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Add tests for 3D local_load local_alloc and relax asserts #5285

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Merged
ThomasRaoux merged 1 commit into from
Nov 29, 2024
Merged

Add tests for 3D local_load local_alloc and relax asserts #5285

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10 changes: 5 additions & 5 deletions lib/Conversion/TritonGPUToLLVM/MemoryOpToLLVM.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -23,9 +23,6 @@ void lowerDistributedToShared(
auto srcTy = cast<RankedTensorType>(src.getType());
auto dstTy = cast<MemDescType>(dst.getType());
auto outOrd = mlir::cast<SharedEncodingAttr>(dstTy.getEncoding()).getOrder();
assert(srcTy.getShape().size() <= 2 ||
(srcTy.getShape().size() == 3 && outOrd[2] == 0) &&
"Unexpected rank of ConvertLayout(blocked->shared)");
auto elemTy = typeConverter->convertType(srcTy.getElementType());

auto smemBase = smemObj.getBase();
Expand Down Expand Up @@ -163,7 +160,9 @@ struct LocalLoadOpConversion : public ConvertOpToLLVMPattern<LocalLoadOp> {
srcTy.getShape()[0] >= 8 && srcTy.getShape()[1] >= 4 * kWidth;
// To be removed in https://github.com/triton-lang/triton/pull/5154
bool legacyLoweringIsBuggy =
(kWidth >= 8 || (kWidth == 4 && bitwidth == 32)) && mma.isAmpere();
(kWidth >= 8 || (kWidth == 4 && bitwidth == 32) ||
dstTy.getRank() == 3) &&
mma.isAmpere();
return (mma.isHopper() && !canUseLdmatrix) ||
(mma.isAmpere() && legacyLoweringIsBuggy);
}
Expand Down Expand Up @@ -220,7 +219,8 @@ struct LocalLoadOpConversion : public ConvertOpToLLVMPattern<LocalLoadOp> {
auto dstTy = op.getResult().getType();
auto dstShape = dstTy.getShape();
auto srcSharedLayout = cast<SharedEncodingAttr>(srcTy.getEncoding());
assert((dstShape.size() <= 2 || isSupportedDotOpLayout(srcTy, dstTy)) &&
assert((!isa<DotOperandEncodingAttr>(dstTy.getEncoding()) ||
isSupportedDotOpLayout(srcTy, dstTy)) &&
"Unexpected rank of ConvertLayout(shared->distributed)");

auto smemObj = LLVM::getSharedMemoryObjectFromStruct(
Expand Down
88 changes: 88 additions & 0 deletions python/test/unit/language/test_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5383,6 +5383,94 @@ def test_convert2d(M, N, src_layout, interm_layout, dst_layout, dtype, device, t
assert torch.equal(z, x)


layouts_3d = [
BlockedLayout([4, 4, 1], [1, 8, THREADS_PER_WARP // 8], [2, 2, 1], [2, 1, 0], [1, 1, 1], [1, 1, 1], [0, 1, 2]),
BlockedLayout([1, 1, 4], [8, THREADS_PER_WARP // 8, 1], [2, 1, 2], [1, 2, 0], [1, 1, 1], [1, 1, 1], [0, 1, 2]),
DotOperandLayout(parent=MmaLayout([2, 0], [4, 1, 1], [1, 1, 1], [1, 1, 1], [2, 1, 0], [1, 16, 8]), op_idx=0,
k_width=1),
]

shared_layout_3d = [
SharedLayout(1, 1, 1, [2, 1, 0], [1, 1, 1], [1, 1, 1], [0, 1, 2]),
SharedLayout(4, 2, 4, [1, 2, 0], [1, 1, 1], [1, 1, 1], [0, 1, 2]),
SharedLayout(8, 2, 4, [0, 2, 1], [1, 1, 1], [1, 1, 1], [0, 1, 2]),
SharedLayout(4, 2, 1, [2, 0, 1], [1, 1, 1], [1, 1, 1], [0, 1, 2]),
]


