[AMD] Reland instruction scheduling hint changes by ravil-mobile · Pull Request #4940 · triton-lang/triton

ravil-mobile · 2024-10-17T09:55:49Z

This commit relands #4819
with the following fixes:

Replaced temlate-based rewindUnaryOps to use regular for-loops. The new way is more robust and can handle other unary ops automatically.
Replaced instr.sched.barriers using the ones from rocdl dialect from the MLIR upstream
Extended lit tests

antiagainst

Thanks @ravil-mobile! A few comments.

This reverts commit 93de426.

Replaced temlate-based impl. of `rewindUnaryOps` in `SchedInstructions.cpp` using regular for-loops. The new impl. is more robust and can handle other unary ops automatically.

* add a test for the presence of OpIdx attribute

The extra check tests whether the data are loaded from HBM using `buffer_load` instructions. The CKV3 scheduling is skipped if the check fails.

zhanglx13 · 2024-10-30T03:33:31Z

+    mod.walk([this, ctx](scf::ForOp forOp) {
+      // Note, instruction schedule barriers are inserted only in the case of
+      // a single `tt.dot` op in a `scf::ForOp` scope in the current
+      // implementation.


Does it mean for attention like kernels, i.e. chained dot in the main loop, we will not have sched.barrier inserted?
May I ask why this is the case?
I'm asking because I see better register pressure if sched.barrier is inserted (yes, two tt.dot means we have two sched.barrier at beginning and end of the loop) for flash-attention kernels.

Hi @zhanglx13,

Does it mean for attention like kernels, i.e. chained dot in the main loop, we will not have sched.barrier inserted?
May I ask why this is the case?

Yes, you are correct. It is the state of the current implementation. There is still some work which need to be done even for a single tt.DotOp per block. The FA kernel is one of the next steps. I would suggest to work iteratively. I am sure that there are going to be some challenges with instruction scheduling for the FA-like kernels

zhanglx13 · 2024-10-30T03:37:53Z

I'm also curious if the current instruction count mechanism can handle the case when the backend decides to combine two ds_read_b64 into one ds_read2st_b64?

ravil-mobile · 2024-10-30T09:20:51Z

I'm also curious if the current instruction count mechanism can handle the case when the backend decides to combine two ds_read_b64 into one ds_read2st_b64?

Hi @zhanglx13, no the current implementation cannot handle it. Instruction folding happens during instruction selection at the level of our compiler-backend. We have no explicit control of it from the MLIR level. This issue should be addressed in one of the follow-up tickets.

giuseros

LGTM

This commit relands triton-lang#4819 with the following fixes: * Changed to a better way to mark opIdx for loads * Replaced temlate-based `rewindUnaryOps` to use regular for-loops. The new way is more robust and can handle other unary ops automatically. * Replaced `instr.sched.barriers` using the ones from `rocdl` dialect from the MLIR upstream * Extended lit tests

This commit relands triton-lang#4819 with the following fixes: * Changed to a better way to mark opIdx for loads * Replaced temlate-based `rewindUnaryOps` to use regular for-loops. The new way is more robust and can handle other unary ops automatically. * Replaced `instr.sched.barriers` using the ones from `rocdl` dialect from the MLIR upstream * Extended lit tests (cherry picked from commit ee5876c)

