From e6f9d9371794706705f06d54c0538395cd7253ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ingo=20M=C3=BCller?= Date: Mon, 5 Jun 2023 09:33:26 +0200 Subject: [PATCH 01/23] [BUILD] Update CMake target name MLIRGPU{Ops => Dialect}. (#1733) This reflects a change upstream in llvm/llvm-project@61223c49d, which renamed that target. --- lib/Conversion/TritonGPUToLLVM/CMakeLists.txt | 2 +- lib/Dialect/TritonGPU/IR/CMakeLists.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/Conversion/TritonGPUToLLVM/CMakeLists.txt b/lib/Conversion/TritonGPUToLLVM/CMakeLists.txt index 98fce466ac01..48a67ab4a6eb 100644 --- a/lib/Conversion/TritonGPUToLLVM/CMakeLists.txt +++ b/lib/Conversion/TritonGPUToLLVM/CMakeLists.txt @@ -35,7 +35,7 @@ add_mlir_conversion_library(TritonGPUToLLVM LINK_LIBS PUBLIC MLIRIR MLIRPass - MLIRGPUOps + MLIRGPUDialect MLIRGPUToNVVMTransforms MLIRGPUToROCDLTransforms MLIRGPUTransforms diff --git a/lib/Dialect/TritonGPU/IR/CMakeLists.txt b/lib/Dialect/TritonGPU/IR/CMakeLists.txt index 20f6f9851e1b..5170f507598e 100644 --- a/lib/Dialect/TritonGPU/IR/CMakeLists.txt +++ b/lib/Dialect/TritonGPU/IR/CMakeLists.txt @@ -7,6 +7,6 @@ add_mlir_dialect_library(TritonGPUIR TritonGPUAttrDefsIncGen LINK_LIBS PUBLIC - MLIRGPUOps + MLIRGPUDialect TritonIR ) From b1a9b623a67c6d4c66cb5dfcd6dd1bc652e143a4 Mon Sep 17 00:00:00 2001 From: Christian Sigg Date: Mon, 12 Jun 2023 09:44:51 +0200 Subject: [PATCH 02/23] [BACKEND] Fixes for https://github.com/llvm/llvm-project/commit/641b12e94b8a4e7befbda691364554c186a61639 --- include/triton/Analysis/Utility.h | 6 +++--- lib/Analysis/Utility.cpp | 10 +++++----- lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp | 6 ++++-- .../TritonGPU/Transforms/RemoveLayoutConversions.cpp | 2 +- 4 files changed, 13 insertions(+), 11 deletions(-) diff --git a/include/triton/Analysis/Utility.h b/include/triton/Analysis/Utility.h index 71a34dc4b993..747e4b094984 100644 --- a/include/triton/Analysis/Utility.h +++ b/include/triton/Analysis/Utility.h @@ -177,9 +177,9 @@ SetVector multiRootTopologicalSort(const SetVector &toSort); /// This uses the toplogicalSort above -SetVector -multiRootGetSlice(Operation *op, TransitiveFilter backwardFilter = nullptr, - TransitiveFilter forwardFilter = nullptr); +SetVector multiRootGetSlice( + Operation *op, SliceOptions::TransitiveFilter backwardFilter = nullptr, + SliceOptions::TransitiveFilter forwardFilter = nullptr); /// Create a basic DataFlowSolver with constant and dead code analysis included. std::unique_ptr createDataFlowSolver(); diff --git a/lib/Analysis/Utility.cpp b/lib/Analysis/Utility.cpp index 9eabdec354ab..4c1df3c2d537 100644 --- a/lib/Analysis/Utility.cpp +++ b/lib/Analysis/Utility.cpp @@ -466,9 +466,9 @@ multiRootTopologicalSort(const SetVector &toSort) { return res; } -SetVector multiRootGetSlice(Operation *op, - TransitiveFilter backwardFilter, - TransitiveFilter forwardFilter) { +SetVector multiRootGetSlice( + Operation *op, SliceOptions::TransitiveFilter backwardFilter, + SliceOptions::TransitiveFilter forwardFilter) { SetVector slice; slice.insert(op); @@ -479,12 +479,12 @@ SetVector multiRootGetSlice(Operation *op, auto *currentOp = (slice)[currentIndex]; // Compute and insert the backwardSlice starting from currentOp. backwardSlice.clear(); - getBackwardSlice(currentOp, &backwardSlice, backwardFilter); + getBackwardSlice(currentOp, &backwardSlice, {{backwardFilter}}); slice.insert(backwardSlice.begin(), backwardSlice.end()); // Compute and insert the forwardSlice starting from currentOp. forwardSlice.clear(); - getForwardSlice(currentOp, &forwardSlice, forwardFilter); + getForwardSlice(currentOp, &forwardSlice, {{forwardFilter}}); slice.insert(forwardSlice.begin(), forwardSlice.end()); ++currentIndex; } diff --git a/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp b/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp index 5119ff760aea..b18b9d56ddfb 100644 --- a/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp +++ b/lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp @@ -44,7 +44,7 @@ SmallVector mmaVersionToShapePerWarp(int version) { SmallVector warpsPerTileV2(triton::DotOp dotOp, const ArrayRef shape, int numWarps) { - auto filter = [&dotOp](Operation *op) { + mlir::TransitiveFilter filter = [&dotOp](Operation *op) { return op->getParentRegion() == dotOp->getParentRegion(); }; auto slices = mlir::getSlice(dotOp, {filter}); @@ -112,7 +112,9 @@ class BlockedToMMA : public mlir::RewritePattern { triton::gpu::MmaEncodingAttr mmaEnc; if (versionMajor == 1) { SetVector aBwdSlices, bBwdSlices; - auto isCvt = [](Operation *op) { return isa(op); }; + mlir::TransitiveFilter isCvt = [](Operation *op) { + return isa(op); + }; getBackwardSlice(a, &aBwdSlices, {isCvt}); getBackwardSlice(b, &bBwdSlices, {isCvt}); // get the source of the first conversion found in slices diff --git a/lib/Dialect/TritonGPU/Transforms/RemoveLayoutConversions.cpp b/lib/Dialect/TritonGPU/Transforms/RemoveLayoutConversions.cpp index 47aa68c372b6..8c80f9fc18c3 100644 --- a/lib/Dialect/TritonGPU/Transforms/RemoveLayoutConversions.cpp +++ b/lib/Dialect/TritonGPU/Transforms/RemoveLayoutConversions.cpp @@ -354,7 +354,7 @@ class RematerializeForward : public mlir::RewritePattern { return failure(); SetVector cvtSlices; - auto filter = [&](Operation *op) { + mlir::TransitiveFilter filter = [&](Operation *op) { return op->getBlock() == cvt->getBlock() && !isa(op) && !(isa(op) && From 1240e9d6400d1988497c6fa22cadd05e2b0d9268 Mon Sep 17 00:00:00 2001 From: Ashay Rane Date: Wed, 14 Jun 2023 14:19:31 -0500 Subject: [PATCH 03/23] [CI] add text file containing LLVM commit hash This patch adds a text file that holds the LLVM commit hash to be used with the Triton code. Once the workflow files are added to the main branch, any updates to this text file in the llvm-head branch will trigger an LLVM build and the corresponding build artifacts will be uploaded to Azure Blob storage. --- llvm-hash.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 llvm-hash.txt diff --git a/llvm-hash.txt b/llvm-hash.txt new file mode 100644 index 000000000000..45dcb3d8e257 --- /dev/null +++ b/llvm-hash.txt @@ -0,0 +1 @@ +c5dede880d175f7229c9b2923f4753e12702305d From 09170bdd1df8ca98355c0cb34b5f8190b5d5b90c Mon Sep 17 00:00:00 2001 From: Christian Sigg Date: Tue, 27 Jun 2023 10:25:49 +0200 Subject: [PATCH 04/23] [BACKEND] Fix for LLVM change b126ee65. --- lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMBase.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMBase.h b/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMBase.h index 99dee8bb322a..fe1073305b2f 100644 --- a/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMBase.h +++ b/lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMBase.h @@ -130,7 +130,7 @@ struct FuncOpConversionBase : public ConvertOpToLLVMPattern { } auto newFuncOp = rewriter.create( funcOp.getLoc(), funcOp.getName(), llvmType, linkage, - /*dsoLocal*/ false, LLVM::CConv::C, attributes); + /*dsoLocal*/ false, LLVM::CConv::C, /*comdat=*/nullptr, attributes); rewriter.inlineRegionBefore(funcOp.getBody(), newFuncOp.getBody(), newFuncOp.end()); if (failed(rewriter.convertRegionTypes(&newFuncOp.getBody(), *typeConverter, From 0e7ea31153f38b540581c8687299ae8968359a8b Mon Sep 17 00:00:00 2001 From: Christian Sigg Date: Tue, 27 Jun 2023 10:32:20 +0200 Subject: [PATCH 05/23] Bump LLVM version. --- llvm-hash.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llvm-hash.txt b/llvm-hash.txt index 45dcb3d8e257..502b75e2f529 100644 --- a/llvm-hash.txt +++ b/llvm-hash.txt @@ -1 +1 @@ -c5dede880d175f7229c9b2923f4753e12702305d +b126ee65fcbb49054e32fd11fdac07279d00f159 From c12fd7fb6421ddf877b2b678fdb6ed9c6178d9fe Mon Sep 17 00:00:00 2001 From: Christian Sigg Date: Mon, 10 Jul 2023 11:15:03 +0200 Subject: [PATCH 06/23] Remove python/, add BUILD files. --- BUILD | 641 + python/MANIFEST.in | 5 - python/README.md | 0 python/examples/copy_strided.py | 18 - python/examples/empty.py | 13 - python/pyproject.toml | 8 - python/setup.py | 326 - python/src/extra/cuda.ll | 17 - python/src/main.cc | 11 - python/src/triton.cc | 1738 -- python/test/backend/extension_backend.c | 42 - python/test/backend/test_device_backend.py | 262 - .../backend/third_party_backends/conftest.py | 14 - .../third_party_backends/test_xpu_backend.py | 33 - python/test/kernel_comparison/kernels.yml | 31 - .../regression/test_functional_regressions.py | 230 - python/test/regression/test_performance.py | 212 - python/test/tools/compare_files.py | 261 - .../test/unit/interpreter/test_interpreter.py | 69 - python/test/unit/language/assert_helper.py | 131 - python/test/unit/language/conftest.py | 14 - python/test/unit/language/print_helper.py | 46 - python/test/unit/language/test_annotations.py | 21 - .../test/unit/language/test_block_pointer.py | 102 - python/test/unit/language/test_core.py | 3271 --- python/test/unit/language/test_line_info.py | 120 - python/test/unit/language/test_random.py | 198 - python/test/unit/language/test_subprocess.py | 80 - .../test/unit/operators/test_blocksparse.py | 219 - .../test/unit/operators/test_cross_entropy.py | 40 - .../unit/operators/test_flash_attention.py | 49 - python/test/unit/operators/test_inductor.py | 155 - python/test/unit/operators/test_matmul.py | 141 - python/test/unit/runtime/test_autotuner.py | 22 - python/test/unit/runtime/test_cache.py | 208 - python/test/unit/runtime/test_driver.py | 14 - python/test/unit/runtime/test_launch.py | 106 - python/test/unit/runtime/test_subproc.py | 83 - python/test/unit/tools/test_aot.py | 205 - python/triton/_C/include | 1 - 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delete mode 100644 python/tutorials/04-low-memory-dropout.py delete mode 100644 python/tutorials/05-layer-norm.py delete mode 100644 python/tutorials/06-fused-attention.py delete mode 100644 python/tutorials/07-math-functions.py delete mode 100644 python/tutorials/08-experimental-block-pointer.py delete mode 100644 python/tutorials/README.rst create mode 100644 test/BUILD diff --git a/BUILD b/BUILD new file mode 100644 index 000000000000..644177fe8abb --- /dev/null +++ b/BUILD @@ -0,0 +1,641 @@ +# This package imports OpenAI's Triton (https://github.com/openai/triton). +# +# There are two versions of Triton in google3 at the moment. The older version +# can be found at //third_party/py/triton. This is the MLIR-based version close +# to head. We expect to transition users to this version in the following +# weeks. +# +# There is no SLA associated with this package and it may get broken by LLVM +# imports at any time. + +load("@llvm-project//mlir:tblgen.bzl", "gentbl_cc_library", "td_library") +# copybara:uncomment load("//tools/build_defs/license:license.bzl", "license") + +package( + # copybara:uncomment_begin + # default_applicable_licenses = [":license"], + # default_compatible_with = ["//buildenv/target:gce"], + # default_visibility = [ + # "//third_party/tensorflow/compiler/xla:__subpackages__", + # "//:__subpackages__", + # ], + # copybara:uncomment_end_and_comment_begin + default_visibility = ["//visibility:public"], + # copybara:comment_end + # TODO(csigg): fix and remove + features = [ + "-parse_headers", + "-use_header_modules", + ], +) + +# copybara:uncomment_begin +# license(name = "license") +# +# licenses(["notice"]) +# +# exports_files(["LICENSE"]) +# copybara:uncomment_end + +config_setting( + name = "compiler_is_msvc", + flag_values = { + # copybara:comment_begin + "@bazel_tools" + + # copybara:comment_end + "//tools/cpp:compiler": "msvc-cl", + }, +) + +# TODO(csigg): fix, enable error upstream, remove. +_no_unused_variable = select({ + ":compiler_is_msvc": [], + "//conditions:default": ["-Wno-unused-variable"], +}) + +td_library( + name = "td_files", + srcs = glob(["include/triton/**/*.td"]), + includes = ["include"], + deps = [ + "@llvm-project//mlir:ArithOpsTdFiles", + "@llvm-project//mlir:CastInterfacesTdFiles", + "@llvm-project//mlir:ControlFlowInterfacesTdFiles", + "@llvm-project//mlir:DestinationStyleOpInterfaceTdFiles", + "@llvm-project//mlir:FunctionInterfacesTdFiles", + "@llvm-project//mlir:InferTypeOpInterfaceTdFiles", + "@llvm-project//mlir:OpBaseTdFiles", + "@llvm-project//mlir:PassBaseTdFiles", + "@llvm-project//mlir:SideEffectInterfacesTdFiles", + "@llvm-project//mlir:ViewLikeInterfaceTdFiles", + ], +) + +gentbl_cc_library( + name = "triton_dialect_inc_gen", + tbl_outs = [ + ( + ["--gen-dialect-decls"], + "include/triton/Dialect/Triton/IR/Dialect.h.inc", + ), + ( + ["--gen-dialect-defs"], + "include/triton/Dialect/Triton/IR/Dialect.cpp.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/Triton/IR/TritonDialect.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_ops_inc_gen", + tbl_outs = [ + ( + ["--gen-enum-decls"], + "include/triton/Dialect/Triton/IR/OpsEnums.h.inc", + ), + ( + ["--gen-enum-defs"], + "include/triton/Dialect/Triton/IR/OpsEnums.cpp.inc", + ), + ( + ["--gen-op-decls"], + "include/triton/Dialect/Triton/IR/Ops.h.inc", + ), + ( + ["--gen-op-defs"], + "include/triton/Dialect/Triton/IR/Ops.cpp.inc", + ), + ( + ["--gen-typedef-decls"], + "include/triton/Dialect/Triton/IR/Types.h.inc", + ), + ( + ["--gen-typedef-defs"], + "include/triton/Dialect/Triton/IR/Types.cpp.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/Triton/IR/TritonOps.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_interfaces_inc_gen", + tbl_outs = [ + ( + ["--gen-attr-interface-decls"], + "include/triton/Dialect/Triton/IR/AttrInterfaces.h.inc", + ), + ( + ["--gen-attr-interface-defs"], + "include/triton/Dialect/Triton/IR/AttrInterfaces.cpp.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/Triton/IR/TritonInterfaces.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_transforms_inc_gen", + tbl_outs = [ + ( + [ + "--gen-pass-decls", + "--name=Triton", + ], + "include/triton/Dialect/Triton/Transforms/Passes.h.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/Triton/Transforms/Passes.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_combine_inc_gen", + # The generated file is #included without relative path. + strip_include_prefix = "lib/Dialect/Triton/Transforms", + tbl_outs = [ + ( + ["--gen-rewriters"], + "lib/Dialect/Triton/Transforms/TritonCombine.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "lib/Dialect/Triton/Transforms/Combine.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_gpu_dialect_inc_gen", + tbl_outs = [ + ( + ["--gen-dialect-decls"], + "include/triton/Dialect/TritonGPU/IR/Dialect.h.inc", + ), + ( + ["--gen-dialect-defs"], + "include/triton/Dialect/TritonGPU/IR/Dialect.cpp.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/TritonGPU/IR/TritonGPUDialect.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_gpu_ops_inc_gen", + tbl_outs = [ + ( + ["--gen-op-decls"], + "include/triton/Dialect/TritonGPU/IR/Ops.h.inc", + ), + ( + ["--gen-op-defs"], + "include/triton/Dialect/TritonGPU/IR/Ops.cpp.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/TritonGPU/IR/TritonGPUOps.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_gpu_attr_inc_gen", + tbl_outs = [ + ( + ["--gen-attrdef-decls"], + "include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.h.inc", + ), + ( + ["--gen-attrdef-defs"], + "include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.cpp.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_gpu_transforms_inc_gen", + tbl_outs = [ + ( + [ + "--gen-pass-decls", + "--name=TritonGPU", + ], + "include/triton/Dialect/TritonGPU/Transforms/Passes.h.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Dialect/TritonGPU/Transforms/Passes.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_conversion_triton_gpu_to_llvm_passes_inc_gen", + tbl_outs = [ + ( + [ + "--gen-pass-decls", + "--name=TritonGPUToLLVM", + ], + "include/triton/Conversion/TritonGPUToLLVM/Passes.h.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Conversion/TritonGPUToLLVM/Passes.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_conversion_triton_to_triton_gpu_passes_inc_gen", + tbl_outs = [ + ( + [ + "--gen-pass-decls", + "--name=TritonToTritonGPU", + ], + "include/triton/Conversion/TritonToTritonGPU/Passes.h.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Conversion/TritonToTritonGPU/Passes.td", + deps = ["td_files"], +) + +gentbl_cc_library( + name = "triton_target_llvmir_passes_inc_gen", + tbl_outs = [ + ( + [ + "--gen-pass-decls", + "--name=TritonLLVMIR", + ], + "include/triton/Target/LLVMIR/Passes.h.inc", + ), + ], + tblgen = "@llvm-project//mlir:mlir-tblgen", + td_file = "include/triton/Target/LLVMIR/Passes.td", + deps = ["td_files"], +) + +cc_library( + name = "TritonAnalysis", + srcs = glob(["lib/Analysis/*.cpp"]), + hdrs = glob(["include/triton/Analysis/*.h"]), + copts = _no_unused_variable, + includes = ["include"], + deps = [ + ":TritonDialects", + ":TritonTools", + ":triton_gpu_attr_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:Analysis", + "@llvm-project//mlir:FuncDialect", + "@llvm-project//mlir:GPUDialect", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:TensorDialect", + ], +) + +cc_library( + name = "TritonDialects", + srcs = glob([ + "lib/Dialect/Triton/IR/*.cpp", + "lib/Dialect/TritonGPU/IR/*.cpp", + ]) + [ + "include/triton/Analysis/Utility.h", # Avoid circular dependency. + ], + hdrs = glob([ + "include/triton/Dialect/Triton/IR/*.h", + "include/triton/Dialect/TritonGPU/IR/*.h", + ]), + copts = _no_unused_variable, + includes = ["include"], + deps = [ + ":triton_dialect_inc_gen", + ":triton_gpu_attr_inc_gen", + ":triton_gpu_dialect_inc_gen", + ":triton_gpu_ops_inc_gen", + ":triton_gpu_transforms_inc_gen", + ":triton_interfaces_inc_gen", + ":triton_ops_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:Analysis", + "@llvm-project//mlir:ArithDialect", + "@llvm-project//mlir:ControlFlowDialect", + "@llvm-project//mlir:ControlFlowInterfaces", + "@llvm-project//mlir:DestinationStyleOpInterface", + "@llvm-project//mlir:FuncDialect", + "@llvm-project//mlir:GPUDialect", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:MathDialect", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:SCFDialect", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:TensorDialect", + "@llvm-project//mlir:Transforms", + ], +) + +cc_library( + name = "TritonTransforms", + srcs = glob(["lib/Dialect/Triton/Transforms/*.cpp"]), + hdrs = glob(["include/triton/Dialect/Triton/Transforms/*.h"]), + includes = ["include"], + deps = [ + ":TritonDialects", + ":triton_combine_inc_gen", + ":triton_transforms_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:ArithDialect", + "@llvm-project//mlir:ControlFlowDialect", + "@llvm-project//mlir:ControlFlowInterfaces", + "@llvm-project//mlir:FuncDialect", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:MathDialect", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:SCFDialect", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:TensorDialect", + "@llvm-project//mlir:Transforms", + ], + alwayslink = True, # TritonDialect uses getCanonicalizationPatterns(). +) + +cc_library( + name = "TritonGPUTransforms", + srcs = glob([ + "lib/Dialect/TritonGPU/Transforms/*.cpp", + "lib/Dialect/TritonGPU/Transforms/*.h", + ]), + hdrs = glob(["include/triton/Dialect/TritonGPU/Transforms/*.h"]), + copts = _no_unused_variable, + includes = ["include"], + deps = [ + ":TritonAnalysis", + ":TritonDialects", + ":triton_gpu_transforms_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:Analysis", + "@llvm-project//mlir:ArithDialect", + "@llvm-project//mlir:ControlFlowDialect", + "@llvm-project//mlir:ControlFlowInterfaces", + "@llvm-project//mlir:FuncDialect", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:InferTypeOpInterface", + "@llvm-project//mlir:MathDialect", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:SCFDialect", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:TensorDialect", + "@llvm-project//mlir:Transforms", + ], +) + +cc_library( + name = "TritonGPUToLLVM", + srcs = glob([ + "lib/Conversion/TritonGPUToLLVM/*.h", + "lib/Conversion/TritonGPUToLLVM/**/*.cpp", + ]) + [ + "include/triton/Conversion/MLIRTypes.h", + ], + hdrs = glob([ + "include/triton/Tools/Sys/*.hpp", + "include/triton/Conversion/TritonGPUToLLVM/*.h", + ]), + copts = _no_unused_variable, + includes = [ + "include", + "lib/Conversion/TritonGPUToLLVM", + ], + deps = [ + ":TritonAnalysis", + ":TritonDialects", + ":triton_conversion_triton_gpu_to_llvm_passes_inc_gen", + ":triton_conversion_triton_to_triton_gpu_passes_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:Analysis", + "@llvm-project//mlir:ArithDialect", + "@llvm-project//mlir:ArithToLLVM", + "@llvm-project//mlir:ControlFlowDialect", + "@llvm-project//mlir:ControlFlowToLLVM", + "@llvm-project//mlir:FuncDialect", + "@llvm-project//mlir:GPUDialect", + "@llvm-project//mlir:GPUToNVVMTransforms", + "@llvm-project//mlir:GPUToROCDLTransforms", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:IndexDialect", + "@llvm-project//mlir:LLVMCommonConversion", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:MathToLLVM", + "@llvm-project//mlir:NVVMDialect", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:ROCDLDialect", + "@llvm-project//mlir:SCFToControlFlow", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:TensorDialect", + "@llvm-project//mlir:Transforms", + ], +) + +cc_library( + name = "TritonToTritonGPU", + srcs = glob([ + "lib/Conversion/TritonToTritonGPU/*.h", + "lib/Conversion/TritonToTritonGPU/*.cpp", + ]), + hdrs = glob(["include/triton/Conversion/TritonToTritonGPU/*.h"]), + includes = ["include"], + deps = [ + ":TritonAnalysis", + ":TritonDialects", + ":TritonGPUTransforms", + ":triton_conversion_triton_gpu_to_llvm_passes_inc_gen", + ":triton_conversion_triton_to_triton_gpu_passes_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:ArithDialect", + "@llvm-project//mlir:ControlFlowDialect", + "@llvm-project//mlir:GPUDialect", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:IndexDialect", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:NVVMDialect", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:Transforms", + ], +) + +cc_library( + name = "TritonLLVMIR", + srcs = glob([ + "lib/Target/LLVMIR/*.cpp", + ]) + [ + "include/triton/Tools/Sys/GetEnv.hpp", + ], + hdrs = glob(["include/triton/Target/LLVMIR/*.h"]), + copts = _no_unused_variable, + includes = ["include"], + deps = [ + ":TritonGPUToLLVM", + ":TritonTransforms", + ":triton_target_llvmir_passes_inc_gen", + "@llvm-project//llvm:BinaryFormat", + "@llvm-project//llvm:Core", + "@llvm-project//llvm:IRReader", + "@llvm-project//llvm:Linker", + "@llvm-project//llvm:Support", + "@llvm-project//mlir:BuiltinToLLVMIRTranslation", + "@llvm-project//mlir:ConversionPasses", + "@llvm-project//mlir:ExecutionEngine", + "@llvm-project//mlir:ExecutionEngineUtils", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:LLVMIRTransforms", + "@llvm-project//mlir:LLVMToLLVMIRTranslation", + "@llvm-project//mlir:NVVMToLLVMIRTranslation", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:ROCDLToLLVMIRTranslation", + "@llvm-project//mlir:ToLLVMIRTranslation", + "@llvm-project//mlir:Transforms", + # copybara:uncomment "//third_party/py/triton/google:find_cuda", + ], +) + +cc_library( + name = "TritonPTX", + srcs = glob([ + "lib/Target/PTX/*.cpp", + ]), + hdrs = glob(["include/triton/Target/PTX/*.h"]), + includes = ["include"], + deps = [ + ":TritonLLVMIR", + "@llvm-project//llvm:Core", + "@llvm-project//llvm:MC", + "@llvm-project//llvm:Support", + "@llvm-project//llvm:Target", + ], +) + +cc_library( + name = "TritonHSACO", + srcs = glob([ + "lib/Target/HSACO/*.cpp", + ]), + hdrs = glob(["include/triton/Target/HSACO/*.h"]), + includes = ["include"], + deps = [ + ":TritonLLVMIR", + ":TritonTools", + "@llvm-project//llvm:Core", + "@llvm-project//llvm:ExecutionEngine", + "@llvm-project//llvm:MC", + "@llvm-project//llvm:Scalar", + "@llvm-project//llvm:Support", + "@llvm-project//llvm:Target", + "@llvm-project//llvm:TransformUtils", + "@llvm-project//mlir:ExecutionEngine", + "@llvm-project//mlir:ExecutionEngineUtils", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:LLVMToLLVMIRTranslation", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:ToLLVMIRTranslation", + ], +) + +cc_library( + name = "TritonTools", + hdrs = ["include/triton/Tools/Sys/GetEnv.hpp"], + includes = ["include"], +) + +cc_binary( + name = "triton-opt", + srcs = [ + "bin/RegisterTritonDialects.h", + "bin/triton-opt.cpp", + "include/triton/Conversion/TritonGPUToLLVM/Passes.h", + "include/triton/Conversion/TritonToTritonGPU/Passes.h", + ], + includes = ["include"], + deps = [ + ":TritonDialects", + ":TritonGPUToLLVM", + ":TritonGPUTransforms", + ":TritonToTritonGPU", + ":TritonTransforms", + ":triton_conversion_triton_gpu_to_llvm_passes_inc_gen", + ":triton_conversion_triton_to_triton_gpu_passes_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//llvm:ir_headers", + "@llvm-project//mlir:AllPassesAndDialects", + "@llvm-project//mlir:ControlFlowDialect", + "@llvm-project//mlir:ConversionPasses", + "@llvm-project//mlir:ExecutionEngine", + "@llvm-project//mlir:ExecutionEngineUtils", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:LLVMCommonConversion", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:LLVMToLLVMIRTranslation", + "@llvm-project//mlir:MlirOptLib", + "@llvm-project//mlir:Parser", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:ToLLVMIRTranslation", + "@llvm-project//mlir:TransformUtils", + "@llvm-project//mlir:Transforms", + "//third_party/triton/test:TritonTestAnalysis", + ], +) + +cc_binary( + name = "triton-translate", + srcs = [ + "bin/triton-translate.cpp", + "include/triton/Conversion/TritonGPUToLLVM/Passes.h", + "include/triton/Conversion/TritonToTritonGPU/Passes.h", + ], + includes = ["include"], + deps = [ + ":TritonDialects", + ":TritonGPUToLLVM", + ":TritonGPUTransforms", + ":TritonHSACO", + ":TritonLLVMIR", + ":TritonPTX", + ":TritonToTritonGPU", + ":TritonTransforms", + ":triton_conversion_triton_gpu_to_llvm_passes_inc_gen", + ":triton_conversion_triton_to_triton_gpu_passes_inc_gen", + "@llvm-project//llvm:Support", + "@llvm-project//llvm:ir_headers", + "@llvm-project//mlir:AllPassesAndDialects", + "@llvm-project//mlir:ConversionPasses", + "@llvm-project//mlir:ExecutionEngine", + "@llvm-project//mlir:ExecutionEngineUtils", + "@llvm-project//mlir:IR", + "@llvm-project//mlir:LLVMCommonConversion", + "@llvm-project//mlir:LLVMDialect", + "@llvm-project//mlir:LLVMToLLVMIRTranslation", + "@llvm-project//mlir:MlirOptLib", + "@llvm-project//mlir:Parser", + "@llvm-project//mlir:Pass", + "@llvm-project//mlir:Support", + "@llvm-project//mlir:ToLLVMIRTranslation", + "@llvm-project//mlir:TransformUtils", + "@llvm-project//mlir:Transforms", + ], +) diff --git a/python/MANIFEST.in b/python/MANIFEST.in deleted file mode 100644 index c8c818919820..000000000000 --- a/python/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -graft src -graft triton/third_party -graft triton/tools -graft triton/runtime/backends/ -graft triton/language/extra diff --git a/python/README.md b/python/README.md deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/examples/copy_strided.py b/python/examples/copy_strided.py deleted file mode 100644 index 34cf12630205..000000000000 --- a/python/examples/copy_strided.py +++ /dev/null @@ -1,18 +0,0 @@ -import triton -import triton.language as tl - - -# triton kernel -@triton.jit -def kernel(X, stride_xm, - Z, stride_zn, - BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): - off_m = tl.arange(0, BLOCK_M) - off_n = tl.arange(0, BLOCK_N) - Xs = X + off_m[:, None] * stride_xm + off_n[None, :] * 1 - Zs = Z + off_m[:, None] * 1 + off_n[None, :] * stride_zn - tl.store(Zs, tl.load(Xs)) - - -ret = triton.compile(kernel, signature="*fp32,i32,*fp32,i32", constants={"BLOCK_M": 64, "BLOCK_N": 64}) -print(ret.asm["ttgir"]) diff --git a/python/examples/empty.py b/python/examples/empty.py deleted file mode 100644 index df313fb85869..000000000000 --- a/python/examples/empty.py +++ /dev/null @@ -1,13 +0,0 @@ -import torch - -import triton -import triton.language as tl - - -@triton.jit -def kernel(X, stride_xm, stride_xn, BLOCK: tl.constexpr): - pass - - -X = torch.randn(1, device="cuda") -pgm = kernel[(1,)](X, 1, 1, BLOCK=1024) diff --git a/python/pyproject.toml b/python/pyproject.toml deleted file mode 100644 index 6430c0c154dc..000000000000 --- a/python/pyproject.toml +++ /dev/null @@ -1,8 +0,0 @@ - -[build-system] -requires = ["setuptools>=40.8.0", "wheel", "cmake>=3.18"] - -[tool.autopep8] -aggressive = 1 -ignore = "E501,E701,E731,W690" -max_line_length = 88 diff --git a/python/setup.py b/python/setup.py deleted file mode 100644 index 13ebcebd6835..000000000000 --- a/python/setup.py +++ /dev/null @@ -1,326 +0,0 @@ -import os -import platform -import re -import shutil -import subprocess -import sys -import sysconfig -import tarfile -import tempfile -import urllib.request -from pathlib import Path -from typing import NamedTuple - -from setuptools import Extension, setup -from setuptools.command.build_ext import build_ext -from setuptools.command.build_py import build_py - - -# Taken from https://github.com/pytorch/pytorch/blob/master/tools/setup_helpers/env.py -def check_env_flag(name: str, default: str = "") -> bool: - return os.getenv(name, default).upper() in ["ON", "1", "YES", "TRUE", "Y"] - - -def get_build_type(): - if check_env_flag("DEBUG"): - return "Debug" - elif check_env_flag("REL_WITH_DEB_INFO"): - return "RelWithDebInfo" - elif check_env_flag("TRITON_REL_BUILD_WITH_ASSERTS"): - return "TritonRelBuildWithAsserts" - else: - # TODO: change to release when stable enough - return "TritonRelBuildWithAsserts" - - -def get_codegen_backends(): - backends = [] - env_prefix = "TRITON_CODEGEN_" - for name, _ in os.environ.items(): - if name.startswith(env_prefix) and check_env_flag(name): - assert name.count(env_prefix) <= 1 - backends.append(name.replace(env_prefix, '').lower()) - return backends - - -# --- third party packages ----- - - -class Package(NamedTuple): - package: str - name: str - url: str - include_flag: str - lib_flag: str - syspath_var_name: str - -# pybind11 - - -def get_pybind11_package_info(): - name = "pybind11-2.10.0" - url = "https://github.com/pybind/pybind11/archive/refs/tags/v2.10.0.tar.gz" - return Package("pybind11", name, url, "PYBIND11_INCLUDE_DIR", "", "PYBIND11_SYSPATH") - -# llvm - - -def get_llvm_package_info(): - # download if nothing is installed - system = platform.system() - if system == "Darwin": - system_suffix = "apple-darwin" - elif system == "Linux": - vglibc = tuple(map(int, platform.libc_ver()[1].split('.'))) - vglibc = vglibc[0] * 100 + vglibc[1] - linux_suffix = 'ubuntu-18.04' if vglibc > 217 else 'centos-7' - system_suffix = f"linux-gnu-{linux_suffix}" - else: - return Package("llvm", "LLVM-C.lib", "", "LLVM_INCLUDE_DIRS", "LLVM_LIBRARY_DIR", "LLVM_SYSPATH") - use_assert_enabled_llvm = check_env_flag("TRITON_USE_ASSERT_ENABLED_LLVM", "False") - release_suffix = "assert" if use_assert_enabled_llvm else "release" - name = f'llvm+mlir-17.0.0-x86_64-{system_suffix}-{release_suffix}' - version = "llvm-17.0.0-c5dede880d17" - url = f"https://github.com/ptillet/triton-llvm-releases/releases/download/{version}/{name}.tar.xz" - return Package("llvm", name, url, "LLVM_INCLUDE_DIRS", "LLVM_LIBRARY_DIR", "LLVM_SYSPATH") - - -def get_thirdparty_packages(triton_cache_path): - packages = [get_pybind11_package_info(), get_llvm_package_info()] - thirdparty_cmake_args = [] - for p in packages: - package_root_dir = os.path.join(triton_cache_path, p.package) - package_dir = os.path.join(package_root_dir, p.name) - if p.syspath_var_name in os.environ: - package_dir = os.environ[p.syspath_var_name] - version_file_path = os.path.join(package_dir, "version.txt") - if p.syspath_var_name not in os.environ and\ - (not os.path.exists(version_file_path) or Path(version_file_path).read_text() != p.url): - try: - shutil.rmtree(package_root_dir) - except Exception: - pass - os.makedirs(package_root_dir, exist_ok=True) - print(f'downloading and extracting {p.url} ...') - ftpstream = urllib.request.urlopen(p.url) - file = tarfile.open(fileobj=ftpstream, mode="r|*") - file.extractall(path=package_root_dir) - # write version url to package_dir - with open(os.path.join(package_dir, "version.txt"), "w") as f: - f.write(p.url) - if p.include_flag: - thirdparty_cmake_args.append(f"-D{p.include_flag}={package_dir}/include") - if p.lib_flag: - thirdparty_cmake_args.append(f"-D{p.lib_flag}={package_dir}/lib") - return thirdparty_cmake_args - -# ---- package data --- - - -def download_and_copy_ptxas(): - base_dir = os.path.dirname(__file__) - src_path = "bin/ptxas" - version = "12.1.105" - url = f"https://conda.anaconda.org/nvidia/label/cuda-12.1.1/linux-64/cuda-nvcc-{version}-0.tar.bz2" - dst_prefix = os.path.join(base_dir, "triton") - dst_suffix = os.path.join("third_party", "cuda", src_path) - dst_path = os.path.join(dst_prefix, dst_suffix) - is_linux = platform.system() == "Linux" - download = False - if is_linux: - download = True - if os.path.exists(dst_path): - curr_version = subprocess.check_output([dst_path, "--version"]).decode("utf-8").strip() - curr_version = re.search(r"V([.|\d]+)", curr_version).group(1) - download = curr_version != version - if download: - print(f'downloading and extracting {url} ...') - ftpstream = urllib.request.urlopen(url) - file = tarfile.open(fileobj=ftpstream, mode="r|*") - with tempfile.TemporaryDirectory() as temp_dir: - file.extractall(path=temp_dir) - src_path = os.path.join(temp_dir, src_path) - os.makedirs(os.path.split(dst_path)[0], exist_ok=True) - shutil.copy(src_path, dst_path) - return dst_suffix - - -# ---- cmake extension ---- - -class CMakeBuildPy(build_py): - def run(self) -> None: - self.run_command('build_ext') - return super().run() - - -class CMakeExtension(Extension): - def __init__(self, name, path, sourcedir=""): - Extension.__init__(self, name, sources=[]) - self.sourcedir = os.path.abspath(sourcedir) - self.path = path - - -class CMakeBuild(build_ext): - - user_options = build_ext.user_options + [('base-dir=', None, 'base directory of Triton')] - - def initialize_options(self): - build_ext.initialize_options(self) - self.base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) - - def finalize_options(self): - build_ext.finalize_options(self) - - def run(self): - try: - out = subprocess.check_output(["cmake", "--version"]) - except OSError: - raise RuntimeError( - "CMake must be installed to build the following extensions: " + ", ".join(e.name for e in self.extensions) - ) - - match = re.search(r"version\s*(?P\d+)\.(?P\d+)([\d.]+)?", out.decode()) - cmake_major, cmake_minor = int(match.group("major")), int(match.group("minor")) - if (cmake_major, cmake_minor) < (3, 18): - raise RuntimeError("CMake >= 3.18.0 is required") - - for ext in self.extensions: - self.build_extension(ext) - - def get_cmake_dir(self): - plat_name = sysconfig.get_platform() - python_version = sysconfig.get_python_version() - dir_name = f"cmake.{plat_name}-{sys.implementation.name}-{python_version}" - cmake_dir = Path(self.base_dir) / "python" / "build" / dir_name - cmake_dir.mkdir(parents=True, exist_ok=True) - return cmake_dir - - def build_extension(self, ext): - lit_dir = shutil.which('lit') - user_home = os.getenv("HOME") or os.getenv("USERPROFILE") or \ - os.getenv("HOMEPATH") or None - if not user_home: - raise RuntimeError("Could not find user home directory") - triton_cache_path = os.path.join(user_home, ".triton") - # lit is used by the test suite - thirdparty_cmake_args = get_thirdparty_packages(triton_cache_path) - extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.path))) - # create build directories - if not os.path.exists(self.build_temp): - os.makedirs(self.build_temp) - # python directories - python_include_dir = sysconfig.get_path("platinclude") - cmake_args = [ - "-DCMAKE_EXPORT_COMPILE_COMMANDS=ON", - "-DLLVM_ENABLE_WERROR=ON", - "-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=" + extdir, - "-DTRITON_BUILD_TUTORIALS=OFF", - "-DTRITON_BUILD_PYTHON_MODULE=ON", - "-DPython3_EXECUTABLE:FILEPATH=" + sys.executable, - "-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON", - "-DPYTHON_INCLUDE_DIRS=" + python_include_dir, - ] - if lit_dir is not None: - cmake_args.append("-DLLVM_EXTERNAL_LIT=" + lit_dir) - cmake_args.extend(thirdparty_cmake_args) - - # configuration - cfg = get_build_type() - build_args = ["--config", cfg] - - codegen_backends = get_codegen_backends() - if len(codegen_backends) > 0: - all_codegen_backends = ';'.join(codegen_backends) - cmake_args += ["-DTRITON_CODEGEN_BACKENDS=" + all_codegen_backends] - - if platform.system() == "Windows": - cmake_args += [f"-DCMAKE_RUNTIME_OUTPUT_DIRECTORY_{cfg.upper()}={extdir}"] - if sys.maxsize > 2**32: - cmake_args += ["-A", "x64"] - build_args += ["--", "/m"] - else: - cmake_args += ["-DCMAKE_BUILD_TYPE=" + cfg] - max_jobs = os.getenv("MAX_JOBS", str(2 * os.cpu_count())) - build_args += ['-j' + max_jobs] - - if check_env_flag("TRITON_BUILD_WITH_CLANG_LLD"): - cmake_args += ["-DCMAKE_C_COMPILER=clang", - "-DCMAKE_CXX_COMPILER=clang++", - "-DCMAKE_LINKER=lld", - "-DCMAKE_EXE_LINKER_FLAGS=-fuse-ld=lld", - "-DCMAKE_MODULE_LINKER_FLAGS=-fuse-ld=lld", - "-DCMAKE_SHARED_LINKER_FLAGS=-fuse-ld=lld"] - - env = os.environ.copy() - cmake_dir = self.get_cmake_dir() - subprocess.check_call(["cmake", self.base_dir] + cmake_args, cwd=cmake_dir, env=env) - subprocess.check_call(["cmake", "--build", "."] + build_args, cwd=cmake_dir) - - -download_and_copy_ptxas() - - -setup( - name="triton", - version="2.1.0", - author="Philippe Tillet", - author_email="phil@openai.com", - description="A language and compiler for custom Deep Learning operations", - long_description="", - packages=[ - "triton", - "triton/_C", - "triton/common", - "triton/compiler", - "triton/interpreter", - "triton/language", - "triton/language/extra", - "triton/ops", - "triton/ops/blocksparse", - "triton/runtime", - "triton/runtime/backends", - "triton/third_party", - "triton/tools", - ], - install_requires=[ - "filelock", - ], - include_package_data=True, - ext_modules=[CMakeExtension("triton", "triton/_C/")], - cmdclass={"build_ext": CMakeBuild, "build_py": CMakeBuildPy}, - zip_safe=False, - # for PyPI - keywords=["Compiler", "Deep Learning"], - url="https://github.com/openai/triton/", - classifiers=[ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "Topic :: Software Development :: Build Tools", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - ], - test_suite="tests", - extras_require={ - "build": [ - "cmake>=3.18", - "lit", - ], - "tests": [ - "autopep8", - "flake8", - "isort", - "numpy", - "pytest", - "scipy>=1.7.1", - ], - "tutorials": [ - "matplotlib", - "pandas", - "tabulate", - ], - }, -) diff --git a/python/src/extra/cuda.ll b/python/src/extra/cuda.ll deleted file mode 100644 index 0ab2f6896bdd..000000000000 --- a/python/src/extra/cuda.ll +++ /dev/null @@ -1,17 +0,0 @@ -; ~/.triton/llvm/llvm+mlir-17.0.0-x86_64-linux-gnu-ubuntu-18.04-release/bin/llvm-as ./src/extra/cuda.ll -o ./triton/language/extra/cuda.bc - -target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128" -target triple = "nvptx64-nvidia-cuda" - - -define i64 @globaltimer() #0 { - %1 = call i64 asm sideeffect "mov.u64 $0, %globaltimer;", "=l"() nounwind - ret i64 %1 -} - -define i32 @smid() #0 { - %1 = call i32 asm "mov.u32 $0, %smid;", "=r"() nounwind - ret i32 %1 -} - -attributes #0 = { alwaysinline nounwind } diff --git a/python/src/main.cc b/python/src/main.cc deleted file mode 100644 index 801a83a4b19f..000000000000 --- a/python/src/main.cc +++ /dev/null @@ -1,11 +0,0 @@ -#include - -void init_superblocking(pybind11::module &m); -void init_torch_utils(pybind11::module &m); -void init_triton(pybind11::module &m); -void init_cutlass(pybind11::module &m); - -PYBIND11_MODULE(libtriton, m) { - m.doc() = "Python bindings to the C++ Triton API"; - init_triton(m); -} diff --git a/python/src/triton.cc b/python/src/triton.cc deleted file mode 100644 index 8221af41b467..000000000000 --- a/python/src/triton.cc +++ /dev/null @@ -1,1738 +0,0 @@ -#include "mlir/IR/Builders.h" -#include "mlir/IR/BuiltinOps.h" -#include "mlir/IR/MLIRContext.h" -#include "mlir/IR/Verifier.h" - -#include "mlir/Bytecode/BytecodeWriter.h" - -#include "mlir/Conversion/Passes.h" -#include "mlir/Pass/Pass.h" -#include "mlir/Pass/PassManager.h" -#include "mlir/Transforms/Passes.h" - -#include "mlir/Parser/Parser.h" -#include "mlir/Support/FileUtilities.h" - -#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h" -#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h" -#include "mlir/Dialect/Index/IR/IndexDialect.h" -#include "mlir/Dialect/Index/IR/IndexOps.h" -#include "mlir/Dialect/LLVMIR/LLVMDialect.h" -#include "triton/Analysis/Allocation.h" -#include "triton/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.h" -#include "triton/Conversion/TritonToTritonGPU/TritonToTritonGPUPass.h" -#include "triton/Dialect/Triton/IR/Dialect.h" -#include "triton/Dialect/Triton/IR/Types.h" -#include "triton/Dialect/Triton/Transforms/Passes.h" -#include "triton/Dialect/TritonGPU/Transforms/Passes.h" -#include "triton/Target/HSACO/HSACOTranslation.h" -#include "triton/Target/LLVMIR/LLVMIRTranslation.h" -#include "triton/Target/PTX/PTXTranslation.h" -#include "triton/Tools/Sys/GetEnv.hpp" -#include "triton/Tools/Sys/GetPlatform.hpp" - -#include "llvm/IR/LegacyPassManager.h" -#include "llvm/IR/Module.h" -#include "llvm/IR/Verifier.h" -#include "llvm/IRReader/IRReader.h" -#include "llvm/Support/FileUtilities.h" -#include "llvm/Support/raw_ostream.h" - -#include "llvm/Support/SourceMgr.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -namespace py = pybind11; - -enum backend_t { - HOST, - CUDA, - ROCM, -}; - -void init_triton_runtime(py::module &&m) { - // wrap backend_t - py::enum_(m, "backend") - .value("HOST", HOST) - .value("CUDA", CUDA) - .value("ROCM", ROCM) - .export_values(); -} - -// A custom op builder that keeps track of the last location -class TritonOpBuilder { -public: - TritonOpBuilder(mlir::MLIRContext *context) { - builder = std::make_unique(context); - lastLoc = std::make_unique(builder->getUnknownLoc()); - } - - mlir::OpBuilder &getBuilder() { return *builder; } - - bool isLineInfoEnabled() { return lineInfoEnabled; } - - void setLastLoc(mlir::Location loc) { - if (lineInfoEnabled) - lastLoc = std::make_unique(loc); - } - - void setLastLoc(const std::string &fileName, int line, int column) { - auto context = builder->getContext(); - setLastLoc(mlir::FileLineColLoc::get(context, fileName, line, column)); - } - - mlir::Location getLastLoc() { - assert(lastLoc); - return *lastLoc; - } - - void setInsertionPointToStart(mlir::Block &block) { - if (!block.empty()) - setLastLoc(block.begin()->getLoc()); - else - setLastLoc(builder->getUnknownLoc()); - builder->setInsertionPointToStart(&block); - } - - void setInsertionPointToEnd(mlir::Block &block) { - if (!block.empty()) - setLastLoc(block.back().getLoc()); - else - setLastLoc(builder->getUnknownLoc()); - builder->setInsertionPointToEnd(&block); - } - - void setInsertionPointAfter(mlir::Operation &op) { - setLastLoc(op.getLoc()); - builder->setInsertionPointAfter(&op); - } - - void restoreInsertionPoint(mlir::OpBuilder::InsertPoint pt) { - if (pt.isSet() && pt.getPoint() != pt.getBlock()->end()) - setLastLoc(pt.getPoint()->getLoc()); - else - setLastLoc(builder->getUnknownLoc()); - builder->restoreInsertionPoint(pt); - } - - template OpTy create(Args &&...args) { - auto loc = getLastLoc(); - return builder->create(loc, std::forward(args)...); - } - - // Overload to create or fold a single result operation. - template - std::enable_if_t(), - mlir::Value> - createOrFold(Args &&...args) { - auto loc = getLastLoc(); - return builder->createOrFold(loc, std::forward(args)...); - } - - // Overload to create or fold a zero result operation. - template - std::enable_if_t(), OpTy> - createOrFold(Args &&...args) { - auto loc = getLastLoc(); - return builder->createOrFold(loc, std::forward(args)...); - } - -private: - std::unique_ptr builder; - std::unique_ptr lastLoc; - bool lineInfoEnabled = !triton::tools::getBoolEnv("TRITON_DISABLE_LINE_INFO"); -}; - -/*****************************************************************************/ -/* Python bindings for triton::ir */ -/*****************************************************************************/ - -void init_triton_ir(py::module &&m) { - using ret = py::return_value_policy; - using namespace pybind11::literals; - - py::enum_(m, "PADDING_OPTION") - .value("PAD_ZERO", mlir::triton::PaddingOption::PAD_ZERO) - .value("PAD_NAN", mlir::triton::PaddingOption::PAD_NAN) - .export_values(); - - py::enum_(m, "CACHE_MODIFIER") - .value("NONE", mlir::triton::CacheModifier::NONE) - .value("CA", mlir::triton::CacheModifier::CA) - .value("CG", mlir::triton::CacheModifier::CG) - .value("WB", mlir::triton::CacheModifier::WB) - .value("CS", mlir::triton::CacheModifier::CS) - .value("WT", mlir::triton::CacheModifier::WT) - .export_values(); - - py::enum_(m, "MEM_SEMANTIC") - .value("ACQUIRE_RELEASE", mlir::triton::MemSemantic::ACQUIRE_RELEASE) - .value("ACQUIRE", mlir::triton::MemSemantic::ACQUIRE) - .value("RELEASE", mlir::triton::MemSemantic::RELEASE) - .value("RELAXED", mlir::triton::MemSemantic::RELAXED) - .export_values(); - - py::enum_(m, "EVICTION_POLICY") - .value("NORMAL", mlir::triton::EvictionPolicy::NORMAL) - .value("EVICT_FIRST", mlir::triton::EvictionPolicy::EVICT_FIRST) - .value("EVICT_LAST", mlir::triton::EvictionPolicy::EVICT_LAST) - .export_values(); - - py::enum_(m, "ATOMIC_OP") - .value("ADD", mlir::triton::RMWOp::ADD) - .value("FADD", mlir::triton::RMWOp::FADD) - .value("AND", mlir::triton::RMWOp::AND) - .value("OR", mlir::triton::RMWOp::OR) - .value("XOR", mlir::triton::RMWOp::XOR) - .value("XCHG", mlir::triton::RMWOp::XCHG) - .value("MAX", mlir::triton::RMWOp::MAX) - .value("MIN", mlir::triton::RMWOp::MIN) - .value("UMIN", mlir::triton::RMWOp::UMIN) - .value("UMAX", mlir::triton::RMWOp::UMAX); - - py::class_(m, "context") - .def(py::init<>()) - .def("load_triton", [](mlir::MLIRContext &self) { - self.getOrLoadDialect(); - self.getOrLoadDialect(); - self.getOrLoadDialect(); - self.getOrLoadDialect(); - // we load LLVM because the frontend uses LLVM.undef for - // some placeholders - self.getOrLoadDialect(); - }); - // .def(py::init([](){ - // mlir::MLIRContext context; - // context.getOrLoadDialect(); - // // TODO: should we return a (raw/unique) pointer here? - // return context; - // })); - - // py::class_(m, "value") - // .def("multiple_of", [](ir::value *self, int val) { - // if (auto *instr = dynamic_cast(self)) { - // instr->set_metadata(ir::metadata::multiple_of, val); - // } else - // throw std::runtime_error("multiple_of"); - // }) - // .def("max_contiguous", [](ir::value *self, int val) { - // if (auto *instr = dynamic_cast(self)) { - // instr->set_metadata(ir::metadata::max_contiguous, val); - // } else - // throw std::runtime_error("max_contiguous"); - // }) - // .def("set_fdiv_ieee_rounding", [](ir::value *self, bool val) { - // if (auto *instr = dynamic_cast(self)) - // instr->set_fdiv_ieee_rounding(val); - // else - // throw std::runtime_error("set_fdiv_ieee_rounding"); - // }) - // .def("ops", [](ir::value *self) { - // if (auto *instr = dynamic_cast(self)) { - // return instr->ops(); - // } - // throw std::runtime_error("cannot use ops()"); - // }) - // .def("replace_all_uses_with", &ir::value::replace_all_uses_with) - // .def("erase_from_parent", [](ir::value *self) { - // if (auto *instr = dynamic_cast(self)) - // return instr->erase_from_parent(); - // throw std::runtime_error("cannot use erase_from_parent"); - // }) - // .def_property("name", &ir::value::get_name, &ir::value::set_name) - // .def_property_readonly("type", &ir::value::get_type); - - // // // Do we need under in TritonIR ? - // // py::class_(m, "undef") - // // .def("get", &ir::undef_value::get, ret::reference); - - py::class_(m, "type") - .def("is_integer", &mlir::Type::isInteger) - .def("is_fp16", &mlir::Type::isF16) - .def("__str__", [](mlir::Type &self) { - std::string str; - llvm::raw_string_ostream os(str); - self.print(os); - return os.str(); - }); - - py::class_(m, "function_type") - .def("param_types", [](mlir::FunctionType &self) { - return std::vector(self.getInputs().begin(), - self.getInputs().end()); - }); - - py::class_(m, "location") - .def("__str__", [](mlir::Location &self) { - std::string str; - llvm::raw_string_ostream os(str); - self.print(os); - return os.str(); - }); - - py::class_(m, "value") - .def("set_attr", - [](mlir::Value &self, std::string &name, - mlir::Attribute &attr) -> void { - if (mlir::Operation *definingOp = self.getDefiningOp()) - definingOp->setAttr(name, attr); - else { - auto arg = self.cast(); - int id = arg.getArgNumber(); - std::string attrName = name + "_arg" + std::to_string(id); - mlir::Block *owner = arg.getOwner(); - if (owner->isEntryBlock() && - !mlir::isa(owner->getParentOp())) { - owner->getParentOp()->setAttr(attrName, attr); - } - } - }) - .def("get_context", &mlir::Value::getContext) - .def("replace_all_uses_with", - [](mlir::Value &self, mlir::Value &newValue) { - self.replaceAllUsesWith(newValue); - }) - .def("get_type", &mlir::Value::getType); - - py::class_(m, "block_argument"); - - py::class_(m, "region") - .def("get_parent_region", &mlir::Region::getParentRegion, ret::reference) - .def("size", [](mlir::Region &self) { return self.getBlocks().size(); }) - .def("empty", &mlir::Region::empty); - - py::class_(m, "block") - .def("arg", - [](mlir::Block &self, int index) -> mlir::BlockArgument { - return self.getArgument(index); - }) - .def("add_argument", - [](mlir::Block &self, mlir::Type ty) { - auto loc = mlir::UnknownLoc::get(ty.getContext()); - self.addArgument(ty, loc); - }) - .def("get_num_arguments", &mlir::Block::getNumArguments) - .def("dump", &mlir::Block::dump) - .def("move_before", &mlir::Block::moveBefore) - .def("insert_before", &mlir::Block::insertBefore) - .def("get_parent", &mlir::Block::getParent, ret::reference) - .def("merge_block_before", - [](mlir::Block &self, mlir::Block &dst) { - // ref: RewriterBase::mergeBlocks() - if (self.getNumArguments() != 0) - throw std::runtime_error( - "This block has arguments, don't merge"); - dst.getOperations().splice(dst.begin(), self.getOperations()); - self.dropAllUses(); - self.erase(); - }) - .def("replace_use_in_block_with", - [](mlir::Block &self, mlir::Value &v, mlir::Value &newVal) { - v.replaceUsesWithIf(newVal, [&](mlir::OpOperand &operand) { - mlir::Operation *user = operand.getOwner(); - mlir::Block *currentBlock = user->getBlock(); - while (currentBlock) { - if (currentBlock == &self) - return true; - // Move up one level - currentBlock = - currentBlock->getParent()->getParentOp()->getBlock(); - } - return false; - }); - }) - .def("__str__", - [](mlir::Block &self) { - std::string str; - llvm::raw_string_ostream os(str); - self.print(os); - return str; - }) - .def("has_terminator", - [](mlir::Block &self) { - return !self.empty() && - self.back().hasTrait(); - }) - .def("has_return", - [](mlir::Block &self) { - return !self.empty() && - self.back().hasTrait(); - }) - .def("erase", [](mlir::Block &self) { self.erase(); }); - - // using eattr = ir::attribute_kind_t; - // py::enum_(m, "attribute_kind") - // .value("readonly", eattr::readonly) - // .value("writeonly", eattr::writeonly) - // .value("noalias", eattr::noalias) - // .value("aligned", eattr::aligned) - // .value("multiple_of", eattr::multiple_of) - // .value("retune", eattr::retune) - // .value("not_implemented", eattr::not_implemented); - - py::class_(m, "attribute"); - py::class_(m, "integer_attr"); - py::class_(m, "bool_attr"); - - // Ops - py::class_(m, "OpState") - .def("set_attr", - [](mlir::OpState &self, std::string &name, - mlir::Attribute &attr) -> void { self->setAttr(name, attr); }) - .def( - "get_num_results", - [](mlir::OpState &self) -> unsigned { return self->getNumResults(); }) - .def("get_result", - [](mlir::OpState &self, unsigned idx) -> mlir::Value { - return self->getResult(idx); - }) - .def( - "get_region", - [](mlir::OpState &self, unsigned idx) -> mlir::Region & { - return self->getRegion(idx); - }, - ret::reference) - .def( - "get_body", - [](mlir::scf::ForOp &self, unsigned idx) -> mlir::Block * { - return self.getBody(idx); - }, - ret::reference) - .def("dump", [](mlir::OpState &self) { self->dump(); }) - .def("__str__", - [](mlir::OpState &self) -> std::string { - std::string str; - llvm::raw_string_ostream os(str); - self->print(os); - return str; - }) - .def("append_operand", - [](mlir::OpState &self, mlir::Value &val) { - self->insertOperands(self->getNumOperands(), val); - }) - .def("verify", [](mlir::OpState &self) -> bool { - return mlir::succeeded(mlir::verify(self.getOperation())); - }); - // scf Ops - py::class_(m, "ForOp") - .def("get_induction_var", &mlir::scf::ForOp::getInductionVar); - - py::class_(m, "IfOp") - .def("get_then_block", &mlir::scf::IfOp::thenBlock, ret::reference) - .def("get_else_block", &mlir::scf::IfOp::elseBlock, ret::reference) - .def("get_then_yield", &mlir::scf::IfOp::thenYield) - .def("get_else_yield", &mlir::scf::IfOp::elseYield); - py::class_(m, "YieldOp"); - py::class_(m, "WhileOp") - .def("get_before", &mlir::scf::WhileOp::getBefore, ret::reference) - .def("get_after", &mlir::scf::WhileOp::getAfter, ret::reference); - py::class_(m, "ConditionOp"); - - // dynamic_attr is used to transfer ownership of the MLIR context to the - // module - py::class_(m, "module", py::dynamic_attr()) - .def("dump", &mlir::ModuleOp::dump) - .def("str", - [](mlir::ModuleOp &self) -> std::string { - std::string str; - llvm::raw_string_ostream os(str); - self.print(os); - return str; - }) - .def("bytecode", - [](mlir::ModuleOp &self) -> py::bytearray { - std::string bytecode; - llvm::raw_string_ostream os(bytecode); - if (failed(mlir::writeBytecodeToFile(self, os))) - throw std::runtime_error("Failed to write module bytecode"); - return py::bytearray(bytecode); - }) - .def("push_back", - [](mlir::ModuleOp &self, mlir::triton::FuncOp &funcOp) -> void { - self.push_back(funcOp); - }) - .def("has_function", - [](mlir::ModuleOp &self, std::string &funcName) -> bool { - if (self.lookupSymbol(funcName)) - return true; - return false; - }) - .def("get_function", - [](mlir::ModuleOp &self, - std::string &funcName) -> mlir::triton::FuncOp { - return self.lookupSymbol(funcName); - }) - .def("get_single_function", - [](mlir::ModuleOp &self) -> mlir::triton::FuncOp { - llvm::SmallVector funcs; - self.walk( - [&](mlir::triton::FuncOp func) { funcs.push_back(func); }); - if (funcs.size() != 1) - throw std::runtime_error("Expected a single function"); - return funcs[0]; - }); - - m.def("make_attr", - [](const std::vector &values, mlir::MLIRContext &context) { - return mlir::DenseIntElementsAttr::get( - mlir::RankedTensorType::get( - {static_cast(values.size())}, - mlir::IntegerType::get(&context, 32)), - values) - .cast(); - }); - - m.def( - "parse_mlir_module", - [](const std::string &inputFilename, mlir::MLIRContext &context) { - // initialize registry - // note: we initialize llvm for undef - mlir::DialectRegistry registry; - registry.insert< - mlir::triton::TritonDialect, mlir::triton::gpu::TritonGPUDialect, - mlir::math::MathDialect, mlir::arith::ArithDialect, - mlir::index::IndexDialect, mlir::scf::SCFDialect, - mlir::cf::ControlFlowDialect, mlir::LLVM::LLVMDialect>(); - context.appendDialectRegistry(registry); - context.loadAllAvailableDialects(); - - // parse module - mlir::OwningOpRef module = - mlir::parseSourceFile(inputFilename, &context); - if (!module) - throw std::runtime_error("Parse MLIR file failed."); - // locations are incompatible with ptx < 7.5 ! - module->walk([](mlir::Operation *op) { - op->setLoc(mlir::UnknownLoc::get(op->getContext())); - }); - - return module->clone(); - }, - ret::take_ownership); - - py::class_(m, "function") - // .def_property_readonly("attrs", &ir::function::attrs) - // .def("add_attr", &ir::function::add_attr); - .def("args", - [](mlir::triton::FuncOp &self, unsigned idx) -> mlir::BlockArgument { - return self.getArgument(idx); - }) - .def( - "add_entry_block", - [](mlir::triton::FuncOp &self) -> mlir::Block * { - return self.addEntryBlock(); - }, - ret::reference) - .def( - "set_arg_attr", - [](mlir::triton::FuncOp &self, int arg_no, const std::string &name, - int val) { - // set arg attributes "name" to value "val" - auto attrTy = mlir::IntegerType::get(self.getContext(), 32); - self.setArgAttr(arg_no, name, mlir::IntegerAttr::get(attrTy, val)); - }, - ret::reference) - .def("finalize", - [](mlir::triton::FuncOp &self) -> void { - // Remove dead code - // 1. Unreachable code after return - self.walk([&](mlir::Block *block) { - mlir::Operation *retOp = nullptr; - // It's better to not use walk here because we only want to - // check operations in the current block - for (auto &op : block->getOperations()) { - if (mlir::isa(op)) - if (retOp == nullptr) { - retOp = &op; - break; - } - } - if (retOp && retOp != &block->back()) { - auto pos = retOp->getIterator(); - pos++; - auto *newBlock = block->splitBlock(pos); - newBlock->erase(); - } - }); - }) - .def_property_readonly("type", &mlir::triton::FuncOp::getFunctionType) - .def("reset_type", &mlir::triton::FuncOp::setType); - - py::class_(m, "InsertPoint"); - - py::class_(m, "builder", py::dynamic_attr()) - .def(py::init()) - // getters - .def("create_module", - [](TritonOpBuilder &self) -> mlir::ModuleOp { - return self.create(); - }) - // insertion block/point - .def("set_insertion_point_to_start", - [](TritonOpBuilder &self, mlir::Block &block) -> void { - self.setInsertionPointToStart(block); - }) - .def("set_insertion_point_to_end", - [](TritonOpBuilder &self, mlir::Block &block) { - self.setInsertionPointToEnd(block); - }) - .def("set_insertion_point_after", - [](TritonOpBuilder &self, mlir::Operation &op) { - self.setInsertionPointAfter(op); - }) - .def( - "get_insertion_block", - [](TritonOpBuilder &self) -> mlir::Block * { - return self.getBuilder().getInsertionBlock(); - }, - ret::reference) - .def("get_insertion_point", - [](TritonOpBuilder &self) { - return self.getBuilder().saveInsertionPoint(); - }) - .def("restore_insertion_point", - [](TritonOpBuilder &self, mlir::OpBuilder::InsertPoint pt) { - self.restoreInsertionPoint(pt); - }) - // Attr - .def("get_bool_attr", - [](TritonOpBuilder &self, bool value) { - return self.getBuilder().getBoolAttr(value); - }) - .def("get_int32_attr", - [](TritonOpBuilder &self, int32_t value) { - return self.getBuilder().getI32IntegerAttr(value); - }) - // Use arith.ConstantOp to create constants - // Constants - .def("get_int1", - [](TritonOpBuilder &self, bool v) -> mlir::Value { - return mlir::Value(self.create( - v, self.getBuilder().getI1Type())); - }) - .def("get_int8", - [](TritonOpBuilder &self, int64_t v) -> mlir::Value { - return mlir::Value(self.create( - v, self.getBuilder().getI8Type())); - }) - .def("get_int16", - [](TritonOpBuilder &self, int64_t v) -> mlir::Value { - return mlir::Value(self.create( - v, self.getBuilder().getI16Type())); - }) - .def("get_int32", - [](TritonOpBuilder &self, int64_t v) -> mlir::Value { - return mlir::Value(self.create( - v, self.getBuilder().getI32Type())); - }) - .def("get_int64", - [](TritonOpBuilder &self, int64_t v) -> mlir::Value { - return mlir::Value(self.create( - v, self.getBuilder().getI64Type())); - }) - .def("get_bf16", - [](TritonOpBuilder &self, float v) -> mlir::Value { - auto type = self.getBuilder().getBF16Type(); - return self.create( - mlir::APFloat(type.getFloatSemantics(), std::to_string(v)), - type); - }) - .def("get_fp16", - [](TritonOpBuilder &self, float v) -> mlir::Value { - return self.create( - self.getBuilder().getF16FloatAttr(v)); - }) - .def("get_fp32", - [](TritonOpBuilder &self, float v) -> mlir::Value { - return self.create( - self.getBuilder().getF32FloatAttr(v)); - }) - .def("get_fp64", - [](TritonOpBuilder &self, double v) -> mlir::Value { - return self.create( - self.getBuilder().getF64FloatAttr(v)); - }) - .def("get_null_value", - [](TritonOpBuilder &self, mlir::Type type) -> mlir::Value { - if (auto floatTy = type.dyn_cast()) - return self.create( - mlir::APFloat(floatTy.getFloatSemantics(), 0), floatTy); - else if (auto intTy = type.dyn_cast()) - return self.create(0, intTy); - else - throw std::runtime_error("Not implemented"); - }) - .def("get_all_ones_value", - [](TritonOpBuilder &self, mlir::Type type) -> mlir::Value { - uint64_t val = 0xFFFFFFFFFFFFFFFF; - if (auto intTy = type.dyn_cast()) - return self.create(val, intTy); - else - throw std::runtime_error("Not implemented"); - }) - - // Types - .def("get_void_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getNoneType(); - }) - .def("get_int1_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getI1Type(); - }) // or ret::copy? - .def("get_int8_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getI8Type(); - }) - .def("get_int16_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getType(16); - }) - .def("get_int32_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getI32Type(); - }) - .def("get_int64_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getI64Type(); - }) - .def("get_fp8e4_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getType(); - }) - .def("get_fp8e4b15_ty", - [](TritonOpBuilder &self) -> mlir::Type { - // TODO: upstream FP8E4B15 into MLIR, or find a way to externally - // have a float-like type compatible with float only native ops - return self.getBuilder().getType(); - }) - .def("get_fp8e5_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getType(); - }) - .def("get_half_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getF16Type(); - }) - .def("get_bf16_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getBF16Type(); - }) - .def("get_float_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getF32Type(); - }) - .def("get_double_ty", - [](TritonOpBuilder &self) -> mlir::Type { - return self.getBuilder().getF64Type(); - }) - .def("get_ptr_ty", - [](TritonOpBuilder &self, mlir::Type &type, - int addrSpace) -> mlir::Type { - return mlir::triton::PointerType::get(type, addrSpace); - }) - .def("get_block_ty", - [](TritonOpBuilder &self, mlir::Type &elementType, - std::vector &shape) -> mlir::Type { - return mlir::RankedTensorType::get(shape, elementType); - }) - .def("get_function_ty", - [](TritonOpBuilder &self, std::vector inTypes, - std::vector outTypes) -> mlir::Type { - return self.getBuilder().getFunctionType(inTypes, outTypes); - }) - // locs - .def("set_loc", [](TritonOpBuilder &self, - mlir::Location loc) { self.setLastLoc(loc); }) - .def("set_loc", - [](TritonOpBuilder &self, const std::string &fileName, int line, - int column) { self.setLastLoc(fileName, line, column); }) - .def("get_loc", - [](TritonOpBuilder &self) -> mlir::Location { - return self.getLastLoc(); - }) - - // Ops - .def("get_or_insert_function", - [](TritonOpBuilder &self, mlir::ModuleOp &module, - std::string &funcName, mlir::Type &funcType, - std::string &visibility, bool noinline) -> mlir::triton::FuncOp { - if (mlir::Operation *funcOperation = module.lookupSymbol(funcName)) - return llvm::dyn_cast(funcOperation); - if (auto funcTy = funcType.dyn_cast()) { - llvm::SmallVector attrs = { - mlir::NamedAttribute( - self.getBuilder().getStringAttr("sym_visibility"), - self.getBuilder().getStringAttr(visibility)), - mlir::NamedAttribute( - self.getBuilder().getStringAttr("noinline"), - self.getBuilder().getBoolAttr(noinline))}; - return self.create(funcName, funcTy, - attrs); - } - throw std::runtime_error("invalid function type"); - }) - .def( - "create_block", - [](TritonOpBuilder &self) -> mlir::Block * { - mlir::Region *parent = self.getBuilder().getBlock()->getParent(); - return self.getBuilder().createBlock(parent); - }, - ret::reference) - .def( - "create_block_with_parent", - [](TritonOpBuilder &self, mlir::Region &parent, - std::vector &argTypes) -> mlir::Block * { - // TODO: update arg loc - auto loc = self.getBuilder().getUnknownLoc(); - llvm::SmallVector argLocs(argTypes.size(), loc); - return self.getBuilder().createBlock(&parent, {}, argTypes, - argLocs); - }, - ret::reference) - .def( - "new_block", - [](TritonOpBuilder &self) -> mlir::Block * { - return new mlir::Block(); - }, - ret::reference) - // Function - .def("ret", - [](TritonOpBuilder &self, - std::vector &vals) -> mlir::OpState { - return self.create(vals); - }) - .def("call", - [](TritonOpBuilder &self, mlir::triton::FuncOp &func, - std::vector &args) -> mlir::OpState { - return self.create(func, args); - }) - // Unstructured control flow - .def("create_cond_branch", - [](TritonOpBuilder &self, mlir::Value condition, - mlir::Block *trueDest, mlir::Block *falseDest) -> mlir::OpState { - return self.create(condition, trueDest, - falseDest); - }) - .def("create_branch", - [](TritonOpBuilder &self, mlir::Block *dest, - std::vector &args) -> mlir::OpState { - return self.create(dest, args); - }) - // Structured control flow - .def("create_for_op", - [](TritonOpBuilder &self, mlir::Value &lb, mlir::Value &ub, - mlir::Value &step, - std::vector &initArgs) -> mlir::scf::ForOp { - return self.create(lb, ub, step, initArgs); - }) - .def("create_if_op", - [](TritonOpBuilder &self, std::vector &retTypes, - mlir::Value &condition, bool withElse) -> mlir::scf::IfOp { - return self.create(retTypes, condition, withElse); - }) - .def("create_yield_op", - [](TritonOpBuilder &self, - std::vector &yields) -> mlir::scf::YieldOp { - return self.create(yields); - }) - .def("create_while_op", - [](TritonOpBuilder &self, std::vector &retTypes, - std::vector &initArgs) -> mlir::scf::WhileOp { - return self.create(retTypes, initArgs); - }) - .def("create_condition_op", - [](TritonOpBuilder &self, mlir::Value &cond, - std::vector &args) -> mlir::scf::ConditionOp { - return self.create(cond, args); - }) - - // miscellaneous - .def("create_make_range", - [](TritonOpBuilder &self, int start, int end) -> mlir::Value { - auto retType = mlir::RankedTensorType::get( - {end - start}, self.getBuilder().getI32Type()); - return self.create(retType, start, end); - }) - - // Cast instructions - // Conversions for custom FP types (FP8) - .def("create_fp_to_fp", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - // Conversions for standard LLVM builtin types - .def("create_bitcast", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_si_to_fp", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_ui_to_fp", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_fp_to_si", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_fp_to_ui", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_fp_ext", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_fp_trunc", - [](TritonOpBuilder &self, mlir::Value &src, - mlir::Type &dstType) -> mlir::Value { - return self.create(dstType, src); - }) - .def("create_int_cast", - [](TritonOpBuilder &self, mlir::Value &src, mlir::Type &dstType, - bool isSigned) -> mlir::Value { - // get element type if necessary - mlir::Type srcType = src.getType(); - auto srcTensorType = srcType.dyn_cast(); - auto dstTensorType = dstType.dyn_cast(); - mlir::Type srcEltType = srcType; - mlir::Type dstEltType = dstType; - if (dstTensorType && srcTensorType) { - dstEltType = dstTensorType.getElementType(); - srcEltType = srcTensorType.getElementType(); - } - unsigned srcWidth = srcEltType.getIntOrFloatBitWidth(); - unsigned dstWidth = dstEltType.getIntOrFloatBitWidth(); - if (srcWidth == dstWidth) - return self.create(dstType, src); - else if (srcWidth > dstWidth) - return self.create(dstType, src); - else if (isSigned) - return self.create(dstType, src); - else - return self.create(dstType, src); - }) - .def("create_to_index", - [](TritonOpBuilder &self, mlir::Value &input) -> mlir::Value { - return self.create( - self.getBuilder().getIndexType(), input); - }) - .def("create_index_to_si", - [](TritonOpBuilder &self, mlir::Value &input) -> mlir::Value { - return self.create( - self.getBuilder().getI64Type(), input); - }) - .def("create_fmul", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_fdiv", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_frem", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_fadd", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_fsub", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_mul", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_sdiv", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_udiv", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_srem", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_urem", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_add", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_sub", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return mlir::Value(self.create(lhs, rhs)); - }) - .def("create_shl", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return mlir::Value(self.create(lhs, rhs)); - }) - .def("create_lshr", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return mlir::Value(self.create(lhs, rhs)); - }) - .def("create_ashr", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return mlir::Value(self.create(lhs, rhs)); - }) - // AddPtr (similar to GEP) - .def("create_addptr", - [](TritonOpBuilder &self, mlir::Value &ptr, - mlir::Value &offset) -> mlir::Value { - return self.create(ptr.getType(), ptr, - offset); - }) - // Comparison (int) - .def("create_icmpSLE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::sle, lhs, rhs); - }) - .def("create_icmpSLT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::slt, lhs, rhs); - }) - .def("create_icmpSGE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::sge, lhs, rhs); - }) - .def("create_icmpSGT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::sgt, lhs, rhs); - }) - .def("create_icmpULE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::ule, lhs, rhs); - }) - .def("create_icmpULT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::ult, lhs, rhs); - }) - .def("create_icmpUGE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::uge, lhs, rhs); - }) - .def("create_icmpUGT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::ugt, lhs, rhs); - }) - .def("create_icmpEQ", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::eq, lhs, rhs); - }) - .def("create_icmpNE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpIPredicate::ne, lhs, rhs); - }) - // Comparison (float) - .def("create_fcmpOLT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::OLT, lhs, rhs); - }) - .def("create_fcmpOGT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::OGT, lhs, rhs); - }) - .def("create_fcmpOLE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::OLE, lhs, rhs); - }) - .def("create_fcmpOGE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::OGE, lhs, rhs); - }) - .def("create_fcmpOEQ", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::OEQ, lhs, rhs); - }) - .def("create_fcmpONE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::ONE, lhs, rhs); - }) - .def("create_fcmpULT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::ULT, lhs, rhs); - }) - .def("create_fcmpUGT", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::UGT, lhs, rhs); - }) - .def("create_fcmpULE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::ULE, lhs, rhs); - }) - .def("create_fcmpUGE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::UGE, lhs, rhs); - }) - .def("create_fcmpUEQ", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::UEQ, lhs, rhs); - }) - .def("create_fcmpUNE", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create( - mlir::arith::CmpFPredicate::UNE, lhs, rhs); - }) - // // Logical - .def("create_and", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_xor", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - .def("create_or", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - return self.create(lhs, rhs); - }) - // Input/Output - .def("create_load", - [](TritonOpBuilder &self, mlir::Value &ptrs, - mlir::triton::CacheModifier cacheModifier, - mlir::triton::EvictionPolicy evictionPolicy, - bool isVolatile) -> mlir::Value { - return self.create( - ptrs, cacheModifier, evictionPolicy, isVolatile); - }) - .def("create_store", - [](TritonOpBuilder &self, mlir::Value &ptrs, mlir::Value &value, - mlir::triton::CacheModifier cacheModifier, - mlir::triton::EvictionPolicy evictionPolicy) -> void { - self.create(ptrs, value, cacheModifier, - evictionPolicy); - }) - .def("create_tensor_pointer_load", - [](TritonOpBuilder &self, mlir::Value &ptr, - std::vector &boundaryCheck, - std::optional paddingOption, - mlir::triton::CacheModifier cacheModifier, - mlir::triton::EvictionPolicy evictionPolicy, - bool isVolatile) -> mlir::Value { - return self.create( - ptr, boundaryCheck, paddingOption, cacheModifier, - evictionPolicy, isVolatile); - }) - .def("create_tensor_pointer_store", - [](TritonOpBuilder &self, mlir::Value &ptr, mlir::Value &val, - std::vector &boundaryCheck, - mlir::triton::CacheModifier cacheModifier, - mlir::triton::EvictionPolicy evictionPolicy) -> void { - self.create(ptr, val, boundaryCheck, - cacheModifier, evictionPolicy); - }) - .def("create_masked_load", - [](TritonOpBuilder &self, mlir::Value &ptrs, mlir::Value &mask, - std::optional &other, - mlir::triton::CacheModifier cacheModifier, - mlir::triton::EvictionPolicy evictionPolicy, - bool isVolatile) -> mlir::Value { - return self.create( - ptrs, mask, other.value_or(mlir::Value()), cacheModifier, - evictionPolicy, isVolatile); - }) - .def("create_masked_store", - [](TritonOpBuilder &self, mlir::Value &ptrs, mlir::Value &val, - mlir::Value &mask, mlir::triton::CacheModifier cacheModifier, - mlir::triton::EvictionPolicy evictionPolicy) -> void { - self.create(ptrs, val, mask, cacheModifier, - evictionPolicy); - }) - .def("create_view", - [](TritonOpBuilder &self, mlir::Value &arg, - std::vector &shape) -> mlir::Value { - auto argType = arg.getType() - .dyn_cast() - .getElementType(); - return self.create( - mlir::RankedTensorType::get(shape, argType), arg); - }) - .def( - "create_expand_dims", - [](TritonOpBuilder &self, mlir::Value &arg, int axis) -> mlir::Value { - auto argType = arg.getType().dyn_cast(); - auto argEltType = argType.getElementType(); - std::vector retShape = argType.getShape(); - retShape.insert(retShape.begin() + axis, 1); - return self.create( - mlir::RankedTensorType::get(retShape, argEltType), arg, axis); - }) - .def("create_cat", - [](TritonOpBuilder &self, mlir::Value &lhs, - mlir::Value &rhs) -> mlir::Value { - auto lhsType = lhs.getType().dyn_cast(); - auto rhsType = rhs.getType().dyn_cast(); - if (!