@pytest.mark.parametrize("M, N, K", [[8, 16, 32]])
@pytest.mark.parametrize("shared_layout", shared_layout_3d)
@pytest.mark.parametrize("dist_layout", layouts_3d)
def test_local_load_store(M, N, K, dist_layout, shared_layout, device, tmp_path: pathlib.Path):
layouts = f"""
#dist = {dist_layout}
#shared = {shared_layout}
"""
ir = layouts + f"""
module attributes {{"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 4 : i32, "ttg.threads-per-warp" = {THREADS_PER_WARP} : i32}} {{
tt.func public @kernel(%arg0: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}) attributes {{noinline = false}} {{
%cst = arith.constant dense<{K}> : tensor<1x{N}x1xi32, #dist>
%cst_0 = arith.constant dense<{K*N}> : tensor<{M}x1x1xi32, #dist>
%cst_1 = arith.constant dense<{K*N}> : tensor<{M}x1x1xi32, #dist>
%cst_2 = arith.constant dense<{K}> : tensor<1x{N}x1xi32, #dist>
%0 = tt.make_range {{end = {K} : i32, start = 0 : i32}} : tensor<{K}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 1, parent = #dist}}>}}>>
%1 = tt.expand_dims %0 {{axis = 0 : i32}} : tensor<{K}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 1, parent = #dist}}>}}>> -> tensor<1x{K}xi32, #ttg.slice<{{dim = 1, parent = #dist}}>>
%2 = tt.expand_dims %1 {{axis = 1 : i32}} : tensor<1x{K}xi32, #ttg.slice<{{dim = 1, parent = #dist}}>> -> tensor<1x1x{K}xi32, #dist>
%3 = tt.splat %arg0 : !tt.ptr<i32> -> tensor<1x1x{K}x!tt.ptr<i32>, #dist>
%4 = tt.addptr %3, %2 : tensor<1x1x{K}x!tt.ptr<i32>, #dist>, tensor<1x1x{K}xi32, #dist>
%5 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>>
%6 = tt.expand_dims %5 {{axis = 0 : i32}} : tensor<{N}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>> -> tensor<1x{N}xi32, #ttg.slice<{{dim = 2, parent = #dist}}>>
%7 = tt.expand_dims %6 {{axis = 2 : i32}} : tensor<1x{N}xi32, #ttg.slice<{{dim = 2, parent = #dist}}>> -> tensor<1x{N}x1xi32, #dist>
%8 = arith.muli %7, %cst_2 : tensor<1x{N}x1xi32, #dist>
%9 = tt.broadcast %4 : tensor<1x1x{K}x!tt.ptr<i32>, #dist> -> tensor<1x{N}x{K}x!tt.ptr<i32>, #dist>
%10 = tt.broadcast %8 : tensor<1x{N}x1xi32, #dist> -> tensor<1x{N}x{K}xi32, #dist>
%11 = tt.addptr %9, %10 : tensor<1x{N}x{K}x!tt.ptr<i32>, #dist>, tensor<1x{N}x{K}xi32, #dist>
%12 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #ttg.slice<{{dim = 1, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>>
%13 = tt.expand_dims %12 {{axis = 1 : i32}} : tensor<{M}xi32, #ttg.slice<{{dim = 1, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>> -> tensor<{M}x1xi32, #ttg.slice<{{dim = 2, parent = #dist}}>>
%14 = tt.expand_dims %13 {{axis = 2 : i32}} : tensor<{M}x1xi32, #ttg.slice<{{dim = 2, parent = #dist}}>> -> tensor<{M}x1x1xi32, #dist>
%15 = arith.muli %14, %cst_1 : tensor<{M}x1x1xi32, #dist>
%16 = tt.broadcast %11 : tensor<1x{N}x{K}x!tt.ptr<i32>, #dist> -> tensor<{M}x{N}x{K}x!tt.ptr<i32>, #dist>
%17 = tt.broadcast %15 : tensor<{M}x1x1xi32, #dist> -> tensor<{M}x{N}x{K}xi32, #dist>
%18 = tt.addptr %16, %17 : tensor<{M}x{N}x{K}x!tt.ptr<i32>, #dist>, tensor<{M}x{N}x{K}xi32, #dist>