@lezcano

* [BACKEND][NVIDIA] Add Lowering for Shared-to-MMAv3-DotOp Copy (triton-lang#5009) Allows for upcasting in DotOp encoding in RF. This lowering path is not currently in use; pending triton-lang#5003 (cherry picked from commit cfddb09) * [AMD] Add initial support for scaled_dot(mxfp8, fp8) (triton-lang#4994) This commit adds initial support for scaled_dot with mxfp8 LHS and fp8 RHS. It supports both mfma32 and mfma16 intrinsic variants. Right now we are missing software emulation for `Float8E4M3FN` type, so this only enables for `Float8E5M2`. (cherry picked from commit 3549db8) * [Frontend][Backend] Implement support for scale_dot(-, bf16) (triton-lang#4996) In the passing we also improve a few other things: - Now `scaled_dot` accepts both uint8/uint16 fp8/bf16 as inputs (before you had to cast it to uint8, which was weird when extending it to bf16). - Add `scaled_dot` to the docs and improve the docs overall (have not render them, might need a few further tweaks) (cherry picked from commit 23c9ec1) * [BACKEND] Improve detection of register to register conversion (triton-lang#4991) Specifically, it fixes problems when `srcLayout` and `dstLayout` have different number of registers but the same number of not free registers. We solved the problem by padding free registers to either `srcLayout` or `dstLayout`, but this can be improved by fixing the `invertAndCompose` function. (cherry picked from commit 15c5e55) * [BACKEND] Replace `isMmaToDotShortcut` with linear layout based logic (triton-lang#4951) This PR removes the legacy `isMmaToDotShortcut` and its associated shortcut conversion. (cherry picked from commit 1d5fdfe) * [BACKEND]Fix DotOperand(Ampere) LinearLayoutConversion (triton-lang#5038) We also clean a bit `TritonGPU/IR/Dialect.cpp` using some auxiliary functions to make the intentions a bit clearer. We add a few asserts in the `LinearLayoutConversion` to make sure it's clear why we do certain things here and there. We also kill `getCvtOrder`, as it was not used anywhere (cherry picked from commit 56584c4) * [BACKEND] Fix uses of getOrder(DotOperand(Nvidia) and MMA(Nvidia)) (triton-lang#5055) We use `getOrder` very liberally throughout the codebase, when we really meant to use `getThreadOrder`. This is an issue with the input layout is an `DotOperand(mma(opIdx=1))`, where the thread order and the matrix order are opposite. Found this to be an issue when a PR changed the `getOrder` of `DotOperand(Hopper)` to an incorrect one and CI still passed! The issue here is that the LLVM lowering for wgmma and the LinearLayout does not use `getOrder`, but there are many other subsystems do, and many heuristics would be getting an incorrect order, and potentially be disabled. This is particularly problematic for `DotOperand(opIdx=1)` in nvidia hardware, as `getThreadOrder` and `getOrder` are different! While doing so we: - Audit most (all?) the calls to `getOrder(dotOperand)`. It turns out that most of them really meant `getThreadOrder` - Fix the ordering methods of `SliceEncodingAttr` to be consistent - Move the implementation of `getWarpOrder` to the Attr classes, because of OOP The test strategy was to add `llvm::report_fatal_error("Testing");` within `getOrder(nvidiaMma)` and `getOrder(DotOperand(nvidiaMma))` and triaging all errors that were raised in CI. (cherry picked from commit 38a11b8) * [AMD] Reland instruction scheduling hint changes (triton-lang#4940) This commit relands triton-lang#4819 with the following fixes: * Changed to a better way to mark opIdx for loads * Replaced temlate-based `rewindUnaryOps` to use regular for-loops. The new way is more robust and can handle other unary ops automatically. * Replaced `instr.sched.barriers` using the ones from `rocdl` dialect from the MLIR upstream * Extended lit tests (cherry picked from commit ee5876c) * [AMD] Enable scaled_dot(-, bf16) (triton-lang#5029) (cherry picked from commit f062540) * [AMD] Add support for scaled_dot(mxfp4, -) (triton-lang#5034) This commit adds support for mxfp4 typed A tensor for sacled dot in the AMD backend. We moved the `convertMxfp4x2ToBf16x2` impl from NVIDIA side to a common path to reuse. (cherry picked from commit edc5c5c) * [BACKEND] Minor Bugfixes for SharedToDotOperand MMAv3 (triton-lang#5030) Two bugfixes following triton-lang#5009. - When `BLOCK_M=64` and `num_warps > 4`, the order of warps for DotOpEncoded tensor should be M-major instead of N-major, since WGMMA expects the 4 warps in each warp group to be stacked along the M dimension. - Should use `mmaBitwidth` instead of `bitwidth` when calculating `numRep` in `SharedToDotOperandMMAv2OrV3`. This was missed in a bad rebase. @lezcano I encountered these bugs when attempting to locally test the [DotOp hoisting PR](triton-lang#5003) after rebasing (they normally would be caught by `test_core.py` but that path was not yet enabled in the last PR). With these fixes added, I was able to successfully validate against pytorch. (cherry picked from commit e82dfd9) (cherry picked from commit 5287a68) * [BACKEND] Get rid of unpack/pack I32 (triton-lang#5044) - Removed functions related to unpacking and packing I32 values. - Updated utilities to handle conversion of mxfp4 values without packing/unpacking I32. - Move the register value ordering logic from the element-wise operation lowering to the dot operation lowering. - Use linear layout to handle conversions between almost all distributed layouts. - Clean up data loading and mma computation involving `repN`, `repK`, and `repM`. (cherry picked from commit 1cf7b1b) (cherry picked from commit 376fe7e) * Consolidate `getOrder` as "element order" and implement `getRepOrder` for general and NVIDIA layouts (triton-lang#5089) This partially reverts commit 38a11b8. Supersedes triton-lang#5085 It also documents that we are implicitly choosing a way to tile a full tensor depending on the layout. See triton-lang#5085 (comment) (cherry picked from commit 57643b3) (cherry picked from commit ffb2032) --------- Co-authored-by: Gary Geng <ggeng@nvidia.com> Co-authored-by: Lei Zhang <antiagainst@gmail.com> Co-authored-by: Mario Lezcano Casado <3291265+lezcano@users.noreply.github.com> Co-authored-by: Keren Zhou <kerenzhou@openai.com> Co-authored-by: ravil-mobile <ravil.aviva.com@gmail.com>