(lhsType.getShape().size() == 1 && - rhsType.getShape().size() == 1)) - throw std::runtime_error( - "shape not supported by cat. Expecting rank-1 inputs"); - std::vector shape{lhsType.getShape()[0] + - rhsType.getShape()[0]}; - return self.create( - mlir::RankedTensorType::get(shape, lhsType.getElementType()), - lhs, rhs); - }) - .def("create_trans", - [](TritonOpBuilder &self, mlir::Value &arg) -> mlir::Value { - auto argType = arg.getType().dyn_cast(); - auto argEltType = argType.getElementType(); - std::vector retShape = argType.getShape(); - std::reverse(retShape.begin(), retShape.end()); - return self.create( - mlir::RankedTensorType::get(retShape, argEltType), arg); - }) - .def("create_broadcast", - [](TritonOpBuilder &self, mlir::Value &arg, - std::vector &shape) -> mlir::Value { - if (auto argType = - arg.getType().dyn_cast()) - return self.createOrFold( - mlir::RankedTensorType::get(shape, argType.getElementType()), - arg); - throw std::runtime_error( - "arg is not of RankedTensorType, use create_splat"); - }) - .def("create_splat", - [](TritonOpBuilder &self, mlir::Value &arg, - std::vector &shape) -> mlir::Value { - auto argType = arg.getType(); - auto ret = self.createOrFold( - mlir::RankedTensorType::get(shape, argType), arg); - return ret; - }) - // // atomic - .def("create_atomic_cas", - [](TritonOpBuilder &self, mlir::Value &ptr, mlir::Value &cmp, - mlir::Value &val, mlir::triton::MemSemantic sem) -> mlir::Value { - mlir::Type dstType; - if (auto srcTensorType = - ptr.getType().dyn_cast()) { - mlir::Type dstElemType = srcTensorType.getElementType() - .cast() - .getPointeeType(); - dstType = mlir::RankedTensorType::get(srcTensorType.getShape(), - dstElemType); - } else { - auto ptrType = mlir::getElementTypeOrSelf(ptr) - .cast(); - dstType = ptrType.getPointeeType(); - } - return self.create(dstType, ptr, cmp, - val, sem); - }) - .def("create_atomic_rmw", - [](TritonOpBuilder &self, mlir::triton::RMWOp rmwOp, - mlir::Value &ptr, mlir::Value &val, mlir::Value &mask, - mlir::triton::MemSemantic sem) -> mlir::Value { - mlir::Type dstType; - if (auto srcTensorType = - ptr.getType().dyn_cast()) { - mlir::Type dstElemType = srcTensorType.getElementType() - .cast() - .getPointeeType(); - dstType = mlir::RankedTensorType::get(srcTensorType.getShape(), - dstElemType); - } else { - auto ptrType = mlir::getElementTypeOrSelf(ptr) - .cast(); - dstType = ptrType.getPointeeType(); - } - return self.create(dstType, rmwOp, ptr, - val, mask, sem); - }) - // External - .def("create_extern_elementwise", - [](TritonOpBuilder &self, const std::string &libName, - const std::string &libPath, const std::string &symbol, - std::vector &argList, mlir::Type retType, - bool isPure) -> mlir::Value { - if (isPure) - return self.create( - retType, argList, libName, libPath, symbol); - else - return self.create( - retType, argList, libName, libPath, symbol); - }) - // Built-in instruction - .def("create_get_program_id", - [](TritonOpBuilder &self, int axis) -> mlir::Value { - if (axis < 0 || axis > 3) - throw std::runtime_error("program_id must be in [0,3]"); - return self.create( - self.getBuilder().getI32Type(), - mlir::triton::ProgramIDDimAttr::get( - self.getBuilder().getContext(), - mlir::triton::ProgramIDDim(axis))); - }) - .def("create_get_num_programs", - [](TritonOpBuilder &self, int axis) -> mlir::Value { - return self.create( - self.getBuilder().getI32Type(), - self.getBuilder().getI32IntegerAttr(axis)); - }) - .def("create_dot", - [](TritonOpBuilder &self, mlir::Value &a, mlir::Value &b, - mlir::Value &c, bool allowTF32) -> mlir::Value { - return self.create(c.getType(), a, b, c, - allowTF32); - }) - .def("create_exp", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_cos", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_sin", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_log", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_sqrt", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_fabs", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_iabs", - [](TritonOpBuilder &self, mlir::Value &val) -> mlir::Value { - return self.create(val); - }) - .def("create_reduce", - [](TritonOpBuilder &self, std::vector operands, - int axis) -> mlir::OpState { - return self.create(operands, axis); - }) - .def("create_reduce_ret", - [](TritonOpBuilder &self, py::args args) -> mlir::OpState { - llvm::SmallVector return_values; - for (const auto &arg : args) { - return_values.push_back(py::cast(arg)); - } - return self.create(return_values); - }) - .def("create_scan", - [](TritonOpBuilder &self, std::vector operands, - int axis) -> mlir::OpState { - return self.create(operands, axis); - }) - .def("create_scan_ret", - [](TritonOpBuilder &self, py::args args) -> mlir::OpState { - llvm::SmallVector return_values; - for (const auto &arg : args) { - return_values.push_back(py::cast(arg)); - } - return self.create(return_values); - }) - .def("create_ptr_to_int", - [](TritonOpBuilder &self, mlir::Value &val, - mlir::Type &type) -> mlir::Value { - return self.create(type, val); - }) - .def("create_int_to_ptr", - [](TritonOpBuilder &self, mlir::Value &val, - mlir::Type &type) -> mlir::Value { - return self.create(type, val); - }) - .def("create_select", - [](TritonOpBuilder &self, mlir::Value &condition, - mlir::Value &trueValue, mlir::Value &falseValue) -> mlir::Value { - return self.create(condition, trueValue, - falseValue); - }) - .def("create_print", - [](TritonOpBuilder &self, const std::string &prefix, - const std::vector &values) -> void { - self.create( - mlir::StringAttr::get(self.getBuilder().getContext(), - llvm::StringRef(prefix)), - values); - }) - .def("create_assert", - [](TritonOpBuilder &self, mlir::Value &condition, - const std::string &message, const std::string &fileName, - const std::string &funcName, unsigned lineNo) -> void { - auto messageAttr = mlir::StringAttr::get( - self.getBuilder().getContext(), llvm::StringRef(message)); - auto fileNameAttr = mlir::StringAttr::get( - self.getBuilder().getContext(), llvm::StringRef(fileName)); - auto funcNameAttr = mlir::StringAttr::get( - self.getBuilder().getContext(), llvm::StringRef(funcName)); - auto lineNoAttr = self.getBuilder().getI32IntegerAttr(lineNo); - self.create(condition, messageAttr, - fileNameAttr, funcNameAttr, - lineNoAttr); - }) - // Undef - .def("create_undef", - [](TritonOpBuilder &self, mlir::Type &type) -> mlir::Value { - return self.create<::mlir::LLVM::UndefOp>(type); - }) - // Force GPU barrier - .def("create_barrier", - [](TritonOpBuilder &self) { self.create(); }) - // Make a block pointer (tensor pointer in Triton IR) - .def("create_make_block_ptr", - [](TritonOpBuilder &self, mlir::Value &base, - std::vector &shape, - std::vector &strides, - std::vector &offsets, - std::vector &tensorShape, - std::vector &order) -> mlir::Value { - return self.create( - base, shape, strides, offsets, tensorShape, order); - }) - // Advance a block pointer - .def("create_advance", - [](TritonOpBuilder &self, mlir::Value &ptr, - std::vector &offsets) -> mlir::Value { - return self.create(ptr.getType(), ptr, - offsets); - }); - - py::class_(m, "pass_manager") - .def(py::init()) - .def("enable_debug", - [](mlir::PassManager &self) { - auto printingFlags = mlir::OpPrintingFlags(); - printingFlags.elideLargeElementsAttrs(16); - printingFlags.enableDebugInfo(); - self.enableIRPrinting( - /*shouldPrintBeforePass=*/nullptr, - /*shouldPrintAfterPass=*/ - [](mlir::Pass *pass, mlir::Operation *) { - return ::triton::tools::getBoolEnv("MLIR_ENABLE_DUMP"); - }, - /*printModuleScope=*/false, - /*printAfterOnlyOnChange=*/true, - /*printAfterOnlyOnFailure*/ false, llvm::dbgs(), - printingFlags); - }) - .def("run", - [](mlir::PassManager &self, mlir::ModuleOp &mod) { - // TODO: maybe dump module to file and print error for better - // diagnostics - if (mlir::failed(self.run(mod.getOperation()))) - throw std::runtime_error("PassManager::run failed"); - }) - .def( - "add_sccp_pass", - [](mlir::PassManager &self) { self.addPass(mlir::createSCCPPass()); }) - .def("add_tritongpu_coalesce_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createTritonGPUCoalescePass()); - }) - .def("add_symbol_dce_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createSymbolDCEPass()); - }) - .def("add_inliner_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createInlinerPass()); - }) - .def("add_canonicalizer_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createCanonicalizerPass()); - }) - .def("add_cse_pass", - [](mlir::PassManager &self) { self.addPass(mlir::createCSEPass()); }) - .def("add_licm_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createLoopInvariantCodeMotionPass()); - }) - .def("add_triton_combine_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::triton::createCombineOpsPass()); - }) - .def("add_rewrite_tensor_pointer_pass", - [](mlir::PassManager &self, int computeCapability) { - self.addPass(mlir::triton::createRewriteTensorPointerPass( - computeCapability)); - }) - .def( - "add_convert_triton_to_tritongpu_pass", - [](mlir::PassManager &self, int numWarps, int threadsPerWarp) { - self.addPass(mlir::triton::createConvertTritonToTritonGPUPass( - numWarps, threadsPerWarp)); - }, - py::arg("numWarps") = 4, py::arg("threadsPerWarp") = 32) - .def("add_tritongpu_pipeline_pass", - [](mlir::PassManager &self, int numStages) { - self.addPass(mlir::createTritonGPUPipelinePass(numStages)); - }) - .def("add_tritongpu_prefetch_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createTritonGPUPrefetchPass()); - }) - .def("add_tritongpu_accelerate_matmul_pass", - [](mlir::PassManager &self, int computeCapability) { - self.addPass( - mlir::createTritonGPUAccelerateMatmulPass(computeCapability)); - }) - .def("add_tritongpu_optimize_dot_operands_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createTritonGPUOptimizeDotOperandsPass()); - }) - .def("add_tritongpu_remove_layout_conversions_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createTritonGPURemoveLayoutConversionsPass()); - }) - .def("add_tritongpu_reorder_instructions_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createTritonGPUReorderInstructionsPass()); - }) - .def("add_tritongpu_decompose_conversions_pass", - [](mlir::PassManager &self) { - self.addPass(mlir::createTritonGPUDecomposeConversionsPass()); - }) - .def("add_triton_gpu_to_llvm", - [](mlir::PassManager &self) { - self.addPass(mlir::triton::createConvertTritonGPUToLLVMPass()); - }) - .def("add_scf_to_cfg", [](mlir::PassManager &self) { - self.addPass(mlir::createConvertSCFToCFPass()); - }); -} - -void init_triton_translation(py::module &m) { - using ret = py::return_value_policy; - - m.def("get_shared_memory_size", [](mlir::ModuleOp mod) { - auto shared = mod->getAttrOfType("triton_gpu.shared"); - return shared.getInt(); - }); - - m.def( - "translate_triton_gpu_to_llvmir", - [](mlir::ModuleOp op, int computeCapability, bool isROCM) { - py::gil_scoped_release allow_threads; - llvm::LLVMContext llvmContext; - auto llvmModule = ::mlir::triton::translateTritonGPUToLLVMIR( - &llvmContext, op, computeCapability, isROCM); - if (!llvmModule) - llvm::report_fatal_error("Failed to translate TritonGPU to LLVM IR."); - - std::string str; - llvm::raw_string_ostream os(str); - llvmModule->print(os, nullptr); - os.flush(); - return str; - }, - ret::take_ownership); - - m.def( - "translate_llvmir_to_ptx", - [](const std::string llvmIR, int capability, int version) -> std::string { - py::gil_scoped_release allow_threads; - // create LLVM module from C++ - llvm::LLVMContext context; - std::unique_ptr buffer = - llvm::MemoryBuffer::getMemBuffer(llvmIR.c_str()); - llvm::SMDiagnostic error; - std::unique_ptr module = - llvm::parseIR(buffer->getMemBufferRef(), error, context); - if (!module) { - llvm::report_fatal_error( - "failed to parse IR: " + error.getMessage() + - "lineno: " + std::to_string(error.getLineNo())); - } - // translate module to PTX - auto ptxCode = - triton::translateLLVMIRToPTX(*module, capability, version); - return ptxCode; - }, - ret::take_ownership); - - m.def( - "compile_ptx_to_cubin", - [](const std::string &ptxCode, const std::string &ptxasPath, - int capability) -> py::object { - std::string cubin; - { - py::gil_scoped_release allow_threads; - - // compile ptx with ptxas - llvm::SmallString<64> fsrc; - llvm::SmallString<64> flog; - llvm::sys::fs::createTemporaryFile("compile-ptx-src", "", fsrc); - llvm::sys::fs::createTemporaryFile("compile-ptx-log", "", flog); - std::string fbin = std::string(fsrc) + ".o"; - llvm::FileRemover logRemover(flog); - llvm::FileRemover binRemover(fbin); - const char *_fsrc = fsrc.c_str(); - const char *_flog = flog.c_str(); - const char *_fbin = fbin.c_str(); - std::ofstream ofs(_fsrc); - ofs << ptxCode << std::endl; - ofs.close(); - - auto lineInfoOption = - triton::tools::getBoolEnv("TRITON_DISABLE_LINE_INFO") - ? "" - : " -lineinfo"; - auto capabilitySuffix = (capability == 90) ? "a " : " "; - auto outputFileName = std::string(_fsrc) + ".o"; - auto logRedirect = " 2> " + std::string(_flog); - std::string cmd = ptxasPath + lineInfoOption + " -v --gpu-name=sm_" + - std::to_string(capability) + capabilitySuffix + - _fsrc + " -o " + outputFileName + logRedirect; - - int err = system(cmd.c_str()); - if (err != 0) { - err >>= 8; - std::ifstream _log(_flog); - std::string log(std::istreambuf_iterator(_log), {}); - if (err == 255) { - throw std::runtime_error("Internal Triton PTX codegen error: \n" + - log); - } else if (err == 128 + SIGSEGV) { - throw std::runtime_error("Please run `ptxas " + fsrc.str().str() + - "` to confirm that this is a " - "bug in `ptxas`\n" + - log); - } else { - throw std::runtime_error("`ptxas` failed with error code " + - std::to_string(err) + ": \n" + log); - } - return {}; - } else { - llvm::FileRemover srcRemover(fsrc); - std::ifstream _cubin(_fbin, std::ios::binary); - cubin = std::string(std::istreambuf_iterator(_cubin), {}); - _cubin.close(); - // Do not return here, exit the gil scope and return below - } - } - py::bytes bytes(cubin); - return std::move(bytes); - }); - - m.def("add_external_libs", - [](mlir::ModuleOp &op, const std::vector &names, - const std::vector &paths) { - ::mlir::triton::addExternalLibs(op, names, paths); - }); - - m.def( - "translate_llvmir_to_hsaco", - [](const std::string llvmIR, std::string gfx_arch, std::string gfx_triple, - std::string gfx_features) -> std::tuple { - // create LLVM module from C++ - llvm::LLVMContext context; - std::unique_ptr buffer = - llvm::MemoryBuffer::getMemBuffer(llvmIR.c_str()); - llvm::SMDiagnostic error; - std::unique_ptr module = - llvm::parseIR(buffer->getMemBufferRef(), error, context); - // translate module to HSACO - auto hsacoCode = triton::translateLLVMIRToHSACO( - *module, gfx_arch, gfx_triple, gfx_features); - return hsacoCode; - }, - ret::take_ownership); -} - -void init_triton(py::module &m) { - py::module subm = m.def_submodule("triton"); - // init_triton_codegen(subm.def_submodule("code_gen")); - init_triton_runtime(subm.def_submodule("runtime")); - init_triton_ir(subm.def_submodule("ir")); - init_triton_translation(subm); -} diff --git a/python/test/backend/extension_backend.c b/python/test/backend/extension_backend.c deleted file mode 100644 index 4a1e08bf0f29..000000000000 --- a/python/test/backend/extension_backend.c +++ /dev/null @@ -1,42 +0,0 @@ -#include -#include -#include - -static PyObject *getDeviceProperties(PyObject *self, PyObject *args) { - // create a struct to hold device properties - return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i}", "max_shared_mem", 1024, - "multiprocessor_count", 16, "sm_clock_rate", 2100, - "mem_clock_rate", 2300, "mem_bus_width", 2400); -} - -static PyObject *loadBinary(PyObject *self, PyObject *args) { - // get allocated registers and spilled registers from the function - int n_regs = 0; - int n_spills = 0; - int mod = 0; - int fun = 0; - return Py_BuildValue("(KKii)", (uint64_t)mod, (uint64_t)fun, n_regs, - n_spills); -} - -static PyMethodDef ModuleMethods[] = { - {"load_binary", loadBinary, METH_VARARGS, - "Load dummy binary for the extension device"}, - {"get_device_properties", getDeviceProperties, METH_VARARGS, - "Get the properties for the extension device"}, - {NULL, NULL, 0, NULL} // sentinel -}; - -static struct PyModuleDef ModuleDef = {PyModuleDef_HEAD_INIT, "ext_utils", - NULL, // documentation - -1, // size - ModuleMethods}; - -PyMODINIT_FUNC PyInit_ext_utils(void) { - PyObject *m = PyModule_Create(&ModuleDef); - if (m == NULL) { - return NULL; - } - PyModule_AddFunctions(m, ModuleMethods); - return m; -} diff --git a/python/test/backend/test_device_backend.py b/python/test/backend/test_device_backend.py deleted file mode 100644 index 238398cdf850..000000000000 --- a/python/test/backend/test_device_backend.py +++ /dev/null @@ -1,262 +0,0 @@ -import functools -import hashlib -import importlib -import os -import shutil -import subprocess -import sysconfig -import tempfile -from pathlib import Path - -import setuptools -import torch - -import triton -import triton.language as tl -from triton.common.backend import BaseBackend, register_backend -from triton.common.build import quiet -from triton.compiler.make_launcher import make_so_cache_key -from triton.runtime.cache import get_cache_manager -from triton.runtime.driver import DriverBase -from triton.runtime.jit import version_key - - -def build_for_backend(name, src, srcdir): - suffix = sysconfig.get_config_var('EXT_SUFFIX') - so = os.path.join(srcdir, '{name}{suffix}'.format(name=name, suffix=suffix)) - # try to avoid setuptools if possible - cc = os.environ.get("CC") - if cc is None: - # TODO: support more things here. - clang = shutil.which("clang") - gcc = shutil.which("gcc") - cc = gcc if gcc is not None else clang - if cc is None: - raise RuntimeError("Failed to find C compiler. Please specify via CC environment variable.") - # This function was renamed and made public in Python 3.10 - if hasattr(sysconfig, 'get_default_scheme'): - scheme = sysconfig.get_default_scheme() - else: - scheme = sysconfig._get_default_scheme() - # 'posix_local' is a custom scheme on Debian. However, starting Python 3.10, the default install - # path changes to include 'local'. This change is required to use triton with system-wide python. - if scheme == 'posix_local': - scheme = 'posix_prefix' - py_include_dir = sysconfig.get_paths(scheme=scheme)["include"] - - ret = subprocess.check_call([cc, src, f"-I{py_include_dir}", f"-I{srcdir}", "-shared", "-fPIC", "-o", so]) - if ret == 0: - return so - # fallback on setuptools - extra_compile_args = [] - library_dirs = [] - include_dirs = [srcdir] - libraries = [] - # extra arguments - extra_link_args = [] - # create extension module - ext = setuptools.Extension( - name=name, - language='c', - sources=[src], - include_dirs=include_dirs, - extra_compile_args=extra_compile_args + ['-O3'], - extra_link_args=extra_link_args, - library_dirs=library_dirs, - libraries=libraries, - ) - # build extension module - args = ['build_ext'] - args.append('--build-temp=' + srcdir) - args.append('--build-lib=' + srcdir) - args.append('-q') - args = dict( - name=name, - ext_modules=[ext], - script_args=args, - ) - with quiet(): - setuptools.setup(**args) - return so - - -class ExtensionUtils: - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(ExtensionUtils, cls).__new__(cls) - return cls.instance - - def __init__(self): - dirname = os.path.dirname(os.path.realpath(__file__)) - src = Path(os.path.join(dirname, "extension_backend.c")).read_text() - key = hashlib.md5(src.encode("utf-8")).hexdigest() - cache = get_cache_manager(key) - fname = "ext_utils.so" - cache_path = cache.get_file(fname) - if cache_path is None: - with tempfile.TemporaryDirectory() as tmpdir: - src_path = os.path.join(tmpdir, "main.c") - with open(src_path, "w") as f: - f.write(src) - so = build_for_backend("ext_utils", src_path, tmpdir) - with open(so, "rb") as f: - cache_path = cache.put(f.read(), fname, binary=True) - import importlib.util - spec = importlib.util.spec_from_file_location("ext_utils", cache_path) - mod = importlib.util.module_from_spec(spec) - spec.loader.exec_module(mod) - self.load_binary = mod.load_binary - self.get_device_properties = mod.get_device_properties - - -class ExtensionDriver(DriverBase): - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(ExtensionDriver, cls).__new__(cls) - return cls.instance - - def __init__(self): - self.utils = ExtensionUtils() - - -class ExtensionBackend(BaseBackend): - stub_so_path = "" - - def __init__(self, device_type: str) -> None: - super(ExtensionBackend, self).__init__(device_type) - self.driver = ExtensionDriver() - - def add_stages(self, arch, extern_libs, stages): - filter_in_stages = ["ast", "ttir", "ttgir"] - filter_out_stages = [] - for key, _ in stages.items(): - if key not in filter_in_stages: - filter_out_stages.append(key) - for filter_out_key in filter_out_stages: - stages.pop(filter_out_key) - - def add_meta_info(self, ir, cur_module, next_module, metadata, asm): - metadata["name"] = "extension_backend_name" - - def get_driver(self): - return self.driver - - def get_stream(self): - return "" - - @functools.lru_cache(None) - def get_device_properties(self, device): - return self.driver.utils.get_device_properties() - - def get_current_device(self): - return torch.device("cpu") - - def set_current_device(self, device): - pass - - def get_load_binary_fn(self): - return self.driver.utils.load_binary - - def get_kernel_bin(self): - return "ttgir" - - def get_architecture_descriptor(self, **kwargs): - return "" - - def make_launcher_stub(self, name, signature, constants): - # name of files that are cached - so_cache_key = make_so_cache_key(version_key(), signature, constants) - so_cache_manager = get_cache_manager(so_cache_key) - so_name = f"{name}.so" - # retrieve stub from cache if it exists - cache_path = so_cache_manager.get_file(so_name) - if cache_path is None: - with tempfile.TemporaryDirectory() as tmpdir: - src = self._generate_launcher(constants, signature) - src_path = os.path.join(tmpdir, "main.c") - with open(src_path, "w") as f: - f.write(src) - so = build_for_backend(name, src_path, tmpdir) - with open(so, "rb") as f: - so_path = so_cache_manager.put(f.read(), so_name, binary=True) - type(self).stub_so_path = so_path - return so_path - else: - type(self).stub_so_path = cache_path - return cache_path - - def _generate_launcher(self, constants, signature): - # generate glue code - src = """ - #define __EXTENSION_BACKEND__ - #include - #include - - static PyObject* launch_counter(PyObject* self, PyObject* args) { - static int64_t launch_counter = 0; - launch_counter += 1; - return PyLong_FromLong(launch_counter); - } - - static PyObject* launch(PyObject* self, PyObject* args) { - if (PyErr_Occurred()) { - return NULL; - } - launch_counter(self, args); - // return None - Py_INCREF(Py_None); - return Py_None; - } - - static PyMethodDef ModuleMethods[] = { - {"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}, - {"launch_counter", launch_counter, METH_VARARGS, "Entry point to get launch counter"}, - {NULL, NULL, 0, NULL} // sentinel - }; - - static struct PyModuleDef ModuleDef = { - PyModuleDef_HEAD_INIT, - \"__triton_launcher\", - NULL, //documentation - -1, //size - ModuleMethods - }; - - PyMODINIT_FUNC PyInit___triton_launcher(void) { - PyObject *m = PyModule_Create(&ModuleDef); - if(m == NULL) { - return NULL; - } - PyModule_AddFunctions(m, ModuleMethods); - return m; - } - """ - - return src - - -def test_dummy_backend(): - register_backend("cpu", ExtensionBackend) - - @triton.jit - def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): - xnumel = 10 - xoffset = tl.program_id(0) * XBLOCK - xindex = xoffset + tl.arange(0, XBLOCK)[:] - xmask = xindex < xnumel - x0 = xindex - tmp0 = tl.load(in_ptr0 + (x0), xmask) - tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp0, xmask) - - inp = torch.randn(10) - out = torch.randn(10) - kernel[(10,)](inp, out, 10, XBLOCK=16) - spec = importlib.util.spec_from_file_location("__triton_launcher", ExtensionBackend.stub_so_path) - mod = importlib.util.module_from_spec(spec) - spec.loader.exec_module(mod) - launch_counter = getattr(mod, "launch_counter") - - for _ in range(100): - kernel[(10,)](inp, out, 10, XBLOCK=16) - - assert launch_counter() > 0 diff --git a/python/test/backend/third_party_backends/conftest.py b/python/test/backend/third_party_backends/conftest.py deleted file mode 100644 index 62ee6c68976e..000000000000 --- a/python/test/backend/third_party_backends/conftest.py +++ /dev/null @@ -1,14 +0,0 @@ -# content of conftest.py - -import pytest - - -def pytest_addoption(parser): - parser.addoption( - "--backend", action="store", default="", help="Codegen backend" - ) - - -@pytest.fixture -def cmdopt(request): - return request.config.getoption("--backend") diff --git a/python/test/backend/third_party_backends/test_xpu_backend.py b/python/test/backend/third_party_backends/test_xpu_backend.py deleted file mode 100644 index f00261f193de..000000000000 --- a/python/test/backend/third_party_backends/test_xpu_backend.py +++ /dev/null @@ -1,33 +0,0 @@ -import torch - -import triton -import triton.language as tl - - -def test_xpu_backend(cmdopt): - if cmdopt == "xpu": - has_ipex = False - try: - # Import IPEX to provide Intel GPU runtime - import intel_extension_for_pytorch # type: ignore # noqa: F401 - has_ipex = True if hasattr(torch, "xpu") else False - except Exception: - has_ipex = False - - @triton.jit() - def kernel(x_ptr, y_ptr, out_ptr): - pid = tl.program_id(axis=0) - x = tl.load(x_ptr + pid) - y = tl.load(y_ptr + pid) - out = x + y - tl.store(out_ptr + pid, out) - - if has_ipex: - for _ in range(1000): - x = torch.randn((65536,), device="xpu", dtype=torch.float32) - y = torch.randn((65536,), device="xpu", dtype=torch.float32) - z = torch.zeros((65536,), device="xpu", dtype=torch.float32) - kernel[(65536,)](x, y, z, num_warps=32) - assert torch.all(x + y == z) - else: - return diff --git a/python/test/kernel_comparison/kernels.yml b/python/test/kernel_comparison/kernels.yml deleted file mode 100644 index 87292ef80735..000000000000 --- a/python/test/kernel_comparison/kernels.yml +++ /dev/null @@ -1,31 +0,0 @@ -name_and_extension: - - name: _kernel_0d1d2d34567c89c1011c - extension: ptx - - name: _kernel_0d1d2d3d4d5d6d7c8d9c10d11c - extension: ptx - - name: _kernel_0d1d2d3d4d5d6d7c8c9d10d11c - extension: ptx - - name: _kernel_0d1d2d3456c789c1011c - extension: ptx - - name: _kernel_0d1d2d345d6d7c8c9d1011c - extension: ptx - - name: _kernel_0d1d2d34567c8c91011c - extension: ptx - - name: _kernel_0d1d2d3456c78c91011c - extension: ptx - - name: _kernel_0d1d2d345d6c78c9d1011c - extension: ptx - - name: _kernel_0d1d2d345d6c789c1011c - extension: ptx - - name: _kernel_0d1d2d3d4d5d6c7d8d9c10d11c - extension: ptx - - name: _kernel_0d1d2d3d4d5d6c7d8c9d10d11c - extension: ptx - - name: _kernel_0d1d2d345d6d7c89c1011c - extension: ptx - - name: _bwd_kernel_0d1d2d34d5d6d7d8d9d10d11d12d13d14d15c16d17d18d19c20d21d22d23c2425d26d27 - extension: ptx - - name: _fwd_kernel_0d1d2d34d5d6d7d8d9d10c11d12d13d14c15d16d17d18c19d20d21d22c2324d25d - extension: ptx - - name: _bwd_preprocess_0d1d2d3d4d - extension: ptx diff --git a/python/test/regression/test_functional_regressions.py b/python/test/regression/test_functional_regressions.py deleted file mode 100644 index 684cbfb4dd6f..000000000000 --- a/python/test/regression/test_functional_regressions.py +++ /dev/null @@ -1,230 +0,0 @@ -import numpy as np -import pytest -import torch -from numpy.random import RandomState - -import triton -import triton.language as tl - - -def test_chained_matmul(): - # Regression test for issue #1601 - def chained_matmul_reference(a, b, c): - intermediate = torch.einsum('MK,NK->MN', a, b) - return torch.einsum('MN,NK->MK', intermediate, c) - - @triton.jit - def chained_matmul_kernel( - A, # shape: (m, k) - B, # shape: (n, k) - C, # shape: (n, k) - out, # shape: (m, k) - m, n, k: tl.constexpr, - block_m: tl.constexpr, - block_n: tl.constexpr, - block_k: tl.constexpr): - - tl.static_assert(block_k == k, - f"expected block_k == k but got {block_k} != {k}") - - block_ix = tl.program_id(0) - a_tile = (block_ix * block_m + tl.arange(0, block_m))[:, None] * block_k \ - + tl.arange(0, block_k)[None, :] - - a = tl.load(A + a_tile, mask=a_tile < m * k, other=0.0) - - acc = tl.zeros([block_m, block_k], dtype=tl.float32) - - for loop_block_start in range(0, n, block_n): - bc_tile = (loop_block_start + tl.arange(0, block_n))[:, None] * block_k \ - + tl.arange(0, block_k)[None, :] - b = tl.load(B + bc_tile, mask=bc_tile < n * k, other=0.0) - - intermediate = tl.dot(a, tl.trans(b)) - intermediate_mask = ((loop_block_start + tl.arange(0, block_n)) < n)[None, :] \ - * (tl.arange(0, block_m) < m)[:, None] - - intermediate = tl.where(intermediate_mask, intermediate, 0.0) - - c = tl.load(C + bc_tile, mask=bc_tile < n * k) - - acc += tl.dot(intermediate.to(A.dtype.element_ty), c) - - tl.store(out + a_tile, acc.to(A.dtype.element_ty), mask=a_tile < m * k) - - m, n, k = 32, 64, 128 - block_m, block_n, block_k = 16, 32, k - - grid = (triton.cdiv(m, block_m),) - a = torch.randint(low=0, high=2, size=(m, k), dtype=torch.float16, - device='cuda') - b = torch.randint(low=0, high=2, size=(n, k), dtype=torch.float16, - device='cuda') - c = torch.randint_like(b, low=0, high=2) - triton_result = torch.zeros_like(a) - - torch_result = chained_matmul_reference(a, b, c) - chained_matmul_kernel[grid](a, b, c, triton_result, m, n, k, - block_m=block_m, block_n=block_n, - block_k=block_k) - - assert (torch_result == triton_result).all() - - -def test_vecmat(): - @triton.jit - def batched_vecmat( - # inputs - A, # shape: [dim_m, dim_k] - B, # shape: [dim_m, dim_n, dim_k] - # dimensions - dim_m, dim_n, dim_k, - # outputs - output, - # block information - block_m: tl.constexpr, block_n: tl.constexpr, block_k: tl.constexpr - ): - m_index = tl.program_id(0) - n_index = tl.program_id(1) - # Output tile - output_tile = (m_index * block_m + tl.arange(0, block_m))[:, None] * dim_n \ - + (n_index * block_n + tl.arange(0, block_n))[None, :] - - vecmat = tl.zeros([block_m, block_n], dtype=A.dtype.element_ty) - k_blocks = dim_k // block_k - for k_index in range(k_blocks): - # Load A tile - a_tile = (m_index * block_m + tl.arange(0, block_m))[:, None] * dim_k \ - + (k_index * block_k + tl.arange(0, block_k))[None, :] - a = tl.load(A + a_tile) - - # Load B tile, transposed to [n, m, k] in order to broadcast A on a - # leading dimension. - b_tile = (m_index * block_m + tl.arange(0, block_m))[None, :, None] * dim_n * dim_k \ - + (n_index * block_n + tl.arange(0, block_n))[:, None, None] * dim_k \ - + (k_index * block_k + tl.arange(0, block_k))[None, None, :] - b = tl.load(B + b_tile) - - expanded_a, _ = tl.broadcast(a, b) - vecmat += tl.trans(tl.sum(expanded_a * b, axis=2)) - - tl.store(output + output_tile, vecmat) - - M, N, K = 128, 128, 128 - block_m, block_n, block_k = 16, 32, 64 - - rs = RandomState(17) - A_vec = rs.randint(0, 4, (M, K)).astype('float32') - B_vec = rs.randint(0, 4, (M, N, K)).astype('float32') - A = A_vec - B = B_vec - - A_tri = torch.tensor(A, device='cuda') - B_tri = torch.tensor(B, device='cuda') - C_tri = torch.zeros((M, N), dtype=torch.float32, device='cuda') - - grid = (M // block_m, N // block_n) - - batched_vecmat[grid](A_tri, B_tri, M, N, K, C_tri, - block_m=block_m, block_n=block_n, block_k=block_k, - num_warps=4, num_stages=1) - - A_expanded = A[:, np.newaxis, :] - A_broadcasted = np.broadcast_to(A_expanded, (M, N, K)) - AB = A_broadcasted * B - C_ref = np.sum(AB, axis=2) - - np.testing.assert_allclose(C_ref, C_tri.cpu().numpy(), rtol=0.01, atol=1e-3) - - -@pytest.mark.parametrize("type", ["pre_load", "post_load", "post_pre_mixed", "post_load_two_iters", "post_load_three_iters"]) -def test_iv_dependent_matmul(type): - @triton.jit - def kernel( - a_ptr, b_ptr, c_ptr, - M, N, K, - stride_am, stride_ak, - stride_bk, stride_bn, - stride_cm, stride_cn, - BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, - type: tl.constexpr - ): - pid = tl.program_id(axis=0) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - pid_m = pid // num_pid_n - pid_n = pid % num_pid_n - - offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M - offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N - offs_k = tl.arange(0, BLOCK_SIZE_K) - a_ptr = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) - b_ptr = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) - a_ptrs = a_ptr - b_ptrs = b_ptr - if type == "post_load_two_iters": - a_ptrs_next = a_ptr + BLOCK_SIZE_K * stride_ak - b_ptrs_next = b_ptr + BLOCK_SIZE_K * stride_bk - elif type == "post_load_three_iters": - a_ptrs_next = a_ptr + BLOCK_SIZE_K * stride_ak - b_ptrs_next = b_ptr + BLOCK_SIZE_K * stride_bk - a_ptrs_next_next = a_ptr + 2 * BLOCK_SIZE_K * stride_ak - b_ptrs_next_next = b_ptr + 2 * BLOCK_SIZE_K * stride_bk - - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): - if type == "pre_load": - a_ptrs = a_ptr + k * BLOCK_SIZE_K * stride_ak - b_ptrs = b_ptr + k * BLOCK_SIZE_K * stride_bk - elif type == "post_pre_mixed": - a_ptrs = a_ptr + k * BLOCK_SIZE_K * stride_ak - a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) - b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) - accumulator += tl.dot(a, b) - if type == "post_load": - a_ptrs = a_ptr + (k + 1) * BLOCK_SIZE_K * stride_ak - b_ptrs = b_ptr + (k + 1) * BLOCK_SIZE_K * stride_bk - elif type == "post_pre_mixed": - b_ptrs = b_ptr + (k + 1) * BLOCK_SIZE_K * stride_bk - elif type == "post_load_two_iters": - a_ptrs = a_ptrs_next - b_ptrs = b_ptrs_next - a_ptrs_next = a_ptr + (k + 2) * BLOCK_SIZE_K * stride_ak - b_ptrs_next = b_ptr + (k + 2) * BLOCK_SIZE_K * stride_bk - elif type == "post_load_three_iters": - a_ptrs = a_ptrs_next - b_ptrs = b_ptrs_next - a_ptrs_next = a_ptrs_next_next - b_ptrs_next = b_ptrs_next_next - a_ptrs_next_next = a_ptr + (k + 3) * BLOCK_SIZE_K * stride_ak - b_ptrs_next_next = b_ptr + (k + 3) * BLOCK_SIZE_K * stride_bk - c = accumulator.to(tl.float16) - - offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] - c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) - tl.store(c_ptrs, c, mask=c_mask) - - M = 256 - K = 256 - N = 256 - BLOCK_SIZE_K = 32 - BLOCK_SIZE_N = 32 - BLOCK_SIZE_M = 32 - - a = torch.rand((M, K), device='cuda') - b = torch.rand((K, N), device='cuda') - - torch_output = torch.mm(a, b) - triton_output = torch.empty_like( - torch_output, device=torch_output.device) - - def grid(META): - return (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),) - - num_stages = 4 if type == "post_load_three_iters" else 3 - kernel[grid](a, b, triton_output, M, N, K, a.stride(0), a.stride(1), - b.stride(0), b.stride(1), triton_output.stride(0), triton_output.stride(1), - BLOCK_SIZE_M=BLOCK_SIZE_M, BLOCK_SIZE_N=BLOCK_SIZE_N, BLOCK_SIZE_K=BLOCK_SIZE_K, - type=type, num_stages=num_stages) - torch.testing.assert_allclose(torch_output, triton_output, rtol=1e-2, atol=1e-2) diff --git a/python/test/regression/test_performance.py b/python/test/regression/test_performance.py deleted file mode 100644 index 4d5e4f5df5c0..000000000000 --- a/python/test/regression/test_performance.py +++ /dev/null @@ -1,212 +0,0 @@ -import subprocess -import sys - -import pytest -import torch - -import triton -import triton.language as tl -import triton.ops -from triton.testing import get_dram_gbps, get_max_tensorcore_tflops - -DEVICE_NAME = {7: 'v100', 8: 'a100'}[torch.cuda.get_device_capability()[0]] - -####################### -# Utilities -####################### - - -def print_perf(cur_ms, cur_util, ref_util): - # print on the same line cur_ms, cur_util and ref_util with 3 decimal places - print(f'{cur_ms:.3f} ms \t cur: {cur_util:.3f} \t ref: {ref_util:.3f} \t dif={cur_util - ref_util:.3f}', end='\t') - - -def nvsmi(attrs): - attrs = ','.join(attrs) - cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits'] - out = subprocess.check_output(cmd) - ret = out.decode(sys.stdout.encoding).split(',') - ret = [int(x) for x in ret] - return ret - - -####################### -# Matrix Multiplication -####################### - -sm_clocks = {'v100': 1350, 'a100': 1350} -mem_clocks = {'v100': 877, 'a100': 1215} - -matmul_data = { - 'v100': { - # square - (512, 512, 512): {'float16': 0.158}, - (1024, 1024, 1024): {'float16': 0.466}, - (2048, 2048, 2048): {'float16': 0.695}, - (4096, 4096, 4096): {'float16': 0.831}, - (8192, 8192, 8192): {'float16': 0.849}, - # tall-skinny - (16, 1024, 1024): {'float16': 0.0128}, - (16, 4096, 4096): {'float16': 0.0883}, - (16, 8192, 8192): {'float16': 0.101}, - (64, 1024, 1024): {'float16': 0.073}, - (64, 4096, 4096): {'float16': 0.270}, - (64, 8192, 8192): {'float16': 0.459}, - (1024, 64, 1024): {'float16': 0.0692}, - (4096, 64, 4096): {'float16': 0.264}, - (8192, 64, 8192): {'float16': 0.452}, - }, - # NOTE: - # A100 in the CI server is slow-ish for some reason. - # On some other servers, we are getting about 90% peak for 8kx8x8k float16 - 'a100': { - (512, 512, 512): {'float16': 0.084, 'float32': 0.13, 'int8': 0.05}, - (1024, 1024, 1024): {'float16': 0.332, 'float32': 0.35, 'int8': 0.169}, - (2048, 2048, 2048): {'float16': 0.641, 'float32': 0.57, 'int8': 0.34}, - (4096, 4096, 4096): {'float16': 0.785, 'float32': 0.75, 'int8': 0.46}, - (8192, 8192, 8192): {'float16': 0.805, 'float32': 0.85, 'int8': 0.51}, - # tall-skinny - (16, 1024, 1024): {'float16': 0.0077, 'float32': 0.0127, 'int8': 0.005}, - (16, 4096, 4096): {'float16': 0.044, 'float32': 0.0457, 'int8': 0.0259}, - (16, 8192, 8192): {'float16': 0.07, 'float32': 0.0648, 'int8': 0.0431}, - (64, 1024, 1024): {'float16': 0.028, 'float32': 0.0509, 'int8': 0.0169}, - (64, 4096, 4096): {'float16': 0.163, 'float32': 0.162, 'int8': 0.097}, - (64, 8192, 8192): {'float16': 0.285, 'float32': 0.257, 'int8': 0.174}, - (1024, 64, 1024): {'float16': 0.033, 'float32': 0.0458, 'int8': 0.017}, - (4096, 64, 4096): {'float16': 0.16, 'float32': 0.177, 'int8': 0.102}, - (8192, 64, 8192): {'float16': 0.254, 'float32': 0.230, 'int8': 0.177}, - } -} - - -@pytest.mark.parametrize('M, N, K, dtype_str', - [(M, N, K, dtype_str) - for M, N, K in matmul_data[DEVICE_NAME].keys() - for dtype_str in ['float16']]) -def test_matmul(M, N, K, dtype_str): - if dtype_str in ['float32', 'int8'] and DEVICE_NAME != 'a100': - pytest.skip('Only test float32 & int8 on a100') - dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str] - torch.manual_seed(0) - ref_gpu_util = matmul_data[DEVICE_NAME][(M, N, K)][dtype_str] - cur_sm_clock = nvsmi(['clocks.current.sm'])[0] - max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3) - if dtype == torch.int8: - a = torch.randint(-128, 127, (M, K), dtype=dtype, device='cuda') - b = torch.randint(-128, 127, (N, K), dtype=dtype, device='cuda') - b = b.t() # only test row-col layout - else: - a = torch.randn((M, K), dtype=dtype, device='cuda') - b = torch.randn((K, N), dtype=dtype, device='cuda') - fn = lambda: triton.ops.matmul(a, b) - ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=300) - cur_gpu_perf = 2. * M * N * K / ms * 1e-9 - cur_gpu_util = cur_gpu_perf / max_gpu_perf - print_perf(ms, cur_gpu_util, ref_gpu_util) - triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05) - - -####################### -# Element-Wise -####################### - - -@triton.jit -def _add(x_ptr, y_ptr, output_ptr, n_elements, - BLOCK_SIZE: tl.constexpr): - pid = tl.program_id(axis=0) - block_start = pid * BLOCK_SIZE - offsets = block_start + tl.arange(0, BLOCK_SIZE) - mask = offsets < n_elements - x = tl.load(x_ptr + offsets, mask=mask) - y = tl.load(y_ptr + offsets, mask=mask) - output = x + y - tl.store(output_ptr + offsets, output, mask=mask) - - -elementwise_data = { - 'v100': { - 1024 * 16: 0.0219, - 1024 * 64: 0.0791, - 1024 * 256: 0.243, - 1024 * 1024: 0.530, - 1024 * 4096: 0.796, - 1024 * 16384: 0.905, - 1024 * 65536: 0.939, - }, - 'a100': { - 1024 * 16: 0.010, - 1024 * 64: 0.040, - 1024 * 256: 0.132, - 1024 * 1024: 0.353, - 1024 * 4096: 0.605, - 1024 * 16384: 0.758, - 1024 * 65536: 0.850, - } -} - - -@pytest.mark.parametrize('N', elementwise_data[DEVICE_NAME].keys()) -def test_elementwise(N): - torch.manual_seed(0) - ref_gpu_util = elementwise_data[DEVICE_NAME][N] - max_gpu_perf = get_dram_gbps() - z = torch.empty((N, ), dtype=torch.float16, device='cuda') - x = torch.randn_like(z) - y = torch.randn_like(z) - grid = lambda args: (triton.cdiv(N, args['BLOCK_SIZE']), ) - fn = lambda: _add[grid](x, y, z, N, BLOCK_SIZE=1024) - ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=500) - cur_gpu_perf = 3. * N * z.element_size() / ms * 1e-6 - cur_gpu_util = cur_gpu_perf / max_gpu_perf - print_perf(ms, cur_gpu_util, ref_gpu_util) - triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05) - -####################### -# Flash-Attention -####################### - - -flash_attention_data = { - "a100": { - (4, 48, 4096, 64, 'forward', 'float16'): 0.37, - (4, 48, 4096, 64, 'backward', 'float16'): 0.25, - } -} - - -@pytest.mark.parametrize("Z, H, N_CTX, D_HEAD", [[4, 48, 4096, 64]]) -@pytest.mark.parametrize("mode", ['forward', 'backward']) -@pytest.mark.parametrize("dtype_str", ['float16']) -def test_flash_attention(Z, H, N_CTX, D_HEAD, mode, dtype_str): - is_backward = mode == 'backward' - capability = torch.cuda.get_device_capability() - if capability[0] < 8: - pytest.skip("Flash attention only supported for compute capability < 80") - torch.manual_seed(20) - dtype = {'float16': torch.float16, 'float32': torch.float32, 'int8': torch.int8}[dtype_str] - # init data - q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).requires_grad_() - k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_() - v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_() - sm_scale = 0.2 - # benchmark - fn = lambda: triton.ops.attention(q, k, v, sm_scale) - if is_backward: - o = fn() - do = torch.randn_like(o) - fn = lambda: o.backward(do, retain_graph=True) - ms = triton.testing.do_bench(fn, return_mode="min", warmup=100, rep=500) - # compute flops - flops_per_matmul = 2. * Z * H * N_CTX * N_CTX * D_HEAD * 0.5 - total_flops = 2 * flops_per_matmul - if is_backward: - total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute) - cur_gpu_perf = total_flops / ms * 1e-9 - # maximum flops - cur_sm_clock = nvsmi(['clocks.current.sm'])[0] - max_gpu_perf = get_max_tensorcore_tflops(dtype, clock_rate=cur_sm_clock * 1e3) - cur_gpu_util = cur_gpu_perf / max_gpu_perf - ref_gpu_util = flash_attention_data[DEVICE_NAME][(Z, H, N_CTX, D_HEAD, mode, dtype_str)] - print_perf(ms, cur_gpu_util, ref_gpu_util) - triton.testing.assert_close(cur_gpu_util, ref_gpu_util, atol=0.01, rtol=0.05) diff --git a/python/test/tools/compare_files.py b/python/test/tools/compare_files.py deleted file mode 100644 index fe3688e91e04..000000000000 --- a/python/test/tools/compare_files.py +++ /dev/null @@ -1,261 +0,0 @@ -import argparse -import difflib -import glob -import os -import sys -from typing import Dict, List, Optional, Tuple - -import yaml - - -class ComparisonResult: - def __init__(self, name: str, extension: str, numComparisons: int, diffs: List[str] = None, errors: List[str] = None): - self.name = name - self.extension = extension - self.numComparisons = numComparisons - self.diffs = [] if diffs is None else diffs - self.errors = [] if errors is None else errors - - def isSuccess(self) -> bool: - return len(self.diffs) == 0 and len(self.errors) == 0 - - def __str__(self) -> str: - return f"name={self.name}, extension={self.extension}, numComparisons={self.numComparisons}, success={self.isSuccess()}" - - -def listFilesWithExtension(path: str, extension: str) -> List[str]: - """ - Returns a list of files in the given path with the given extension - The files are returned with their full path - """ - files = glob.glob(os.path.join(path, f'*.{extension}')) - return files - - -def getFileWithExtension(path: str, ext: str) -> Optional[str]: - """ - Returns a single file in the given path with the given extension - """ - # get all files in directory with extension - files = listFilesWithExtension(path, ext) - if len(files) == 0: - return None - # filter out files with grp in their name - files = [f for f in files if "__grp__" not in f] - if len(files) != 1: - print(f"Found {len(files)} files in {path} with extension {ext}!") - sys.exit(2) - return files[0] - - -def loadYamlFile(filePath: str) -> List[Dict[str, str]]: - """ - Loads a yaml file and returns its content as a list of dictionaries - """ - with open(filePath, 'r') as file: - content = yaml.safe_load(file) - return content - - -def compareFiles(file1: str, file2: str) -> bool: - """ - Compares two files and returns True if they are the same, False otherwise - """ - with open(file1, 'rb') as f1, open(file2, 'rb') as f2: - content1 = f1.read() - content2 = f2.read() - - return content1 == content2 - - -def diffFiles(file1, file2): - with open(file1, 'r') as f1: - file1_lines = f1.readlines() - with open(file2, 'r') as f2: - file2_lines = f2.readlines() - - diff = list(difflib.unified_diff(file1_lines, file2_lines, file1, file2)) - return diff - - -def getFileVec(path: str) -> List[Tuple[str, str]]: - """ - Returns a list of tuples (extension, file) for the given path (note: the path includes the hash) - The returned list must have extensions (json, ttir, ttgir) - in this particular order, unless a file with a certain extension does not exist - """ - vec = [] - for ext in ["json", "ttir", "ttgir"]: - file = getFileWithExtension(path, ext) - if file is not None: - vec.append((ext, file)) - return vec - - -def getNameToHashesDict(path: str) -> Dict[str, List[str]]: - """ - Returns a dictionary that maps kernel names to a list of hashes that have the same kernel name - in the given path - Note: the hashes must have a json file and either a ttir or ttgir file, otherwise they are ignored - """ - nameToHashes = {} - for hash in os.listdir(path): - fullPath = os.path.join(path, hash) - if not os.path.isdir(fullPath): - print(f"Path {fullPath} is not a directory!") - sys.exit(2) - fileVec = getFileVec(fullPath) - if len(fileVec) < 2 or fileVec[0][0] != "json": - continue - jsonFile = fileVec[0][1] - # load json file - with open(jsonFile, 'r') as file: - content = yaml.safe_load(file) - # get name - name = content["name"] - nameToHashes.setdefault(name, []).append(hash) - return nameToHashes - - -def doFilesMatch(path1: str, path2: str) -> bool: - """ - Returns True if the files in the given paths match, False otherwise - The files are considered to match if: - 1. The number of files in both paths match - 2. The json files match - 3. Both paths have a ttir that match, if a ttir does not exist, the ttgir file must exist and match - """ - filesVec1 = getFileVec(path1) - filesVec2 = getFileVec(path2) - # The number of files must match - if len(filesVec1) != len(filesVec2): - return False - - for (ext1, file1), (ext2, file2) in zip(filesVec1, filesVec2): - if ext1 != ext2: - return False - if not compareFiles(file1, file2): - return False - else: - # once we actually compared a ttir or ttgir file, we can break - if ext1 in ("ttir", "ttgir"): - break - return True - - -def compareMatchingFiles(name: str, extension: str, nameToHashes1: Dict[str, List[str]], nameToHashes2: Dict[str, List[str]], args) -> ComparisonResult: - """ - Compare files with the given name/extension in all hashes in both paths - Return the first mismatching files as a tuple (file1, file2), otherwise, return an empty tuple - """ - hashes1 = nameToHashes1.get(name, []) - hashes2 = nameToHashes2.get(name, []) - diffs = [] - errors = [] - numComparisons = 0 - for hash1 in hashes1: - path1 = os.path.join(args.path1, hash1) - for hash2 in hashes2: - path2 = os.path.join(args.path2, hash2) - # check whether both paths have: - # 1. json files that match - # 2. ttir files that match (if they exist), otherwise ttgir files that match (if they exist) - # if any of these contraints is not met, then we can skip this pair of hashes since they are not a match - if not doFilesMatch(path1, path2): - continue - numComparisons += 1 - extFile1 = listFilesWithExtension(path1, extension)[0] - extFile2 = listFilesWithExtension(path2, extension)[0] - diff = diffFiles(extFile1, extFile2) - if len(diff) > 0: - diffs.append(diffFiles(extFile2, extFile1)) - if numComparisons == 0: - errors.append(f"Did not find any matching files for {name}") - return ComparisonResult(name=name, extension=extension, numComparisons=numComparisons, diffs=diffs, errors=errors) - - -def dumpResults(results: List[ComparisonResult], fileName: str): - """ - Dumps the results to the given file - """ - with open(fileName, 'w') as file: - for result in results: - file.write(str(result) + "\n") - file.write("Diffs:\n") - for diff in result.diffs: - for line in diff: - file.write(line) - file.write("Errors:\n") - for error in result.errors: - file.write(error) - file.write("\n\n") - - -def main(args) -> bool: - """ - Iterates over all kernels in the given yaml file and compares them - in the given paths - """ - if args.path1 == args.path2: - print("Cannot compare files in the same directory!") - sys.exit(2) - # Get kernel name to hashes dict, these hashes would have the same kernel name - nameToHashes1 = getNameToHashesDict(args.path1) - nameToHashes2 = getNameToHashesDict(args.path2) - - yamlFilePath = args.kernels - if not os.path.exists(yamlFilePath): - print(f"Path {yamlFilePath} does not exist!") - sys.exit(2) - nameAndExtension = loadYamlFile(yamlFilePath)["name_and_extension"] - - results = [] - # iterate over the kernels that need to be checked - for d in nameAndExtension: - name = d["name"] # kernel name - extension = d["extension"] # extension of the file to be compared (e.g. ptx) - # Compare all hashes on path 1 with all hashes on path 2 - # result is either the mismatching (file1, file2) with "extension" or empty tuple if no mismatch - result = compareMatchingFiles(name, extension, nameToHashes1, nameToHashes2, args) - print(result) - # Otherwise, add it to the mismatches - results.append(result) - - # Dump results - dumpResults(results, "kernels_reference_check.txt") - - success = all(result.isSuccess() for result in results) - - if not success: - print("Failed!") - sys.exit(1) - - print("Passed!") - sys.exit(0) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--path1", - type=str, - default=None, - required=True, - help=("Path to first cache directory"), - ) - parser.add_argument( - "--path2", - type=str, - default=None, - required=True, - help=("Path to second cache directory"), - ) - parser.add_argument( - "--kernels", - type=str, - default=None, - required=True, - help=("Path to kernels yaml file"), - ) - args = parser.parse_args() - main(args) diff --git a/python/test/unit/interpreter/test_interpreter.py b/python/test/unit/interpreter/test_interpreter.py deleted file mode 100644 index b6bb6b79c206..000000000000 --- a/python/test/unit/interpreter/test_interpreter.py +++ /dev/null @@ -1,69 +0,0 @@ -import random - -import torch - -import triton -import triton.language as tl -from triton.interpreter.interpreter import program_ids_from_grid - - -def test_addition(): - - @triton.jit(interpret=True) - def add_kernel( - x_ptr, - y_ptr, - output_ptr, - n_elements, - BLOCK_SIZE: tl.constexpr, - ): - pid = tl.program_id(axis=0) - block_start = pid * BLOCK_SIZE - offsets = block_start + tl.arange(0, BLOCK_SIZE) - mask = offsets < n_elements - x = tl.load(x_ptr + offsets, mask=mask) - y = tl.load(y_ptr + offsets, mask=mask) - output = x + y - tl.store(output_ptr + offsets, output, mask=mask) - - a = torch.rand((128,), device="cuda") - b = torch.rand((128,), device="cuda") - expected = a + b - output = torch.empty((128,), device="cuda") - - def grid(meta): - return (triton.cdiv(128, meta["BLOCK_SIZE"]),) - - add_kernel[grid](a, b, output, 128, BLOCK_SIZE=32) - - assert torch.allclose(expected, output, atol=1e-2, rtol=0) - - -def test_program_ids_from_grid(): - random.seed(123) - grid = (3, 4) - expected_combinations = 3 * 4 - unique_combinations = set(program_ids_from_grid(grid)) - assert len(unique_combinations) == expected_combinations - - first_run = list(program_ids_from_grid(grid)) - second_run = list(program_ids_from_grid(grid)) - assert first_run != second_run - - -def test_atomic(): - @triton.jit(interpret=True) - def atomic( - x_ptr, - ): - pid = tl.program_id(axis=0) - tl.atomic_add(x_ptr + pid, 1) - t = tl.atomic_xchg(x_ptr + pid, 3) - t += 1 # 2 - tl.atomic_cas(x_ptr + pid, 3, t) # match - tl.atomic_cas(x_ptr + pid, 40, 9) # no match - nb_dim = 16 - a = torch.zeros((nb_dim, ), dtype=torch.int32, device="cuda") - - atomic[(nb_dim, )](a) - assert torch.allclose(a, torch.full_like(a, 2)) diff --git a/python/test/unit/language/assert_helper.py b/python/test/unit/language/assert_helper.py deleted file mode 100644 index 1b15f915b5d7..000000000000 --- a/python/test/unit/language/assert_helper.py +++ /dev/null @@ -1,131 +0,0 @@ -import sys - -import torch -from torch.testing import assert_close - -import triton -import triton.language as tl - - -@triton.jit -def kernel_device_assert(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - tl.device_assert(x == 0, "x != 0") - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit -def kernel_device_assert_scalar(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - # Trivial assert - tl.device_assert(0 == 0, "x != 0") - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit(debug=False) -def kernel_device_assert_no_debug(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - tl.device_assert(x == 0, "x != 0") - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit -def kernel_assert(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - assert x == 0, "x != 0" - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit -def kernel_static_assert(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - tl.static_assert(BLOCK == 128, "BLOCK != 128") - tl.store(Y + tl.arange(0, BLOCK), x) - - -def test_assert(func: str): - shape = (128, ) - x = torch.arange(0, shape[0], dtype=torch.int32, device='cuda') - y = torch.zeros(shape, dtype=x.dtype, device="cuda") - if func == "device_assert": - kernel_device_assert[(1,)](x, y, BLOCK=shape[0]) - kernel_device_assert_scalar[(1,)](x, y, BLOCK=shape[0]) - elif func == "no_debug": - # TRITON_DEBUG=1 can override the debug flag - kernel_device_assert_no_debug[(1,)](x, y, BLOCK=shape[0]) - elif func == "assert": - kernel_assert[(1,)](x, y, BLOCK=shape[0]) - elif func == "static_assert": - kernel_static_assert[(1,)](x, y, BLOCK=shape[0]) - assert_close(y, x) - - -@triton.jit -def jit_device_assert_none(x): - tl.device_assert(x == 0, "x != 0") - - -@triton.jit(debug=True) -def jit_device_assert_true(x): - tl.device_assert(x == 0, "x != 0") - - -@triton.jit(debug=False) -def jit_device_assert_false(x): - tl.device_assert(x == 0, "x != 0") - - -@triton.jit -def kernel_device_assert_nested(X, Y, BLOCK: tl.constexpr, jit_debug: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - if jit_debug == "true": - jit_device_assert_true(x) - elif jit_debug == "false": - jit_device_assert_false(x) - else: - jit_device_assert_none(x) - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit(debug=True) -def kernel_device_assert_nested_true(X, Y, BLOCK: tl.constexpr, jit_debug: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - if jit_debug == "true": - jit_device_assert_true(x) - elif jit_debug == "false": - jit_device_assert_false(x) - else: - jit_device_assert_none(x) - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit(debug=False) -def kernel_device_assert_nested_false(X, Y, BLOCK: tl.constexpr, jit_debug: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - if jit_debug == "true": - jit_device_assert_true(x) - elif jit_debug == "false": - jit_device_assert_false(x) - else: - jit_device_assert_none(x) - tl.store(Y + tl.arange(0, BLOCK), x) - - -def test_assert_nested(caller: str, callee: str): - shape = (128, ) - x = torch.arange(0, shape[0], dtype=torch.int32, device='cuda') - y = torch.zeros(shape, dtype=x.dtype, device="cuda") - if caller == "none": - kernel_device_assert_nested[(1,)](x, y, BLOCK=shape[0], jit_debug=callee) - elif caller == "true": - kernel_device_assert_nested_true[(1,)](x, y, BLOCK=shape[0], jit_debug=callee) - elif caller == "false": - kernel_device_assert_nested_false[(1,)](x, y, BLOCK=shape[0], jit_debug=callee) - assert_close(y, x) - - -if __name__ == "__main__": - if len(sys.argv) == 3: - test_assert_nested(sys.argv[1], sys.argv[2]) - else: - test_assert(sys.argv[1]) diff --git a/python/test/unit/language/conftest.py b/python/test/unit/language/conftest.py deleted file mode 100644 index f9e96688b921..000000000000 --- a/python/test/unit/language/conftest.py +++ /dev/null @@ -1,14 +0,0 @@ -# content of conftest.py - -import pytest - - -def pytest_addoption(parser): - parser.addoption( - "--device", action="store", default='cuda' - ) - - -@pytest.fixture -def device(request): - return request.config.getoption("--device") diff --git a/python/test/unit/language/print_helper.py b/python/test/unit/language/print_helper.py deleted file mode 100644 index afdd12960737..000000000000 --- a/python/test/unit/language/print_helper.py +++ /dev/null @@ -1,46 +0,0 @@ -import sys - -import torch -from torch.testing import assert_close - -import triton -import triton.language as tl - - -@triton.jit -def kernel_device_print(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - tl.device_print("", x) - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit -def kernel_print(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - print("", x) - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit -def kernel_static_print(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - tl.static_print(x) - tl.store(Y + tl.arange(0, BLOCK), x) - - -def test_print(func: str, data_type: str): - shape = (128, ) - # limit the range of integers so that the sum does not overflow - x = torch.arange(0, shape[0], dtype=torch.int32, device='cuda').to(getattr(torch, data_type)) - y = torch.zeros(shape, dtype=x.dtype, device="cuda") - if func == "device_print": - kernel_device_print[(1,)](x, y, BLOCK=shape[0]) - elif func == "print": - kernel_print[(1,)](x, y, BLOCK=shape[0]) - elif func == "static_print": - kernel_static_print[(1,)](x, y, BLOCK=shape[0]) - assert_close(y, x) - - -if __name__ == "__main__": - test_print(sys.argv[1], sys.argv[2]) diff --git a/python/test/unit/language/test_annotations.py b/python/test/unit/language/test_annotations.py deleted file mode 100644 index 0e18c950c47d..000000000000 --- a/python/test/unit/language/test_annotations.py +++ /dev/null @@ -1,21 +0,0 @@ - -from __future__ import annotations - -import torch - -import triton -import triton.language as tl - - -def test_annotations(device): - - @triton.jit - def _kernel(X: torch.Tensor, N: int, BLOCK_SIZE: tl.constexpr): - pass - - x = torch.empty(1, device=device) - _kernel[(1,)](x, x.shape[0], 32) - try: - _kernel[(1,)](x.shape[0], x.shape[0], 32) - except AttributeError: - pass diff --git a/python/test/unit/language/test_block_pointer.py b/python/test/unit/language/test_block_pointer.py deleted file mode 100644 index 147249076181..000000000000 --- a/python/test/unit/language/test_block_pointer.py +++ /dev/null @@ -1,102 +0,0 @@ -import pytest -import torch - -import triton -import triton.language as tl - - -@triton.jit -def block_copy_kernel(a_ptr, b_ptr, N, BLOCK_SIZE: tl.constexpr, padding_option: tl.constexpr): - pid = tl.program_id(0) - # We only copy half of the data to see if the padding works - a_block_ptr = tl.make_block_ptr(base=a_ptr, shape=(N // 2, ), strides=(1, ), offsets=(pid * BLOCK_SIZE, ), - block_shape=(BLOCK_SIZE, ), order=(0, )) - b_block_ptr = tl.make_block_ptr(base=b_ptr, shape=(N, ), strides=(1, ), offsets=(pid * BLOCK_SIZE, ), - block_shape=(BLOCK_SIZE, ), order=(0, )) - a = tl.load(a_block_ptr, boundary_check=(0, ), padding_option=padding_option) - tl.store(b_block_ptr, a, boundary_check=(0, )) - - -@pytest.mark.parametrize("dtype_str, n, padding_option", - [(dtype_str, n, padding) for dtype_str in ("bool", "int16", "float16") - for n in (64, 128, 256, 512, 1024) - for padding in ("zero", "nan")]) -def test_block_copy(dtype_str, n, padding_option): - capability = torch.cuda.get_device_capability() - if capability[0] >= 9: - pytest.skip("Hopper support is working in progress") - - dtype = getattr(torch, dtype_str) - if dtype_str in ("bool", "int16"): - if padding_option == "nan": - pytest.skip("Padding with NaN is not supported for integer types") - a = torch.randint(0, 2, (n, ), device="cuda", dtype=dtype) - else: - a = torch.randn((n, ), device="cuda", dtype=dtype) - b = torch.zeros((n, ), device="cuda", dtype=dtype) - - grid = lambda meta: (triton.cdiv(n, meta["BLOCK_SIZE"]),) - block_copy_kernel[grid](a_ptr=a, b_ptr=b, N=n, BLOCK_SIZE=64, padding_option=padding_option) - - assert torch.all(a[0: n // 2] == b[0: n // 2]) - if padding_option == "zero": - assert torch.all(b[n // 2: n] == 0) - else: - assert torch.all(torch.isnan(b[n // 2: n])) - - -@triton.jit -def matmul_no_scf_with_advance_kernel( - a_ptr, b_ptr, c_ptr, - M, N, K, - stride_am, stride_ak, - stride_bk, stride_bn, - stride_cm, stride_cn, - BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr -): - offs_m = tl.arange(0, BLOCK_M) - offs_n = tl.arange(0, BLOCK_N) - a_block_ptr = tl.make_block_ptr(base=a_ptr, shape=(M, K), strides=(stride_am, stride_ak), - offsets=(0, 0), block_shape=(BLOCK_M, BLOCK_K), order=(1, 0)) - b_block_ptr = tl.make_block_ptr(base=b_ptr, shape=(K, N), strides=(stride_bk, stride_bn), - offsets=(0, 0), block_shape=(BLOCK_K, BLOCK_N), order=(1, 0)) - # Below two lines are just for testing negative offsets for the `advance` API, which could be removed - a_block_ptr = tl.advance(a_block_ptr, (BLOCK_M, -BLOCK_K)) - a_block_ptr = tl.advance(a_block_ptr, (-BLOCK_M, BLOCK_K)) - a = tl.load(a_block_ptr, boundary_check=(1, ), padding_option="zero") - b = tl.load(b_block_ptr, boundary_check=(0, ), padding_option="zero") - - c = tl.dot(a, b) - c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn - tl.store(c_ptrs, c) - - -@pytest.mark.parametrize("shape, num_warps", [ - (shape, num_warps) - for shape in [ - [64, 64, 16], - [64, 64, 32], - [64, 64, 64], - ] - for num_warps in [4, 8] -]) -def test_block_ptr_matmul_no_scf(shape, num_warps): - capability = torch.cuda.get_device_capability() - if capability[0] >= 9: - pytest.skip("Hopper support is working in progress") - - m, n, k = shape - a = torch.randn((m, k), device="cuda", dtype=torch.float16) - b = torch.randn((k, n), device="cuda", dtype=torch.float16) - c = torch.empty((m, n), device="cuda", dtype=torch.float32) - - grid = lambda META: (1, ) - matmul_no_scf_with_advance_kernel[grid](a_ptr=a, b_ptr=b, c_ptr=c, - M=m, N=n, K=k, - stride_am=a.stride(0), stride_ak=a.stride(1), - stride_bk=b.stride(0), stride_bn=b.stride(1), - stride_cm=c.stride(0), stride_cn=c.stride(1), - BLOCK_M=m, BLOCK_N=n, BLOCK_K=k, - num_warps=num_warps) - golden = torch.matmul(a, b) - torch.testing.assert_allclose(c, golden) diff --git a/python/test/unit/language/test_core.py b/python/test/unit/language/test_core.py deleted file mode 100644 index 8dfe87eae2a5..000000000000 --- a/python/test/unit/language/test_core.py +++ /dev/null @@ -1,3271 +0,0 @@ -# flake8: noqa: F821,F841 -import itertools -import os -import re -from typing import Optional, Union - -import numpy as np -import pytest -import torch -from numpy.random import RandomState - -import triton -import triton._C.libtriton.triton as _triton -import triton.language as tl -from triton.runtime.jit import JITFunction, TensorWrapper, reinterpret - -int_dtypes = ['int8', 'int16', 'int32', 'int64'] -uint_dtypes = ['uint8', 'uint16', 'uint32', 'uint64'] -float_dtypes = ['float16', 'float32', 'float64'] -dtypes = int_dtypes + uint_dtypes + float_dtypes -dtypes_with_bfloat16 = dtypes + ['bfloat16'] -torch_dtypes = ['bool'] + int_dtypes + ['uint8'] + float_dtypes + ['bfloat16'] - - -def _bitwidth(dtype: str) -> int: - # ex.: "int64" -> 64 - return int(re.search(r'(\d+)$', dtype).group(1)) - - -def numpy_random(shape, dtype_str, rs: Optional[RandomState] = None, low=None, high=None): - """ - Override `rs` if you're calling this function twice and don't want the same - result for both calls. - """ - if isinstance(shape, int): - shape = (shape, ) - if rs is None: - rs = RandomState(seed=17) - if dtype_str in int_dtypes + uint_dtypes: - iinfo = np.iinfo(getattr(np, dtype_str)) - low = iinfo.min if low is None else max(low, iinfo.min) - high = iinfo.max if high is None else min(high, iinfo.max) - dtype = getattr(np, dtype_str) - x = rs.randint(low, high, shape, dtype=dtype) - x[x == 0] = 1 # Hack. Never return zero so tests of division don't error out. - return x - elif dtype_str in float_dtypes: - return rs.normal(0, 1, shape).astype(dtype_str) - elif dtype_str == 'bfloat16': - return (rs.normal(0, 1, shape).astype('float32').view('uint32') - & np.uint32(0xffff0000)).view('float32') - elif dtype_str in ['bool', 'int1', 'bool_']: - return rs.normal(0, 1, shape) > 0.0 - else: - raise RuntimeError(f'Unknown dtype {dtype_str}') - - -def to_triton(x: np.ndarray, device='cuda', dst_type=None) -> Union[TensorWrapper, torch.Tensor]: - ''' - Note: We need dst_type because the type of x can be different from dst_type. - For example: x is of type `float32`, dst_type is `bfloat16`. - If dst_type is None, we infer dst_type from x. - ''' - t = x.dtype.name - if t in uint_dtypes: - signed_type_name = t.lstrip('u') # e.g. "uint16" -> "int16" - x_signed = x.astype(getattr(np, signed_type_name)) - return reinterpret(torch.tensor(x_signed, device=device), getattr(tl, t)) - else: - if t == 'float32' and dst_type == 'bfloat16': - return torch.tensor(x, device=device).bfloat16() - return torch.tensor(x, device=device) - - -def torch_dtype_name(dtype) -> str: - if isinstance(dtype, triton.language.dtype): - return dtype.name - elif isinstance(dtype, torch.dtype): - # 'torch.int64' -> 'int64' - m = re.match(r'^torch\.(\w+)$', str(dtype)) - return m.group(1) - else: - raise TypeError(f'not a triton or torch dtype: {type(dtype)}') - - -def to_numpy(x): - if isinstance(x, TensorWrapper): - return x.base.cpu().numpy().astype(getattr(np, torch_dtype_name(x.dtype))) - elif isinstance(x, torch.Tensor): - if x.dtype is torch.bfloat16: - return x.cpu().float().numpy() - return x.cpu().numpy() - else: - raise ValueError(f"Not a triton-compatible tensor: {x}") - - -def patch_kernel(template, to_replace): - kernel = triton.JITFunction(template.fn) - for key, value in to_replace.items(): - kernel.src = kernel.src.replace(key, value) - return kernel - - -def check_cuda_only(device): - if device not in ['cuda']: - pytest.skip("Only for cuda") - - -def check_type_supported(dtype, device): - ''' - skip test if dtype is not supported on the current device - ''' - if device in ['cuda']: - cc = torch.cuda.get_device_capability() - if cc[0] < 8 and (dtype is tl.bfloat16 or dtype == "bfloat16" or dtype is torch.bfloat16): - pytest.skip("bfloat16 is only supported on NVGPU with cc >= 80") - - -class MmaLayout: - def __init__(self, version, warps_per_cta): - self.version = version - self.warps_per_cta = str(warps_per_cta) - - def __str__(self): - return f"#triton_gpu.mma<{{versionMajor={self.version[0]}, versionMinor={self.version[1]}, warpsPerCTA={self.warps_per_cta}}}>" - - -class BlockedLayout: - def __init__(self, size_per_thread, threads_per_warp, warps_per_cta, order): - self.sz_per_thread = str(size_per_thread) - self.threads_per_warp = str(threads_per_warp) - self.warps_per_cta = str(warps_per_cta) - self.order = str(order) - - def __str__(self): - return f"#triton_gpu.blocked<{{sizePerThread={self.sz_per_thread}, threadsPerWarp={self.threads_per_warp}, warpsPerCTA={self.warps_per_cta}, order={self.order}}}>" - - -class SharedLayout: - def __init__(self, vec, per_phase, max_phase, order): - self.vec = str(vec) - self.per_phase = str(per_phase) - self.max_phase = str(max_phase) - self.order = str(order) - - def __str__(self): - return f"#triton_gpu.shared<{{vec={self.vec}, perPhase={self.per_phase}, maxPhase={self.max_phase}, order={self.order}}}>" - - -@pytest.mark.parametrize("dtype_x", list(dtypes) + ["bfloat16"]) -def test_empty_kernel(dtype_x, device): - SIZE = 128 - - @triton.jit - def kernel(X, SIZE: tl.constexpr): - pass - check_type_supported(dtype_x, device) - x = to_triton(numpy_random(SIZE, dtype_str=dtype_x), device=device, dst_type=dtype_x) - kernel[(1, )](x, SIZE=SIZE, num_warps=4) - - -# generic test functions -def _test_unary(dtype_x, expr, numpy_expr=None, device='cuda'): - check_type_supported(dtype_x, device) # early return if dtype_x is not supported - SIZE = 128 - # define the kernel / launch-grid - - @triton.jit - def kernel(Z, X, SIZE: tl.constexpr): - off = tl.arange(0, SIZE) - x = tl.load(X + off) - z = GENERATE_TEST_HERE - tl.store(Z + off, z) - - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': expr}) - # inputs - x = numpy_random(SIZE, dtype_str=dtype_x) - if 'log' in expr: - x = np.abs(x) + 0.01 - # reference result - z_ref = eval(expr if numpy_expr is None else numpy_expr) - # triton result - x_tri = to_triton(x, device=device, dst_type=dtype_x) - z_tri = to_triton(np.empty_like(z_ref), device=device, dst_type=dtype_x) - kernel[(1, )](z_tri, x_tri, SIZE=SIZE, num_warps=4) - # compare - np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01) - - -def _binary_op_dtype_override(a: str, b: str) -> Optional[np.dtype]: - """ - Given two dtype strings, returns the numpy dtype Triton thinks binary - operations on the two types should return. Returns None if the return value - matches numpy. This is generally needed because Triton and pytorch return - narrower floating point types than numpy in mixed operations, and because - Triton follows C/C++ semantics around mixed signed/unsigned operations, and - numpy/pytorch do not. - """ - overrides = { - ('float16', 'int16'): np.float16, - ('float16', 'int32'): np.float16, - ('float16', 'int64'): np.float16, - ('float16', 'uint16'): np.float16, - ('float16', 'uint32'): np.float16, - ('float16', 'uint64'): np.float16, - ('int8', 'uint8'): np.uint8, - ('int8', 'uint16'): np.uint16, - ('int8', 'uint32'): np.uint32, - ('int8', 'uint64'): np.uint64, - ('int16', 'uint16'): np.uint16, - ('int16', 'uint32'): np.uint32, - ('int16', 'uint64'): np.uint64, - ('int32', 'uint32'): np.uint32, - ('int32', 'uint64'): np.uint64, - ('int64', 'uint64'): np.uint64, - } - key = (a, b) if a < b else (b, a) - return overrides.get(key) - - -def _test_binary(dtype_x, dtype_y, expr, numpy_expr=None, mode_x='real', mode_y='real', device='cuda', y_low=None, y_high=None): - check_type_supported(dtype_x, device) # early return if dtype_x is not supported - check_type_supported(dtype_y, device) - SIZE = 128 - # define the kernel / launch-grid - - @triton.jit - def kernel(Z, X, Y, SIZE: tl.constexpr): - off = tl.arange(0, SIZE) - x = tl.load(X + off) - y = tl.load(Y + off) - z = GENERATE_TEST_HERE - tl.store(Z + off, z) - - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': expr}) - # inputs - rs = RandomState(17) - x = numpy_random(SIZE, dtype_str=dtype_x, rs=rs) - y = numpy_random(SIZE, dtype_str=dtype_y, rs=rs, low=y_low, high=y_high) - if mode_x == 'nan': - x[:] = float('nan') - if mode_y == 'nan': - y[:] = float('nan') - # reference result - z_ref = eval(expr if numpy_expr is None else numpy_expr) - dtype_z = _binary_op_dtype_override(dtype_x, dtype_y) - if dtype_z is not None: - z_ref = z_ref.astype(dtype_z) - # triton result - x_tri = to_triton(x, device=device, dst_type=dtype_x) - y_tri = to_triton(y, device=device, dst_type=dtype_y) - z_tri = to_triton(np.empty(SIZE, dtype=z_ref.dtype), device=device) - kernel[(1, )](z_tri, x_tri, y_tri, SIZE=SIZE, num_warps=4) - np.testing.assert_allclose(z_ref, to_numpy(z_tri), err_msg=expr, rtol=0.01) - - -def _mod_operation_ill_conditioned(dtype_x, dtype_y) -> bool: - # The result of x % y is ill-conditioned if x % y is much smaller than x. - # pytorch/CUDA has slightly different (probably better) rounding on - # remainders than stock LLVM. We currently don't expect to match it - # bit-for-bit. - return (dtype_x, dtype_y) in [ - ('int32', 'bfloat16'), - ('int32', 'float16'), - ('int32', 'float32'), - ('int64', 'bfloat16'), - ('int64', 'float16'), - ('int64', 'float32'), - ('int64', 'float64'), - ('uint16', 'bfloat16'), - ('uint16', 'float16'), - ('uint16', 'float32'), - ('uint32', 'bfloat16'), - ('uint32', 'float16'), - ('uint32', 'float32'), - ('uint64', 'bfloat16'), - ('uint64', 'float16'), - ('uint64', 'float32'), - ('uint64', 'float64'), - ] - -# --------------- -# test binary ops -# --------------- - - -@pytest.mark.parametrize("dtype_x, dtype_y, op", [ - (dtype_x, dtype_y, op) - for op in ['+', '-', '*', '/', '%'] - for dtype_x in dtypes_with_bfloat16 - for dtype_y in dtypes_with_bfloat16 -]) -def test_bin_op(dtype_x, dtype_y, op, device): - expr = f' x {op} y' - if op == '%' and dtype_x in int_dtypes + uint_dtypes and dtype_y in int_dtypes + uint_dtypes: - # LLVM has 'numpy.fmod', not 'numpy.remainder', semantics on integer remainders. - numpy_expr = 'np.fmod(x, y)' - elif op in ('/', '%') and dtype_x in ('int16', 'float16', 'bfloat16') and dtype_y in ('int16', 'float16', 'bfloat16'): - # Triton promotes 16-bit floating-point / and % to 32-bit because there - # are no native div or FRem operations on float16. Since we have to - # convert anyway, we may as well take the accuracy bump. - numpy_expr = f'x.astype(np.float32) {op} y.astype(np.float32)' - elif (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)): - numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})' - elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)): - numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})' - else: - numpy_expr = None - if op == '%' and _mod_operation_ill_conditioned(dtype_x, dtype_y): - with pytest.raises(AssertionError, match='Not equal to tolerance'): - _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) - elif (op in ('%', '/') and - ((dtype_x in int_dtypes and dtype_y in uint_dtypes) or - (dtype_x in uint_dtypes and dtype_y in int_dtypes))): - with pytest.raises(triton.CompilationError) as exc_info: - _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) - assert re.match('Cannot use .* because they have different signedness', str(exc_info.value.__cause__)) - else: - _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) - - -@pytest.mark.parametrize("dtype_x, dtype_y", - [(dtype_x, dtype_y) for dtype_x in int_dtypes for dtype_y in int_dtypes] + - [(dtype_x, dtype_y) for dtype_x in uint_dtypes for dtype_y in uint_dtypes] - ) -def test_floordiv(dtype_x, dtype_y, device): - # Triton has IEEE, not numpy/torch, semantics for %, and those carry - # through to //, so we have to use a nonstandard expression to get a - # reference result for //. - expr = 'x // y' - numpy_expr = '((x - np.fmod(x, y)) / y)' - _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) - - -def test_unsigned_name_mangling(device): - # Test that uint32 and int32 are mangled differently by the compiler - SIZE = 128 - # define the kernel / launch-grid - - @triton.jit - def kernel(O1, O2, X, Y, SIZE: tl.constexpr): - off = tl.arange(0, SIZE) - x = tl.load(X + off) - y = tl.load(Y + off) - out1 = tl.abs(x) # uint32 -> nop - out2 = tl.abs(-y) # int32 -> should have an effect - tl.store(O1 + off, out1) - tl.store(O2 + off, out2) - - dtype_x = 'uint32' - dtype_y = 'int32' - # inputs - rs = RandomState(17) - x = numpy_random(SIZE, dtype_str=dtype_x, rs=rs) - y = numpy_random(SIZE, dtype_str=dtype_y, rs=rs) - # reference result - expect = (np.abs(x), np.abs(-y)) - # triton result - x_tri = to_triton(x, device=device, dst_type=dtype_x) - y_tri = to_triton(y, device=device, dst_type=dtype_y) - actual = tuple( - to_triton(np.empty_like(e), device=device) - for e in expect - ) - kernel[(1, )](actual[0], actual[1], x_tri, y_tri, SIZE=SIZE, num_warps=4) - - # Bitwise op, so expect exact equality - assert (expect[0] == to_numpy(actual[0])).all() - assert (expect[1] == to_numpy(actual[1])).all() - - -# --------------- -# test bitwise ops -# --------------- -@pytest.mark.parametrize("dtype_x, dtype_y, op", [ - (dtype_x, dtype_y, op) - for op in ['&', '|', '^'] - for dtype_x in dtypes + dtypes_with_bfloat16 - for dtype_y in dtypes + dtypes_with_bfloat16 -]) -def test_bitwise_op(dtype_x, dtype_y, op, device): - expr = f'x {op} y' - if (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)): - numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})' - elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)): - numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})' - else: - numpy_expr = None - if 'float' in dtype_x + dtype_y: - with pytest.raises(triton.CompilationError) as exc_info: - _test_binary(dtype_x, dtype_y, expr, numpy_expr='np.array([])', device=device) - # The CompilationError must have been caused by a C++ exception with this text. - assert re.match('invalid operands of type', str(exc_info.value.__cause__)) - else: - _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device) - - -@pytest.mark.parametrize("dtype_x, dtype_y, op", [ - (dtype_x, dtype_y, op) - for op in ['<<', '>>'] - for dtype_x in int_dtypes + uint_dtypes - for dtype_y in int_dtypes + uint_dtypes -]) -def test_shift_op(dtype_x, dtype_y, op, device): - expr = f'x {op} y' - bw = max(_bitwidth(dtype_x), _bitwidth(dtype_y)) - if dtype_x.startswith('int'): - dtype_z = f'int{bw}' - else: - dtype_z = f'uint{bw}' - numpy_expr = f'x.astype(np.{dtype_z}) {op} y.astype(np.{dtype_z})' - _test_binary(dtype_x, dtype_y, expr, numpy_expr, device=device, y_low=0, y_high=65) - - -# --------------- -# test compare ops -# --------------- -ops = ['==', '!=', '>', '<', '>=', '<='] - - -@pytest.mark.parametrize("dtype_x, dtype_y, op, mode_x, mode_y", - # real - [ - (dtype_x, dtype_y, op, 'real', 'real') - for op in ops - for dtype_x in dtypes - for dtype_y in dtypes - ] + - # NaNs - [('float32', 'float32', op, mode_x, mode_y) - for op in ops - for mode_x, mode_y in [('nan', 'real'), - ('real', 'nan'), - ('nan', 'nan')] - - ]) -def test_compare_op(dtype_x, dtype_y, op, mode_x, mode_y, device): - expr = f'x {op} y' - if (dtype_x in uint_dtypes and dtype_y in int_dtypes and _bitwidth(dtype_x) >= _bitwidth(dtype_y)): - numpy_expr = f'x.astype(np.{dtype_x}) {op} y.astype(np.{dtype_x})' - elif (dtype_y in uint_dtypes and dtype_x in int_dtypes and _bitwidth(dtype_y) >= _bitwidth(dtype_x)): - numpy_expr = f'x.astype(np.{dtype_y}) {op} y.astype(np.{dtype_y})' - else: - numpy_expr = None - _test_binary(dtype_x, dtype_y, expr, numpy_expr, mode_x=mode_x, mode_y=mode_y, device=device) - - -# --------------- -# test broadcast -# --------------- -@pytest.mark.parametrize("dtype", dtypes_with_bfloat16) -def test_broadcast(dtype, device): - @triton.jit - def broadcast_kernel(x_ptr, y_ptr, y_broadcasted_ptr, M: tl.constexpr, N: tl.constexpr): - offset1 = tl.arange(0, M) - offset2 = tl.arange(0, N) - x = tl.load(x_ptr + N * offset1[:, None] + offset2[None, :]) - y = tl.load(y_ptr + offset2) - _, y_broadcasted = tl.broadcast(x, y) - tl.store(y_broadcasted_ptr + N * offset1[:, None] + offset2[None, :], y_broadcasted) - - M = 32 - N = 64 - rs = RandomState(17) - x = numpy_random((M, N), dtype_str=dtype, rs=rs) - y = numpy_random(N, dtype_str=dtype, rs=rs) - _, y_broadcasted_np = np.broadcast_arrays(x, y) - - x_tri = to_triton(x, device=device, dst_type=dtype) - y_tri = to_triton(y, device=device, dst_type=dtype) - y_broadcasted_tri = to_triton(np.empty((M, N), dtype=y_broadcasted_np.dtype), device=device, dst_type=dtype) - - broadcast_kernel[(1,)](x_tri, y_tri, y_broadcasted_tri, M=M, N=N) - assert (y_broadcasted_np == to_numpy(y_broadcasted_tri)).all() - -# ------------------ -# test invalid slice -# ------------------ - - -def test_invalid_slice(device): - dst = torch.empty(128, device=device) - - @triton.jit - def _kernel(dst): - dst[10:] - - with pytest.raises(triton.CompilationError, match='unsupported tensor index'): - _kernel[(1,)](dst=dst) - - -# ---------------- -# test expand_dims -# ---------------- -def test_expand_dims(device): - @triton.jit - def expand_dims_kernel(dummy, N: tl.constexpr): - offset1 = tl.arange(0, N) - - t = tl.expand_dims(offset1, 0) - tl.static_assert(t.shape == [1, N]) - - t = tl.expand_dims(offset1, 1) - tl.static_assert(t.shape == [N, 1]) - - t = tl.expand_dims(offset1, -1) - tl.static_assert(t.shape == [N, 1]) - - t = tl.expand_dims(offset1, -2) - tl.static_assert(t.shape == [1, N]) - - t = tl.expand_dims(offset1, (0, -1)) - tl.static_assert(t.shape == [1, N, 1]) - - t = tl.expand_dims(offset1, (0, 1, 3)) - tl.static_assert(t.shape == [1, 1, N, 1]) - - t = tl.expand_dims(offset1, (-4, 2, -1)) - tl.static_assert(t.shape == [1, N, 1, 1]) - - t = tl.expand_dims(offset1, (3, 1, 2)) - tl.static_assert(t.shape == [N, 1, 1, 1]) - - N = 32 - dummy_tensor = torch.empty((), device=device) - expand_dims_kernel[(1,)](dummy_tensor, N) - - -def test_expand_dims_error_cases(device): - @triton.jit - def dim_out_of_range1(dummy, N: tl.constexpr): - offset1 = tl.arange(0, N) - - t = tl.expand_dims(offset1, -2) - t = tl.expand_dims(offset1, -3) - - @triton.jit - def dim_out_of_range2(dummy, N: tl.constexpr): - offset1 = tl.arange(0, N) - - t = tl.expand_dims(offset1, 1) - t = tl.expand_dims(offset1, 2) - - @triton.jit - def duplicate_dim1(dummy, N: tl.constexpr): - offset1 = tl.arange(0, N) - - t = tl.expand_dims(offset1, (0, 0)) - - @triton.jit - def duplicate_dim2(dummy, N: tl.constexpr): - offset1 = tl.arange(0, N) - - t = tl.expand_dims(offset1, (0, -3)) - - N = 32 - dummy_tensor = torch.empty((), device=device) - - with pytest.raises(triton.CompilationError, match="invalid axis -3"): - dim_out_of_range1[(1,)](dummy_tensor, N) - - with pytest.raises(triton.CompilationError, match="invalid axis 2"): - dim_out_of_range2[(1,)](dummy_tensor, N) - - with pytest.raises(triton.CompilationError, match=r"duplicate axes, normalized axes = \[0, 0\]"): - duplicate_dim1[(1,)](dummy_tensor, N) - - with pytest.raises(triton.CompilationError, match=r"duplicate axes, normalized axes = \[0, 0\]"): - duplicate_dim2[(1,)](dummy_tensor, N) - - -# ---------------------------- -# test invalid program id axis -# ---------------------------- -def test_invalid_pid_axis(device): - dst = torch.empty(128, device=device) - - @triton.jit - def _kernel(dst): - pid = tl.program_id(20) - - with pytest.raises(triton.CompilationError, match=r"program_id must be in \[0,3\]"): - _kernel[(1,)](dst) - - -# --------------- -# test where -# --------------- -@pytest.mark.parametrize("dtype", dtypes_with_bfloat16 + ["*int32"]) -def test_where(dtype, device): - select_ptrs = False - if dtype == "*int32": - dtype = "int64" - select_ptrs = True - check_type_supported(dtype, device) - - @triton.jit - def where_kernel(cond_ptr, a_ptr, b_ptr, output_ptr, n_elements, - BLOCK_SIZE: tl.constexpr, - TEST_POINTERS: tl.constexpr, - TEST_SCALAR_POINTERS: tl.constexpr): - offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = offsets < n_elements - decide = tl.load(cond_ptr + offsets, mask=mask) - if TEST_SCALAR_POINTERS: - ptr = tl.where(tl.load(cond_ptr), a_ptr, b_ptr) - output = tl.load(ptr + offsets, mask=mask) - else: - if TEST_POINTERS: - a = tl.load(a_ptr + offsets, mask=mask).to(tl.pi32_t) - b = tl.load(b_ptr + offsets, mask=mask).to(tl.pi32_t) - else: - a = tl.load(a_ptr + offsets, mask=mask) - b = tl.load(b_ptr + offsets, mask=mask) - output = tl.where(decide, a, b) - tl.store(output_ptr + offsets, output, mask=mask) - - SIZE = 1_000 - rs = RandomState(17) - cond = numpy_random(SIZE, 'bool', rs) - x = numpy_random(SIZE, dtype_str=dtype, rs=rs) - y = numpy_random(SIZE, dtype_str=dtype, rs=rs) - z = np.where(cond, x, y) - - cond_tri = to_triton(cond, device=device) - x_tri = to_triton(x, device=device, dst_type=dtype) - y_tri = to_triton(y, device=device, dst_type=dtype) - z_tri = to_triton(np.empty(SIZE, dtype=z.dtype), device=device, dst_type=dtype) - - grid = lambda meta: (triton.cdiv(SIZE, meta['BLOCK_SIZE']),) - where_kernel[grid](cond_tri, x_tri, y_tri, z_tri, SIZE, BLOCK_SIZE=1024, TEST_POINTERS=select_ptrs, TEST_SCALAR_POINTERS=False) - assert (z == to_numpy(z_tri)).all() - if select_ptrs: - where_kernel[grid](cond_tri, x_tri, y_tri, z_tri, SIZE, BLOCK_SIZE=1024, TEST_POINTERS=select_ptrs, TEST_SCALAR_POINTERS=True) - z = np.where(cond[0], x, y) - assert (z == to_numpy(z_tri)).all() - - -def test_where_broadcast(device): - @triton.jit - def where_kernel(cond_ptr, a_ptr, out_ptr, BLOCK_SIZE: tl.constexpr): - xoffsets = tl.arange(0, BLOCK_SIZE)[:, None] - yoffsets = tl.arange(0, BLOCK_SIZE)[None, :] - - mask = tl.load(cond_ptr + yoffsets) - vals = tl.load(a_ptr + yoffsets + BLOCK_SIZE * xoffsets) - res = tl.where(mask, vals, 0.) - tl.store(out_ptr + yoffsets + BLOCK_SIZE * xoffsets, res) - - @triton.jit - def where_scalar_condition(a_ptr, out_ptr, BLOCK_SIZE: tl.constexpr): - xoffsets = tl.arange(0, BLOCK_SIZE)[:, None] - yoffsets = tl.arange(0, BLOCK_SIZE)[None, :] - mask = 0 - vals = tl.load(a_ptr + yoffsets + BLOCK_SIZE * xoffsets) - res = tl.where(mask, vals, 0.) - tl.store(out_ptr + yoffsets + BLOCK_SIZE * xoffsets, res) - - SIZE = 32 - dtype = 'float32' - rs = RandomState(17) - x = numpy_random((SIZE, SIZE), dtype_str=dtype, rs=rs) - mask = numpy_random(SIZE, 'bool', rs=rs) - z = np.where(mask, x, 0) - cond_tri = to_triton(mask, device=device) - x_tri = to_triton(x, device=device, dst_type=dtype) - z_tri = to_triton(np.empty((SIZE, SIZE), dtype=z.dtype), device=device, dst_type=dtype) - where_kernel[(1,)](cond_tri, x_tri, z_tri, SIZE) - assert (z == to_numpy(z_tri)).all() - where_scalar_condition[(1,)](x_tri, z_tri, SIZE) - z = np.where(0, x, 0) - assert (z == to_numpy(z_tri)).all() - -# --------------- -# test unary ops -# --------------- - - -@pytest.mark.parametrize("dtype_x, expr", [ - (dtype_x, ' -x') for dtype_x in dtypes_with_bfloat16 -] + [ - (dtype_x, ' ~x') for dtype_x in int_dtypes -]) -def test_unary_op(dtype_x, expr, device): - _test_unary(dtype_x, expr, device=device) - -# ---------------- -# test math ops -# ---------------- - - -@pytest.mark.parametrize("dtype_x, expr", [(dtype_x, expr) for dtype_x in ["float32", "float64"] for expr in ['exp', 'log', 'cos', 'sin']]) -def test_math_op(dtype_x, expr, device): - _test_unary(dtype_x, f'tl.