%19 = tt.load %18 : tensor<{M}x{N}x{K}x!tt.ptr<i32>, #dist>
%20 = ttg.local_alloc %19 : (tensor<{M}x{N}x{K}xi32, #dist>) -> !ttg.memdesc<{M}x{N}x{K}xi32, #shared, #ttg.shared_memory>
%21 = ttg.local_load %20 : !ttg.memdesc<{M}x{N}x{K}xi32, #shared, #ttg.shared_memory> -> tensor<{M}x{N}x{K}xi32, #dist>
%22 = tt.make_range {{end = {K} : i32, start = 0 : i32}} : tensor<{K}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 1, parent = #dist}}>}}>>
%23 = tt.expand_dims %22 {{axis = 0 : i32}} : tensor<{K}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 1, parent = #dist}}>}}>> -> tensor<1x{K}xi32, #ttg.slice<{{dim = 1, parent = #dist}}>>
%24 = tt.expand_dims %23 {{axis = 1 : i32}} : tensor<1x{K}xi32, #ttg.slice<{{dim = 1, parent = #dist}}>> -> tensor<1x1x{K}xi32, #dist>
%25 = tt.splat %arg1 : !tt.ptr<i32> -> tensor<1x1x{K}x!tt.ptr<i32>, #dist>
%26 = tt.addptr %25, %24 : tensor<1x1x{K}x!tt.ptr<i32>, #dist>, tensor<1x1x{K}xi32, #dist>
%27 = tt.make_range {{end = {N} : i32, start = 0 : i32}} : tensor<{N}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>>
%28 = tt.expand_dims %27 {{axis = 0 : i32}} : tensor<{N}xi32, #ttg.slice<{{dim = 0, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>> -> tensor<1x{N}xi32, #ttg.slice<{{dim = 2, parent = #dist}}>>
%29 = tt.expand_dims %28 {{axis = 2 : i32}} : tensor<1x{N}xi32, #ttg.slice<{{dim = 2, parent = #dist}}>> -> tensor<1x{N}x1xi32, #dist>
%30 = arith.muli %29, %cst : tensor<1x{N}x1xi32, #dist>
%31 = tt.broadcast %26 : tensor<1x1x{K}x!tt.ptr<i32>, #dist> -> tensor<1x{N}x{K}x!tt.ptr<i32>, #dist>
%32 = tt.broadcast %30 : tensor<1x{N}x1xi32, #dist> -> tensor<1x{N}x{K}xi32, #dist>
%33 = tt.addptr %31, %32 : tensor<1x{N}x{K}x!tt.ptr<i32>, #dist>, tensor<1x{N}x{K}xi32, #dist>
%34 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #ttg.slice<{{dim = 1, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>>
%35 = tt.expand_dims %34 {{axis = 1 : i32}} : tensor<{M}xi32, #ttg.slice<{{dim = 1, parent = #ttg.slice<{{dim = 2, parent = #dist}}>}}>> -> tensor<{M}x1xi32, #ttg.slice<{{dim = 2, parent = #dist}}>>
%36 = tt.expand_dims %35 {{axis = 2 : i32}} : tensor<{M}x1xi32, #ttg.slice<{{dim = 2, parent = #dist}}>> -> tensor<{M}x1x1xi32, #dist>
%37 = arith.muli %36, %cst_0 : tensor<{M}x1x1xi32, #dist>
%38 = tt.broadcast %33 : tensor<1x{N}x{K}x!tt.ptr<i32>, #dist> -> tensor<{M}x{N}x{K}x!tt.ptr<i32>, #dist>
%39 = tt.broadcast %37 : tensor<{M}x1x1xi32, #dist> -> tensor<{M}x{N}x{K}xi32, #dist>
%40 = tt.addptr %38, %39 : tensor<{M}x{N}x{K}x!tt.ptr<i32>, #dist>, tensor<{M}x{N}x{K}xi32, #dist>
tt.store %40, %21 : tensor<{M}x{N}x{K}x!tt.ptr<i32>, #dist>
tt.return
}}
}}
"""

x = torch.arange(0, M * N * K, device=device, dtype=torch.int32).reshape(M, N, K)
z = torch.empty_like(x, device=device)

temp_file = tmp_path / "test_local_load_store.ttgir"
temp_file.write_text(ir)
kernel = triton.compile(str(temp_file))

kernel[(1, 1, 1)](x, z)
assert torch.equal(z, x)


mma_pairs = [
[
MmaLayout((2, 0), [1, 4], [1, 1], [1, 1], [0, 1], [16, 8]),
Expand Down