This commit relands triton-lang#4819 with the following fixes: * Changed to a better way to mark opIdx for loads * Replaced temlate-based `rewindUnaryOps` to use regular for-loops. The new way is more robust and can handle other unary ops automatically. * Replaced `instr.sched.barriers` using the ones from `rocdl` dialect from the MLIR upstream * Extended lit tests (cherry picked from commit ee5876c) (cherry picked from commit 8dd9226)

This commit relands triton-lang#4819 with the following fixes: * Changed to a better way to mark opIdx for loads * Replaced temlate-based `rewindUnaryOps` to use regular for-loops. The new way is more robust and can handle other unary ops automatically. * Replaced `instr.sched.barriers` using the ones from `rocdl` dialect from the MLIR upstream * Extended lit tests (cherry picked from commit ee5876c) (cherry picked from commit 8dd9226) (cherry picked from commit aed3efc)

@lezcano

* [AMD] Emit vectorized 16-bit float LLVM atomic ops (triton-lang#4925) In the case of 16 bit floats operands for tt::AtomicRMWOp, construct only one LLVM::AtomicRMWOp but use vector of elements. Such approach allows to generate packed intrinsics and process 2 elements at once. Added a lit test for f16 vectorized case. (cherry picked from commit 78c8054) (cherry picked from commit 4d70942) (cherry picked from commit 2f8aacc) * [AMD] Restructure ReorderInstructions pass (triton-lang#4998) (cherry picked from commit 86a2ac7) (cherry picked from commit 4c7d56e) (cherry picked from commit 0529343) * [AMD] Support warp-level reduction with DPP (triton-lang#5019) This commit adds support for warp-level reduction with DPP instructions, which can improve performance. See https://gpuopen.com/learn/amd-gcn-assembly-cross-lane-operations/ (cherry picked from commit 21119e3) (cherry picked from commit d0142d3) (cherry picked from commit 9f2b69b) * [AMD] Add missing dependency to TritonAMDGPUIR (triton-lang#5053) TritonAMDGPUTransforms now depends on it. (cherry picked from commit 0b443ce) (cherry picked from commit 37cec47) (cherry picked from commit 1ab334d) * [AMD] Support warp-level reduction with DPP (triton-lang#5019) This commit adds support for warp-level reduction with DPP instructions, which can improve performance. See https://gpuopen.com/learn/amd-gcn-assembly-cross-lane-operations/ (cherry picked from commit 21119e3) (cherry picked from commit ca8842c) (cherry picked from commit 3a3902d) * [AMD] Reland sinking the 2nd tt.load after local_load's (triton-lang#4935) This PR adds more restrictions about when should we apply the sched-load optimizations and un-revert triton-lang#4823. We will only apply the optimization when all of the following is satisfied: 1. pureMatmulProblem, i.e. 1 `tt.dot` in the main loop 2. two `tt.load`s in the main loop 3. 2nd `tt.load` is ahead of the `tt.dot` 4. 1st user of 2nd `tt.load` is after the `tt.dot` 5. tile size is large enough, i.e. nonKDim >= 128 and kDim >= 64 (cherry picked from commit 4f6f768) (cherry picked from commit f6053a3) (cherry picked from commit 72d1575) * [BACKEND][NVIDIA] Add Lowering for Shared-to-MMAv3-DotOp Copy (triton-lang#5009) Allows for upcasting in DotOp encoding in RF. This lowering path is not currently in use; pending triton-lang#5003 (cherry picked from commit cfddb09) (cherry picked from commit f8c2c30) (cherry picked from commit 73ef337) * [AMD] Add initial support for scaled_dot(mxfp8, fp8) (triton-lang#4994) This commit adds initial support