{expr}(x)', f'np.{expr}(x) ', device=device) - -# ---------------- -# test abs -# ---------------- - - -@pytest.mark.parametrize("dtype_x", [ - (dtype_x) - for dtype_x in dtypes_with_bfloat16 -]) -def test_abs(dtype_x, device): - _test_unary(dtype_x, 'tl.abs(x)', 'np.abs(x) ', device=device) - - -@pytest.mark.parametrize("in_dtype", [tl.float8e4b15, tl.float8e4, tl.float8e5]) -def test_abs_fp8(in_dtype, device): - - @triton.jit - def abs_kernel(X, Z, SIZE: tl.constexpr): - off = tl.arange(0, SIZE) - x = tl.load(X + off) - z = tl.abs(x) - tl.store(Z + off, z) - - f8_tensor = torch.tensor(range(-128, 128), dtype=torch.int8, device=device) - # f32_to_f8 doesn't handle nan, so we make sure f8_tensor doesn't contain any nan - all_exp_ones = (f8_tensor & 0b01111100) == 128 - 2**in_dtype.fp_mantissa_width - f8_tensor[all_exp_ones] = 0 - f8 = triton.reinterpret(f8_tensor, in_dtype) - n_elements = f8_tensor.numel() - out_f8 = torch.empty_like(f8_tensor) - abs_kernel[(1,)](f8, triton.reinterpret(out_f8, in_dtype), n_elements) - - f32_tensor = convert_float_to_float32(f8_tensor, in_dtype) - expect = f32_tensor.abs() - actual_f8 = convert_float_to_float32(out_f8, in_dtype) - torch.testing.assert_allclose(actual_f8, expect) - - -# ---------------- -# test indexing -# ---------------- - - -def make_ptr_str(name, shape): - rank = len(shape) - offsets = [] - stride = 1 - for i in reversed(range(rank)): - idx = ', '.join([':' if ii == i else 'None' for ii in range(rank)]) - offsets += [f'tl.arange(0, {shape[i]})[{idx}]*{stride}'] - stride *= shape[i] - return f"{name} + {' + '.join(offsets)}" - - -# TODO: handle `%4 = triton_gpu.convert_layout %3 : (tensor<32xi32, #blocked0>) -> tensor<32xi32, #triton_gpu.slice<{dim = 0, parent = #blocked1}>>`` -@pytest.mark.parametrize("expr, dtype_str", [ - (f'x[{s}]', d) - for s in ['None, :', ':, None', - 'None, :, :', - ':, :, None'] - for d in ['int32', 'uint32', 'uint16'] -]) -def test_index1d(expr, dtype_str, device): - rank_x = expr.count(':') - rank_y = expr.count(',') + 1 - shape_x = [32 for _ in range(rank_x)] - shape_z = [32 for _ in range(rank_y)] - shape_z_rank_mismatch = [32 for _ in range(rank_y + 1)] - shape_z_dim_mismatch = [64 for _ in range(rank_y)] - - # Triton kernel - @triton.jit - def kernel(Z, X, SIZE: tl.constexpr): - m = tl.arange(0, SIZE) - n = tl.arange(0, SIZE) - x = tl.load(X_PTR_EXPR) - z = GENERATE_TEST_HERE - tl.store(Z_PTR_EXPR, z) - - def generate_kernel(shape_x, shape_z): - to_replace = { - 'X_PTR_EXPR': make_ptr_str('X', shape_x), - 'Z_PTR_EXPR': make_ptr_str('Z', shape_z), - 'GENERATE_TEST_HERE': expr, - } - return patch_kernel(kernel, to_replace) - - kernel_match = generate_kernel(shape_x, shape_z) - kernel_dim_mismatch = generate_kernel(shape_x, shape_z_dim_mismatch) - kernel_rank_mismatch = generate_kernel(shape_x, shape_z_rank_mismatch) - - # torch result - x = numpy_random(shape_x, dtype_str=dtype_str) - y = np.zeros(shape_z, dtype=getattr(np, dtype_str)) - z_ref = eval(expr) + y - # triton result - z_tri = to_triton(np.empty_like(z_ref), device=device) - x_tri = to_triton(x, device=device) - kernel_match[(1, )](z_tri, x_tri, num_warps=1, SIZE=shape_x[0]) - # compare - assert (z_ref == to_numpy(z_tri)).all() - - def catch_compilation_error(kernel): - try: - kernel[(1, )](z_tri, x_tri, num_warps=1, SIZE=shape_x[0]) - except triton.CompilationError as e: - np.testing.assert_(True) - except BaseException: - np.testing.assert_(False) - - catch_compilation_error(kernel_dim_mismatch) - catch_compilation_error(kernel_rank_mismatch) - - -# --------------- -# test tuples -# --------------- - - -@triton.jit -def tuples_fn(a, b): - return a + b, \ - a - b, \ - a * b - - -def test_tuples(device): - @triton.jit - def with_fn(X, Y, A, B, C): - x = tl.load(X) - y = tl.load(Y) - a, b, c = tuples_fn(x, y) - tl.store(A, a) - tl.store(B, b) - tl.store(C, c) - - @triton.jit - def without_fn(X, Y, A, B, C): - x = tl.load(X) - y = tl.load(Y) - a, b, c = x + y, x - y, x * y - tl.store(A, a) - tl.store(B, b) - tl.store(C, c) - - x = torch.tensor([1.3], device=device, dtype=torch.float32) - y = torch.tensor([1.9], device=device, dtype=torch.float32) - a_tri = torch.tensor([0], device=device, dtype=torch.float32) - b_tri = torch.tensor([0], device=device, dtype=torch.float32) - c_tri = torch.tensor([0], device=device, dtype=torch.float32) - for kernel in [with_fn, without_fn]: - kernel[(1, )](x, y, a_tri, b_tri, c_tri, num_warps=1) - a_ref, b_ref, c_ref = x + y, x - y, x * y - assert a_tri == a_ref - assert b_tri == b_ref - assert c_tri == c_ref - - -@triton.jit(noinline=True) -def noinline_simple_fn(x, y, Z): - z = x + y - tl.store(Z, z) - - -@triton.jit(noinline=True) -def noinline_call_graph_fn1(x): - return x + 1 - - -@triton.jit(noinline=True) -def noinline_call_graph_fn2(y): - return y + 2 - - -@triton.jit(noinline=True) -def noinline_call_graph_fn(x, y, Z): - t0 = noinline_call_graph_fn1(x) - t1 = noinline_call_graph_fn2(y) - z = t0 + t1 - tl.store(Z, z) - - -@triton.jit(noinline=True) -def noinline_shared_fn(x, y, Z): - offs = tl.arange(0, 16)[:, None] * 16 + tl.arange(0, 16)[None, :] - z = tl.load(Z + offs) - z = tl.dot(z, z) + x + y - tl.store(Z + offs, z) - - -@triton.jit(noinline=True) -def noinline_dynamic_fn(x, y, Z): - if x >= 1: - x = noinline_call_graph_fn1(x) - else: - x = noinline_call_graph_fn2(x) - if y >= 2: - y = noinline_call_graph_fn2(y) - else: - y = noinline_call_graph_fn1(y) - z = x + y - tl.store(Z, z) - - -@triton.jit(noinline=True) -def noinline_call_multi_values_fn(x, y): - return x + 1, y + 2 - - -@triton.jit(noinline=True) -def noinline_multi_values_fn(x, y, Z): - x, y = noinline_call_multi_values_fn(x, y) - z = x + y - tl.store(Z, z) - - -@pytest.mark.parametrize("mode", ["simple", "call_graph", "shared", "dynamic", "multi_values"]) -def test_noinline(mode, device): - @triton.jit - def kernel(X, Y, Z): - x = tl.load(X) - y = tl.load(Y) - GENERATE_TEST_HERE(x, y, Z) - - func_name = f'noinline_{mode}_fn' - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': func_name}) - x = torch.tensor([1.0], device=device, dtype=torch.float32) - y = torch.tensor([2.0], device=device, dtype=torch.float32) - if mode == "shared": - z = torch.ones((16, 16), device=device, dtype=torch.float32) - else: - z = torch.tensor([0.0], device=device, dtype=torch.float32) - kernel[(1,)](x, y, z, num_warps=1) - if mode == "simple": - assert torch.equal(z, x + y) - elif mode == "call_graph" or mode == "dynamic" or mode == "multi_values": - assert torch.equal(z, x + 1 + y + 2) - elif mode == "shared": - ref = torch.full((16, 16), 16, device=device, dtype=torch.float32) - assert torch.equal(z, ref + x + y) - - -# --------------- -# test atomics -# --------------- -@pytest.mark.parametrize("op, dtype_x_str, mode, sem", itertools.chain.from_iterable([ - [ - ('add', 'float16', mode, sem), - ('add', 'uint32', mode, sem), ('add', 'int32', mode, sem), ('add', 'float32', mode, sem), - ('max', 'uint32', mode, sem), ('max', 'int32', mode, sem), ('max', 'float32', mode, sem), - ('min', 'uint32', mode, sem), ('min', 'int32', mode, sem), ('min', 'float32', mode, sem), - ] - for mode in ['all_neg', 'all_pos', 'min_neg', 'max_pos'] - for sem in [None, 'acquire', 'release', 'acq_rel', 'relaxed']])) -def test_atomic_rmw(op, dtype_x_str, mode, sem, device): - check_cuda_only(device) - - capability = torch.cuda.get_device_capability() - if capability[0] < 7: - if dtype_x_str == 'float16': - pytest.skip("Only test atomic float16 ops on devices with sm >= 70") - n_programs = 5 - - # triton kernel - @triton.jit - def kernel(X, Z): - pid = tl.program_id(0) - x = tl.load(X + pid) - old = GENERATE_TEST_HERE - - sem_arg = sem if sem is None else f'"{sem}"' - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.atomic_{op}(Z, x, sem={sem_arg})'}) - numpy_op = {'add': np.sum, 'max': np.max, 'min': np.min}[op] - max_neutral = float('-inf') if dtype_x_str in float_dtypes else np.iinfo(getattr(np, dtype_x_str)).min - min_neutral = float('inf') if dtype_x_str in float_dtypes else np.iinfo(getattr(np, dtype_x_str)).max - neutral = {'add': 0, 'max': max_neutral, 'min': min_neutral}[op] - - # triton result - rs = RandomState(17) - x = np.array([2**i for i in range(n_programs)], dtype=getattr(np, dtype_x_str)) - if mode == 'all_neg': - x = -np.abs(x) - if mode == 'all_pos': - x = np.abs(x) - if mode == 'min_neg': - idx = rs.randint(n_programs, size=(1, )).item() - x[idx] = -np.max(np.abs(x)) - 1 - if mode == 'max_pos': - idx = rs.randint(n_programs, size=(1, )).item() - x[idx] = np.max(np.abs(x)) + 1 - x_tri = to_triton(x, device=device) - - z_tri = to_triton(np.array([neutral], dtype=getattr(np, dtype_x_str)), device=device) - h = kernel[(n_programs, )](x_tri, z_tri) - # torch result - z_ref = numpy_op(x).astype(getattr(np, dtype_x_str)) - # compare - exact = op not in ['add'] - if exact: - assert z_ref.item() == to_numpy(z_tri).item() - else: - np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01) - sem_str = "acq_rel" if sem is None else sem - assert f"atom.global.gpu.{sem_str}" in h.asm["ptx"] - - -def test_atomic_rmw_predicate(device): - @triton.jit - def kernel(X): - val = tl.program_id(0) - if val < 64: - tl.atomic_max(X, val) - x = torch.zeros((1,), device=device, dtype=torch.int32) - kernel[(4096,)](x) - assert x.item() == 63 - - -@pytest.mark.parametrize("shape, axis", - [(shape, axis) for shape in [(2, 2), (2, 8), (8, 2), (8, 8), (32, 32)] for axis in [0, 1]]) -def test_tensor_atomic_rmw(shape, axis, device): - shape0, shape1 = shape - # triton kernel - - @triton.jit - def kernel(Z, X, AXIS: tl.constexpr, SHAPE0: tl.constexpr, SHAPE1: tl.constexpr): - off0 = tl.arange(0, SHAPE0) - off1 = tl.arange(0, SHAPE1) - x = tl.load(X + off0[:, None] * SHAPE1 + off1[None, :]) - z = tl.sum(x, axis=AXIS) - if AXIS == 1: - tl.atomic_add(Z + off0, z) - else: - tl.atomic_add(Z + off1, z) - rs = RandomState(17) - x = numpy_random((shape0, shape1), dtype_str="float32", rs=rs) - # reference result - z_ref = np.sum(x, axis=axis, keepdims=False) - # triton result - x_tri = to_triton(x, device=device) - z_shape = (shape0, ) if axis == 1 else (shape1, ) - z_tri = to_triton(np.zeros(z_shape, dtype="float32"), device=device) - kernel[(1,)](z_tri, x_tri, axis, shape0, shape1) - np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=1e-4) - - -def test_tensor_atomic_rmw_block(device): - shape = (8, 8) - - @triton.jit - def kernel(X, SHAPE0: tl.constexpr, SHAPE1: tl.constexpr): - off0 = tl.arange(0, SHAPE0) - off1 = tl.arange(0, SHAPE1) - offs = off0[:, None] * SHAPE1 + off1[None, :] - val = offs.to(tl.float32) - x = X + offs - tl.atomic_min(x, val) - x = torch.ones((8, 8), device=device, dtype=torch.float32) - kernel[(2,)](x, shape[0], shape[1]) - assert torch.min(x).item() == 0.0 - - -@pytest.mark.parametrize("sem", [None, 'acquire', 'release', 'acq_rel', 'relaxed']) -def test_atomic_cas(sem, device): - # 1. make sure that atomic_cas changes the original value (Lock) - @triton.jit - def change_value(Lock): - tl.atomic_cas(Lock, 0, 1) - - Lock = torch.zeros((1,), device=device, dtype=torch.int32) - change_value[(1,)](Lock) - - assert (Lock[0] == 1) - - # 2. only one block enters the critical section - @triton.jit - def serialized_add(data, Lock, SEM: tl.constexpr): - ptrs = data + tl.arange(0, 128) - while tl.atomic_cas(Lock, 0, 1, SEM) == 1: - pass - - tl.store(ptrs, tl.load(ptrs) + 1.0) - - # release lock - tl.atomic_xchg(Lock, 0) - - Lock = torch.zeros((1,), device=device, dtype=torch.int32) - data = torch.zeros((128,), device=device, dtype=torch.float32) - ref = torch.full((128,), 64.0) - h = serialized_add[(64,)](data, Lock, SEM=sem) - sem_str = "acq_rel" if sem is None else sem - np.testing.assert_allclose(to_numpy(data), to_numpy(ref)) - assert f"atom.global.{sem_str}" in h.asm["ptx"] - - -# --------------- -# test cast -# --------------- - - -@pytest.mark.parametrize("dtype_x, dtype_z, bitcast", [ - (dtype_x, dtype_z, False) - for dtype_x in dtypes - for dtype_z in dtypes -] + [ - ('float32', 'bfloat16', False), - ('bfloat16', 'float32', False), - ('float32', 'int32', True), - ('float32', 'int1', False), -] + [ - (f'uint{x}', f'int{x}', True) for x in [8, 16, 32, 64] -] + [ - (f'int{x}', f'uint{x}', True) for x in [8, 16, 32, 64] -]) -def test_cast(dtype_x, dtype_z, bitcast, device): - # bfloat16 on cc < 80 will not be tested - check_type_supported(dtype_x, device) - check_type_supported(dtype_z, device) - - # This is tricky because numpy doesn't have bfloat, and torch doesn't have uints. - x0 = 43 if dtype_x in int_dtypes else 43.5 - if dtype_x in float_dtypes and dtype_z == 'int1': - x0 = 0.5 - if dtype_x.startswith('bfloat'): - x_tri = torch.tensor([x0], dtype=getattr(torch, dtype_x), device=device) - else: - x = np.array([x0], dtype=getattr(np, dtype_x)) - x_tri = to_triton(x, device=device) - - # triton kernel - @triton.jit - def kernel(X, Z, BITCAST: tl.constexpr): - x_ptr = X + tl.arange(0, 1) - z_ptr = Z + tl.arange(0, 1) - x = tl.load(x_ptr) - z = x.to(Z.dtype.element_ty, bitcast=BITCAST) - tl.store(z_ptr, z) - - dtype_z_np = dtype_z if dtype_z != 'int1' else 'bool_' - # triton result - if dtype_z.startswith('bfloat'): - z_tri = torch.empty((1,), dtype=getattr(torch, dtype_z), device=device) - else: - z_tri = to_triton(np.empty((1, ), dtype=getattr(np, dtype_z_np)), device=device) - kernel[(1, )](x_tri, z_tri, BITCAST=bitcast) - # torch result - if dtype_z.startswith('bfloat') or dtype_x.startswith('bfloat'): - assert bitcast is False - z_ref = x_tri.to(z_tri.dtype) - assert z_tri == z_ref - else: - if bitcast: - z_ref = x.view(getattr(np, dtype_z_np)) - else: - z_ref = x.astype(getattr(np, dtype_z_np)) - assert to_numpy(z_tri) == z_ref - - -@pytest.mark.parametrize("dtype_str, num_warps", [(dtype_str, num_warps) for dtype_str in int_dtypes + float_dtypes for num_warps in [4, 8]]) -def test_cat(dtype_str, num_warps, device): - check_type_supported(dtype_str, device) - - @triton.jit - def kernel(X, Y, Z, N: tl.constexpr): - offs = tl.arange(0, N) - x = tl.load(X + offs) - y = tl.load(Y + offs) - z = tl.cat(x, y, can_reorder=True) - tl.store(Z + tl.arange(0, 2 * N), z) - - x = torch.arange(0, 128, device=device).to(getattr(torch, dtype_str)) - y = torch.arange(-128, 0, device=device).to(getattr(torch, dtype_str)) - z_ref = torch.cat([x, y], dim=0).sum() - z = torch.zeros((256,), dtype=getattr(torch, dtype_str), device=device) - kernel[(1, )](x, y, z, N=128, num_warps=num_warps) - assert z.sum() == z_ref - # check if there's no duplicate value in z - assert z.unique().size(0) == z.size(0) - - -@pytest.mark.parametrize("dtype_str", list(torch_dtypes)) -def test_store_constant(dtype_str, device): - check_type_supported(dtype_str, device) - - """Tests that boolean True is stored as 1""" - @triton.jit - def kernel(output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): - offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = offsets < n_elements - output = GENERATE_TEST_HERE - tl.store(output_ptr + offsets, output, mask=mask) - - triton_dtype_str = 'uint8' if dtype_str == 'bool' else dtype_str - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.zeros([BLOCK_SIZE], dtype=tl.{triton_dtype_str}) + 1'}) - block_size = 128 - ref = torch.ones([block_size], dtype=getattr(torch, dtype_str), device=device) - output = torch.zeros([block_size], dtype=getattr(torch, dtype_str), device=device) - kernel[(1,)](output, block_size, BLOCK_SIZE=block_size) - - assert torch.all(output == ref) - - -def test_load_store_same_ptr(device): - @triton.jit() - def kernel(in_out_ptr): - pid = tl.program_id(axis=0) - x = tl.load(in_out_ptr + pid) - out = x * 2 - tl.store(in_out_ptr + pid, out) - - for _ in range(1000): - x = torch.ones((65536,), device=device, dtype=torch.float32) - kernel[(65536,)](x, num_warps=32) - assert torch.all(x == 2) - - -def convert_float_to_float32(fp: torch.tensor, dtype=None): - if not dtype: - dtype = getattr(tl, torch_dtype_name(fp.dtype)) - - fp = fp.view(getattr(torch, f"int{dtype.primitive_bitwidth}")) - exp_width = dtype.primitive_bitwidth - dtype.fp_mantissa_width - 1 - exp_bias = dtype.exponent_bias - sign = ((fp >> (dtype.primitive_bitwidth - 1)) & 0x01).int() - exp = ((fp >> dtype.fp_mantissa_width) & ((1 << exp_width) - 1)).int() - frac = (fp & ((1 << dtype.fp_mantissa_width) - 1)).int() - - output = torch.where(exp == 0, - # subnormal - ((-1.0) ** sign) * (2.0 ** (1 - exp_bias)) * (frac / (2.0 ** dtype.fp_mantissa_width)), - # normal - ((-1.0) ** sign) * (2.0 ** (exp - exp_bias)) * (1.0 + frac / (2.0 ** dtype.fp_mantissa_width))).float() - - extended_exp = ((1 << (tl.float32.primitive_bitwidth - tl.float32.fp_mantissa_width - 1)) - 1) << tl.float32.fp_mantissa_width - # special cases, exp is 0b11..1 - if dtype in [tl.float8e4, tl.float8e4b15]: - # float8e4m3 does not have infinities - output[fp == 0b01111111] = torch.nan - output[fp == 0b11111111] = torch.nan - else: - output = torch.where(exp == (1 << exp_width) - 1, - ((sign << (tl.float32.primitive_bitwidth - 1)) | extended_exp | (frac << (tl.float32.fp_mantissa_width - dtype.fp_mantissa_width))).view(torch.float32), - output) - return output - - -@pytest.mark.parametrize("in_dtype", [torch.float16, torch.bfloat16]) -def test_convert_float16_to_float32(in_dtype, device): - """Tests that check convert_float_to_float32 function""" - check_type_supported(in_dtype, device) - - f16_input = torch.tensor(range(-int(2 ** (16 - 1)), int(2 ** (16 - 1))), dtype=torch.int16).view(in_dtype) - f32_output = convert_float_to_float32(f16_input) - - nan = f16_input.isnan() - assert torch.all(f32_output[nan].isnan()) - inf = f16_input.isinf() - assert torch.all(f32_output[inf].isinf()) - other = torch.logical_not(torch.logical_or(nan, inf)) - assert torch.all(f16_input[other] == f32_output[other]) - - -def serialize_fp8(np_data, in_dtype): - if in_dtype == tl.float8e4b15: - # triton's f8e4b15 format is optimized for software emulation - # as a result, each pack of 4xfp8 values: - # s0b0s1b1s2b2s3b3 (for s, b sign and bits respectively) - # is actually internally stored as - # s0s2b0b2s1s3b1b3 - # we apply the conversion here - f8x4 = np_data.view(np.uint32) - s = [(f8x4 & (0x80000000 >> i)) << i for i in range(0, 32, 8)] - b = [(f8x4 & (0x7f000000 >> i)) << i for i in range(0, 32, 8)] - signs = (s[0] >> 0) | (s[1] >> 16) | (s[2] >> 1) | (s[3] >> 17) - bits = (b[0] >> 1) | (b[1] >> 17) | (b[2] >> 8) | (b[3] >> 24) - # tensor of triton fp8 data - return (signs | bits).view(np.int8) - else: - return np_data - - -@pytest.mark.parametrize("in_dtype", [tl.float8e4b15, tl.float8e4, tl.float8e5]) -@pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16, torch.float32]) -def test_fp8_fpN_roundtrip(in_dtype, out_dtype, device): - """ - For all possible float8 values (ref_fp8 = range(0, 256)), test that: - - conversion tri_fp16 = convert(input=ref_fp8, out=out_dtype) matches the reference - - conversion tri_fp8 = convert(input=tri_fp16, out=out_dtype) matches the original - this is only possible if both conversions are correct - """ - check_type_supported(out_dtype, device) - from contextlib import nullcontext as does_not_raise - expectation = does_not_raise() - err_msg = None - if (in_dtype == tl.float8e4b15 and out_dtype != torch.float16) or\ - (in_dtype != torch.float16 and out_dtype == tl.float8e4b15): - expectation = pytest.raises(triton.CompilationError) - err_msg = "fp8e4b15 can only be converted to/from fp16" - - @triton.jit - def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): - offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = offsets < n_elements - input = tl.load(input_ptr + offsets, mask=mask) - output = input - tl.store(output_ptr + offsets, output, mask=mask) - - # initialize array containing all possible f8 values except NaN - ref_fp8 = np.array(range(-128, 128), dtype=np.int8) - is_nan = (ref_fp8 & 0b01111100) == 128 - 2**in_dtype.fp_mantissa_width - exp_mask = 0b01111111 ^ ((1 << in_dtype.fp_mantissa_width) - 1) - is_subnormal = np.logical_or((ref_fp8 & exp_mask) == 0, (ref_fp8 & exp_mask) == exp_mask) - ref_fp8[is_nan] = 0 - ref_fp8[is_subnormal] = 0 - tri_fp8 = torch.from_numpy(serialize_fp8(ref_fp8, in_dtype)).cuda() - tri_fp16 = torch.empty(256, dtype=out_dtype, device="cuda") - with expectation as e: - copy_kernel[(1,)](triton.reinterpret(tri_fp8, in_dtype), tri_fp16, tri_fp16.shape[0], BLOCK_SIZE=1024) - - ref_fp8 = torch.from_numpy(ref_fp8).cuda() - ref_fp16 = convert_float_to_float32(ref_fp8, in_dtype) - assert torch.all(tri_fp16[~is_subnormal] == ref_fp16[~is_subnormal]) - - ref_fp8 = torch.empty_like(tri_fp16, dtype=torch.int8) - copy_kernel[(1,)](tri_fp16, triton.reinterpret(ref_fp8, in_dtype), tri_fp16.shape[0], BLOCK_SIZE=1024) - assert torch.all(tri_fp8 == ref_fp8) - - if err_msg is not None: - assert err_msg in str(e) - -# --------------- -# test reduce -# --------------- - - -def get_reduced_dtype(dtype_str, op): - if op in ('argmin', 'argmax'): - return 'int32' - if dtype_str in ['int8', 'uint8', 'int16', 'uint16']: - return 'int32' - if dtype_str == 'bfloat16': - return 'float32' - return dtype_str - - -@pytest.mark.parametrize("op, dtype_str, shape", - [(op, dtype, shape) - for op in ['min', 'max', - 'min-with-indices', - 'max-with-indices', - 'argmin-tie-break-left', - 'argmax-tie-break-left', - 'sum'] - for dtype in dtypes_with_bfloat16 - for shape in [32, 64, 128, 512]]) -def test_reduce1d(op, dtype_str, shape, device): - check_type_supported(dtype_str, device) # bfloat16 on cc < 80 will not be tested - - # triton kernel - @triton.jit - def kernel(X, Z, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - GENERATE_TEST_HERE - tl.store(Z, z) - - if 'with-indices' in op: - patch = f'z, _ = tl.{op.split("-")[0]}(x, axis=0, return_indices=True)' - elif 'arg' in op: - tie_break_left = 'tie-break-left' in op - patch = f'z = tl.{op.split("-")[0]}(x, axis=0, tie_break_left={tie_break_left})' - else: - patch = f'z = tl.{op}(x, axis=0)' - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': patch}) - # input - rs = RandomState(17) - # limit the range of integers so that the sum does not overflow - x = numpy_random((shape,), dtype_str=dtype_str, rs=rs) - numpy_op = {'sum': np.sum, 'max': np.max, 'min': np.min, - 'max-with-indices': np.max, - 'min-with-indices': np.min, - 'argmin-tie-break-fast': np.argmin, - 'argmin-tie-break-left': np.argmin, - 'argmax-tie-break-fast': np.argmax, - 'argmax-tie-break-left': np.argmax}[op] - if 'tie-break-left' in op: - x[3:10] = numpy_op(x) - x_tri = to_triton(x, device=device) - # numpy result - z_dtype_str = 'int32' if op in ('argmin', 'argmax') else dtype_str - z_tri_dtype_str = z_dtype_str - if op not in ['argmin', 'argmax'] and dtype_str == 'bfloat16': - z_dtype_str = 'float32' - z_ref = numpy_op(x).astype(getattr(np, z_dtype_str)) - # trunc mantissa for a fair comparison of accuracy - z_ref = (z_ref.view('uint32') & np.uint32(0xffff0000)).view('float32') - z_tri_dtype_str = 'bfloat16' - else: - z_ref = numpy_op(x).astype(getattr(np, z_dtype_str)) - # triton result - z_tri = to_triton(numpy_random((1,), dtype_str=z_dtype_str, rs=rs), - device=device, dst_type=z_tri_dtype_str) - kernel[(1,)](x_tri, z_tri, BLOCK=shape) - z_tri = to_numpy(z_tri) - # compare - if op == 'sum': - np.testing.assert_allclose(z_ref, z_tri, rtol=0.01) - else: - if op in ('argmin', 'argmax'): - # argmin and argmax can have multiple valid indices. - # so instead we compare the values pointed by indices - np.testing.assert_equal(x[z_ref], x[z_tri]) - else: - np.testing.assert_equal(z_ref, z_tri) - - -# TODO: [Qingyi] Fix argmin / argmax -reduce_configs1 = [ - (op, dtype, (1, 1024), axis) for dtype in dtypes_with_bfloat16 - for op in ['min', 'max', 'sum', 'argmin', 'argmax'] - for axis in [1] -] - - -# shape (128, 256) and (32, 1024) are not enabled on sm86 because the required shared memory -# exceeds the limit of 99KB -reduce2d_shapes = [(2, 32), (4, 32), (4, 128)] -# TODO: fix and uncomment -# , (32, 64), (64, 128)] -if torch.cuda.is_available() and 'V100' in torch.cuda.get_device_name(0): - reduce2d_shapes += [(128, 256) and (32, 1024)] - - -reduce_configs2 = [ - (op, 'float32', shape, axis) - for op in ['min', 'max', 'sum', 'argmin', 'argmax'] - for shape in reduce2d_shapes - for axis in [0, 1] -] + [ - (op, 'float32', [16, 32], None) - for op in ['min', 'max', 'sum'] -] - - -@pytest.mark.parametrize("op, dtype_str, shape, axis", reduce_configs1 + reduce_configs2) -def test_reduce2d(op, dtype_str, shape, axis, device): - check_type_supported(dtype_str, device) # bfloat16 on cc < 80 will not be tested - - # triton kernel - @triton.jit - def kernel(X, Z, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, AXIS: tl.constexpr): - range_m = tl.arange(0, BLOCK_M) - range_n = tl.arange(0, BLOCK_N) - x = tl.load(X + range_m[:, None] * BLOCK_N + range_n[None, :]) - z = GENERATE_TEST_HERE - if AXIS is None: - tl.store(Z, z) - elif AXIS == 1: - tl.store(Z + range_m, z) - else: - tl.store(Z + range_n, z) - - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{op}(x, axis=AXIS)'}) - # input - rs = RandomState(17) - # limit the range of integers so that the sum does not overflow - x = numpy_random(shape, dtype_str=dtype_str, rs=rs) - x_tri = to_triton(x, device=device) - numpy_op = {'sum': np.sum, 'max': np.max, 'min': np.min, - 'argmin': np.argmin, 'argmax': np.argmax}[op] - z_dtype_str = get_reduced_dtype(dtype_str, op) - z_tri_dtype_str = z_dtype_str - # numpy result - if op not in ['argmin', 'argmax'] and dtype_str == 'bfloat16': - z_dtype_str = 'float32' - z_tri_dtype_str = 'bfloat16' - z_ref = numpy_op(x, axis=axis).astype(getattr(np, z_dtype_str)) - # trunc mantissa for a fair comparison of accuracy - z_ref = (z_ref.view('uint32') & np.uint32(0xffff0000)).view('float32') - else: - z_ref = numpy_op(x, axis=axis).astype(getattr(np, z_dtype_str)) - # triton result - ret_numel = 1 if axis is None else shape[1 - axis] - z_tri = to_triton(numpy_random((ret_numel,), dtype_str=z_dtype_str, rs=rs), - device=device, dst_type=z_tri_dtype_str) - kernel[(1,)](x_tri, z_tri, BLOCK_M=shape[0], BLOCK_N=shape[1], AXIS=axis) - z_tri = to_numpy(z_tri) - # compare - if op == 'sum': - np.testing.assert_allclose(z_ref, z_tri, rtol=0.01) - else: - if op in ('argmin', 'argmax'): - # argmin and argmax can have multiple valid indices. - # so instead we compare the values pointed by indices - z_ref_index = np.expand_dims(z_ref, axis=axis) - z_tri_index = np.expand_dims(z_tri, axis=axis) - z_ref_value = np.take_along_axis(x, z_ref_index, axis=axis) - z_tri_value = np.take_along_axis(x, z_tri_index, axis=axis) - np.testing.assert_equal(z_ref_value, z_tri_value) - else: - np.testing.assert_equal(z_ref, z_tri) - - -scan2d_shapes = [(8, 32), (16, 32), (32, 16), (2, 1024), (1024, 2), (32, 32), (1, 1024)] - -scan_configs = [ - (op, type, shape, axis, num_warps) - for num_warps in [4, 16] - for type in ['int32', 'float32'] - for axis in [1, 0] - for shape in scan2d_shapes - for op in ['cumsum', 'cumprod'] -] - - -@pytest.mark.parametrize("op, dtype_str, shape, axis, num_warps", scan_configs) -def test_scan2d(op, dtype_str, shape, axis, num_warps, device): - check_type_supported(dtype_str, device) - - # triton kernel - @triton.jit - def kernel(X, Z, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, AXIS: tl.constexpr): - range_m = tl.arange(0, BLOCK_M) - range_n = tl.arange(0, BLOCK_N) - x = tl.load(X + range_m[:, None] * BLOCK_N + range_n[None, :]) - z = GENERATE_TEST_HERE - tl.store(Z + range_m[:, None] * BLOCK_N + range_n[None, :], z) - - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{op}(x, axis={axis})'}) - # input - rs = RandomState(17) - x = numpy_random(shape, dtype_str=dtype_str, rs=rs) - z = np.empty_like(x) - x_tri = to_triton(x, device=device) - numpy_op = {'cumsum': np.cumsum, 'cumprod': np.cumprod}[op] - z_dtype_str = dtype_str - z_ref = numpy_op(x, axis=axis).astype(getattr(np, z_dtype_str)) - # triton result - z_tri = to_triton(z, device=device) - kernel[(1,)](x_tri, z_tri, BLOCK_M=shape[0], BLOCK_N=shape[1], AXIS=axis, num_warps=num_warps) - z_tri = to_numpy(z_tri) - # compare - if dtype_str == 'float32': - if op == 'cumprod': - np.testing.assert_allclose(z_ref, z_tri, rtol=0.01, atol=1e-3) - else: - np.testing.assert_allclose(z_ref, z_tri, rtol=0.01) - else: - np.testing.assert_equal(z_ref, z_tri) - - -scan_layouts = [ - BlockedLayout([1, 4], [4, 8], [4, 1], [0, 1]), - BlockedLayout([1, 4], [8, 4], [4, 1], [0, 1]), - BlockedLayout([4, 1], [4, 8], [1, 4], [0, 1]), - BlockedLayout([2, 2], [4, 8], [2, 2], [0, 1]), - BlockedLayout([2, 2], [8, 4], [2, 2], [0, 1]), - - BlockedLayout([1, 4], [4, 8], [4, 1], [1, 0]), - BlockedLayout([1, 4], [8, 4], [4, 1], [1, 0]), - BlockedLayout([4, 1], [4, 8], [1, 4], [1, 0]), - BlockedLayout([2, 2], [4, 8], [2, 2], [1, 0]), - BlockedLayout([2, 2], [8, 4], [2, 2], [1, 0]), -] - - -@pytest.mark.parametrize("M, N", [[32, 32], [32, 64], [64, 32]]) -@pytest.mark.parametrize("src_layout", scan_layouts) -@pytest.mark.parametrize("axis", [0, 1]) -def test_scan_layouts(M, N, src_layout, axis, device): - ir = f""" - #blocked = {src_layout} - module attributes {{"triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 32 : i32}} {{ - tt.func public @kernel_0d1d(%arg0: !tt.ptr {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr {{tt.divisibility = 16 : i32}}) {{ - %cst = arith.constant dense<{N}> : tensor<{M}x1xi32, #blocked> - %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #blocked}}>> - %1 = tt.expand_dims %0 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #blocked}}>>) -> tensor<{M}x1xi32, #blocked> - %2 = arith.muli %1, %cst : tensor<{M}x1xi32, #blocked> - %3 = tt.splat %arg0 : (!tt.ptr) -> tensor<{M}x1x!tt.ptr, #blocked> - %4 = tt.addptr %3, %2 : tensor<{M}x1x!tt.ptr, #blocked>, tensor<{M}x1xi32, #blocked> - %5 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #blocked}}>> - %6 = tt.expand_dims %5 {{axis = 0 : i32}} : (tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #blocked}}>>) -> tensor<1x{N}xi32, #blocked> - %7 = tt.broadcast %4 : (tensor<{M}x1x!tt.ptr, #blocked>) -> tensor<{M}x{N}x!tt.ptr, #blocked> - %8 = tt.broadcast %6 : (tensor<1x{N}xi32, #blocked>) -> tensor<{M}x{N}xi32, #blocked> - %9 = tt.addptr %7, %8 : tensor<{M}x{N}x!tt.ptr, #blocked>, tensor<{M}x{N}xi32, #blocked> - %10 = tt.load %9 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}x{N}xi32, #blocked> - %11 = "tt.scan"(%10) <{{axis = {axis} : i32}}> ({{ - ^bb0(%arg2: i32, %arg3: i32): - %16 = arith.addi %arg2, %arg3 : i32 - tt.scan.return %16 : i32 - }}) : (tensor<{M}x{N}xi32, #blocked>) -> tensor<{M}x{N}xi32, #blocked> - %12 = tt.splat %arg1 : (!tt.ptr) -> tensor<{M}x1x!tt.ptr, #blocked> - %13 = tt.addptr %12, %2 : tensor<{M}x1x!tt.ptr, #blocked>, tensor<{M}x1xi32, #blocked> - %14 = tt.broadcast %13 : (tensor<{M}x1x!tt.ptr, #blocked>) -> tensor<{M}x{N}x!tt.ptr, #blocked> - %15 = tt.addptr %14, %8 : tensor<{M}x{N}x!tt.ptr, #blocked>, tensor<{M}x{N}xi32, #blocked> - tt.store %15, %11 {{cache = 1 : i32, evict = 1 : i32}} : tensor<{M}x{N}xi32, #blocked> - tt.return - }} - }} - """ - - import tempfile - with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: - f.write(ir) - f.flush() - kernel = triton.compile(f.name) - rs = RandomState(17) - x = rs.randint(-100, 100, (M, N)).astype('int32') - - z = np.zeros((M, N)).astype('int32') - x_tri = torch.tensor(x, device=device) - z_tri = torch.tensor(z, device=device) - - kernel[(1, 1, 1)](x_tri, z_tri) - - z_ref = np.cumsum(x, axis=axis) - - np.testing.assert_equal(z_ref, z_tri.cpu().numpy()) - - -layouts = [ - BlockedLayout([1, 4], [8, 4], [4, 1], [1, 0]), - BlockedLayout([1, 4], [8, 4], [4, 1], [0, 1]), - BlockedLayout([4, 4], [2, 16], [4, 1], [1, 0]), - MmaLayout(version=(2, 0), warps_per_cta=[4, 1]), - MmaLayout(version=(2, 0), warps_per_cta=[2, 2]) -] - - -@pytest.mark.parametrize("M, N", [[128, 16], [128, 128], [32, 128], [32, 32]]) -@pytest.mark.parametrize("src_layout", layouts) -@pytest.mark.parametrize("axis", [0, 1]) -def test_reduce_layouts(M, N, src_layout, axis, device): - rdims_2d = f"1x{N}" if axis == 0 else f"{M}x1" - rdims_1d = f"{N}" if axis == 0 else f"{M}" - store_range = "%7" if axis == 0 else "%1" - ir = f""" - #blocked = #triton_gpu.blocked<{{sizePerThread = [1, 1], threadsPerWarp = [32, 1], warpsPerCTA = [4, 1], order = [0, 1]}}> - #src = {src_layout} - module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ - tt.func public @kernel_0d1d2c3d4c(%arg0: !tt.ptr {{tt.divisibility = 16 : i32}}, %arg1: i32 {{tt.divisibility = 16 : i32}}, %arg2: !tt.ptr {{tt.divisibility = 16 : i32}}) {{ - %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #blocked}}>> - %1 = tt.expand_dims %0 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #blocked}}>>) -> tensor<{M}x1xi32, #blocked> - %2 = tt.splat %arg1 : (i32) -> tensor<{M}x1xi32, #blocked> - %3 = arith.muli %1, %2 : tensor<{M}x1xi32, #blocked> - %4 = tt.splat %arg0 : (!tt.ptr) -> tensor<{M}x1x!tt.ptr, #blocked> - %5 = tt.addptr %4, %3 : tensor<{M}x1x!tt.ptr, #blocked>, tensor<{M}x1xi32, #blocked> - %6 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #blocked}}>> - %7 = tt.expand_dims %6 {{axis = 0 : i32}} : (tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #blocked}}>>) -> tensor<1x{N}xi32, #blocked> - %8 = tt.broadcast %5 : (tensor<{M}x1x!tt.ptr, #blocked>) -> tensor<{M}x{N}x!tt.ptr, #blocked> - %9 = tt.broadcast %7 : (tensor<1x{N}xi32, #blocked>) -> tensor<{M}x{N}xi32, #blocked> - %10 = tt.addptr %8, %9 : tensor<{M}x{N}x!tt.ptr, #blocked>, tensor<{M}x{N}xi32, #blocked> - %11 = tt.splat %arg2 : (!tt.ptr) -> tensor<{rdims_2d}x!tt.ptr, #blocked> - %12 = tt.addptr %11, {store_range} : tensor<{rdims_2d}x!tt.ptr, #blocked>, tensor<{rdims_2d}xi32, #blocked> - %13 = tt.load %10 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}x{N}xi32, #blocked> - %14 = triton_gpu.convert_layout %13 : (tensor<{M}x{N}xi32, #blocked>) -> tensor<{M}x{N}xi32, #src> - %15 = "tt.reduce"(%14) ({{ - ^bb0(%arg3: i32, %arg4: i32): - %17 = arith.addi %arg3, %arg4 : i32 - tt.reduce.return %17 : i32 - }}) {{axis = {axis} : i32}} : (tensor<{M}x{N}xi32, #src>) -> tensor<{rdims_1d}xi32, #triton_gpu.slice<{{dim = {axis}, parent = #src}}>> - %18 = triton_gpu.convert_layout %15 : (tensor<{rdims_1d}xi32, #triton_gpu.slice<{{dim = {axis}, parent = #src}}>>) -> tensor<{rdims_1d}xi32, #triton_gpu.slice<{{dim = {axis}, parent = #blocked}}>> - %19 = tt.expand_dims %18 {{axis = {axis} : i32}} : (tensor<{rdims_1d}xi32, #triton_gpu.slice<{{dim = {axis}, parent = #blocked}}>>) -> tensor<{rdims_2d}xi32, #blocked> - tt.store %12, %19 {{cache = 1 : i32, evict = 1 : i32}} : tensor<{rdims_2d}xi32, #blocked> - tt.return - }} - }} - """ - - import tempfile - with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: - f.write(ir) - f.flush() - kernel = triton.compile(f.name) - - rs = RandomState(17) - x = rs.randint(0, 20, (M, N)).astype('int32') - - if axis == 0: - z = np.zeros((1, N)).astype('int32') - else: - z = np.zeros((M, 1)).astype('int32') - - x_tri = torch.tensor(x, device=device) - z_tri = torch.tensor(z, device=device) - - pgm = kernel[(1, 1, 4)](x_tri, x_tri.stride(0), z_tri) - - z_ref = np.sum(x, axis=axis, keepdims=True) - - np.testing.assert_equal(z_ref, z_tri.cpu().numpy()) - - -layouts = [ - BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]), - BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]), - MmaLayout(version=(2, 0), warps_per_cta=[4, 1]) -] - - -@pytest.mark.parametrize("M", [32, 64, 128, 256]) -@pytest.mark.parametrize("src_layout", layouts) -def test_store_op(M, src_layout, device): - ir = f""" - #src = {src_layout} - module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ - tt.func public @kernel(%arg0: !tt.ptr {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr {{tt.divisibility = 16 : i32}}) {{ - %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> - %1 = tt.splat %arg0 : (!tt.ptr) -> tensor<{M}x!tt.ptr, #triton_gpu.slice<{{dim = 1, parent = #src}}>> - %2 = tt.addptr %1, %0 : tensor<{M}x!tt.ptr, #triton_gpu.slice<{{dim = 1, parent = #src}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> - %3 = tt.load %2 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}xf32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> - %4 = tt.expand_dims %3 {{axis = 1 : i32}} : (tensor<{M}xf32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> tensor<{M}x1xf32, #src> - %5 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> - %6 = tt.expand_dims %5 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> tensor<{M}x1xi32, #src> - %7 = tt.splat %arg1 : (!tt.ptr) -> tensor<{M}x1x!tt.ptr, #src> - %8 = tt.addptr %7, %6 : tensor<{M}x1x!tt.ptr, #src>, tensor<{M}x1xi32, #src> - tt.store %8, %4 : tensor<{M}x1xf32, #src> - tt.return - }} - }} - """ - - import tempfile - with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: - f.write(ir) - f.flush() - store_kernel = triton.compile(f.name) - - rs = RandomState(17) - x = rs.randint(0, 4, (M, 1)).astype('float32') - y = np.zeros((M, 1), dtype='float32') - x_tri = torch.tensor(x, device=device) - y_tri = torch.tensor(y, device=device) - - pgm = store_kernel[(1, 1, 1)](x_tri, y_tri) - y_ref = x - np.testing.assert_allclose(y_ref, y_tri.cpu().numpy(), rtol=0.01, atol=1e-3) - - -layouts = [ - BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]), - BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]), - MmaLayout(version=(2, 0), warps_per_cta=[4, 1]) -] - - -@pytest.mark.parametrize("M", [64, 128, 256]) -@pytest.mark.parametrize("src_layout", layouts) -@pytest.mark.parametrize("dst_layout", layouts) -@pytest.mark.parametrize("src_dim", [0, 1]) -@pytest.mark.parametrize("dst_dim", [0, 1]) -def test_convert1d(M, src_layout, dst_layout, src_dim, dst_dim, device): - ir = f""" - #dst = {dst_layout} - #src = {src_layout} - module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ - tt.func public @kernel(%arg0: !tt.ptr {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr {{tt.divisibility = 16 : i32}}) {{ - %0 = tt.splat %arg0 : (!tt.ptr) -> tensor<{M}x!tt.ptr, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> - %1 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> - %2 = tt.addptr %0, %1 : tensor<{M}x!tt.ptr, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> - %3 = tt.load %2 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>> - %4 = tt.splat %arg1 : (!tt.ptr) -> tensor<{M}x!tt.ptr, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> - %5 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> - %6 = tt.addptr %4, %5 : tensor<{M}x!tt.ptr, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> - %7 = triton_gpu.convert_layout %3 : (tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>) -> tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> - tt.store %6, %7 : tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>> - tt.return - }} - }} - """ - import tempfile - with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: - f.write(ir) - f.flush() - kernel = triton.compile(f.name) - - rs = RandomState(17) - x = rs.randint(0, 4, (M, )).astype('int32') - y = np.zeros((M, ), dtype='int32') - x_tri = torch.tensor(x, device=device) - y_tri = torch.tensor(y, device=device) - pgm = kernel[(1, 1, 1)](x_tri, y_tri) - y_ref = x - np.testing.assert_allclose(y_ref, y_tri.cpu().numpy(), rtol=0.01, atol=1e-3) - - -@triton.jit -def _welford_combine(mean_1, m2_1, weight_1, mean_2, m2_2, weight_2): - delta = mean_2 - mean_1 - new_weight = weight_1 + weight_2 - w2_over_w = weight_2 / new_weight - return ( - mean_1 + delta * w2_over_w, - m2_1 + m2_2 + delta * delta * weight_1 * w2_over_w, - new_weight, - ) - - -layouts = [ - BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]), - BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]), - BlockedLayout([1, 4], [1, 32], [1, 4], [1, 0]), - BlockedLayout([1, 4], [8, 4], [2, 2], [0, 1]) -] - - -@pytest.mark.parametrize("M, N", [[128, 128], [256, 128], [256, 256], [128, 256]]) -@pytest.mark.parametrize("src_layout", layouts) -@pytest.mark.parametrize("op", ["sum", "max"]) -@pytest.mark.parametrize("first_axis", [0, 1]) -def test_chain_reduce(M, N, src_layout, op, device, first_axis): - op_str = "" - if op == "sum": - op_str = f""" - %13 = arith.addi %arg2, %arg3 : i32 - tt.reduce.return %13 : i32""" - elif op == "max": - op_str = f""" - %13 = "triton_gpu.cmpi"(%arg2, %arg3) <{{predicate = 4 : i64}}> : (i32, i32) -> i1 - %14 = arith.select %13, %arg2, %arg3 : i32 - tt.reduce.return %14 : i32""" - ir = f""" - #src = {src_layout} - module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{ - tt.func public @sum_kernel_0d1d(%arg0: !tt.ptr {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr {{tt.divisibility = 16 : i32}}) {{ - %cst = arith.constant dense<{N}> : tensor<{M}x1xi32, #src> - %0 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>> - %1 = tt.expand_dims %0 {{axis = 1 : i32}} : (tensor<{M}xi32, #triton_gpu.slice<{{dim = 1, parent = #src}}>>) -> tensor<{M}x1xi32, #src> - %2 = arith.muli %1, %cst : tensor<{M}x1xi32, #src> - %3 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #src}}>> - %4 = tt.expand_dims %3 {{axis = 0 : i32}} : (tensor<{N}xi32, #triton_gpu.slice<{{dim = 0, parent = #src}}>>) -> tensor<1x{N}xi32, #src> - %5 = tt.broadcast %2 : (tensor<{M}x1xi32, #src>) -> tensor<{M}x{N}xi32, #src> - %6 = tt.broadcast %4 : (tensor<1x{N}xi32, #src>) -> tensor<{M}x{N}xi32, #src> - %7 = arith.addi %5, %6 : tensor<{M}x{N}xi32, #src> - %8 = tt.splat %arg0 : (!tt.ptr) -> tensor<{M}x{N}x!tt.ptr, #src> - %9 = tt.addptr %8, %7 : tensor<{M}x{N}x!tt.ptr, #src>, tensor<{M}x{N}xi32, #src> - %10 = tt.load %9 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}x{N}xi32, #src> - %11 = "tt.reduce"(%10) ({{ - ^bb0(%arg2: i32, %arg3: i32): - {op_str} - }}) {{axis = {first_axis} : i32}} : (tensor<{M}x{N}xi32, #src>) -> tensor<{M if first_axis == 1 else N}xi32, #triton_gpu.slice<{{dim = {first_axis}, parent = #src}}>> - %12 = "tt.reduce"(%11) ({{ - ^bb0(%arg2: i32, %arg3: i32): - {op_str} - }}) {{axis = 0 : i32}} : (tensor<{M if first_axis == 1 else N}xi32, #triton_gpu.slice<{{dim = {first_axis}, parent = #src}}>>) -> i32 - tt.store %arg1, %12 {{cache = 1 : i32, evict = 1 : i32}} : i32 - tt.return - }} - }} - """ - import tempfile - with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: - f.write(ir) - f.flush() - kernel = triton.compile(f.name) - - rs = RandomState(17) - x = rs.randint(0, 4, (M, N)).astype('int32') - - z = np.zeros((1,)).astype('int32') - - x_tri = torch.tensor(x, device=device) - z_tri = torch.tensor(z, device=device) - - pgm = kernel[(1, 1, 1)](x_tri, z_tri) - if op == "sum": - z_ref = np.sum(x) - elif op == "max": - z_ref = np.max(x) - - np.testing.assert_allclose(z_ref, z_tri.cpu().numpy(), rtol=0.01, atol=1e-3) - - -def test_generic_reduction(device): - - @triton.jit - def var_mean_kernel(X, out_mean, out_var, BLOCK: tl.constexpr): - xindex = tl.arange(0, BLOCK) - x = tl.load(X + xindex) - mean = x - m2 = tl.zeros_like(x) - weight = tl.full(x.shape, 1, x.dtype) - (mean, m2, weight) = tl.reduce((mean, m2, weight), 0, _welford_combine) - tl.store(out_mean, mean) - tl.store(out_var, m2 / weight) - - SIZE = 512 - x = torch.rand(SIZE, device=device) - out_mean = torch.empty((), device=device) - out_var = torch.empty((), device=device) - - var_mean_kernel[(1,)](x, out_mean, out_var, BLOCK=SIZE) - - expect_var, expect_mean = torch.var_mean(x, dim=0, correction=0) - torch.testing.assert_close(out_mean, expect_mean) - torch.testing.assert_close(out_var, expect_var) - - -# --------------- -# test permute -# --------------- - - -@pytest.mark.parametrize("dtype_str, shape, perm", - [(dtype, shape, perm) - # TODO: bfloat16 - for dtype in ['float16', 'float32'] - for shape in [(64, 64), (128, 128)] - for perm in [(1, 0)]]) -def test_permute(dtype_str, shape, perm, device): - check_type_supported(dtype_str, device) # bfloat16 on cc < 80 will not be tested - - # triton kernel - @triton.jit - def kernel(X, stride_xm, stride_xn, - Z, stride_zm, stride_zn, - BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr): - off_m = tl.arange(0, BLOCK_M) - off_n = tl.arange(0, BLOCK_N) - Xs = X + off_m[:, None] * stride_xm + off_n[None, :] * stride_xn - Zs = Z + off_m[:, None] * stride_zm + off_n[None, :] * stride_zn - tl.store(Zs, tl.load(Xs)) - # input - x = numpy_random(shape, dtype_str=dtype_str) - # triton result - z_tri = to_triton(np.empty_like(x), device=device, dst_type=dtype_str) - z_tri_contiguous = to_triton(np.empty_like(x), device=device, dst_type=dtype_str) - x_tri = to_triton(x, device=device, dst_type=dtype_str) - pgm = kernel[(1, 1)](x_tri, x_tri.stride(0), x_tri.stride(1), - z_tri, z_tri.stride(1), z_tri.stride(0), - BLOCK_M=shape[0], BLOCK_N=shape[1]) - pgm_contiguous = kernel[(1, 1)](x_tri, x_tri.stride(1), x_tri.stride(0), - z_tri_contiguous, z_tri_contiguous.stride(0), z_tri_contiguous.stride(1), - BLOCK_M=shape[0], BLOCK_N=shape[1]) - # numpy result - z_ref = x.transpose(*perm) - # compare - np.testing.assert_allclose(to_numpy(z_tri), z_ref) - np.testing.assert_allclose(to_numpy(z_tri_contiguous), z_ref) - # parse ptx to make sure ld/st are vectorized - ptx = pgm.asm['ptx'] - assert 'ld.global.v4' in ptx - assert 'st.global.v4' in ptx - ptx = pgm_contiguous.asm['ptx'] - assert 'ld.global.v4' in ptx - assert 'st.global.v4' in ptx - -# --------------- -# test dot -# --------------- - - -@pytest.mark.parametrize("M, N, K, num_warps, col_a, col_b, epilogue, allow_tf32, in_dtype, out_dtype", - [(*shape, 4, False, False, epilogue, allow_tf32, in_dtype, out_dtype) - for shape in [(64, 64, 64), (16, 16, 16)] - for epilogue in ['none', 'trans', 'add-matrix', 'add-rows', 'add-cols', 'softmax', 'chain-dot'] - for allow_tf32 in [True, False] - for in_dtype, out_dtype in [('float16', 'float16'), - ('float16', 'float32'), - ('float32', 'float32')] - if not (allow_tf32 and (in_dtype in ['float16']))] + - - [(*shape_nw, col_a, col_b, 'none', allow_tf32, in_dtype, out_dtype) - for shape_nw in [[128, 256, 32, 8], - [128, 16, 32, 4], - [32, 128, 64, 4], - [128, 128, 64, 4], - [64, 128, 128, 4], - [32, 128, 64, 2], - [64, 64, 32, 4], - [32, 32, 128, 16], - [128, 128, 64, 2], - [64, 128, 128, 2]] - for allow_tf32 in [True] - for col_a in [True, False] - for col_b in [True, False] - for in_dtype, out_dtype in [('int8', 'int8'), - ('float16', 'float16'), - ('float16', 'float32'), - ('float32', 'float32')]]) -def test_dot(M, N, K, num_warps, col_a, col_b, epilogue, allow_tf32, in_dtype, out_dtype, device): - check_cuda_only(device) - - capability = torch.cuda.get_device_capability() - if capability[0] < 7: - pytest.skip("Only test tl.dot() on devices with sm >= 70") - if capability[0] < 8: - if in_dtype == 'int8': - pytest.skip("Only test int8 on devices with sm >= 80") - elif in_dtype == 'float32' and allow_tf32: - pytest.skip("Only test tf32 on devices with sm >= 80") - if capability[0] == 7: - if (M, N, K, num_warps) == (128, 256, 32, 8): - pytest.skip("shared memory out of resource") - if out_dtype == 'float16': - # TODO: support out_dtype=float16 for tl.dot on V100 - pytest.skip("Only test out_dtype=float16 on devices with sm >=80") - - torch.backends.cuda.matmul.allow_tf32 = allow_tf32 - - # triton kernel - @triton.jit - def kernel(X, stride_xm, stride_xk, - Y, stride_yk, stride_yn, - W, stride_wn, stride_wl, - Z, stride_zm, stride_zn, - out_dtype: tl.constexpr, - BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, - ADD_MATRIX: tl.constexpr, ADD_ROWS: tl.constexpr, ADD_COLS: tl.constexpr, - ALLOW_TF32: tl.constexpr, - DO_SOFTMAX: tl.constexpr, CHAIN_DOT: tl.constexpr, - COL_A: tl.constexpr, COL_B: tl.constexpr): - off_m = tl.arange(0, BLOCK_M) - off_n = tl.arange(0, BLOCK_N) - off_l = tl.arange(0, BLOCK_N) - off_k = tl.arange(0, BLOCK_K) - Xs = X + off_m[:, None] * stride_xm + off_k[None, :] * stride_xk - Ys = Y + off_k[:, None] * stride_yk + off_n[None, :] * stride_yn - Ws = W + off_n[:, None] * stride_wn + off_l[None, :] * stride_wl - Zs = Z + off_m[:, None] * stride_zm + off_n[None, :] * stride_zn - x = tl.load(Xs) - y = tl.load(Ys) - z = tl.dot(x, y, allow_tf32=ALLOW_TF32, out_dtype=out_dtype) - if ADD_MATRIX: - z += tl.load(Zs) - if ADD_ROWS: - ZRs = Z + off_m * stride_zm - z += tl.load(ZRs)[:, None] - if ADD_COLS: - ZCs = Z + off_n * stride_zn - z += tl.load(ZCs)[None, :] - if DO_SOFTMAX: - max = tl.max(z, 1) - z = z - max[:, None] - num = tl.exp(z.to(tl.float32)).to(max.dtype) - den = tl.sum(num, 1) - z = num / den[:, None] - if CHAIN_DOT: - w = tl.load(Ws) - z = tl.dot(z.to(w.dtype), w, out_dtype=out_dtype) - tl.store(Zs, z) - # input - rs = RandomState(17) - if col_a: - x = numpy_random((K, M), dtype_str=in_dtype, rs=rs).T - else: - x = numpy_random((M, K), dtype_str=in_dtype, rs=rs) - if col_b: - y = numpy_random((N, K), dtype_str=in_dtype, rs=rs).T - else: - y = numpy_random((K, N), dtype_str=in_dtype, rs=rs) - w = numpy_random((N, N), dtype_str=in_dtype, rs=rs) - if 'int' not in in_dtype: - x *= .1 - y *= .1 - if in_dtype == 'float32' and allow_tf32: - x = (x.view('uint32') & np.uint32(0xffffe000)).view('float32') - y = (y.view('uint32') & np.uint32(0xffffe000)).view('float32') - w = (w.view('uint32') & np.uint32(0xffffe000)).view('float32') - x_tri = to_triton(x, device=device) - y_tri = to_triton(y, device=device) - w_tri = to_triton(w, device=device) - # triton result - if out_dtype == 'int8': - z = 1 + numpy_random((M, N), dtype_str='int32', rs=rs) - else: - z = 1 + numpy_random((M, N), dtype_str=in_dtype, rs=rs) * .1 - - z_tri = to_triton(z, device=device) - if epilogue == 'trans': - z_tri = torch.as_strided(z_tri, (M, N), z_tri.stride()[::-1]) - - if out_dtype == 'int8': - out_dtype = tl.int8 - elif out_dtype == 'float16' and epilogue != 'softmax': - # TODO: for out_dtype == 'float16' and epilogue == 'softmax', it will - # fail with the following error: 'llvm.fmul' op requires the same type - # for all operands and results - out_dtype = tl.float16 - else: - out_dtype = tl.float32 - - pgm = kernel[(1, 1)](x_tri, x_tri.stride(0), x_tri.stride(1), - y_tri, y_tri.stride(0), y_tri.stride(1), - w_tri, w_tri.stride(0), w_tri.stride(1), - z_tri, z_tri.stride(0), z_tri.stride(1), - out_dtype, - COL_A=col_a, COL_B=col_b, - BLOCK_M=M, BLOCK_K=K, BLOCK_N=N, - ADD_MATRIX=epilogue == 'add-matrix', - ADD_ROWS=epilogue == 'add-rows', - ADD_COLS=epilogue == 'add-cols', - DO_SOFTMAX=epilogue == 'softmax', - CHAIN_DOT=epilogue == 'chain-dot', - ALLOW_TF32=allow_tf32, - num_warps=num_warps) - # torch result - if in_dtype == 'int8': - z_ref = np.matmul(x.astype(np.float32), - y.astype(np.float32())).astype(np.int32) - else: - z_ref = np.matmul(x, y) - - if epilogue == 'add-matrix': - z_ref += z - if epilogue == 'add-rows': - z_ref += z[:, 0][:, None] - if epilogue == 'add-cols': - z_ref += z[0, :][None, :] - if epilogue == 'softmax': - num = np.exp(z_ref - np.max(z_ref, axis=-1, keepdims=True)) - denom = np.sum(num, axis=-1, keepdims=True) - z_ref = num / denom - if epilogue == 'chain-dot': - z_ref = np.matmul(z_ref, w) - # compare - # print(z_ref[:,0], z_tri[:,0]) - if in_dtype == 'float32': - # XXX: Somehow there's a larger difference when we use float32 - np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01, atol=1e-3) - elif out_dtype == tl.float16: - np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01, atol=1e-3) - else: - np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=0.01) - # make sure ld/st are vectorized - ptx = pgm.asm['ptx'] - if (K > 16 or N > 16 or M > 16) and (M * N // (num_warps * 32) >= 4): - # XXX: skip small sizes because they are not vectorized - assert 'ld.global.v4' in ptx - assert 'st.global.v4' in ptx - if in_dtype == 'float32' and allow_tf32: - assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' in ptx - elif in_dtype == 'float32' and allow_tf32: - assert 'mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32' not in ptx - elif in_dtype == 'int8': - assert 'mma.sync.aligned.m16n8k32.row.col.satfinite.s32.s8.s8.s32' in ptx - elif out_dtype == tl.float16: - assert 'mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16' in ptx - - -@pytest.mark.parametrize('in_dtype', ['float32']) -def test_dot_mulbroadcastred(in_dtype, device): - @triton.jit - def kernel(Z, X, Y, - M: tl.constexpr, N: tl.constexpr, K: tl.constexpr, - BM: tl.constexpr, BN: tl.constexpr, BK: tl.constexpr): - pidn = tl.program_id(1) - pidm = tl.program_id(0) - offm = tl.arange(0, BM)[:, None] - offn = tl.arange(0, BN)[None, :] - offak = tl.arange(0, BK)[None, :] - offbk = tl.arange(0, BK)[:, None] - acc = tl.full((BM, BN), 0.0, tl.float32) - for ridx5 in range(0, K // BK): - x = tl.load(X + ((pidm * K * BM) + (offm * K) + (ridx5 * BK) + offak)) - y = tl.load(Y + ((pidn * BN) + (offbk * N) + (ridx5 * N * BK) + offn)) - x = tl.expand_dims(x, axis=2) - y = tl.expand_dims(y, axis=0) - t = tl.sum(x * y, axis=1) - acc = t + acc - tl.store(Z + ((pidm * BM * N) + (pidn * BN) + (offm * N) + offn), acc) - M, N, K = 256, 192, 160 - BM, BN, BK = 128, 32, 32 - rs = RandomState(17) - x = numpy_random((M, K), dtype_str=in_dtype, rs=rs) - y = numpy_random((K, N), dtype_str=in_dtype, rs=rs) - x = x * 0.1 - y = y * 0.1 - z = numpy_random((M, N), dtype_str=in_dtype, rs=rs) - x_tri = to_triton(x, device=device) - y_tri = to_triton(y, device=device) - z_tri = to_triton(z, device=device) - grid = M // BM, N // BN - h = kernel[grid](z_tri, x_tri, y_tri, M, N, K, BM, BN, BK) - z_ref = np.matmul(x, y) - np.testing.assert_allclose(z_ref, to_numpy(z_tri), atol=0.01) - assert "tt.dot" in h.asm['ttir'] - assert "triton_gpu.async_wait {num = 2 : i32}" in h.asm['ttgir'] - - -@pytest.mark.parametrize("dtype_str", int_dtypes + float_dtypes + ['bfloat16']) -def test_full(dtype_str, device): - dtype = getattr(torch, dtype_str) - check_type_supported(dtype, device) # bfloat16 on cc < 80 will not be tested - - @triton.jit - def kernel_static(out): - a = GENERATE_TEST_HERE - out_ptr = out + tl.arange(0, 128)[:] - tl.store(out_ptr, a) - - @triton.jit - def kernel_dynamic(out, val, dtype: tl.constexpr): - a = tl.full((128,), val, dtype) - out_ptr = out + tl.arange(0, 128)[:] - tl.store(out_ptr, a) - - kernel_static_patched = patch_kernel(kernel_static, {'GENERATE_TEST_HERE': f"tl.full((128,), 2, tl.{dtype_str})"}) - out_static = torch.zeros((128), dtype=dtype, device=device) - kernel_static_patched[(1,)](out_static) - out_dynamic = torch.zeros((128), dtype=dtype, device=device) - kernel_dynamic[(1,)](out_dynamic, 2, getattr(triton.language, dtype_str)) - assert torch.all(out_static == 2) - assert torch.all(out_dynamic == 2) - - -@pytest.mark.parametrize("literal, dtype_str", - [(1e+50, "f64"), (1e+10, "f32"), (1.0, "f32"), - ('float("inf")', "f32"), ('float("-inf")', "f32"), - ('float("nan")', "f32"), ('float("-nan")', "f32"), - (0., "f32"), - (5, "i32"), (2**40, "i64"),]) -def test_constexpr(literal, dtype_str, device): - @triton.jit - def kernel(out_ptr): - val = GENERATE_TEST_HERE - tl.store(out_ptr.to(tl.pointer_type(val.dtype)), val) - - kernel_patched = patch_kernel(kernel, {'GENERATE_TEST_HERE': f"{literal}"}) - out = torch.zeros((1,), dtype=torch.float32, device=device) - h = kernel_patched[(1,)](out) - assert re.search(r"arith.constant .* : " + dtype_str, h.asm["ttir"]) is not None - - -@pytest.mark.parametrize("dtype_str", ['float32', 'float16']) -def test_dot_without_load(dtype_str, device): - @triton.jit - def _kernel(out): - a = GENERATE_TEST_HERE - b = GENERATE_TEST_HERE - c = tl.dot(a, b) - out_ptr = out + tl.arange(0, 32)[:, None] * 32 + tl.arange(0, 32)[None, :] - tl.store(out_ptr, c) - kernel = patch_kernel(_kernel, {'GENERATE_TEST_HERE': f"tl.full((32, 32), 1.0, tl.{dtype_str})"}) - a = torch.ones((32, 32), dtype=getattr(torch, dtype_str), device=device) - b = torch.ones((32, 32), dtype=getattr(torch, dtype_str), device=device) - out_ref = torch.matmul(a, b) - out = torch.zeros((32, 32), dtype=getattr(torch, dtype_str), device=device) - kernel[(1,)](out) - assert torch.all(out == out_ref) - -# --------------- -# test arange -# --------------- - - -@pytest.mark.parametrize("start", [0, 1, 7, 16]) -def test_arange(start, device): - BLOCK = 128 - z_tri = torch.empty(BLOCK, dtype=torch.int32, device=device) - - @triton.jit - def _kernel(z, BLOCK: tl.constexpr, - START: tl.constexpr, END: tl.constexpr): - off = tl.arange(0, BLOCK) - val = tl.arange(START, END) - tl.store(z + off, val) - _kernel[(1,)](z_tri, START=start, END=start + BLOCK, BLOCK=BLOCK) - z_ref = torch.arange(start, BLOCK + start, dtype=torch.int32, device=device) - np.testing.assert_allclose(to_numpy(z_tri), to_numpy(z_ref)) - -# --------------- -# test load -# --------------- - - -@pytest.mark.parametrize("dtype_str, size, size_diff", [(dtype_str, size, size_diff) for dtype_str in torch_dtypes for size in [128, 512] for size_diff in [0, 1, 2, 3, 4]]) -def test_masked_load(dtype_str, size, size_diff, device): - dtype = getattr(torch, dtype_str) - check_type_supported(dtype, device) # bfloat16 on cc < 80 will not be tested - - input_size = size - size_diff - output_size = size - if dtype_str == 'bool': - input = torch.randint(0, 2, (input_size,), dtype=dtype, device=device) - elif dtype_str in int_dtypes or dtype_str in uint_dtypes: - input = torch.randint(0, 127, (input_size,), dtype=dtype, device=device) - else: - input = torch.rand(input_size, dtype=dtype, device=device) - output = torch.zeros((output_size,), dtype=dtype, device=device) - - @triton.jit - def _kernel(in_ptr, out_ptr, in_size: tl.constexpr, out_size: tl.constexpr): - in_offsets = tl.arange(0, out_size) - # Load inputs. - x = GENERATE_TEST_HERE - # Store output - output_offsets = tl.arange(0, out_size) - tl.store(out_ptr + output_offsets, x) - - mask_str = "mask=in_offsets < in_size, other=1" if size_diff > 0 else "None" - kernel = patch_kernel(_kernel, {'GENERATE_TEST_HERE': f"tl.load(in_ptr + in_offsets, {mask_str})"}) - kernel[(1,)](input, output, input_size, output_size) - - reference_out = torch.cat((input, torch.ones((size_diff,), dtype=dtype, device=device))) - # print((output - reference_out).nonzero()) - torch.testing.assert_allclose(output, reference_out) - -# Testing masked loads with an intermate copy to shared memory run. - - -@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32]) -def test_masked_load_shared_memory(dtype, device): - check_type_supported(dtype, device) # bfloat16 on cc < 80 will not be tested - - M = 32 - N = 32 - K = 16 - - in1 = torch.rand((M, K), dtype=dtype, device=device) - in2 = torch.rand((K, N), dtype=dtype, device=device) - out = torch.zeros((M, N), dtype=dtype, device=device) - - @triton.jit - def _kernel(in1_ptr, in2_ptr, output_ptr, - in_stride, in2_stride, out_stride, - in_numel, in2_numel, out_numel, - M: tl.constexpr, N: tl.constexpr, K: tl.constexpr): - - M_offsets = tl.arange(0, M) - N_offsets = tl.arange(0, N) - K_offsets = tl.arange(0, K) - - in_offsets = M_offsets[:, None] * in_stride + K_offsets[None, :] - in2_offsets = K_offsets[:, None] * in2_stride + N_offsets[None, :] - - # Load inputs. - x = tl.load(in1_ptr + in_offsets, mask=in_offsets < M * K) - w = tl.load(in2_ptr + in2_offsets, mask=in2_offsets < K * N) - - # Without a dot product the memory doesn't get promoted to shared. - o = tl.dot(x, w, out_dtype=tl.float32) - - # Store output - output_offsets = M_offsets[:, None] * out_stride + N_offsets[None, :] - tl.store(output_ptr + output_offsets, o, mask=output_offsets < M * N) - - pgm = _kernel[(1,)](in1, in2, out, - in1.stride()[0], - in2.stride()[0], - out.stride()[0], - in1.numel(), - in2.numel(), - out.numel(), - M=M, N=N, K=K) - - reference_out = torch.matmul(in1, in2) - torch.testing.assert_allclose(out, reference_out, atol=1e-2, rtol=0) - - -@pytest.mark.parametrize("cache", ["", ".ca", ".cg"]) -def test_load_cache_modifier(cache, device): - src = torch.empty(128, device=device) - dst = torch.empty(128, device=device) - - @triton.jit - def _kernel(dst, src, CACHE: tl.constexpr): - offsets = tl.arange(0, 128) - x = tl.load(src + offsets, cache_modifier=CACHE) - tl.store(dst + offsets, x) - - pgm = _kernel[(1,)](dst, src, CACHE=cache) - ptx = pgm.asm['ptx'] - if cache == '': - assert 'ld.global.ca' not in ptx - assert 'ld.global.cg' not in ptx - if cache == '.cg': - assert 'ld.global.cg' in ptx - assert 'ld.global.ca' not in ptx - if cache == '.ca': - assert 'ld.global.ca' in ptx - assert 'ld.global.cg' not in ptx - - -@pytest.mark.parametrize("N", [16, 10, 11, 1024]) -def test_vectorization(N, device): - src = torch.empty(1024, device=device) - dst = torch.empty(1024, device=device) - - @triton.jit - def _kernel(dst, src, N, BLOCK_SIZE: tl.constexpr): - offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - x = tl.load(src + offsets, mask=offsets < N) - tl.store(dst + offsets, x, mask=offsets < N) - pgm = _kernel[(1,)](dst, src, N=N, BLOCK_SIZE=src.shape[0]) - ptx = pgm.asm["ptx"] - if N % 16 == 0: - assert "ld.global.v4.b32" in ptx - else: - assert "ld.global.b32" in ptx - # np.testing.assert_allclose(dst, src[:N]) - - -@pytest.mark.parametrize("has_hints", [False, True]) -def test_vectorization_hints(has_hints, device): - src = torch.empty(1024, device=device) - dst = torch.empty(1024, device=device) - off = torch.zeros(1, device=device, dtype=torch.int32) - - @triton.jit - def _kernel(dst, src, off, N, BLOCK_SIZE: tl.constexpr, HINT: tl.constexpr): - offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - offsets = offsets + tl.load(off) - if HINT: - tl.max_contiguous(tl.multiple_of(offsets, 1024), 1024) - x = tl.load(src + offsets, mask=offsets < N) - tl.store(dst + offsets, x, mask=offsets < N) - pgm = _kernel[(1,)](dst, src, off, N=1024, BLOCK_SIZE=src.shape[0], HINT=has_hints) - ptx = pgm.asm["ptx"] - if has_hints: - assert "ld.global.v4.b32" in ptx - else: - assert "ld.global.v4.b32" not in ptx - -# --------------- -# test store -# --------------- - - -@pytest.mark.parametrize("cache", ["", ".wb", ".cg", ".cs", ".wt"]) -def test_store_cache_modifier(cache): - src = torch.empty(128, device='cuda') - dst = torch.empty(128, device='cuda') - - @triton.jit - def _kernel(dst, src, CACHE: tl.constexpr): - offsets = tl.arange(0, 128) - x = tl.load(src + offsets) - tl.store(dst + offsets, x, cache_modifier=CACHE) - - pgm = _kernel[(1,)](dst, src, CACHE=cache) - ptx = pgm.asm['ptx'] - if cache == '': - assert 'st.global.wb' not in ptx - assert 'st.global.cg' not in ptx - assert 'st.global.cs' not in ptx - assert 'st.global.wt' not in ptx - if cache == '.wb': - assert 'st.global.wb' in ptx - assert 'st.global.cg' not in ptx - assert 'st.global.cs' not in ptx - assert 'st.global.wt' not in ptx - if cache == '.cg': - assert 'st.global.wb' not in ptx - assert 'st.global.cg' in ptx - assert 'st.global.cs' not in ptx - assert 'st.global.wt' not in ptx - if cache == '.cs': - assert 'st.global.wb' not in ptx - assert 'st.global.cg' not in ptx - assert 'st.global.cs' in ptx - assert 'st.global.wt' not in ptx - if cache == '.wt': - assert 'st.global.wb' not in ptx - assert 'st.global.cg' not in ptx - assert 'st.global.cs' not in ptx - assert 'st.global.wt' in ptx - -# --------------- -# test if -# --------------- - -# --------------- -# test for -# --------------- - -# --------------- -# test while -# --------------- - -# --------------- -# test default -# --------------- -# TODO: can't be local to test_default - - -@triton.jit -def _impl(value=10): - return value - - -def test_default(device): - value = 5 - ret0 = torch.zeros(1, dtype=torch.int32, device=device) - ret1 = torch.zeros(1, dtype=torch.int32, device=device) - - @triton.jit - def _kernel(ret0, ret1, value): - tl.store(ret0, _impl()) - tl.store(ret1, _impl(value)) - - _kernel[(1,)](ret0, ret1, value) - assert ret0.item() == 10 - assert ret1.item() == value - -# --------------- -# test noop -# ---------------- - - -def test_noop(device): - @triton.jit - def kernel(x): - pass - x = to_triton(numpy_random((1,), dtype_str='int32'), device=device) - kernel[(1, )](x) - - -@pytest.mark.parametrize("device", ['cuda', 'cpu', 'cpu_pinned']) -def test_pointer_arguments(device): - @triton.jit - def kernel(x): - pass - pin_memory = 'pinned' in device - x = torch.empty(1024, device=device.split('_')[0], pin_memory=pin_memory) - if device == "cpu": - with pytest.raises(ValueError): - kernel[(1,)](x) - else: - kernel[(1, )](x) - - -@pytest.mark.parametrize("value, value_type", [ - (-1, 'i32'), (0, 'i32'), (-2**31, 'i32'), (2**31 - 1, 'i32'), - (2**31, 'i64'), (2**32 - 1, 'i64'), (2**32, 'i64'), (2**63 - 1, 'i64'), - (-2**63, 'i64'), (2**63, 'u64'), (2**64 - 1, 'u64') -]) -def test_value_specialization(value: int, value_type: str, device) -> None: - spec_type = None - - def cache_hook(*args, **kwargs): - nonlocal spec_type - spec_type = kwargs["compile"]["signature"][0] - JITFunction.cache_hook = cache_hook - - @triton.jit - def kernel(VALUE, X): - pass - - x = torch.tensor([3.14159], device=device) - pgm = kernel[(1, )](value, x) - - JITFunction.cache_hook = None - assert spec_type == value_type - -# -------------------- -# value specialization -# -------------------- - - -@pytest.mark.parametrize( - "value, overflow", - [(2**64 - 1, False), (2**64, True), (-2**63, False), (-2**63 - 1, True)] -) -def test_value_specialization_overflow(value: int, overflow: bool, device) -> None: - - @triton.jit - def kernel(VALUE, X): - pass - - x = torch.tensor([3.14159], device=device) - - if overflow: - with pytest.raises(OverflowError): - kernel[(1, )](value, x) - else: - kernel[(1, )](value, x) - - -# ---------------- -# test constexpr -# ---------------- - -@pytest.mark.parametrize("op", ['+', '-', '*', '/', '%', '<', '>', '<<', '>>', '&', '^', '|']) -@pytest.mark.parametrize("is_lhs_constexpr", [False, True]) -@pytest.mark.parametrize("is_rhs_constexpr", [True, False]) -def test_bin_op_constexpr(op, is_lhs_constexpr, is_rhs_constexpr, device): - - @triton.jit - def kernel(Z, X, Y): - x = tl.load(X) - y = tl.load(Y) - z = GENERATE_TEST_HERE - tl.store(Z, z) - - if op in ['<<', '>>', '&', '^', '|']: # int op - x_str = "3" if is_lhs_constexpr else "x" - y_str = "4" if is_rhs_constexpr else "y" - x = numpy_random((1,), dtype_str="int32") - y = numpy_random((1,), dtype_str="int32") - else: - x_str = "3.14" if is_lhs_constexpr else "x" - y_str = "4.13" if is_rhs_constexpr else "y" - x = numpy_random((1,), dtype_str="float32") - y = numpy_random((1,), dtype_str="float32") - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f"{x_str} {op} {y_str}"}) - z = np.array(eval(f"{x_str} {op} {y_str}")) - x_tri = to_triton(x, device=device) - y_tri = to_triton(y, device=device) - z_tri = to_triton(np.empty((1,), dtype=z.dtype), device=device) - kernel[(1,)](z_tri, x_tri, y_tri) - np.testing.assert_allclose(z, to_numpy(z_tri)) - - -def test_constexpr_shape(device): - - @triton.jit - def kernel(X): - off = tl.arange(0, 128 + 128) - tl.store(X + off, off) - - x_tri = to_triton(np.empty((256, ), dtype=np.int32), device=device) - kernel[(1,)](x_tri) - np.testing.assert_equal(to_numpy(x_tri), np.arange(0, 256)) - - -def test_constexpr_scalar_shape(device): - - @triton.jit - def kernel(X, s): - off = tl.arange(0, 256) - val = off % (256 // s) - tl.store(X + off, val) - - x_tri = to_triton(np.empty((256, ), dtype=np.int32), device=device) - kernel[(1,)](x_tri, 32) - np.testing.assert_equal(to_numpy(x_tri), np.arange(0, 256) % 8) - -# ------------- -# test call -# ------------- - - -@triton.jit -def val_multiplier(val, i): - return val * i - - -@triton.jit(noinline=True) -def val_multiplier_noinline(val, i): - return val * i - - -@triton.jit -def vecmul_kernel(ptr, n_elements, rep, type: tl.constexpr): - pid = tl.program_id(axis=0) - offsets = pid * 128 + tl.arange(0, 128) - mask = offsets < n_elements - vec = tl.load(ptr + offsets, mask=mask) - for i in range(1, rep): - if type == "inline": - vec = val_multiplier(vec, i) - else: - vec = val_multiplier_noinline(vec, i) - tl.store(ptr + offsets, vec, mask=mask) - - -@pytest.mark.parametrize("type", ["inline", "noinline"]) -def test_call(type, device): - - @triton.jit - def kernel(ptr, n_elements, num1, num2, type: tl.constexpr): - vecmul_kernel(ptr, n_elements, num1, type) - vecmul_kernel(ptr, n_elements, num2, type) - - size = 1024 - rand_val = numpy_random((size,), dtype_str="float32") - rand_val_tri = to_triton(rand_val, device=device) - err_msg = "" - try: - kernel[(size // 128,)](rand_val_tri, size, 3, 5, type) - except Exception as e: - err_msg = str(e) - - if type == "noinline": - assert err_msg is not "" - else: - ans = rand_val * 1 * 2 * 1 * 2 * 3 * 4 - np.testing.assert_equal(to_numpy(rand_val_tri), ans) - -# ------------- -# test if -# ------------- - - -@pytest.mark.parametrize("if_type", ["if", "if_exp", "if_and"]) -def test_if(if_type, device): - - @triton.jit - def kernel(Cond, XTrue, XFalse, Ret, IfType: tl.constexpr, BoolVar: tl.constexpr, StaticVaue: tl.constexpr): - pid = tl.program_id(0) - cond = tl.load(Cond) - if IfType == "if": - if pid % 2 == 0: - tl.store(Ret, tl.load(XTrue)) - else: - tl.store(Ret, tl.load(XFalse)) - elif IfType == "if_exp": - tl.store(Ret, tl.load(XTrue)) if pid % 2 else tl.store(Ret, tl.load(XFalse)) - elif IfType == "if_and_dynamic": - if BoolVar and pid % 2 == 0: - tl.store(Ret, tl.load(XTrue)) - else: - tl.store(Ret, tl.load(XFalse)) - elif IfType == "if_and_static": - if StaticVaue != 0 and StaticVaue != 0: - tl.store(Ret, tl.load(XTrue)) - else: - tl.store(Ret, tl.load(XFalse)) - - cond = torch.ones(1, dtype=torch.int32, device=device) - x_true = torch.tensor([3.14], dtype=torch.float32, device=device) - x_false = torch.tensor([1.51], dtype=torch.float32, device=device) - ret = torch.empty(1, dtype=torch.float32, device=device) - - kernel[(1,)](cond, x_true, x_false, ret, if_type, True, 1) - assert torch.equal(ret, x_true) - - -def test_num_warps_pow2(device): - dst = torch.empty(128, device=device) - - @triton.jit - def _kernel(dst): - pass - - with pytest.raises(AssertionError, match='must be a power of 2'): - _kernel[(1,)](dst=dst, num_warps=3) - _kernel[(1,)](dst=dst, num_warps=1) - _kernel[(1,)](dst=dst, num_warps=2) - _kernel[(1,)](dst=dst, num_warps=4) - -# ------------- -# test extern -# ------------- - - -@pytest.mark.parametrize("dtype_str, expr, lib_path", - [('int32', 'math.ffs', ''), - ('float32', 'math.log2', ''), - ('float32', 'math.scalbn', ''), - ('float32', 'math.pow', tl.math.libdevice_path()), - ('float64', 'math.pow_dtype', tl.math.libdevice_path()), - ('float64', 'math.norm4d', '')]) -def test_math_tensor(dtype_str, expr, lib_path, device): - - @triton.jit - def kernel(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - y = GENERATE_TEST_HERE - tl.store(Y + tl.arange(0, BLOCK), y) - - shape = (128, ) - rs = RandomState(17) - # limit the range of integers so that the sum does not overflow - x = numpy_random(shape, dtype_str=dtype_str, rs=rs) - - if expr == 'math.log2': - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.broadcast_to(tl.{expr}(5.0), x.shape)'}) - y_ref = np.log2(5.0) - elif expr == 'math.ffs': - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x)'}) - y_ref = np.zeros(shape, dtype=x.dtype) - for i in range(shape[0]): - y_ref[i] = (int(x[i]) & int(-x[i])).bit_length() - elif expr == 'math.scalbn': - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x, 2)'}) - y_ref = x * pow(2, 2) - elif expr == 'math.pow_dtype': - x = np.abs(x) - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.math.pow(x, 0.5)'}) - y_ref = np.power(x, 0.5) - elif expr == 'math.pow': - # numpy does not allow negative factors in power, so we use abs() - x = np.abs(x) - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x, x)'}) - y_ref = np.power(x, x) - elif expr == 'math.pow_dtype': - x = np.abs(x) - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.math.pow(x, 0.5)'}) - y_ref = np.power(x, 0.5) - elif expr == 'math.norm4d': - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': f'tl.{expr}(x, x, x, x)'}) - y_ref = np.sqrt(4 * np.power(x, 2)) - - x_tri = to_triton(x, device=device) - # triton result - y_tri = to_triton(numpy_random((shape[0],), dtype_str=dtype_str, rs=rs), device=device) - kernel[(1,)](x_tri, y_tri, BLOCK=shape[0], extern_libs={'libdevice': lib_path}) - # compare - if expr == 'math.ffs': - np.testing.assert_equal(y_ref, to_numpy(y_tri)) - else: - np.testing.assert_allclose(y_ref, to_numpy(y_tri), rtol=0.01) - - -@pytest.mark.parametrize("dtype_str, expr, lib_path", - [('float32', 'math.pow', ''), - ('float64', 'math.pow_dtype', ''), - ('float64', 'math.pow', tl.math.libdevice_path())]) -def test_math_scalar(dtype_str, expr, lib_path, device): - - @triton.jit - def kernel(X, Y, BLOCK: tl.constexpr): - x = X - y = GENERATE_TEST_HERE - tl.store(Y + tl.arange(0, BLOCK), y) - - shape = (128, ) - rs = RandomState(17) - # limit the range of integers so that the sum does not overflow - x = numpy_random((1,), dtype_str=dtype_str, rs=rs) - y_ref = np.zeros(shape, dtype=x.dtype) - - # numpy does not allow negative factors in power, so we use abs() - if expr == 'math.pow': - x = np.abs(x) - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.math.pow(x, x)'}) - y_ref[:] = np.power(x, x) - elif expr == 'math.pow_dtype': - x = np.abs(x) - kernel = patch_kernel(kernel, {'GENERATE_TEST_HERE': 'tl.math.pow(x, 0.5)'}) - y_ref[:] = np.power(x, 0.5) - - # triton result - x_tri = to_triton(x, device=device)[0].item() - y_tri = to_triton(numpy_random((shape[0],), dtype_str=dtype_str, rs=rs), device=device) - kernel[(1,)](x_tri, y_tri, BLOCK=shape[0], extern_libs={'libdevice': lib_path}) - # compare - np.testing.assert_allclose(y_ref, to_numpy(y_tri), rtol=0.01) - -# ----------------------- -# test control flow -# ----------------------- - - -@pytest.mark.parametrize("lo, hi, iv", [(2**35, 2**35 + 20, 1), (2**35, 2**35 + 20, 2), (2**35, 2**35 + 20, 3), - (15, -16, -1), (15, -16, -2), (15, -16, -3), - (-18, -22, -1), (22, 18, -1)]) -def test_for_iv(lo, hi, iv, device): - - @triton.jit - def kernel(Out, lo, hi, iv: tl.constexpr): - acc = 0 - acc = acc.to(tl.int64) - for i in range(lo, hi, iv): - acc += i - tl.store(Out, acc) - - lo = 2**35 - hi = 2**35 + 20 - out = to_triton(np.zeros((1,), dtype=np.int64), device=device) - kernel[(1,)](out, lo, hi, iv) - assert out[0] == sum(range(lo, hi, iv)) - - -def test_if_else(device): - - @triton.jit - def kernel(Cond, TrueVal, FalseVal, Out): - if tl.load(Cond): - val = tl.load(TrueVal) - else: - val = tl.load(FalseVal) - tl.store(Out, val) - - out = to_triton(np.zeros((1,), dtype=np.int32), device=device) - true_val = to_triton(np.full((1,), 1, dtype=np.int32), device=device) - false_val = to_triton(np.full((1,), 2, dtype=np.int32), device=device) - cond = to_triton(np.zeros((1,), dtype=np.int32), device=device) - # True - cond[0] = True - kernel[(1,)](cond, true_val, false_val, out) - assert to_numpy(out)[0] == true_val[0] - # False - cond[0] = False - kernel[(1,)](cond, true_val, false_val, out) - assert to_numpy(out)[0] == false_val[0] - - -@pytest.mark.parametrize("mode", ["dynamic", "static"]) -def test_if_return(mode, device): - - @triton.jit - def kernel(ExitEarly, Out, cond: tl.constexpr, mode: tl.constexpr): - if mode == "dynamic": - if tl.load(ExitEarly): - tl.store(Out, 0) - return - else: - if cond: - tl.store(Out, 0) - return - tl.store(Out, 1) - - out = to_triton(np.zeros((1,), dtype=np.int32), device=device) - exit_early = to_triton(np.zeros((1,), dtype=np.int32), device=device) - # exit early path taken - exit_early[0] = 1 - kernel[(1,)](exit_early, out, True, mode) - assert to_numpy(out)[0] == 0 - # exit early path not taken - exit_early[0] = 0 - kernel[(1,)](exit_early, out, False, mode) - assert to_numpy(out)[0] == 1 - - -@triton.jit -def add_fn(x): - return x + 1 - - -@triton.jit(noinline=True) -def add_fn_noinline(x): - return x + 1 - - -@triton.jit -def add_fn_return(x, pid): - if pid == 0: - return x + 1 - else: - return x + 2 - - -@triton.jit -def add_fn_expr(Out, x): - tl.store(Out, x) - - -@triton.jit -def add_fn_static_cond(x, cond: tl.constexpr): - if cond == "": - return x - else: - return x + 1 - - -@pytest.mark.parametrize("call_type", ["attribute", "attribute_jit", - "jit", "jit_if", "jit_ifexp", "jit_expr", - "jit_static_cond", "jit_noinline", "jit_extern"]) -def test_if_call(call_type, device): - @triton.jit - def kernel(Out, call_type: tl.constexpr): - pid = tl.program_id(0) - o = tl.load(Out) - if call_type == "attribute": - # call attribute - if pid == 0: - a = o - a = a.to(tl.int32).to(tl.int32) + 1 - o = a - elif call_type == "attribute_jit": - # call attribute and jit function - if pid == 0: - a = o - a = tl.load(Out + add_fn(a) - 1).to(tl.int32) + 1 - o = a - elif call_type == "jit": - if pid == 0: - # regular function call - a = o - a = add_fn(a) - o = a - elif call_type == "jit_if": - # function without end_if block - if pid == 0: - a = o - a = add_fn_return(a, pid) - o = a - elif call_type == "jit_ifexp": - # ifexp expression - if pid == 0: - a = o - a = add_fn(a) if pid == 0 else add_fn_return(a, pid) - o = a - elif call_type == "jit_expr": - # call without return - if pid == 0: - a = o + 1 - add_fn_expr(Out, a) - o = a - elif call_type == "jit_static_cond": - if pid == 0: - a = o + 1 - add_fn_static_cond(o, call_type) - o = a - elif call_type == "jit_noinline": - if pid == 0: - a = o + 1 - add_fn_noinline(a) - o = a - elif call_type == "jit_extern": - if pid == 0: - a = o + 1 - tl.cdiv(a, a) - o = a - - tl.store(Out, o) - - out = to_triton(np.zeros((1,), dtype=np.int32), device=device) - kernel[(1,)](out, call_type) - assert to_numpy(out)[0] == 1 - - -@pytest.mark.parametrize("_cond1", [True, False]) -@pytest.mark.parametrize("_cond2", [True, False]) -@pytest.mark.parametrize("_cond3", [True, False]) -def test_nested_if_else_return(_cond1, _cond2, _cond3, device): - - @triton.jit - def kernel(Cond1, Cond2, Cond3, Val1, Val2, Val3, Out): - val = 0 - if tl.load(Cond1): - if tl.load(Cond2): - val = tl.load(Val1) - else: - return - else: - if tl.load(Cond3): - val = tl.load(Val2) - else: - val = tl.load(Val3) - tl.store(Out, val) - - out = to_triton(np.full((1,), -1, dtype=np.int32), device=device) - cond1 = to_triton(np.full((1,), _cond1, dtype=np.int32), device=device) - cond2 = to_triton(np.full((1,), _cond2, dtype=np.int32), device=device) - cond3 = to_triton(np.full((1,), _cond3, dtype=np.int32), device=device) - val1 = to_triton(np.full((1,), 1, dtype=np.int32), device=device) - val2 = to_triton(np.full((1,), 2, dtype=np.int32), device=device) - val3 = to_triton(np.full((1,), 3, dtype=np.int32), device=device) - kernel[(1,)](cond1, cond2, cond3, val1, val2, val3, out) - targets = { - (True, True, True): val1[0], - (True, True, False): val1[0], - (True, False, True): out[0], - (True, False, False): out[0], - (False, True, True): val2[0], - (False, True, False): val3[0], - (False, False, True): val2[0], - (False, False, False): val3[0], - } - assert out[0] == targets[(_cond1, _cond2, _cond3)] - - -def test_while(device): - - @triton.jit - def kernel(InitI, Bound, CutOff, OutI, OutInitI, OutJ): - init_i = tl.load(InitI) - curr_i = init_i - j = 0 - # Check that init_i is not updated by the loop - while j < tl.load(Bound): - curr_i = curr_i + (j == tl.load(CutOff)) - j += 1 - tl.store(OutInitI, init_i) - tl.store(OutI, curr_i) - tl.store(OutJ, j) - - out_i = to_triton(np.zeros((1,), dtype=np.int32), device=device) - out_j = to_triton(np.zeros((1,), dtype=np.int32), device=device) - init_i = to_triton(np.full((1,), 1, dtype=np.int32), device=device) - out_init_i = to_triton(np.full((1,), 0, dtype=np.int32), device=device) - bound = to_triton(np.full((1,), 10, dtype=np.int32), device=device) - cut_off = to_triton(np.full((1,), 5, dtype=np.int32), device=device) - kernel[(1,)](init_i, bound, cut_off, out_i, out_init_i, out_j) - assert out_init_i[0] == init_i[0] - assert out_i[0] == init_i[0] + 1 - assert out_j[0] == bound[0] - -# def test_for_if(device): - -# @triton.jit -# def kernel(bound, cutoff, M, N): -# m = 0 -# n = 0 -# for i in range(bound): -# if i > cutoff: -# m = m + 1 -# else: -# n = n + 1 -# tl.store(M, m) -# tl.store(N, n) - -# m = to_triton(np.zeros((1,), dtype=np.int32), device=device) -# n = to_triton(np.zeros((1,), dtype=np.int32), device=device) -# kernel[(1,)](10, 7, m, n) -# print(m[0]) -# print(n[0]) - -# ----------------------- -# test extra -# ----------------------- - - -def test_globaltimer(device): - check_cuda_only(device) - - @triton.jit - def kernel(Out1, Out2): - start = tl.extra.cuda.globaltimer() - off = tl.arange(0, 128) - for i in range(10000): - tl.store(Out1 + off, tl.load(Out1 + off) + 1) - end = tl.extra.cuda.globaltimer() - tl.store(Out2, end - start) - - out1 = to_triton(np.zeros((128,), dtype=np.int64), device=device) - out2 = to_triton(np.zeros((1,), dtype=np.int64), device=device) - h = kernel[(1,)](out1, out2) - assert out2[0] > 0 - # 2 inlined globaltimers + one extra in the wrapper extern function - assert h.asm["ptx"].count("%globaltimer") == 3 - - -def test_smid(device): - check_cuda_only(device) - - @triton.jit - def kernel(Out): - tl.store(Out + tl.program_id(0), tl.extra.cuda.smid()) - - out = to_triton(np.zeros((1024,), dtype=np.int32), device=device) - h = kernel[(out.shape[0],)](out) - assert out.sort()[0].unique().shape[0] > 0 - assert h.asm["ptx"].count("%smid") == 2 - -# ----------------------- -# test layout conversions -# ----------------------- -# TODO: backend should be tested separately - - -layouts = [ - # MmaLayout(version=1, warps_per_cta=[1, 4]), - MmaLayout(version=(2, 0), warps_per_cta=[1, 4]), - # MmaLayout(version=1, warps_per_cta=[4, 1]), - MmaLayout(version=(2, 0), warps_per_cta=[4, 1]), - BlockedLayout([1, 8], [2, 16], [4, 1], [1, 0]), - BlockedLayout([1, 4], [4, 8], [2, 2], [1, 0]), - BlockedLayout([1, 1], [1, 32], [2, 2], [1, 0]), - BlockedLayout([8, 1], [16, 2], [1, 4], [0, 1]), - BlockedLayout([4, 1], [8, 4], [2, 2], [0, 1]), - BlockedLayout([1, 1], [32, 1], [2, 2], [0, 1]), - BlockedLayout([4, 4], [1, 32], [4, 1], [1, 0]) -] - -intermediate_layouts = [ - None, - SharedLayout(1, 1, 1, [1, 0]), - SharedLayout(4, 2, 4, [1, 0]), - SharedLayout(2, 2, 4, [1, 0]), -] - - -@pytest.mark.parametrize("shape", [(128, 128)]) -@pytest.mark.parametrize("dtype", ['float16']) -@pytest.mark.parametrize("src_layout", layouts) -@pytest.mark.parametrize("interm_layout", intermediate_layouts) -@pytest.mark.parametrize("dst_layout", layouts) -def test_convert2d(dtype, shape, src_layout, interm_layout, dst_layout, device): - if str(src_layout) == str(dst_layout): - pytest.skip() - if 'mma' in str(src_layout) and 'mma' in str(dst_layout): - pytest.skip() - - layouts = f""" - #src = {src_layout} - #dst = {dst_layout} - """ if interm_layout is None else f""" - #src = {src_layout} - #interm = {interm_layout} - #dst = {dst_layout} - """ - - conversion = f""" - %12 = triton_gpu.convert_layout %9 : (tensor<128x128xi32, #src>) -> tensor<128x128xi32, #dst> - %13 = triton_gpu.convert_layout %11 : (tensor<128x128xf16, #src>) -> tensor<128x128xf16, #dst> - """ if interm_layout is None else f""" - %15 = triton_gpu.convert_layout %9 : (tensor<128x128xi32, #src>) -> tensor<128x128xi32, #interm> - %16 = triton_gpu.convert_layout %15 : (tensor<128x128xi32, #interm>) -> tensor<128x128xi32, #src> - %17 = triton_gpu.convert_layout %11 : (tensor<128x128xf16, #src>) -> tensor<128x128xf16, #interm> - %18 = triton_gpu.convert_layout %17 : (tensor<128x128xf16, #interm>) -> tensor<128x128xf16, #src> - - %12 = triton_gpu.convert_layout %16 : (tensor<128x128xi32, #src>) -> tensor<128x128xi32, #dst> - %13 = triton_gpu.convert_layout %18 : (tensor<128x128xf16, #src>) -> tensor<128x128xf16, #dst> - """ - - ir = layouts + """ - module attributes {"triton_gpu.num-warps" = 4 : i32} { - tt.func public @kernel_0d1d(%arg0: !tt.ptr {tt.divisibility = 16 : i32}, %arg1: !tt.ptr {tt.divisibility = 16 : i32}) { - %cst = arith.constant dense<128> : tensor<128x1xi32, #src> - %0 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #src}>> - %1 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #src}>> - %2 = tt.splat %arg0 : (!tt.ptr) -> tensor<128x128x!tt.ptr, #src> - %4 = tt.expand_dims %0 {axis = 1 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 1, parent = #src}>>) -> tensor<128x1xi32, #src> - %5 = arith.muli %4, %cst : tensor<128x1xi32, #src> - %6 = tt.expand_dims %1 {axis = 0 : i32} : (tensor<128xi32, #triton_gpu.slice<{dim = 0, parent = #src}>>) -> tensor<1x128xi32, #src> - %7 = tt.broadcast %6 : (tensor<1x128xi32, #src>) -> tensor<128x128xi32, #src> - %8 = tt.broadcast %5 : (tensor<128x1xi32, #src>) -> tensor<128x128xi32, #src> - %9 = arith.addi %8, %7 : tensor<128x128xi32, #src> - %10 = tt.addptr %2, %9 : tensor<128x128x!tt.ptr, #src>, tensor<128x128xi32, #src> - %11 = tt.load %10 {cache = 1 : i32, evict = 1 : i32, isVolatile = false} : tensor<128x128xf16, #src> - %3 = tt.splat %arg1 : (!tt.ptr) -> tensor<128x128x!tt.ptr, #dst> - """ + conversion + """ - %14 = tt.addptr %3, %12 : tensor<128x128x!tt.ptr, #dst>, tensor<128x128xi32, #dst> - tt.store %14, %13 : tensor<128x128xf16, #dst> - tt.return - } -} -""" - - x = to_triton(numpy_random(shape, dtype_str=dtype), device=device) - z = torch.empty_like(x) - - # write the IR to a temporary file using mkstemp - import tempfile - with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f: - f.write(ir) - f.flush() - kernel = triton.compile(f.name) - kernel[(1, 1, 1)](x.data_ptr(), z.data_ptr()) - - assert torch.equal(z, x) - - -def test_load_scalar_with_mask(device): - @triton.jit - def kernel(Input, Index, Out, N: int): - index = tl.load(Index) - scalar = tl.load(Input + index, mask=index < N, other=0) - tl.store(Out, scalar, mask=index < N) - Index = torch.tensor([0], dtype=torch.int32, device=device) - Input = torch.tensor([0], dtype=torch.int32, device=device) - Out = torch.empty_like(Index, device=device) - kernel[(1,)](Input, Index, Out, Index.numel()) - assert Out.data[0] == 0 - - -# This test is used to test our own PTX codegen for float16 and int16 conversions -# maybe delete it later after ptxas has been fixed -@pytest.mark.parametrize("dtype_str", ['float16', 'int16']) -def test_ptx_cast(dtype_str, device): - @triton.jit - def kernel(in_ptr0, out_ptr2, xnumel, rnumel, dtype: tl.constexpr, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr): - xoffset = tl.program_id(0) * XBLOCK - xindex = xoffset + tl.arange(0, XBLOCK)[:, None] - xmask = xindex < xnumel - rbase = tl.arange(0, RBLOCK)[None, :] - x0 = xindex - _tmp4 = (tl.zeros([XBLOCK, RBLOCK], dtype) - 10000).to(dtype) - for roffset in range(0, rnumel, RBLOCK): - rindex = roffset + rbase - rmask = rindex < rnumel - r1 = rindex - tmp0 = tl.load(in_ptr0 + (r1 + (197 * x0)), rmask & xmask).to(dtype) - tmp1 = 2 - tmp2 = tmp0 * tmp1 - tmp3 = tmp2.to(dtype) - tmp5 = _tmp4 < tmp3 - _tmp4 = tl.where(rmask & xmask & tmp5, tmp3, _tmp4) - tl.store(out_ptr2 + (r1 + (197 * x0) + tl.zeros([XBLOCK, RBLOCK], tl.int32)), _tmp4, rmask & xmask) - - torch.manual_seed(123) - if dtype_str == 'int16': - torch_dtype = torch.int16 - triton_dtype = tl.int32 - else: - torch_dtype = torch.float16 - triton_dtype = tl.float32 - - s0 = 4 - buf11 = -torch.ones((6 * s0, 197, 197), device=device, dtype=torch_dtype) - buf14 = -torch.ones((s0, 6, 197, 197), device=device, dtype=torch_dtype) - kernel[(4728,)](buf11, buf14, 1182 * s0, 197, triton_dtype, 1, 256, num_warps=2) - assert buf14.to(torch.float32).mean() == -2.0 diff --git a/python/test/unit/language/test_line_info.py b/python/test/unit/language/test_line_info.py deleted file mode 100644 index 8a482d5f5cc3..000000000000 --- a/python/test/unit/language/test_line_info.py +++ /dev/null @@ -1,120 +0,0 @@ -import subprocess -import tempfile - -import pytest -import torch - -import triton -import triton.language as tl - - -@triton.jit -def kernel_single(X, - Y, - BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - tl.store(Y + tl.arange(0, BLOCK), x) - - -@triton.jit -def device_inline(x): - return x + x - - -@triton.jit -def kernel_call(X, - Y, - BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - y = device_inline(x) - tl.store(Y + tl.arange(0, BLOCK), y) - - -@triton.jit(noinline=True) -def device_noinline(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - y = x + x - tl.store(Y + tl.arange(0, BLOCK), y) - - -@triton.jit -def kernel_call_noinline(X, Y, BLOCK: tl.constexpr): - device_noinline(X, Y, BLOCK) - - -@triton.jit -def kernel_multi_files(X, Y, BLOCK: tl.constexpr): - x = tl.load(X + tl.arange(0, BLOCK)) - y = tl.softmax(x) - tl.store(Y + tl.arange(0, BLOCK), y) - - -def extract_file_lines(asm): - fd, path = tempfile.mkstemp() - with open(fd, 'wb') as cubin: - cubin.write(asm) - asm = subprocess.check_output(["nvdisasm", "-g", path]).decode("utf-8") - file_lines = [] - lines = asm.splitlines() - for line in lines: - if "## File" in line: - entries = line[line.index("## File"):].split(",") - file_lines.append((entries[0].strip(), entries[1].strip())) - return file_lines - - -def check_file_lines(file_lines, file_name, lineno): - for file, line in file_lines: - # -1 means do not check line number - if lineno == -1: - if file_name in file: - return True - if file_name in file and str(lineno) in line: - return True - return False - - -func_types = ["single", "call", "call_noinline", "multi_files"] - - -@pytest.mark.parametrize("func", func_types) -def test_line_info(func: str): - try: - subprocess.check_output(["nvdisasm", "-h"]) - except BaseException: - pytest.skip("nvdisasm is not available") - - shape = (128, ) - x = torch.arange(0, shape[0], dtype=torch.float32, device='cuda') - y = torch.zeros(shape, dtype=x.dtype, device="cuda") - kernel_info = {} - if func == "single": - kernel_info = kernel_single[(1,)](x, y, BLOCK=shape[0]) - elif func == "call": - kernel_info = kernel_call[(1,)](x, y, BLOCK=shape[0]) - elif func == "call_noinline": - kernel_info = kernel_call_noinline[(1,)](x, y, BLOCK=shape[0]) - elif func == "multi_files": - kernel_info = kernel_multi_files[(1,)](x, y, BLOCK=shape[0]) - - file_lines = extract_file_lines(kernel_info.asm["cubin"]) - if func == "single": - assert (check_file_lines(file_lines, "test_line_info.py", 15)) - assert (check_file_lines(file_lines, "test_line_info.py", 16)) - elif func == "call": - assert (check_file_lines(file_lines, "test_line_info.py", 28)) - assert (check_file_lines(file_lines, "test_line_info.py", 21)) - assert (check_file_lines(file_lines, "test_line_info.py", 30)) - elif func == "call_noinline": - assert (check_file_lines(file_lines, "test_line_info.py", 42)) - assert (check_file_lines(file_lines, "test_line_info.py", 35)) - assert (check_file_lines(file_lines, "test_line_info.py", 36)) - assert (check_file_lines(file_lines, "test_line_info.py", 37)) - elif func == "multi_files": - assert (check_file_lines(file_lines, "test_line_info.py", 47)) - assert (check_file_lines(file_lines, "test_line_info.py", 49)) - assert (check_file_lines(file_lines, "standard.py", 33)) - assert (check_file_lines(file_lines, "standard.py", 34)) - assert (check_file_lines(file_lines, "standard.py", 36)) - # core.py is changed frequently, so we only check if it exists - assert (check_file_lines(file_lines, "core.py", -1)) diff --git a/python/test/unit/language/test_random.py b/python/test/unit/language/test_random.py deleted file mode 100644 index e12adff1e7ee..000000000000 --- a/python/test/unit/language/test_random.py +++ /dev/null @@ -1,198 +0,0 @@ -import numpy as np -import pytest -import scipy.stats -import torch - -import triton -import triton.language as tl - -##################################### -# Reference Philox Implementation -##################################### - - -class PhiloxConfig: - def __init__(self, PHILOX_ROUND_A, PHILOX_ROUND_B, PHILOX_KEY_A, PHILOX_KEY_B, DTYPE): - self.PHILOX_ROUND_A = np.array(PHILOX_ROUND_A, dtype=DTYPE) - self.PHILOX_ROUND_B = np.array(PHILOX_ROUND_B, dtype=DTYPE) - self.PHILOX_KEY_A = np.array(PHILOX_KEY_A, dtype=DTYPE) - self.PHILOX_KEY_B = np.array(PHILOX_KEY_B, dtype=DTYPE) - self.DTYPE = DTYPE - - -# This is better for GPU -PHILOX_32 = PhiloxConfig( - PHILOX_KEY_A=0x9E3779B9, - PHILOX_KEY_B=0xBB67AE85, - PHILOX_ROUND_A=0xD2511F53, - PHILOX_ROUND_B=0xCD9E8D57, - DTYPE=np.uint32, -) - -# This is what numpy implements -PHILOX_64 = PhiloxConfig( - PHILOX_KEY_A=0x9E3779B97F4A7C15, - PHILOX_KEY_B=0xBB67AE8584CAA73B, - PHILOX_ROUND_A=0xD2E7470EE14C6C93, - PHILOX_ROUND_B=0xCA5A826395121157, - DTYPE=np.uint64, -) - - -class CustomPhilox4x: - def __init__(self, seed, config): - self._config = config - seed = self._into_pieces(seed) - self._key = np.array(seed[:2], dtype=self._dtype) - self._counter = np.array((0, 0) + seed[2:], dtype=self._dtype) - - @property - def _dtype(self): - return self._config.DTYPE - - def _into_pieces(self, n, pad=4): - res = [] - while len(res) < pad: - res.append(np.array(n, dtype=self._dtype)) - n >>= (np.dtype(self._dtype).itemsize * 8) - assert n == 0 - return tuple(res) - - def _multiply_low_high(self, a, b): - low = a * b - high = int(a) * int(b) - high = np.array(high >> (np.dtype(self._dtype).itemsize * 8), dtype=self._dtype) - return low, high - - def _single_round(self, counter, key): - lo0, hi0 = self._multiply_low_high(self._config.PHILOX_ROUND_A, counter[0]) - lo1, hi1 = self._multiply_low_high(self._config.PHILOX_ROUND_B, counter[2]) - ret0 = hi1 ^ counter[1] ^ key[0] - ret1 = lo1 - ret2 = hi0 ^ counter[3] ^ key[1] - ret3 = lo0 - return np.array([ret0, ret1, ret2, ret3], dtype=self._dtype) - - def _raise_key(self, key): - pk = [self._config.PHILOX_KEY_A, self._config.PHILOX_KEY_B] - return key + np.array(pk, dtype=self._dtype) - - def random_raw(self): - counter = self._counter - key = self._key - for _ in range(10): - counter = self._single_round(counter, key) - key = self._raise_key(key) - self.advance(1) - return counter - - def advance(self, n_steps): - self._counter[0] += n_steps - assert self._counter[0] < 2**32, "FIXME: doesn't work for large offsets" - - -class CustomPhilox(CustomPhilox4x): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.buffer = [] - - def random_raw(self): - if len(self.buffer) == 0: - self.buffer = list(super().random_raw())[::-1] - return int(self.buffer.pop()) - - -##################################### -# Unit Tests -##################################### - -BLOCK = 1024 - -# test generation of random uint32 - - -@pytest.mark.parametrize('size, seed', - [(size, seed) for size in ['10', '4,53', '10000'] - for seed in [0, 42, 124, 54, 0xffffffff, 0xdeadbeefcafeb0ba]] - ) -def test_randint(size, seed, device): - size = list(map(int, size.split(','))) - - @triton.jit - def kernel(X, N, seed): - offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) - rand = tl.randint(seed, offset) - tl.store(X + offset, rand, mask=offset < N) - # triton result - x = torch.empty(size, dtype=torch.int32, device=device) - N = x.numel() - grid = (triton.cdiv(N, BLOCK),) - kernel[grid](x, N, seed) - out_tri = x.cpu().numpy().astype(np.uint32).flatten().tolist() - # reference result - gen = CustomPhilox4x(seed, config=PHILOX_32) - out_ref = [gen.random_raw()[0] for _ in out_tri] - assert out_tri == out_ref - -# test uniform PRNG - - -@pytest.mark.parametrize('size, seed', - [(size, seed) for size in [1000000] - for seed in [0, 42, 124, 54]] - ) -def test_rand(size, seed, device): - @triton.jit - def kernel(X, N, seed): - offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) - rand = tl.rand(seed, offset) - tl.store(X + offset, rand, mask=offset < N) - # triton result - x = torch.empty(size, dtype=torch.float32, device=device) - N = x.numel() - grid = (triton.cdiv(N, BLOCK),) - kernel[grid](x, N, seed) - assert all((x >= 0) & (x <= 1)) - assert scipy.stats.kstest(x.tolist(), 'uniform', args=(0, 1)).statistic < 0.01 - -# test normal PRNG - - -@pytest.mark.parametrize('size, seed', - [(size, seed) for size in [1000000] - for seed in [0, 42, 124, 54]] - ) -def test_randn(size, seed, device): - @triton.jit - def kernel(X, N, seed): - offset = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) - rand = tl.randn(seed, offset) - tl.store(X + offset, rand, mask=offset < N) - # triton result - x = torch.empty(size, dtype=torch.float32, device=device) - N = x.numel() - grid = (triton.cdiv(N, BLOCK),) - kernel[grid](x, N, seed) - assert abs(x.mean()) < 1e-2 - assert abs(x.std() - 1) < 1e-2 - - -# tl.rand() should never produce >=1.0 - -def test_rand_limits(device): - @triton.jit - def kernel(input, output, n: tl.constexpr): - idx = tl.arange(0, n) - x = tl.load(input + idx) - y = tl.random.uint32_to_uniform_float(x) - tl.store(output + idx, y) - - min_max_int32 = torch.tensor([ - torch.iinfo(torch.int32).min, - torch.iinfo(torch.int32).max, - ], dtype=torch.int32, device=device) - output = torch.empty(2, dtype=torch.float32, device=device) - kernel[(1,)](min_max_int32, output, 2) - - assert output[0] == output[1] - assert 1.0 - torch.finfo(torch.float32).eps <= output[0].item() < 1.0 diff --git a/python/test/unit/language/test_subprocess.py b/python/test/unit/language/test_subprocess.py deleted file mode 100644 index 0baf89fce851..000000000000 --- a/python/test/unit/language/test_subprocess.py +++ /dev/null @@ -1,80 +0,0 @@ -import os -import subprocess -import sys - -import pytest - -dir_path = os.path.dirname(os.path.realpath(__file__)) -print_path = os.path.join(dir_path, "print_helper.py") -assert_path = os.path.join(dir_path, "assert_helper.py") - -# TODO: bfloat16 after LLVM-15 -assert_types = ["device_assert", "assert", "static_assert", "no_debug"] -nested_types = [(caller, callee) for caller in ["true", "false", "none"] for callee in ["true", "false", "none"]] -torch_types = ["int8", "uint8", "int16", "int32", "long", "float16", "float32", "float64"] - - -@pytest.mark.parametrize("func_type, data_type", - [("device_print", data_type) for data_type in torch_types] + [("print", "int32"), ("static_print", "int32")]) -def test_print(func_type: str, data_type: str): - proc = subprocess.Popen([sys.executable, print_path, func_type, data_type], stdout=subprocess.PIPE, shell=False) - outs, _ = proc.communicate() - outs = outs.split() - new_lines = set() - for line in outs: - try: - value = line - if func_type != "static_print": - value = int(float(line)) - new_lines.add(value) - except Exception as e: - print(e) - if func_type != "static_print": - for i in range(128): - assert i in new_lines - assert len(new_lines) == 128 - else: - assert len(new_lines) == 1 - - -@pytest.mark.parametrize("func_type", assert_types) -def test_assert(func_type: str): - os.environ["TRITON_DEBUG"] = "1" - proc = subprocess.Popen([sys.executable, assert_path, func_type], stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False) - _, errs = proc.communicate() - errs = errs.splitlines() - num_errs = 0 - for err in errs: - if "x != 0" in err.decode("utf-8"): - num_errs += 1 - os.environ["TRITON_DEBUG"] = "0" - if func_type != "static_assert": - assert num_errs == 127 - else: - assert num_errs == 0 - - -@pytest.mark.parametrize("caller_type, callee_type", nested_types) -def test_assert_nested(caller_type, callee_type): - proc = subprocess.Popen([sys.executable, assert_path, caller_type, callee_type], stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False) - _, errs = proc.communicate() - errs = errs.splitlines() - num_errs = 0 - for err in errs: - if "x != 0" in err.decode("utf-8"): - num_errs += 1 - if caller_type == "none": - if callee_type == "true": - assert num_errs == 127 - else: - assert num_errs == 0 - elif caller_type == "true": - if callee_type == "false": - assert num_errs == 0 - else: - assert num_errs == 127 - elif caller_type == "false": - if callee_type == "true": - assert num_errs == 127 - else: - assert num_errs == 0 diff --git a/python/test/unit/operators/test_blocksparse.py b/python/test/unit/operators/test_blocksparse.py deleted file mode 100644 index 5f94cd8b31bf..000000000000 --- a/python/test/unit/operators/test_blocksparse.py +++ /dev/null @@ -1,219 +0,0 @@ -import pytest -import torch - -import triton -import triton.ops - - -def sparsify_tensor(x, mask, block): - ret = torch.empty((x.size(0), mask.sum(), block, block), dtype=x.dtype, device=x.device) - for idx, (h, i, j) in enumerate(zip(*mask.nonzero(as_tuple=True))): - ret[:, idx, :, :] = x[:, h, i * block:(i + 1) * block, j * block:(j + 1) * block] - return ret - - -def make_pair(shape, device="cuda", alpha=1e-2, beta=0., trans=False, data=None, dtype=torch.float32): - if data is None: - data = torch.randn(shape, dtype=torch.float32, requires_grad=True, device=device) - ref_ret = data - ref_ret = ref_ret * alpha + beta - ref_ret = ref_ret.half().to(dtype) - if trans: - ref_ret = ref_ret.t().requires_grad_() - ref_ret = ref_ret.detach().requires_grad_() - tri_ret = ref_ret.clone().detach().requires_grad_() - return ref_ret, tri_ret - - -def mask_tensor(x, mask, block, value=0): - ret = x.clone() - for h, i, j in zip(*(mask == 0).nonzero(as_tuple=True)): - ret[:, h, i * block:(i + 1) * block, j * block:(j + 1) * block] = value - return ret - - -@pytest.mark.parametrize("MODE", ["sdd", "dds", "dsd"]) -@pytest.mark.parametrize("TRANS_A", [False, True]) -@pytest.mark.parametrize("TRANS_B", [False, True]) -@pytest.mark.parametrize("BLOCK", [16, 32, 64]) -@pytest.mark.parametrize("DTYPE", [torch.float16]) -def test_matmul(MODE, TRANS_A, TRANS_B, BLOCK, DTYPE, Z=3, H=2, M=512, N=384, K=256): - seed = 0 - torch.manual_seed(seed) - is_sdd = MODE == "sdd" - is_dsd = MODE == "dsd" - is_dds = MODE == "dds" - do_sparsify = lambda x: sparsify_tensor(x, layout, BLOCK) - do_mask = lambda x: mask_tensor(x, layout, BLOCK) - # create inputs - # create op - a_shape = (Z, H, K, M) if TRANS_A else (Z, H, M, K) - b_shape = (Z, H, N, K) if TRANS_B else (Z, H, K, N) - c_shape = (Z, H, M, N) - shape = { - "sdd": (M, N), - "dsd": (a_shape[2], a_shape[3]), - "dds": (b_shape[2], b_shape[3]), - }[MODE] - layout = torch.randint(2, (H, shape[0] // BLOCK, shape[1] // BLOCK)) - layout[1, 2, :] = 0 - layout[1, :, 1] = 0 - # create data - a_ref, a_tri = make_pair(a_shape, alpha=.1, dtype=DTYPE) - b_ref, b_tri = make_pair(b_shape, alpha=.1, dtype=DTYPE) - dc_ref, dc_tri = make_pair(c_shape, dtype=DTYPE) - # compute [torch] - dc_ref = do_mask(dc_ref) if is_sdd else dc_ref - a_ref = do_mask(a_ref) if is_dsd else a_ref - b_ref = do_mask(b_ref) if is_dds else b_ref - a_ref.retain_grad() - b_ref.retain_grad() - c_ref = torch.matmul(a_ref.transpose(2, 3) if TRANS_A else a_ref, - b_ref.transpose(2, 3) if TRANS_B else b_ref) - c_ref.backward(dc_ref) - c_ref = do_sparsify(c_ref) if is_sdd else c_ref - da_ref = do_sparsify(a_ref.grad) if is_dsd else a_ref.grad - db_ref = do_sparsify(b_ref.grad) if is_dds else b_ref.grad - # triton result - dc_tri = do_sparsify(dc_tri) if is_sdd else dc_tri - a_tri = do_sparsify(a_tri) if is_dsd else a_tri - b_tri = do_sparsify(b_tri) if is_dds else b_tri - a_tri.retain_grad() - b_tri.retain_grad() - op = triton.ops.blocksparse.matmul(layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B, device="cuda") - c_tri = op(a_tri, b_tri) - c_tri.backward(dc_tri) - da_tri = a_tri.grad - db_tri = b_tri.grad - # compare - torch.testing.assert_allclose(c_ref, c_tri) - torch.testing.assert_allclose(da_ref, da_tri) - torch.testing.assert_allclose(db_ref, db_tri) - - -configs = [ - (16, 256), - (32, 576), - (64, 1871), - (128, 2511), -] - - -@pytest.mark.parametrize("is_dense", [False, True]) -@pytest.mark.parametrize("BLOCK, WIDTH", configs) -def test_softmax(BLOCK, WIDTH, is_dense, Z=2, H=2, is_causal=True, scale=0.4): - # set seed - torch.random.manual_seed(0) - Z, H, M, N = 2, 3, WIDTH, WIDTH - # initialize layout - # make sure each row has at least one non-zero element - layout = torch.randint(2, (H, M // BLOCK, N // BLOCK)) - if is_dense: - layout[:] = 1 - else: - layout[1, 2, :] = 0 - layout[1, :, 1] = 0 - # initialize data - a_shape = (Z, H, M, N) - a_ref, a_tri = make_pair(a_shape) - dout_ref, dout_tri = make_pair(a_shape) - # compute [torch] - a_ref = mask_tensor(a_ref, layout, BLOCK, value=float("-inf")) - a_ref.retain_grad() - at_mask = torch.ones((M, N), device="cuda") - if is_causal: - at_mask = torch.tril(at_mask) - M = at_mask[None, None, :, :] + torch.zeros_like(a_ref) - a_ref[M == 0] = float("-inf") - out_ref = torch.softmax(a_ref * scale, -1) - out_ref.backward(dout_ref) - out_ref = sparsify_tensor(out_ref, layout, BLOCK) - da_ref = sparsify_tensor(a_ref.grad, layout, BLOCK) - # compute [triton] - a_tri = sparsify_tensor(a_tri, layout, BLOCK) - a_tri.retain_grad() - dout_tri = sparsify_tensor(dout_tri, layout, BLOCK) - op = triton.ops.blocksparse.softmax(layout, BLOCK, device="cuda", is_dense=is_dense) - out_tri = op(a_tri, scale=scale, is_causal=is_causal) - out_tri.backward(dout_tri) - da_tri = a_tri.grad - # compare - torch.testing.assert_allclose(out_tri, out_ref) - torch.testing.assert_allclose(da_tri, da_ref) - - -@pytest.mark.parametrize("block", [16, 32, 64]) -@pytest.mark.parametrize("dtype", [torch.float16, torch.float32]) -def test_attention_fwd_bwd( - block, - dtype, - input_scale=1.0, - scale=1 / 8.0, - n_ctx=256, - batch_size=2, - n_heads=2, -): - capability = torch.cuda.get_device_capability() - if capability[0] < 7: - pytest.skip("Only test tl.dot() on devices with sm >= 70") - - # inputs - qkv_shape = (batch_size, n_heads, n_ctx, 64) - qkvs = [ - torch.nn.Parameter(input_scale * torch.randn(qkv_shape), requires_grad=True).to(dtype).cuda() for _ in range(3) - ] - - # Triton: - n_blocks = n_ctx // block - layout = torch.tril(torch.ones([n_heads, n_blocks, n_blocks], dtype=torch.long)) - query, key, value = [x.clone() for x in qkvs] - query.retain_grad() - key.retain_grad() - value.retain_grad() - attn_out = triton_attention(layout, block, query=query, key=key, value=value, scale=scale) - # ad hoc loss - loss = (attn_out ** 2).mean() - loss.backward() - grads = [query.grad, key.grad, value.grad] - - # Torch version: - torch_q, torch_k, torch_v = [x.clone() for x in qkvs] - attn_mask = torch.ones([n_ctx, n_ctx], device="cuda", dtype=dtype) - attn_mask = torch.tril(attn_mask, diagonal=0) - attn_mask = 1e6 * (-1 + (attn_mask.reshape((1, 1, n_ctx, n_ctx)).cuda())) - torch_q.retain_grad() - torch_k.retain_grad() - torch_v.retain_grad() - scores = scale * torch.einsum("bhsd,bhtd->bhst", torch_q, torch_k) - scores = scores + attn_mask - probs = torch.softmax(scores, dim=-1) - torch_attn_out = torch.einsum("bhst,bhtd->bhsd", probs, torch_v) - # ad hoc loss - torch_loss = (torch_attn_out ** 2).mean() - torch_loss.backward() - torch_grads = [torch_q.grad, torch_k.grad, torch_v.grad] - - # comparison - # print(f"Triton loss {loss} and torch loss {torch_loss}. Also checking grads...") - torch.testing.assert_allclose(loss, torch_loss, atol=1e-3, rtol=0) - for g1, g2 in zip(grads, torch_grads): - torch.testing.assert_allclose(g1, g2) - - -@pytest.mark.parametrize("block", [16, 32, 64]) -def triton_attention( - layout, - block: int, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - scale: float, -): - sparse_dot_sdd_nt = triton.ops.blocksparse.matmul(layout, block, "sdd", trans_a=False, trans_b=True, device=value.device) - sparse_dot_dsd_nn = triton.ops.blocksparse.matmul(layout, block, "dsd", trans_a=False, trans_b=False, device=value.device) - sparse_softmax = triton.ops.blocksparse.softmax(layout, block, device=value.device) - - w = sparse_dot_sdd_nt(query, key) - w = sparse_softmax(w, scale=scale, is_causal=True) - a = sparse_dot_dsd_nn(w, value) - return a diff --git a/python/test/unit/operators/test_cross_entropy.py b/python/test/unit/operators/test_cross_entropy.py deleted file mode 100644 index f4e40d3a65a7..000000000000 --- a/python/test/unit/operators/test_cross_entropy.py +++ /dev/null @@ -1,40 +0,0 @@ -import pytest -import torch - -import triton -import triton.ops - - -@pytest.mark.parametrize("M, N, dtype, mode", - [ - (M, N, dtype, mode) for M in [1024, 821] - for N in [512, 857, 1871, 2089, 8573, 31000] - for dtype in ['float16', 'float32'] - for mode in ['forward', 'backward'] - ] - ) -def test_op(M, N, dtype, mode): - capability = torch.cuda.get_device_capability() - if capability[0] < 8 and dtype == "bfloat16": - pytest.skip("Only test bfloat16 on devices with sm >= 80") - dtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16, 'float32': torch.float32}[dtype] - # create inputs - x = torch.randn(M, N, dtype=dtype, device='cuda', requires_grad=True) - idx = 4 + torch.ones(M, dtype=torch.int64, device='cuda') - # forward pass - tt_y = triton.ops.cross_entropy(x, idx) - th_y = torch.nn.CrossEntropyLoss(reduction="none")(x, idx) - if mode == 'forward': - torch.testing.assert_allclose(th_y, tt_y) - # backward pass - elif mode == 'backward': - dy = torch.randn_like(tt_y) - # triton backward - tt_y.backward(dy) - tt_dx = x.grad.clone() - # torch backward - x.grad = None - th_y.backward(dy) - th_dx = x.grad.clone() - - torch.testing.assert_allclose(th_dx, tt_dx) diff --git a/python/test/unit/operators/test_flash_attention.py b/python/test/unit/operators/test_flash_attention.py deleted file mode 100644 index 3c3e8a568839..000000000000 --- a/python/test/unit/operators/test_flash_attention.py +++ /dev/null @@ -1,49 +0,0 @@ -import pytest -import torch - -import triton -import triton.ops - - -@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(4, 48, 1024, 16), - (4, 48, 1024, 32), - (4, 48, 1024, 64), - (4, 48, 1024, 128)]) -@pytest.mark.parametrize('dtype', [torch.float16, torch.bfloat16]) -def test_op(Z, H, N_CTX, D_HEAD, dtype): - capability = torch.cuda.get_device_capability() - if capability[0] < 8: - pytest.skip("Flash attention only supported for compute capability < 80") - torch.manual_seed(20) - q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2).requires_grad_() - k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2).requires_grad_() - v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2).requires_grad_() - sm_scale = 0.2 - dout = torch.randn_like(q) - # reference implementation - M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda")) - p = torch.matmul(q, k.transpose(2, 3)) * sm_scale - for z in range(Z): - for h in range(H): - p[:, :, M == 0] = float("-inf") - p = torch.softmax(p.float(), dim=-1).to(dtype) - # p = torch.exp(p) - ref_out = torch.matmul(p, v) - ref_out.backward(dout) - ref_dv, v.grad = v.grad.clone(), None - ref_dk, k.grad = k.grad.clone(), None - ref_dq, q.grad = q.grad.clone(), None - # # triton implementation - tri_out = triton.ops.attention(q, k, v, sm_scale) - # print(ref_out) - # print(tri_out) - tri_out.backward(dout) - tri_dv, v.grad = v.grad.clone(), None - tri_dk, k.grad = k.grad.clone(), None - tri_dq, q.grad = q.grad.clone(), None - # compare - atol = 1e-1 if dtype == torch.bfloat16 else 1e-2 - torch.testing.assert_allclose(ref_out, tri_out, atol=atol, rtol=0) - torch.testing.assert_allclose(ref_dv, tri_dv, atol=atol, rtol=0) - torch.testing.assert_allclose(ref_dk, tri_dk, atol=atol, rtol=0) - torch.testing.assert_allclose(ref_dq, tri_dq, atol=atol, rtol=0) diff --git a/python/test/unit/operators/test_inductor.py b/python/test/unit/operators/test_inductor.py deleted file mode 100644 index f7e2ce2aa7e0..000000000000 --- a/python/test/unit/operators/test_inductor.py +++ /dev/null @@ -1,155 +0,0 @@ -import torch - -import triton -import triton.language as tl - - -def test_normalization_with_remat(): - - @triton.jit - def triton_(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr): - xnumel = 512 - rnumel = 4096 - xoffset = tl.program_id(0) * XBLOCK - xindex = xoffset + tl.arange(0, XBLOCK)[:, None] - xmask = xindex < xnumel - rbase = tl.arange(0, RBLOCK)[None, :] - x3 = xindex - x0 = xindex % 64 - tmp1 = tl.load(in_ptr0 + (x0), xmask) - tmp3 = tl.load(in_ptr1 + (x0), xmask) - tmp11 = tl.load(in_ptr2 + (x0), xmask) - tmp13 = tl.load(in_ptr3 + (x0), xmask) - _tmp17 = tl.zeros([XBLOCK, RBLOCK], tl.float32) + 0 - for roffset in range(0, rnumel, RBLOCK): - rindex = roffset + rbase - rmask = rindex < rnumel - r2 = rindex - tmp0 = tl.load(in_out_ptr0 + (r2 + (4096 * x3)), rmask & xmask, eviction_policy='evict_last', other=0) - tmp2 = tmp0 - tmp1 - tmp4 = 1e-05 - tmp5 = tmp3 + tmp4 - tmp6 = tl.sqrt(tmp5) - tmp7 = 1 / tmp6 - tmp8 = 1.0 - tmp9 = tmp7 * tmp8 - tmp10 = tmp2 * tmp9 - tmp12 = tmp10 * tmp11 - tmp14 = tmp12 + tmp13 - _tmp17 = tl.where(rmask & xmask, _tmp17 + tmp14, _tmp17) - tl.store(in_out_ptr0 + (r2 + (4096 * x3) + tl.zeros([XBLOCK, RBLOCK], tl.int32)), tmp14, rmask & xmask) - tmp17 = tl.sum(_tmp17, 1)[:, None] - tmp18 = 4096.0 - tmp19 = tmp17 / tmp18 - tl.store(in_out_ptr1 + (x3 + tl.zeros([XBLOCK, 1], tl.int32)), tmp19, xmask) - - torch.manual_seed(123) - - buf14 = torch.rand(8, 64, 64, 64, device="cuda") - buf16 = torch.rand(8, 1, 64, device="cuda") - arg114_1 = torch.rand(64, device="cuda") - arg115_1 = torch.rand(64, device="cuda") - arg8_1 = torch.rand(64, device="cuda") - arg9_1 = torch.rand(64, device="cuda") - triton_[(512,)](buf14, buf16, arg114_1, arg115_1, arg8_1, arg9_1, 512, 4096, 1, 2048) - torch.testing.assert_allclose(buf16.mean().item(), buf14.mean().item(), atol=1e-7, rtol=0) - - -def test_avg_pool_bw(): - - @triton.jit - def triton_(in_ptr0, out_ptr0, XBLOCK: tl.constexpr): - xoffset = tl.program_id(0) * XBLOCK - xindex = xoffset + tl.arange(0, XBLOCK)[:] - x1 = (xindex // 8) % 8 - x0 = xindex % 8 - x2 = (xindex // 64) - x5 = xindex - tmp0 = (-1) + x1 - tmp1 = (-1) + x0 - tmp2 = 2 + x1 - tmp3 = 2 + x0 - tmp4 = 0 - tmp5 = tl.where(tmp0 != tmp0, tmp0, tl.where(tmp0 > tmp4, tmp0, tmp4)) - tmp6 = tl.where(tmp1 != tmp1, tmp1, tl.where(tmp1 > tmp4, tmp1, tmp4)) - tmp7 = 8 - tmp8 = tl.where(tmp2 != tmp2, tmp2, tl.where(tmp2 < tmp7, tmp2, tmp7)) - tmp9 = tl.where(tmp3 != tmp3, tmp3, tl.where(tmp3 < tmp7, tmp3, tmp7)) - tmp10 = tmp5 + tmp4 - tmp11 = tmp6 + tmp4 - tmp12 = 1 - tmp13 = tmp8 - tmp12 - tmp14 = tl.where(tmp10 != tmp10, tmp10, tl.where(tmp10 < tmp13, tmp10, tmp13)) - tmp15 = tmp9 - tmp12 - tmp16 = tl.where(tmp11 != tmp11, tmp11, tl.where(tmp11 < tmp15, tmp11, tmp15)) - tmp17 = tl.load(in_ptr0 + (tmp16 + (8 * tmp14) + (64 * x2)), None).to(tl.float32) - tmp18 = tmp17 / 9 - tmp19 = tmp10 < tmp8 - tmp20 = tmp11 < tmp9 - tmp21 = tmp19 & tmp20 - tmp22 = 0.0 - tmp23 = tl.where(tmp21, tmp18, tmp22) - tmp24 = tmp6 + tmp12 - tmp25 = tl.where(tmp24 != tmp24, tmp24, tl.where(tmp24 < tmp15, tmp24, tmp15)) - tmp26 = tl.load(in_ptr0 + (tmp25 + (8 * tmp14) + (64 * x2)), None).to(tl.float32) - tmp27 = tmp26 / 9 - tmp28 = tmp24 < tmp9 - tmp29 = tmp19 & tmp28 - tmp30 = tmp23 + tmp27 - tmp31 = tl.where(tmp29, tmp30, tmp23) - tmp32 = 2 - tmp33 = tmp6 + tmp32 - tmp34 = tl.where(tmp33 != tmp33, tmp33, tl.where(tmp33 < tmp15, tmp33, tmp15)) - tmp35 = tl.load(in_ptr0 + (tmp34 + (8 * tmp14) + (64 * x2)), None).to(tl.float32) - tmp36 = tmp35 / 9 - tmp37 = tmp33 < tmp9 - tmp38 = tmp19 & tmp37 - tmp39 = tmp31 + tmp36 - tmp40 = tl.where(tmp38, tmp39, tmp31) - tmp41 = tmp5 + tmp12 - tmp42 = tl.where(tmp41 != tmp41, tmp41, tl.where(tmp41 < tmp13, tmp41, tmp13)) - tmp43 = tl.load(in_ptr0 + (tmp16 + (8 * tmp42) + (64 * x2)), None).to(tl.float32) - tmp44 = tmp43 / 9 - tmp45 = tmp41 < tmp8 - tmp46 = tmp45 & tmp20 - tmp47 = tmp40 + tmp44 - tmp48 = tl.where(tmp46, tmp47, tmp40) - tmp49 = tl.load(in_ptr0 + (tmp25 + (8 * tmp42) + (64 * x2)), None).to(tl.float32) - tmp50 = tmp49 / 9 - tmp51 = tmp45 & tmp28 - tmp52 = tmp48 + tmp50 - tmp53 = tl.where(tmp51, tmp52, tmp48) - tmp54 = tl.load(in_ptr0 + (tmp34 + (8 * tmp42) + (64 * x2)), None).to(tl.float32) - tmp55 = tmp54 / 9 - tmp56 = tmp45 & tmp37 - tmp57 = tmp53 + tmp55 - tmp58 = tl.where(tmp56, tmp57, tmp53) - tmp59 = tmp5 + tmp32 - tmp60 = tl.where(tmp59 != tmp59, tmp59, tl.where(tmp59 < tmp13, tmp59, tmp13)) - tmp61 = tl.load(in_ptr0 + (tmp16 + (8 * tmp60) + (64 * x2)), None).to(tl.float32) - tmp62 = tmp61 / 9 - tmp63 = tmp59 < tmp8 - tmp64 = tmp63 & tmp20 - tmp65 = tmp58 + tmp62 - tmp66 = tl.where(tmp64, tmp65, tmp58) - tmp67 = tl.load(in_ptr0 + (tmp25 + (8 * tmp60) + (64 * x2)), None).to(tl.float32) - tmp68 = tmp67 / 9 - tmp69 = tmp63 & tmp28 - tmp70 = tmp66 + tmp68 - tmp71 = tl.where(tmp69, tmp70, tmp66) - tmp72 = tl.load(in_ptr0 + (tmp34 + (8 * tmp60) + (64 * x2)), None).to(tl.float32) - tmp73 = tmp72 / 9 - tmp74 = tmp63 & tmp37 - tmp75 = tmp71 + tmp73 - tmp76 = tl.where(tmp74, tmp75, tmp71) - tl.store(out_ptr0 + (x5 + tl.zeros([XBLOCK], tl.int32)), tmp76, None) - - inp = torch.ones(8, 2048, 8, 8, device="cuda", dtype=torch.half) - out = torch.ones_like(inp) * 3 - numel = inp.numel() - triton_[(numel // 1024,)](inp, out, 1024) - out_ref = torch.ones_like(inp) - out_ref[:, :, 1:7, 0::7] = 2 / 3 - out_ref[:, :, 0::7, 1:7] = 2 / 3 - out_ref[:, :, 0::7, 0::7] = 4 / 9 - torch.testing.assert_allclose(out, out_ref) diff --git a/python/test/unit/operators/test_matmul.py b/python/test/unit/operators/test_matmul.py deleted file mode 100644 index 01dd813537ee..000000000000 --- a/python/test/unit/operators/test_matmul.py +++ /dev/null @@ -1,141 +0,0 @@ -import itertools - -import pytest -import torch - -import triton -import triton.language as tl -import triton.ops - - -def f8_to_f16(x): - - @triton.jit - def kernel(Y, X, N, BLOCK_SIZE: tl.constexpr): - pid = tl.program_id(0) - offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = offs < N - x = tl.load(X + offs, mask=mask) - y = x.to(tl.float8e5) - tl.store(Y + offs, y, mask=mask) - - ret = torch.empty(x.shape, dtype=torch.float16, device=x.device) - grid = lambda META: (triton.cdiv(x.numel(), META['BLOCK_SIZE']),) - kernel[grid](ret, triton.reinterpret(x, tl.float8e5), ret.numel(), BLOCK_SIZE=1024) - return ret - - -@pytest.mark.parametrize( - "BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, ADTYPE, BDTYPE", - itertools.chain( - *[ - [ - # 1 warp - (16, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (16, 32, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (16, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (16, 32, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (16, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (64, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (16, 64, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE, DTYPE), - # 2 warp - (64, 32, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 64, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (64, 32, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 64, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (128, 32, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 128, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE, DTYPE), - # 4 warp - (128, 64, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (64, 128, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (128, 32, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 128, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (128, 32, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (32, 128, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - # 8 warp - (128, 256, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (256, 128, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (256, 128, 32, 1, 8, 2, None, None, None, AT, BT, DTYPE, DTYPE), - # split-k - (64, 64, 16, 2, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (64, 64, 16, 4, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - (64, 64, 16, 8, 4, 2, None, None, None, AT, BT, DTYPE, DTYPE), - # variable input - (128, 128, 32, 1, 4, 2, 256, 384, 160, AT, BT, DTYPE, DTYPE), - (128, 128, 32, 1, 4, 2, 107, 233, 256, AT, BT, DTYPE, DTYPE), - (128, 128, 32, 1, 4, 2, 107, 233, 311, AT, BT, DTYPE, DTYPE), - (128, 256, 64, 1, 8, 3, 256, 512, 160, AT, BT, DTYPE, DTYPE), - ] for DTYPE in ["float16", "bfloat16", "float32"] for AT in [False, True] for BT in [False, True] - ], - # n-stage - *[ - [ - (16, 16, 16, 1, 1, STAGES, 32, 32, 80, AT, BT, DTYPE, DTYPE), - (64, 32, 64, 1, 2, STAGES, 128, 64, 128, AT, BT, DTYPE, DTYPE), - (128, 64, 16, 1, 4, STAGES, 256, 128, 80, AT, BT, DTYPE, DTYPE), - (256, 128, 32, 1, 8, STAGES, 512, 256, 160, AT, BT, DTYPE, DTYPE), - (128, 128, 32, 1, 4, STAGES, 256, 256, 160, AT, BT, DTYPE, DTYPE), - # split-k - (64, 64, 16, 8, 4, STAGES, 128, 128, 768, AT, BT, DTYPE, DTYPE), - (64, 64, 16, 8, 4, STAGES, 128, 128, 32, AT, BT, DTYPE, DTYPE), - ] for DTYPE in ["float16", "bfloat16", "float32"] for AT in [False, True] for BT in [False, True] for STAGES in [4] - ], - # mixed-precision - *[ - [ - (16, 16, 16, 1, 1, 2, None, None, None, AT, BT, ADTYPE, BDTYPE), - (128, 32, 32, 1, 2, 2, None, None, None, AT, BT, ADTYPE, BDTYPE), - (128, 256, 16, 1, 8, 2, None, None, None, AT, BT, ADTYPE, BDTYPE), - (32, 64, 16, 1, 1, 2, 64, 128, 32, AT, BT, ADTYPE, BDTYPE), - (128, 128, 32, 8, 4, 2, 256, 256, 128, AT, BT, ADTYPE, BDTYPE), - ] for ADTYPE, BDTYPE in [("float8", "float16"), ("float16", "float32"), ("float32", "float16"), - ("bfloat16", "float32"), ("float32", "bfloat16")] for AT in [False, True] for BT in [False, True] - ] - ), -) -def test_op(BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, ADTYPE, BDTYPE): - capability = torch.cuda.get_device_capability() - if capability[0] < 7: - pytest.skip("Only test tl.dot() on devices with sm >= 70") - if capability[0] < 8 and (ADTYPE == "bfloat16" or BDTYPE == "bfloat16"): - pytest.skip("Only test bfloat16 on devices with sm >= 80") - if (ADTYPE == "bfloat16" or BDTYPE == "bfloat16") and SPLIT_K != 1: - pytest.skip("bfloat16 matmuls don't allow split_k for now") - torch.manual_seed(0) - # nuke kernel decorators -- will set meta-parameters manually - kwargs = {'BLOCK_M': BLOCK_M, 'BLOCK_N': BLOCK_N, 'BLOCK_K': BLOCK_K, 'SPLIT_K': SPLIT_K} - pre_hook = None if SPLIT_K == 1 else lambda nargs: nargs['C'].zero_() - configs = [triton.Config(kwargs=kwargs, num_warps=NWARP, num_stages=NSTAGE, pre_hook=pre_hook)] - kernel = triton.ops._matmul.kernel - kernel.configs = configs - # kernel.run = kernel.run.run.run - - # get matrix shape - M = BLOCK_M if M is None else M - N = BLOCK_N if N is None else N - K = BLOCK_K * SPLIT_K if K is None else K - - def get_input(n, m, t, dtype): - if t: - return get_input(m, n, False, dtype).t() - if dtype == "float8": - x = torch.randint(10, 50, (n, m), device="cuda", dtype=torch.int8) - return f8_to_f16(x) - dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32}[dtype] - return .1 * torch.randn((n, m), device="cuda", dtype=dtype) - - # allocate/transpose inputs - a = get_input(M, K, AT, ADTYPE) - b = get_input(K, N, BT, BDTYPE) - # run test - th_c = torch.matmul(a.to(torch.float32), b.to(torch.float32)) - try: - tt_c = triton.ops.matmul(a, b) - atol, rtol = 1e-2, 0 - if ADTYPE == torch.bfloat16 or BDTYPE == torch.bfloat16: - atol, rtol = 3.5e-2, 0 - torch.testing.assert_allclose(th_c, tt_c, atol=atol, rtol=rtol) - except triton.OutOfResources as e: - pytest.skip(str(e)) diff --git a/python/test/unit/runtime/test_autotuner.py b/python/test/unit/runtime/test_autotuner.py deleted file mode 100644 index c425a36697f6..000000000000 --- a/python/test/unit/runtime/test_autotuner.py +++ /dev/null @@ -1,22 +0,0 @@ -import torch - -import triton -import triton.language as tl - - -def test_kwargs(): - N = 1024 - src = torch.empty(N, device='cuda') - dst = torch.empty(N, device='cuda') - - configs = [triton.Config(kwargs={'BLOCK_SIZE': 32}), triton.Config(kwargs={'BLOCK_SIZE': 128})] - - @triton.autotune(configs=configs, key=['N']) - @triton.jit - def _kernel(dst, src, N, BLOCK_SIZE: tl.constexpr): - offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - x = tl.load(src + offsets, mask=offsets < N) - tl.store(dst + offsets, x, mask=offsets < N) - grid = lambda META: (triton.cdiv(N, META['BLOCK_SIZE']),) - _kernel[grid](dst, src, N) - _kernel[grid](dst=dst, src=src, N=N) diff --git a/python/test/unit/runtime/test_cache.py b/python/test/unit/runtime/test_cache.py deleted file mode 100644 index e13921079992..000000000000 --- a/python/test/unit/runtime/test_cache.py +++ /dev/null @@ -1,208 +0,0 @@ -import os -import shutil - -import pytest -import torch - -import triton -import triton.language as tl -from triton.runtime.jit import JITFunction - -tmpdir = ".tmp" - - -@triton.jit -def function_1(i): - i = i + 1 - i = function_2(i) - return i - - -@triton.jit -def function_2(i): - i = i + 1 - return i - - -@triton.jit -def kernel(X, i, BLOCK: tl.constexpr): - i = i + 1 - i = function_1(i) - tl.store(X, i) - - -@triton.jit(do_not_specialize=["i"]) -def kernel_nospec(X, i, BLOCK: tl.constexpr): - i = i + 1 - i = function_1(i) - tl.store(X, i) - - -def apply_src_change(target, old, new): - kernel.hash = None - function_1.hash = None - function_2.hash = None - function_1.src = function_1.src.replace(old, new) - target.src = target.src.replace(old, new) - ret = target.cache_key - target.src = target.src.replace(new, old) - return ret - - -def test_nochange(): - baseline = kernel.cache_key - updated = apply_src_change(kernel, 'i + 1', 'i + 1') - assert baseline == updated - - -def test_toplevel_change(): - baseline = kernel.cache_key - updated = apply_src_change(kernel, 'i + 1', 'i + 2') - assert baseline != updated - - -def test_nested1_change(): - baseline = kernel.cache_key - updated = apply_src_change(function_1, 'i + 1', 'i + 2') - assert baseline != updated - - -def reset_tmp_dir(): - os.environ["TRITON_CACHE_DIR"] = tmpdir - if os.path.exists(tmpdir): - shutil.rmtree(tmpdir) - - -def test_reuse(): - counter = 0 - - def inc_counter(*args, **kwargs): - nonlocal counter - counter += 1 - JITFunction.cache_hook = inc_counter - reset_tmp_dir() - x = torch.empty(1, dtype=torch.int32, device='cuda') - for i in range(10): - kernel[(1,)](x, 1, BLOCK=1024) - assert counter == 1 - - -@pytest.mark.parametrize('mode', ['enable', 'disable']) -def test_specialize(mode): - counter = 0 - - def inc_counter(*args, **kwargs): - nonlocal counter - counter += 1 - JITFunction.cache_hook = inc_counter - reset_tmp_dir() - x = torch.empty(1, dtype=torch.int32, device='cuda') - function = {'enable': kernel, 'disable': kernel_nospec}[mode] - target = {'enable': 3, 'disable': 1}[mode] - for i in [1, 2, 4, 8, 16, 32]: - function[(1,)](x, i, BLOCK=512) - assert counter == target - - -def test_constexpr_not_callable() -> None: - @triton.jit - def kernel(X, c: tl.constexpr): - tl.store(X, 2) - - x = torch.empty(1, dtype=torch.int32, device='cuda') - error = False - try: - kernel[(1, )](x, c="str") - except BaseException: - error = True - assert error is False - # try and catch - try: - kernel[(1, )](x, c=tl.abs) - except BaseException: - error = True - assert error is True - - -def test_jit_warmup_cache() -> None: - @triton.jit - def kernel_add(a, b, o, N: tl.constexpr): - idx = tl.arange(0, N) - tl.store(o + idx, - tl.load(a + idx) + tl.load(b + idx)) - - args = [ - torch.randn(32, dtype=torch.float32, device="cuda"), - torch.randn(32, dtype=torch.float32, device="cuda"), - torch.randn(32, dtype=torch.float32, device="cuda"), - 32, - ] - assert len(kernel_add.cache) == 0 - kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,)) - assert len(kernel_add.cache) == 1 - kernel_add.warmup(*args, grid=(1,)) - assert len(kernel_add.cache) == 1 - kernel_add.warmup(*args, grid=(1,)) - assert len(kernel_add.cache) == 1 - - -def test_jit_debug() -> None: - @triton.jit - def kernel_add(a, b, o, N: tl.constexpr): - idx = tl.arange(0, N) - tl.device_assert(idx < 32, "idx < 32") - tl.store(o + idx, - tl.load(a + idx) + tl.load(b + idx)) - - device = torch.cuda.current_device() - assert len(kernel_add.cache[device]) == 0 - kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,)) - assert len(kernel_add.cache[device]) == 1 - kernel_add.debug = False - kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,)) - assert len(kernel_add.cache[device]) == 2 - kernel_add.debug = True - kernel_add.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,)) - assert len(kernel_add.cache[device]) == 3 - bins = list(kernel_add.cache[device].values()) - assert bins[2].asm['ttir'] != bins[1].asm['ttir'] - - -@triton.jit -def add_fn(a, b, o, N: tl.constexpr): - idx = tl.arange(0, N) - tl.store(o + idx, tl.load(a + idx) + tl.load(b + idx)) - - -def test_jit_noinline() -> None: - @triton.jit - def kernel_add_device(a, b, o, N: tl.constexpr): - add_fn(a, b, o, N) - - device = torch.cuda.current_device() - assert len(kernel_add_device.cache[device]) == 0 - kernel_add_device.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,)) - assert len(kernel_add_device.cache[device]) == 1 - bins = list(kernel_add_device.cache[device].values()) - inline_ttir = bins[0].asm['ttir'] - add_fn.noinline = True - add_fn.hash = None - kernel_add_device.hash = None - kernel_add_device.cache[device].clear() - kernel_add_device.warmup(torch.float32, torch.float32, torch.float32, 32, grid=(1,)) - assert len(kernel_add_device.cache[device]) == 1 - bins = list(kernel_add_device.cache[device].values()) - noinline_ttir = bins[0].asm['ttir'] - assert inline_ttir != noinline_ttir - - -def test_memory_leak() -> None: - @triton.jit - def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): - xnumel = 10 - xoffset = tl.program_id(0) * XBLOCK - xindex = xoffset + tl.arange(0, XBLOCK)[:] - xmask = xindex < xnumel - x0 = xindex - tmp0 = tl.load(in_ptr0 + (x0), xmask) - tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp0, xmask) diff --git a/python/test/unit/runtime/test_driver.py b/python/test/unit/runtime/test_driver.py deleted file mode 100644 index b63927d89bfa..000000000000 --- a/python/test/unit/runtime/test_driver.py +++ /dev/null @@ -1,14 +0,0 @@ -import sys - -import triton - - -def test_is_lazy(): - from importlib import reload - reload(sys.modules["triton.runtime.driver"]) - reload(sys.modules["triton.runtime"]) - mod = sys.modules[triton.runtime.driver.__module__] - assert isinstance(triton.runtime.driver, getattr(mod, "LazyProxy")) - assert triton.runtime.driver._obj is None - utils = triton.runtime.driver.utils # noqa: F841 - assert issubclass(triton.runtime.driver._obj.__class__, getattr(mod, "DriverBase")) diff --git a/python/test/unit/runtime/test_launch.py b/python/test/unit/runtime/test_launch.py deleted file mode 100644 index fc79ab67aac6..000000000000 --- a/python/test/unit/runtime/test_launch.py +++ /dev/null @@ -1,106 +0,0 @@ -import gc -# import importlib -# import os -# import sys -# import tempfile -# import textwrap -# import time -import tracemalloc - -import torch - -import triton -import triton.language as tl - -# from typing import Tuple - - -def test_memory_leak() -> None: - - @triton.jit - def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): - xnumel = 10 - xoffset = tl.program_id(0) * XBLOCK - xindex = xoffset + tl.arange(0, XBLOCK)[:] - xmask = xindex < xnumel - x0 = xindex - tmp0 = tl.load(in_ptr0 + (x0), xmask) - tl.store(out_ptr0 + (x0 + tl.zeros([XBLOCK], tl.int32)), tmp0, xmask) - - tracemalloc.start() - try: - inp = torch.randn(10, device='cuda') - out = torch.randn(10, device='cuda') - kernel[(10,)](inp, out, 10, XBLOCK=16) - gc.collect() - begin, _ = tracemalloc.get_traced_memory() - for _ in range(100): - kernel[(10,)](inp, out, 10, XBLOCK=16) - gc.collect() - end, _ = tracemalloc.get_traced_memory() - assert end - begin < 5000 - finally: - tracemalloc.stop() - - -# LATENCY_THRESHOLD_US = 46 - -# def test_kernel_launch_latency() -> None: -# def define_kernel(kernel_name: str, num_tensor_args: int) -> str: -# arg_str = ",".join([f"arg{i}: torch.Tensor" for i in range(num_tensor_args)]) -# arg_str += ", n_elements: int, BLOCK_SIZE: tl.constexpr" -# func_str = f""" -# import torch - -# import triton -# import triton.language as tl - -# @triton.jit -# def {kernel_name}({arg_str}): -# pass -# """ -# with tempfile.NamedTemporaryFile(mode="w+t", suffix=".py", delete=False) as temp_file: -# temp_file.write(textwrap.dedent(func_str)) -# temp_file_path = temp_file.name - -# return temp_file_path - -# def import_kernel(file_path, kernel_name): -# directory, filename = os.path.split(file_path) -# module_name, _ = os.path.splitext(filename) -# sys.path.insert(0, directory) - -# module = importlib.import_module(module_name) -# kernel = getattr(module, kernel_name) -# return kernel - -# def empty(*kernel_args: Tuple[torch.Tensor]): -# first_arg = kernel_args[0] -# n_elements = first_arg.numel() -# grid = (triton.cdiv(n_elements, 1024),) -# device = torch.cuda.current_device() -# # Warmup -# empty_kernel[grid](*kernel_args, n_elements, BLOCK_SIZE=1024, device=device) -# torch.cuda.synchronize() -# # Measure launch overhead at steady state -# num_runs = 1000 -# start_time = time.time() -# for i in range(num_runs): -# empty_kernel[grid](*kernel_args, n_elements, BLOCK_SIZE=1024, device=device) -# end_time = time.time() -# latency_us = (end_time - start_time) / num_runs * 1e6 - -# assert latency_us < LATENCY_THRESHOLD_US, "Kernel launch time has increased!" - -# num_tensor_args = 40 -# kernel_name = 'empty_kernel' -# file_path = define_kernel(kernel_name, num_tensor_args) -# empty_kernel = import_kernel(file_path, kernel_name) - -# # Initialize random tensors for the empty_kernel -# torch.manual_seed(0) -# size = 1024 -# kernel_args = (torch.rand(size, device='cuda') for i in range(num_tensor_args)) - -# # Run empty, which would run empty_kernel internally -# empty(*kernel_args) diff --git a/python/test/unit/runtime/test_subproc.py b/python/test/unit/runtime/test_subproc.py deleted file mode 100644 index 0e0d33c6fd21..000000000000 --- a/python/test/unit/runtime/test_subproc.py +++ /dev/null @@ -1,83 +0,0 @@ -import multiprocessing -import os -import shutil -from collections import namedtuple - -import torch - -import triton -import triton.language as tl - -tmpdir = ".tmp" - - -def reset_tmp_dir(): - os.environ["TRITON_CACHE_DIR"] = tmpdir - if os.path.exists(tmpdir): - shutil.rmtree(tmpdir) - - -instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"]) - - -def compile_fn(config, cc): - @triton.jit - def kernel_sub(a, b, o, N: tl.constexpr): - idx = tl.arange(0, N) - tl.store(o + idx, tl.load(a + idx) - tl.load(b + idx) * 777) - triton.compile( - fn=kernel_sub, - signature={0: "*fp32", 1: "*fp32", 2: "*fp32"}, - device=0, - constants={3: 32}, - configs=[config], - warm_cache_only=True, - cc=cc, - ) - - -def test_compile_in_subproc() -> None: - major, minor = torch.cuda.get_device_capability(0) - cc = major * 10 + minor - config = instance_descriptor(tuple(range(4)), ()) - - multiprocessing.set_start_method('fork') - proc = multiprocessing.Process( - target=compile_fn, - args=(config, cc)) - proc.start() - proc.join() - assert proc.exitcode == 0 - - -def compile_fn_dot(config, cc): - @triton.jit - def kernel_dot(Z): - offs = tl.arange(0, 16)[:, None] * 16 + tl.arange(0, 16)[None, :] - z = tl.load(Z + offs) - z = tl.dot(z, z) - tl.store(Z + offs, z) - - triton.compile( - fn=kernel_dot, - signature={0: "*fp32"}, - device=0, - configs=[config], - warm_cache_only=True, - cc=cc, - ) - - -def test_compile_in_forked_subproc() -> None: - reset_tmp_dir() - major, minor = torch.cuda.get_device_capability(0) - cc = major * 10 + minor - config = instance_descriptor(tuple(range(1)), ()) - - assert multiprocessing.get_start_method() == 'fork' - proc = multiprocessing.Process( - target=compile_fn_dot, - args=(config, cc)) - proc.start() - proc.join() - assert proc.exitcode == 0 diff --git a/python/test/unit/tools/test_aot.py b/python/test/unit/tools/test_aot.py deleted file mode 100644 index 53879052a548..000000000000 --- a/python/test/unit/tools/test_aot.py +++ /dev/null @@ -1,205 +0,0 @@ -import glob -import os -import subprocess -import sys -import tempfile - -import numpy as np - -import triton -from triton.common import cuda_include_dir, libcuda_dirs - -kernel_utils_src = """ -import triton - -@triton.jit -def mul(x, y): - return x * y -""" - -kernel_src = """ -import triton -import triton.language as tl -import kernel_utils - -@triton.jit -def kernel(C, A, B, - stride_cm, stride_cn, - stride_am, stride_ak, - stride_bk, stride_bn, - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, - BLOCK_K: tl.constexpr): - ms = tl.arange(0, BLOCK_M) - ns = tl.arange(0, BLOCK_N) - ks = tl.arange(0, BLOCK_K) - a = tl.load(A + ms[:, None] * stride_am + ks[None, :] * stride_ak) - b = tl.load(B + ks[:, None] * stride_bk + ns[None, :] * stride_bn) - c = tl.dot(a, b) - c = kernel_utils.mul(c, c) - tl.store(C + ms[:, None] * stride_cm + ns[None, :] * stride_cn, c) -""" - -test_src = """ -#include -#include -#include -#include -#include "kernel.h" - -static void write_buffer_to_csv(char *filename, int32_t *buffer, int size) { - FILE *file = fopen(filename, "w"); - if (file == NULL) { - printf(\"Could not open file %s\\n\", filename); - return; - } - for (int i = 0; i < size; i++) { - fprintf(file, "%d", buffer[i]); - if (i < size - 1) { - fprintf(file, ","); - } - } - fclose(file); -} - -static void read_csv_to_buffer(char *filename, int16_t *buffer, int size) { - FILE *file = fopen(filename, "r"); - if (file == NULL) { - printf(\"Could not open file %s\\n\", filename); - return; - } - int index = 0; - while (fscanf(file, "%hd,", &buffer[index]) != EOF && index < size) { - index++; - } - fclose(file); -} - -int main(int argc, char **argv) { - int M = 16, N = 16, K = 16; - int BM = 16, BN = 16, BK = 16; - - // initialize CUDA handles - CUdevice dev; - CUcontext ctx; - CUstream stream; - CUdeviceptr A, B, C; - CUresult err = 0; - cuInit(0); - cuDeviceGet(&dev, 0); - cuCtxCreate(&ctx, 0, dev); - cuMemAlloc(&A, M * K * 2); - cuMemAlloc(&B, K * N * 2); - cuMemAlloc(&C, M * N * 4); - cuStreamCreate(&stream, 0); - load_kernel(); - - // initialize input data - int16_t hA[M*K]; - int16_t hB[K*N]; - memset(hA, 0, M*K*2); - memset(hB, 0, K*N*2); - read_csv_to_buffer(argv[1], hA, M*K); - read_csv_to_buffer(argv[2], hB, K*N); - cuMemcpyHtoD(A, hA, M*K*2); - cuMemcpyHtoD(B, hB, K*N*2); - - // launch kernel - int numWarps = 1; - int gX = 1, gY = 1, gZ = 1; - cuStreamSynchronize(stream); - kernel(stream, M/BM, N/BN, 1, numWarps, C, A, B, N, K, N); - cuStreamSynchronize(stream); - - // read data - int32_t hC[M*N]; - memset(hC, 0, M*N*4); - cuMemcpyDtoH(hC, C, M*N*4); - write_buffer_to_csv(argv[3], hC, M*N); - - - // free cuda handles - unload_kernel(); - cuMemFree(A); - cuMemFree(B); - cuMemFree(C); - cuCtxDestroy(ctx); -} -""" - - -def test_compile_link_matmul(): - np.random.seed(3) - - with tempfile.TemporaryDirectory() as tmp_dir: - kernel_path = os.path.join(tmp_dir, "kernel.py") - with open(kernel_path, "w") as file: - file.write(kernel_src) - - kernel_utils_path = os.path.join(tmp_dir, "kernel_utils.py") - with open(kernel_utils_path, "w") as file: - file.write(kernel_utils_src) - - compiler_path = os.path.join(triton.tools.__path__[0], "compile.py") - linker_path = os.path.join(triton.tools.__path__[0], "link.py") - - dtype = "fp16" - M, N, K = 16, 16, 16 - BM, BN, BK = 16, 16, 16 - - # compile all desired configs - hints = [":16", ""] - for ha in hints: - for hb in hints: - sig = f'*fp32:16, *{dtype}:16, *{dtype}:16, i32{ha}, 1, i32{hb}, 1, i32:16, 1, {BM}, {BN}, {BK}' - name = f"matmul_{dtype}x{dtype}_{BM}x{BN}x{BK}" - subprocess.run([sys.executable, compiler_path, "-n", "kernel", "--signature", sig, "--out-name", name, "-o", name, kernel_path], check=True, cwd=tmp_dir) - - # link all desired configs - h_files = glob.glob(os.path.join(tmp_dir, "*.h")) - subprocess.run([sys.executable, linker_path] + h_files + ["-o", "kernel"], check=True, cwd=tmp_dir) - - # compile test case - with open(os.path.join(tmp_dir, "test.c"), "w") as file: - file.write(test_src) - c_files = glob.glob(os.path.join(tmp_dir, "*.c")) - subprocess.run(["gcc"] + c_files + ["-I", cuda_include_dir(), - "-L", libcuda_dirs()[0], - "-l", "cuda", - "-o", "test"], check=True, cwd=tmp_dir) - - # initialize test data - a = np.random.randn(M * K).astype(np.float16).reshape((M, K)) - b = np.random.randn(M * K).astype(np.float16).reshape((K, N)) - a_path = os.path.join(tmp_dir, "a.csv") - b_path = os.path.join(tmp_dir, "b.csv") - c_path = os.path.join(tmp_dir, "c.csv") - for x, path in [(a, a_path), (b, b_path)]: - x.view(np.int16).ravel().tofile(path, sep=",") - - # run test case - subprocess.run(["./test", a_path, b_path, c_path], check=True, cwd=tmp_dir) - - # read data and compare against reference - c = np.genfromtxt(c_path, delimiter=",", dtype=np.int32) - c_tri = c.reshape((M, N)).view(np.float32) - c_ref = np.matmul(a.astype(np.float32), b.astype(np.float32)) - np.testing.assert_allclose(c_tri, c_ref * c_ref, atol=1e-4, rtol=0.) - - -def test_ttgir_to_ptx(): - src = """ -module attributes {"triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 32 : i32} { - tt.func public @sum_kernel_0d1d(%arg0: !tt.ptr, %arg1: !tt.ptr) { - tt.return - } -} -""" - with tempfile.TemporaryDirectory() as tmp_dir: - kernel_path = os.path.join(tmp_dir, "empty_kernel.ttgir") - with open(kernel_path, "w") as fp: - fp.write(src) - k = triton.compile(kernel_path, cc=80) - ptx = k.asm["ptx"] - assert ".target sm_80" in ptx - assert ".address_size 64" in ptx diff --git a/python/triton/_C/include b/python/triton/_C/include deleted file mode 120000 index b85a409837d1..000000000000 --- a/python/triton/_C/include +++ /dev/null @@ -1 +0,0 @@ -../../../include/ \ No newline at end of file diff --git a/python/triton/__init__.py b/python/triton/__init__.py deleted file mode 100644 index aca365dad396..000000000000 --- a/python/triton/__init__.py +++ /dev/null @@ -1,68 +0,0 @@ -"""isort:skip_file""" -__version__ = '2.1.0' - -# --------------------------------------- -# Note: import order is significant here. - -# submodules -from .runtime import ( - autotune, - Config, - heuristics, - JITFunction, - KernelInterface, - reinterpret, - TensorWrapper, - OutOfResources, - MockTensor, -) -from .runtime.jit import jit -from .compiler import compile, CompilationError - -from . import language -from . import testing - -__all__ = [ - "autotune", - "cdiv", - "CompilationError", - "compile", - "Config", - "heuristics", - "impl", - "jit", - "JITFunction", - "KernelInterface", - "language", - "MockTensor", - "next_power_of_2", - "ops", - "OutOfResources", - "reinterpret", - "runtime", - "TensorWrapper", - "testing", - "tools", -] - - -# ------------------------------------- -# misc. utilities that don't fit well -# into any specific module -# ------------------------------------- - -def cdiv(x: int, y: int): - return (x + y - 1) // y - - -def next_power_of_2(n: int): - """Return the smallest power of 2 greater than or equal to n""" - n -= 1 - n |= n >> 1 - n |= n >> 2 - n |= n >> 4 - n |= n >> 8 - n |= n >> 16 - n |= n >> 32 - n += 1 - return n diff --git a/python/triton/common/__init__.py b/python/triton/common/__init__.py deleted file mode 100644 index dfb6f8870e48..000000000000 --- a/python/triton/common/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .build import _build, cuda_include_dir, libcuda_dirs - -__all__ = ["_build", "libcuda_dirs", "cuda_include_dir"] diff --git a/python/triton/common/backend.py b/python/triton/common/backend.py deleted file mode 100644 index 3f9bcb477f5a..000000000000 --- a/python/triton/common/backend.py +++ /dev/null @@ -1,96 +0,0 @@ - -import importlib -import importlib.util -from typing import Dict - -from ..runtime.driver import DriverBase - - -class BaseBackend: - def __init__(self, device_type: str) -> None: - self.device_type = device_type - - def add_stages(self, arch, extern_libs, stages): - """ - Custom the arch, extern_libs and stages per backend specific requirement - """ - raise NotImplementedError - - def add_meta_info(self, ir, cur_module, next_module, metadata, asm): - """ - Custom the ir, module, metadata and asm per backend specific requirement - """ - raise NotImplementedError - - def get_load_binary_fn(self): - """ - Return a callable to load binary - """ - raise NotImplementedError - - def get_driver(self) -> DriverBase: - """ - Get the backend driver. Please refer to "DriverBase" for more details - """ - raise NotImplementedError - - def get_stream(self): - """ - Get stream for current device - """ - raise NotImplementedError - - def get_device_properties(self, device): - raise NotImplementedError - - def get_current_device(self): - """ - Get current device - """ - raise NotImplementedError - - def set_current_device(self, device): - """ - Set current device as the given device - """ - raise NotImplementedError - - def get_kernel_bin(self): - raise NotImplementedError - - def make_launcher_stub(self, name, signature, constants): - """ - Generate the launcher stub to launch the kernel - """ - raise NotImplementedError - - def get_architecture_descriptor(self, **kwargs): - """ - Get the architecture descriptor the backend - """ - raise NotImplementedError - - @classmethod - def create_backend(cls, device_type: str): - return cls(device_type) - - -_backends: Dict[str, BaseBackend] = {} - - -def register_backend(device_type: str, backend_cls: type): - if device_type not in _backends: - _backends[device_type] = backend_cls.create_backend(device_type) - - -def get_backend(device_type: str): - if device_type not in _backends: - device_backend_package_name = f"...third_party.{device_type}" - if importlib.util.find_spec(device_backend_package_name, package=__spec__.name): - try: - importlib.import_module(device_backend_package_name, package=__spec__.name) - except Exception: - return None - else: - return None - return _backends[device_type] if device_type in _backends else None diff --git a/python/triton/common/build.py b/python/triton/common/build.py deleted file mode 100644 index a3114adef162..000000000000 --- a/python/triton/common/build.py +++ /dev/null @@ -1,115 +0,0 @@ -import contextlib -import functools -import io -import os -import shutil -import subprocess -import sys -import sysconfig - -import setuptools - - -# TODO: is_hip shouldn't be here -def is_hip(): - import torch - return torch.version.hip is not None - - -@functools.lru_cache() -def libcuda_dirs(): - locs = subprocess.check_output(["whereis", "libcuda.so"]).decode().strip().split()[1:] - return [os.path.dirname(loc) for loc in locs] - - -@functools.lru_cache() -def rocm_path_dir(): - return os.getenv("ROCM_PATH", default="/opt/rocm") - - -@contextlib.contextmanager -def quiet(): - old_stdout, old_stderr = sys.stdout, sys.stderr - sys.stdout, sys.stderr = io.StringIO(), io.StringIO() - try: - yield - finally: - sys.stdout, sys.stderr = old_stdout, old_stderr - - -@functools.lru_cache() -def cuda_include_dir(): - base_dir = os.path.join(os.path.dirname(__file__), os.path.pardir) - cuda_path = os.path.join(base_dir, "third_party", "cuda") - return os.path.join(cuda_path, "include") - - -def _build(name, src, srcdir): - if is_hip(): - hip_lib_dir = os.path.join(rocm_path_dir(), "lib") - hip_include_dir = os.path.join(rocm_path_dir(), "include") - else: - cuda_lib_dirs = libcuda_dirs() - cu_include_dir = cuda_include_dir() - suffix = sysconfig.get_config_var('EXT_SUFFIX') - so = os.path.join(srcdir, '{name}{suffix}'.format(name=name, suffix=suffix)) - # try to avoid setuptools if possible - cc = os.environ.get("CC") - if cc is None: - # TODO: support more things here. - clang = shutil.which("clang") - gcc = shutil.which("gcc") - cc = gcc if gcc is not None else clang - if cc is None: - raise RuntimeError("Failed to find C compiler. Please specify via CC environment variable.") - # This function was renamed and made public in Python 3.10 - if hasattr(sysconfig, 'get_default_scheme'): - scheme = sysconfig.get_default_scheme() - else: - scheme = sysconfig._get_default_scheme() - # 'posix_local' is a custom scheme on Debian. However, starting Python 3.10, the default install - # path changes to include 'local'. This change is required to use triton with system-wide python. - if scheme == 'posix_local': - scheme = 'posix_prefix' - py_include_dir = sysconfig.get_paths(scheme=scheme)["include"] - - if is_hip(): - ret = subprocess.check_call([cc, src, f"-I{hip_include_dir}", f"-I{py_include_dir}", f"-I{srcdir}", "-shared", "-fPIC", f"-L{hip_lib_dir}", "-lamdhip64", "-o", so]) - else: - cc_cmd = [cc, src, "-O3", f"-I{cu_include_dir}", f"-I{py_include_dir}", f"-I{srcdir}", "-shared", "-fPIC", "-lcuda", "-o", so] - cc_cmd += [f"-L{dir}" for dir in cuda_lib_dirs] - ret = subprocess.check_call(cc_cmd) - - if ret == 0: - return so - # fallback on setuptools - extra_compile_args = [] - library_dirs = cuda_lib_dirs - include_dirs = [srcdir, cu_include_dir] - libraries = ['cuda'] - # extra arguments - extra_link_args = [] - # create extension module - ext = setuptools.Extension( - name=name, - language='c', - sources=[src], - include_dirs=include_dirs, - extra_compile_args=extra_compile_args + ['-O3'], - extra_link_args=extra_link_args, - library_dirs=library_dirs, - libraries=libraries, - ) - # build extension module - args = ['build_ext'] - args.append('--build-temp=' + srcdir) - args.append('--build-lib=' + srcdir) - args.append('-q') - args = dict( - name=name, - ext_modules=[ext], - script_args=args, - ) - with quiet(): - setuptools.setup(**args) - return so diff --git a/python/triton/compiler/__init__.py b/python/triton/compiler/__init__.py deleted file mode 100644 index 9593deafbdb9..000000000000 --- a/python/triton/compiler/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .compiler import CompiledKernel, compile, instance_descriptor -from .errors import CompilationError - -__all__ = ["compile", "instance_descriptor", "CompiledKernel", "CompilationError"] diff --git a/python/triton/compiler/code_generator.py b/python/triton/compiler/code_generator.py deleted file mode 100644 index 651a811a0a73..000000000000 --- a/python/triton/compiler/code_generator.py +++ /dev/null @@ -1,1135 +0,0 @@ -import ast -import inspect -import re -import sys -import warnings -from typing import Any, Callable, Dict, Optional, Tuple, Type, Union - -from .. import language -from .._C.libtriton.triton import ir -from ..language import constexpr, tensor -# ideally we wouldn't need any runtime component -from ..runtime import JITFunction -from .errors import (CompilationError, CompileTimeAssertionFailure, - UnsupportedLanguageConstruct) - - -def mangle_ty(ty): - if ty.is_ptr(): - return 'P' + mangle_ty(ty.element_ty) - if ty.is_int(): - SIGNED = language.dtype.SIGNEDNESS.SIGNED - prefix = 'i' if ty.int_signedness == SIGNED else 'u' - return prefix + str(ty.int_bitwidth) - if ty.is_fp8(): - return 'fp8' - if ty.is_fp16(): - return 'fp16' - if ty.is_bf16(): - return 'bf16' - if ty.is_fp32(): - return 'fp32' - if ty.is_fp64(): - return 'fp64' - if ty.is_block(): - elt = mangle_ty(ty.scalar) - shape = '_'.join(map(str, ty.shape)) - return f'{elt}S{shape}S' - if ty.is_void(): - return 'V' - assert False, "Unsupported type" - - -def mangle_fn(name, arg_tys, constants): - # doesn't mangle ret type, which must be a function of arg tys - mangled_arg_names = '_'.join([mangle_ty(ty) for ty in arg_tys]) - mangled_constants = '_'.join([f'{i}c{repr(constants[i])}' for i in sorted(constants)]) - mangled_constants = mangled_constants.replace('.', '_d_') - mangled_constants = mangled_constants.replace("'", '_sq_') - # [ and ] are not allowed in LLVM identifiers - mangled_constants = mangled_constants.replace('[', '_').replace(']', '_') - ret = f'{name}__{mangled_arg_names}__{mangled_constants}' - return ret - - -def _is_triton_tensor(o: Any) -> bool: - return isinstance(o, tensor) - - -def _is_constexpr(o: Any) -> bool: - return isinstance(o, constexpr) - - -def _is_triton_scalar(o: Any) -> bool: - return _is_triton_tensor(o) and (not o.type.is_block() or o.type.numel == 1) - - -def _unwrap_if_constexpr(o: Any): - return o.value if isinstance(o, constexpr) else o - - -def _check_fn_args(node, fn, args): - if fn.noinline: - for idx, arg in enumerate(args): - if not _is_constexpr(arg) and not _is_triton_scalar(arg): - raise UnsupportedLanguageConstruct(fn.src, node, f'Function {fn.__name__} is marked noinline, but was called with non-scalar argument {fn.arg_names[idx]}:{arg}') - - -def _get_fn_file_line(fn): - base_fn = fn - while not isinstance(base_fn, JITFunction): - base_fn = base_fn.fn - file_name = base_fn.fn.__code__.co_filename - lines, begin_line = inspect.getsourcelines(base_fn.fn) - for line in lines: - if line.strip().startswith('@'): - begin_line += 1 - else: - break - return file_name, begin_line - - -_condition_types = {bool, int, type(None)} # Python types accepted for conditionals inside kernels - - -class enter_sub_region: - def __init__(self, generator): - self.generator = generator - - def __enter__(self): - # record lscope & local_defs in the parent scope - self.liveins = self.generator.lscope.copy() - self.prev_defs = self.generator.local_defs.copy() - self.generator.local_defs = {} - self.insert_block = self.generator.builder.get_insertion_block() - self.insert_point = self.generator.builder.get_insertion_point() - return self.liveins, self.insert_block - - def __exit__(self, *args, **kwargs): - self.generator.builder.restore_insertion_point(self.insert_point) - self.generator.lscope = self.liveins - self.generator.local_defs = self.prev_defs - - -# Check if the given syntax node has an "early" return -class ContainsReturnChecker(ast.NodeVisitor): - def __init__(self, gscope): - self.gscope = gscope - - def _visit_stmts(self, body) -> bool: - for s in body: - if self.visit(s): - return True - return False - - def _visit_function(self, fn) -> bool: - # Currently we only support JITFunctions defined in the global scope - if isinstance(fn, JITFunction) and not fn.noinline: - fn_node = fn.parse() - return ContainsReturnChecker(self.gscope).visit(fn_node) - return False - - def generic_visit(self, node) -> bool: - ret = False - for _, value in ast.iter_fields(node): - if isinstance(value, list): - for item in value: - if isinstance(item, ast.AST): - ret = ret or self.visit(item) - elif isinstance(value, ast.AST): - ret = ret or self.visit(value) - return ret - - def visit_Attribute(self, node: ast.Attribute) -> bool: - # If the left part is a name, it's possible that - # we call triton native function or a jit function from another module. - # If the left part is not a name, it must return a tensor or a constexpr - # whose methods do not contain return statements - # e.g., (tl.load(x)).to(y) - # So we only check if the expressions within value have return or not - if isinstance(node.value, ast.Name): - if node.value.id in self.gscope: - value = self.gscope[node.value.id] - fn = getattr(value, node.attr) - return self._visit_function(fn) - return False - return self.visit(node.value) - - def visit_Name(self, node: ast.Name) -> bool: - if type(node.ctx) == ast.Store: - return False - if node.id in self.gscope: - fn = self.gscope[node.id] - return self._visit_function(fn) - return False - - def visit_Return(self, node: ast.Return) -> bool: - return True - - def visit_Assign(self, node: ast.Assign) -> bool: - # There couldn't be an early return - # x = ... - return False - - def visit_AugAssign(self, node: ast.AugAssign) -> bool: - # There couldn't be an early return - # x += ... - return False - - def visit_Module(self, node: ast.Module) -> bool: - return self._visit_stmts(node.body) - - def visit_FunctionDef(self, node: ast.FunctionDef) -> bool: - return self._visit_stmts(node.body) - - def visit_If(self, node: ast.If) -> bool: - # TODO: optimize the following case in which we actually don't have - # a return when static_cond is false: - # if dynamic_cond - # if static_cond - # func_with_return - # else - # func_without_return - ret = self._visit_stmts(node.body) - if node.orelse: - ret = ret or self._visit_stmts(node.orelse) - return ret - - def visit_IfExp(self, node: ast.IfExp) -> bool: - return self.visit(node.body) or self.visit(node.orelse) - - def visit_Call(self, node: ast.Call) -> bool: - return self.visit(node.func) - - -class CodeGenerator(ast.NodeVisitor): - def __init__(self, context, prototype, gscope, attributes, constants, function_name, arch, - module=None, is_kernel=False, function_types: Optional[Dict] = None, - debug=False, noinline=False, file_name: Optional[str] = None, begin_line=0): - self.context = context - self.builder = ir.builder(context) - self.file_name = file_name - # node.lineno starts from 1, so we need to subtract 1 - self.begin_line = begin_line - 1 - self.builder.set_loc(file_name, begin_line, 0) - self.builder.arch = arch - self.module = self.builder.create_module() if module is None else module - self.function_ret_types = {} if function_types is None else function_types - self.prototype = prototype - self.gscope = gscope - self.lscope = dict() - self.attributes = attributes - self.constants = constants - self.function_name = function_name - self.is_kernel = is_kernel - self.last_node = None - self.debug = debug - self.noinline = noinline - self.scf_stack = [] - self.last_ret_type = None - # SSA-construction - # name => language.tensor - self.local_defs: Dict[str, tensor] = {} - self.global_uses: Dict[str, tensor] = {} - self.dereference_name: Callable[[str], Any] = self._define_name_lookup() - - builtin_namespace: Dict[str, Any] = {_.