for scaled_dot with mxfp8 LHS and fp8 RHS. It supports both mfma32 and mfma16 intrinsic variants. Right now we are missing software emulation for `Float8E4M3FN` type, so this only enables for `Float8E5M2`. (cherry picked from commit 3549db8) (cherry picked from commit efe0ec4) (cherry picked from commit 010fe45) * [Frontend][Backend] Implement support for scale_dot(-, bf16) (triton-lang#4996) In the passing we also improve a few other things: - Now `scaled_dot` accepts both uint8/uint16 fp8/bf16 as inputs (before you had to cast it to uint8, which was weird when extending it to bf16). - Add `scaled_dot` to the docs and improve the docs overall (have not render them, might need a few further tweaks) (cherry picked from commit 23c9ec1) (cherry picked from commit 675758b) (cherry picked from commit 4e04af0) * [BACKEND] Improve detection of register to register conversion (triton-lang#4991) Specifically, it fixes problems when `srcLayout` and `dstLayout` have different number of registers but the same number of not free registers. We solved the problem by padding free registers to either `srcLayout` or `dstLayout`, but this can be improved by fixing the `invertAndCompose` function. (cherry picked from commit 15c5e55) (cherry picked from commit 6537eb6) (cherry picked from commit 4ca5013) * [BACKEND] Replace `isMmaToDotShortcut` with linear layout based logic (triton-lang#4951) This PR removes the legacy `isMmaToDotShortcut` and its associated shortcut conversion. (cherry picked from commit 1d5fdfe) (cherry picked from commit fc6d96b) (cherry picked from commit 9f67c54) * [BACKEND]Fix DotOperand(Ampere) LinearLayoutConversion (triton-lang#5038) We also clean a bit `TritonGPU/IR/Dialect.cpp` using some auxiliary functions to make the intentions a bit clearer. We add a few asserts in the `LinearLayoutConversion` to make sure it's clear why we do certain things here and there. We also kill `getCvtOrder`, as it was not used anywhere (cherry picked from commit 56584c4) (cherry picked from commit 276d182) (cherry picked from commit 72651c2) * [BACKEND] Fix uses of getOrder(DotOperand(Nvidia) and MMA(Nvidia)) (triton-lang#5055) We use `getOrder` very liberally throughout the codebase, when we really meant to use `getThreadOrder`. This is an issue with the input layout is an `DotOperand(mma(opIdx=1))`, where the thread order and the matrix order are opposite. Found this to be an issue when a PR changed the `getOrder` of `DotOperand(Hopper)` to an incorrect one and CI still passed! The issue here is that the LLVM lowering for wgmma and the LinearLayout does not use `getOrder`, but there are many other subsystems do, and many heuristics would be getting an incorrect order, and potentially be disabled. This is particularly problematic for `DotOperand(opIdx=1)` in nvidia hardware, as `getThreadOrder` and `getOrder` are different! While doing so we: - Audit most (all?) the calls to `getOrder(dotOperand)`. It turns out that most of them really meant `getThreadOrder` - Fix the ordering methods of `SliceEncodingAttr` to be consistent - Move the implementation of `getWarpOrder` to the Attr classes, because of OOP The test strategy was to add `llvm::report_fatal_error("Testing");` within `getOrder(nvidiaMma)` and `getOrder(DotOperand(nvidiaMma))` and triaging all errors that were raised in CI. (cherry picked from commit 38a11b8) (cherry picked from commit 8412154) (cherry picked from commit a569c3e) * [AMD] Reland instruction scheduling