__name__: _ for _ in (range, float, int, isinstance, getattr)} - builtin_namespace.update(( - ('print', language.core.device_print), - ('min', language.minimum), - )) - - def _define_name_lookup(self): - def local_lookup(name: str, absent): - value = self.lscope.get(name, absent) # this needs to be re-fetched from `self` every time, because it gets switched occasionally - if value is not absent and name not in self.local_defs: - self.global_uses[name] = value - return value - - absent_marker = object() - - def name_lookup(name: str) -> Any: - absent = absent_marker - for lookup_function in local_lookup, self.gscope.get, self.builtin_namespace.get: - value = lookup_function(name, absent) - if value is not absent: - return value - raise NameError(f'{name} is not defined') - - return name_lookup - - def set_value(self, name: str, - value: Union[tensor, constexpr]) -> None: - ''' This function: - called by visit_Assign() & visit_FunctionDef() to store left value (lvalue) - 1. record local defined name (FIXME: should consider control flow) - 2. store tensor in self.lvalue - ''' - self.lscope[name] = value - self.local_defs[name] = value - - def _get_insertion_point_and_loc(self): - # XXX: this is a hack to get the location of the insertion point. - # The insertion point's location could be invalid sometimes, - # so we need to explicitly set the location - loc = self.builder.get_loc() - ip = self.builder.get_insertion_point() - return ip, loc - - def _set_insertion_point_and_loc(self, ip, loc): - self.builder.restore_insertion_point(ip) - self.builder.set_loc(loc) - - # - # AST visitor - # - def visit_compound_statement(self, stmts): - for stmt in stmts: - ret_type = self.visit(stmt) - if ret_type is not None and isinstance(stmt, ast.Return): - self.last_ret_type = ret_type - - def visit_Module(self, node): - ast.NodeVisitor.generic_visit(self, node) - - def visit_List(self, node): - ctx = self.visit(node.ctx) - assert ctx is None - elts = [self.visit(elt) for elt in node.elts] - return elts - - # By design, only non-kernel functions can return - def visit_Return(self, node): - ret_value = self.visit(node.value) - # ret_block = self.builder.create_block() - # post_ret_block = self.builder.create_block() - # self.builder.create_branch(ret_block) - # self.builder.set_insertion_point_to_end(ret_block) - if ret_value is None: - self.builder.ret([]) - ret_ty = None - elif isinstance(ret_value, tuple): - ret_values = [language.core._to_tensor(v, self.builder) for v in ret_value] - ret_types = [v.type for v in ret_values] - self.builder.ret([v.handle for v in ret_values]) - ret_ty = tuple(ret_types) - else: - ret = language.core._to_tensor(ret_value, self.builder) - self.builder.ret([ret.handle]) - ret_ty = ret.type - # self.builder.create_branch(post_ret_block) - # self.builder.set_insertion_point_to_end(post_ret_block) - return ret_ty - - def visit_FunctionDef(self, node): - arg_names, kwarg_names = self.visit(node.args) - # initialize defaults - for i, default_value in enumerate(node.args.defaults): - arg_node = node.args.args[-i - 1] - annotation = arg_node.annotation - name = arg_node.arg - st_target = ast.Name(id=name, ctx=ast.Store()) - if annotation is None: - init_node = ast.Assign(targets=[st_target], value=default_value) - else: - init_node = ast.AnnAssign(target=st_target, value=default_value, annotation=annotation) - self.visit(init_node) - # initialize function - visibility = "public" if self.is_kernel else "private" - fn = self.builder.get_or_insert_function(self.module, self.function_name, self.prototype.to_ir(self.builder), visibility, self.noinline) - self.module.push_back(fn) - entry = fn.add_entry_block() - arg_values = [] - idx = 0 - for i, arg_name in enumerate(arg_names): - if i in self.constants: - cst = self.constants[i] - if not _is_constexpr(cst): - cst = constexpr(self.constants[i]) - arg_values.append(cst) - continue - else: - if i in self.attributes: - fn.set_arg_attr(idx, "tt.divisibility", self.attributes[i][1]) - arg_values.append(tensor(fn.args(idx), self.prototype.param_types[idx])) - idx += 1 - - insert_pt = self.builder.get_insertion_block() - for arg_name, arg_value in zip(arg_names, arg_values): - self.set_value(arg_name, arg_value) - self.builder.set_insertion_point_to_start(entry) - # visit function body - self.visit_compound_statement(node.body) - # finalize function - if self.last_ret_type is None: - self.builder.ret([]) - else: - # update return type - if isinstance(self.last_ret_type, tuple): - self.prototype.ret_types = list(self.last_ret_type) - fn.reset_type(self.prototype.to_ir(self.builder)) - else: - self.prototype.ret_types = [self.last_ret_type] - fn.reset_type(self.prototype.to_ir(self.builder)) - if insert_pt: - self.builder.set_insertion_point_to_end(insert_pt) - # Remove dead code - fn.finalize() - - def visit_arguments(self, node): - arg_names = [] - for arg in node.args: - arg_names += [self.visit(arg)] - kwarg_names = self.visit(node.kwarg) - return arg_names, kwarg_names - - def visit_arg(self, node): - ast.NodeVisitor.generic_visit(self, node) - return node.arg - - def visit_AnnAssign(self, node): - # extract attributes - annotation = self.visit(node.annotation) - target = self.visit(node.target) - value = self.visit(node.value) - # constexpr - if annotation == constexpr: - if target in self.lscope: - raise ValueError(f'{target} is already defined.' - f' constexpr cannot be reassigned.') - if not _is_constexpr(value): - value = constexpr(value) - self.lscope[target] = value - return self.lscope[target] - # default: call visit_Assign - return self.visit_Assign(node) - - def visit_Assign(self, node): - _names = [] - for target in node.targets: - _names += [self.visit(target)] - if len(_names) > 1: - raise UnsupportedLanguageConstruct(None, node, "simultaneous multiple assignment is not supported.") - names = _names[0] - values = self.visit(node.value) - if not isinstance(names, tuple): - names = [names] - if not isinstance(values, tuple): - values = [values] - native_nontensor_types = (language.dtype, ) - for name, value in zip(names, values): - # by default, constexpr are assigned into python variable - value = _unwrap_if_constexpr(value) - if value is not None and \ - not _is_triton_tensor(value) and \ - not isinstance(value, native_nontensor_types): - value = language.core._to_tensor(value, self.builder) - self.set_value(name, value) - - def visit_AugAssign(self, node): - name = node.target.id - lhs = ast.Name(id=name, ctx=ast.Load()) - rhs = ast.BinOp(lhs, node.op, node.value) - assign = ast.Assign(targets=[node.target], value=rhs) - self.visit(assign) - return self.dereference_name(name) - - def visit_Name(self, node): - if type(node.ctx) == ast.Store: - return node.id - return self.dereference_name(node.id) - - def visit_Store(self, node): - ast.NodeVisitor.generic_visit(self, node) - - def visit_Load(self, node): - ast.NodeVisitor.generic_visit(self, node) - - def visit_Tuple(self, node): - args = [self.visit(x) for x in node.elts] - return tuple(args) - - def _apply_binary_method(self, method_name, lhs, rhs): - # TODO: raise something meaningful if getattr fails below, esp for reverse method - if _is_triton_tensor(lhs): - return getattr(lhs, method_name)(rhs, _builder=self.builder) - if _is_triton_tensor(rhs): - reverse_method_name = re.sub(r"__(.*)__", r"__r\1__", method_name) - return getattr(rhs, reverse_method_name)(lhs, _builder=self.builder) - return getattr(lhs, method_name)(rhs) - - def visit_BinOp(self, node): - lhs = self.visit(node.left) - rhs = self.visit(node.right) - method_name = self._method_name_for_bin_op.get(type(node.op)) - if method_name is None: - raise UnsupportedLanguageConstruct(None, node, "AST binary operator '{}' is not (currently) implemented.".format(node.op.__name__)) - return self._apply_binary_method(method_name, lhs, rhs) - _method_name_for_bin_op: Dict[Type[ast.operator], str] = { - ast.Add: '__add__', ast.Sub: '__sub__', ast.Mult: '__mul__', ast.Div: '__truediv__', - ast.FloorDiv: '__floordiv__', ast.Mod: '__mod__', ast.Pow: '__pow__', - ast.LShift: '__lshift__', ast.RShift: '__rshift__', ast.BitAnd: '__and__', ast.BitOr: '__or__', ast.BitXor: '__xor__', - } - - def visit_then_else_blocks(self, node, liveins, then_block, else_block): - # then block - self.builder.set_insertion_point_to_start(then_block) - self.visit_compound_statement(node.body) - then_block = self.builder.get_insertion_block() - then_defs = self.local_defs.copy() - # else block - else_defs = {} - if node.orelse: - self.builder.set_insertion_point_to_start(else_block) - self.lscope = liveins.copy() - self.local_defs = {} - self.visit_compound_statement(node.orelse) - else_defs = self.local_defs.copy() - else_block = self.builder.get_insertion_block() - - # update block arguments - names = [] - ret_types = [] - ir_ret_types = [] - # variables in livein whose value is updated in `if` - for name in liveins: - # check type - for defs, block_name in [(then_defs, 'then'), (else_defs, 'else')]: - if name in defs: - assert defs[name].type == liveins[name].type,\ - f'initial value for `{name}` is of type {liveins[name].type}, '\ - f'but the {block_name} block redefines it as {defs[name].type}' - if name in then_defs or name in else_defs: - names.append(name) - ret_types.append(then_defs[name].type if name in then_defs else else_defs[name].type) - ir_ret_types.append(then_defs[name].handle.get_type() if name in then_defs else else_defs[name].handle.get_type()) - # variable defined in then but not in else - if name in then_defs and name not in else_defs: - else_defs[name] = liveins[name] - # variable defined in else but not in then - if name in else_defs and name not in then_defs: - then_defs[name] = liveins[name] - # variables that are both in then and else but not in liveins - # TODO: could probably be cleaned up - for name in then_defs.keys() & else_defs.keys(): - if name in names: - continue - then_ty = then_defs[name].type - else_ty = else_defs[name].type - assert then_ty == else_ty,\ - f'mismatched type for {name} between then block ({then_ty}) '\ - f'and else block ({else_ty})' - names.append(name) - ret_types.append(then_ty) - ir_ret_types.append(then_defs[name].handle.get_type()) - - return then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types - - def visit_if_top_level(self, cond, node): - has_endif_block = True - with enter_sub_region(self) as sr: - liveins, ip_block = sr - then_block = self.builder.create_block() - else_block = self.builder.create_block() - # create basic-block after conditional - endif_block = self.builder.create_block() - # create branch - self.builder.set_insertion_point_to_end(ip_block) - self.builder.create_cond_branch(cond.handle, then_block, else_block) - # visit then and else blocks - then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types = \ - self.visit_then_else_blocks(node, liveins, then_block, else_block) - # then terminator - self.builder.set_insertion_point_to_end(then_block) - if then_block.has_return() and else_block.has_return(): - has_endif_block = False - endif_block.erase() - if not then_block.has_terminator() and has_endif_block: - self.builder.create_branch(endif_block, [then_defs[n].handle for n in names]) - # else terminator - self.builder.set_insertion_point_to_end(else_block) - if not else_block.has_terminator() and has_endif_block: - self.builder.create_branch(endif_block, [else_defs[n].handle for n in names]) - if has_endif_block: - for ty in ir_ret_types: - endif_block.add_argument(ty) - if has_endif_block: - # change block - self.builder.set_insertion_point_to_start(endif_block) - # update value - for i, name in enumerate(names): - new_tensor = language.core.tensor(endif_block.arg(i), ret_types[i]) - self.set_value(name, new_tensor) - - # TODO: refactor - def visit_if_scf(self, cond, node): - with enter_sub_region(self) as sr: - liveins, _ = sr - ip, last_loc = self._get_insertion_point_and_loc() - then_block = self.builder.create_block() - else_block = self.builder.create_block() if node.orelse else None - then_defs, else_defs, then_block, else_block, names, ret_types, _ = \ - self.visit_then_else_blocks(node, liveins, then_block, else_block) - # create if op - self._set_insertion_point_and_loc(ip, last_loc) - if_op = self.builder.create_if_op([ty.to_ir(self.builder) for ty in ret_types], cond.handle, True) - then_block.merge_block_before(if_op.get_then_block()) - self.builder.set_insertion_point_to_end(if_op.get_then_block()) - if len(names) > 0: - self.builder.create_yield_op([then_defs[n].handle for n in names]) - if not node.orelse: - else_block = if_op.get_else_block() - else: - else_block.merge_block_before(if_op.get_else_block()) - self.builder.set_insertion_point_to_end(if_op.get_else_block()) - if len(names) > 0: - self.builder.create_yield_op([else_defs[n].handle for n in names]) - # update values - for i, name in enumerate(names): - new_tensor = language.core.tensor(if_op.get_result(i), ret_types[i]) - self.set_value(name, new_tensor) - - def visit_If(self, node): - cond = self.visit(node.test) - if _is_triton_tensor(cond): - cond = cond.to(language.int1, _builder=self.builder) - contains_return = ContainsReturnChecker(self.gscope).visit(node) - if self.scf_stack and contains_return: - raise UnsupportedLanguageConstruct( - None, node, - "Cannot have `return` statements inside `while` or `for` statements in triton " - "(note that this also applies to `return` statements that are inside functions " - "transitively called from within `while`/`for` statements)") - elif self.scf_stack or not contains_return: - self.visit_if_scf(cond, node) - else: - self.visit_if_top_level(cond, node) - else: - cond = _unwrap_if_constexpr(cond) - if type(cond) not in _condition_types: # not isinstance - we insist the real thing, no subclasses and no ducks - raise UnsupportedLanguageConstruct( - None, node, "`if` conditionals can only accept values of type {{{}}}, not objects of type {}".format( - ', '.join(_.__name__ for _ in _condition_types), type(cond).__name__)) - if cond: - self.visit_compound_statement(node.body) - else: - self.visit_compound_statement(node.orelse) - - def visit_IfExp(self, node): - cond = self.visit(node.test) - if _is_triton_tensor(cond): - cond = cond.to(language.int1, _builder=self.builder) - if _unwrap_if_constexpr(cond): - return self.visit(node.body) - else: - return self.visit(node.orelse) - - def visit_Pass(self, node): - pass - - def visit_Compare(self, node): - if not (len(node.comparators) == 1 and len(node.ops) == 1): - raise UnsupportedLanguageConstruct(None, node, "simultaneous multiple comparison is not supported") - lhs = self.visit(node.left) - rhs = self.visit(node.comparators[0]) - lhs_value = _unwrap_if_constexpr(lhs) - rhs_value = _unwrap_if_constexpr(rhs) - if type(node.ops[0]) == ast.Is: - return constexpr(lhs_value is rhs_value) - if type(node.ops[0]) == ast.IsNot: - return constexpr(lhs_value is not rhs_value) - method_name = self._method_name_for_comp_op.get(type(node.ops[0])) - if method_name is None: - raise UnsupportedLanguageConstruct(None, node, "AST comparison operator '{}' is not (currently) implemented.".format(node.ops[0].__name__)) - return self._apply_binary_method(method_name, lhs, rhs) - _method_name_for_comp_op: Dict[Type[ast.cmpop], str] = { - ast.Eq: '__eq__', ast.NotEq: '__ne__', ast.Lt: '__lt__', ast.LtE: '__le__', ast.Gt: '__gt__', ast.GtE: '__ge__' - } - - def visit_UnaryOp(self, node): - op = self.visit(node.operand) - fn = self._method_name_for_unary_op.get(type(node.op)) - if fn is None: - raise UnsupportedLanguageConstruct(None, node, "AST unary operator '{}' is not (currently) implemented.".format(node.op.__name__)) - if _is_triton_tensor(op): - return getattr(op, fn)(_builder=self.builder) - return getattr(op, fn)() - _method_name_for_unary_op: Dict[Type[ast.unaryop], str] = {ast.USub: '__neg__', ast.UAdd: '__pos__', ast.Not: '__not__', ast.Invert: '__invert__'} - - def visit_While(self, node): - with enter_sub_region(self) as sr: - liveins, insert_block = sr - - # loop body (the after region) - # loop_block = self.builder.create_block() - dummy = self.builder.create_block() - self.builder.set_insertion_point_to_start(dummy) - self.scf_stack.append(node) - self.visit_compound_statement(node.body) - self.scf_stack.pop() - loop_defs = self.local_defs - - # collect loop-carried values - names = [] - ret_types = [] - init_args = [] - for name in loop_defs: - if name in liveins: - # We should not def new constexpr - assert _is_triton_tensor(loop_defs[name]) - assert _is_triton_tensor(liveins[name]) - assert loop_defs[name].type == liveins[name].type - # these are loop-carried values - names.append(name) - ret_types.append(loop_defs[name].type) - init_args.append(liveins[name]) - - self.builder.set_insertion_point_to_end(insert_block) - while_op = self.builder.create_while_op([ty.to_ir(self.builder) for ty in ret_types], - [arg.handle for arg in init_args]) - # merge the condition region - before_block = self.builder.create_block_with_parent(while_op.get_before(), - [ty.to_ir(self.builder) for ty in ret_types]) - self.builder.set_insertion_point_to_start(before_block) - for i, name in enumerate(names): - self.lscope[name] = language.core.tensor(before_block.arg(i), ret_types[i]) - self.local_defs[name] = self.lscope[name] - cond = self.visit(node.test) - self.builder.set_insertion_point_to_end(before_block) - # create ConditionOp: e.g., scf.condition(%cond) %arg0, %arg1, ... - self.builder.create_condition_op(cond.handle, [before_block.arg(i) for i in range(len(init_args))]) - # merge the loop body - after_block = self.builder.create_block_with_parent(while_op.get_after(), - [ty.to_ir(self.builder) for ty in ret_types]) - - # generate loop body - self.builder.set_insertion_point_to_start(after_block) - for i, name in enumerate(names): - self.lscope[name] = language.core.tensor(after_block.arg(i), ret_types[i]) - self.local_defs[name] = self.lscope[name] - self.scf_stack.append(node) - self.visit_compound_statement(node.body) - self.scf_stack.pop() - loop_defs = self.local_defs - yields = [] - for name in loop_defs: - if name in liveins: - yields.append(loop_defs[name]) - self.builder.create_yield_op([y.handle for y in yields]) - - # WhileOp defines new values, update the symbol table (lscope, local_defs) - for i, name in enumerate(names): - new_def = language.core.tensor(while_op.get_result(i), ret_types[i]) - self.lscope[name] = new_def - self.local_defs[name] = new_def - - for stmt in node.orelse: - assert False, "Not implemented" - ast.NodeVisitor.generic_visit(self, stmt) - - def visit_Subscript(self, node): - assert node.ctx.__class__.__name__ == "Load" - lhs = self.visit(node.value) - slices = self.visit(node.slice) - if _is_triton_tensor(lhs): - return lhs.__getitem__(slices, _builder=self.builder) - return lhs[slices] - - def visit_ExtSlice(self, node): - return [self.visit(dim) for dim in node.dims] - - def visit_For(self, node): - IteratorClass = self.visit(node.iter.func) - iter_args = [self.visit(arg) for arg in node.iter.args] - if IteratorClass == language.static_range: - iterator = IteratorClass(*iter_args) - static_range = range(iterator.start.value, - iterator.end.value, - iterator.step.value) - for i in static_range: - self.lscope[node.target.id] = constexpr(i) - self.visit_compound_statement(node.body) - for stmt in node.orelse: - ast.NodeVisitor.generic_visit(self, stmt) - return - - if IteratorClass is not range: - raise RuntimeError('Only `range` and `static_range` iterators are currently supported') - - # visit iterator arguments - # note: only `range` iterator is supported now - # collect lower bound (lb), upper bound (ub), and step - lb = iter_args[0] if len(iter_args) > 1 else self.visit(ast.Num(0)) - ub = iter_args[1] if len(iter_args) > 1 else self.visit(node.iter.args[0]) - step = iter_args[2] if len(iter_args) > 2 else self.visit(ast.Num(1)) - # handle negative constant step (not supported by scf.for in MLIR) - negative_step = False - if _is_constexpr(step) and step.value < 0: - step = constexpr(-step.value) - negative_step = True - lb, ub = ub, lb - lb = language.core._to_tensor(lb, self.builder) - ub = language.core._to_tensor(ub, self.builder) - step = language.core._to_tensor(step, self.builder) - # induction variable type - if not lb.dtype.is_int() or not ub.dtype.is_int() or not step.dtype.is_int(): - raise TypeError(f"For loop bounds and step must all be ints, are ({lb.dtype}, {ub.dtype}, {step.dtype})") - iv_type = language.semantic.integer_promote_impl(lb.dtype, ub.dtype) - iv_type = language.semantic.integer_promote_impl(iv_type, step.dtype) - iv_ir_type = iv_type.to_ir(self.builder) - iv_is_signed = iv_type.int_signedness == language.core.dtype.SIGNEDNESS.SIGNED - # lb/ub/step might be constexpr, we need to cast them to tensor - lb = lb.handle - ub = ub.handle - step = step.handle - # ForOp can only accept IndexType as lb/ub/step. Cast integer to Index - lb = self.builder.create_int_cast(lb, iv_ir_type, iv_is_signed) - ub = self.builder.create_int_cast(ub, iv_ir_type, iv_is_signed) - step = self.builder.create_int_cast(step, iv_ir_type, iv_is_signed) - # Create placeholder for the loop induction variable - iv = self.builder.create_undef(iv_ir_type) - self.set_value(node.target.id, language.core.tensor(iv, iv_type)) - - with enter_sub_region(self) as sr: - liveins, insert_block = sr - ip, last_loc = self._get_insertion_point_and_loc() - - # create loop body block - block = self.builder.create_block() - self.builder.set_insertion_point_to_start(block) - # dry visit loop body - self.scf_stack.append(node) - self.visit_compound_statement(node.body) - self.scf_stack.pop() - block.erase() - - # If a variable (name) is defined in both its parent & itself, then it's - # a loop-carried variable. (They must be of the same type) - init_args = [] - yields = [] - names = [] - for name in self.local_defs: - if name in liveins: - assert _is_triton_tensor(self.local_defs[name]), f'{name} is not tensor' - assert _is_triton_tensor(liveins[name]) - assert self.local_defs[name].type == liveins[name].type,\ - f'Loop-carried variable {name} has initial type {liveins[name].type} '\ - f'but is re-assigned to {self.local_defs[name].type} in loop! '\ - f'Please make sure that the type stays consistent.' - - names.append(name) - init_args.append(language.core._to_tensor(liveins[name], self.builder)) - yields.append(language.core._to_tensor(self.local_defs[name], self.builder)) - - # create ForOp - self._set_insertion_point_and_loc(ip, last_loc) - for_op = self.builder.create_for_op(lb, ub, step, [arg.handle for arg in init_args]) - - self.scf_stack.append(node) - self.builder.set_insertion_point_to_start(for_op.get_body(0)) - for i, name in enumerate(names): - self.set_value(name, language.core.tensor(for_op.get_body(0).arg(i + 1), yields[i].type)) - self.visit_compound_statement(node.body) - self.scf_stack.pop() - yields = [] - for name in self.local_defs: - if name in liveins: - yields.append(language.core._to_tensor(self.local_defs[name], self.builder)) - - # create YieldOp - if len(yields) > 0: - self.builder.create_yield_op([y.handle for y in yields]) - for_op_region = for_op.get_body(0).get_parent() - assert for_op_region.size() == 1, "We use SCF, so the loop body should only have one block" - - # update induction variable with actual value, and replace all uses - self.builder.set_insertion_point_to_start(for_op.get_body(0)) - iv = for_op.get_induction_var() - if negative_step: - iv = self.builder.create_sub(ub, iv) - iv = self.builder.create_add(iv, lb) - self.lscope[node.target.id].handle.replace_all_uses_with(iv) - self.set_value(node.target.id, language.core.tensor(iv, iv_type)) - - # update lscope & local_defs (ForOp defines new values) - for i, name in enumerate(names): - self.set_value(name, language.core.tensor(for_op.get_result(i), yields[i].type)) - - for stmt in node.orelse: - assert False, "Don't know what to do with else after for" - ast.NodeVisitor.generic_visit(self, stmt) - - def visit_Slice(self, node): - lower = self.visit(node.lower) - upper = self.visit(node.upper) - step = self.visit(node.step) - return slice(lower, upper, step) - - def visit_Index(self, node): - return self.visit(node.value) - - def visit_keyword(self, node) -> Tuple[str, Any]: - return node.arg, self.visit(node.value) - - def visit_Assert(self, node) -> Any: - if not self.debug: - return - test = self.visit(node.test) - msg = self.visit(node.msg) - # Convert assert to triton's device_assert which happens on the device - return language.core.device_assert(test, msg, _builder=self.builder) - - def call_JitFunction(self, fn: JITFunction, args, kwargs): - args = inspect.getcallargs(fn.fn, *args, **kwargs) - args = [args[name] for name in fn.arg_names] - args = [arg if _is_triton_tensor(arg) - else constexpr(arg) for arg in args] - # generate function def - attributes = dict() - constexprs = [i for i, arg in enumerate(args) if _is_constexpr(arg)] - constants = {i: args[i] for i in constexprs} - # generate call - args = [None if i in constexprs else arg for i, arg in enumerate(args)] - arg_vals = [arg.handle for arg in args if arg is not None] - arg_types = [arg.type for arg in args if arg is not None] - fn_name = mangle_fn(fn.__name__, arg_types, constants) - # generate function def if necessary - if not self.module.has_function(fn_name): - prototype = language.function_type([], arg_types) - gscope = sys.modules[fn.fn.__module__].__dict__ - # If the callee is not set, we use the same debug setting as the caller - debug = self.debug if fn.debug is None else fn.debug - file_name, begin_line = _get_fn_file_line(fn) - generator = CodeGenerator(self.context, prototype, gscope, attributes, constants, module=self.module, - function_name=fn_name, function_types=self.function_ret_types, debug=debug, noinline=fn.noinline, - file_name=file_name, begin_line=begin_line, arch=self.builder.arch) - generator.visit(fn.parse()) - callee_ret_type = generator.last_ret_type - self.function_ret_types[fn_name] = callee_ret_type - else: - callee_ret_type = self.function_ret_types[fn_name] - symbol = self.module.get_function(fn_name) - call_op = self.builder.call(symbol, arg_vals) - if call_op.get_num_results() == 0 or callee_ret_type is None: - return None - elif call_op.get_num_results() == 1: - return tensor(call_op.get_result(0), callee_ret_type) - else: - # should return a tuple of tl.tensor - results = [] - for i in range(call_op.get_num_results()): - results.append(tensor(call_op.get_result(i), callee_ret_type[i])) - return tuple(results) - - def visit_Call(self, node): - fn = _unwrap_if_constexpr(self.visit(node.func)) - - static_implementation = self.statically_implemented_functions.get(fn) - if static_implementation is not None: - return static_implementation(self, node) - - kws = dict(self.visit(keyword) for keyword in node.keywords) - args = [self.visit(arg) for arg in node.args] - if fn is language.core.device_assert: # TODO: this should not be so hardcoded - if not self.debug: - return - if isinstance(fn, JITFunction): - _check_fn_args(node, fn, args) - return self.call_JitFunction(fn, args, kws) - if (hasattr(fn, '__self__') and _is_triton_tensor(fn.__self__)) or language.core.is_builtin(fn): - extra_kwargs = dict(_builder=self.builder) - sig = inspect.signature(fn) - if '_generator' in sig.parameters: - extra_kwargs['_generator'] = self - return fn(*args, **extra_kwargs, **kws) - if fn in self.builtin_namespace.values(): - args = map(_unwrap_if_constexpr, args) - return fn(*args, **kws) - - def visit_Constant(self, node): - return constexpr(node.value) - - def visit_BoolOp(self, node: ast.BoolOp): - if len(node.values) != 2: - raise UnsupportedLanguageConstruct(None, node, "chained boolean operators (A or B or C) are not supported; use parentheses to split the chain.") - lhs = self.visit(node.values[0]) - rhs = self.visit(node.values[1]) - method_name = self._method_name_for_bool_op.get(type(node.op)) - if method_name is None: - raise UnsupportedLanguageConstruct(None, node, "AST boolean operator '{}' is not (currently) implemented.".format(node.op.__name__)) - return self._apply_binary_method(method_name, lhs, rhs) - _method_name_for_bool_op: Dict[Type[ast.boolop], str] = {ast.And: 'logical_and', ast.Or: 'logical_or'} - - if sys.version_info < (3, 8): - def visit_NameConstant(self, node): - return constexpr(node.value) - - def visit_Num(self, node): - return constexpr(node.n) - - def visit_Str(self, node): - return constexpr(ast.literal_eval(node)) - - def visit_Attribute(self, node): - lhs = self.visit(node.value) - if _is_triton_tensor(lhs): - if node.attr == "T": - return language.semantic.trans(lhs, builder=self.builder) - return getattr(lhs, node.attr) - - def visit_Expr(self, node): - ast.NodeVisitor.generic_visit(self, node) - - def visit_NoneType(self, node): - return None - - def visit_JoinedStr(self, node): - values = list(node.values) - for i, value in enumerate(values): - if isinstance(value, ast.Constant): - values[i] = str(value.value) - elif isinstance(value, ast.FormattedValue): - conversion_code = value.conversion - evaluated = self.visit(value.value) - if not _is_constexpr(evaluated): - raise UnsupportedLanguageConstruct( - None, node, "Cannot evaluate f-string containing non-constexpr conversion values, found conversion of type " + str(type(evaluated))) - values[i] = ("{}" if conversion_code < 0 else "{!" + chr(conversion_code) + "}").format(evaluated.value) - else: - raise AssertionError("encountered unexpected node of type {} in a JoinedStr node".format(type(value))) - return ''.join(values) - - def visit(self, node): - if node is None: - return - with warnings.catch_warnings(): - # The ast library added visit_Constant and deprecated some other - # methods but we can't move to that without breaking Python 3.6 and 3.7. - warnings.simplefilter("ignore", DeprecationWarning) # python 3.9 - warnings.simplefilter("ignore", PendingDeprecationWarning) # python 3.8 - self.last_node = node - last_loc = self.builder.get_loc() - if hasattr(node, 'lineno') and hasattr(node, 'col_offset'): - self.builder.set_loc(self.file_name, self.begin_line + node.lineno, node.col_offset) - last_loc = self.builder.get_loc() - ret = super().visit(node) - # Reset the location to the last one before the visit - if last_loc: - self.builder.set_loc(last_loc) - return ret - - def generic_visit(self, node): - raise UnsupportedLanguageConstruct(None, node, "unsupported AST node type: {}".format(type(node).__name__)) - - def execute_static_print(self, node: ast.Call) -> None: - # TODO: too simplistic? Perhaps do something else with non-constexpr - - kws = {name: _unwrap_if_constexpr(value) for name, value in (self.visit(keyword) for keyword in node.keywords)} - args = [_unwrap_if_constexpr(self.visit(arg)) for arg in node.args] - print(*args, **kws) - - def execute_static_assert(self, node: ast.Call) -> None: - arg_count = len(node.args) - if not (0 < arg_count <= 2) or len(node.keywords): - raise TypeError("`static_assert` requires one or two positional arguments only") - - passed = _unwrap_if_constexpr(self.visit(node.args[0])) - if not isinstance(passed, bool): - raise NotImplementedError("Assertion condition could not be determined at compile-time. Make sure that it depends only on `constexpr` values") - if not passed: - if arg_count == 1: - message = "" - else: - try: - message = self.visit(node.args[1]) - except Exception as e: - message = "" - - raise CompileTimeAssertionFailure(None, node, _unwrap_if_constexpr(message)) - return None - - statically_implemented_functions: Dict[object, Callable[[ast.Call], Any]] = { - language.core.static_assert: execute_static_assert, - language.core.static_print: execute_static_print, - } - - -def str_to_ty(name): - if name[0] == "*": - ty = str_to_ty(name[1:]) - return language.pointer_type(ty) - tys = { - "fp8e4": language.float8e4, - "fp8e5": language.float8e5, - "fp8e4b15": language.float8e4b15, - "fp16": language.float16, - "bf16": language.bfloat16, - "fp32": language.float32, - "fp64": language.float64, - "i1": language.int1, - "i8": language.int8, - "i16": language.int16, - "i32": language.int32, - "i64": language.int64, - "u8": language.uint8, - "u16": language.uint16, - "u32": language.uint32, - "u64": language.uint64, - "B": language.int1, - } - return tys[name] - - -def kernel_suffix(signature, specialization): - # suffix format: - # <'c' if equal to 1><'d' if divisible by 16> - suffix = '' - for i, _ in enumerate(signature): - suffix += str(i) - if i in specialization.equal_to_1: - suffix += 'c' - if i in specialization.divisible_by_16: - suffix += 'd' - return suffix - - -def ast_to_ttir(fn, signature, specialization, constants, debug, arch): - # canonicalize signature - if isinstance(signature, str): - signature = {k: v.strip() for k, v in enumerate(signature.split(","))} - context = ir.context() - context.load_triton() - # create kernel prototype - cst_key = lambda i: fn.arg_names.index(i) if isinstance(i, str) else i - constants = {cst_key(key): value for key, value in constants.items()} - # visit kernel AST - gscope = fn.__globals__.copy() - function_name = '_'.join([fn.__name__, kernel_suffix(signature.values(), specialization)]) - tys = list(signature.values()) - new_constants = {k: True if k in tys and tys[k] == "i1" else 1 for k in specialization.equal_to_1} - new_attrs = {k: ("multiple_of", 16) for k in specialization.divisible_by_16} - all_constants = constants.copy() - all_constants.update(new_constants) - arg_types = [str_to_ty(v) for k, v in signature.items() if k not in constants] - file_name, begin_line = _get_fn_file_line(fn) - - prototype = language.function_type([], arg_types) - generator = CodeGenerator(context, prototype, gscope=gscope, constants=all_constants, - function_name=function_name, attributes=new_attrs, - is_kernel=True, debug=debug, file_name=file_name, begin_line=begin_line, - arch=arch) - try: - generator.visit(fn.parse()) - except CompilationError as e: - if e.src is None: - e.set_source_code(fn.src) - raise - except Exception as e: - node = generator.last_node - if node is None: - raise - raise CompilationError(fn.src, node, repr(e)) from e - ret = generator.module - # module takes ownership of the context - ret.context = context - return ret diff --git a/python/triton/compiler/compiler.py b/python/triton/compiler/compiler.py deleted file mode 100644 index 946f31370bfe..000000000000 --- a/python/triton/compiler/compiler.py +++ /dev/null @@ -1,624 +0,0 @@ -from __future__ import annotations - -import functools -import hashlib -import json -import os -import re -import subprocess -import tempfile -from collections import namedtuple -from pathlib import Path -from typing import Any, Tuple - -from .._C.libtriton.triton import (add_external_libs, compile_ptx_to_cubin, - get_shared_memory_size, ir, - translate_llvmir_to_hsaco, translate_llvmir_to_ptx, - translate_triton_gpu_to_llvmir) -from ..common.backend import get_backend -# from ..runtime import driver, jit, JITFunction -# TODO: runtime.errors -from ..runtime.autotuner import OutOfResources -from ..runtime.cache import get_cache_manager -from ..runtime.driver import driver -from ..runtime.jit import (JITFunction, get_cuda_stream, get_current_device, - get_device_capability, version_key) -from ..tools.disasm import extract -from .code_generator import ast_to_ttir -from .make_launcher import make_stub - - -def inline_triton_ir(mod): - pm = ir.pass_manager(mod.context) - pm.enable_debug() - pm.add_inliner_pass() - pm.run(mod) - return mod - - -def ttir_compute_capability_rewrite(mod, arch): - # For hardware without support, we must rewrite all load/store - # with block (tensor) pointers into tensors of pointers - pm = ir.pass_manager(mod.context) - pm.enable_debug() - if _is_cuda(arch): - pm.add_rewrite_tensor_pointer_pass(arch) - pm.run(mod) - return mod - - -def optimize_ttir(mod, arch): - mod = inline_triton_ir(mod) - mod = ttir_compute_capability_rewrite(mod, arch) - pm = ir.pass_manager(mod.context) - pm.enable_debug() - pm.add_inliner_pass() - pm.add_triton_combine_pass() - pm.add_canonicalizer_pass() - pm.add_cse_pass() - pm.add_licm_pass() - pm.add_symbol_dce_pass() - pm.run(mod) - return mod - - -def ttir_to_ttgir(mod, num_warps): - pm = ir.pass_manager(mod.context) - pm.enable_debug() - pm.add_convert_triton_to_tritongpu_pass(num_warps) - pm.run(mod) - return mod - - -def optimize_ttgir(mod, num_stages, arch): - pm = ir.pass_manager(mod.context) - pm.enable_debug() - pm.add_tritongpu_coalesce_pass() - pm.add_tritongpu_remove_layout_conversions_pass() - if isinstance(arch, int): - pm.add_tritongpu_accelerate_matmul_pass(arch) - pm.add_tritongpu_remove_layout_conversions_pass() - pm.add_tritongpu_optimize_dot_operands_pass() - pm.add_tritongpu_pipeline_pass(num_stages) - pm.add_tritongpu_prefetch_pass() - pm.add_tritongpu_optimize_dot_operands_pass() - pm.add_tritongpu_remove_layout_conversions_pass() - pm.add_tritongpu_decompose_conversions_pass() - pm.add_tritongpu_reorder_instructions_pass() - pm.add_cse_pass() - pm.add_symbol_dce_pass() - pm.run(mod) - return mod - - -def _add_external_libs(mod, libs): - for name, path in libs.items(): - if len(name) == 0 or len(path) == 0: - return - add_external_libs(mod, list(libs.keys()), list(libs.values())) - - -def ttgir_to_llir(mod, extern_libs, arch): - if extern_libs: - _add_external_libs(mod, extern_libs) - # TODO: separate tritongpu_to_llvmir for different backends - if _is_cuda(arch): - return translate_triton_gpu_to_llvmir(mod, arch, False) - else: - return translate_triton_gpu_to_llvmir(mod, 0, True) - - -# PTX translation - -@functools.lru_cache() -def ptx_get_version(cuda_version) -> int: - ''' - Get the highest PTX version supported by the current CUDA driver. - ''' - assert isinstance(cuda_version, str) - major, minor = map(int, cuda_version.split('.')) - if major == 12: - return 80 + minor - if major == 11: - return 70 + minor - if major == 10: - return 63 + minor - raise RuntimeError("Triton only support CUDA 10.0 or higher") - - -@functools.lru_cache() -def path_to_ptxas(): - base_dir = os.path.join(os.path.dirname(__file__), os.pardir) - paths = [ - os.environ.get("TRITON_PTXAS_PATH", ""), - os.path.join(base_dir, "third_party", "cuda", "bin", "ptxas") - ] - - for ptxas in paths: - ptxas_bin = ptxas.split(" ")[0] - if os.path.exists(ptxas_bin) and os.path.isfile(ptxas_bin): - result = subprocess.check_output([ptxas_bin, "--version"], stderr=subprocess.STDOUT) - if result is not None: - version = re.search(r".*release (\d+\.\d+).*", result.decode("utf-8"), flags=re.MULTILINE) - if version is not None: - return ptxas, version.group(1) - raise RuntimeError("Cannot find ptxas") - - -def llir_to_ptx(mod: Any, arch: int, ptx_version: int = None) -> str: - ''' - Translate TritonGPU module to PTX code. - :param mod: a TritonGPU dialect module - :return: PTX code - ''' - if ptx_version is None: - _, cuda_version = path_to_ptxas() - ptx_version = ptx_get_version(cuda_version) - return translate_llvmir_to_ptx(mod, arch, ptx_version) - - -def ptx_to_cubin(ptx: str, arch: int): - ''' - Compile TritonGPU module to cubin. - :param ptx: ptx code - :param compute_capability: compute capability - :return: str - ''' - ptxas, _ = path_to_ptxas() - return compile_ptx_to_cubin(ptx, ptxas, arch) - - -# AMDGCN translation - -def get_amdgcn_bitcode_paths(arch): - gpu_arch_agnostic_bitcode_libraries = ["opencl.bc", - "ocml.bc", - "ockl.bc", - "oclc_finite_only_off.bc", - "oclc_daz_opt_off.bc", - "oclc_correctly_rounded_sqrt_on.bc", - "oclc_unsafe_math_off.bc", - "oclc_wavefrontsize64_on.bc"] - - gfx_arch = arch[1] - gfx_arch_id = re.search('gfx(\\w+)', gfx_arch).group(1).strip() - - gpu_arch_specific_bitcode_library = 'oclc_isa_version_' + gfx_arch_id + ".bc" - bitcode_path_dir = os.path.join(Path(__file__).parent.resolve(), "third_party/rocm/lib/bitcode/") - - amdgcn_bitcode_paths = {} - i = 1 - for bc_lib in gpu_arch_agnostic_bitcode_libraries: - bc_path = bitcode_path_dir + bc_lib - if os.path.exists(bc_path): - amdgcn_bitcode_paths['library_' + str(i)] = bc_path - i += 1 - bc_gfx_path = bitcode_path_dir + gpu_arch_specific_bitcode_library - if os.path.exists(bc_gfx_path): - amdgcn_bitcode_paths['library_' + str(i)] = bc_gfx_path - - return amdgcn_bitcode_paths - - -def get_amdgpu_arch_fulldetails(): - """ - get the amdgpu fulll ISA details for compiling: - i.e., arch_triple: amdgcn-amd-amdhsa; arch_name: gfx906; arch_features: sramecc+:xnack- - """ - try: - # TODO: package rocm.cc with Triton - rocm_path_dir = os.getenv("ROCM_PATH", default="/opt/rocm") - rocminfo = subprocess.check_output(rocm_path_dir + '/bin/rocminfo').decode() - gfx_arch_details = re.search('amd.*', rocminfo).group(0).strip().split('--') - arch_triple = gfx_arch_details[0] - arch_name_features = gfx_arch_details[1].split(':') - arch_name = arch_name_features[0] - arch_features = "" - - if (len(arch_name_features) == 3): - arch_features = "+" + re.search('\\w+', arch_name_features[1]).group(0) + ","\ - "-" + re.search('\\w+', arch_name_features[2]).group(0) - return [arch_triple, arch_name, arch_features] - except BaseException: - return None - - -def llir_to_amdgcn_and_hsaco(mod: Any, gfx_arch: str, gfx_triple: str, gfx_features: str) -> Tuple[str, str]: - ''' - Translate TritonGPU module to HSACO code based on full details of gpu architecture. - :param mod: a TritonGPU dialect module - :return: - - AMDGCN code - - Path to HSACO object - ''' - return translate_llvmir_to_hsaco(mod, gfx_arch, gfx_triple, gfx_features) - - -# ------------------------------------------------------------------------------ -# compiler -# ------------------------------------------------------------------------------ -def get_kernel_name(src: str, pattern: str) -> str: - ''' - Get kernel name from PTX code. - This Kernel name is required when launching the kernel. - ''' - # There is a name mangling in PTX codegen, so the original kernel names in Triton IR are not available in PTX/cubin. - assert src - for line in src.split('\n'): - line = line.strip() - if line.startswith(pattern): - return line.split()[-1] - - -def convert_type_repr(x): - match = re.search(r'!tt\.ptr<(.*)>', x) - if match is not None: - return '*' + convert_type_repr(match.group(1)) - return x - - -def make_hash(fn, arch, **kwargs): - if isinstance(fn, JITFunction): - configs = kwargs["configs"] - signature = kwargs["signature"] - constants = kwargs.get("constants", dict()) - num_warps = kwargs.get("num_warps", 4) - num_stages = kwargs.get("num_stages", 3) - debug = kwargs.get("debug", False) - # Get unique key for the compiled code - get_conf_key = lambda conf: (sorted(conf.divisible_by_16), sorted(conf.equal_to_1)) - configs_key = [get_conf_key(conf) for conf in configs] - key = f"{fn.cache_key}-{''.join(signature.values())}-{configs_key}-{constants}-{num_warps}-{num_stages}-{debug}-{arch}" - return hashlib.md5(key.encode("utf-8")).hexdigest() - assert isinstance(fn, str) - return hashlib.md5((Path(fn).read_text() + version_key()).encode("utf-8")).hexdigest() - - -# - ^\s*tt\.func\s+ : match the start of the string, any leading whitespace, the keyword func, -# and any following whitespace -# - (public\s+)? : optionally match the keyword public and any following whitespace -# - (@\w+) : match an @ symbol followed by one or more word characters -# (letters, digits, or underscores), and capture it as group 1 (the function name) -# - (\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\)) : match a pair of parentheses enclosing -# zero or more arguments separated by commas, and capture it as group 2 (the argument list) -mlir_prototype_pattern = r'^\s*tt\.func\s+(?:public\s+)?(@\w+)(\((?:%\w+: \S+(?: \{\S+ = \S+ : \S+\})?(?:, )?)*\))\s*\{\s*$' -ptx_prototype_pattern = r"\.(?:visible|extern)\s+\.(?:entry|func)\s+(\w+)\s*\(([^)]*)\)" -prototype_pattern = { - "ttir": mlir_prototype_pattern, - "ttgir": mlir_prototype_pattern, - "ptx": ptx_prototype_pattern, -} - -mlir_arg_type_pattern = r'%\w+: ([^,^\)\s]+)(?: \{\S+ = \S+ : \S+\})?,?' -ptx_arg_type_pattern = r"\.param\s+\.(\w+)" -arg_type_pattern = { - "ttir": mlir_arg_type_pattern, - "ttgir": mlir_arg_type_pattern, - "ptx": ptx_arg_type_pattern, -} - -ttgir_num_warps_pattern = r'"triton_gpu.num-warps"\s?=\s?(\d+)\s?:' - - -def _get_jsonable_constants(constants): - def _is_jsonable(x): - try: - json.dumps(x) - return True - except (TypeError, OverflowError): - return False - serialized_constants = {} - for constant in constants: - if _is_jsonable(constants[constant]): - serialized_constants[constant] = constants[constant] - return serialized_constants - - -def parse_mlir_module(path, context): - module = ir.parse_mlir_module(path, context) - # module takes ownership of the context - module.context = context - return module - - -instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"], defaults=[set(), set()]) - - -# TODO: architecture descriptor class -def _is_cuda(arch): - return isinstance(arch, int) - - -def get_architecture_descriptor(capability): - try: - import torch - except ImportError: - raise ImportError("Triton requires PyTorch to be installed") - if capability is None: - if torch.version.hip is None: - device = get_current_device() - capability = get_device_capability(device) - capability = capability[0] * 10 + capability[1] - else: - capability = get_amdgpu_arch_fulldetails() - return capability - - -def add_rocm_stages(arch, extern_libs, stages): - extern_libs.update(get_amdgcn_bitcode_paths(arch)) - - for key in list(extern_libs): - if extern_libs[key] == '' or extern_libs[key] is None: - extern_libs.pop(key) - - gfx_arch_full_details = arch - gfx_arch = os.environ.get('MI_GPU_ARCH', gfx_arch_full_details[1]) - if gfx_arch is None: - raise RuntimeError('gfx_arch is None (not specified)') - stages["amdgcn"] = (lambda path: Path(path).read_text(), - lambda src: llir_to_amdgcn_and_hsaco(src, gfx_arch, - gfx_arch_full_details[0], - gfx_arch_full_details[2])) - - -def add_cuda_stages(arch, extern_libs, stages): - - stages["ptx"] = (lambda path: Path(path).read_text(), - lambda src: llir_to_ptx(src, arch)) - stages["cubin"] = (lambda path: Path(path).read_bytes(), - lambda src: ptx_to_cubin(src, arch)) - - -def compile(fn, **kwargs): - # Get device type to decide which backend should be used - device_type = kwargs.get("device_type", "cuda") - _device_backend = get_backend(device_type) - - if device_type in ["cuda", "hip"]: - arch = get_architecture_descriptor(kwargs.get("cc", None)) - else: - _device_backend = get_backend(device_type) - assert _device_backend - arch = _device_backend.get_architecture_descriptor(**kwargs) - - is_cuda = device_type == "cuda" and _is_cuda(arch) - is_hip = device_type in ["cuda", "hip"] and not is_cuda - context = ir.context() - constants = kwargs.get("constants", dict()) - num_warps = kwargs.get("num_warps", 4) - num_stages = kwargs.get("num_stages", 3 if is_cuda and arch >= 75 else 2) - extern_libs = kwargs.get("extern_libs", dict()) - if extern_libs is None: - extern_libs = dict() - debug = kwargs.get("debug", False) - - # build compilation stages - stages = dict() - stages["ast"] = (lambda path: fn, None) - stages["ttir"] = (lambda path: parse_mlir_module(path, context), - lambda src: optimize_ttir(ast_to_ttir(src, signature, configs[0], constants, debug=debug, arch=arch), arch)) - stages["ttgir"] = (lambda path: parse_mlir_module(path, context), - lambda src: optimize_ttgir(ttir_to_ttgir(src, num_warps), num_stages, arch)) - stages["llir"] = (lambda path: Path(path).read_text(), - lambda src: ttgir_to_llir(src, extern_libs, arch)) - if is_cuda: - add_cuda_stages(arch, extern_libs, stages) - elif is_hip: - add_rocm_stages(arch, extern_libs, stages) - else: - _device_backend.add_stages(arch, extern_libs, stages) - - # find out the signature of the function - if isinstance(fn, JITFunction): - configs = kwargs.get("configs", None) - signature = kwargs["signature"] - if configs is None: - configs = [instance_descriptor()] - assert len(configs) == 1 - kwargs["configs"] = configs - name = fn.__name__ - first_stage = 0 - if isinstance(signature, str): - signature = {k: v.strip() for k, v in enumerate(signature.split(","))} - kwargs["signature"] = signature - else: - assert isinstance(fn, str) - _, ir_name = os.path.basename(fn).split(".") - src = Path(fn).read_text() - import re - match = re.search(prototype_pattern[ir_name], src, re.MULTILINE) - name, signature = match.group(1), match.group(2) - types = re.findall(arg_type_pattern[ir_name], signature) - if ir_name == 'ttgir': - num_warps_matches = re.findall(ttgir_num_warps_pattern, src) - assert len(num_warps_matches) == 1, "Expected exactly one match for num_warps" - assert "num_warps" not in kwargs or int(num_warps_matches[0]) == num_warps, "num_warps in ttgir does not match num_warps in compile" - num_warps = int(num_warps_matches[0]) - param_tys = [convert_type_repr(ty) for ty in types] - signature = {k: v for k, v in enumerate(param_tys)} - first_stage = list(stages.keys()).index(ir_name) - - # cache manager - if is_cuda or is_hip: - so_path = make_stub(name, signature, constants) - else: - so_path = _device_backend.make_launcher_stub(name, signature, constants) - - # create cache manager - fn_cache_manager = get_cache_manager(make_hash(fn, arch, **kwargs)) - # determine name and extension type of provided function - if isinstance(fn, JITFunction): - name, ext = fn.__name__, "ast" - else: - name, ext = os.path.basename(fn).split(".") - - # load metadata if any - metadata = None - metadata_filename = f"{name}.json" - - # The group is addressed by the metadata - metadata_group = fn_cache_manager.get_group( - metadata_filename - ) or {} - - metadata_path = metadata_group.get(metadata_filename) - - if metadata_path is not None: - with open(metadata_path) as f: - metadata = json.load(f) - else: - metadata = {"num_warps": num_warps, - "num_stages": num_stages, - "constants": _get_jsonable_constants(constants), - "debug": debug, - "arch": arch, } - if ext == "ptx": - assert "shared" in kwargs, "ptx compilation must provide shared memory size" - metadata["shared"] = kwargs["shared"] - - # Add device type to meta information - metadata["device_type"] = device_type - - first_stage = list(stages.keys()).index(ext) - asm = dict() - module = fn - # run compilation pipeline and populate metadata - for ir_name, (parse, compile_kernel) in list(stages.items())[first_stage:]: - ir_filename = f"{name}.{ir_name}" - - if ir_name == ext: - next_module = parse(fn) - else: - path = metadata_group.get(ir_filename) - if path is None: - next_module = compile_kernel(module) - if ir == "amdgcn": - extra_file_name = f"{name}.hsaco_path" - metadata_group[ir_filename] = fn_cache_manager.put(next_module[0], ir_filename) - metadata_group[extra_file_name] = fn_cache_manager.put(next_module[1], extra_file_name) - else: - metadata_group[ir_filename] = fn_cache_manager.put(next_module, ir_filename) - fn_cache_manager.put(next_module, ir_filename) - else: - if ir_name == "amdgcn": - extra_file_name = f"{name}.hsaco_path" - hasco_path = metadata_group.get(extra_file_name) - assert hasco_path is not None, "Expected to have hsaco_path in metadata when we have the amdgcn" - next_module = (parse(path), parse(hasco_path)) - else: - next_module = parse(path) - - if ir_name == "cubin": - asm[ir_name] = next_module - elif ir_name == "amdgcn": - asm[ir_name] = str(next_module[0]) - else: - asm[ir_name] = str(next_module) - if ir_name == "llir" and "shared" not in metadata: - metadata["shared"] = get_shared_memory_size(module) - if ir_name == "ptx": - metadata["name"] = get_kernel_name(next_module, pattern='// .globl') - if ir_name == "amdgcn": - metadata["name"] = get_kernel_name(next_module[0], pattern='.globl') - asm["hsaco_path"] = next_module[1] - if not is_cuda and not is_hip: - _device_backend.add_meta_info(ir_name, module, next_module, metadata, asm) - module = next_module - # write-back metadata, if it didn't come from the cache - if metadata_path is None: - metadata_group[metadata_filename] = fn_cache_manager.put(json.dumps(metadata), metadata_filename, binary=False) - fn_cache_manager.put_group(metadata_filename, metadata_group) - - # return handle to compiled kernel - return CompiledKernel(fn, so_path, metadata, asm) - - -class CompiledKernel: - - # Hooks for external tools to monitor the execution of triton kernels - launch_enter_hook = None - launch_exit_hook = None - - def __init__(self, fn, so_path, metadata, asm): - # initialize launcher - import importlib.util - spec = importlib.util.spec_from_file_location("__triton_launcher", so_path) - mod = importlib.util.module_from_spec(spec) - self.fn = fn - spec.loader.exec_module(mod) - self.c_wrapper = getattr(mod, "launch") - # initialize metadata - self.shared = metadata["shared"] if "shared" in metadata else 0 - self.num_warps = metadata["num_warps"] - self.num_stages = metadata["num_stages"] - self.constants = metadata["constants"] - self.device_type = metadata["device_type"] - self.device_backend = get_backend(self.device_type) if self.device_type not in ["cuda", "hip"] else None - # initialize asm dict - self.asm = asm - # binaries are lazily initialized - # because it involves doing runtime things - # (e.g., checking amount of shared memory on current device) - self.metadata = metadata - self.cu_module = None - self.cu_function = None - - def _init_handles(self): - if self.cu_module is not None: - return - - if self.device_type in ["cuda", "hip"]: - device = get_current_device() - bin_path = { - driver.HIP: "hsaco_path", - driver.CUDA: "cubin" - }[driver.backend] - max_shared = driver.utils.get_device_properties(device)["max_shared_mem"] - fn_load_binary = driver.utils.load_binary - else: - assert self.device_backend - device = self.device_backend.get_current_device() - bin_path = self.device_backend.get_kernel_bin() - max_shared = self.device_backend.get_device_properties(device)["max_shared_mem"] - fn_load_binary = self.device_backend.get_load_binary_fn() - - if self.shared > max_shared: - raise OutOfResources(self.shared, max_shared, "shared memory") - - mod, func, n_regs, n_spills = fn_load_binary(self.metadata["name"], self.asm[bin_path], self.shared, device) - - self.n_spills = n_spills - self.n_regs = n_regs - self.cu_module = mod - self.cu_function = func - - def __getattribute__(self, name): - if name == 'c_wrapper': - self._init_handles() - return super().__getattribute__(name) - - def __getitem__(self, grid): - self._init_handles() - - def runner(*args, stream=None): - if stream is None: - if self.device_type in ["cuda", "rocm"]: - stream = get_cuda_stream() - else: - stream = get_backend(self.device_type).get_stream(None) - self.c_wrapper(grid[0], grid[1], grid[2], self.num_warps, self.shared, stream, self.cu_function, - CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, self, *args) - return runner - - def get_sass(self, fun=None): - if 'sass' in self.asm: - return self.asm['sass'] - fd, path = tempfile.mkstemp() - try: - with open(fd, 'wb') as cubin: - cubin.write(self.asm['cubin']) - self.sass = extract(path, fun) - finally: - os.remove(path) - self.asm['sass'] = self.sass - return self.sass diff --git a/python/triton/compiler/errors.py b/python/triton/compiler/errors.py deleted file mode 100644 index 2930117b5170..000000000000 --- a/python/triton/compiler/errors.py +++ /dev/null @@ -1,52 +0,0 @@ -import ast -from typing import Optional, Union - - -class CompilationError(Exception): - source_line_count_max_in_message = 12 - - def _format_message(self) -> str: - node = self.node - if self.src is None: - source_excerpt = " " - else: - source_excerpt = self.src.split('\n')[:node.lineno][-self.source_line_count_max_in_message:] - if source_excerpt: - source_excerpt.append(' ' * node.col_offset + '^') - source_excerpt = '\n'.join(source_excerpt) - else: - source_excerpt = " " - - message = "at {}:{}:{}".format(node.lineno, node.col_offset, source_excerpt) - if self.error_message: - message += '\n' + self.error_message - return message - - def __init__(self, src: Optional[str], node: ast.AST, error_message: Union[str, None]): - self.src = src - self.node = node - self.error_message = error_message - self.message = self._format_message() - - def set_source_code(self, src: Optional[str]): - self.src = src - self.message = self._format_message() - - def __str__(self): - return self.message - - def __repr__(self): - return "{}({!r})".format(type(self).__name__, self.message) - - def __reduce__(self): - # this is necessary to make CompilationError picklable - return type(self), (self.src, self.node, self.error_message) - - -class CompileTimeAssertionFailure(CompilationError): - """Specific exception for failed tests in `static_assert` invocations""" - pass - - -class UnsupportedLanguageConstruct(CompilationError): - pass diff --git a/python/triton/compiler/make_launcher.py b/python/triton/compiler/make_launcher.py deleted file mode 100644 index 3da8ddccf5c5..000000000000 --- a/python/triton/compiler/make_launcher.py +++ /dev/null @@ -1,373 +0,0 @@ -import hashlib -import os -import tempfile - -from ..common import _build -from ..runtime.cache import get_cache_manager -from ..runtime.jit import version_key - - -def is_hip(): - import torch - return torch.version.hip is not None - - -# ----- stub -------- - - -def make_so_cache_key(version_hash, signature, constants): - # Get unique key for the compiled code - signature = {k: 'ptr' if v[0] == '*' else v for k, v in signature.items()} - key = f"{version_hash}-{''.join(signature.values())}{constants}" - key = hashlib.md5(key.encode("utf-8")).hexdigest() - return key - - -def make_stub(name, signature, constants): - # name of files that are cached - so_cache_key = make_so_cache_key(version_key(), signature, constants) - so_cache_manager = get_cache_manager(so_cache_key) - so_name = f"{name}.so" - # retrieve stub from cache if it exists - cache_path = so_cache_manager.get_file(so_name) - if cache_path is None: - with tempfile.TemporaryDirectory() as tmpdir: - src = generate_launcher(constants, signature) - src_path = os.path.join(tmpdir, "main.c") - with open(src_path, "w") as f: - f.write(src) - so = _build(name, src_path, tmpdir) - with open(so, "rb") as f: - return so_cache_manager.put(f.read(), so_name, binary=True) - else: - return cache_path - -# ----- source code generation -------- - - -def ty_to_cpp(ty): - if ty[0] == '*': - return "hipDeviceptr_t" if is_hip() else "CUdeviceptr" - return { - "i1": "int32_t", - "i8": "int8_t", - "i16": "int16_t", - "i32": "int32_t", - "i64": "int64_t", - "u32": "uint32_t", - "u64": "uint64_t", - "fp16": "float", - "bf16": "float", - "fp32": "float", - "f32": "float", - "fp64": "double", - }[ty] - - -def generate_launcher(constants, signature): - arg_decls = ', '.join(f"{ty_to_cpp(ty)} arg{i}" for i, ty in signature.items()) - - def _extracted_type(ty): - if ty[0] == '*': - return "PyObject*" - return { - 'i1': 'int32_t', - 'i32': 'int32_t', - 'i64': 'int64_t', - 'u32': 'uint32_t', - 'u64': 'uint64_t', - 'fp16': 'float', - 'bf16': 'float', - 'fp32': 'float', - 'f32': 'float', - 'fp64': 'double', - }[ty] - - def format_of(ty): - return { - "PyObject*": "O", - "float": "f", - "double": "d", - "long": "l", - "uint32_t": "I", - "int32_t": "i", - "uint64_t": "K", - "int64_t": "L", - }[ty] - - format = "iiiiiKKOOO" + ''.join([format_of(_extracted_type(ty)) for ty in signature.values()]) - - # generate glue code - if is_hip(): - src = f""" - #define __HIP_PLATFORM_AMD__ - #include - #include - #include - - static inline void gpuAssert(hipError_t code, const char *file, int line) - {{ - if (code != HIP_SUCCESS) - {{ - const char* prefix = "Triton Error [HIP]: "; - const char* str = hipGetErrorString(code); - char err[1024] = {{0}}; - snprintf(err, 1024, "%s Code: %d, Messsage: %s", prefix, code, str ); - PyErr_SetString(PyExc_RuntimeError, err); - }} - }} - - #define HIP_CHECK(ans) {{ gpuAssert((ans), __FILE__, __LINE__); }} - - static void _launch(int gridX, int gridY, int gridZ, int num_warps, int shared_memory, hipStream_t stream, hipFunction_t function, {arg_decls}) {{ - void *params[] = {{ {', '.join(f"&arg{i}" for i in signature.keys() if i not in constants)} }}; - if (gridX*gridY*gridZ > 0) {{ - HIP_CHECK(hipModuleLaunchKernel(function, gridX, gridY, gridZ, 64*num_warps, 1, 1, shared_memory, stream, params, 0)); - }} - }} - - typedef struct _DevicePtrInfo {{ - hipDeviceptr_t dev_ptr; - bool valid; - }} DevicePtrInfo; - - static inline DevicePtrInfo getPointer(PyObject *obj, int idx) {{ - DevicePtrInfo ptr_info; - ptr_info.dev_ptr = 0; - ptr_info.valid = true; - - if (PyLong_Check(obj)) {{ - ptr_info.dev_ptr = (hipDeviceptr_t)PyLong_AsUnsignedLongLong(obj); - return ptr_info; - }} - - if (obj == Py_None) {{ - // valid nullptr - return ptr_info; - }} - - PyObject *ptr = PyObject_GetAttrString(obj, "data_ptr"); - - if (ptr) {{ - PyObject *empty_tuple = PyTuple_New(0); - PyObject *ret = PyObject_Call(ptr, empty_tuple, NULL); - Py_DECREF(empty_tuple); - Py_DECREF(ptr); - - if (!PyLong_Check(ret)) {{ - PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int"); - ptr_info.valid = false; - return ptr_info; - }} - - ptr_info.dev_ptr = (hipDeviceptr_t)PyLong_AsUnsignedLongLong(ret); - - if (!ptr_info.dev_ptr) - return ptr_info; - - uint64_t dev_ptr; - hipError_t status = hipPointerGetAttribute(&dev_ptr, HIP_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr); - if (status == hipErrorInvalidValue) {{ - PyErr_Format(PyExc_ValueError, - "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx); - ptr_info.valid = false; - }} - - ptr_info.dev_ptr = (hipDeviceptr_t)dev_ptr; - return ptr_info; - }} - - PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method"); - return ptr_info; - }} - - static PyObject* launch(PyObject* self, PyObject* args) {{ - - int gridX, gridY, gridZ; - uint64_t _stream; - uint64_t _function; - int num_warps; - int shared_memory; - PyObject *launch_enter_hook = NULL; - PyObject *launch_exit_hook = NULL; - PyObject *compiled_kernel = NULL; - - {' '.join([f"{_extracted_type(ty)} _arg{i}; " for i, ty in signature.items()])} - if (!PyArg_ParseTuple(args, \"{format}\", &gridX, &gridY, &gridZ, &num_warps, &shared_memory, &_stream, &_function, &launch_enter_hook, &launch_exit_hook, &compiled_kernel, {', '.join(f"&_arg{i}" for i, ty in signature.items())})) {{ - return NULL; - }} - - if (launch_enter_hook != Py_None) {{ - PyObject_CallObject(launch_enter_hook, args); - }} - - // raise exception asap - {"; ".join([f"DevicePtrInfo ptr_info{i} = getPointer(_arg{i}, {i}); if (!ptr_info{i}.valid) return NULL;" if ty[0] == "*" else "" for i, ty in signature.items()])}; - _launch(gridX, gridY, gridZ, num_warps, shared_memory, (hipStream_t)_stream, (hipFunction_t)_function, {', '.join(f"ptr_info{i}.dev_ptr" if ty[0]=="*" else f"_arg{i}" for i, ty in signature.items())}); - if (launch_exit_hook != Py_None) {{ - PyObject_CallObject(launch_exit_hook, args); - }} - if (PyErr_Occurred()) {{ - return NULL; - }} - - // return None - Py_INCREF(Py_None); - return Py_None; - }} - - static PyMethodDef ModuleMethods[] = {{ - {{"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}}, - {{NULL, NULL, 0, NULL}} // sentinel - }}; - - static struct PyModuleDef ModuleDef = {{ - PyModuleDef_HEAD_INIT, - \"__triton_launcher\", - NULL, //documentation - -1, //size - ModuleMethods - }}; - - PyMODINIT_FUNC PyInit___triton_launcher(void) {{ - PyObject *m = PyModule_Create(&ModuleDef); - if(m == NULL) {{ - return NULL; - }} - PyModule_AddFunctions(m, ModuleMethods); - return m; - }} - """ - else: - src = f""" -#include \"cuda.h\" -#include -#include - -static inline void gpuAssert(CUresult code, const char *file, int line) -{{ - if (code != CUDA_SUCCESS) - {{ - const char* prefix = "Triton Error [CUDA]: "; - const char* str; - cuGetErrorString(code, &str); - char err[1024] = {{0}}; - strcat(err, prefix); - strcat(err, str); - PyErr_SetString(PyExc_RuntimeError, err); - }} -}} - -#define CUDA_CHECK(ans) {{ gpuAssert((ans), __FILE__, __LINE__); }} - -static void _launch(int gridX, int gridY, int gridZ, int num_warps, int shared_memory, CUstream stream, CUfunction function, {arg_decls}) {{ - void *params[] = {{ {', '.join(f"&arg{i}" for i in signature.keys() if i not in constants)} }}; - if(gridX*gridY*gridZ > 0){{ - CUDA_CHECK(cuLaunchKernel(function, gridX, gridY, gridZ, 32*num_warps, 1, 1, shared_memory, stream, params, 0)); - }} -}} - -typedef struct _DevicePtrInfo {{ - CUdeviceptr dev_ptr; - bool valid; -}} DevicePtrInfo; - -static inline DevicePtrInfo getPointer(PyObject *obj, int idx) {{ - DevicePtrInfo ptr_info; - ptr_info.dev_ptr = 0; - ptr_info.valid = true; - if (PyLong_Check(obj)) {{ - ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(obj); - return ptr_info; - }} - if (obj == Py_None) {{ - // valid nullptr - return ptr_info; - }} - PyObject *ptr = PyObject_GetAttrString(obj, "data_ptr"); - if(ptr){{ - PyObject *empty_tuple = PyTuple_New(0); - PyObject *ret = PyObject_Call(ptr, empty_tuple, NULL); - Py_DECREF(empty_tuple); - Py_DECREF(ptr); - if (!PyLong_Check(ret)) {{ - PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int"); - ptr_info.valid = false; - return ptr_info; - }} - ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(ret); - if(!ptr_info.dev_ptr) - return ptr_info; - uint64_t dev_ptr; - int status = cuPointerGetAttribute(&dev_ptr, CU_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr); - if (status == CUDA_ERROR_INVALID_VALUE) {{ - PyErr_Format(PyExc_ValueError, - "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx); - ptr_info.valid = false; - }} - ptr_info.dev_ptr = dev_ptr; - Py_DECREF(ret); // Thanks ChatGPT! - return ptr_info; - }} - PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method"); - return ptr_info; -}} - -static PyObject* launch(PyObject* self, PyObject* args) {{ - int gridX, gridY, gridZ; - uint64_t _stream; - uint64_t _function; - int num_warps; - int shared_memory; - PyObject *launch_enter_hook = NULL; - PyObject *launch_exit_hook = NULL; - PyObject *compiled_kernel = NULL; - {' '.join([f"{_extracted_type(ty)} _arg{i}; " for i, ty in signature.items()])} - if(!PyArg_ParseTuple(args, \"{format}\", &gridX, &gridY, &gridZ, &num_warps, &shared_memory, &_stream, &_function, &launch_enter_hook, &launch_exit_hook, &compiled_kernel, {', '.join(f"&_arg{i}" for i, ty in signature.items())})) {{ - return NULL; - }} - - if (launch_enter_hook != Py_None) {{ - PyObject_CallObject(launch_enter_hook, args); - }} - - - // raise exception asap - {"; ".join([f"DevicePtrInfo ptr_info{i} = getPointer(_arg{i}, {i}); if (!ptr_info{i}.valid) return NULL;" if ty[0] == "*" else "" for i, ty in signature.items()])}; - _launch(gridX, gridY, gridZ, num_warps, shared_memory, (CUstream)_stream, (CUfunction)_function, {', '.join(f"ptr_info{i}.dev_ptr" if ty[0]=="*" else f"_arg{i}"for i, ty in signature.items())}); - - if (launch_exit_hook != Py_None) {{ - PyObject_CallObject(launch_exit_hook, args); - }} - - if(PyErr_Occurred()) {{ - return NULL; - }} - // return None - Py_INCREF(Py_None); - return Py_None; -}} - -static PyMethodDef ModuleMethods[] = {{ - {{"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}}, - {{NULL, NULL, 0, NULL}} // sentinel -}}; - -static struct PyModuleDef ModuleDef = {{ - PyModuleDef_HEAD_INIT, - \"__triton_launcher\", - NULL, //documentation - -1, //size - ModuleMethods -}}; - -PyMODINIT_FUNC PyInit___triton_launcher(void) {{ - PyObject *m = PyModule_Create(&ModuleDef); - if(m == NULL) {{ - return NULL; - }} - PyModule_AddFunctions(m, ModuleMethods); - return m; -}} -""" - return src diff --git a/python/triton/interpreter/__init__.py b/python/triton/interpreter/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/triton/interpreter/core.py b/python/triton/interpreter/core.py deleted file mode 100644 index 82f3f43a25a0..000000000000 --- a/python/triton/interpreter/core.py +++ /dev/null @@ -1,9 +0,0 @@ -from typing import Tuple - -import dataclasses - - -@dataclasses.dataclass -class ExecutionContext: - program_id: Tuple[int] - program_size: Tuple[int] diff --git a/python/triton/interpreter/interpreter.py b/python/triton/interpreter/interpreter.py deleted file mode 100644 index 001b80ec9855..000000000000 --- a/python/triton/interpreter/interpreter.py +++ /dev/null @@ -1,171 +0,0 @@ -import itertools -import random -from typing import Tuple - -from .. import language as tl -# import .language.core as lcore -from ..language import core as lcore -from . import torch_wrapper -from .core import ExecutionContext -from .memory_map import MemoryMap -from .tl_lang import (TritonLangProxy, WrappedTensor, _primitive_to_tensor, - debugger_constexpr) - -torch = torch_wrapper.torch -tl_method_backup = {} - - -def get_proxy_method(proxy, name): - method = getattr(proxy, name) - - def fun(*args, **kwarg): - return method(*args, **kwarg) - - return fun - - -def attach_triton(module, proxy): - method_list = [func for func in dir(TritonLangProxy) if func[0] != "_"] - for name in method_list: - if hasattr(module, name): - attr = getattr(module, name) - tl_method_backup[name] = attr - if callable(attr): - setattr(module, name, get_proxy_method(proxy, name)) - else: - setattr(module, name, getattr(proxy, name)) - - -def detach_triton(module): - for name, method in tl_method_backup.items(): - setattr(module, name, method) - - -def program_ids_from_grid(grid: Tuple[int, ...]) -> Tuple[int, ...]: - # reverse the grid dimensions and generate the range for each dimension - reversed_grid = reversed(grid) - ranges_for_each_dimension = [range(dim) for dim in reversed_grid] - - # gen all combinations - index_combinations = list(itertools.product(*ranges_for_each_dimension)) - random.shuffle(index_combinations) - - for index_combination in index_combinations: - yield index_combination - - -class DebuggerFunction: - def __init__(self, func, grid=(1,)): - self.func = func - self.grid = grid - - def _is_constexpr(self, name): - return name in self.func.__annotations__ and self.func.__annotations__[name] is lcore.constexpr - - def _get_constexpr(self): - result = [] - for name, annotation in self.func.__annotations__.items(): - if annotation is lcore.constexpr: - result.append(name) - return result - - def _assert_constexpr(self, **kwargs): - constexp = self._get_constexpr() - missing = [i for i in constexp if i not in kwargs.keys()] - assert len(missing) == 0, f"You must specify constexpr {missing}" - - def _get_grid(self, **kwargs): - if callable(self.grid): - return self.grid(kwargs) - else: - return self.grid - - def __call__(self, *args, **kwargs): - self._assert_constexpr(**kwargs) - - memory = MemoryMap() - - def convert_arg(v): - name, arg = v - if torch.is_tensor(arg): - ptr = memory.add_tensor(arg) - return WrappedTensor(torch.tensor([ptr], dtype=torch.int64, device="cuda")) - if self._is_constexpr(name): - return debugger_constexpr(arg) - return WrappedTensor(_primitive_to_tensor(arg)) - - new_args = tuple(map(convert_arg, zip(self.func.__code__.co_varnames, args))) - new_kwargs = {k: convert_arg((k, v)) for (k, v) in kwargs.items() if k not in ["num_warps", "num_stages"]} - - grid = self._get_grid(**kwargs) - for program_id in program_ids_from_grid(grid): - proxy = TritonLangProxy(memory, ExecutionContext(program_id, grid)) - attach_triton(tl, proxy) - self.func(*new_args, **new_kwargs) - detach_triton(tl) - - -class GridSelector: - """ - Entry point of the debugger - """ - - def __init__(self, func): - version = torch.__version__ - assert version[0] == "2", f"Triton Debugger only supports torch >= 2.0, using {version}" - self.func = func - - def __getitem__(self, grid): - return DebuggerFunction(self.func, grid) - - def __call__(self, *args, **kwargs): - return DebuggerFunction(self.func)(*args, **kwargs) - - -class AutotuneGridSelector: - def __init__(self, func, autotune_params): - self.func = func - self.autotune_params = autotune_params - - def __getitem__(self, grid): - return AutotuneRunner(self.func, self.autotune_params, grid) - - def __call__(self, *args, **kwargs): - return AutotuneRunner(self.func, self.autotune_params)(*args, **kwargs) - - -class AutotuneRunner: - def __init__(self, func, autotune_params, grid=None): - self.func = func - self.autotune_params = autotune_params - self.grid = grid - - def __call__(self, *args, **kwargs): - assert len(self.autotune_params["configs"]) >= 1 - - for config in self.autotune_params["configs"][1:]: - - def convert_arg(v): - if torch.is_tensor(v): - return torch.clone(v) - return v - - new_args = tuple(map(convert_arg, args)) - new_kwargs = {k: convert_arg(v) for k, v in kwargs.items()} - if self.grid: - self.func[self.grid](*new_args, **new_kwargs, **config.kwargs) - else: - self.func(*new_args, **new_kwargs, **config.kwargs) - - main_config = self.autotune_params["configs"][0] - if self.grid: - self.func[self.grid](*args, **kwargs, **main_config.kwargs) - else: - self.func(*args, **kwargs, **main_config.kwargs) - - -def triton_debug_autotune(**kwars): - def wrapper(func): - return AutotuneGridSelector(func, kwars) - - return wrapper diff --git a/python/triton/interpreter/memory_map.py b/python/triton/interpreter/memory_map.py deleted file mode 100644 index d0ff732a74b9..000000000000 --- a/python/triton/interpreter/memory_map.py +++ /dev/null @@ -1,102 +0,0 @@ -from __future__ import annotations - -import dataclasses - -from . import torch_wrapper - -torch = torch_wrapper.torch - - -@dataclasses.dataclass -class RegisteredStorage: - storage: torch.Storage - dtype: torch.dtype - size: int - ptr: int - - @property - def end_ptr(self) -> int: - return self.ptr + self.size - - @property - def access_tensor(self) -> torch.Tensor: - return torch.tensor(self.storage, dtype=self.dtype, device=self.storage.device) - - def ensure_immutable(self): - assert self.storage.data_ptr() == self.ptr and self.storage.size() == self.size - - -class MemoryMap: - storages: [RegisteredStorage] - - def __init__(self): - self.storages = [] - - def _get_registered_storage(self, pointer: torch.Tensor): - max_pointer = torch.max(pointer).item() - min_pointer = torch.min(pointer).item() - - registered_storage = next( - filter( - lambda registered: min_pointer >= registered.ptr and max_pointer < registered.end_ptr, self.storages - ), - None, - ) - if registered_storage is None: - raise Exception("Storage not found or pointers spanning multiple tensors") - registered_storage.ensure_immutable() - return registered_storage - - def add_tensor(self, t: torch.Tensor): - storage = t.untyped_storage() - self.storages.append(RegisteredStorage(storage, t.dtype, storage.size(), storage.data_ptr())) - return t.data_ptr() - - def load( - self, - pointer: torch.Tensor, - mask: torch.Tensor = None, - other=0.0, - ): - assert pointer.is_cuda - assert 0 < pointer.dim() < 3 - assert pointer.dtype == torch.int64 - - if mask is None: - mask = torch.ones_like(pointer).bool() - assert mask.is_cuda - assert 0 < mask.dim() < 3 - assert mask.dtype == torch.bool - mask = mask.expand(pointer.size()) - - if torch.all(~mask): - # Todo: The type is wrong here, we can't determine the correct type - return torch.full_like(pointer, fill_value=other, dtype=torch.float16, device="cuda") - - registered_storage = self._get_registered_storage(pointer[mask]) - access_tensor = registered_storage.access_tensor - - index_tensor = pointer - registered_storage.ptr - - block = torch.full_like(pointer, fill_value=other, dtype=access_tensor.dtype, device="cuda") - block[mask] = access_tensor[index_tensor[mask]] - return block - - def store(self, pointer: torch.Tensor, value: torch.Tensor, mask=None): - assert 0 < pointer.dim() < 3 - assert pointer.dtype == torch.int64 - - if mask is None: - mask = torch.ones_like(pointer).bool() - assert 0 < mask.dim() < 3 - assert mask.dtype == torch.bool - mask = mask.expand(pointer.size()) - - if torch.all(~mask): - return - - registered_storage = self._get_registered_storage(pointer[mask]) - access_tensor = registered_storage.access_tensor - - index_tensor = pointer - registered_storage.ptr - access_tensor[index_tensor[mask]] = value[mask].to(access_tensor.dtype) diff --git a/python/triton/interpreter/tl_lang.py b/python/triton/interpreter/tl_lang.py deleted file mode 100644 index cbe601324960..000000000000 --- a/python/triton/interpreter/tl_lang.py +++ /dev/null @@ -1,637 +0,0 @@ -from __future__ import annotations - -from ..language import core as lcore -from . import torch_wrapper -from .core import ExecutionContext -from .memory_map import MemoryMap - -torch = torch_wrapper.torch - - -def _primitive_to_tensor(x): - """ - Converts various Python primitive data types to PyTorch tensor. - """ - tensor_args = {"device": "cuda"} - if isinstance(x, bool): - return torch.tensor([x], dtype=torch.bool, **tensor_args) - elif isinstance(x, int): - if -(2**31) <= x < 2**31: - return torch.tensor([x], dtype=torch.int32, **tensor_args) - elif -(2**63) <= x < 2**63: - return torch.tensor([x], dtype=torch.int64, **tensor_args) - else: - raise RuntimeError(f"Nonrepresentable integer {x}.") - elif isinstance(x, float): - return torch.tensor([x], dtype=torch.float32, **tensor_args) - elif torch.is_tensor(x): - return x - elif isinstance(x, WrappedTensor): - return x - elif isinstance(x, debugger_constexpr): - if x.value is None: - return None - return _primitive_to_tensor(x.value) - elif x is None: - return None - assert False, f"cannot convert {x} of type {type(x)} to tensor" - - -def _infer_tensor(func): - """ - A decorator function to harmonize function args: - - converts primitives to PyTorch tensors - - wraps PyTorch tensors with WrappedTensors - """ - def wrapper(*args): - new_args = tuple(map(lambda v: _primitive_to_tensor(v), args)) - new_args = tuple(map(lambda v: WrappedTensor(v) if torch.is_tensor(v) else v, new_args)) - - return func(*new_args) - - return wrapper - - -def _tensor_operation(func): - """ - A decorator function to unwrap WrappedTensors and debugger_constexpr before calling the function. - Can be combined with _infer_tensor decorator to harmonize args (everything to torch tensor). - """ - def wrapper(*args, **kwargs): - for arg in args: - assert not torch.is_tensor(arg), "unexpected tensor argument" - - def unwrap_tensor(v): - if isinstance(v, WrappedTensor): - return v.tensor - if isinstance(v, debugger_constexpr): - return v.value - return v - - new_args = tuple(map(unwrap_tensor, args)) - new_kwargs = {k: unwrap_tensor(v) for k, v in kwargs.items()} - - result = func(args[0], *new_args[1:], **new_kwargs) - return WrappedTensor(result) if torch.is_tensor(result) else result - - return wrapper - - -class debugger_constexpr: - def __init__(self, value): - if isinstance(value, debugger_constexpr): - self.value = value.value - else: - self.value = value - - def __str__(self) -> str: - return "debugger_constexpr(" + str(self.value) + ")" - - def __index__(self) -> int: - return self.value - - def __bool__(self): - return bool(self.value) - - def __ge__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value >= other - - def __gt__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value > other - - def __le__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value <= other - - def __lt__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value < other - - def __eq__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value == other - - def __or__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value | other - - def __ror__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value | other - - def __and__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value & other - - def __rand__(self, other): - other = other.value if isinstance(other, debugger_constexpr) else other - return self.value & other - - def to(self, dtype, bitcast=False, _builder=None): - if dtype in [torch.int64]: - ret_ty = int - elif dtype == torch.bool: - ret_ty = bool - elif dtype in [torch.float64]: - ret_ty = float - else: - raise ValueError("dtype not supported in debugger") - return debugger_constexpr(ret_ty(self.value)) - - -class WrappedTensor: - def __init__(self, tensor): - self.tensor = tensor - - def __index__(self) -> int: - return self.tensor.item() - - def __str__(self) -> str: - return "wrapped_" + str(self.tensor) - - def __bool__(self) -> bool: - return torch.all(self.tensor == True).item() # noqa: E712 - - @property - def dtype(self): - return self.tensor.dtype - - @_infer_tensor - @_tensor_operation - def __add__(self, other): - return torch.add(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __radd__(self, other): - return self.