hint changes (triton-lang#4940) This commit relands triton-lang#4819 with the following fixes: * Changed to a better way to mark opIdx for loads * Replaced temlate-based `rewindUnaryOps` to use regular for-loops. The new way is more robust and can handle other unary ops automatically. * Replaced `instr.sched.barriers` using the ones from `rocdl` dialect from the MLIR upstream * Extended lit tests (cherry picked from commit ee5876c) (cherry picked from commit 8dd9226) (cherry picked from commit aed3efc) * [AMD] Enable scaled_dot(-, bf16) (triton-lang#5029) (cherry picked from commit f062540) (cherry picked from commit ca75b5f) (cherry picked from commit 98149dd) * [AMD] Add support for scaled_dot(mxfp4, -) (triton-lang#5034) This commit adds support for mxfp4 typed A tensor for sacled dot in the AMD backend. We moved the `convertMxfp4x2ToBf16x2` impl from NVIDIA side to a common path to reuse. (cherry picked from commit edc5c5c) (cherry picked from commit ac9f0d0) (cherry picked from commit c0710dc) * [BACKEND] Minor Bugfixes for SharedToDotOperand MMAv3 (triton-lang#5030) Two bugfixes following triton-lang#5009. - When `BLOCK_M=64` and `num_warps > 4`, the order of warps for DotOpEncoded tensor should be M-major instead of N-major, since WGMMA expects the 4 warps in each warp group to be stacked along the M dimension. - Should use `mmaBitwidth` instead of `bitwidth` when calculating `numRep` in `SharedToDotOperandMMAv2OrV3`. This was missed in a bad rebase. @lezcano I encountered these bugs when attempting to locally test the [DotOp hoisting PR](triton-lang#5003) after rebasing (they normally would be caught by `test_core.py` but that path was not yet enabled in the last PR). With these fixes added, I was able to successfully validate against pytorch. (cherry picked from commit e82dfd9) (cherry picked from commit 5287a68) (cherry picked from commit 8d70247) (cherry picked from commit 302de9d) * [BACKEND] Get rid of unpack/pack I32 (triton-lang#5044) - Removed functions related to unpacking and packing I32 values. - Updated utilities to handle conversion of mxfp4 values without packing/unpacking I32. - Move the register value ordering logic from the element-wise operation lowering to the dot operation lowering. - Use linear layout to handle conversions between almost all distributed layouts. - Clean up data loading and mma computation involving `repN`, `repK`, and `repM`. (cherry picked from commit 1cf7b1b) (cherry picked from commit 376fe7e) (cherry picked from commit 2141a4e) (cherry picked from commit d0e4abc) * Consolidate `getOrder` as "element order" and implement `getRepOrder` for general and NVIDIA layouts (triton-lang#5089) This partially reverts commit 38a11b8. Supersedes triton-lang#5085 It also documents that we are implicitly choosing a way to tile a full tensor depending on the layout. See triton-lang#5085 (comment) (cherry picked from commit 57643b3) (cherry picked from commit ffb2032) (cherry picked from commit a11c6be) (cherry picked from commit 9d080b4) --------- Co-authored-by: Ilya V <152324710+joviliast@users.noreply.github.com> Co-authored-by: Lei Zhang <antiagainst@gmail.com> Co-authored-by: Kyle Wang <ec1wng@gmail.com> Co-authored-by: Lixun Zhang <Lixun.Zhang@amd.com> Co-authored-by: Gary Geng <ggeng@nvidia.com> Co-authored-by: Mario Lezcano Casado <3291265+lezcano@users.noreply.github.com> Co-authored-by: Keren Zhou <kerenzhou@openai.com> Co-authored-by: ravil-mobile <ravil.aviva.com@gmail.com>