__add__(other) - - @_infer_tensor - @_tensor_operation - def __sub__(self, other): - return torch.sub(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __rsub__(self, other): - return torch.sub(other, self.tensor) - - @_infer_tensor - @_tensor_operation - def __mul__(self, other): - return torch.mul(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __rmul__(self, other): - return self.__mul__(other) - - @_infer_tensor - @_tensor_operation - def __truediv__(self, other): - return torch.div(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __rtruediv__(self, other): - return torch.div(other, self.tensor) - - @_infer_tensor - @_tensor_operation - def __floordiv__(self, other): - return torch.floor_divide(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __rfloordiv__(self, other): - return torch.floor_divide(other, self.tensor) - - @_infer_tensor - @_tensor_operation - def __mod__(self, other): - return torch.remainder(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __rmod__(self, other): - return torch.remainder(other, self.tensor) - - @_infer_tensor - @_tensor_operation - def __neg__(self): - return -self.tensor - - @_infer_tensor - @_tensor_operation - def __invert__(self): - return ~self.tensor - - @_infer_tensor - @_tensor_operation - def __and__(self, other): - return torch.bitwise_and(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __or__(self, other): - return torch.bitwise_or(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __xor__(self, other): - return torch.bitwise_xor(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __lshift__(self, other): - return torch.bitwise_left_shift(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __rshift__(self, other): - return torch.bitwise_right_shift(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __gt__(self, other): - return self.tensor > other - - @_infer_tensor - @_tensor_operation - def __rgt__(self, other): - return other > self.tensor - - @_infer_tensor - @_tensor_operation - def __ge__(self, other): - return self.tensor >= other - - @_infer_tensor - @_tensor_operation - def __rge__(self, other): - return other >= self.tensor - - @_infer_tensor - @_tensor_operation - def __lt__(self, other): - return self.tensor < other - - @_infer_tensor - @_tensor_operation - def __rlt__(self, other): - return other < self.tensor - - @_infer_tensor - @_tensor_operation - def __le__(self, other): - return self.tensor <= other - - @_infer_tensor - @_tensor_operation - def __rle__(self, other): - return other <= self.tensor - - @_infer_tensor - @_tensor_operation - def __eq__(self, other): - return torch.equal(self.tensor, other) - - @_infer_tensor - @_tensor_operation - def __ne__(self, other): - return not torch.equal(self.tensor, other) - - @_tensor_operation - def __getitem__(self, slices): - return self.tensor.__getitem__(slices) - # if isinstance(slices, slice): - # slices = [slices] - # src_shape = self.shape - # dst_shape = [] - # curr = 0 - # for sl in slices: - # if isinstance(sl, constexpr) and sl.value is None: - # dst_shape.append(1) - # elif sl == slice(None, None, None): - # dst_shape.append(src_shape[curr].value) - # curr += 1 - # ret = torch.reshape(self.tensor, dst_shape, ) - # return ret - - @_tensor_operation - def to(self, dtype, bitcast=False): - return self.tensor.to(dtype) - # if isinstance(bitcast, constexpr): - # bitcast = bitcast.value - # if bitcast: - # return semantic.bitcast(self, dtype, ) - # return semantic.cast(self, dtype, ) - - -def _constexpr_to_value(v): - if isinstance(v, debugger_constexpr): - return v.value - return v - - -class TritonLangProxy: - _memory_map: MemoryMap - _context: ExecutionContext - - def __init__(self, memory_map: MemoryMap, context: ExecutionContext): - self._memory_map = memory_map - self._context = context - - # Types - # Removed void, int1, float8, uint16, uint32, uint64, pi32_t - - # constexpr = debugger_constexpr - - # Program functions - - @_tensor_operation - def load( - self, - pointer: torch.Tensor, - mask: torch.Tensor = None, - other=0.0, - cache_modifier="", - eviction_policy="", - volatile=False, - ): - return self._memory_map.load(pointer, mask, other) - - @_tensor_operation - def store(self, pointer: torch.Tensor, value: torch.Tensor, mask=None): - return self._memory_map.store(pointer, value, mask) - - @_tensor_operation - def program_id(self, axis): - assert axis < len(self._context.program_id) - return torch.tensor([self._context.program_id[axis]], dtype=torch.int32, device="cuda") - - @_tensor_operation - def num_programs(self, axis): - assert axis < len(self._context.program_size) - return torch.tensor([self._context.program_size[axis]], dtype=torch.int32, device="cuda") - - @_tensor_operation - def arange(self, start, end): - return torch.arange(start=start, end=end, dtype=torch.int32, device="cuda") - - @_tensor_operation - def zeros(self, shape, dtype): - for i, d in enumerate(shape): - if not isinstance(d, debugger_constexpr): - raise TypeError(f"Shape element {i} must have type `constexpr`") - if not isinstance(d.value, int): - raise TypeError(f"Shape element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]") - shape = [x.value for x in shape] - if isinstance(dtype, lcore.dtype): - if dtype.is_fp32(): - dtype = torch.float32 - elif dtype.is_fp16(): - dtype = torch.float16 - elif dtype.is_bf16(): - dtype = torch.bfloat16 - elif dtype.is_int32(): - dtype = torch.int32 - elif dtype.is_int16(): - dtype = torch.int16 - elif dtype.is_int8(): - dtype = torch.int8 - else: - raise TypeError(f"Unsupported dtype {dtype}") - return torch.zeros(size=shape, dtype=dtype, device="cuda") - - @_tensor_operation - def dequantize(self, input, scale, shift, nbit, dst_ty=None): - if dst_ty is None: - dst_ty = torch.float16 - raise NotImplementedError() - - @_tensor_operation - def broadcast(self, input, other): - raise NotImplementedError() - - @_tensor_operation - def broadcast_to(self, input, shape): - raise NotImplementedError() - - @_tensor_operation - def cat(self, input, shape): - raise NotImplementedError() - - @_tensor_operation - def reshape(self, input, shape): - raise NotImplementedError() - - @_tensor_operation - def dot(self, input, other, trans_a=False, trans_b=False, allow_tf32=True): - assert input.dtype == other.dtype - if trans_a: - input = input.T - if trans_b: - other = other.T - return torch.matmul(input=input, other=other) - - @_tensor_operation - def atomic_cas(self, pointer, cmp, val): - stored = self._memory_map.load(pointer, None, 0.0) - if not isinstance(cmp, torch.Tensor): - cmp = torch.tensor([cmp], dtype=stored.dtype, device="cuda") - if not isinstance(val, torch.Tensor): - val = torch.tensor([val], dtype=stored.dtype, device="cuda") - if stored == cmp: - self._memory_map.store(pointer, val, None) - return stored - - @_tensor_operation - def atomic_xchg(self, pointer, val, mask=None): - if isinstance(val, int): - val = torch.tensor([val], dtype=torch.int32, device="cuda") - stored = self._memory_map.load(pointer, mask, 0.0) - self._memory_map.store(pointer, val, mask) - return stored - - @_tensor_operation - def atomic_add(self, pointer, val, mask=None): - # arbitrary other value as it will masked during storing - stored = self._memory_map.load(pointer, mask, 0.0) - result = stored + val - self._memory_map.store(pointer, result, mask) - return stored - - @_tensor_operation - def atomic_max(self, pointer, val, mask=None): - stored = self._memory_map.load(pointer, mask, 0.0) - result = torch.maximum(stored, val) - self._memory_map.store(pointer, result, mask) - return stored - - @_tensor_operation - def atomic_min(self, pointer, val, mask=None): - stored = self._memory_map.load(pointer, mask, 0.0) - result = torch.minimum(stored, val) - self._memory_map.store(pointer, result, mask) - return stored - - @_tensor_operation - def atomic_and(self, pointer, val, mask=None): - stored = self._memory_map.load(pointer, mask, 0) - result = torch.bitwise_and(stored, val) - self._memory_map.store(pointer, result, mask) - return stored - - @_tensor_operation - def atomic_or(self, pointer, val, mask=None): - stored = self._memory_map.load(pointer, mask, 0) - result = torch.bitwise_or(stored, val) - self._memory_map.store(pointer, result, mask) - return stored - - @_tensor_operation - def atomic_xor(self, pointer, val, mask=None): - stored = self._memory_map.load(pointer, mask, 0) - result = torch.bitwise_xor(stored, val) - self._memory_map.store(pointer, result, mask) - return stored - - @_tensor_operation - def where(self, condition, x, y): - condition = _primitive_to_tensor(condition) - x = _primitive_to_tensor(x) - y = _primitive_to_tensor(y) - return torch.where(condition, x, y) - - @_tensor_operation - def umulhi(self, x, y): - raise NotImplementedError() - - @_tensor_operation - def fdiv(self, x, y, ieee_rounding=False): - raise NotImplementedError() - - @_tensor_operation - def exp(self, x): - return torch.exp(x) - - @_tensor_operation - def log(self, x): - return torch.log(x) - - @_tensor_operation - def cos(self, x): - return torch.cos(x) - - @_tensor_operation - def sin(self, x): - return torch.sin(x) - - @_tensor_operation - def sqrt(self, x): - return torch.sqrt(x) - - @_tensor_operation - def globaltimer(self): - raise NotImplementedError() - - @_tensor_operation - def clock(self): - raise NotImplementedError() - - @_tensor_operation - def debug_barrier(self): - raise NotImplementedError() - - @_tensor_operation - def multiple_of(self, input, values): - return input - - @_tensor_operation - def max_contiguous(self, input, values): - return input - - @_tensor_operation - def abs(self, x): - return torch.abs(x) - - @_tensor_operation - def cdiv(self, x, div): - return (x + div - 1) // div - - @_tensor_operation - def minimum(self, x, y): - if isinstance(x, int): - x = torch.tensor(x, device="cuda") - if isinstance(y, int): - y = torch.tensor(y, device="cuda") - return torch.minimum(x, y) - - @_tensor_operation - def maximum(self, x, y): - return torch.maximum(x, y) - - @_tensor_operation - def sigmoid(self, x): - raise NotImplementedError() - - @_tensor_operation - def softmax(self, x, ieee_rounding=False): - raise NotImplementedError() - - @_tensor_operation - def ravel(self, x): - raise NotImplementedError() - - @_tensor_operation - def swizzle2d(self, i, j, size_i, size_j, size_g): - raise NotImplementedError() - - @_tensor_operation - def zeros_like(self, input): - raise NotImplementedError() - - @_tensor_operation - def max(self, input, axis=None): - if axis is None: - return torch.max(input) - return torch.max(input, dim=axis).values - - @_tensor_operation - def argmax(self, input, axis): - raise NotImplementedError() - - @_tensor_operation - def min(self, input, axis=None): - if axis is None: - return torch.min(input) - return torch.min(input, dim=axis).values - - @_tensor_operation - def argmin(self, input, axis): - raise NotImplementedError() - - @_tensor_operation - def sum(self, input, axis=None): - if axis is None: - return torch.sum(input) - return torch.sum(input, dim=axis) - - @_tensor_operation - def xor_sum(self, input, axis): - raise NotImplementedError() - - @_tensor_operation - def cumsum(self, input, axis=None): - if axis is None: - return torch.cumsum(input) - return torch.cumsum(input, dim=axis) - - @_tensor_operation - def cumprod(self, input, axis=None): - if axis is None: - return torch.cumprod(input) - return torch.cumprod(input, dim=axis) diff --git a/python/triton/interpreter/torch_wrapper.py b/python/triton/interpreter/torch_wrapper.py deleted file mode 100644 index 44aa17eb1355..000000000000 --- a/python/triton/interpreter/torch_wrapper.py +++ /dev/null @@ -1,18 +0,0 @@ -try: - import torch as _torch -except ImportError: - _torch = None - - -class TorchWrapper: - """ - Helps in making torch an optional dependency - """ - - def __getattr__(self, name): - if _torch is None: - raise ImportError("Triton requires PyTorch to be installed") - return getattr(_torch, name) - - -torch = TorchWrapper() diff --git a/python/triton/language/__init__.py b/python/triton/language/__init__.py deleted file mode 100644 index 8134daea9277..000000000000 --- a/python/triton/language/__init__.py +++ /dev/null @@ -1,211 +0,0 @@ -"""isort:skip_file""" -# Import order is significant here. - -from . import math -from . import extra -from .standard import ( - cdiv, - sigmoid, - softmax, - ravel, - swizzle2d, - zeros, - zeros_like, -) -from .core import ( - TRITON_MAX_TENSOR_NUMEL, - abs, - advance, - arange, - argmin, - argmax, - associative_scan, - atomic_add, - atomic_and, - atomic_cas, - atomic_max, - atomic_min, - atomic_or, - atomic_xchg, - atomic_xor, - bfloat16, - block_type, - broadcast, - broadcast_to, - cat, - constexpr, - cos, - cumprod, - cumsum, - debug_barrier, - device_assert, - device_print, - dot, - dtype, - exp, - expand_dims, - full, - fdiv, - float16, - float32, - float64, - float8e4b15, - float8e4, - float8e5, - function_type, - int1, - int16, - int32, - int64, - int8, - load, - log, - make_block_ptr, - max, - max_contiguous, - maximum, - min, - minimum, - multiple_of, - num_programs, - pi32_t, - pointer_type, - program_id, - reduce, - reshape, - sin, - sqrt, - static_assert, - static_print, - store, - sum, - static_range, - tensor, - trans, - # triton, - uint16, - uint32, - uint64, - uint8, - umulhi, - view, - void, - where, - xor_sum, -) -from .random import ( - pair_uniform_to_normal, - philox, - philox_impl, - rand, - rand4x, - randint, - randint4x, - randn, - randn4x, - uint32_to_uniform_float, -) - - -__all__ = [ - "TRITON_MAX_TENSOR_NUMEL", - "abs", - "advance", - "arange", - "argmin", - "argmax", - "associative_scan", - "atomic_add", - "atomic_and", - "atomic_cas", - "atomic_max", - "atomic_min", - "atomic_or", - "atomic_xchg", - "atomic_xor", - "bfloat16", - "block_type", - "broadcast", - "broadcast_to", - "builtin", - "cat", - "cdiv", - "constexpr", - "cos", - "cumprod", - "cumsum", - "debug_barrier", - "device_assert", - "device_print", - "dot", - "dtype", - "exp", - "expand_dims", - "extra", - "fdiv", - "float16", - "float32", - "float64", - "float8e4b15", - "float8e4", - "float8e5", - "full", - "function_type", - "int1", - "int16", - "int32", - "int64", - "int8", - "ir", - "math", - "load", - "log", - "make_block_ptr", - "max", - "max_contiguous", - "maximum", - "min", - "minimum", - "multiple_of", - "num_programs", - "pair_uniform_to_normal", - "philox", - "philox_impl", - "pi32_t", - "pointer_type", - "program_id", - "rand", - "rand4x", - "randint", - "randint4x", - "randn", - "randn4x", - "ravel", - "reduce", - "reshape", - "sigmoid", - "sin", - "softmax", - "sqrt", - "static_range", - "static_assert", - "static_print", - "store", - "sum", - "swizzle2d", - "tensor", - "trans", - "triton", - "uint16", - "uint32", - "uint32_to_uniform_float", - "uint64", - "uint8", - "umulhi", - "view", - "void", - "where", - "xor_sum", - "zeros", - "zeros_like", -] diff --git a/python/triton/language/core.py b/python/triton/language/core.py deleted file mode 100644 index b18743683be2..000000000000 --- a/python/triton/language/core.py +++ /dev/null @@ -1,1888 +0,0 @@ -from __future__ import annotations - -from contextlib import contextmanager -from enum import Enum -from functools import wraps -from typing import Callable, List, Sequence, TypeVar - -from .._C.libtriton.triton import ir -from ..runtime.jit import jit -from . import semantic - -T = TypeVar('T') - -TRITON_MAX_TENSOR_NUMEL = 131072 - -TRITON_BUILTIN = "__triton_builtin__" - - -def builtin(fn: T) -> T: - """Mark a function as a builtin.""" - assert callable(fn) - - @wraps(fn) - def wrapper(*args, **kwargs): - if "_builder" not in kwargs or kwargs["_builder"] is None: - raise ValueError( - "Did you forget to add @triton.jit ? " - "(`_builder` argument must be provided outside of JIT functions.)" - ) - return fn(*args, **kwargs) - - setattr(wrapper, TRITON_BUILTIN, True) - - return wrapper - - -def is_builtin(fn) -> bool: - """Is this a registered triton builtin function?""" - return getattr(fn, TRITON_BUILTIN, False) - - -def _to_tensor(x, builder): - if isinstance(x, bool): - return tensor(builder.get_int1(x), int1) - # Note: compile-time const integers are represented by unsigned values - elif isinstance(x, int): - if -2**31 <= x < 2**31: - return tensor(builder.get_int32(x), int32) - elif 2**31 <= x < 2**32: - return tensor(builder.get_int32(x), uint32) - elif -2**63 <= x < 2**63: - return tensor(builder.get_int64(x), int64) - elif 2**63 <= x < 2**64: - return tensor(builder.get_int64(x), uint64) - else: - raise RuntimeError(f'Nonrepresentable integer {x}.') - elif isinstance(x, float): - min_float32 = 2 ** -126 - max_float32 = (2 - 2**-23) * 2**127 - abs_x = __builtins__['abs'](x) - if abs_x == float("inf") or\ - abs_x == 0.0 or \ - x != x or \ - min_float32 <= abs_x <= max_float32: - return tensor(builder.get_fp32(x), float32) - else: - return tensor(builder.get_fp64(x), float64) - - elif isinstance(x, constexpr): - return _to_tensor(x.value, builder) - elif isinstance(x, tensor): - return x - assert False, f"cannot convert {x} of type {type(x)} to tensor" - - -class dtype: - SINT_TYPES = ['int8', 'int16', 'int32', 'int64'] - UINT_TYPES = ['int1', 'uint8', 'uint16', 'uint32', 'uint64'] - FP_TYPES = ['fp8e4b15', 'fp8e4', 'fp8e5', 'fp16', 'bf16', 'fp32', 'fp64'] - STANDARD_FP_TYPES = ['fp16', 'bf16', 'fp32', 'fp64'] - OTHER_TYPES = ['void'] - - class SIGNEDNESS(Enum): - SIGNED = 0 - UNSIGNED = 1 - - def __init__(self, name): - self.name = name - assert name in dtype.SINT_TYPES + dtype.UINT_TYPES + dtype.FP_TYPES + dtype.OTHER_TYPES, name - if name in dtype.SINT_TYPES: - self.int_signedness = dtype.SIGNEDNESS.SIGNED - self.int_bitwidth = int(name.split('int')[-1]) - self.primitive_bitwidth = self.int_bitwidth - elif name in dtype.UINT_TYPES: - self.int_signedness = dtype.SIGNEDNESS.UNSIGNED - self.int_bitwidth = int(name.split('int')[-1]) - self.primitive_bitwidth = self.int_bitwidth - elif name in dtype.FP_TYPES: - if name == 'fp8e4b15': - self.fp_mantissa_width = 3 - self.primitive_bitwidth = 8 - self.exponent_bias = 15 - elif name == 'fp8e4': - self.fp_mantissa_width = 3 - self.primitive_bitwidth = 8 - self.exponent_bias = 7 - elif name == 'fp8e5': - self.fp_mantissa_width = 2 - self.primitive_bitwidth = 8 - self.exponent_bias = 15 - elif name == 'fp16': - self.fp_mantissa_width = 10 - self.primitive_bitwidth = 16 - self.exponent_bias = 15 - elif name == 'bf16': - self.fp_mantissa_width = 7 - self.primitive_bitwidth = 16 - self.exponent_bias = 127 - elif name == 'fp32': - self.fp_mantissa_width = 23 - self.primitive_bitwidth = 32 - self.exponent_bias = 127 - elif name == 'fp64': - self.fp_mantissa_width = 53 - self.primitive_bitwidth = 64 - self.exponent_bias = 1023 - else: - raise RuntimeError(f'Unsupported floating-point type {name}') - elif name == 'void': - self.primitive_bitwidth = 0 - - def is_fp8(self): - return 'fp8' in self.name - - def is_fp8e4(self): - return self.name == 'fp8e4' - - def is_fp8e4b15(self): - return self.name == 'fp8e4b15' - - def is_fp16(self): - return self.name == 'fp16' - - def is_bf16(self): - return self.name == 'bf16' - - def is_fp32(self): - return self.name == 'fp32' - - def is_fp64(self): - return self.name == 'fp64' - - def is_int1(self): - return self.name == 'int1' - - def is_int8(self): - return self.name == 'int8' - - def is_int16(self): - return self.name == 'int16' - - def is_int32(self): - return self.name == 'int32' - - def is_int64(self): - return self.name == 'int64' - - def is_uint8(self): - return self.name == 'uint8' - - def is_uint16(self): - return self.name == 'uint16' - - def is_uint32(self): - return self.name == 'uint32' - - def is_uint64(self): - return self.name == 'uint64' - - def is_floating(self): - return self.name in dtype.FP_TYPES - - def is_standard_floating(self): - return self.name in dtype.STANDARD_FP_TYPES - - def is_int_signed(self): - return self.name in dtype.SINT_TYPES - - def is_int_unsigned(self): - return self.name in dtype.UINT_TYPES - - def is_int(self): - return self.name in dtype.SINT_TYPES + dtype.UINT_TYPES - - def is_bool(self): - return self.is_int1() - - @staticmethod - def is_void(): - raise RuntimeError("Not implemented") - - @staticmethod - def is_block(): - return False - - @staticmethod - def is_ptr(): - return False - - def __eq__(self, other: dtype): - if not isinstance(other, dtype): - return False - return self.name == other.name - - def __ne__(self, other: dtype): - return not self.__eq__(other) - - def __hash__(self): - return hash((self.name,)) - - @property - def scalar(self): - return self - - def to_ir(self, builder: ir.builder) -> ir.type: - if self.name == 'void': - return builder.get_void_ty() - elif self.name == 'int1': - return builder.get_int1_ty() - elif self.name in ('int8', 'uint8'): - return builder.get_int8_ty() - elif self.name in ('int16', 'uint16'): - return builder.get_int16_ty() - elif self.name in ('int32', 'uint32'): - return builder.get_int32_ty() - elif self.name in ('int64', 'uint64'): - return builder.get_int64_ty() - elif self.name == 'fp8e5': - return builder.get_fp8e5_ty() - elif self.name == 'fp8e4': - return builder.get_fp8e4_ty() - elif self.name == 'fp8e4b15': - return builder.get_fp8e4b15_ty() - elif self.name == 'fp16': - return builder.get_half_ty() - elif self.name == 'bf16': - return builder.get_bf16_ty() - elif self.name == 'fp32': - return builder.get_float_ty() - elif self.name == 'fp64': - return builder.get_double_ty() - raise ValueError(f'fail to convert {self} to ir type') - - def __str__(self): - return self.name - - @property - def cache_key_part(self) -> str: - """See cache_key_part() in triton.cc.""" - return self.name - - def __repr__(self): - return f'triton.language.{self.name}' - - -class pointer_type(dtype): - def __init__(self, element_ty: dtype, address_space: int = 1): - if not isinstance(element_ty, dtype): - raise TypeError('element_ty is a {type(element_ty).__name__}.') - self.element_ty = element_ty - self.address_space = address_space - - self.name = self.__str__() - - def to_ir(self, builder: ir.builder) -> ir.pointer_type: - return builder.get_ptr_ty(self.element_ty.to_ir(builder), 1) - - def __str__(self): - return f'pointer<{self.element_ty}>' - - def __repr__(self): - return self.__str__() - - def is_ptr(self): - return True - - def __eq__(self, other: pointer_type) -> bool: - if not isinstance(other, pointer_type): - return False - return self.element_ty == other.element_ty and self.address_space == other.address_space - - def __ne__(self, other: pointer_type) -> bool: - return not self.__eq__(other) - - @property - def scalar(self): - return self - - -class block_type(dtype): - def __init__(self, element_ty: dtype, shape: List): - self.element_ty = element_ty - - # Note that block_type's shape is a list of int - # while tensor's shape is a list of constexpr. - - # shape can be empty ([]) when an input is a 0D tensor. - if not shape: - raise TypeError('0d block_type is forbidden') - if isinstance(shape[0], constexpr): - shape = [s.value for s in shape] - - self.shape = shape - self.numel = 1 - for s in self.shape: - self.numel *= s - if self.numel > TRITON_MAX_TENSOR_NUMEL: - raise ValueError(f"numel ({self.numel}) exceeds triton maximum tensor numel ({TRITON_MAX_TENSOR_NUMEL})") - - self.name = self.__str__() - - def to_ir(self, builder: ir.builder) -> ir.block_type: - return builder.get_block_ty(self.element_ty.to_ir(builder), self.shape) - - def __str__(self): - return f'<{self.shape}, {self.element_ty}>' - - def __repr__(self): - return self.__str__() - - def is_block(self): - return True - - def get_block_shapes(self) -> List[int]: - return self.shape - - def __eq__(self, other: block_type) -> bool: - if not isinstance(other, block_type): - return False - return self.element_ty == other.element_ty and self.shape == other.shape - - def __ne__(self, other: block_type) -> bool: - return not self.__eq__(other) - - @property - def scalar(self): - return self.element_ty - - -class function_type(dtype): - def __init__(self, ret_types: List[dtype], param_types: List[dtype]) -> None: - self.ret_types = ret_types - self.param_types = param_types - - def __str__(self): - return f'fn ({self.param_types}) -> {self.ret_types}' - - def to_ir(self, builder: ir.builder): - ir_param_types = [ty.to_ir(builder) for ty in self.param_types] - ret_types = [ret_type.to_ir(builder) for ret_type in self.ret_types] - return builder.get_function_ty(ir_param_types, ret_types) - - -# scalar types -void = dtype('void') -int1 = dtype('int1') -int8 = dtype('int8') -int16 = dtype('int16') -int32 = dtype('int32') -int64 = dtype('int64') -uint8 = dtype('uint8') -uint16 = dtype('uint16') -uint32 = dtype('uint32') -uint64 = dtype('uint64') -float8e5 = dtype('fp8e5') -float8e4 = dtype('fp8e4') -float8e4b15 = dtype('fp8e4b15') -float16 = dtype('fp16') -bfloat16 = dtype('bf16') -float32 = dtype('fp32') -float64 = dtype('fp64') -# pointer types -pi32_t = pointer_type(int32) - -# ----------------------- -# constexpr -# ----------------------- - - -class constexpr: - """ - This class is used to store a value that is known at compile-time. - """ - - def __init__(self, value): - if isinstance(value, constexpr): - self.value = value.value - else: - self.value = value - - def __repr__(self) -> str: - return f"constexpr[{self.value}]" - - def __add__(self, other): - return constexpr(self.value + other.value) - - def __radd__(self, other): - return constexpr(other.value + self.value) - - def __sub__(self, other): - return constexpr(self.value - other.value) - - def __rsub__(self, other): - return constexpr(other.value - self.value) - - def __mul__(self, other): - return constexpr(self.value * other.value) - - def __mod__(self, other): - return constexpr(self.value % other.value) - - def __rmul__(self, other): - return constexpr(other.value * self.value) - - def __truediv__(self, other): - return constexpr(self.value / other.value) - - def __rtruediv__(self, other): - return constexpr(other.value / self.value) - - def __floordiv__(self, other): - return constexpr(self.value // other.value) - - def __rfloordiv__(self, other): - return constexpr(other.value // self.value) - - def __gt__(self, other): - return constexpr(self.value > other.value) - - def __rgt__(self, other): - return constexpr(other.value > self.value) - - def __ge__(self, other): - return constexpr(self.value >= other.value) - - def __rge__(self, other): - return constexpr(other.value >= self.value) - - def __lt__(self, other): - return constexpr(self.value < other.value) - - def __rlt__(self, other): - return constexpr(other.value < self.value) - - def __le__(self, other): - return constexpr(self.value <= other.value) - - def __rle__(self, other): - return constexpr(other.value <= self.value) - - def __eq__(self, other): - return constexpr(self.value == other.value) - - def __ne__(self, other): - return constexpr(self.value != other.value) - - def __bool__(self): - return bool(self.value) - - def __neg__(self): - return constexpr(-self.value) - - def __and__(self, other): - return constexpr(self.value & other.value) - - def logical_and(self, other): - return constexpr(self.value and other.value) - - def __or__(self, other): - return constexpr(self.value | other.value) - - def __xor__(self, other): - return constexpr(self.value ^ other.value) - - def logical_or(self, other): - return constexpr(self.value or other.value) - - def __pos__(self): - return constexpr(+self.value) - - def __invert__(self): - return constexpr(~self.value) - - def __pow__(self, other): - return constexpr(self.value ** other.value) - - def __rshift__(self, other): - return constexpr(self.value >> other.value) - - def __lshift__(self, other): - return constexpr(self.value << other.value) - - def __not__(self): - return constexpr(not self.value) - - def __call__(self, *args, **kwds): - return self.value(*args, **kwds) - - -class tensor: - def __init__(self, handle, type: dtype): - # IR handle - self.handle = handle - # Block shape - self.shape = (1, ) - if type.is_block(): - self.shape = type.shape - self.numel = 1 - for s in self.shape: - self.numel *= s - self.numel = constexpr(self.numel) - self.type = type # Tensor type (can be block_type) - # Following the practice in pytorch, dtype is scalar type - self.dtype = type.scalar - self.shape = [constexpr(s) for s in self.shape] - - def __str__(self) -> str: - # ex. "float32[3,4]" - return str(self.dtype) + '[' + ','.join(str(s) for s in self.shape) + ']' - - @builtin - def __add__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.add(self, other, _builder) - - def __radd__(self, other, _builder=None): - return self.__add__(other, _builder=_builder) - - @builtin - def __sub__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.sub(self, other, _builder) - - def __rsub__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.sub(other, self, _builder) - - @builtin - def __mul__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.mul(self, other, _builder) - - def __rmul__(self, other, _builder=None): - return self.__mul__(other, _builder=_builder) - - @builtin - def __truediv__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.truediv(self, other, _builder) - - def __rtruediv__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.truediv(other, self, _builder) - - @builtin - def __floordiv__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.floordiv(self, other, _builder) - - @builtin - def __rfloordiv__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.floordiv(other, self, _builder) - - @builtin - def __mod__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.mod(self, other, _builder) - - @builtin - def __rmod__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.mod(other, self, _builder) - - # unary operators - @builtin - def __neg__(self, _builder=None): - return semantic.minus(self, _builder) - - @builtin - def __invert__(self, _builder=None): - return semantic.invert(self, _builder) - - # bitwise operators - - @builtin - def __and__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.and_(self, other, _builder) - - @builtin - def __rand__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.and_(other, self, _builder) - - @builtin - def __or__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.or_(self, other, _builder) - - @builtin - def __ror__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.or_(other, self, _builder) - - @builtin - def __xor__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.xor_(self, other, _builder) - - @builtin - def __rxor__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.xor_(other, self, _builder) - - @builtin - def __lshift__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.shl(self, other, _builder) - - @builtin - def __rlshift__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.shl(other, self, _builder) - - @builtin - def __rshift__(self, other, _builder=None): - other = _to_tensor(other, _builder) - if self.dtype.is_int_signed(): - return semantic.ashr(self, other, _builder) - else: - return semantic.lshr(self, other, _builder) - - @builtin - def __rrshift__(self, other, _builder=None): - other = _to_tensor(other, _builder) - if self.dtype.is_int_signed(): - return semantic.ashr(other, self, _builder) - else: - return semantic.lshr(other, self, _builder) - - # comparison operators - - # > - @builtin - def __gt__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.greater_than(self, other, _builder) - - @builtin - def __rgt__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.greater_than(other, self, _builder) - - # >= - @builtin - def __ge__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.greater_equal(self, other, _builder) - - @builtin - def __rge__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.greater_equal(other, self, _builder) - - # < - @builtin - def __lt__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.less_than(self, other, _builder) - - @builtin - def __rlt__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.less_than(other, self, _builder) - - # <= - @builtin - def __le__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.less_equal(self, other, _builder) - - @builtin - def __rle__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.less_equal(other, self, _builder) - - # == - @builtin - def __eq__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.equal(self, other, _builder) - - @builtin - def __ne__(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.not_equal(self, other, _builder) - - @builtin - def logical_and(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.logical_and(self, other, _builder) - - @builtin - def logical_or(self, other, _builder=None): - other = _to_tensor(other, _builder) - return semantic.logical_or(self, other, _builder) - - # note: __not__ isn't actually a magic method in python - # but it's ok because our ASTVisitor handles it - @builtin - def __not__(self, _builder=None): - return semantic.not_(self, _builder) - - @builtin - def __getitem__(self, slices, _builder=None): - if isinstance(slices, slice): - slices = [slices] - ret = self - for dim, sl in enumerate(slices): - if isinstance(sl, constexpr) and sl.value is None: - ret = semantic.expand_dims(ret, dim, _builder) - elif isinstance(sl, slice) and sl.start is None and sl.stop is None and sl.step is None: - pass - else: - assert False, f"unsupported tensor index: {sl}" - return ret - - @property - def T(self): - assert False, "Transposition must be created by the AST Visitor" - - @builtin - def to(self, dtype, bitcast=False, _builder=None): - if isinstance(bitcast, constexpr): - bitcast = bitcast.value - if bitcast: - return semantic.bitcast(self, dtype, _builder) - return semantic.cast(self, dtype, _builder) - - -# ----------------------- -# SPMD Programming Model -# ----------------------- -def _constexpr_to_value(v): - if isinstance(v, constexpr): - return v.value - return v - - -@builtin -def program_id(axis, _builder=None): - """ - Returns the id of the current program instance along the given :code:`axis`. - - :param axis: The axis of the 3D launch grid. Has to be either 0, 1 or 2. - :type axis: int - """ - # if axis == -1: - # pid0 = program_id(0, _builder) - # pid1 = program_id(1, _builder) - # pid2 = program_id(2, _builder) - # npg0 = num_programs(0, _builder) - # npg1 = num_programs(0, _builder) - # return pid0 + pid1*npg0 + pid2*npg0*npg1 - axis = _constexpr_to_value(axis) - return semantic.program_id(axis, _builder) - - -@builtin -def num_programs(axis, _builder=None): - """ - Returns the number of program instances launched along the given :code:`axis`. - - :param axis: The axis of the 3D launch grid. Has to be either 0, 1 or 2. - :type axis: int - """ - axis = _constexpr_to_value(axis) - return semantic.num_programs(axis, _builder) - - -# ----------------------- -# Block Initialization -# ----------------------- - - -@builtin -def arange(start, end, _builder=None): - """ - Returns contiguous values within the left-closed and right-open interval [:code:`start`, :code:`end`). \ - End - Start must be less than or equal to TRITON_MAX_TENSOR_NUMEL = 131072 - - :param start: Start of the interval. Must be a power of two. - :type start: int32 - :param end: End of the interval. Must be a power of two > start. - :type end: int32 - """ - start = _constexpr_to_value(start) - end = _constexpr_to_value(end) - return semantic.arange(start, end, _builder) - - -def _shape_check_impl(shape): - shape = _constexpr_to_value(shape) - for i, d in enumerate(shape): - if not isinstance(d, constexpr): - raise TypeError(f"Shape element {i} must have type `constexpr`") - if not isinstance(d.value, int): - raise TypeError(f"Shape element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]") - return [_constexpr_to_value(x) for x in shape] - - -@builtin -def full(shape, value, dtype, _builder=None): - """ - Returns a tensor filled with the scalar value for the given :code:`shape` and :code:`dtype`. - - :param shape: Shape of the new array, e.g., (8, 16) or (8, ) - :value value: A scalar value to fill the array with - :type shape: tuple of ints - :param dtype: Data-type of the new array, e.g., :code:`tl.float16` - :type dtype: DType - """ - shape = _shape_check_impl(shape) - value = _constexpr_to_value(value) - dtype = _constexpr_to_value(dtype) - return semantic.full(shape, value, dtype, _builder) - - -# ----------------------- -# Shape Manipulation -# ----------------------- - - -@builtin -def broadcast(input, other, _builder=None): - """ - Tries to broadcast the two given blocks to a common compatible shape. - - :param input: The first input tensor. - :type input: Block - :param other: The second input tensor. - :type other: Block - """ - return semantic.broadcast_impl_value(input, other, _builder) - - -@builtin -def broadcast_to(input, shape, _builder=None): - """ - Tries to broadcast the given tensor to a new :code:`shape`. - - :param input: The input tensor. - :type input: Block - :param shape: The desired shape. - :type shape: Tuple[int] - """ - shape = _shape_check_impl(shape) - return semantic.broadcast_impl_shape(input, shape, _builder) - - -@builtin -def trans(input, _builder=None): - return semantic.trans(input, _builder) - - -@builtin -def cat(input, other, can_reorder=False, _builder=None): - """ - Concatenate the given blocks - - :param input: The first input tensor. - :type input: - :param other: The second input tensor. - :type other: - :param reorder: Compiler hint. If true, the compiler is - allowed to reorder elements while concatenating inputs. - Only use if the order does not matter (e.g., result is - only used in reduction ops) - """ - return semantic.cat(input, other, can_reorder, _builder) - - -@builtin -def view(input, shape, _builder=None): - """ - Returns a tensor with the same elements as `input` but a different shape. - The order of the elements may not be preserved. - - :param input: The input tensor. - :type input: - :param shape: The desired shape. - :type shape: Tuple[int] - - """ - shape = _shape_check_impl(shape) - return semantic.view(input, shape, _builder) - - -@builtin -def reshape(input, shape, _builder=None): - shape = _shape_check_impl(shape) - return semantic.reshape(input, shape, _builder) - - -def _wrap_axis(axis, ndim): - if not (-ndim <= axis < ndim): - raise ValueError(f"invalid axis {axis}. Expected {-ndim} <= axis < {ndim}") - - return axis if axis >= 0 else axis + ndim - - -@builtin -def expand_dims(input, axis, _builder=None): - """ - Expand the shape of a tensor, by inserting new length-1 dimensions. - - Axis indices are with respect to the resulting tensor, so - ``result.shape[axis]`` will be 1 for each axis. - - :param input: The input tensor. - :type input: tl.tensor - :param axis: The indices to add new axes - :type axis: int | Sequence[int] - - """ - axis = _constexpr_to_value(axis) - axes = list(axis) if isinstance(axis, Sequence) else [axis] - new_ndim = len(input.shape) + len(axes) - axes = [_wrap_axis(_constexpr_to_value(d), new_ndim) for d in axes] - - if len(set(axes)) != len(axes): - raise ValueError(f"expand_dims recieved duplicate axes, normalized axes = {axes}") - - ret = input - for a in sorted(axes): - ret = semantic.expand_dims(ret, a, _builder) - return ret - -# ----------------------- -# Linear Algebra -# ----------------------- - - -@builtin -def dot(input, other, allow_tf32=True, out_dtype=float32, _builder=None): - """ - Returns the matrix product of two blocks. - - The two blocks must be two-dimensional and have compatible inner dimensions. - - :param input: The first tensor to be multiplied. - :type input: 2D tensor of scalar-type in {:code:`float16`, :code:`bfloat16`, :code:`float32`} - :param other: The second tensor to be multiplied. - :type other: 2D tensor of scalar-type in {:code:`float16`, :code:`bfloat16`, :code:`float32`} - """ - allow_tf32 = _constexpr_to_value(allow_tf32) - out_dtype = _constexpr_to_value(out_dtype) - return semantic.dot(input, other, allow_tf32, out_dtype, _builder) - - -# ----------------------- -# Non-Atomic Memory Operations -# ----------------------- - - -@builtin -def load(pointer, mask=None, other=None, boundary_check=tuple(), padding_option="", cache_modifier="", - eviction_policy="", volatile=False, _builder=None): - """ - Return a tensor of data whose values are loaded from memory at location defined by `pointer`: - (1) `pointer` could be a single element pointer, then a scalar will be loaded - - `mask` and `other` must be scalar too - - `other` is implicitly typecast to `pointer.dtype.element_ty` - - `boundary_check` and `padding_option` must be empty - (2) `pointer` could be element-wise tensor of pointers, in which case: - - `mask` and `other` are implicitly broadcast to `pointer.shape` - - `other` is implicitly typecast to `pointer.dtype.element_ty` - - `boundary_check` and `padding_option` must be empty - (3) `pointer` could be a block pointer defined by `make_block_ptr`, in which case: - - `mask` and `other` must be None - - `boundary_check` and `padding_option` can be specified to control the behavior of out-of-bound access - - :param pointer: Pointer to the data to be loaded - :type pointer: `triton.PointerType`, or block of `dtype=triton.PointerType` - :param mask: if `mask[idx]` is false, do not load the data at address `pointer[idx]` - (must be `None` with block pointers) - :type mask: Block of `triton.int1`, optional - :param other: if `mask[idx]` is false, return `other[idx]` - :type other: Block, optional - :param boundary_check: tuple of integers, indicating the dimensions which should do the boundary check - :type boundary_check: tuple of ints, optional - :param padding_option: should be one of {"", "zero", "nan"}, do padding while out of bound - :param cache_modifier: changes cache option in NVIDIA PTX - :type cache_modifier: str, optional - :param eviction_policy: changes eviction policy in NVIDIA PTX - :type eviction_policy: str, optional - :param volatile: changes volatile option in NVIDIA PTX - :type volatile: bool, optional - """ - # `mask` and `other` can be constexpr - if _constexpr_to_value(mask) is not None: - mask = _to_tensor(mask, _builder) - if _constexpr_to_value(other) is not None: - other = _to_tensor(other, _builder) - padding_option = _constexpr_to_value(padding_option) - cache_modifier = _constexpr_to_value(cache_modifier) - eviction_policy = _constexpr_to_value(eviction_policy) - volatile = _constexpr_to_value(volatile) - return semantic.load(pointer, mask, other, boundary_check, padding_option, cache_modifier, eviction_policy, - volatile, _builder) - - -@builtin -def store(pointer, value, mask=None, boundary_check=(), cache_modifier="", eviction_policy="", _builder=None): - """ - Store a tensor of data into memory locations defined by `pointer`: - (1) `pointer` could be a single element pointer, then a scalar will be stored - - `mask` must be scalar too - - `boundary_check` and `padding_option` must be empty - (2) `pointer` could be element-wise tensor of pointers, in which case: - - `mask` is implicitly broadcast to `pointer.shape` - - `boundary_check` must be empty - (3) or `pointer` could be a block pointer defined by `make_block_ptr`, in which case: - - `mask` must be None - - `boundary_check` can be specified to control the behavior of out-of-bound access - `value` is implicitly broadcast to `pointer.shape` and typecast to `pointer.dtype.element_ty`. - - :param pointer: The memory location where the elements of `value` are stored - :type pointer: `triton.PointerType`, or block of `dtype=triton.PointerType` - :param value: The tensor of elements to be stored - :type value: Block - :param mask: If `mask[idx]` is false, do not store `value[idx]` at `pointer[idx]` - :type mask: Block of triton.int1, optional - :param boundary_check: tuple of integers, indicating the dimensions which should do the boundary check - :type boundary_check: tuple of ints, optional - :param cache_modifier: changes cache option in NVIDIA PTX - :type cache_modifier: str, optional - :param eviction_policy: changes eviction policy in NVIDIA PTX - :type eviction_policy: str, optional - """ - # `value` can be constexpr - value = _to_tensor(value, _builder) - if _constexpr_to_value(mask) is not None: - mask = _to_tensor(mask, _builder) - cache_modifier = _constexpr_to_value(cache_modifier) - eviction_policy = _constexpr_to_value(eviction_policy) - return semantic.store(pointer, value, mask, boundary_check, cache_modifier, eviction_policy, _builder) - - -@builtin -def make_block_ptr(base: tensor, shape, strides, offsets, block_shape, order, _builder=None): - """ - Returns a pointer to a block in a parent tensor - - :param base: The base pointer to the parent tensor - :param shape: The shape of the parent tensor - :param strides: The strides of the parent tensor - :param offsets: The offsets to the block - :param block_shape: The shape of the block - :param order: The order of the original data format - """ - return semantic.make_block_ptr(base, shape, strides, offsets, block_shape, order, _builder) - - -@builtin -def advance(base: tensor, offsets, _builder=None): - """ - Advance a block pointer - - :param base: the block pointer to advance - :param offsets: the offsets to advance, a tuple by dimension - """ - return semantic.advance(base, offsets, _builder) - -# ----------------------- -# Atomic Memory Operations -# ----------------------- - - -def _add_atomic_docstr(name: str) -> Callable[[T], T]: - - def _decorator(func: T) -> T: - docstr = """ - Performs an atomic {name} at the memory location specified by :code:`pointer`. - - Return the data stored at :code:`pointer` before the atomic operation. - - :param pointer: The memory locations to compare-and-swap. - :type pointer: Block of dtype=triton.PointerDType - :param cmp: The values expected to be found in the atomic object - :type cmp: Block of dtype=`pointer.dtype.element_ty` - :param val: The values to copy in case the expected value matches the contained value. - :type val: Block of dtype=`pointer.dtype.element_ty` - """ - func.__doc__ = docstr.format(name=name) - return func - - return _decorator - - -@builtin -@_add_atomic_docstr("compare-and-swap") -def atomic_cas(pointer, cmp, val, sem=None, _builder=None): - cmp = _to_tensor(cmp, _builder) - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_cas(pointer, cmp, val, sem, _builder) - - -@builtin -@_add_atomic_docstr("exchange") -def atomic_xchg(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_xchg(pointer, val, mask, sem, _builder) - - -@builtin -@_add_atomic_docstr("add") -def atomic_add(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_add(pointer, val, mask, sem, _builder) - - -@builtin -@_add_atomic_docstr("max") -def atomic_max(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_max(pointer, val, mask, sem, _builder) - - -@builtin -@_add_atomic_docstr("min") -def atomic_min(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_min(pointer, val, mask, sem, _builder) - - -@builtin -@_add_atomic_docstr("logical and") -def atomic_and(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_and(pointer, val, mask, sem, _builder) - - -@builtin -@_add_atomic_docstr("logical or") -def atomic_or(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_or(pointer, val, mask, sem, _builder) - - -@builtin -@_add_atomic_docstr("logical xor") -def atomic_xor(pointer, val, mask=None, sem=None, _builder=None): - val = _to_tensor(val, _builder) - sem = _constexpr_to_value(sem) - return semantic.atomic_xor(pointer, val, mask, sem, _builder) - - -# ----------------------- -# Conditioning -# ----------------------- - -@builtin -def where(condition, x, y, _builder=None): - """ - Returns a tensor of elements from either :code:`x` or :code:`y`, depending on :code:`condition`. - - Note that :code:`x` and :code:`y` are always evaluated regardless of the value of :code:`condition`. - - If you want to avoid unintended memory operations, use the :code:`mask` arguments in `triton.load` and `triton.store` instead. - - The shape of :code:`x` and :code:`y` are both broadcast to the shape of :code:`condition`. - :code:`x` and :code:`y` must have the same data type. - - :param condition: When True (nonzero), yield x, otherwise yield y. - :type condition: Block of triton.bool - :param x: values selected at indices where condition is True. - :param y: values selected at indices where condition is False. - """ - condition = _to_tensor(condition, _builder) - x = _to_tensor(x, _builder) - y = _to_tensor(y, _builder) - return semantic.where(condition, x, y, _builder) - - -# ----------------------- -# Math -# ----------------------- - -@builtin -def umulhi(x, y, _builder=None): - x = _to_tensor(x, _builder) - y = _to_tensor(y, _builder) - return semantic.umulhi(x, y, _builder) - - -@builtin -def fdiv(x, y, ieee_rounding=False, _builder=None): - ieee_rounding = _constexpr_to_value(ieee_rounding) - return semantic.fdiv(x, y, ieee_rounding, _builder) - - -def _add_math_1arg_docstr(name: str) -> Callable[[T], T]: - - def _decorator(func: T) -> T: - docstr = """ - Computes the element-wise {name} of :code:`x`. - - :param x: the input values - :type x: Block - """ - func.__doc__ = docstr.format(name=name) - return func - - return _decorator - - -@builtin -@_add_math_1arg_docstr("exponential") -def exp(x, _builder=None): - return semantic.exp(x, _builder) - - -@builtin -@_add_math_1arg_docstr("natural logarithm") -def log(x, _builder=None): - return semantic.log(x, _builder) - - -@builtin -@_add_math_1arg_docstr("cosine") -def cos(x, _builder=None): - return semantic.cos(x, _builder) - - -@builtin -@_add_math_1arg_docstr("sine") -def sin(x, _builder=None): - return semantic.sin(x, _builder) - - -@builtin -@_add_math_1arg_docstr("square root") -def sqrt(x, _builder=None): - return semantic.sqrt(x, _builder) - - -@builtin -@_add_math_1arg_docstr("absolute value") -def abs(x, _builder=None): - return semantic.abs(x, _builder) - - -# ----------------------- -# Reductions -# ----------------------- - -def _add_reduction_docstr(name: str, return_indices_arg: str = None, tie_break_arg: str = None) -> Callable[[T], T]: - - def _decorator(func: T) -> T: - docstr = """ - Returns the {name} of all elements in the :code:`input` tensor along the provided :code:`axis` - - :param input: the input values - :param axis: the dimension along which the reduction should be done""" - if return_indices_arg is not None: - docstr += f""" - :param {return_indices_arg}: if true, return index corresponding to the {name} value""" - if tie_break_arg is not None: - docstr += f""" - :param {tie_break_arg}: if true, return the left-most indices in case of ties for values that aren't NaN""" - - func.__doc__ = docstr.format(name=name) - return func - - return _decorator - - -@contextmanager -def _insertion_guard(builder): - ip = builder.get_insertion_point() - yield - builder.restore_insertion_point(ip) - - -@builtin -def reduce(input, axis, combine_fn, _builder=None, _generator=None): - """Applies the combine_fn to all elements in :code:`input` tensors along the provided :code:`axis` - - :param input: the input tensor, or tuple of tensors - :param axis: the dimension along which the reduction should be done - :param combine_fn: a function to combine two groups of scalar tensors (must be marked with @triton.jit) - - """ - if isinstance(input, tensor): - return reduce((input,), axis, combine_fn, - _builder=_builder, _generator=_generator)[0] - - def make_combine_region(reduce_op): - in_scalar_tys = [t.type.scalar for t in input] - prototype = function_type(in_scalar_tys, in_scalar_tys * 2) - - region = reduce_op.get_region(0) - with _insertion_guard(_builder): - param_types = [ty.to_ir(_builder) for ty in prototype.param_types] - block = _builder.create_block_with_parent(region, param_types) - args = [tensor(block.arg(i), ty) - for i, ty in enumerate(prototype.param_types)] - results = _generator.call_JitFunction(combine_fn, args, kwargs={}) - if isinstance(results, tensor): - handles = [results.handle] - else: - handles = [r.handle for r in results] - _builder.create_reduce_ret(*handles) - if axis is not None: - axis = _constexpr_to_value(axis) - return semantic.reduction(input, axis, make_combine_region, _builder) - - -@builtin -def _promote_reduction_input(t, _builder=None): - scalar_ty = t.type.scalar - # input is extended to 32-bits if necessary - # this increases numerical accuracy and can be done pretty much for free - # on GPUs - if scalar_ty.is_int() and scalar_ty.int_bitwidth < 32: - return t.to(int32, _builder=_builder) - - # hardware doesn't support FMAX, FMIN, CMP for bfloat16 - if scalar_ty is bfloat16: - return t.to(float32, _builder=_builder) - - return t - - -@builtin -def _reduce_with_indices(input, axis, combine_fn, _builder=None, _generator=None): - axis = _constexpr_to_value(axis) - n = input.shape[axis] - index = arange(0, n, _builder=_builder) - - if len(input.shape) > 1: - # Broadcast index across the non-reduced axes - axes_to_expand = [constexpr(d) for d in range(len(input.shape))] - del axes_to_expand[axis] - index = expand_dims(index, axes_to_expand, _builder=_builder) - index = broadcast_to(index, input.shape, _builder=_builder) - - rvalue, rindices = reduce((input, index), axis, combine_fn, - _builder=_builder, _generator=_generator) - return rvalue, rindices - - -@jit -def minimum(x, y): - """ - Computes the element-wise minimum of :code:`x` and :code:`y`. - - :param input: the first input tensor - :type input: Block - :param other: the second input tensor - :type other: Block - """ - return where(x < y, x, y) - - -@jit -def maximum(x, y): - """ - Computes the element-wise maximum of :code:`x` and :code:`y`. - - :param input: the first input tensor - :type input: Block - :param other: the second input tensor - :type other: Block - """ - return where(x > y, x, y) - -# max and argmax - - -@jit -def _argmax_combine(value1, index1, value2, index2, tie_break_left): - if tie_break_left: - tie = value1 == value2 and index1 < index2 - else: - tie = False - gt = value1 > value2 or tie - v_ret = where(gt, value1, value2) - i_ret = where(gt, index1, index2) - return v_ret, i_ret - - -@jit -def _argmax_combine_tie_break_left(value1, index1, value2, index2): - return _argmax_combine(value1, index1, value2, index2, True) - - -@jit -def _argmax_combine_tie_break_fast(value1, index1, value2, index2): - return _argmax_combine(value1, index1, value2, index2, False) - - -@jit -@_add_reduction_docstr("maximum", - return_indices_arg="return_indices", - tie_break_arg="return_indices_tie_break_left") -def max(input, axis=None, return_indices=False, return_indices_tie_break_left=True): - input = _promote_reduction_input(input) - if return_indices: - if return_indices_tie_break_left: - return _reduce_with_indices(input, axis, _argmax_combine_tie_break_left) - else: - return _reduce_with_indices(input, axis, _argmax_combine_tie_break_fast) - else: - return reduce(input, axis, maximum) - - -@jit -@_add_reduction_docstr("maximum index", tie_break_arg="tie_break_left") -def argmax(input, axis, tie_break_left=True): - (_, ret) = max(input, axis, return_indices=True, return_indices_tie_break_left=tie_break_left) - return ret - -# min and argmin - - -@jit -def _argmin_combine(value1, index1, value2, index2, tie_break_left): - if tie_break_left: - tie = value1 == value2 and index1 < index2 - else: - tie = False - lt = value1 < value2 or tie - value_ret = where(lt, value1, value2) - index_ret = where(lt, index1, index2) - return value_ret, index_ret - - -@jit -def _argmin_combine_tie_break_left(value1, index1, value2, index2): - return _argmin_combine(value1, index1, value2, index2, True) - - -@jit -def _argmin_combine_tie_break_fast(value1, index1, value2, index2): - return _argmin_combine(value1, index1, value2, index2, False) - - -@jit -@_add_reduction_docstr("minimum", - return_indices_arg="return_indices", - tie_break_arg="return_indices_tie_break_left") -def min(input, axis=None, return_indices=False, return_indices_tie_break_left=True): - input = _promote_reduction_input(input) - if return_indices: - if return_indices_tie_break_left: - return _reduce_with_indices(input, axis, _argmin_combine_tie_break_left) - else: - return _reduce_with_indices(input, axis, _argmin_combine_tie_break_fast) - else: - return reduce(input, axis, minimum) - - -@jit -@_add_reduction_docstr("minimum index", - tie_break_arg="tie_break_left") -def argmin(input, axis, tie_break_left=True): - _, ret = min(input, axis, return_indices=True, return_indices_tie_break_left=tie_break_left) - return ret - - -@jit -def _sum_combine(a, b): - return a + b - -# sum - - -@jit -@_add_reduction_docstr("sum") -def sum(input, axis=None): - input = _promote_reduction_input(input) - return reduce(input, axis, _sum_combine) - - -@jit -def _xor_combine(a, b): - return a ^ b - - -# xor sum - -@builtin -@_add_reduction_docstr("xor sum") -def xor_sum(input, axis=None, _builder=None, _generator=None): - scalar_ty = input.type.scalar - if not scalar_ty.is_int(): - raise ValueError("xor_sum only supported for integers") - - input = _promote_reduction_input(input, _builder=_builder) - return reduce(input, axis, _xor_combine, - _builder=_builder, _generator=_generator) - - -# ----------------------- -# Scans -# ----------------------- - -def _add_scan_docstr(name: str, return_indices_arg: str = None, tie_break_arg: str = None) -> Callable[[T], T]: - - def _decorator(func: T) -> T: - docstr = """ - Returns the {name} of all elements in the :code:`input` tensor along the provided :code:`axis` - - :param input: the input values - :param axis: the dimension along which the scan should be done""" - func.__doc__ = docstr.format(name=name) - return func - - return _decorator - - -@builtin -def associative_scan(input, axis, combine_fn, _builder=None, _generator=None): - """Applies the combine_fn to each elements with a carry in :code:`input` tensors along the provided :code:`axis` and update the carry - - :param input: the input tensor, or tuple of tensors - :param axis: the dimension along which the reduction should be done - :param combine_fn: a function to combine two groups of scalar tensors (must be marked with @triton.jit) - - """ - if isinstance(input, tensor): - return associative_scan((input,), axis, combine_fn, - _builder=_builder, _generator=_generator)[0] - - def make_combine_region(scan_op): - in_scalar_tys = [t.type.scalar for t in input] - prototype = function_type(in_scalar_tys, in_scalar_tys * 2) - - region = scan_op.get_region(0) - with _insertion_guard(_builder): - param_types = [ty.to_ir(_builder) for ty in prototype.param_types] - block = _builder.create_block_with_parent(region, param_types) - args = [tensor(block.arg(i), ty) - for i, ty in enumerate(prototype.param_types)] - results = _generator.call_JitFunction(combine_fn, args, kwargs={}) - if isinstance(results, tensor): - handles = [results.handle] - else: - handles = [r.handle for r in results] - _builder.create_scan_ret(*handles) - axis = _constexpr_to_value(axis) - return semantic.associative_scan(input, axis, make_combine_region, _builder) - -# cumsum - - -@jit -@_add_scan_docstr("cumsum") -def cumsum(input, axis=0): - # todo rename this to a generic function name - input = _promote_reduction_input(input) - return associative_scan(input, axis, _sum_combine) - -# cumprod - - -@jit -def _prod_combine(a, b): - return a * b - - -@jit -@_add_scan_docstr("cumprod") -def cumprod(input, axis=0): - # todo rename this to a generic function name - input = _promote_reduction_input(input) - return associative_scan(input, axis, _prod_combine) - -# ----------------------- -# Compiler Hint Ops -# ----------------------- - - -@builtin -def debug_barrier(_builder=None): - ''' - Insert a barrier to synchronize all threads in a block. - ''' - return semantic.debug_barrier(_builder) - - -@builtin -def multiple_of(input, values, _builder=None): - """ - Let the compiler knows that the values in :code:`input` are all multiples of :code:`value`. - """ - if isinstance(values, constexpr): - values = [values] - for i, d in enumerate(values): - if not isinstance(d, constexpr): - raise TypeError(f"values element {i} must have type `constexpr`") - if not isinstance(d.value, int): - raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]") - values = [x.value for x in values] - return semantic.multiple_of(input, values) - - -@builtin -def max_contiguous(input, values, _builder=None): - """ - Let the compiler knows that the `value` first values in :code:`input` are contiguous. - """ - if isinstance(values, constexpr): - values = [values] - for i, d in enumerate(values): - if not isinstance(d, constexpr): - raise TypeError(f"values element {i} must have type `constexpr`") - if not isinstance(d.value, int): - raise TypeError(f"values element {i} must have type `constexpr[int]`, got `constexpr[{type(d.value)}]") - values = [x.value for x in values] - return semantic.max_contiguous(input, values) - -# ----------------------- -# Debugging functions -# ----------------------- - - -@builtin -def static_print(*values, sep: str = " ", end: str = "\n", file=None, flush=False, _builder=None): - ''' - Print the values at compile time. The parameters are the same as the builtin :code:`print`. - - NOTE: Calling the Python builtin :code:`print` is not the same as calling this, it instead maps to :code:`device_print`, - which has special requirements for the arguments. - - .. highlight:: python - .. code-block:: python - - tl.static_print(f"{BLOCK_SIZE=}") - ''' - pass - - -@builtin -def static_assert(cond, msg="", _builder=None): - ''' - Assert the condition at compile time. Does not require that the :code:`TRITON_DEBUG` environment variable - is set. - - .. highlight:: python - .. code-block:: python - - tl.static_assert(BLOCK_SIZE == 1024) - ''' - pass - - -@builtin -def device_print(prefix, *args, _builder=None): - ''' - Print the values at runtime from the device. String formatting does not work for runtime values, so you should - provide the values you want to print as arguments. The first value must be a string, all following values must - be scalars or tensors. - - Calling the Python builtin :code:`print` is the same as calling this function, and the requirements for the arguments will match - this function (not the normal requirements for :code:`print`). - - .. highlight:: python - .. code-block:: python - - tl.device_print("pid", pid) - print("pid", pid) - - :param prefix: a prefix to print before the values. This is required to be a string literal. - :param args: the values to print. They can be any tensor or scalar. - ''' - import string - prefix = _constexpr_to_value(prefix) - assert isinstance(prefix, str), f"{prefix} is not string" - b_ascii = True - for ch in prefix: - if ch not in string.printable: - b_ascii = False - break - assert b_ascii, f"{prefix} is not an ascii string" - new_args = [] - for arg in args: - new_args.append(_to_tensor(arg, _builder)) - return semantic.device_print(prefix, new_args, _builder) - - -@builtin -def device_assert(cond, msg="", _builder=None): - ''' - Assert the condition at runtime from the device. Requires that the environment variable :code:`TRITON_DEBUG` - is set to a value besides :code:`0` in order for this to have any effect. - - Using the Python :code:`assert` statement is the same as calling this function, except that the second argument - must be provided and must be a string, e.g. :code:`assert pid == 0, "pid != 0"`. The environment variable must - be set for this :code:`assert` statement to have any effect. - - .. highlight:: python - .. code-block:: python - - tl.device_assert(pid == 0) - assert pid == 0, f"pid != 0" - - :param cond: the condition to assert. This is required to be a boolean tensor. - :param msg: the message to print if the assertion fails. This is required to be a string literal. - ''' - msg = _constexpr_to_value(msg) - import inspect - frame = inspect.currentframe() - module = inspect.getmodule(frame) - # The triton function module doesn't have the name attribute. - # We use this trick to find the caller. - while hasattr(module, "__name__"): - frame = frame.f_back - module = inspect.getmodule(frame) - lineno = 0 - func_name = 'unknown' - file_name = 'unknown' - if frame is not None: - func_name = frame.f_code.co_name - file_name = frame.f_back.f_code.co_filename - # TODO: The line number currently indicates the line - # where the triton function is called but not where the - # device_assert is called. Need to enhance this. - lineno = frame.f_back.f_lineno - return semantic.device_assert(_to_tensor(cond, _builder), msg, file_name, func_name, lineno, _builder) - - -# ----------------------- -# Iterators -# ----------------------- - - -class static_range: - - """ - Iterator that counts upward forever. - - .. highlight:: python - .. code-block:: python - - @triton.jit - def kernel(...): - for i in tl.static_range(10): - ... - :note: This is a special iterator used to implement similar semantics to Python's :code:`range` in the context of - :code:`triton.jit` functions. In addition, it also guides the compiler to unroll the loop aggressively. - :param arg1: the start value. - :param arg2: the end value. - :param step: the step value. - """ - - def __init__(self, arg1, arg2=None, step=None): - assert isinstance(arg1, constexpr) - if step is None: - self.step = constexpr(1) - else: - assert isinstance(step, constexpr) - self.step = step - if arg2 is None: - self.start = constexpr(0) - self.end = arg1 - else: - assert isinstance(arg2, constexpr) - self.start = arg1 - self.end = arg2 - - def __iter__(self): - raise RuntimeError("static_range can only be used in @triton.jit'd functions") - - def __next__(self): - raise RuntimeError("static_range can only be used in @triton.jit'd functions") - - -# ----------------------- -# Extern functions -# ----------------------- - -def dispatch(func, lib_name: str, lib_path: str, args: list, arg_type_symbol_dict: dict, ret_shape: tuple, is_pure: bool, _builder=None): - ''' - Dispatch a function to a library - :param func: the function to dispatch - :param lib_name: the name of the library - :param lib_path: the path of the library - :param args: the arguments of the function - :param arg_type_symbol_dict: the type of the arguments - :param ret_shape: the shape of the return value - :param _builder: the builder - :return: the return value of the function - ''' - if len(arg_type_symbol_dict) == 0: - raise ValueError("arg_type_symbol_dict is empty") - - num_args = len(list(arg_type_symbol_dict.keys())[0]) - if len(args) != num_args: - raise ValueError(f"length of input args does not match." - f"Expect {len(args)}, got {num_args}") - - arg_types = [] - arg_list = [] - for arg in args: - if isinstance(arg, tensor): - arg_types.append(arg.dtype) - arg_list.append(arg.handle) - else: - arg_types.append(type(arg)) - arg_list.append(arg) - arg_types = tuple(arg_types) - - if arg_types not in arg_type_symbol_dict: - raise ValueError(f"input arg type does not match." - f"Expect one of {arg_type_symbol_dict.keys()}, got {arg_types}") - else: - symbol = arg_type_symbol_dict[arg_types][0] - ret_type = arg_type_symbol_dict[arg_types][1] - if ret_shape: - ret_type = block_type(ret_type, ret_shape) - return tensor(func(lib_name, lib_path, symbol, arg_list, ret_type.to_ir(_builder), is_pure), ret_type) - - -def extern_elementwise(lib_name: str, lib_path: str, args: list, arg_type_symbol_dict: dict, is_pure: bool, _builder=None): - ''' - Dispatch an elementwise function to a library - :param lib_name: the name of the library - :param lib_path: the path of the library - :param args: the arguments of the function - :param arg_type_symbol_dict: the type of the arguments - :param is_pure: whether the function is pure - :param _builder: the builder - :return: the return value of the function - ''' - dispatch_args = args.copy() - all_scalar = True - ret_shape = None - arg_types = [] - for i in range(len(dispatch_args)): - dispatch_args[i] = _to_tensor(dispatch_args[i], _builder) - arg_types.append(dispatch_args[i].dtype) - if dispatch_args[i].type.is_block(): - all_scalar = False - if len(arg_types) > 0: - arg_types = tuple(arg_types) - arithmetic_check = True - # If there's a type tuple that is not supported by the library, we will do arithmetic check - if arg_types in arg_type_symbol_dict: - arithmetic_check = False - broadcast_arg = dispatch_args[0] - # Get the broadcast shape over all the arguments - for i, item in enumerate(dispatch_args): - _, broadcast_arg = semantic.binary_op_type_checking_impl( - item, broadcast_arg, _builder, arithmetic_check=arithmetic_check) - # Change the shape of each argument based on the broadcast shape - for i in range(len(dispatch_args)): - dispatch_args[i], _ = semantic.binary_op_type_checking_impl( - dispatch_args[i], broadcast_arg, _builder, arithmetic_check=arithmetic_check) - if not all_scalar: - ret_shape = broadcast_arg.shape - func = getattr(_builder, "create_extern_elementwise") - return dispatch(func, lib_name, lib_path, dispatch_args, arg_type_symbol_dict, ret_shape, is_pure, _builder) - - -def extern(fn): - """A decorator for external functions.""" - return builtin(fn) diff --git a/python/triton/language/extra/__init__.py b/python/triton/language/extra/__init__.py deleted file mode 100644 index 2fd0ff3eeee3..000000000000 --- a/python/triton/language/extra/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import cuda - -__all__ = ['cuda'] diff --git a/python/triton/language/extra/cuda.bc b/python/triton/language/extra/cuda.bc deleted file mode 100644 index 4538ac35446a74fab56fc6424aef0c5b99311d93..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1808 zcmX|BZ){Ul6u;fudh1s1t1zKs?>*jg0~ba1C<-lY$=a%!im{*{W}=~QjL8J%TUWX= z2x-^i8*wp(?8Bm%bcPT5t>`|WnM(I2mf4U=j4rw?t?R}xV@^S0B8i^cF>i9tx##{l z_jmr>_?I!Dsl{Q;{vpO!-J+oFDuz1dw zwPj70szu*({o*x)&6cn}-tc&G`>FKn=R`dVcF&3k!E@-*_HxUo-o9&={lJo4WB%sK zhqm~xR(Ne&OyvDKbY^Yad&vAkIu?BxP9wC>ijWax?Lhut7`O>tT$5O)Q$_^n=y}+u z*w^(1ydsrbdeS(O!l%;A?!!z^S__F<$fWJGYQ3VSP6aCGCXKgK82GY?dl2rGaWI9u zMSKjgK^1qE=5%r8fQWkoIF!QSCVVVOfKzqSI3LAo3U`S(GHG0j;$905uUstcerCbJ zDBdXTC6E1dWZR~%Cn8_C?r;3*nzG>o_r(C0uqOi^yUz_nK!-2Fa#LLcx(@relT9_ljBBlfn1+>MeQX0I@o;ja&K?Q%uw zSiCmJxU)X@0OQU@20|)6X~HL(HC4sEDie-qs;JfE9wv)PQjpnBnVnrG%YL%MGS1IP z+?2$RDMGt^);St?PWl`pjB7ORN-)mca&Paej*4r@W+QB6&1Y5<2DaQN>EFxwN= z4w$qqs}>y6P7KAiE-jO#Jo)PiS(KU9>@Zgx=5i%It#A_(Hy-0B6yuLcpCjvY43+q0 z-9G5E_cP8xy%8S0-{<^+ai(?iw;h_=q6O7>=rE21w2)N`rL>ysudqvq{nJm1Niy5S z7DTcLUWg6+bVeBS2sb2dIwnl#xnhC;%^EOKWz{wV2NK=jIW-F+jF`z?Q=sw?vJ^OK4(9u`oK4p;HlOFcucsr z0SAX-ug_Y@;uSLICwJt!%ngrFENqx6@G}6QxyG4}yGP=+3Ee*6bL4c_<%}b%gO9FU z^GKHo1F9|vV2f7s%T=-%U~lK4RM|P1ofFx6e$r_nvoZnoPTqJcPl|r>w_Nw@qQZ^E z_^HB%MTwu6_;HDc3@%8U+Qko@qdxa##xWRo(77Q~xu)?HKQYYbJ;F?eYp*{jlLE~z z3r6N-pAxNPai`)gDAuG-dyyg{v=-#La?e)!p=rNM z{IN&M{#@V}Gi!d!kOgGUA~J={6L}8mVH`eMOt>-3Pk4lTO$$sIp*mv%^##Q8TDYr- zD~(~*US-LiT$X%bPa(zs?FEFNB|fXLi9N{@))hNVB|BYUhk?5`VtF)yrpv6T5j2Dj z0l>{5*D`|*6hA$~0zCw@7l^L40MS7u?X&$> 32) & 0xffffffff).to(tl.uint32) - seed_lo = (seed & 0xffffffff).to(tl.uint32) - c0 = c0.to(tl.uint32, bitcast=True) - c1 = c1.to(tl.uint32, bitcast=True) - c2 = c2.to(tl.uint32, bitcast=True) - c3 = c3.to(tl.uint32, bitcast=True) - return philox_impl(c0, c1, c2, c3, seed_lo, seed_hi, n_rounds) - - -@jit -def randint(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): - """ - Given a :code:`seed` scalar and an :code:`offset` block, returns a single - block of random :code:`int32`. - - If you need multiple streams of random numbers, - using `randint4x` is likely to be faster than calling `randint` 4 times. - - :param seed: The seed for generating random numbers. - :param offsets: The offsets to generate random numbers for. - """ - ret, _, _, _ = randint4x(seed, offset, n_rounds) - return ret - - -@jit -def randint4x(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): - """ - Given a :code:`seed` scalar and an :code:`offset` block, returns four - blocks of random :code:`int32`. - - This is the maximally efficient entry point - to Triton's Philox pseudo-random number generator. - - :param seed: The seed for generating random numbers. - :param offsets: The offsets to generate random numbers for. - """ - # _0 = tl.zeros(offset.shape, offset.dtype) - _0 = offset * 0 - return philox(seed, offset, _0, _0, _0, n_rounds) - - -# ------------------- -# rand -# ------------------- - -# @jit -# def uint32_to_uniform_float(x): -# """ -# Numerically stable function to convert a random uint32 into a random float uniformly sampled in [0, 1). -# """ -# two_to_the_minus_32: tl.constexpr = 2.328306e-10 -# return x * two_to_the_minus_32 - -@jit -def uint32_to_uniform_float(x): - """ - Numerically stable function to convert a random uint32 into a random float uniformly sampled in [0, 1). - """ - x = x.to(tl.int32, bitcast=True) - # maximum value such that `MAX_INT * scale < 1.0` (with float rounding) - scale = 4.6566127342e-10 - x = tl.where(x < 0, -x - 1, x) - return x * scale - - -@jit -def rand(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): - """ - Given a :code:`seed` scalar and an :code:`offset` block, - returns a block of random :code:`float32` in :math:`U(0, 1)`. - - :param seed: The seed for generating random numbers. - :param offsets: The offsets to generate random numbers for. - """ - offset = offset.to(tl.uint32, bitcast=True) - source = randint(seed, offset, n_rounds) - return uint32_to_uniform_float(source) - - -@jit -def rand4x(seed, offsets, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): - """ - Given a :code:`seed` scalar and an :code:`offsets` block, - returns a 4 blocks of random :code:`float32` in :math:`U(0, 1)`. - - :param seed: The seed for generating random numbers. - :param offsets: The offsets to generate random numbers for. - """ - offsets = offsets.to(tl.uint32, bitcast=True) - i1, i2, i3, i4 = randint4x(seed, offsets, n_rounds) - u1 = uint32_to_uniform_float(i1) - u2 = uint32_to_uniform_float(i2) - u3 = uint32_to_uniform_float(i3) - u4 = uint32_to_uniform_float(i4) - return u1, u2, u3, u4 - -# ------------------- -# randn -# ------------------- - - -@jit -def pair_uniform_to_normal(u1, u2): - """Box-Muller transform""" - u1 = tl.maximum(1.0e-7, u1) - th = 6.283185307179586 * u2 - r = tl.sqrt(-2.0 * tl.log(u1)) - return r * tl.cos(th), r * tl.sin(th) - - -@jit -def randn(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): - """ - Given a :code:`seed` scalar and an :code:`offset` block, - returns a block of random :code:`float32` in :math:`\\mathcal{N}(0, 1)`. - - :param seed: The seed for generating random numbers. - :param offsets: The offsets to generate random numbers for. - """ - i1, i2, _, _ = randint4x(seed, offset, n_rounds) - u1 = uint32_to_uniform_float(i1) - u2 = uint32_to_uniform_float(i2) - n1, _ = pair_uniform_to_normal(u1, u2) - return n1 - - -@jit -def randn4x(seed, offset, n_rounds: tl.constexpr = N_ROUNDS_DEFAULT): - """ - Given a :code:`seed` scalar and an :code:`offset` block, - returns a 4 blocks of random :code:`float32` in :math:`\\mathcal{N}(0, 1)`. - - :param seed: The seed for generating random numbers. - :param offsets: The offsets to generate random numbers for. - """ - u1, u2, u3, u4 = rand4x(seed, offset, n_rounds) - n1, n2 = pair_uniform_to_normal(u1, u2) - n3, n4 = pair_uniform_to_normal(u3, u4) - return n1, n2, n3, n4 diff --git a/python/triton/language/semantic.py b/python/triton/language/semantic.py deleted file mode 100644 index a402384c8c73..000000000000 --- a/python/triton/language/semantic.py +++ /dev/null @@ -1,1538 +0,0 @@ -from __future__ import annotations # remove after python 3.11 - -import warnings -from functools import wraps -from typing import List, Optional, Sequence, Tuple, TypeVar - -from .._C.libtriton.triton import ir -from . import core as tl - -T = TypeVar('T') - -# Create custom exception that prints message "hello" - - -class IncompatibleTypeErrorImpl(Exception): - def __init__(self, type_a, type_b): - self.type_a = type_a - self.type_b = type_b - self.message = "invalid operands of type " + self.type_a.__repr__() + " and " + self.type_b.__repr__() - super(IncompatibleTypeErrorImpl, self).__init__(self.message) - - -# ===----------------------------------------------------------------------===## -# Programming Model -# ===----------------------------------------------------------------------===## - -def program_id(axis: int, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_get_program_id(axis), tl.int32) - - -def num_programs(axis: int, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_get_num_programs(axis), tl.int32) - -# ===----------------------------------------------------------------------===// -# Implicit Casting Utilities -# ===----------------------------------------------------------------------===// - - -def integer_promote_impl(a_ty: tl.dtype, b_ty: tl.dtype) -> tl.dtype: - a_rank = a_ty.int_bitwidth - b_rank = b_ty.int_bitwidth - a_sn = a_ty.int_signedness - b_sn = b_ty.int_signedness - # Rules for signedness taken from "Usual arithmetic conversions" on - # https://en.cppreference.com/w/c/language/conversion. - if a_sn == b_sn: - return a_ty if a_rank > b_rank else b_ty - elif a_sn == tl.dtype.SIGNEDNESS.UNSIGNED: - return a_ty if a_rank >= b_rank else b_ty - elif b_sn == tl.dtype.SIGNEDNESS.UNSIGNED: - return b_ty if b_rank >= a_rank else a_ty - assert False - - -def computation_type_impl(a_ty: tl.dtype, b_ty: tl.dtype, div_or_mod: bool) -> tl.dtype: - # 1) if one operand is double, the other is implicitly - # converted to double - if a_ty.is_fp64() or b_ty.is_fp64(): - return tl.float64 - # 2) if one operand is float, the other is implicitly - # converted to float - if a_ty.is_fp32() or b_ty.is_fp32(): - return tl.float32 - # 3 ) if one operand is half, the other is implicitly converted to half - # unless we're doing / or %, which do not exist natively in PTX for fp16. - # Supported PTX op: add, sub, mul, fma, neg, abs, min, max, tanh, ex2, setp - if a_ty.is_fp16() or b_ty.is_fp16(): - if div_or_mod: - return tl.float32 - else: - return tl.float16 - # 4) return bf16 only if both operands are of bf16 - if a_ty.is_bf16() or b_ty.is_bf16(): - if div_or_mod: - return tl.float32 - if a_ty.is_bf16() and b_ty.is_bf16(): - return tl.bfloat16 - return tl.float32 - if not a_ty.is_int() or not b_ty.is_int(): - assert False - # 5 ) both operands are integer and undergo - # integer promotion - if div_or_mod and a_ty.int_signedness != b_ty.int_signedness: - raise ValueError("Cannot use /, #, or % with " + a_ty.__repr__() + " and " + b_ty.__repr__() + " because they have different signedness;" - "this is unlikely to result in a useful answer. Cast them to the same signedness.") - return integer_promote_impl(a_ty, b_ty) - -# ===----------------------------------------------------------------------===// -# Binary Operators -# ===----------------------------------------------------------------------===// - - -def check_ptr_type_impl(type_a: tl.dtype, type_b: tl.dtype, allow_ptr_a: bool) -> None: - if type_a.is_ptr(): - if not allow_ptr_a: - raise IncompatibleTypeErrorImpl(type_a, type_b) - # T* + U* with T != U - if type_b.is_ptr() and (type_a != type_b): - raise IncompatibleTypeErrorImpl(type_a, type_b) - # T* + float - if type_b.is_floating(): - raise IncompatibleTypeErrorImpl(type_a, type_b) - - -def binary_op_type_checking_impl(lhs: tl.tensor, - rhs: tl.tensor, - builder: ir.builder, - allow_lhs_ptr=False, allow_rhs_ptr=False, - arithmetic_check=True, div_or_mod=False - ) -> Tuple[tl.tensor, tl.tensor]: - # implicit broadcasting - lhs, rhs = broadcast_impl_value(lhs, rhs, builder) - # implicit typecasting - lhs_sca_ty = lhs.type.scalar - rhs_sca_ty = rhs.type.scalar - check_ptr_type_impl(lhs_sca_ty, rhs_sca_ty, allow_lhs_ptr) - check_ptr_type_impl(rhs_sca_ty, lhs_sca_ty, allow_rhs_ptr) - if arithmetic_check and not lhs_sca_ty.is_ptr() and not rhs_sca_ty.is_ptr(): - ret_sca_ty = computation_type_impl(lhs_sca_ty, rhs_sca_ty, div_or_mod) - lhs = cast(lhs, ret_sca_ty, builder) - rhs = cast(rhs, ret_sca_ty, builder) - return lhs, rhs - - -def add(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder, True, True) - input_scalar_ty = input.type.scalar - other_scalar_ty = other.type.scalar - - # offset + ptr - # ptr + offset - if other_scalar_ty.is_ptr() and not input_scalar_ty.is_ptr(): - input, other = other, input - if input_scalar_ty.is_ptr(): - return tl.tensor(builder.create_addptr(input.handle, other.handle), input.type) - # float + float - elif input_scalar_ty.is_floating(): - return tl.tensor(builder.create_fadd(input.handle, other.handle), input.type) - # int + int - elif input_scalar_ty.is_int(): - return tl.tensor(builder.create_add(input.handle, other.handle), input.type) - assert False - - -def sub(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder, True, False) - scalar_ty = input.type.scalar - # ptr - offset - if scalar_ty.is_ptr(): - return tl.tensor(builder.create_addptr(input.handle, minus(other, builder).handle), - input.type) - # float - float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fsub(input.handle, other.handle), input.type) - # int - int - elif scalar_ty.is_int(): - return tl.tensor(builder.create_sub(input.handle, other.handle), input.type) - assert False - - -def mul(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float * float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fmul(input.handle, other.handle), input.type) - # * int - elif scalar_ty.is_int(): - return tl.tensor(builder.create_mul(input.handle, other.handle), input.type) - assert False - - -def truediv(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder, False, False, True, True) - input_scalar_ty = input.type.scalar - other_scalar_ty = other.type.scalar - # float / int - if input_scalar_ty.is_floating() and other_scalar_ty.is_int(): - other = cast(other, input_scalar_ty, builder) - # int / float - elif input_scalar_ty.is_int() and other_scalar_ty.is_floating(): - input = cast(input, other_scalar_ty, builder) - # int / int (cast to tl.float32) - elif input_scalar_ty.is_int() and other_scalar_ty.is_int(): - input = cast(input, tl.float32, builder) - other = cast(other, tl.float32, builder) - # float / float (cast to highest exponent type) - elif input_scalar_ty.is_floating() and other_scalar_ty.is_floating(): - if input_scalar_ty.fp_mantissa_width > other_scalar_ty.fp_mantissa_width: - other = cast(other, input_scalar_ty, builder) - else: - input = cast(input, other_scalar_ty, builder) - # unreachable - else: - assert False - return tl.tensor(builder.create_fdiv(input.handle, other.handle), input.type) - - -def floordiv(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder, False, False, True, True) - input_scalar_ty = input.type.scalar - other_scalar_ty = other.type.scalar - if input_scalar_ty.is_int() and other_scalar_ty.is_int(): - ret_ty = integer_promote_impl(input_scalar_ty, other_scalar_ty) - input = cast(input, ret_ty, builder) - other = cast(other, ret_ty, builder) - if ret_ty.is_int_signed(): - return tl.tensor(builder.create_sdiv(input.handle, other.handle), input.type) - else: - return tl.tensor(builder.create_udiv(input.handle, other.handle), input.type) - assert False - - -def fdiv(input: tl.tensor, - other: tl.tensor, - ieee_rounding: bool, - builder: ir.builder) -> tl.tensor: - input_scalar_ty = input.type.scalar - other_scalar_ty = other.type.scalar - if not input_scalar_ty.is_floating() or not other_scalar_ty.is_floating(): - raise ValueError("both operands of fdiv must have floating scalar type") - input, other = binary_op_type_checking_impl(input, other, builder, False, False, False, True) - ret = builder.create_fdiv(input.handle, other.handle) - return tl.tensor(ret, input.type) - - -def mod(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder, False, False, True, True) - scalar_ty = input.type.scalar - other_scalar_ty = other.type.scalar - # float % float - if scalar_ty.is_floating(): - # input - input.div(other, rounding_mode="floor") * other - ret = sub(input, mul(floor(fdiv(input, other, False, builder), builder), - other, builder), - builder) - return ret - # % int - elif scalar_ty.is_int(): - if scalar_ty.int_signedness != other_scalar_ty.int_signedness: - raise ValueError("Cannot mod " + scalar_ty.__repr__() + " by " + other_scalar_ty.__repr__() + " " - "because they have different signedness;" - "this is unlikely to result in a useful answer. Cast them to the same signedness.") - if scalar_ty.is_int_signed(): - return tl.tensor(builder.create_srem(input.handle, other.handle), input.type) - else: - return tl.tensor(builder.create_urem(input.handle, other.handle), input.type) - assert False - -############## -# bitwise ops -############## - - -def bitwise_op_type_checking_impl(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> Tuple[tl.tensor, tl.tensor]: - input, other = binary_op_type_checking_impl(input, other, builder, False, False, False) - input_sca_ty = input.type.scalar - other_sca_ty = other.type.scalar - if not input_sca_ty.is_int() or not other_sca_ty.is_int(): - raise IncompatibleTypeErrorImpl(input_sca_ty, other_sca_ty) - ret_sca_ty = integer_promote_impl(input_sca_ty, other_sca_ty) - if ret_sca_ty != input_sca_ty: - input = cast(input, ret_sca_ty, builder) - if ret_sca_ty != other_sca_ty: - other = cast(other, ret_sca_ty, builder) - return input, other - - -def and_(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = bitwise_op_type_checking_impl(input, other, builder) - return tl.tensor(builder.create_and(input.handle, other.handle), input.type) - - -def or_(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = bitwise_op_type_checking_impl(input, other, builder) - return tl.tensor(builder.create_or(input.handle, other.handle), input.type) - - -def xor_(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = bitwise_op_type_checking_impl(input, other, builder) - return tl.tensor(builder.create_xor(input.handle, other.handle), input.type) - - -def logical_and(input: tl.tensor, other: tl.tensor, builder: ir.builder) -> tl.tensor: - if not input.type.is_int1(): - input = bitcast(input, tl.dtype("int1"), builder) - if not other.type.is_int1(): - other = bitcast(other, tl.dtype("int1"), builder) - return and_(input, other, builder) - - -def logical_or(input: tl.tensor, other: tl.tensor, builder: ir.builder) -> tl.tensor: - if not input.type.is_int1(): - input = bitcast(input, tl.dtype("int1"), builder) - if not other.type.is_int1(): - other = bitcast(other, tl.dtype("int1"), builder) - return or_(input, other, builder) - - -def not_(input: tl.tensor, builder: ir.builder): - if not input.type.is_int1(): - input = bitcast(input, tl.dtype("int1"), builder) - return invert(input, builder) - - -def lshr(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = bitwise_op_type_checking_impl(input, other, builder) - return tl.tensor(builder.create_lshr(input.handle, other.handle), input.type) - - -def ashr(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = bitwise_op_type_checking_impl(input, other, builder) - return tl.tensor(builder.create_ashr(input.handle, other.handle), input.type) - - -def shl(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = bitwise_op_type_checking_impl(input, other, builder) - return tl.tensor(builder.create_shl(input.handle, other.handle), input.type) - -# ===----------------------------------------------------------------------===// -# Unary Operators -# ===----------------------------------------------------------------------===// - - -def plus(input: tl.tensor) -> tl.tensor: - return input - - -def minus(input: tl.tensor, - builder: ir.builder) -> tl.tensor: - input_sca_ty = input.type.scalar - if input_sca_ty.is_ptr(): - raise ValueError("wrong type argument to unary minus (" + input_sca_ty.__repr__() + ")") - _0 = tl.tensor(builder.get_null_value(input_sca_ty.to_ir(builder)), input_sca_ty) - return sub(_0, input, builder) - - -def invert(input: tl.tensor, - builder: tl.tensor) -> tl.tensor: - input_sca_ty = input.type.scalar - if input_sca_ty.is_ptr() or input_sca_ty.is_floating(): - raise ValueError("wrong type argument to unary invert (" + input_sca_ty.__repr__() + ")") - _1 = tl.tensor(builder.get_all_ones_value(input_sca_ty.to_ir(builder)), input_sca_ty) - return xor_(input, _1, builder) - - -# ===----------------------------------------------------------------------===// -# Comparison Operators -# ===----------------------------------------------------------------------===// -def _bool_like(v: tl.tensor) -> tl.block_type: - if not v.type.is_block(): - return tl.int1 - shape = v.type.shape - return tl.block_type(tl.int1, shape) - - -def greater_than(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float > float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fcmpOGT(input.handle, other.handle), _bool_like(input)) - # > int - elif scalar_ty.is_int(): - if scalar_ty.is_int_signed(): - return tl.tensor(builder.create_icmpSGT(input.handle, other.handle), _bool_like(input)) - else: - return tl.tensor(builder.create_icmpUGT(input.handle, other.handle), _bool_like(input)) - assert False - - -def greater_equal(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float >= float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fcmpOGE(input.handle, other.handle), _bool_like(input)) - # >= int - elif scalar_ty.is_int(): - if scalar_ty.is_int_signed(): - return tl.tensor(builder.create_icmpSGE(input.handle, other.handle), _bool_like(input)) - else: - return tl.tensor(builder.create_icmpUGE(input.handle, other.handle), _bool_like(input)) - assert False - - -def less_than(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float < float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fcmpOLT(input.handle, other.handle), _bool_like(input)) - # < int - elif scalar_ty.is_int(): - if scalar_ty.is_int_signed(): - return tl.tensor(builder.create_icmpSLT(input.handle, other.handle), _bool_like(input)) - else: - return tl.tensor(builder.create_icmpULT(input.handle, other.handle), _bool_like(input)) - assert False - - -def less_equal(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float < float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fcmpOLE(input.handle, other.handle), _bool_like(input)) - # < int - elif scalar_ty.is_int(): - if scalar_ty.is_int_signed(): - return tl.tensor(builder.create_icmpSLE(input.handle, other.handle), _bool_like(input)) - else: - return tl.tensor(builder.create_icmpULE(input.handle, other.handle), _bool_like(input)) - assert False - - -def equal(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float == float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fcmpOEQ(input.handle, other.handle), _bool_like(input)) - # == int - elif scalar_ty.is_int(): - return tl.tensor(builder.create_icmpEQ(input.handle, other.handle), _bool_like(input)) - assert False - - -def not_equal(input: tl.tensor, - other: tl.tensor, - builder: ir.builder) -> tl.tensor: - input, other = binary_op_type_checking_impl(input, other, builder) - scalar_ty = input.type.scalar - # float == float - if scalar_ty.is_floating(): - return tl.tensor(builder.create_fcmpUNE(input.handle, other.handle), _bool_like(input)) - # == int - elif scalar_ty.is_int(): - return tl.tensor(builder.create_icmpNE(input.handle, other.handle), _bool_like(input)) - assert False - -# ===----------------------------------------------------------------------===// -# Block Creation -# ===----------------------------------------------------------------------===// - - -def arange(start: int, end: int, builder: ir.builder) -> tl.tensor: - if not isinstance(start, int) or not isinstance(end, int): - raise ValueError("arange's arguments must be of type tl.constexpr") - is_start_int64 = bool(start >> 32) - is_end_int64 = bool(end >> 32) - if is_start_int64 or is_end_int64: - raise ValueError("arange must fit in int32") - if end <= start: - raise ValueError("arange's end argument must be greater than the start argument") - - shape = [end - start] - ret_ty = tl.block_type(tl.int32, shape) - return tl.tensor(builder.create_make_range(start, end), ret_ty) - - -def full(shape: List[int], value, dtype: tl.dtype, builder: ir.builder) -> tl.tensor: - if isinstance(value, tl.tensor): - assert value.numel.value == 1, "only accepts size-1 tensor" - value = cast(value, dtype, builder) - ret_ty = tl.block_type(value.dtype, shape) - return tl.tensor(builder.create_splat(value.handle, shape), ret_ty) - else: - # scalar - if value == 0: - value = builder.get_null_value(dtype.to_ir(builder)) - else: - get_value_fn = getattr(builder, f"get_{dtype.name}") - value = get_value_fn(value) - if dtype is None: - raise ValueError("dtype must be specified when value is not a tensor") - ret_ty = tl.block_type(dtype, shape) - return tl.tensor(builder.create_splat(value, shape), ret_ty) - - -# ===----------------------------------------------------------------------===// -# Shape Manipulation -# ===----------------------------------------------------------------------===// - - -def view(input: tl.tensor, - dst_shape: List[int], - builder: ir.builder) -> tl.tensor: - # TODO: disable when TritonToTritonGPU handles views properly - - # assert len(input.shape) == len(dst_shape) - numel = 1 - for s in dst_shape: - numel *= s - if input.type.numel != numel: - raise ValueError("cannot view block of different shape") - ret_ty = tl.block_type(input.type.scalar, dst_shape) - return tl.tensor(builder.create_view(input.handle, dst_shape), ret_ty) - - -def reshape(input: tl.tensor, - dst_shape: List[int], - builder: ir.builder) -> tl.tensor: - raise ValueError("`reshape` is not supported yet. Please use `view` instead if applicable. " - "Note that view may reorder elements in an implementation- and context- dependent way.") - - -def expand_dims(input: tl.tensor, axis: int, builder: ir.builder) -> tl.tensor: - dst_shape = list(input.type.shape) - dst_shape.insert(axis, 1) - ret_ty = tl.block_type(input.type.scalar, dst_shape) - return tl.tensor(builder.create_expand_dims(input.handle, axis), ret_ty) - - -def cat(lhs: tl.tensor, rhs: tl.tensor, can_reorder: bool, builder: ir.builder) -> tl.tensor: - assert can_reorder, "current implementation of `cat` always may reorder elements" - assert len(lhs.shape) == 1 - ret_type = tl.block_type(lhs.type.scalar, [lhs.shape[0] + rhs.shape[0]]) - return tl.tensor(builder.create_cat(lhs.handle, rhs.handle), ret_type) - - -def trans(input: tl.tensor, builder: ir.builder) -> tl.tensor: - if len(input.shape) != 2: - raise ValueError("Only 2D tensors can be transposed") - ret_type = tl.block_type(input.type.scalar, [input.shape[1], input.shape[0]]) - return tl.tensor(builder.create_trans(input.handle), ret_type) - - -def broadcast_impl_shape(input: tl.tensor, - shape: List[int], - builder: ir.builder) -> tl.tensor: - if not input.type.is_block(): - ret_ty = tl.block_type(input.type, shape) - return tl.tensor(builder.create_splat(input.handle, shape), ret_ty) - src_shape = input.type.get_block_shapes() - if len(src_shape) != len(shape): - raise ValueError(f"Cannot broadcast, rank mismatch: {src_shape}, {shape}") - if shape == src_shape: - return input - for i, item in enumerate(src_shape): - if shape[i] != item and item != 1: - raise ValueError(f"Cannot broadcast, the expanded size of the tensor ({shape[i]})" - f" must match the existing size ({item}) at non-singleton dimension" - f" {i}: {src_shape}, {shape}") - ret_ty = tl.block_type(input.type.scalar, shape) - return tl.tensor(builder.create_broadcast(input.handle, shape), ret_ty) - - -def broadcast_impl_value(lhs: tl.tensor, - rhs: tl.tensor, - builder: ir.builder) -> tl.tensor: - lhs_ty = lhs.type - rhs_ty = rhs.type - - # make_shape_compatible(block, scalar) - if lhs_ty.is_block() and not rhs_ty.is_block(): - rhs_ty = tl.block_type(rhs_ty.scalar, lhs_ty.shape) - rhs = tl.tensor(builder.create_splat(rhs.handle, lhs_ty.get_block_shapes()), rhs_ty) - # make_shape_compatible(scalar, block) - elif not lhs_ty.is_block() and rhs_ty.is_block(): - lhs_ty = tl.block_type(lhs_ty.scalar, rhs_ty.shape) - lhs = tl.tensor(builder.create_splat(lhs.handle, rhs_ty.get_block_shapes()), lhs_ty) - # make_shape_compatible(block, block) - elif lhs_ty.is_block() and rhs_ty.is_block(): - lhs_shape = lhs_ty.get_block_shapes() - rhs_shape = rhs_ty.get_block_shapes() - - if len(lhs_shape) < len(rhs_shape): - # Add new axes to lhs - for dim in range(len(lhs_shape), len(rhs_shape)): - lhs = tl.tensor(builder.create_expand_dims(lhs.handle, 0), tl.block_type(lhs_ty.scalar, [1] + lhs_shape)) - lhs_ty = lhs.type - lhs_shape = lhs_ty.get_block_shapes() - elif len(rhs_shape) < len(lhs_shape): - # Add new axes to rhs - for dim in range(len(rhs_shape), len(lhs_shape)): - rhs = tl.tensor(builder.create_expand_dims(rhs.handle, 0), tl.block_type(rhs_ty.scalar, [1] + rhs_shape)) - rhs_ty = rhs.type - rhs_shape = rhs_ty.get_block_shapes() - assert len(rhs_shape) == len(lhs_shape) - - ret_shape = [] - for i, left in enumerate(lhs_shape): - right = rhs_shape[i] - if left == 1: - ret_shape.append(right) - elif right == 1: - ret_shape.append(left) - elif left == right: - ret_shape.append(left) - else: - raise ValueError("Cannot make_shape_compatible: incompatible dimensions " - "at index " + str(i) + ": " + str(left) + " and " + str(right)) - if lhs_shape != ret_shape: - ret_ty = tl.block_type(lhs_ty.scalar, ret_shape) - lhs = tl.tensor(builder.create_broadcast(lhs.handle, ret_shape), ret_ty) - if rhs_shape != ret_shape: - ret_ty = tl.block_type(rhs_ty.scalar, ret_shape) - rhs = tl.tensor(builder.create_broadcast(rhs.handle, ret_shape), ret_ty) - # (scalar, scalar) => returns original blocks - return lhs, rhs - -####### -# cast -####### - - -def bitcast(input: tl.tensor, - dst_ty: tl.dtype, - builder: ir.builder) -> tl.tensor: - src_ty = input.type - if src_ty.is_block(): - dst_ty = tl.block_type(dst_ty.scalar, input.type.get_block_shapes()) - if src_ty == dst_ty: - return input - src_sca_ty = src_ty.scalar - dst_sca_ty = dst_ty.scalar - if src_sca_ty.is_ptr() or dst_sca_ty.is_ptr(): - return cast(input, dst_ty, builder) - # Bitcast - src_bits = src_sca_ty.primitive_bitwidth - dst_bits = dst_sca_ty.primitive_bitwidth - if src_bits != dst_bits: - raise ValueError("Cannot bitcast data-type of size " + str(src_bits) + " to " - "data-type of size " + str(dst_bits)) - return tl.tensor(builder.create_bitcast(input.handle, dst_ty.to_ir(builder)), - dst_ty) - - -def cast(input: tl.tensor, - dst_ty: tl.dtype, - builder: ir.builder) -> tl.tensor: - src_ty = input.type - if isinstance(dst_ty, tl.constexpr): - dst_ty = dst_ty.value - if src_ty.is_block(): - dst_ty = tl.block_type(dst_ty.scalar, input.type.get_block_shapes()) - if src_ty == dst_ty: - return input - - src_sca_ty = src_ty.scalar - dst_sca_ty = dst_ty.scalar - - if builder.arch < 89 and \ - (src_sca_ty.is_fp8e4() or dst_sca_ty.is_fp8e4()): - warnings.warn("Standard tl.float8e4 format will be deprecated on SM < 89. " - "Please use tl.float8e4b15.", DeprecationWarning) - - # Unsupported conversion: - if (src_sca_ty.is_fp8e4b15() and not dst_sca_ty.is_fp16()) or \ - (dst_sca_ty.is_fp8e4b15() and not src_sca_ty.is_fp16()): - raise ValueError('fp8e4b15 can only be converted to/from fp16') - - # Casting with customized floating types involved: fp8 <=> bf16, fp16, fp32, fp64 - if (src_sca_ty.is_fp8() and dst_sca_ty.is_floating()) or \ - (src_sca_ty.is_floating() and dst_sca_ty.is_fp8()): - return tl.tensor(builder.create_fp_to_fp(input.handle, dst_ty.to_ir(builder)), - dst_ty) - - # bf16 <=> (not fp32) - if (src_sca_ty.is_fp16() and not dst_sca_ty.is_fp32()) or \ - (src_sca_ty.is_bf16() and not dst_sca_ty.is_fp32()): - return cast(cast(input, tl.float32, builder), dst_sca_ty, builder) - - # Standard floating types' casting: truncation - # fp64 => fp32, fp16, bf16 - # fp32 => fp16, bf16 - truncate_fp = src_sca_ty.is_floating() and \ - dst_sca_ty.is_floating() and \ - src_sca_ty.primitive_bitwidth > dst_sca_ty.primitive_bitwidth - if truncate_fp: - return tl.tensor(builder.create_fp_trunc(input.handle, - dst_ty.to_ir(builder)), - dst_ty) - - # Standard floating types' casting: extension - # fp32 => fp64 - # fp16 => fp32, fp64 - # bf16 => fp32, fp64 - ext_fp = src_sca_ty.is_floating() and \ - dst_sca_ty.is_floating() and \ - src_sca_ty.primitive_bitwidth < dst_sca_ty.primitive_bitwidth - if ext_fp: - return tl.tensor(builder.create_fp_ext(input.handle, - dst_ty.to_ir(builder)), - dst_ty) - - # Casting between integer types - if src_sca_ty.is_int() and dst_sca_ty.is_int() and \ - (src_sca_ty.int_bitwidth != dst_sca_ty.int_bitwidth or src_sca_ty.int_signedness != dst_sca_ty.int_signedness): - sign_extend = src_sca_ty.is_int_signed() and not src_sca_ty.is_bool() - if dst_sca_ty.is_bool(): - ty = input.dtype.to_ir(builder) - _0 = tl.tensor(builder.get_null_value(ty), input.dtype) - return not_equal(input, _0, builder) - else: - return tl.tensor(builder.create_int_cast(input.handle, - dst_ty.to_ir(builder), sign_extend), - dst_ty) - - # Casting standard floating types to integer types - if src_sca_ty.is_standard_floating() and dst_sca_ty.is_int(): - if dst_sca_ty.is_bool(): - ty = input.dtype.to_ir(builder) - _0 = tl.tensor(builder.get_null_value(ty), input.dtype) - return not_equal(input, _0, builder) - elif dst_sca_ty.is_int_signed(): - return tl.tensor(builder.create_fp_to_si(input.handle, - dst_ty.to_ir(builder)), - dst_ty) - else: - return tl.tensor(builder.create_fp_to_ui(input.handle, - dst_ty.to_ir(builder)), - dst_ty) - - # Casting integer types to standard floating types - if src_sca_ty.is_int() and dst_sca_ty.is_standard_floating(): - if src_sca_ty.is_bool() or not src_sca_ty.is_int_signed(): - return tl.tensor(builder.create_ui_to_fp(input.handle, - dst_ty.to_ir(builder)), - dst_ty) - else: - return tl.tensor(builder.create_si_to_fp(input.handle, - dst_ty.to_ir(builder)), - dst_ty) - - # Casting pointer types to integer types - if src_sca_ty.is_ptr() and dst_sca_ty.is_int(): - bitwidth = dst_sca_ty.int_bitwidth - if bitwidth == 64: - return tl.tensor(builder.create_ptr_to_int(input.handle, dst_ty.to_ir(builder)), - dst_ty) - if bitwidth == 1: - return not_equal(cast(input, tl.int64, builder), - tl.tensor(builder.get_int64(0), tl.int64), - builder) - - # Casting integer types to pointer types - if src_sca_ty.is_int() and dst_sca_ty.is_ptr(): - return tl.tensor(builder.create_int_to_ptr(input.handle, dst_ty.to_ir(builder)), dst_ty) - - # Casting pointer types to pointer types - if src_sca_ty.is_ptr() and dst_sca_ty.is_ptr(): - return tl.tensor(builder.create_bitcast(input.handle, dst_ty.to_ir(builder)), dst_ty) - - assert False, f'cannot cast {input} to {dst_ty}' - -# ===----------------------------------------------------------------------===// -# Memory Operators -# ===----------------------------------------------------------------------===// - - -def _str_to_load_cache_modifier(cache_modifier): - cache = ir.CACHE_MODIFIER.NONE # default - if cache_modifier: - if cache_modifier == ".ca": - cache = ir.CACHE_MODIFIER.CA - elif cache_modifier == ".cg": - cache = ir.CACHE_MODIFIER.CG - else: - raise ValueError(f"Cache modifier {cache_modifier} not supported") - return cache - - -def _str_to_store_cache_modifier(cache_modifier): - cache = ir.CACHE_MODIFIER.NONE # default - if cache_modifier: - if cache_modifier == ".wb": - cache = ir.CACHE_MODIFIER.WB - elif cache_modifier == ".cg": - cache = ir.CACHE_MODIFIER.CG - elif cache_modifier == ".cs": - cache = ir.CACHE_MODIFIER.CS - elif cache_modifier == ".wt": - cache = ir.CACHE_MODIFIER.WT - else: - raise ValueError(f"Cache modifier {cache_modifier} not supported") - return cache - - -def _str_to_eviction_policy(eviction_policy): - eviction = ir.EVICTION_POLICY.NORMAL # default - if eviction_policy: - if eviction_policy == "evict_last": - eviction = ir.EVICTION_POLICY.EVICT_LAST - elif eviction_policy == "evict_first": - eviction = ir.EVICTION_POLICY.EVICT_FIRST - else: - raise ValueError(f"Eviction policy {eviction_policy} not supported") - return eviction - - -def _str_to_padding_option(padding_option): - padding = None # default - if padding_option: - if padding_option == "zero": - padding = ir.PADDING_OPTION.PAD_ZERO - elif padding_option == "nan": - padding = ir.PADDING_OPTION.PAD_NAN - else: - raise ValueError(f"Padding option {padding_option} not supported") - return padding - - -def _str_to_sem(sem_option): - sem = ir.MEM_SEMANTIC.ACQUIRE_RELEASE - if sem_option: - if sem_option == "acquire": - sem = ir.MEM_SEMANTIC.ACQUIRE - elif sem_option == "release": - sem = ir.MEM_SEMANTIC.RELEASE - elif sem_option == "acq_rel": - sem = ir.MEM_SEMANTIC.ACQUIRE_RELEASE - elif sem_option == "relaxed": - sem = ir.MEM_SEMANTIC.RELAXED - else: - raise ValueError(f"Memory semantic {sem_option} not supported") - return sem - - -def _canonicalize_boundary_check(boundary_check, block_shape): - if boundary_check: - if not hasattr(boundary_check, "__iter__"): - boundary_check = [boundary_check] - boundary_check = [elem.value if isinstance(elem, tl.constexpr) else elem for elem in boundary_check] - for dim in boundary_check: - assert isinstance(dim, int) and 0 <= dim < len(block_shape) - assert len(boundary_check) > 0 - assert len(boundary_check) == len(set(boundary_check)), "Duplicate dimension in `boundary_check`" - return sorted(boundary_check) - return tuple() - - -def _load_block_pointer(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder): - # Load by a block pointer: `pointer_type>` - # Block pointer can not have `mask` and `other` arguments - if mask or other: - raise ValueError("`mask` and `other` arguments cannot be specified for loading block pointers") - - elt_ty = ptr.type.element_ty.element_ty - assert elt_ty != tl.int1, "`tl.int1` should be rewrited in `tl.make_block_ptr`" - if elt_ty.is_int() and padding == ir.PADDING_OPTION.PAD_NAN: - raise ValueError("Padding option `nan` is not supported for integer block pointers") - - # `dst_ty` is de-referenced type of the pointer type - dst_ty = ptr.type.element_ty - - # Check `boundary_check` argument - boundary_check = _canonicalize_boundary_check(boundary_check, dst_ty.get_block_shapes()) - - # Build IR - return tl.tensor(builder.create_tensor_pointer_load(ptr.handle, boundary_check, padding, cache, eviction, - is_volatile), dst_ty) - - -def _load_legacy(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder): - # Load by a tensor of pointers or a pointer of scalar: `block_type>` or `pointer_type<>` - if not ptr.type.scalar.is_ptr(): - raise ValueError(f"Unsupported ptr type {ptr.type.__repr__()} in `tl.load`") - - # Check `mask`, `other`, `boundary_check`, and `padding` arguments - if not mask and other: - raise ValueError("`other` cannot be provided without `mask`") - if padding or boundary_check: - raise ValueError("`padding_option` or `boundary_check` argument is not supported for loading a tensor of" - "pointers or loading a scalar. Because the compiler does not know the boundary; please " - "use block pointers (defined by `make_block_ptr`) instead") - - # For a pointer of scalar, check the type of `mask` and `other` - if not ptr.type.is_block(): - if mask and mask.type.is_block(): - raise ValueError("Mask argument cannot be block type if pointer argument is not a block") - if other and other.type.is_block(): - raise ValueError("Other argument cannot be block type if pointer argument is not a block") - - # Make `mask` and `other` into the same shape as `ptr` - if ptr.type.is_block(): - if mask: - mask = broadcast_impl_shape(mask, ptr.type.get_block_shapes(), builder) - if other: - other = broadcast_impl_shape(other, ptr.type.get_block_shapes(), builder) - - # Get `pointer_type` and `elt_ty` - ptr_ty = ptr.type.scalar - elt_ty = ptr_ty.element_ty - - # Treat `pointer_type` as `pointer_type` - if elt_ty == tl.int1: - elt_ty = tl.int8 - ptr_ty = tl.pointer_type(elt_ty, ptr_ty.address_space) - ptr = cast(ptr, ptr_ty, builder) - - # Cast `other` into `ele_ty` type - if other: - other = cast(other, elt_ty, builder) - - # Create loaded result type `dst_ty` - if ptr.type.is_block(): - shape = ptr.type.get_block_shapes() - dst_ty = tl.block_type(elt_ty, shape) - else: - # Load by de-referencing the pointer of scalar - dst_ty = elt_ty - - # Build IR - if not mask: - return tl.tensor(builder.create_load(ptr.handle, cache, eviction, is_volatile), dst_ty) - else: - return tl.tensor(builder.create_masked_load(ptr.handle, mask.handle, other.handle if other else None, cache, - eviction, is_volatile), dst_ty) - - -def load(ptr: tl.tensor, - mask: Optional[tl.tensor], - other: Optional[tl.tensor], - boundary_check, - padding_option: str, - cache_modifier: str, - eviction_policy: str, - is_volatile: bool, - builder: ir.builder) -> tl.tensor: - # Cache, eviction and padding options - cache = _str_to_load_cache_modifier(cache_modifier) - eviction = _str_to_eviction_policy(eviction_policy) - padding = _str_to_padding_option(padding_option) - - if ptr.type.is_ptr() and ptr.type.element_ty.is_block(): - # Load by a block pointer: `pointer_type>` - return _load_block_pointer(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder) - else: - # Load by a tensor of pointers or a pointer of scalar: `block_type>` or `pointer_type<>` - return _load_legacy(ptr, mask, other, boundary_check, padding, cache, eviction, is_volatile, builder) - - -def _store_block_pointer(ptr, val, mask, boundary_check, cache, eviction, builder): - # Store by a block pointer: `pointer_type>` - # Block pointers can not have the `mask` argument - if mask: - raise ValueError("`mask` and `other` arguments cannot be specified for loading block pointers") - - # Check same shape and element type - block_shape = ptr.type.element_ty.get_block_shapes() - if not val.type.is_block(): - val = broadcast_impl_shape(val, block_shape, builder) - assert val.type.is_block(), "Value argument must be block type or a scalar" - assert block_shape == val.type.get_block_shapes(), "Block shape and value shape mismatch" - assert ptr.type.element_ty.element_ty == val.type.element_ty, "Block element type and value element type mismatch" - - elt_ty = ptr.type.element_ty.element_ty - assert elt_ty != tl.int1, "`tl.int1` should be rewrited in `tl.make_block_ptr`" - - # Check `boundary_check` argument - boundary_check = _canonicalize_boundary_check(boundary_check, block_shape) - - # Build IR - return tl.tensor(builder.create_tensor_pointer_store(ptr.handle, val.handle, boundary_check, cache, eviction), - tl.void) - - -def _store_legacy(ptr, val, mask, boundary_check, cache, eviction, builder): - # Store by a tensor of pointers or a pointer of scalar: `block_type>` or `pointer_type<>` - if not ptr.type.scalar.is_ptr(): - raise ValueError(f"Unsupported ptr type {ptr.type.__repr__()} in `tl.store`") - - # Check `boundary_check` argument - if boundary_check: - raise ValueError("`boundary_check` argument is not supported for storing a tensor of pointers or storing a " - "scalar. Because the compiler does not know the boundary; please use block pointers " - "(defined by `make_block_ptr`) instead") - - # For a pointer of scalar, check the type of `val` and `mask` - if not ptr.type.is_block(): - if val.type.is_block(): - raise ValueError("Value argument cannot be block type if pointer argument is not a block") - if mask and mask.type.is_block(): - raise ValueError("Mask argument cannot be block type if pointer argument is not a block") - - # Make `mask` and `val` into the same shape as `ptr` - if ptr.type.is_block(): - val = broadcast_impl_shape(val, ptr.type.get_block_shapes(), builder) - if mask: - mask = broadcast_impl_shape(mask, ptr.type.get_block_shapes(), builder) - - ptr_ty = ptr.type.scalar - elt_ty = ptr_ty.element_ty - - # Treat `pointer_type` as `pointer_type` - if elt_ty == tl.int1: - elt_ty = tl.int8 - ptr_ty = tl.pointer_type(elt_ty, ptr_ty.address_space) - ptr = cast(ptr, ptr_ty, builder) - - # Cast to target data type - val = cast(val, elt_ty, builder) - - # Build IR - if not mask: - return tl.tensor(builder.create_store(ptr.handle, val.handle, cache, eviction), tl.void) - if not mask.type.scalar.is_bool(): - raise ValueError("Mask must have boolean scalar type") - return tl.tensor(builder.create_masked_store(ptr.handle, val.handle, mask.handle, cache, eviction), tl.void) - - -def store(ptr: tl.tensor, - val: tl.tensor, - mask: Optional[tl.tensor], - boundary_check, - cache_modifier: str, - eviction_policy: str, - builder: ir.builder) -> tl.tensor: - # Cache and eviction options - cache = _str_to_store_cache_modifier(cache_modifier) - eviction = _str_to_eviction_policy(eviction_policy) - - if ptr.type.is_ptr() and ptr.type.element_ty.is_block(): - # Store by a block pointer: `pointer_type>` - return _store_block_pointer(ptr, val, mask, boundary_check, cache, eviction, builder) - else: - # Store by a tensor of pointers or a pointer of scalar: `block_type>` or `pointer_type<>` - return _store_legacy(ptr, val, mask, boundary_check, cache, eviction, builder) - - -######### -# atomic -######### - - -def atomic_cas(ptr: tl.tensor, - cmp: tl.tensor, - val: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - sem = _str_to_sem(sem) - element_ty = ptr.type.scalar.element_ty - if element_ty.primitive_bitwidth not in [16, 32, 64]: - raise ValueError("atomic_cas only supports elements with width {16, 32, 64}") - return tl.tensor(builder.create_atomic_cas(ptr.handle, cmp.handle, val.handle, sem), val.type) - - -def atom_red_typechecking_impl(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - op: str, - builder: ir.builder) -> Tuple[tl.tensor, tl.tensor, tl.tensor]: - if not ptr.type.scalar.is_ptr(): - raise ValueError("Pointer argument of store instruction is " + ptr.type.__repr__()) - element_ty = ptr.type.scalar.element_ty - if element_ty is tl.float16 and op != 'add': - raise ValueError("atomic_" + op + " does not support fp16") - if element_ty in [tl.int1, tl.int8, tl.int16, tl.bfloat16]: - raise ValueError("atomic_" + op + " does not support " + str(element_ty)) - if ptr.type.is_block(): - if mask: - mask = broadcast_impl_shape(mask, ptr.type.get_block_shapes(), builder) - if val: - val = broadcast_impl_shape(val, ptr.type.get_block_shapes(), builder) - val = cast(val, ptr.type.scalar.element_ty, builder) - if not mask: - mask_ir = builder.get_int1(True) - mask_ty = tl.int1 - if ptr.type.is_block(): - mask_ir = builder.create_splat(mask_ir, ptr.type.get_block_shapes()) - mask_ty = tl.block_type(tl.int1, ptr.type.get_block_shapes()) - mask = tl.tensor(mask_ir, mask_ty) - return ptr, val, mask - - -def atomic_max(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'max', builder) - sem = _str_to_sem(sem) - sca_ty = val.type.scalar - # direct call to atomic_max for integers - if sca_ty.is_int(): - if sca_ty.is_int_signed(): - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MAX, - ptr.handle, - val.handle, - mask.handle, - sem), - val.type) - else: - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMAX, - ptr.handle, - val.handle, - mask.handle, - sem), - val.type) - # for float - # return atomic_smax(i_ptr, i_val) if val >= 0 - # return atomic_umin(i_ptr, i_val) if val < 0 - i_val = bitcast(val, tl.int32, builder) - i_ptr = bitcast(ptr, tl.pointer_type(tl.int32, 1), builder) - pos = greater_equal(val, tl.tensor(builder.get_fp32(0), sca_ty), builder) - neg = less_than(val, tl.tensor(builder.get_fp32(0), sca_ty), builder) - pos_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MAX, i_ptr.handle, i_val.handle, and_(mask, pos, builder).handle, sem), i_val.type) - neg_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMIN, i_ptr.handle, i_val.handle, and_(mask, neg, builder).handle, sem), i_val.type) - return where(pos, pos_ret, neg_ret, builder) - - -def atomic_min(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'min', builder) - sem = _str_to_sem(sem) - sca_ty = val.type.scalar - # direct call to atomic_min for integers - if sca_ty.is_int(): - if sca_ty.is_int_signed(): - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MIN, - ptr.handle, - val.handle, - mask.handle, - sem), - val.type) - else: - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMIN, - ptr.handle, - val.handle, - mask.handle, - sem), - val.type) - # for float - # return atomic_smin(i_ptr, i_val) if val >= 0 - # return atomic_umax(i_ptr, i_val) if val < 0 - i_val = bitcast(val, tl.int32, builder) - i_ptr = bitcast(ptr, tl.pointer_type(tl.int32, 1), builder) - pos = greater_equal(val, tl.tensor(builder.get_fp32(0), sca_ty), builder) - neg = less_than(val, tl.tensor(builder.get_fp32(0), sca_ty), builder) - pos_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.MIN, - i_ptr.handle, - i_val.handle, - and_(mask, pos, builder).handle, - sem), - i_val.type) - neg_ret = tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.UMAX, - i_ptr.handle, - i_val.handle, - and_(mask, neg, builder).handle, - sem), - i_val.type) - return where(pos, pos_ret, neg_ret, builder) - - -def atomic_add(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'add', builder) - sem = _str_to_sem(sem) - sca_ty = val.type.scalar - op = ir.ATOMIC_OP.FADD if sca_ty.is_floating() else ir.ATOMIC_OP.ADD - return tl.tensor(builder.create_atomic_rmw(op, ptr.handle, val.handle, mask.handle, sem), val.type) - - -def atomic_and(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'and', builder) - sem = _str_to_sem(sem) - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.AND, ptr.handle, val.handle, mask.handle, sem), val.type) - - -def atomic_or(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'or', builder) - sem = _str_to_sem(sem) - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.OR, ptr.handle, val.handle, mask.handle, sem), val.type) - - -def atomic_xor(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'xor', builder) - sem = _str_to_sem(sem) - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.XOR, ptr.handle, val.handle, mask.handle, sem), val.type) - - -def atomic_xchg(ptr: tl.tensor, - val: tl.tensor, - mask: tl.tensor, - sem: str, - builder: ir.builder) -> tl.tensor: - ptr, val, mask = atom_red_typechecking_impl(ptr, val, mask, 'xchg', builder) - sem = _str_to_sem(sem) - return tl.tensor(builder.create_atomic_rmw(ir.ATOMIC_OP.XCHG, ptr.handle, val.handle, mask.handle, sem), val.type) - -# ===----------------------------------------------------------------------===// -# Linear Algebra -# ===----------------------------------------------------------------------===// - - -def dot(lhs: tl.tensor, - rhs: tl.tensor, - allow_tf32: bool, - out_dtype: tl.dtype, - builder: ir.builder) -> tl.tensor: - assert lhs.type.is_block() and rhs.type.is_block() - assert lhs.dtype == rhs.dtype, f"First input ({lhs.dtype}) and second input ({rhs.dtype}) must have the same dtype!" - assert len(lhs.shape) == 2, f"First input shape ({lhs.shape}) is not two dimensional!" - assert len(rhs.shape) == 2, f"Second input shape ({rhs.shape}) is not two dimensional!" - assert lhs.shape[1].value == rhs.shape[0].value, f"First input shape ({lhs.shape}) and second input shape {rhs.shape} are not compatible for matmul (second index of first shape ({lhs.shape[1].value}) must be equal to first index of second shape ({rhs.shape[0].value})" - assert lhs.shape[0].value >= 16 and lhs.shape[1].value >= 16 \ - and rhs.shape[1].value >= 16,\ - f"All values in both first input shape ({lhs.shape}) and second input shape ({rhs.shape}) must be >= 16!" - if lhs.type.scalar.is_int(): - assert lhs.type.scalar == tl.int8, "only int8 supported!" - # TODO: This is CUDA specific, check if ROCm has the same limitation - assert lhs.shape[1].value >= 32, "small blocks not supported!" - _0 = builder.get_int32(0) - ret_scalar_ty = tl.int32 - elif lhs.type.scalar.is_fp32() or lhs.type.scalar.is_bf16(): - _0 = builder.get_fp32(0) - ret_scalar_ty = tl.float32 - else: - _0 = builder.get_fp16(0) if out_dtype.is_fp16() else builder.get_fp32(0) - ret_scalar_ty = out_dtype - - M = lhs.type.shape[0] - N = rhs.type.shape[1] - _0 = builder.create_splat(_0, [M, N]) - ret_ty = tl.block_type(ret_scalar_ty, [M, N]) - return tl.tensor(builder.create_dot(lhs.handle, rhs.handle, _0, allow_tf32), - ret_ty) - - -# ===----------------------------------------------------------------------===// -# Indexing -# ===----------------------------------------------------------------------===// - -def where(condition: tl.tensor, - x: tl.tensor, - y: tl.tensor, - builder: ir.builder) -> tl.tensor: - condition = cast(condition, tl.int1, builder) - if condition.type.is_block(): - condition, x = broadcast_impl_value(condition, x, builder) - x, y = broadcast_impl_value(x, y, builder) - condition, x = broadcast_impl_value(condition, x, builder) - - x, y = binary_op_type_checking_impl(x, y, builder, True, True) - if not condition.type.is_block(): - condition, _ = broadcast_impl_value(condition, x, builder) - ret_ty = x.type - return tl.tensor(builder.create_select(condition.handle, x.handle, y.handle), ret_ty) - -# ===----------------------------------------------------------------------===// -# Reduction -# ===----------------------------------------------------------------------=== - - -def reduction( - inputs: Sequence[tl.tensor], axis: int, region_builder_fn, builder: ir.builder -) -> Tuple[tl.tensor, ...]: - if axis is None: - new_inputs = [] - for i in range(len(inputs)): - new_shape = [inputs[i].numel.value] - new_inputs.append(view(inputs[i], new_shape, builder)) - inputs = tuple(new_inputs) - axis = 0 - # get result shape - shape = inputs[0].type.shape - ret_shape = [s for i, s in enumerate(shape) if i != axis] - for t in inputs: - assert t.type.shape == shape - - def wrap_tensor(x, scalar_ty): - if ret_shape: - res_ty = tl.block_type(scalar_ty, ret_shape) - else: - # 0d-tensor -> scalar - res_ty = scalar_ty - return tl.tensor(x, res_ty) - - reduce_op = builder.create_reduce([t.handle for t in inputs], axis) - region_builder_fn(reduce_op) - reduce_op.verify() - - return tuple( - wrap_tensor(reduce_op.get_result(i), inputs[i].type.scalar) - for i in range(len(inputs)) - ) - - -# ===----------------------------------------------------------------------=== -# Associative Scan -# ===----------------------------------------------------------------------=== - - -def associative_scan( - inputs: Sequence[tl.tensor], axis: int, region_builder_fn, builder: ir.builder -) -> Tuple[tl.tensor, ...]: - if len(inputs) != 1: - raise ValueError("Current implementation only support single tensor input") - shape = inputs[0].type.shape - - def wrap_tensor(x, scalar_ty): - res_ty = tl.block_type(scalar_ty, shape) - return tl.tensor(x, res_ty) - - scan_op = builder.create_scan([t.handle for t in inputs], axis) - region_builder_fn(scan_op) - scan_op.verify() - - return tuple( - wrap_tensor(scan_op.get_result(i), inputs[i].type.scalar) - for i in range(len(inputs)) - ) - - -# ===----------------------------------------------------------------------=== -# Math -# ===----------------------------------------------------------------------=== - -def _check_dtype(dtypes: List[str]) -> T: - """ - We following libdevice's convention to check accepted data types for math functions. - It is not a good practice to support all data types as accelerators/GPUs don't support - many float16 and bfloat16 math operations. - We should let the users know that they are using and invoke explicit cast to convert - the data type to the supported one. - """ - def wrapper(fn): - @wraps(fn) - def check(*args, **kwargs): - # concatenate args and kwargs - all_args = list(args) + list(kwargs.values()) - for arg in [a for a in all_args if isinstance(a, tl.tensor)]: - if arg.type.scalar.name not in dtypes: - raise ValueError(f"Expected dtype {dtypes} but got {arg.type.scalar.name}") - return fn(*args, **kwargs) - return check - - return wrapper - - -def umulhi(x: tl.tensor, y: tl.tensor, builder: ir.builder) -> tl.tensor: - x, y = binary_op_type_checking_impl(x, y, builder) - # FIXME(Keren): not portable, should be fixed - from . import math - return math.mulhi(x, y, _builder=builder) - - -@_check_dtype(dtypes=["fp32", "fp64"]) -def floor(x: tl.tensor, builder: ir.builder) -> tl.tensor: - # FIXME(Keren): not portable, should be fixed - from . import math - return math.floor(x, _builder=builder) - - -@_check_dtype(dtypes=["fp32", "fp64"]) -def exp(x: tl.tensor, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_exp(x.handle), x.type) - - -@_check_dtype(dtypes=["fp32", "fp64"]) -def log(x: tl.tensor, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_log(x.handle), x.type) - - -@_check_dtype(dtypes=["fp32", "fp64"]) -def cos(x: tl.tensor, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_cos(x.handle), x.type) - - -@_check_dtype(dtypes=["fp32", "fp64"]) -def sin(x: tl.tensor, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_sin(x.handle), x.type) - - -@_check_dtype(dtypes=["fp32", "fp64"]) -def sqrt(x: tl.tensor, builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_sqrt(x.handle), x.type) - - -def abs(x: tl.tensor, builder: ir.builder) -> tl.tensor: - dtype = x.dtype - if dtype.is_floating(): - return tl.tensor(builder.create_fabs(x.handle), x.type) - elif dtype.is_int_signed(): - return tl.tensor(builder.create_iabs(x.handle), x.type) - elif dtype.is_int_unsigned(): - return x # no-op - else: - assert False, f"Unexpected dtype {dtype}" - - -## - - -def multiple_of(x: tl.tensor, values: List[int]) -> tl.tensor: - if len(x.shape) != len(values): - raise ValueError("Shape of input to multiple_of does not match the length of values") - x.handle.set_attr("tt.divisibility", ir.make_attr(values, x.handle.get_context())) - return x - - -def max_contiguous(x: tl.tensor, values: List[int]) -> tl.tensor: - if len(x.shape) != len(values): - raise ValueError("Shape of input to max_contiguous does not match the length of values") - x.handle.set_attr("tt.contiguity", ir.make_attr(values, x.handle.get_context())) - return x - - -def debug_barrier(builder: ir.builder) -> tl.tensor: - return tl.tensor(builder.create_barrier(), tl.void) - - -def device_print(prefix: str, args: List[tl.tensor], builder: ir.builder) -> tl.tensor: - new_args = [] - for arg in args: - new_args.append(arg.handle) - return tl.tensor(builder.create_print(prefix, new_args), tl.void) - - -def device_assert(cond: tl.tensor, msg: str, file_name: str, func_name, lineno: int, builder: ir.builder) -> tl.tensor: - cond_ty = cond.type - if not cond_ty.is_block(): - cond_ty = tl.block_type(cond_ty.scalar, (1,)) - cond = tl.tensor(builder.create_splat(cond.handle, (1,)), cond_ty) - return tl.tensor(builder.create_assert(cond.handle, msg, file_name, func_name, lineno), tl.void) - - -def _convert_elem_to_ir_value(builder, elem, require_i64): - if isinstance(elem, tl.constexpr): - return builder.get_int64(elem.value) if require_i64 else builder.get_int32(elem.value) - elif isinstance(elem, tl.tensor): - assert elem.numel.value == 1, "Expected a scalar in shape/strides/offsets" - assert elem.dtype.is_int(), "Expected an integer scalar type in shape/strides/offsets" - if elem.dtype != tl.int64 and require_i64: - return builder.create_int_cast(elem.handle, builder.get_int64_ty(), elem.dtype.is_int_signed()) - elif elem.dtype != tl.int32: - return builder.create_int_cast(elem.handle, builder.get_int32_ty(), elem.dtype.is_int_signed()) - return elem.handle - assert False, f"Unsupported element type in shape/strides/offsets: {type(elem)}" - - -def _convert_to_ir_values(builder, list_like, require_i64=True): - if hasattr(list_like, "__iter__"): - return [_convert_elem_to_ir_value(builder, elem, require_i64) for elem in list_like] - return [_convert_elem_to_ir_value(builder, list_like, require_i64)] - - -def make_block_ptr(base: tl.tensor, shape, strides, offsets, block_shape, order, builder: ir.builder) -> tl.tensor: - # Convert dynamic arguments to IR values - # NOTES(Chenggang): current `shape/strides` are `int64_t`, while `offsets/block_shape` are `int32_t` - shape = _convert_to_ir_values(builder, shape) - strides = _convert_to_ir_values(builder, strides) - offsets = _convert_to_ir_values(builder, offsets, require_i64=False) - - # Check `base` type - if not base.type.is_ptr() or base.type.element_ty.is_block(): - raise ValueError("Expected `base` to be a pointer type (but not a block pointer type or others)") - - # Treat `pointer_type` as `pointer_type` - if base.type.element_ty == tl.int1: - base = cast(base, tl.pointer_type(tl.int8, base.type.address_space), builder) - - # Check whether `block_shape` is static - if not hasattr(block_shape, "__iter__"): - block_shape = [block_shape] - block_shape = [elem.value if isinstance(elem, tl.constexpr) else elem for elem in block_shape] - assert all([isinstance(elem, int) and -2**31 <= elem < 2**31 for elem in block_shape]), \ - "Expected a list of constant integers (`int32_t` range) in `block_shape`" - - # Check `order` - if not hasattr(order, "__iter__"): - order = [order] - order = [elem.value if isinstance(elem, tl.constexpr) else elem for elem in order] - assert sorted(order) == list(range(len(order))), "Expected a permutation of (0, 1, ..., len(order)-1) in order" - - # Must have same length - assert all([len(block_shape) == len(list_like) for list_like in [shape, strides, offsets, order]]), \ - "Expected shape/strides/offsets/block_shape to have the same length" - - # Build value, the type is: - # `pointer_type>` in Python - # `tt.ptr>` in MLIR - handle = builder.create_make_block_ptr(base.handle, shape, strides, offsets, block_shape, order) - return tl.tensor(handle, tl.pointer_type(tl.block_type(base.type.element_ty, block_shape))) - - -def advance(base: tl.tensor, offsets, builder: ir.builder) -> tl.tensor: - # Convert dynamic offsets to IR values - offsets = _convert_to_ir_values(builder, offsets, require_i64=False) - - # Advanced block pointer type is the same as before - return tl.tensor(builder.create_advance(base.handle, offsets), base.type) diff --git a/python/triton/language/standard.py b/python/triton/language/standard.py deleted file mode 100644 index b997674c91b6..000000000000 --- a/python/triton/language/standard.py +++ /dev/null @@ -1,98 +0,0 @@ -from __future__ import annotations - -from ..runtime.jit import jit -from . import core - -# ----------------------- -# Standard library -# ----------------------- - - -@jit -def cdiv(x, div): - """ - Computes the ceiling division of :code:`x` by :code:`div` - - :param x: the input number - :type input: Block - :param div: the divisor - :param div: Block - """ - return (x + div - 1) // div - - -@jit -@core._add_math_1arg_docstr("sigmoid") -def sigmoid(x): - return 1 / (1 + core.exp(-x)) - - -@jit -@core._add_math_1arg_docstr("softmax") -def softmax(x, ieee_rounding=False): - z = x - core.max(x, 0) - num = core.exp(z) - den = core.sum(num, 0) - return core.fdiv(num, den, ieee_rounding) - - -@jit -def ravel(x): - """ - Returns a contiguous flattened view of :code:`x`. - - :param x: the input tensor - :type x: Block - """ - return core.view(x, [x.numel]) - - -@jit -def swizzle2d(i, j, size_i, size_j, size_g): - """ - Transforms indices of a row-major size_i*size_j matrix into those - of one where indices are row major for each group of size_j rows. - For example, for size_i = size_j = 4 and size_g = 2, it will transform - [[0 , 1 , 2 , 3 ], - [4 , 5 , 6 , 7 ], - [8 , 9 , 10, 11], - [12, 13, 14, 15]] - into - [[0, 2, 4 , 6 ], - [1, 3, 5 , 7 ], - [8, 10, 12, 14], - [9, 11, 13, 15]] - """ - # "unrolled index in array" - ij = i * size_j + j - # number of elements in `size_g` groups - # of `size_j` columns - size_gj = size_g * size_j - # index of the group in which (i,j) is - group_id = ij // size_gj - # row-index of the first element of this group - off_i = group_id * size_g - # last group may have fewer rows - size_g = core.minimum(size_i - off_i, size_g) - # new row and column indices - new_i = off_i + (ij % size_g) - new_j = (ij % size_gj) // size_g - return new_i, new_j - - -@jit -def zeros(shape, dtype): - """ - Returns a tensor filled with the scalar value 0 for the given :code:`shape` and :code:`dtype`. - - :param shape: Shape of the new array, e.g., (8, 16) or (8, ) - :type shape: tuple of ints - :param dtype: Data-type of the new array, e.g., :code:`tl.float16` - :type dtype: DType - """ - return core.full(shape, 0, dtype) - - -@jit -def zeros_like(input): - return zeros(input.shape, input.dtype) diff --git a/python/triton/ops/__init__.py b/python/triton/ops/__init__.py deleted file mode 100644 index 6ceec8b56a00..000000000000 --- a/python/triton/ops/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# from .conv import _conv, conv -from . import blocksparse -from .cross_entropy import _cross_entropy, cross_entropy -from .flash_attention import attention -from .matmul import _matmul, matmul - -__all__ = [ - "blocksparse", - "_cross_entropy", - "cross_entropy", - "_matmul", - "matmul", - "attention", -] diff --git a/python/triton/ops/blocksparse/__init__.py b/python/triton/ops/blocksparse/__init__.py deleted file mode 100644 index 6b24b5377fab..000000000000 --- a/python/triton/ops/blocksparse/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .matmul import matmul -from .softmax import softmax - -__all__ = [ - "matmul", - "softmax", -] diff --git a/python/triton/ops/blocksparse/matmul.py b/python/triton/ops/blocksparse/matmul.py deleted file mode 100644 index 5366302068a6..000000000000 --- a/python/triton/ops/blocksparse/matmul.py +++ /dev/null @@ -1,437 +0,0 @@ -import torch - -from ... import cdiv, heuristics, jit -from ... import language as tl - -# ******************************************************** -# -------------------------------------------------------- -# Sparse = Dense x Dense (SDD) -# This operation uses super-blocking to make sure that -# it's done efficiently when small blocks can be grouped -# together -# -------------------------------------------------------- -# ******************************************************** - - -@heuristics({ - 'EVEN_K': lambda nargs: nargs['K'] % nargs['TILE_K'] == 0, -}) -@jit -def _sdd_kernel( - A, B, C, - stride_za, stride_ha, stride_ma, stride_ak, - stride_zb, stride_hb, stride_bk, stride_nb, - stride_zc, stride_hc, stride_mc, stride_nc, - K, grid_offset, lut, - TILE_M: tl.constexpr, TILE_N: tl.constexpr, TILE_K: tl.constexpr, - BLOCK: tl.constexpr, EVEN_K: tl.constexpr -): - # ------------ # - # - Prologue - # - # ------------ # - block_id = tl.program_id(0) + grid_offset - lut += block_id * 3 - # offsets - off_z = tl.program_id(2) # batch - off_h = tl.load(lut + 0) # head - - # initialize pointers to A - start_am = tl.load(lut + 1) - offs_am = start_am * BLOCK + (tl.arange(0, TILE_M) % BLOCK) - offs_ak = tl.arange(0, TILE_K) - a_ptrs = A \ - + off_z * stride_za \ - + off_h * stride_ha \ - + offs_am[:, None] * stride_ma \ - + offs_ak[None, :] * stride_ak - # initialize pointers to B - start_bn = tl.load(lut + 2) - offs_bn = start_bn * BLOCK + (tl.arange(0, TILE_N) % BLOCK) - offs_bk = tl.arange(0, TILE_K) - b_ptrs = B \ - + off_z * stride_zb \ - + off_h * stride_hb \ - + offs_bn[None, :] * stride_nb \ - + offs_bk[:, None] * stride_bk - # ---------------- # - # Inner Loop # - # ---------------- # - acc = tl.zeros((TILE_M, TILE_N), dtype=tl.float32) - for k in range(K, 0, -TILE_K): - if EVEN_K: - a = tl.load(a_ptrs) - b = tl.load(b_ptrs) - else: - a = tl.load(a_ptrs, mask=offs_ak[None, :] < k, other=0.) - b = tl.load(b_ptrs, mask=offs_bk[:, None] < k, other=0.) - acc += tl.dot(a, b, out_dtype=tl.float32) - a_ptrs += TILE_K * stride_ak - b_ptrs += TILE_K * stride_bk - c = acc.to(C.dtype.element_ty) - # ---------------- # - # Epilogue # - # ---------------- # - offs_cm = tl.arange(0, TILE_M) % BLOCK - offs_cn = tl.arange(0, TILE_N) % BLOCK - pc = C \ - + off_z * stride_zc \ - + block_id * stride_hc \ - + offs_cm[:, None] * stride_mc \ - + offs_cn[None, :] * stride_nc - tl.store(pc, c, mask=True) - - -def sdd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, widths, out=None): - if a.stride(2) != 1 and a.stride(3) != 1: - a = a.contiguous() - if b.stride(2) != 1 and b.stride(3) != 1: - b = b.contiguous() - # (A * B)^T = B^T * A^T - if trans_c: - a, b = b, a - trans_a, trans_b = not trans_b, not trans_a - # shape constraints - a_dim = -2 if trans_a else -1 - b_dim = -1 if trans_b else -2 - Ka, Kb = a.shape[a_dim], b.shape[b_dim] - if Ka != Kb: - raise ValueError(f"Inner dimension mismatch (A: {Ka} vs B: {Kb})") - # allocate output - if out is None: - c = torch.empty((a.shape[0], lut.shape[0], block, block), dtype=a.dtype, device=a.device) - else: - assert out.shape == (a.shape[0], lut.shape[0], block, block) - c = out - grid = [c.shape[1], 1, c.shape[0]] - _sdd_kernel[grid]( - a, b, c, - a.stride(0), a.stride(1), a.stride(3 if trans_a else 2), a.stride(2 if trans_a else 3), - b.stride(0), b.stride(1), b.stride(3 if trans_b else 2), b.stride(2 if trans_b else 3), - c.stride(0), c.stride(1), c.stride(2), c.stride(3), - Ka, 0, lut, - TILE_M=block, TILE_N=block, TILE_K=32, BLOCK=block, num_stages=4, - num_warps=4, - ) - return c - - -def sdd_lut(layout, block, device): - lut = layout.nonzero(as_tuple=False).to(device).int() - lut = lut.contiguous() - return lut, None - -# ----------------------------- -# Dense = Sparse x Dense (DSD) -# This operation uses a look-up table that contains pre-computed pointer increments -# in order to minimize computations in the inner loop of the matmul kernel. -# ----------------------------- - - -@jit -def _dsd_kernel( - A, B, C, - stride_az, stride_ha, stride_am, stride_ak, - stride_zb, stride_hb, stride_bk, stride_bn, - stride_zc, stride_hc, stride_cm, stride_cn, - DS0, DS1, lut, - TILE_M: tl.constexpr, TILE_N: tl.constexpr, TILE_K: tl.constexpr, - GROUP_SIZE_M: tl.constexpr, BLOCK: tl.constexpr -): - # ------------ # - # - Prologue - # - # ------------ # - pid_m = tl.program_id(0) - pid_n = tl.program_id(1) - num_pid_m = tl.num_programs(0) - num_pid_n = tl.num_programs(1) - pid_n, pid_m = tl.swizzle2d(pid_n, pid_m, num_pid_n, num_pid_m, GROUP_SIZE_M) - pidz = tl.program_id(2) - header = lut + pid_n * 4 - offset = tl.load(header + 0) - K = tl.load(header + 1) - column = tl.load(header + 2) - off_h = tl.load(header + 3) - pinc = lut + offset - # initialize pointers to A (sparse) - block_id = tl.load(pinc + 1) - block_id = tl.multiple_of(block_id, 8) # compiler hint - offs_am = tl.arange(0, TILE_M) - offs_ak = tl.arange(0, TILE_K) - pa = A + pidz * stride_az \ - + block_id * stride_ha \ - + offs_am[:, None] * stride_am \ - + offs_ak[None, :] * stride_ak - # initialize pointers to B (dense) - offs_bn = pid_m * TILE_N + tl.arange(0, TILE_N) - offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn % DS0, TILE_N), TILE_N) - start_bk = tl.load(pinc) - start_bk = tl.multiple_of(start_bk, 8) # compiler hint - offs_bk = start_bk + tl.arange(0, TILE_K) - pb = B + pidz * stride_zb \ - + off_h * stride_hb \ - + offs_bn[None, :] * stride_bn \ - + offs_bk[:, None] * stride_bk - # ---------------- # - # Inner Loop # - # ---------------- # - acc = tl.zeros((TILE_M, TILE_N), dtype=tl.float32) - pinc += 2 - inc_a = tl.load(pinc + 1) - inc_a = tl.multiple_of(inc_a, 8) - inc_b = tl.load(pinc) - inc_b = tl.multiple_of(inc_b, 8) - for k in range(K, 0, -TILE_K): - a = tl.load(pa) - b = tl.load(pb) - acc += tl.dot(a, b, out_dtype=tl.float32) - pa += inc_a - pb += inc_b * stride_bk - pinc += 2 - inc_a = tl.load(pinc + 1) - inc_a = tl.multiple_of(inc_a, 8) - inc_b = tl.load(pinc) - inc_b = tl.multiple_of(inc_b, 8) - c = acc.to(C.dtype.element_ty) - # initialize pointers to C - offs_cm = column * TILE_M + tl.arange(0, TILE_M) - offs_cn = pid_m * TILE_N + tl.arange(0, TILE_N) - pc = C \ - + off_h * stride_hc \ - + pidz * stride_zc \ - + offs_cm[:, None] * stride_cm \ - + offs_cn[None, :] * stride_cn - tl.store(pc, c, mask=offs_cn[None, :] < DS0) - - -def dsd_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, width, out=None): - if a.stride(2) != 1 and a.stride(3) != 1: - a = a.contiguous() - if b.stride(2) != 1 and b.stride(3) != 1: - b = b.contiguous() - # shapes / dtypes - AS1 = block * spdims[2 if trans_a else 1] - BS0 = b.size(0) - BS1 = b.size(1) - BS3 = b.size(2 if trans_b else 3) - dtype = a.dtype - # allocate output - CS0 = BS0 - CS1 = BS1 - CS2 = BS3 if trans_c else AS1 - CS3 = AS1 if trans_c else BS3 - if out is None: - c = torch.empty((CS0, CS1, CS2, CS3), dtype=dtype, device=a.device) - else: - assert out.shape == (CS0, CS1, CS2, CS3) - c = out - # meta-parameter heuristics - TILE_N = 128 - # compute output - grid = lambda meta: [cdiv(BS3, meta['TILE_N']), width, BS0] - _dsd_kernel[grid]( - a, b, c, - a.stride(0), a.stride(1), a.stride(3 if trans_a else 2), a.stride(2 if trans_a else 3), - b.stride(0), b.stride(1), b.stride(3 if trans_b else 2), b.stride(2 if trans_b else 3), - c.stride(0), c.stride(1), c.stride(3 if trans_c else 2), c.stride(2 if trans_c else 3), - BS3, AS1, lut, - TILE_M=block, TILE_N=TILE_N, TILE_K=min(block, 32), BLOCK=block, num_stages=4, - num_warps=4, GROUP_SIZE_M=4, - ) - # exit() - return c - - -def dsd_lut(layout, block, step, trans, device): - """ - Generates the look-up table for incrementing pointers in the DSD/DDS matmul. - Example (BLOCK=32, STEP=16) - [[1, 0, 0, 1, 0], - [0, 1, 1, 0, 1], - [1, 0, 1, 0, 0]] - - Then the offsets for A are - [0 , 16, 32, 48] <- row 0 - \\----/ \\----/ - col=0 col=3 - [64, 80, 96, 112, 128, 144] <- row 1 - \\----/ \\----/ \\------/ - col=1 col=2 col=3 - [160, 176, 192, 208] - which leads to increments table - [0, 16, 16, 16, || 64, 16, 16, 16, 16, 16, || 160, 16, 16, 16] - - Because B is dense, the offsets are - [0, 16, 96, 112] <- row 0 - [32, 48, 64, 80] <- row 1 - [0, 16, 64, 80] <- row 2 - """ - sizes = torch.sum(layout, 2 if trans else 1) - head_id, col_id = torch.ones_like(sizes).nonzero(as_tuple=True) - sizes = sizes.flatten() - segments = sizes * step - # pointer increments - if trans: - nnz = layout.nonzero(as_tuple=False) - else: - nnz = layout.transpose(1, 2).nonzero(as_tuple=False) - num_blocks = nnz.size(0) - offsets = torch.zeros_like(sizes) - offsets[1:] = torch.cumsum(sizes[:-1], dim=0) - offsets = torch.min(offsets, (num_blocks - 1) * torch.ones_like(offsets)) - # ------------------------------- - # dense input pointer increments - # ------------------------------- - # Note that the inner loop matmul kernel may have a fixed step size (e.g., TILE_K) - # that is smaller than the block size, so we need to do a bit of extra work - # to handle this case - B_idx = nnz[:, 2] * block - B_incs = B_idx.clone() - B_incs[1:] -= B_idx[:-1] - div = block // step - B_incs = B_incs.view(-1, 1).repeat(1, div) - B_incs[:, 1:] = step - B_incs[:, 0] -= (div - 1) * step - # first increment for each reduction is actually the offset - B_incs[offsets[segments > 0], 0] = B_idx[offsets[segments > 0]] - B_incs = B_incs.view(-1) - # ------------------------------- - # sparse input pointer increments - # ------------------------------- - # same as above, except that the increments are in the sparse memory layout - if trans: - A_idx = torch.arange(num_blocks, device=layout.device) - else: - A_idx = torch.tensor([], dtype=torch.int64, device=layout.device) - current_offset = 0 - for z in range(layout.size(0)): - layoutw = layout[z, :, :].clone().long() - msum = layoutw.sum() - layoutw[layoutw > 0] = 1 + torch.arange(msum, device=layout.device) - A_idx = torch.cat((A_idx, current_offset + layoutw.T[layoutw.T > 0] - 1)) - current_offset += msum - A_incs = A_idx * block * block - A_incs[1:] -= A_idx[:-1] * block * block - A_incs = A_incs.view(-1, 1).repeat(1, div) - if trans: - A_incs[:, 1:] = step - A_incs[:, 0] -= (div - 1) * step - else: - A_incs[:, 1:] = step * block - A_incs[:, 0] -= (div - 1) * step * block - A_incs[offsets[segments > 0], 0] = A_idx[offsets[segments > 0]] - A_incs = A_incs.view(-1) - # create header - width = col_id.size(0) - offsets = offsets * 2 * div + 4 * width - segments = segments * div - header = torch.stack((offsets, segments, col_id, head_id), dim=1).view(-1).contiguous() - # create increments - incs = torch.stack((B_incs, A_incs), dim=1).view(-1).contiguous() - # pad by a factor 2*MAX_NUM_STAGES - # to accommodate pre-fetching inside the kernel - pad = torch.zeros(20, device=incs.device, dtype=incs.dtype) - incs = torch.cat((incs, pad)) - # create lut - lut = torch.cat((header, incs)) - lut = lut.type(torch.int32).to(device) - # create locks - return lut, width - -# ----------------------------- -# Dense = Dense x Sparse (DDS) -# ----------------------------- -# AB = (B^T A^T)^T - - -def dds_matmul(a, b, trans_a, trans_b, trans_c, spdims, block, lut, width, out=None): - return dsd_matmul(b, a, not trans_b, not trans_a, not trans_c, spdims, block, lut, width, out=out) - -############## -# MAIN API # -############## - - -class _matmul(torch.autograd.Function): - - fn = {'sdd': sdd_matmul, 'dsd': dsd_matmul, 'dds': dds_matmul} - - @staticmethod - def forward( - ctx, a, b, trans_a, trans_b, trans_c, mode, spdims, block, - c_lut, c_width, da_lut, da_width, db_lut, db_width, out - ): - c = _matmul.fn[mode](a, b, trans_a, trans_b, trans_c, spdims, block, c_lut, c_width, out=out) - # save for backward - ctx.save_for_backward(a, b) - ctx.da_lut = da_lut - ctx.da_width = da_width - ctx.db_lut = db_lut - ctx.db_width = db_width - ctx.mode = mode - ctx.spdims = spdims - ctx.block = block - ctx.trans_a = trans_a - ctx.trans_b = trans_b - ctx.trans_c = trans_c - ctx.has_out = out is not None - return c - - @staticmethod - def backward(ctx, dc): - # saved for backward - a, b = ctx.saved_tensors - da, db = None, None - mode = ctx.mode - # gradients w.r.t. a - if ctx.needs_input_grad[0]: - mode_da = mode[1] + mode[0] + mode[2] - da = _matmul.fn[mode_da]( - dc, b, ctx.trans_c, not ctx.trans_b, ctx.trans_a, ctx.spdims, ctx.block, ctx.da_lut, ctx.da_width, - ) - # gradients w.r.t. b - if ctx.needs_input_grad[1]: - mode_db = mode[2] + mode[1] + mode[0] - db = _matmul.fn[mode_db]( - a, dc, not ctx.trans_a, ctx.trans_c, ctx.trans_b, ctx.spdims, ctx.block, ctx.db_lut, ctx.db_width, - ) - dout = dc if ctx.has_out else None - return da, db, None, None, None,\ - None, None, None, None,\ - None, None, None, None, None, dout - - -class matmul: - - def __init__(self, layout, block, mode, device, trans_a=False, trans_b=False, trans_c=False): - if mode not in ['sdd', 'dsd', 'dds']: - raise NotImplementedError('Supported modes are: sdd, dsd, dds') - self.block = block - self.mode = mode - self.trans_a = trans_a - self.trans_b = trans_b - self.trans_c = trans_c - self.layout = layout - self.spdims = layout.shape - step = min(block, 32) - if self.mode == 'sdd': - self.c_lut, self.c_width = sdd_lut(layout, block, device) - self.da_lut, self.da_width = dsd_lut(layout, block, step, True, device) - self.db_lut, self.db_width = dsd_lut(layout, block, step, False, device) - if self.mode == 'dsd': - self.c_lut, self.c_width = dsd_lut(layout, block, step, not self.trans_a, device) - self.da_lut, self.da_width = sdd_lut(layout, block, device) - self.db_lut, self.db_width = dsd_lut(layout, block, step, self.trans_a, device) - if self.mode == 'dds': - self.c_lut, self.c_width = dsd_lut(layout, block, step, self.trans_b, device) - self.da_lut, self.da_width = dsd_lut(layout, block, step, not self.trans_b, device) - self.db_lut, self.db_width = sdd_lut(layout, block, device) - - def __call__(self, a, b, out=None): - c = _matmul.apply( - a, b, self.trans_a, self.trans_b, self.trans_c, self.mode, self.spdims, self.block, - self.c_lut, self.c_width, - self.da_lut, self.da_width, - self.db_lut, self.db_width, - out - ) - return c diff --git a/python/triton/ops/blocksparse/softmax.py b/python/triton/ops/blocksparse/softmax.py deleted file mode 100644 index c045b11a539c..000000000000 --- a/python/triton/ops/blocksparse/softmax.py +++ /dev/null @@ -1,240 +0,0 @@ -import torch - -from ... import jit -from ... import language as tl -from ... import next_power_of_2 - - -def num_warps(n): - if n <= 128: - return 1 - if n <= 256: - return 2 - if n <= 512: - return 4 - if n <= 4096: - return 8 - return 16 - - -@jit -def _blocksparse_softmax_fwd( - Out, A, stride_xz, LUT, - R, extent, stride_zr, stride_hr, # relative attention - scale, is_causal, - ROW_SIZE: tl.constexpr, - BLOCK_SIZE: tl.constexpr, - IS_DENSE: tl.constexpr, -): - h = tl.program_id(0) - m = tl.program_id(1) - z = tl.program_id(2) - # create index ranges - hm = h * tl.num_programs(1) + m - lane_n = tl.arange(0, ROW_SIZE) % BLOCK_SIZE - block_n = tl.arange(0, ROW_SIZE) // BLOCK_SIZE - # extract information from LUT - header = LUT + (hm // BLOCK_SIZE) * 2 - size = tl.load(header + 0) - offset = tl.load(header + 1) - # pointer offset - off_a = z * stride_xz - off_a += (offset + block_n) * BLOCK_SIZE * BLOCK_SIZE # block indx - off_a += (m % BLOCK_SIZE) * BLOCK_SIZE # row indx - # do not need to read column indices in the dense case - if IS_DENSE: - ns = tl.arange(0, ROW_SIZE) - else: - off_lut = offset + 2 * tl.num_programs(0) * tl.num_programs(1) // BLOCK_SIZE - start_n = tl.load(LUT + off_lut + block_n, mask=block_n < size, other=0) - ns = start_n * BLOCK_SIZE + lane_n - # load X - mask = block_n < size - a = tl.load(A + off_a + lane_n, mask=mask, other=-float("inf")) - a = a.to(tl.float32) - # compute - out = a - out *= scale - # apply relative attention - if R is not None: - R += z * stride_zr - R += h * stride_hr - off_lo = (extent - m - 1) + ns - mask_lo = (off_lo >= 0) & (off_lo < extent) - rel_logits = tl.load(R + m * extent + off_lo, mask=mask_lo, other=0.0) - out += rel_logits - out = out.to(tl.float32) - # apply causal mask - out = tl.where((ns > m) & is_causal, -float("inf"), out) - # computation - out = tl.softmax(out) - # write-back - tl.store(Out + off_a + lane_n, out, mask=mask) - - -@jit -def _blocksparse_softmax_bwd( - DA, stride_zdx, - DOut, stride_zdout, - Out, stride_zout, - scale, - LUT, - DR, extent, stride_zr, stride_hr, stride_er, - is_causal, - ROW_SIZE: tl.constexpr, - BLOCK_SIZE: tl.constexpr, - IS_DENSE: tl.constexpr, -): - h = tl.program_id(0) - m = tl.program_id(1) - z = tl.program_id(2) - # create index ranges - hm = h * tl.num_programs(1) + m - lane_n = tl.arange(0, ROW_SIZE) % BLOCK_SIZE - block_n = tl.arange(0, ROW_SIZE) // BLOCK_SIZE - # extract information from LUT - header = LUT + (hm // BLOCK_SIZE) * 2 - size = tl.load(header + 0) - offset = tl.load(header + 1) - # row-col offset - off_mn = (offset + block_n) * BLOCK_SIZE * BLOCK_SIZE - off_mn += (m % BLOCK_SIZE) * BLOCK_SIZE - mask = block_n < size - # pointers - As = Out + z * stride_zout + off_mn - DOuts = DOut + z * stride_zdout + off_mn - # do not need to read column indices in the dense case - if IS_DENSE: - ns = tl.arange(0, ROW_SIZE) - else: - off_lut = offset + 2 * tl.num_programs(0) * tl.num_programs(1) // BLOCK_SIZE - start_n = tl.load(LUT + off_lut + block_n, mask=mask, other=0) - ns = start_n * BLOCK_SIZE + lane_n - # load data - a = tl.load(As + lane_n, mask=mask, other=0.0) - a = a.to(tl.float32) - dout = tl.load(DOuts + lane_n, mask=mask, other=0.0) - dout = dout.to(tl.float32) - # compute - a = tl.where((ns > m) & is_causal & (a == a), 0., a) - da = a * (dout - tl.sum(a * dout, 0)) - # apply relative attention - if DR is not None: - DR += z * stride_zr - DR += h * stride_hr - off_lo = (extent - m - 1) + ns - mask_lo = (off_lo >= 0) & (off_lo < extent) & mask - tl.store(DR + m * extent + off_lo, da, mask=mask_lo) - da = da * scale - # convert da - # write-back - DAs = DA + z * stride_zdx + off_mn - tl.store(DAs + lane_n, da, mask=mask) - - -class _softmax(torch.autograd.Function): - @staticmethod - def make_lut(layout, block, device): - _empty = torch.tensor([], dtype=torch.int64, device=layout.device) - sizes = _empty.clone() - # sizes along rows - for h in range(layout.shape[0]): - sizes = torch.cat((sizes, layout[h, :, :].sum(-1))) - total_sizes = sizes * block - # offsets in block format - offsets = torch.zeros_like(sizes) - offsets[1:] = torch.cumsum(sizes[:-1], dim=0) - # block indices - columns = layout.nonzero(as_tuple=False)[:, 2] - header = torch.stack((sizes, offsets), dim=1).view(-1) - lut = torch.cat((header, columns)).type(torch.int32).to(device) - return lut, int(total_sizes.max()) - - @staticmethod - def forward( - ctx, a, scale, rel_logits, is_causal, - spdims, block, lut, maxlut, is_dense - ): - if scale is not None and isinstance(scale, torch.Tensor): - assert scale.device.type == "cpu" - scale = scale.item() - M = a.shape[0] - grid = [spdims[0], spdims[1] * block, M] - rel_shape = (1, 1, 1, 1) if rel_logits is None else rel_logits.shape - rel_strides = (1, 1, 1, 1) if rel_logits is None else rel_logits.stride() - # enqueue kernel - out = torch.empty_like(a) - _blocksparse_softmax_fwd[grid]( - out, a, a.stride(0), lut, - rel_logits, rel_shape[-1], rel_strides[0], rel_strides[1], # relative attn - scale, - is_causal, - BLOCK_SIZE=block, - ROW_SIZE=next_power_of_2(maxlut), - IS_DENSE=is_dense, - num_warps=num_warps(maxlut) - ) - # save to context - # ctx.mark_dirty(x) - ctx.save_for_backward(out, lut) - ctx.spdims = spdims - ctx.block = block - ctx.maxlut = maxlut - ctx.scale = scale - ctx.rel_shape = rel_shape - ctx.rel_strides = rel_strides - ctx.rel_dtype = a.dtype - ctx.is_dense = is_dense - ctx.is_causal = is_causal - return out - - @staticmethod - def backward(ctx, dout): - # retrieve from context - out, lut = ctx.saved_tensors - # relative logits gradients - dr = None - if ctx.needs_input_grad[3]: - dr = torch.zeros(ctx.rel_shape, dtype=ctx.rel_dtype, device=out.device) - # run kernel - M = out.shape[0] - grid = (ctx.spdims[0], ctx.spdims[1] * ctx.block, M) - da = torch.empty_like(dout) - _blocksparse_softmax_bwd[grid]( - da, da.stride(0), - dout, dout.stride(0), - out, out.stride(0), - ctx.scale, - lut, - dr, ctx.rel_shape[-1], ctx.rel_strides[0], ctx.rel_strides[1], ctx.rel_strides[2], - ctx.is_causal, - BLOCK_SIZE=ctx.block, - ROW_SIZE=next_power_of_2(ctx.maxlut), - IS_DENSE=ctx.is_dense, - num_warps=num_warps(ctx.maxlut) - ) - return (da, None, None, dr, None, - None, None, None, None, None, - None, - None, None, None, - None, - None, None, None - ) - - -class softmax: - def __init__(self, layout, block, device, is_dense=False): - self.spdims = layout.shape - self.layout = layout - self.block = block - self.lut, self.maxlut = _softmax.make_lut(self.layout, self.block, device) - self.is_dense = is_dense - - def __call__(self, a, *, scale=1.0, rel_logits=None, is_causal=False): - if rel_logits is not None and rel_logits.dtype != a.dtype: - raise ValueError(f"relative position embedding must be {a.dtype}") - a = _softmax.apply( - a, scale, rel_logits, is_causal, - self.spdims, self.block, self.lut, self.maxlut, self.is_dense, - ) - return a diff --git a/python/triton/ops/cross_entropy.py b/python/triton/ops/cross_entropy.py deleted file mode 100644 index 5b0bcf8367bd..000000000000 --- a/python/triton/ops/cross_entropy.py +++ /dev/null @@ -1,95 +0,0 @@ -import torch - -from .. import heuristics, jit -from .. import language as tl -from .. import next_power_of_2 - - -def num_warps(N): - if N < 2048: - return 4 - elif N < 8192: - return 8 - return 16 - - -@heuristics({'num_warps': lambda nargs: num_warps(nargs['N'])}) -@heuristics({'BLOCK': lambda nargs: next_power_of_2(nargs['N'])}) -@jit -def _forward(LOGITS, PROBS, IDX, LOSS, N, BLOCK: tl.constexpr): - row = tl.program_id(0) - cols = tl.arange(0, BLOCK) - idx = tl.load(IDX + row) - # pointers to logit and probs - LOGITS = LOGITS + row * N + cols - WRIT_PROBS = PROBS + row * N + cols - READ_PROBS = PROBS + row * N + idx - # write-back negative log-probs - logits = tl.load(LOGITS, mask=cols < N, other=-float('inf')) - logits = logits.to(tl.float32) - logits = logits - tl.max(logits, 0) - probs = tl.log(tl.sum(tl.exp(logits), 0)) - logits - tl.store(WRIT_PROBS, probs, mask=cols < N) - # There is a bug in the compiler, which fails to insert a barrier here. - # We add it explicitly for now. Will be fixed soon. - tl.debug_barrier() - # write-back loss - probs = tl.load(READ_PROBS) - tl.store(LOSS + row, probs) - - -@heuristics({'num_warps': lambda nargs: num_warps(nargs['N'])}) -@heuristics({'BLOCK': lambda nargs: next_power_of_2(nargs['N'])}) -@jit -def _backward(PROBS, IDX, DPROBS, N, BLOCK: tl.constexpr): - row = tl.program_id(0) - cols = tl.arange(0, BLOCK) - idx = tl.load(IDX + row) - # pointers to probs - PROBS = PROBS + row * N + cols - # We know d(-log(p[i])/dlogit[k] = -id_mat[i,k] + p[k] - # and we have -log(p[k]) stored in PROBS, so this is easy - probs = -tl.load(PROBS, mask=cols < N, other=float('inf')) - probs = tl.exp(probs.to(tl.float32)) - delta = cols == idx - # write result in-place in PROBS - dout = tl.load(DPROBS + row) - din = (probs - delta) * dout - tl.store(PROBS, din.to(PROBS.dtype.element_ty), mask=cols < N) - - -class _cross_entropy(torch.autograd.Function): - @classmethod - def forward(cls, ctx, logits, indices): - # make sure we can use triton - assert (indices.dtype == torch.int64), "Indices are expected to be of type long." - # make kernel - device, dtype = logits.device, logits.dtype - n_cols = logits.shape[-1] - # run the kernel - result = torch.empty_like(indices, dtype=dtype, device=device) - neg_logprobs = torch.empty_like(logits, dtype=dtype, device=device) - grid = lambda opt: (logits.numel() // n_cols, ) - _forward[grid](logits, neg_logprobs, indices, result, n_cols) - # save for backward - ctx.save_for_backward(neg_logprobs, indices) - return result - - @classmethod - def backward(cls, ctx, dneg_logprobs): - """We know d(-log(p[i])/dlogit[k] = -id_mat[i,k] + p[k] - so we initialize the gradient as neg_logprobs, so we can just exponentiate - to get p[k], which is most of what we need... neg_logprobs will be - modified in place to become the gradient we want - """ - # load saved tensors - neg_logprobs, indices = ctx.saved_tensors - # run the kernel - # neg_logprobs will be modified in place to become our gradient: - n_cols = neg_logprobs.shape[-1] - grid = lambda opt: (neg_logprobs.numel() // n_cols, ) - _backward[grid](neg_logprobs, indices, dneg_logprobs, n_cols) - return neg_logprobs, None - - -cross_entropy = _cross_entropy.apply diff --git a/python/triton/ops/flash_attention.py b/python/triton/ops/flash_attention.py deleted file mode 100644 index d17fe515e317..000000000000 --- a/python/triton/ops/flash_attention.py +++ /dev/null @@ -1,266 +0,0 @@ -""" -Fused Attention -=============== -This is a Triton implementation of the Flash Attention algorithm -(see: Dao et al., https://arxiv.org/pdf/2205.14135v2.pdf; Rabe and Staats https://arxiv.org/pdf/2112.05682v2.pdf) -""" - -import torch - -from .. import cdiv, jit -from .. import language as tl - - -@jit -def _fwd_kernel( - Q, K, V, sm_scale, - L, M, - Out, - stride_qz, stride_qh, stride_qm, stride_qk, - stride_kz, stride_kh, stride_kn, stride_kk, - stride_vz, stride_vh, stride_vk, stride_vn, - stride_oz, stride_oh, stride_om, stride_on, - Z, H, N_CTX, - BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, - BLOCK_N: tl.constexpr, -): - start_m = tl.program_id(0) - off_hz = tl.program_id(1) - # initialize offsets - offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) - offs_n = tl.arange(0, BLOCK_N) - offs_d = tl.arange(0, BLOCK_DMODEL) - off_q = off_hz * stride_qh + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk - off_k = off_hz * stride_qh + offs_n[None, :] * stride_kn + offs_d[:, None] * stride_kk - off_v = off_hz * stride_qh + offs_n[:, None] * stride_qm + offs_d[None, :] * stride_qk - # Initialize pointers to Q, K, V - q_ptrs = Q + off_q - k_ptrs = K + off_k - v_ptrs = V + off_v - # initialize pointer to m and l - m_prev = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") - l_prev = tl.zeros([BLOCK_M], dtype=tl.float32) - acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) - # load q: it will stay in SRAM throughout - q = tl.load(q_ptrs) - # loop over k, v and update accumulator - for start_n in range(0, (start_m + 1) * BLOCK_M, BLOCK_N): - # -- compute qk ---- - k = tl.load(k_ptrs) - qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - qk += tl.dot(q, k) - qk *= sm_scale - qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf")) - # compute new m - m_curr = tl.maximum(tl.max(qk, 1), m_prev) - # correct old l - l_prev *= tl.exp(m_prev - m_curr) - # attention weights - p = tl.exp(qk - m_curr[:, None]) - l_curr = tl.sum(p, 1) + l_prev - # rescale operands of matmuls - l_rcp = 1. / l_curr - p *= l_rcp[:, None] - acc *= (l_prev * l_rcp)[:, None] - # update acc - p = p.to(Q.dtype.element_ty) - v = tl.load(v_ptrs) - acc += tl.dot(p, v) - # update m_i and l_i - l_prev = l_curr - m_prev = m_curr - # update pointers - k_ptrs += BLOCK_N * stride_kn - v_ptrs += BLOCK_N * stride_vk - # rematerialize offsets to save registers - start_m = tl.program_id(0) - offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) - # write back l and m - l_ptrs = L + off_hz * N_CTX + offs_m - m_ptrs = M + off_hz * N_CTX + offs_m - tl.store(l_ptrs, l_prev) - tl.store(m_ptrs, m_prev) - # initialize pointers to output - offs_n = tl.arange(0, BLOCK_DMODEL) - off_o = off_hz * stride_oh + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on - out_ptrs = Out + off_o - tl.store(out_ptrs, acc) - - -@jit -def _bwd_preprocess( - Out, DO, L, - NewDO, Delta, - BLOCK_M: tl.constexpr, D_HEAD: tl.constexpr, -): - off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) - off_n = tl.arange(0, D_HEAD) - # load - o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) - do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) - denom = tl.load(L + off_m).to(tl.float32) - # compute - do = do / denom[:, None] - delta = tl.sum(o * do, axis=1) - # write-back - tl.store(NewDO + off_m[:, None] * D_HEAD + off_n[None, :], do) - tl.store(Delta + off_m, delta) - - -@jit -def _bwd_kernel( - Q, K, V, sm_scale, Out, DO, - DQ, DK, DV, - L, M, - D, - stride_qz, stride_qh, stride_qm, stride_qk, - stride_kz, stride_kh, stride_kn, stride_kk, - stride_vz, stride_vh, stride_vk, stride_vn, - Z, H, N_CTX, - num_block, - BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, - BLOCK_N: tl.constexpr, -): - off_hz = tl.program_id(0) - off_z = off_hz // H - off_h = off_hz % H - # offset pointers for batch/head - Q += off_z * stride_qz + off_h * stride_qh - K += off_z * stride_qz + off_h * stride_qh - V += off_z * stride_qz + off_h * stride_qh - DO += off_z * stride_qz + off_h * stride_qh - DQ += off_z * stride_qz + off_h * stride_qh - DK += off_z * stride_qz + off_h * stride_qh - DV += off_z * stride_qz + off_h * stride_qh - for start_n in range(0, num_block): - lo = start_n * BLOCK_M - # initialize row/col offsets - offs_qm = lo + tl.arange(0, BLOCK_M) - offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M) - offs_m = tl.arange(0, BLOCK_N) - offs_k = tl.arange(0, BLOCK_DMODEL) - # initialize pointers to value-like data - q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk) - k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk) - v_ptrs = V + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk) - do_ptrs = DO + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk) - dq_ptrs = DQ + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk) - # pointer to row-wise quantities in value-like data - D_ptrs = D + off_hz * N_CTX - m_ptrs = M + off_hz * N_CTX - # initialize dv amd dk - dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) - dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) - # k and v stay in SRAM throughout - k = tl.load(k_ptrs) - v = tl.load(v_ptrs) - # loop over rows - for start_m in range(lo, num_block * BLOCK_M, BLOCK_M): - offs_m_curr = start_m + offs_m - # load q, k, v, do on-chip - q = tl.load(q_ptrs) - # recompute p = softmax(qk, dim=-1).T - # NOTE: `do` is pre-divided by `l`; no normalization here - qk = tl.dot(q, tl.trans(k)) - qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf")) - m = tl.load(m_ptrs + offs_m_curr) - p = tl.exp(qk * sm_scale - m[:, None]) - # compute dv - do = tl.load(do_ptrs) - dv += tl.dot(tl.trans(p.to(Q.dtype.element_ty)), do) - # compute dp = dot(v, do) - Di = tl.load(D_ptrs + offs_m_curr) - dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None] - dp += tl.dot(do, tl.trans(v)) - # compute ds = p * (dp - delta[:, None]) - ds = p * dp * sm_scale - # compute dk = dot(ds.T, q) - dk += tl.dot(tl.trans(ds.to(Q.dtype.element_ty)), q) - # compute dq - dq = tl.load(dq_ptrs) - dq += tl.dot(ds.to(Q.dtype.element_ty), k) - tl.store(dq_ptrs, dq) - # increment pointers - dq_ptrs += BLOCK_M * stride_qm - q_ptrs += BLOCK_M * stride_qm - do_ptrs += BLOCK_M * stride_qm - # write-back - dv_ptrs = DV + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk) - dk_ptrs = DK + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk) - tl.store(dv_ptrs, dv) - tl.store(dk_ptrs, dk) - - -class _attention(torch.autograd.Function): - - @staticmethod - def forward(ctx, q, k, v, sm_scale): - # only support for Ampere now - capability = torch.cuda.get_device_capability() - if capability[0] < 8: - raise RuntimeError("Flash attention currently only supported for compute capability >= 80") - BLOCK = 128 - # shape constraints - Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1] - assert Lq == Lk and Lk == Lv - assert Lk in {16, 32, 64, 128} - o = torch.empty_like(q) - grid = (cdiv(q.shape[2], BLOCK), q.shape[0] * q.shape[1], 1) - L = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32) - m = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32) - num_warps = 4 if Lk <= 64 else 8 - - _fwd_kernel[grid]( - q, k, v, sm_scale, - L, m, - o, - q.stride(0), q.stride(1), q.stride(2), q.stride(3), - k.stride(0), k.stride(1), k.stride(2), k.stride(3), - v.stride(0), v.stride(1), v.stride(2), v.stride(3), - o.stride(0), o.stride(1), o.stride(2), o.stride(3), - q.shape[0], q.shape[1], q.shape[2], - BLOCK_M=BLOCK, BLOCK_N=BLOCK, - BLOCK_DMODEL=Lk, num_warps=num_warps, - num_stages=2, - ) - - ctx.save_for_backward(q, k, v, o, L, m) - ctx.grid = grid - ctx.sm_scale = sm_scale - ctx.BLOCK_DMODEL = Lk - return o - - @staticmethod - def backward(ctx, do): - BLOCK = 128 - q, k, v, o, l, m = ctx.saved_tensors - do = do.contiguous() - dq = torch.zeros_like(q, dtype=torch.float32) - dk = torch.empty_like(k) - dv = torch.empty_like(v) - do_scaled = torch.empty_like(do) - delta = torch.empty_like(l) - _bwd_preprocess[(ctx.grid[0] * ctx.grid[1], )]( - o, do, l, - do_scaled, delta, - BLOCK_M=BLOCK, D_HEAD=ctx.BLOCK_DMODEL, - ) - _bwd_kernel[(ctx.grid[1],)]( - q, k, v, ctx.sm_scale, - o, do_scaled, - dq, dk, dv, - l, m, - delta, - q.stride(0), q.stride(1), q.stride(2), q.stride(3), - k.stride(0), k.stride(1), k.stride(2), k.stride(3), - v.stride(0), v.stride(1), v.stride(2), v.stride(3), - q.shape[0], q.shape[1], q.shape[2], - ctx.grid[0], - BLOCK_M=BLOCK, BLOCK_N=BLOCK, - BLOCK_DMODEL=ctx.BLOCK_DMODEL, num_warps=8, - num_stages=1, - ) - return dq, dk, dv, None - - -attention = _attention.apply diff --git a/python/triton/ops/matmul.py b/python/triton/ops/matmul.py deleted file mode 100644 index 0448df39b61c..000000000000 --- a/python/triton/ops/matmul.py +++ /dev/null @@ -1,182 +0,0 @@ -import torch - -from .. import Config, autotune, cdiv, heuristics, jit -from .. import language as tl -from .matmul_perf_model import early_config_prune, estimate_matmul_time - -_ordered_datatypes = [torch.float16, torch.bfloat16, torch.float32] - - -def get_higher_dtype(a, b): - if a is b: - return a - - assert a in _ordered_datatypes - assert b in _ordered_datatypes - - for d in _ordered_datatypes: - if a is d: - return b - if b is d: - return a - - -def init_to_zero(name): - return lambda nargs: nargs[name].zero_() - - -def get_configs_io_bound(): - configs = [] - for num_stages in [2, 3, 4, 5, 6]: - for block_m in [16, 32]: - for block_k in [32, 64]: - for block_n in [32, 64, 128, 256]: - num_warps = 2 if block_n <= 64 else 4 - configs.append( - Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1}, - num_stages=num_stages, num_warps=num_warps)) - # split_k - for split_k in [2, 4, 8, 16]: - configs.append(Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k}, - num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C'))) - return configs - - -@autotune( - configs=[ - # basic configs for compute-bound matmuls - Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8), - Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8), - Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2), - # good for int8 - Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8), - Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8), - Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4), - Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2), - ] + get_configs_io_bound(), - key=['M', 'N', 'K'], - prune_configs_by={ - 'early_config_prune': early_config_prune, - 'perf_model': estimate_matmul_time, - 'top_k': 10 - }, -) -@heuristics({ - 'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0, -}) -@jit -def _kernel(A, B, C, M, N, K, - stride_am, stride_ak, - stride_bk, stride_bn, - stride_cm, stride_cn, - dot_out_dtype: tl.constexpr, - BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr, - GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr, - ): - # matrix multiplication - pid = tl.program_id(0) - pid_z = tl.program_id(1) - grid_m = tl.cdiv(M, BLOCK_M) - grid_n = tl.cdiv(N, BLOCK_N) - # re-order program ID for better L2 performance - width = GROUP_M * grid_n - group_id = pid // width - group_size = min(grid_m - group_id * GROUP_M, GROUP_M) - pid_m = group_id * GROUP_M + (pid % group_size) - pid_n = (pid % width) // (group_size) - # do matrix multiplication - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) - rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) - rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K) - # pointers - A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) - B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) - acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=dot_out_dtype) - for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)): - if EVEN_K: - a = tl.load(A) - b = tl.load(B) - else: - k_remaining = K - k * (BLOCK_K * SPLIT_K) - a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.) - b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.) - a = a.to(C.dtype.element_ty) - b = b.to(C.dtype.element_ty) - acc += tl.dot(a, b, out_dtype=dot_out_dtype) - A += BLOCK_K * SPLIT_K * stride_ak - B += BLOCK_K * SPLIT_K * stride_bk - acc = acc.to(C.dtype.element_ty) - # rematerialize rm and rn to save registers - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn) - mask = (rm < M)[:, None] & (rn < N)[None, :] - # handles write-back with reduction-splitting - if SPLIT_K == 1: - tl.store(C, acc, mask=mask) - else: - tl.atomic_add(C, acc, mask=mask) - - -class _matmul(torch.autograd.Function): - kernel = _kernel - - _locks = {} - - @staticmethod - def _call(a, b, dot_out_dtype): - device = a.device - # handle non-contiguous inputs if necessary - if a.stride(0) > 1 and a.stride(1) > 1: - a = a.contiguous() - if b.stride(0) > 1 and b.stride(1) > 1: - b = b.contiguous() - # checks constraints - assert a.shape[1] == b.shape[0], "incompatible dimensions" - M, K = a.shape - _, N = b.shape - # allocates output - c_dtype = get_higher_dtype(a.dtype, b.dtype) - c = torch.empty((M, N), device=device, dtype=c_dtype) - if dot_out_dtype is None: - if c_dtype in [torch.float16, torch.float32, torch.bfloat16]: - dot_out_dtype = tl.float32 - else: - dot_out_dtype = tl.int32 - else: - assert isinstance(dot_out_dtype, torch.dtype), "dot_out_dtype must be a torch.dtype" - if dot_out_dtype == torch.float16: - dot_out_dtype = tl.float16 - elif dot_out_dtype in [torch.float32, torch.bfloat16]: - dot_out_dtype = tl.float32 - else: - dot_out_dtype = tl.int32 - # launch kernel - grid = lambda META: (cdiv(M, META['BLOCK_M']) * cdiv(N, META['BLOCK_N']), META['SPLIT_K']) - _kernel[grid](a, b, c, M, N, K, - a.stride(0), a.stride(1), - b.stride(0), b.stride(1), - c.stride(0), c.stride(1), - dot_out_dtype=dot_out_dtype, - GROUP_M=8) - return c - - @staticmethod - def forward(ctx, a, b, dot_out_dtype=None): - return _matmul._call(a, b, dot_out_dtype=dot_out_dtype) - - -matmul = _matmul.apply diff --git a/python/triton/ops/matmul_perf_model.py b/python/triton/ops/matmul_perf_model.py deleted file mode 100644 index cdb66bc9cad2..000000000000 --- a/python/triton/ops/matmul_perf_model.py +++ /dev/null @@ -1,158 +0,0 @@ -import heapq - -import torch - -from .. import cdiv -from .._C.libtriton.triton import runtime -from ..runtime import driver -from ..testing import get_dram_gbps, get_max_simd_tflops, get_max_tensorcore_tflops - - -def get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype): - ''' return compute throughput in TOPS ''' - total_warps = num_ctas * min(num_warps, 4) - num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs - tflops = min(num_subcores, total_warps) / num_subcores * get_max_tensorcore_tflops(dtype, backend, device) - return tflops - - -def get_simd_tflops(backend, device, num_ctas, num_warps, dtype): - ''' return compute throughput in TOPS ''' - total_warps = num_ctas * min(num_warps, 4) - num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 # on recent GPUs - tflops = min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, backend, device) - return tflops - - -def get_tflops(backend, device, num_ctas, num_warps, dtype): - capability = torch.cuda.get_device_capability(device) - if capability[0] < 8 and dtype == torch.float32: - return get_simd_tflops(backend, device, num_ctas, num_warps, dtype) - return get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype) - - -def estimate_matmul_time( - # backend, device, - num_warps, num_stages, - A, B, C, - M, N, K, - BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, - debug=False, **kwargs -): - ''' return estimated running time in ms - = max(compute, loading) + store ''' - backend = runtime.backend.CUDA - device = torch.cuda.current_device() - dtype = A.dtype - dtsize = A.element_size() - - num_cta_m = cdiv(M, BLOCK_M) - num_cta_n = cdiv(N, BLOCK_N) - num_cta_k = SPLIT_K - num_ctas = num_cta_m * num_cta_n * num_cta_k - - # If the input is smaller than the block size - M, N = max(M, BLOCK_M), max(N, BLOCK_N) - - # time to compute - total_ops = 2 * M * N * K / (1024 * 1024 * 1024) # GOPS - tput = get_tflops(backend, device, num_ctas, num_warps, dtype) - compute_ms = total_ops / tput - - # time to load data - num_sm = driver.utils.get_device_properties(device)["multiprocessor_count"] - active_cta_ratio = min(1, num_ctas / num_sm) - active_cta_ratio_bw1 = min(1, num_ctas / 32) # 32 active ctas are enough to saturate - active_cta_ratio_bw2 = max(min(1, (num_ctas - 32) / (108 - 32)), 0) # 32-108, remaining 5% - dram_bw = get_dram_gbps(backend, device) * (active_cta_ratio_bw1 * 0.95 + active_cta_ratio_bw2 * 0.05) # in GB/s - l2_bw = dram_bw * 4 # rough estimation (should be 4.7 for A100?) - # assume 80% of (following) loads are in L2 cache - load_a_dram = M * K * dtsize * (1 + 0.2 * (num_cta_n - 1)) - load_a_l2 = M * K * dtsize * 0.8 * (num_cta_n - 1) - load_b_dram = N * K * dtsize * (1 + 0.2 * (num_cta_m - 1)) - load_b_l2 = N * K * dtsize * 0.8 * (num_cta_m - 1) - # total - total_dram = (load_a_dram + load_b_dram) / (1024 * 1024) # MB - total_l2 = (load_a_l2 + load_b_l2) / (1024 * 1024) - # loading time in ms - load_ms = total_dram / dram_bw + total_l2 / l2_bw - - # estimate storing time - store_bw = dram_bw * 0.6 # :o - store_c_dram = M * N * dtsize * SPLIT_K / (1024 * 1024) # MB - if SPLIT_K == 1: - store_ms = store_c_dram / store_bw - else: - reduce_bw = store_bw - store_ms = store_c_dram / reduce_bw - # c.zero_() - zero_ms = M * N * 2 / (1024 * 1024) / store_bw - store_ms += zero_ms - - total_time_ms = max(compute_ms, load_ms) + store_ms - if debug: - print(f'Total time: {total_time_ms}ms, compute time: {compute_ms}ms, ' - f'loading time: {load_ms}ms, store time: {store_ms}ms, ' - f'Activate CTAs: {active_cta_ratio*100}%') - return total_time_ms - - -def early_config_prune(configs, named_args): - device = torch.cuda.current_device() - capability = torch.cuda.get_device_capability() - # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages - dtsize = named_args['A'].element_size() - dtype = named_args['A'].dtype - - # 1. make sure we have enough smem - pruned_configs = [] - for config in configs: - kw = config.kwargs - BLOCK_M, BLOCK_N, BLOCK_K, num_stages = \ - kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], config.num_stages - - max_shared_memory = driver.utils.get_device_properties(device)["max_shared_mem"] - required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize - if required_shared_memory <= max_shared_memory: - pruned_configs.append(config) - configs = pruned_configs - - # Some dtypes do not allow atomic_add - if dtype not in [torch.float16, torch.float32]: - configs = [config for config in configs if config.kwargs['SPLIT_K'] == 1] - - # group configs by (BLOCK_M,_N,_K, SPLIT_K, num_warps) - configs_map = {} - for config in configs: - kw = config.kwargs - BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages = \ - kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], kw['SPLIT_K'], config.num_warps, config.num_stages - - key = (BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps) - if key in configs_map: - configs_map[key].append((config, num_stages)) - else: - configs_map[key] = [(config, num_stages)] - - pruned_configs = [] - for k, v in configs_map.items(): - BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k - if capability[0] >= 8: - # compute cycles (only works for ampere GPUs) - mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16) - mma_cycles = mmas / min(4, num_warps) * 8 - - ldgsts_latency = 300 # Does this matter? - optimal_num_stages = ldgsts_latency / mma_cycles - - # nearest stages, prefer large #stages - nearest = heapq.nsmallest(2, v, key=lambda x: 10 + abs(x[1] - optimal_num_stages) - if (x[1] - optimal_num_stages) < 0 else x[1] - optimal_num_stages) - - for n in nearest: - pruned_configs.append(n[0]) - else: # Volta & Turing only supports num_stages <= 2 - random_config = v[0][0] - random_config.num_stages = 2 - pruned_configs.append(random_config) - return pruned_configs diff --git a/python/triton/runtime/__init__.py b/python/triton/runtime/__init__.py deleted file mode 100644 index a4291ab31c8e..000000000000 --- a/python/triton/runtime/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -from .autotuner import (Autotuner, Config, Heuristics, OutOfResources, autotune, - heuristics) -from .driver import driver -from .jit import (JITFunction, KernelInterface, MockTensor, TensorWrapper, reinterpret, - version_key) - -__all__ = [ - "driver", - "Config", - "Heuristics", - "autotune", - "heuristics", - "JITFunction", - "KernelInterface", - "version_key", - "reinterpret", - "TensorWrapper", - "OutOfResources", - "MockTensor", - "Autotuner", -] diff --git a/python/triton/runtime/autotuner.py b/python/triton/runtime/autotuner.py deleted file mode 100644 index 8784f88765d1..000000000000 --- a/python/triton/runtime/autotuner.py +++ /dev/null @@ -1,254 +0,0 @@ -from __future__ import annotations - -import builtins -import time -from typing import Dict - -from ..testing import do_bench -from .jit import KernelInterface - - -class OutOfResources(Exception): - def __init__(self, required, limit, name): - self.message = f'out of resource: {name}, '\ - f'Required: {required}, '\ - f'Hardware limit: {limit}' - self.message += '. Reducing block sizes or `num_stages` may help.' - self.required = required - self.limit = limit - self.name = name - super().__init__(self.message) - - def __reduce__(self): - # this is necessary to make CompilationError picklable - return (type(self), (self.required, self.limit, self.name)) - - -class Autotuner(KernelInterface): - def __init__(self, fn, arg_names, configs, key, reset_to_zero, prune_configs_by: Dict = None, warmup=25, rep=100): - ''' - :param prune_configs_by: a dict of functions that are used to prune configs, fields: - 'perf_model': performance model used to predicate running time with different configs, returns running time - 'top_k': number of configs to bench - 'prune_num_stages_by'(optional): a function used to prune num_stages. It takes configs:List[Config] as its input, and returns pruned configs. - ''' - if not configs: - self.configs = [Config({}, num_warps=4, num_stages=2)] - else: - self.configs = configs - self.key_idx = [arg_names.index(k) for k in key] - self.cache = {} - # hook to reset all required tensor to zeros before relaunching a kernel - self.hook = lambda args: 0 - if reset_to_zero is not None: - self.reset_idx = [arg_names.index(k) for k in reset_to_zero] - - def _hook(args): - for i in self.reset_idx: - args[i].zero_() - self.hook = _hook - self.arg_names = arg_names - # prune configs - if prune_configs_by: - perf_model, top_k = prune_configs_by['perf_model'], prune_configs_by['top_k'] - if 'early_config_prune' in prune_configs_by: - early_config_prune = prune_configs_by['early_config_prune'] - else: - perf_model, top_k, early_config_prune = None, None, None - self.perf_model, self.configs_top_k = perf_model, top_k - self.early_config_prune = early_config_prune - self.fn = fn - self.warmup = warmup - self.rep = rep - - def _bench(self, *args, config, **meta): - # check for conflicts, i.e. meta-parameters both provided - # as kwargs and by the autotuner - conflicts = meta.keys() & config.kwargs.keys() - if conflicts: - raise ValueError( - f"Conflicting meta-parameters: {', '.join(conflicts)}." - " Make sure that you don't re-define auto-tuned symbols." - ) - # augment meta-parameters with tunable ones - current = dict(meta, **config.kwargs) - full_nargs = {**self.nargs, **current} - - def kernel_call(): - if config.pre_hook: - config.pre_hook(full_nargs) - self.hook(args) - self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **current) - try: - return do_bench(kernel_call, warmup=self.warmup, rep=self.rep, quantiles=(0.5, 0.2, 0.8)) - except OutOfResources: - return [float('inf'), float('inf'), float('inf')] - - def run(self, *args, **kwargs): - self.nargs = dict(zip(self.arg_names, args)) - if len(self.configs) > 1: - all_args = {**self.nargs, **kwargs} - _args = [] - for name in self.arg_names: - if name in all_args: - _args.append(all_args[name]) - key = tuple(_args[i] for i in self.key_idx) - if key not in self.cache: - # prune configs - pruned_configs = self.prune_configs(kwargs) - bench_start = time.time() - timings = {config: self._bench(*args, config=config, **kwargs) - for config in pruned_configs} - bench_end = time.time() - self.bench_time = bench_end - bench_start - self.cache[key] = builtins.min(timings, key=timings.get) - self.hook(args) - self.configs_timings = timings - config = self.cache[key] - else: - config = self.configs[0] - self.best_config = config - if config.pre_hook is not None: - full_nargs = {**self.nargs, **kwargs, **self.best_config.kwargs} - config.pre_hook(full_nargs) - ret = self.fn.run(*args, num_warps=config.num_warps, num_stages=config.num_stages, **kwargs, **config.kwargs) - self.nargs = None - return ret - - def prune_configs(self, kwargs): - pruned_configs = self.configs - if self.early_config_prune: - pruned_configs = self.early_config_prune(self.configs, self.nargs) - if self.perf_model: - top_k = self.configs_top_k - if isinstance(top_k, float) and top_k <= 1.0: - top_k = int(len(self.configs) * top_k) - if len(pruned_configs) > top_k: - est_timing = { - config: self.perf_model(**self.nargs, **kwargs, **config.kwargs, num_stages=config.num_stages, - num_warps=config.num_warps) - for config in pruned_configs - } - pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k] - return pruned_configs - - def warmup(self, *args, **kwargs): - self.nargs = dict(zip(self.arg_names, args)) - for config in self.prune_configs(kwargs): - self.fn.warmup( - *args, - num_warps=config.num_warps, - num_stages=config.num_stages, - **kwargs, - **config.kwargs, - ) - self.nargs = None - - -class Config: - """ - An object that represents a possible kernel configuration for the auto-tuner to try. - - :ivar meta: a dictionary of meta-parameters to pass to the kernel as keyword arguments. - :type meta: dict[Str, Any] - :ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if - `num_warps=8`, then each kernel instance will be automatically parallelized to - cooperatively execute using `8 * 32 = 256` threads. - :type num_warps: int - :ivar num_stages: the number of stages that the compiler should use when software-pipelining loops. - Mostly useful for matrix multiplication workloads on SM80+ GPUs. - :type num_stages: int - :ivar pre_hook: a function that will be called before the kernel is called. Parameters of this - function are args. - """ - - def __init__(self, kwargs, num_warps=4, num_stages=2, pre_hook=None): - self.kwargs = kwargs - self.num_warps = num_warps - self.num_stages = num_stages - self.pre_hook = pre_hook - - def __str__(self): - res = [] - for k, v in self.kwargs.items(): - res.append(f'{k}: {v}') - res.append(f'num_warps: {self.num_warps}') - res.append(f'num_stages: {self.num_stages}') - return ', '.join(res) - - -def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, warmup=25, rep=100): - """ - Decorator for auto-tuning a :code:`triton.jit`'d function. - - .. highlight:: python - .. code-block:: python - - @triton.autotune(configs=[ - triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4), - triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8), - ], - key=['x_size'] # the two above configs will be evaluated anytime - # the value of x_size changes - ) - @triton.jit - def kernel(x_ptr, x_size, **META): - BLOCK_SIZE = META['BLOCK_SIZE'] - :note: When all the configurations are evaluated, the kernel will run multiple times. - This means that whatever value the kernel updates will be updated multiple times. - To avoid this undesired behavior, you can use the `reset_to_zero` argument, which - resets the value of the provided tensor to `zero` before running any configuration. - :param configs: a list of :code:`triton.Config` objects - :type configs: list[triton.Config] - :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs. - :type key: list[str] - :param prune_configs_by: a dict of functions that are used to prune configs, fields: - 'perf_model': performance model used to predicate running time with different configs, returns running time - 'top_k': number of configs to bench - 'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It takes configs:List[Config] as its input, and returns pruned configs. - :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs. - :type reset_to_zero: list[str] - :param warmup: Warmup time (in ms) to pass to benchmarking, defaults to 25. - :type warmup: int - :param rep: Repetition time (in ms) to pass to benchmarking, defaults to 100. - :type rep: int - """ - def decorator(fn): - return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, prune_configs_by, warmup, rep) - - return decorator - - -class Heuristics(KernelInterface): - - def __init__(self, fn, arg_names, values) -> None: - self.fn = fn - self.values = values - self.arg_names = arg_names - - def run(self, *args, **kwargs): - for v, heur in self.values.items(): - kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs}) - return self.fn.run(*args, **kwargs) - - -def heuristics(values): - """ - Decorator for specifying how the values of certain meta-parameters may be computed. - This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable. - - .. highlight:: python - .. code-block:: python - - @triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))}) - @triton.jit - def kernel(x_ptr, x_size, **META): - BLOCK_SIZE = META['BLOCK_SIZE'] # smallest power-of-two >= x_size - :param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter. - each such function takes a list of positional arguments as input. - :type values: dict[str, Callable[[list[Any]], Any]] - """ - def decorator(fn): - return Heuristics(fn, fn.arg_names, values) - - return decorator diff --git a/python/triton/runtime/backends/cuda.c b/python/triton/runtime/backends/cuda.c deleted file mode 100644 index 009d52679830..000000000000 --- a/python/triton/runtime/backends/cuda.c +++ /dev/null @@ -1,131 +0,0 @@ -#include "cuda.h" -#define PY_SSIZE_T_CLEAN -#include - -static inline void gpuAssert(CUresult code, const char *file, int line) { - if (code != CUDA_SUCCESS) { - const char *prefix = "Triton Error [CUDA]: "; - const char *str; - cuGetErrorString(code, &str); - char err[1024] = {0}; - strcat(err, prefix); - strcat(err, str); - PyErr_SetString(PyExc_RuntimeError, err); - } -} - -#define CUDA_CHECK(ans) \ - { \ - gpuAssert((ans), __FILE__, __LINE__); \ - if (PyErr_Occurred()) \ - return NULL; \ - } - -static PyObject *getDeviceProperties(PyObject *self, PyObject *args) { - int device_id; - if (!PyArg_ParseTuple(args, "i", &device_id)) - return NULL; - // Get device handle - CUdevice device; - cuDeviceGet(&device, device_id); - - // create a struct to hold device properties - int max_shared_mem; - int multiprocessor_count; - int sm_clock_rate; - int mem_clock_rate; - int mem_bus_width; - CUDA_CHECK(cuDeviceGetAttribute( - &max_shared_mem, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN, - device)); - CUDA_CHECK(cuDeviceGetAttribute( - &multiprocessor_count, CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, device)); - CUDA_CHECK(cuDeviceGetAttribute(&sm_clock_rate, - CU_DEVICE_ATTRIBUTE_CLOCK_RATE, device)); - CUDA_CHECK(cuDeviceGetAttribute( - &mem_clock_rate, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, device)); - CUDA_CHECK(cuDeviceGetAttribute( - &mem_bus_width, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, device)); - - return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i}", "max_shared_mem", - max_shared_mem, "multiprocessor_count", - multiprocessor_count, "sm_clock_rate", sm_clock_rate, - "mem_clock_rate", mem_clock_rate, "mem_bus_width", - mem_bus_width); -} - -static PyObject *loadBinary(PyObject *self, PyObject *args) { - const char *name; - const char *data; - Py_ssize_t data_size; - int shared; - int device; - if (!PyArg_ParseTuple(args, "ss#ii", &name, &data, &data_size, &shared, - &device)) { - return NULL; - } - CUfunction fun; - CUmodule mod; - int32_t n_regs = 0; - int32_t n_spills = 0; - // create driver handles - CUcontext pctx = 0; - CUDA_CHECK(cuCtxGetCurrent(&pctx)); - if (!pctx) { - CUDA_CHECK(cuDevicePrimaryCtxRetain(&pctx, device)); - CUDA_CHECK(cuCtxSetCurrent(pctx)); - } - - CUDA_CHECK(cuModuleLoadData(&mod, data)); - CUDA_CHECK(cuModuleGetFunction(&fun, mod, name)); - // get allocated registers and spilled registers from the function - CUDA_CHECK(cuFuncGetAttribute(&n_regs, CU_FUNC_ATTRIBUTE_NUM_REGS, fun)); - CUDA_CHECK( - cuFuncGetAttribute(&n_spills, CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES, fun)); - n_spills /= 4; - // set dynamic shared memory if necessary - int shared_optin; - CUDA_CHECK(cuDeviceGetAttribute( - &shared_optin, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN, - device)); - if (shared > 49152 && shared_optin > 49152) { - CUDA_CHECK(cuFuncSetCacheConfig(fun, CU_FUNC_CACHE_PREFER_SHARED)); - int shared_total, shared_static; - CUDA_CHECK(cuDeviceGetAttribute( - &shared_total, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR, - device)); - CUDA_CHECK(cuFuncGetAttribute(&shared_static, - CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES, fun)); - CUDA_CHECK( - cuFuncSetAttribute(fun, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, - shared_optin - shared_static)); - } - - if (PyErr_Occurred()) { - return NULL; - } - return Py_BuildValue("(KKii)", (uint64_t)mod, (uint64_t)fun, n_regs, - n_spills); -} - -static PyMethodDef ModuleMethods[] = { - {"load_binary", loadBinary, METH_VARARGS, - "Load provided cubin into CUDA driver"}, - {"get_device_properties", getDeviceProperties, METH_VARARGS, - "Get the properties for a given device"}, - {NULL, NULL, 0, NULL} // sentinel -}; - -static struct PyModuleDef ModuleDef = {PyModuleDef_HEAD_INIT, "cuda_utils", - NULL, // documentation - -1, // size - ModuleMethods}; - -PyMODINIT_FUNC PyInit_cuda_utils(void) { - PyObject *m = PyModule_Create(&ModuleDef); - if (m == NULL) { - return NULL; - } - PyModule_AddFunctions(m, ModuleMethods); - return m; -} diff --git a/python/triton/runtime/backends/hip.c b/python/triton/runtime/backends/hip.c deleted file mode 100644 index 5ed5f19ce837..000000000000 --- a/python/triton/runtime/backends/hip.c +++ /dev/null @@ -1,120 +0,0 @@ -#define __HIP_PLATFORM_AMD__ -#include -#define PY_SSIZE_T_CLEAN -#include -#include -#include - -static inline void gpuAssert(hipError_t code, const char *file, int line) { - { - if (code != HIP_SUCCESS) { - { - const char *prefix = "Triton Error [HIP]: "; - const char *str = hipGetErrorString(code); - char err[1024] = {0}; - snprintf(err, 1024, "%s Code: %d, Messsage: %s", prefix, code, str); - PyErr_SetString(PyExc_RuntimeError, err); - } - } - } -} - -#define HIP_CHECK(ans) \ - { \ - gpuAssert((ans), __FILE__, __LINE__); \ - if (PyErr_Occurred()) \ - return NULL; \ - } - -static PyObject *getDeviceProperties(PyObject *self, PyObject *args) { - int device_id; - if (!PyArg_ParseTuple(args, "i", &device_id)) - return NULL; - - hipDeviceProp_t props; - HIP_CHECK(hipGetDeviceProperties(&props, device_id)); - - // create a struct to hold device properties - return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i}", "max_shared_mem", - props.sharedMemPerBlock, "multiprocessor_count", - props.multiProcessorCount, "sm_clock_rate", - props.clockRate, "mem_clock_rate", props.memoryClockRate, - "mem_bus_width", props.memoryBusWidth); -} - -static PyObject *loadBinary(PyObject *self, PyObject *args) { - const char *name; - const char *data; - Py_ssize_t data_size; - int shared; - int device; - if (!PyArg_ParseTuple(args, "ss#ii", &name, &data, &data_size, &shared, - &device)) { - return NULL; - } - - // Open HSACO file - FILE *hsaco_file; - if ((hsaco_file = fopen(data, "rb")) == NULL) { - return NULL; - } - - // Read HSCAO file into Buffer - fseek(hsaco_file, 0L, SEEK_END); - size_t hsaco_file_size = ftell(hsaco_file); - unsigned char *hsaco = - (unsigned char *)malloc(hsaco_file_size * sizeof(unsigned char)); - rewind(hsaco_file); - fread(hsaco, sizeof(unsigned char), hsaco_file_size, hsaco_file); - fclose(hsaco_file); - - // set HIP options - hipJitOption opt[] = {hipJitOptionErrorLogBufferSizeBytes, - hipJitOptionErrorLogBuffer, - hipJitOptionInfoLogBufferSizeBytes, - hipJitOptionInfoLogBuffer, hipJitOptionLogVerbose}; - const unsigned int errbufsize = 8192; - const unsigned int logbufsize = 8192; - char _err[errbufsize]; - char _log[logbufsize]; - void *optval[] = {(void *)(uintptr_t)errbufsize, (void *)_err, - (void *)(uintptr_t)logbufsize, (void *)_log, (void *)1}; - - // launch HIP Binary - hipModule_t mod; - hipFunction_t fun; - hipModuleLoadDataEx(&mod, hsaco, 5, opt, optval); - hipModuleGetFunction(&fun, mod, name); - free(hsaco); - - // get allocated registers and spilled registers from the function - int n_regs = 0; - int n_spills = 0; - if (PyErr_Occurred()) { - return NULL; - } - return Py_BuildValue("(KKii)", (uint64_t)mod, (uint64_t)fun, n_regs, - n_spills); -} - -static PyMethodDef ModuleMethods[] = { - {"load_binary", loadBinary, METH_VARARGS, - "Load provided hsaco into HIP driver"}, - {"get_device_properties", getDeviceProperties, METH_VARARGS, - "Get the properties for a given device"}, - {NULL, NULL, 0, NULL} // sentinel -}; - -static struct PyModuleDef ModuleDef = {PyModuleDef_HEAD_INIT, "hip_utils", - NULL, // documentation - -1, // size - ModuleMethods}; - -PyMODINIT_FUNC PyInit_hip_utils(void) { - PyObject *m = PyModule_Create(&ModuleDef); - if (m == NULL) { - return NULL; - } - PyModule_AddFunctions(m, ModuleMethods); - return m; -} diff --git a/python/triton/runtime/cache.py b/python/triton/runtime/cache.py deleted file mode 100644 index 43e6660a59df..000000000000 --- a/python/triton/runtime/cache.py +++ /dev/null @@ -1,131 +0,0 @@ -import json -import os -import random -from abc import ABC, abstractmethod -from pathlib import Path -from typing import Dict, Optional - - -def default_cache_dir(): - return os.path.join(Path.home(), ".triton", "cache") - - -class CacheManager(ABC): - def __init__(self, key): - pass - - @abstractmethod - def get_file(self, filename) -> Optional[str]: - pass - - @abstractmethod - def has_file(self, filename) -> bool: - pass - - @abstractmethod - def put(self, data, filename, binary=True) -> str: - pass - - @abstractmethod - def get_group(self, filename: str) -> Optional[Dict[str, str]]: - pass - - @abstractmethod - def put_group(self, filename: str, group: Dict[str, str]): - pass - - -class FileCacheManager(CacheManager): - def __init__(self, key): - self.key = key - self.lock_path = None - # create cache directory if it doesn't exist - self.cache_dir = os.environ.get('TRITON_CACHE_DIR', default_cache_dir()) - if self.cache_dir: - self.cache_dir = os.path.join(self.cache_dir, self.key) - self.lock_path = os.path.join(self.cache_dir, "lock") - os.makedirs(self.cache_dir, exist_ok=True) - - def _make_path(self, filename) -> str: - return os.path.join(self.cache_dir, filename) - - def has_file(self, filename): - if not self.cache_dir: - return False - return os.path.exists(self._make_path(filename)) - - def get_file(self, filename) -> Optional[str]: - if self.has_file(filename): - return self._make_path(filename) - else: - return None - - def get_group(self, filename: str) -> Optional[Dict[str, str]]: - grp_filename = f"__grp__{filename}" - if not self.has_file(grp_filename): - return None - grp_filepath = self._make_path(grp_filename) - with open(grp_filepath) as f: - grp_data = json.load(f) - child_paths = grp_data.get("child_paths", None) - # Invalid group data. - if child_paths is None: - return None - result = {} - for c in child_paths: - p = self._make_path(c) - if not os.path.exists(p): - raise Exception(f"Group file {p} does not exist from group {grp_filename} ") - result[c] = p - return result - - # Note a group of pushed files as being part of a group - def put_group(self, filename: str, group: Dict[str, str]): - if not self.cache_dir: - return - grp_contents = json.dumps({"child_paths": sorted(list(group.keys()))}) - grp_filename = f"__grp__{filename}" - return self.put(grp_contents, grp_filename, binary=False) - - def put(self, data, filename, binary=True) -> str: - if not self.cache_dir: - return - binary = isinstance(data, bytes) - if not binary: - data = str(data) - assert self.lock_path is not None - filepath = self._make_path(filename) - # Random ID to avoid any collisions - rnd_id = random.randint(0, 1000000) - # we use the PID incase a bunch of these around so we can see what PID made it - pid = os.getpid() - # use tempfile to be robust against program interruptions - temp_path = f"{filepath}.tmp.pid_{pid}_{rnd_id}" - mode = "wb" if binary else "w" - with open(temp_path, mode) as f: - f.write(data) - # Replace is guaranteed to be atomic on POSIX systems if it succeeds - # so filepath cannot see a partial write - os.replace(temp_path, filepath) - return filepath - - -__cache_cls = FileCacheManager -__cache_cls_nme = "DEFAULT" - - -def get_cache_manager(key) -> CacheManager: - import os - - user_cache_manager = os.environ.get("TRITON_CACHE_MANAGER", None) - global __cache_cls - global __cache_cls_nme - - if user_cache_manager is not None and user_cache_manager != __cache_cls_nme: - import importlib - module_path, clz_nme = user_cache_manager.split(":") - module = importlib.import_module(module_path) - __cache_cls = getattr(module, clz_nme) - __cache_cls_nme = user_cache_manager - - return __cache_cls(key) diff --git a/python/triton/runtime/driver.py b/python/triton/runtime/driver.py deleted file mode 100644 index 3850821536c5..000000000000 --- a/python/triton/runtime/driver.py +++ /dev/null @@ -1,174 +0,0 @@ -import abc -import hashlib -import os -import tempfile -from pathlib import Path - -from ..common.build import _build -from .cache import get_cache_manager - - -class DriverBase(metaclass=abc.ABCMeta): - - CUDA = 0 - HIP = 1 - - @staticmethod - def third_party_dir(): - return os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "third_party") - - def __init__(self) -> None: - pass -# ----------------------------- -# CUDA -# ----------------------------- - - -class CudaUtils(object): - - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(CudaUtils, cls).__new__(cls) - return cls.instance - - def __init__(self): - dirname = os.path.dirname(os.path.realpath(__file__)) - src = Path(os.path.join(dirname, "backends", "cuda.c")).read_text() - key = hashlib.md5(src.encode("utf-8")).hexdigest() - cache = get_cache_manager(key) - fname = "cuda_utils.so" - cache_path = cache.get_file(fname) - if cache_path is None: - with tempfile.TemporaryDirectory() as tmpdir: - src_path = os.path.join(tmpdir, "main.c") - with open(src_path, "w") as f: - f.write(src) - so = _build("cuda_utils", src_path, tmpdir) - with open(so, "rb") as f: - cache_path = cache.put(f.read(), fname, binary=True) - import importlib.util - spec = importlib.util.spec_from_file_location("cuda_utils", cache_path) - mod = importlib.util.module_from_spec(spec) - spec.loader.exec_module(mod) - self.load_binary = mod.load_binary - self.get_device_properties = mod.get_device_properties - - -class CudaDriver(DriverBase): - - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(CudaDriver, cls).__new__(cls) - return cls.instance - - def __init__(self): - self.utils = CudaUtils() - self.backend = self.CUDA - -# ----------------------------- -# HIP -# ----------------------------- - - -class HIPUtils(object): - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(HIPUtils, cls).__new__(cls) - return cls.instance - - def __init__(self): - dirname = os.path.dirname(os.path.realpath(__file__)) - src = Path(os.path.join(dirname, "backends", "hip.c")).read_text() - key = hashlib.md5(src.encode("utf-8")).hexdigest() - cache = get_cache_manager(key) - fname = "hip_utils.so" - cache_path = cache.get_file(fname) - if cache_path is None: - with tempfile.TemporaryDirectory() as tmpdir: - src_path = os.path.join(tmpdir, "main.c") - with open(src_path, "w") as f: - f.write(src) - so = _build("hip_utils", src_path, tmpdir) - with open(so, "rb") as f: - cache_path = cache.put(f.read(), fname, binary=True) - import importlib.util - spec = importlib.util.spec_from_file_location("hip_utils", cache_path) - mod = importlib.util.module_from_spec(spec) - spec.loader.exec_module(mod) - self.load_binary = mod.load_binary - self.get_device_properties = mod.get_device_properties - - -class HIPDriver(DriverBase): - - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(HIPDriver, cls).__new__(cls) - return cls.instance - - def __init__(self): - self.utils = HIPUtils() - self.backend = self.HIP - - -class UnsupportedDriver(DriverBase): - - def __new__(cls): - if not hasattr(cls, 'instance'): - cls.instance = super(UnsupportedDriver, cls).__new__(cls) - return cls.instance - - def __init__(self): - self.utils = None - self.backend = None - -# ----------------------------- -# Driver -# ----------------------------- - - -class LazyProxy: - def __init__(self, init_fn): - self._init_fn = init_fn - self._obj = None - - def _initialize_obj(self): - if self._obj is None: - self._obj = self._init_fn() - - def __getattr__(self, name): - self._initialize_obj() - return getattr(self._obj, name) - - def __setattr__(self, name, value): - if name in ['_init_fn', '_obj']: - super().__setattr__(name, value) - else: - self._initialize_obj() - setattr(self._obj, name, value) - - def __delattr__(self, name): - self._initialize_obj() - delattr(self._obj, name) - - def __repr__(self): - if self._obj is None: - return f"<{self.__class__.__name__} for {self._init_fn} not yet initialized>" - return repr(self._obj) - - def __str__(self): - self._initialize_obj() - return str(self._obj) - - -def initialize_driver(): - import torch - if torch.version.hip is not None: - return HIPDriver() - elif torch.cuda.is_available(): - return CudaDriver() - else: - return UnsupportedDriver() - - -driver = LazyProxy(initialize_driver) diff --git a/python/triton/runtime/errors.py b/python/triton/runtime/errors.py deleted file mode 100644 index 4ff900574c4f..000000000000 --- a/python/triton/runtime/errors.py +++ /dev/null @@ -1,15 +0,0 @@ - -class OutOfResources(Exception): - def __init__(self, required, limit, name): - self.message = f'out of resource: {name}, '\ - f'Required: {required}, '\ - f'Hardware limit: {limit}' - self.message += '. Reducing block sizes or `num_stages` may help.' - self.required = required - self.limit = limit - self.name = name - super().__init__(self.message) - - def __reduce__(self): - # this is necessary to make CompilationError picklable - return (type(self), (self.required, self.limit, self.name)) diff --git a/python/triton/runtime/jit.py b/python/triton/runtime/jit.py deleted file mode 100644 index cc0fcafb2695..000000000000 --- a/python/triton/runtime/jit.py +++ /dev/null @@ -1,597 +0,0 @@ -from __future__ import annotations, division - -import ast -import functools -import hashlib -import inspect -import os -import subprocess -import textwrap -from collections import defaultdict, namedtuple -from typing import (Callable, Generic, Iterable, List, Optional, TypeVar, Union, cast, - overload) - -from ..common.backend import get_backend - -TRITON_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -TRITON_VERSION = "2.1.0" - - -def get_cuda_stream(idx=None): - if idx is None: - idx = get_current_device() - try: - from torch._C import _cuda_getCurrentRawStream - return _cuda_getCurrentRawStream(idx) - except ImportError: - import torch - return torch.cuda.current_stream(idx).cuda_stream - - -def get_current_device(): - import torch - return torch.cuda.current_device() - - -def set_current_device(idx): - import torch - torch.cuda.set_device(idx) - - -def get_device_capability(idx): - import torch - return torch.cuda.get_device_capability(idx) - - -T = TypeVar('T') - -# ----------------------------------------------------------------------------- -# Dependencies Finder -# ----------------------------------------------------------------------------- - - -class DependenciesFinder(ast.NodeVisitor): - """ - This AST visitor is used to find dependencies of a JITFunction. This can - be used to invalidate a JITFunction's hash when its source code -- or - that of its dependencies -- changes. - """ - - def __init__(self, globals, src) -> None: - super().__init__() - self.ret = hashlib.md5(src.encode("utf-8")).hexdigest() - self.globals = globals - - def visit_Name(self, node): - return self.globals.get(node.id, None) - - def visit_Attribute(self, node): - lhs = self.visit(node.value) - while isinstance(lhs, ast.Attribute): - lhs = self.visit(lhs.value) - if lhs is None or (getattr(lhs, "__name__", "") == "triton" or getattr(lhs, "__name__", "").endswith(".triton")): - return None - return getattr(lhs, node.attr) - - def visit_Call(self, node): - func = self.visit(node.func) - if func is None: - return - if inspect.isbuiltin(func): - return - if func.__module__ and (func.__module__.startswith('triton.') or '.triton.' in func.__module__): - return - assert isinstance(func, JITFunction), f"Function \"{func.__name__}\" is being called from a Triton function but is not a Triton function itself. Decorate it with @triton.jit to fix this" - if func.hash is None: - tree = ast.parse(func.src) - finder = DependenciesFinder(func.__globals__, func.src) - finder.visit(tree) - func.hash = finder.ret - noinline = str(getattr(func, 'noinline', False)) - self.ret = (self.ret + func.hash + noinline).encode("utf-8") - self.ret = hashlib.md5(self.ret).hexdigest() - -# ----------------------------------------------------------------------------- -# JITFunction -# ----------------------------------------------------------------------------- - - -@functools.lru_cache() -def version_key(): - import pkgutil - contents = [] - # frontend - with open(__file__, "rb") as f: - contents += [hashlib.md5(f.read()).hexdigest()] - # compiler - compiler_path = os.path.join(TRITON_PATH, 'compiler') - for lib in pkgutil.iter_modules([compiler_path]): - with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f: - contents += [hashlib.md5(f.read()).hexdigest()] - # backend - with open(os.path.join(TRITON_PATH, "_C/libtriton.so"), "rb") as f: - contents += [hashlib.md5(f.read()).hexdigest()] - # language - language_path = os.path.join(TRITON_PATH, 'language') - for lib in pkgutil.iter_modules([language_path]): - with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f: - contents += [hashlib.md5(f.read()).hexdigest()] - # ptxas version - try: - ptxas_version = hashlib.md5(subprocess.check_output(["ptxas", "--version"])).hexdigest() - except Exception: - ptxas_version = '' - return '-'.join(TRITON_VERSION) + '-' + ptxas_version + '-' + '-'.join(contents) - - -class KernelInterface(Generic[T]): - run: T - - def __getitem__(self, grid) -> T: - """ - A JIT function is launched with: fn[grid](*args, **kwargs). - Hence JITFunction.__getitem__ returns a callable proxy that - memorizes the grid. - """ - return cast(T, functools.partial(cast(Callable, self.run), grid=grid)) - - -class JITFunction(KernelInterface[T]): - - # Hook for inspecting compiled functions and modules - cache_hook = None - divisibility = 16 - - @staticmethod - def _key_of(arg): - if hasattr(arg, "dtype"): - return arg.dtype - elif isinstance(arg, bool): - return "i1" - elif isinstance(arg, int): - if -2**31 <= arg and arg <= 2**31 - 1: - return "i32" - elif 2**63 <= arg and arg <= 2**64 - 1: - return "u64" - else: - return "i64" - elif isinstance(arg, float): - return 'fp32' - elif arg is None: - return None - else: - raise TypeError(f'Unsupported type {type(arg)} for {arg}') - - @staticmethod - def _device_of(arg): - if hasattr(arg, "device"): - if hasattr(arg.device, 'type'): - return arg.device.type - - return '' - - @staticmethod - def _pinned_memory_of(arg): - if hasattr(arg, "is_pinned"): - if isinstance(arg.is_pinned, Callable): - return arg.is_pinned() - - return False - - @staticmethod - def _spec_of(arg): - if hasattr(arg, "data_ptr"): - return (arg.data_ptr() % JITFunction.divisibility == 0) - elif isinstance(arg, int): - return (arg % 16 == 0, arg == 1) - return (arg is None, ) - - def _get_config(self, *args): - def is_divisible_by_16(x): - if hasattr(x, "data_ptr"): - return x.data_ptr() % JITFunction.divisibility == 0 - elif isinstance(x, int): - return x % JITFunction.divisibility == 0 - if x is None: - return True - return False - divisible_by_16 = {i for i, arg in enumerate(args) if is_divisible_by_16(arg) and i not in self.do_not_specialize} - equal_to_1 = {i for i, arg in enumerate(args) if not isinstance(arg, bool) and isinstance(arg, int) and arg == 1 and i not in self.do_not_specialize} - return namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"])(tuple(divisible_by_16), tuple(equal_to_1)) - # return _triton.code_gen.instance_descriptor(divisible_by_16, equal_to_1) - - @staticmethod - def _type_of(key): - # None are nullptr -- implicitly converted to *i8 - if key is None: - return '*i8' - dtype_str = str(key).split(".")[-1] - tys = { - "bool": "i1", - "float8e4": "fp8e4", - "float8e5": "fp8e5", - "float8e4b15": "fp8e4b15", - "float16": "fp16", - "bfloat16": "bf16", - "float32": "fp32", - "float64": "fp64", - "int8": "i8", - "int16": "i16", - "int32": "i32", - "int64": "i64", - "uint8": "u8", - "uint16": "u16", - "uint32": "u32", - "uint64": "u64", - } - # reinterpret can create triton type - for v in list(tys.values()): - tys[v] = v - return key if isinstance(key, str) else f"*{tys[dtype_str]}" - - def _make_signature(self, sig_key): - signature = ",".join([self._type_of(k) for i, k in enumerate(sig_key)]) - return signature - - def _make_constants(self, constexpr_key): - constants = dict(zip(self.constexprs, constexpr_key)) - return constants - - def _call_hook(self, key, signature, device, constants, num_warps, num_stages, extern_libs, configs): - if JITFunction.cache_hook is None: - return False - name = self.fn.__name__ - module = self.fn.__module__ - arg_reprs = ', '.join([f'{name}: {ty}' for name, ty in zip(self.arg_names, key[1])]) - repr = f"{name}[num_warps={num_warps}, num_stages={num_stages}]({arg_reprs})" - key = str(key) - - class LegacyCompiler: - def __init__(self, module, name): - self.module = module - self.name = name - pass - - kwargs = dict(signature=signature, device=device, constants=constants, - num_warps=num_warps, num_stages=num_stages, extern_libs=extern_libs, - configs=configs) - - return JITFunction.cache_hook(key=key, repr=repr, fn=LegacyCompiler(module, name), compile={"key": key, **kwargs}, is_manual_warmup=False, already_compiled=False) - - def _get_arg_specialization_key(self, arg) -> str: - arg_annotation = self.__annotations__.get(arg, '') - if arg_annotation == '': - return f'({arg}.data_ptr() % {JITFunction.divisibility} == 0) if hasattr({arg}, "data_ptr") \ - else ({arg} % {JITFunction.divisibility} == 0, {arg} == 1) if isinstance({arg}, int) \ - else (False,)' - elif 'Tensor' in arg_annotation: - return f'({arg}.data_ptr() % {JITFunction.divisibility} == 0)' - elif arg_annotation == 'int': - return f'({arg} % {JITFunction.divisibility} == 0, {arg} == 1)' - else: - return '(False,)' - - def _get_arg_sig_key(self, arg) -> str: - arg_annotation = self.__annotations__.get(arg, '') - if 'Tensor' in arg_annotation: - return f'{arg}.dtype' - elif arg_annotation == 'bool': - return "i1" - elif arg_annotation == 'float': - return 'fp32' - else: - return f'_key_of({arg})' - - def _conclude_device_type(self, device_types: List[str], pinned_memory_flags: List[bool]) -> str: - device_types = [device_type for device_type in device_types if device_type != ''] - # Return cuda if one of the input tensors is cuda - if 'cuda' in device_types: - import torch - return 'hip' if torch.version.hip else 'cuda' - - is_cpu = all(device_type == 'cpu' for device_type in device_types) - is_pinned_memory = any(pinned_memory_flag for pinned_memory_flag in pinned_memory_flags) - # Return cuda if all the input tensors are cpu while the memory is pinned - if is_cpu and is_pinned_memory: - return 'cuda' - - return device_types[0] if len(device_types) > 0 else 'cuda' - - def _make_launcher(self): - regular_args = [f'{arg}' for i, arg in enumerate(self.arg_names) if i not in self.constexprs] - constexpr_args = [f'{arg}' for i, arg in enumerate(self.arg_names) if i in self.constexprs] - args = ', '.join(regular_args) - # cache key for regular argument type - sig_keys = ', '.join([self._get_arg_sig_key(arg) for arg in regular_args]) - device_types = '[' + ', '.join([f'_device_of({arg})' for arg in regular_args]) + ']' - pinned_memory_flags = '[' + ', '.join([f'_pinned_memory_of({arg})' for arg in regular_args]) + ']' - # cache key for constexpr argument values - constexpr_keys = ', '.join(constexpr_args) - # cache key for argument specialization - specializations = [] - for i, arg in enumerate(regular_args): - if i in self.do_not_specialize: - continue - specializations += [self._get_arg_specialization_key(arg)] - - spec_keys = ', '.join(specializations) - grid_args = ','.join([f'"{arg}": {arg}' for arg in self.arg_names]) - - src = f""" -def {self.fn.__name__}({', '.join(self.arg_names)}, grid, num_warps=4, num_stages=3, extern_libs=None, stream=None, warmup=False, device=None, device_type=None): - from ..compiler import compile, CompiledKernel - sig_key = {sig_keys}, - constexpr_key = {f'{constexpr_keys},' if len(constexpr_keys) > 0 else ()} - spec_key = {f'{spec_keys},' if len(spec_keys) > 0 else ()} - key = (version_key, sig_key, constexpr_key, spec_key, num_warps, num_stages, self.debug) - if not extern_libs is None: - key = (key, tuple(extern_libs.items())) - assert num_warps > 0 and (num_warps & (num_warps - 1)) == 0, "num_warps must be a power of 2" - if callable(grid): - grid = grid({{{grid_args}}}) - grid_size = len(grid) - grid_0 = grid[0] - grid_1 = grid[1] if grid_size > 1 else 1 - grid_2 = grid[2] if grid_size > 2 else 1 - - if device_type is None: - device_types = [_device_type for _device_type in {device_types} if _device_type != ''] - device_type = self._conclude_device_type(device_types, {pinned_memory_flags}) - - device_backend = None - if device_type not in ['cuda', 'hip']: - device_backend = get_backend(device_type) - if device_backend is None: - raise ValueError('Cannot find backend for ' + device_type) - - if device is None: - if device_type in ['cuda', 'hip']: - device = get_current_device() - set_current_device(device) - else: - device = device_backend.get_current_device() - device_backend.set_current_device(device) - if stream is None and not warmup: - if device_type in ['cuda', 'hip']: - stream = get_cuda_stream(device) - else: - stream = device_backend.get_stream() - - bin = cache[device].get(key, None) - if bin is not None: - if not warmup: - bin.c_wrapper(grid_0, grid_1, grid_2, bin.num_warps, bin.shared, stream, bin.cu_function, CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, bin, {args}) - return bin - # kernel not cached -- compile - else: - # build dict of constant values - args = [{args}] - all_args = {', '.join([f'{arg}' for arg in self.arg_names])}, - configs = self._get_config(*all_args), - constants = self._make_constants(constexpr_key) - constants.update({{i: None for i, arg in enumerate(all_args) if arg is None}}) - constants.update({{i: 1 for i in configs[0].equal_to_1}}) - # build kernel signature -- doesn't include specialized arguments - signature = {{ i: self._type_of(_key_of(arg)) for i, arg in enumerate(all_args) if i not in self.constexprs }} - # build stub signature -- includes arguments that are specialized - for i, arg in constants.items(): - if callable(arg): - raise TypeError(f"Callable constexpr at index {{i}} is not supported") - if not self._call_hook(key, signature, device, constants, num_warps, num_stages, extern_libs, configs): - bin = compile(self, signature=signature, device=device, constants=constants, num_warps=num_warps, num_stages=num_stages, extern_libs=extern_libs, configs=configs, debug=self.debug, device_type=device_type) - if not warmup: - bin.c_wrapper(grid_0, grid_1, grid_2, bin.num_warps, bin.shared, stream, bin.cu_function, CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, bin, *args) - self.cache[device][key] = bin - return bin - return None -""" - scope = {"version_key": version_key(), - "get_cuda_stream": get_cuda_stream, - "self": self, - "_spec_of": self._spec_of, - "_key_of": self._key_of, - "_device_of": self._device_of, - "_pinned_memory_of": self._pinned_memory_of, - "cache": self.cache, - "__spec__": __spec__, - "get_backend": get_backend, - "get_current_device": get_current_device, - "set_current_device": set_current_device} - exec(src, scope) - return scope[self.fn.__name__] - - def __init__(self, fn, version=None, do_not_specialize=None, debug=None, noinline=None): - self.fn = fn - self.module = fn.__module__ - self.version = version - # function signature information - signature = inspect.signature(fn) - self.arg_names = [v.name for v in signature.parameters.values()] - self.has_defaults = any(v.default != inspect._empty for v in signature.parameters.values()) - # specialization hints - self.do_not_specialize = [] if do_not_specialize is None else do_not_specialize - self.do_not_specialize = {self.arg_names.index(arg) if isinstance(arg, str) else arg for arg in self.do_not_specialize} - # function source code (without decorators) - self.src = textwrap.dedent(inspect.getsource(fn)) - self.src = self.src[self.src.find("def"):] - # cache of just-in-time compiled kernels - self.cache = defaultdict(dict) - self.hash = None - # JITFunction can be instantiated as kernel - # when called with a grid using __getitem__ - self.kernel_decorators = [] - self.kernel = None - self.debug = True if os.environ.get("TRITON_DEBUG", "0") == "1" else debug - self.noinline = noinline - # annotations - normalize_ty = lambda ty: ty.__name__ if isinstance(ty, type) else ty - self.__annotations__ = {name: normalize_ty(ty) for name, ty in fn.__annotations__.items()} - # index of constexprs - self.constexprs = [self.arg_names.index(name) for name, ty in self.__annotations__.items() if 'constexpr' in ty] - # launcher - self.run = self._make_launcher() - # re-use docs of wrapped function - self.__doc__ = fn.__doc__ - self.__name__ = fn.__name__ - self.__globals__ = fn.__globals__ - self.__module__ = fn.__module__ - - @property - def cache_key(self): - # TODO : hash should be attribute of `self` - if self.hash is None: - dependencies_finder = DependenciesFinder(globals=self.__globals__, src=self.src) - dependencies_finder.visit(self.parse()) - self.hash = dependencies_finder.ret + version_key() - return self.hash - - def warmup(self, *args, **kwargs): - return self.run(*map(MockTensor.wrap_dtype, args), **kwargs, warmup=True) - - # we do not parse `src` in the constructor because - # the user might want to monkey-patch self.src dynamically. - # Our unit tests do this, for example. - def parse(self): - tree = ast.parse(self.src) - assert isinstance(tree, ast.Module) - assert len(tree.body) == 1 - assert isinstance(tree.body[0], ast.FunctionDef) - return tree - - def __call__(self, *args, **kwargs): - raise RuntimeError("Cannot call @triton.jit'd outside of the scope of a kernel") - - def __setattr__(self, name, value): - # - when kernel decorators change, cached kernel - # needs to be cleared - if name == 'kernel_decorators': - self.kernel = None - super(JITFunction, self).__setattr__(name, value) - # - when `.src` attribute is set, cache path needs - # to be reinitialized - if name == 'src': - self.hash = None - - def __repr__(self): - return f"JITFunction({self.module}:{self.fn.__name__})" - - -# ----------------------------------------------------------------------------- -# `jit` decorator -# ----------------------------------------------------------------------------- - - -@overload -def jit(fn: T) -> JITFunction[T]: - ... - - -@overload -def jit( - *, - version=None, - do_not_specialize: Optional[Iterable[int]] = None, - debug: Optional[bool] = None, - noinline: Optional[bool] = None, -) -> Callable[[T], JITFunction[T]]: - ... - - -def jit( - fn: Optional[T] = None, - *, - version=None, - do_not_specialize: Optional[Iterable[int]] = None, - debug: Optional[bool] = None, - noinline: Optional[bool] = None, - interpret: Optional[bool] = None, -) -> Union[JITFunction[T], Callable[[T], JITFunction[T]]]: - """ - Decorator for JIT-compiling a function using the Triton compiler. - - :note: When a jit'd function is called, arguments are - implicitly converted to pointers if they have a :code:`.data_ptr()` method - and a `.dtype` attribute. - - :note: This function will be compiled and run on the GPU. It will only have access to: - - * python primitives, - * builtins within the triton package, - * arguments to this function, - * other jit'd functions - - :param fn: the function to be jit-compiled - :type fn: Callable - """ - - def decorator(fn: T) -> JITFunction[T]: - assert callable(fn) - if interpret: - from ..interpreter.interpreter import GridSelector - return GridSelector(fn) - else: - return JITFunction( - fn, - version=version, - do_not_specialize=do_not_specialize, - debug=debug, - noinline=noinline, - ) - if fn is not None: - return decorator(fn) - - else: - return decorator - -# ----------------------------------------------------------------------------- -# Utilities for mocking tensors -# ----------------------------------------------------------------------------- - - -class MockTensor: - """ - Can be used in place of real tensors when calling: - kernel.warmup(MockTensor(torch.float32), ...) - """ - @staticmethod - def wrap_dtype(arg): - if arg.__class__.__name__ == "dtype" and\ - arg.__module__ == "torch": - return MockTensor(arg) - return arg - - def __init__(self, dtype): - self.dtype = dtype - - @staticmethod - def data_ptr(): - return 0 # optimistically assumes multiple of 16 - - -class TensorWrapper: - def __init__(self, base, dtype): - self.dtype = dtype - self.base = base - self.is_cuda = base.is_cuda - self.device = base.device - - def data_ptr(self): - return self.base.data_ptr() - - def __str__(self) -> str: - return f'TensorWrapper[{self.dtype}]({self.base})' - - -def reinterpret(tensor, dtype): - if isinstance(tensor, TensorWrapper): - if dtype == tensor.base.dtype: - # Reinterpreting to the original interpretation; return the base. - return tensor.base - else: - # Reinterpreting a wrapped tensor to a different type. - return TensorWrapper(tensor.base, dtype) - elif hasattr(tensor, "data_ptr"): - # A new wrapper is needed around an unwrapped tensor. - return TensorWrapper(tensor, dtype) - else: - raise TypeError(f'Cannot reinterpret a {type(tensor)}.') diff --git a/python/triton/testing.py b/python/triton/testing.py deleted file mode 100644 index b6d217ec4d88..000000000000 --- a/python/triton/testing.py +++ /dev/null @@ -1,423 +0,0 @@ -import functools -import os -import subprocess -import sys -from contextlib import contextmanager - -from ._C.libtriton.triton import runtime - - -def nvsmi(attrs): - attrs = ','.join(attrs) - cmd = ['nvidia-smi', '-i', '0', '--query-gpu=' + attrs, '--format=csv,noheader,nounits'] - out = subprocess.check_output(cmd) - ret = out.decode(sys.stdout.encoding).split(',') - ret = [int(x) for x in ret] - return ret - - -def do_bench(fn, warmup=25, rep=100, grad_to_none=None, - quantiles=None, - fast_flush=True, - return_mode="mean"): - assert return_mode in ["min", "max", "mean", "median"] - import torch - """ - Benchmark the runtime of the provided function. By default, return the median runtime of :code:`fn` along with - the 20-th and 80-th performance percentile. - - :param fn: Function to benchmark - :type fn: Callable - :param warmup: Warmup time (in ms) - :type warmup: int - :param rep: Repetition time (in ms) - :type rep: int - :param grad_to_none: Reset the gradient of the provided tensor to None - :type grad_to_none: torch.tensor, optional - :param quantiles: Performance percentile to return in addition to the median. - :type quantiles: list[float] - :param fast_flush: Use faster kernel to flush L2 between measurements - :type fast_flush: bool - """ - - fn() - torch.cuda.synchronize() - - # We maintain a buffer of 256 MB that we clear - # before each kernel call to make sure that the L2 - # doesn't contain any input data before the run - if fast_flush: - cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda') - else: - cache = torch.empty(int(256e6), dtype=torch.int8, device='cuda') - - # Estimate the runtime of the function - start_event = torch.cuda.Event(enable_timing=True) - end_event = torch.cuda.Event(enable_timing=True) - start_event.record() - for _ in range(5): - cache.zero_() - fn() - end_event.record() - torch.cuda.synchronize() - estimate_ms = start_event.elapsed_time(end_event) / 5 - - # compute number of warmup and repeat - n_warmup = max(1, int(warmup / estimate_ms)) - n_repeat = max(1, int(rep / estimate_ms)) - start_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)] - end_event = [torch.cuda.Event(enable_timing=True) for i in range(n_repeat)] - # Warm-up - for _ in range(n_warmup): - fn() - # Benchmark - for i in range(n_repeat): - # we don't want `fn` to accumulate gradient values - # if it contains a backward pass. So we clear the - # provided gradients - if grad_to_none is not None: - for x in grad_to_none: - x.grad = None - # we clear the L2 cache before each run - cache.zero_() - # record time of `fn` - start_event[i].record() - fn() - end_event[i].record() - # Record clocks - torch.cuda.synchronize() - times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)], dtype=torch.float) - if quantiles is not None: - ret = torch.quantile(times, torch.tensor(quantiles, dtype=torch.float)).tolist() - if len(ret) == 1: - ret = ret[0] - return ret - return getattr(torch, return_mode)(times).item() - - -def assert_close(x, y, atol=None, rtol=None, err_msg=''): - import numpy as np - import torch - - # canonicalize arguments to be tensors - if not isinstance(x, torch.Tensor): - x = torch.tensor(x) - if not isinstance(y, torch.Tensor): - y = torch.tensor(y) - # absolute tolerance - if atol is None: - atol = 1e-2 - atol = atol(x.dtype) if callable(atol) else atol - # relative tolerance hook - if rtol is None: - rtol = 0. - rtol = rtol(x.dtype) if callable(rtol) else rtol - # we use numpy instead of pytorch - # as it seems more memory efficient - # pytorch tends to oom on large tensors - if isinstance(x, torch.Tensor): - if x.dtype == torch.bfloat16: - x = x.float() - x = x.cpu().detach().numpy() - if isinstance(y, torch.Tensor): - if y.dtype == torch.bfloat16: - y = y.float() - y = y.cpu().detach().numpy() - # we handle size==1 case separately as we can - # provide better error message there - if x.size > 1 or y.size > 1: - np.testing.assert_allclose(x, y, atol=atol, rtol=rtol, equal_nan=True) - return - if not np.allclose(x, y, atol=atol, rtol=rtol): - raise AssertionError(f'{err_msg} {x} is not close to {y} (atol={atol}, rtol={rtol})') - - -class Benchmark: - """ - This class is used by the :code:`perf_report` function to generate line plots with a concise API. - """ - - def __init__( - self, - x_names, - x_vals, - line_arg, - line_vals, - line_names, - plot_name, - args, - xlabel='', - ylabel='', - x_log=False, - y_log=False, - color=None, - styles=None, - ): - """ - Constructor - - :param x_names: Name of the arguments that should appear on the x axis of the plot. If the list contains more than one element, all the arguments are assumed to have the same value. - :type x_names: List[str] - :param x_vals: List of values to use for the arguments in :code:`x_names`. - :type x_vals: List[Any] - :param line_arg: Argument name for which different values correspond to different lines in the plot. - :type line_arg: str - :param line_vals: List of values to use for the arguments in :code:`line_arg`. - :type line_vals: List[str] - :param line_names: Label names for the different lines. - :type line_names: List[str] - :param plot_name: Name of the plot. - :type plot_name: str - :param args: List of arguments to remain fixed throughout the benchmark. - :type args: List[str] - :param xlabel: Label for the x axis of the plot. - :type xlabel: str, optional - :param ylabel: Label for the y axis of the plot. - :type ylabel: str, optional - :param x_log: Whether the x axis should be log scale. - :type x_log: bool, optional - :param y_log: Whether the y axis should be log scale. - :type y_log: bool, optional - """ - self.x_names = x_names - self.x_vals = x_vals - self.x_log = x_log - self.line_arg = line_arg - self.line_vals = line_vals - self.line_names = line_names - self.y_log = y_log - self.styles = styles - # plot info - self.xlabel = xlabel - self.ylabel = ylabel - self.plot_name = plot_name - self.args = args - - -class Mark: - def __init__(self, fn, benchmarks): - self.fn = fn - self.benchmarks = benchmarks - - def _run(self, bench, save_path, show_plots, print_data): - import os - - import matplotlib.pyplot as plt - import pandas as pd - y_mean = bench.line_names - y_min = [f'{x}-min' for x in bench.line_names] - y_max = [f'{x}-max' for x in bench.line_names] - df = pd.DataFrame(columns=[bench.x_names[0]] + y_mean + y_min + y_max) - for x in bench.x_vals: - x_args = {x_name: x for x_name in bench.x_names} - row_mean, row_min, row_max = [], [], [] - for y in bench.line_vals: - ret = self.fn(**x_args, **{bench.line_arg: y}, **bench.args) - try: - y_mean, y_min, y_max = ret - except TypeError: - y_mean, y_min, y_max = ret, None, None - row_mean += [y_mean] - row_min += [y_min] - row_max += [y_max] - df.loc[len(df)] = [x] + row_mean + row_min + row_max - if bench.plot_name: - plt.figure() - ax = plt.subplot() - x = bench.x_names[0] - for i, y in enumerate(bench.line_names): - y_min, y_max = df[y + '-min'], df[y + '-max'] - col = bench.styles[i][0] if bench.styles else None - sty = bench.styles[i][1] if bench.styles else None - ax.plot(df[x], df[y], label=y, color=col, ls=sty) - if y_min is not None and y_max is not None: - ax.fill_between(df[x], y_min, y_max, alpha=0.15, color=col) - ax.legend() - xlabel = bench.xlabel if bench.xlabel else " = ".join(bench.x_names) - ax.set_xlabel(xlabel) - ax.set_ylabel(bench.ylabel) - # ax.set_title(bench.plot_name) - ax.set_xscale("log" if bench.x_log else "linear") - ax.set_yscale("log" if bench.y_log else "linear") - if show_plots: - plt.show() - if save_path: - plt.savefig(os.path.join(save_path, f"{bench.plot_name}.png")) - df = df[[bench.x_names[0]] + bench.line_names] - if print_data: - print(bench.plot_name + ':') - print(df) - if save_path: - df.to_csv(os.path.join(save_path, f"{bench.plot_name}.csv"), float_format='%.1f', index=False) - - def run(self, show_plots=False, print_data=False, save_path=''): - has_single_bench = isinstance(self.benchmarks, Benchmark) - benchmarks = [self.benchmarks] if has_single_bench else self.benchmarks - if save_path: - html = open(os.path.join(save_path, "results.html"), "w") - html.write("\n") - for bench in benchmarks: - self._run(bench, save_path, show_plots, print_data) - if save_path: - html.write(f"\n") - if save_path: - html.write("\n") - - -def perf_report(benchmarks): - """ - Mark a function for benchmarking. The benchmark can then be executed by using the :code:`.run` method on the return value. - - :param benchmarks: Benchmarking configurations. - :type benchmarks: List of :class:`Benchmark` - """ - wrapper = lambda fn: Mark(fn, benchmarks) - return wrapper - - -def get_dram_gbps(backend=None, device=None): - ''' return DRAM bandwidth in GB/s ''' - import torch - - from .runtime import driver - if not backend: - backend = runtime.backend.CUDA - if not device: - device = torch.cuda.current_device() - mem_clock_khz = driver.utils.get_device_properties(device)["mem_clock_rate"] # in kHz - bus_width = driver.utils.get_device_properties(device)["mem_bus_width"] - bw_gbps = mem_clock_khz * bus_width * 2 / 1e6 / 8 # In GB/s - return bw_gbps - - -def get_max_tensorcore_tflops(dtype, backend=None, device=None, clock_rate=None): - import torch - - from .runtime import driver - if not backend: - backend = runtime.backend.CUDA - if not device: - device = torch.cuda.current_device() - - num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 - if not clock_rate: - clock_rate = driver.utils.get_device_properties(device)["sm_clock_rate"] # in kHz - capability = torch.cuda.get_device_capability(device) - if capability[0] < 8: - assert dtype == torch.float16 - ops_per_sub_core = 256 # 2 4x4x4 Tensor Cores - else: - if dtype == torch.float32: - ops_per_sub_core = 256 - elif dtype in [torch.float16, torch.bfloat16]: - ops_per_sub_core = 512 - elif dtype == torch.int8: - ops_per_sub_core = 1024 - else: - raise RuntimeError("dtype not supported") - tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9 - return tflops - -# create decorator that wraps test function into -# a cuda-memcheck system call - - -def cuda_memcheck(**target_kwargs): - def decorator(test_fn): - @functools.wraps(test_fn) - def wrapper(*args, **kwargs): - import psutil - ppid_name = psutil.Process(os.getppid()).name() - run_cuda_memcheck = target_kwargs.items() <= kwargs.items() - if run_cuda_memcheck and ppid_name != "cuda-memcheck": - path = os.path.realpath(test_fn.__globals__["__file__"]) - # get path of current file - env = {"PATH": os.environ["PATH"], "PYTORCH_NO_CUDA_MEMORY_CACHING": "1"} - assert 'request' in kwargs, "memcheck'ed test must have a (possibly unused) `request` fixture" - test_id = kwargs['request'].node.callspec.id - cmd = f"{path}::{test_fn.__name__}[{test_id}]" - out = subprocess.run(["cuda-memcheck", "pytest", "-vs", cmd], capture_output=True, env=env) - assert out.returncode == 0, "cuda-memcheck returned an error: bounds checking failed" - assert "ERROR SUMMARY: 0 errors" in str(out.stdout) - else: - test_fn(*args, **kwargs) - return wrapper - return decorator - - -def nvsmi_attr(attrs): - attrs = ",".join(attrs) - cmd = [ - "nvidia-smi", - "-i", - "0", - "--query-gpu=" + attrs, - "--format=csv,noheader,nounits", - ] - out = subprocess.check_output(cmd) - ret = out.decode(sys.stdout.encoding).split(",") - ret = [int(x) for x in ret] - return ret - - -@contextmanager -def set_gpu_clock(ref_sm_clock=1350, ref_mem_clock=1215): - try: - subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "1"]) - subprocess.check_output( - [ - "nvidia-smi", - "-i", - "0", - f"--lock-gpu-clocks={ref_sm_clock},{ref_sm_clock}", - ] - ) - subprocess.check_output( - [ - "nvidia-smi", - "-i", - "0", - f"--lock-memory-clocks={ref_mem_clock},{ref_mem_clock}", - ] - ) - cur_sm_clock = nvsmi_attr(["clocks.current.sm"])[0] - cur_mem_clock = nvsmi_attr(["clocks.current.memory"])[0] - assert abs(cur_sm_clock - ref_sm_clock) < 10, f"GPU SMs must run at {ref_sm_clock} MHz" - assert abs(cur_mem_clock - ref_mem_clock) < 10, f"GPU SMs must run at {ref_mem_clock} MHz" - tflops = 1e-6 * 2 * 108 * 4 * 256 * ref_sm_clock - gbps = 640 * 2 * ref_mem_clock * 1e-3 - yield tflops, gbps - finally: - subprocess.check_output(["nvidia-smi", "-i", "0", "-pm", "0"]) - subprocess.check_output(["nvidia-smi", "-i", "0", "-rgc"]) - subprocess.check_output(["nvidia-smi", "-i", "0", "-rmc"]) - - -def get_max_simd_tflops(dtype, backend=None, device=None): - import torch - - from .runtime import driver - if not backend: - backend = runtime.backend.CUDA - if not device: - device = torch.cuda.current_device() - - num_subcores = driver.utils.get_device_properties(device)["multiprocessor_count"] * 4 - clock_rate = driver.utils.get_device_properties(device)["sm_clock_rate"] # in kHz - capability = torch.cuda.get_device_capability() - if capability[0] < 8: - if dtype == torch.float32: - ops_per_sub_core = 32 # 2*16 - elif dtype == torch.float16: - ops_per_sub_core = 64 - else: - raise RuntimeError("dtype not supported") - else: - if dtype == torch.float32: - ops_per_sub_core = 32 - elif dtype in [torch.float16, torch.bfloat16]: - ops_per_sub_core = 64 - else: - raise RuntimeError("dtype not supported") - tflops = num_subcores * clock_rate * ops_per_sub_core * 1e-9 - return tflops diff --git 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