ravil-mobile force-pushed the ravil/bug-fix branch from 48cc87a to dfcb55e Compare October 17, 2024 09:56

ravil-mobile changed the title ~~Ravil/bug fix~~ [AMD] Fixed a bug resulted in reverting PR#4919 Oct 17, 2024

antiagainst mentioned this pull request Oct 17, 2024

[AMD] Use rocdl instr.sched.barriers from upstream MLIR/ROCDL #4939

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antiagainst changed the title ~~[AMD] Fixed a bug resulted in reverting PR#4919~~ [AMD] Reland instruction scheduling hint changes Oct 17, 2024

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ravil-mobile force-pushed the ravil/bug-fix branch 2 times, most recently from 354fd36 to 343603e Compare October 29, 2024 10:37

Revert "[AMD] revert optimizations (triton-lang#4919)"

4b50c48

This reverts commit 93de426.

ravil-mobile added 9 commits October 29, 2024 13:17

[AMD] use rocdl instr.sched.barriers from upstream MLIR/ROCDL

d53c499

[AMD] fixed a bug resulted in reverting PR#4919

ce02968

Replaced temlate-based impl. of `rewindUnaryOps` in `SchedInstructions.cpp` using regular for-loops. The new impl. is more robust and can handle other unary ops automatically.

[AMD] Moved annotateDotUsageOnLoadStore to stream pipeliner

2aecafa

[AMD] Fixed bug in setNumGeneratedGlobalLoads

088fbd9

* add a test for the presence of OpIdx attribute

[AMD] added additional check into createCKV3Schedule

c72bfb9

The extra check tests whether the data are loaded from HBM using `buffer_load` instructions. The CKV3 scheduling is skipped if the check fails.

[AMD] Udated tests for SchedInstructions passes

68e7fac

[AMD] Addressed comments of PR#4940

dd7d2c6

[AMD] Fixed propagation of OpIdx attribute in LoadToBufferLoad pass

a2f8874

[AMD] Fixed instruction.sched lit tests

ae8c3c8

ravil-mobile force-pushed the ravil/bug-fix branch from 343603e to ae8c3c8 Compare October 29, 2024 13:19

Drop unnecessary mlir::prefix and early return if none choice

2504666

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Merge branch 'main' into ravil/bug-fix

63a552f

antiagainst merged commit ee5876c into triton-lang:main Oct 31, 2024

jataylo mentioned this pull request Dec 13, 2024

[CP] Many layout cherry picks for gemm perf ROCm/triton#683

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[AMD] AMD perf cherry picks for rc/3.2.x #5909

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[AMD] Reland instruction scheduling hint changes#4940

[AMD] Reland instruction scheduling hint changes#4940
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