[Inductor][CPU] Add torchao da8w8 pattern with sym quantized act & wgt #142110
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/142110
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (2 Unrelated Failures)As of commit 604e4ec with merge base b31d3b2 ( FLAKY - The following job failed but was likely due to flakiness present on trunk:
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leslie-fang-intel
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Dec 5, 2024
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Will rebase with the latest main-branch to avoid a CI failure that's also been happening in other PRs - |
jgong5
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Dec 5, 2024
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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pytorch#142110) ### Summary Extends pytorch#142036 for Inductor pattern-matching pattern covered for torchao API `int8_dynamic_activation_int8_weight` in the following scenario (inference-only, freezing enabled) - - int8 quantized (symmetrically) activation (per token quantized). - Statically (so, scales are also constant. But then they would have been constant even in case of dynamic quantization due to constant weights, anyway) per-channel int8 quantized (symmetrically) weights (which are also constant because freezing is enabled). The pattern that's matched is `torch._intmm` -> convert to FP32/BF16 -> [optional expand for activation scale] ->`mul` -> `mul`. We don't check if the activation is dynamically quantized or whether the weights are statically quantized, though (since the implementation won't have have any side-effects even if that wouldn't be true). In practice, it also matches the smooth-quant int8 quantized linear pattern if its output is not reshaped (if activation is 2D). ### More details oneDNN int8 matmul supports application of per-channel weight scale but not a vector activation scale, which could be applied as a post op, but is currently unsupported in ATen. Bias addition (which could be supported with an add post-op) is also unfused. The fusion pattern used in this PR is `torch._intmm` -> convert to FP32/BF16 ->`mul`, which will be replaced by oneDNN qlinear op. The speedup over eager-mode is due to 2 reasons - 1. fusion of int8xint8 -> int32 GEMM, conversion to FP32/BF16 & application of weight scale. (In case of BF16, many intermediate conversions are also avoided). 2. weight is pre-packed & cached by Inductor, so a reorder is avoided at run-time. But, in the future, the whole pattern (including application of activation scale, which would be a mul post-op) + bias could be fused if corresponding support would be enabled in ATen. ### Verification Added UT in this PR ``` python test/inductor/test_mkldnn_pattern_matcher.py -v -k test_da8w8_sym_act_sym_wgt_with_int_mm ``` #### Corresponding torchao UTs 1. int8 Smoothquant legacy API - `TORCHINDUCTOR_FREEZING=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+inductor" python test/integration/test_integration.py -v -k test_non_dynamically_quantizable_linear`. The difference from pytorch#139595 is that there are no reshapes of the linear output in this pattern. 2. int8 da8w8 - symmetrically quantized activation (dynamically) & statically quantized weights - ` TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+inductor" TORCHINDUCTOR_FREEZING=1 python test/integration/test_integration.py -v -k test_int8_dynamic_quant_subclass_api_0_cpu` Pull Request resolved: pytorch#142110 Approved by: https://github.com/leslie-fang-intel, https://github.com/jgong5 ghstack dependencies: pytorch#142036
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Stack from ghstack (oldest at bottom):
Summary
Extends #142036 for Inductor pattern-matching pattern covered for torchao API
int8_dynamic_activation_int8_weightin the following scenario (inference-only, freezing enabled) -The pattern that's matched is
torch._intmm-> convert to FP32/BF16 -> [optional expand for activation scale] ->mul->mul.We don't check if the activation is dynamically quantized or whether the weights are statically quantized, though (since the implementation won't have have any side-effects even if that wouldn't be true).
In practice, it also matches the smooth-quant int8 quantized linear pattern if its output is not reshaped (if activation is 2D).
More details
oneDNN int8 matmul supports application of per-channel weight scale but not a vector activation scale, which could be applied as a post op, but is currently unsupported in ATen. Bias addition (which could be supported with an add post-op) is also unfused.
The fusion pattern used in this PR is
torch._intmm-> convert to FP32/BF16 ->mul, which will be replaced by oneDNN qlinear op.The speedup over eager-mode is due to 2 reasons -
But, in the future, the whole pattern (including application of activation scale, which would be a mul post-op) + bias could be fused if corresponding support would be enabled in ATen.
Verification
Added UT in this PR
Corresponding torchao UTs< 8000 /h4>-
-
int8 Smoothquant legacy API -
TORCHINDUCTOR_FREEZING=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+inductor" python test/integration/test_integration.py -v -k test_non_dynamically_quantizable_linear.The difference from [Inductor][CPU] Fuse SmoothQuant int8 linear pattern #139595 is that there are no reshapes of the linear output in this pattern.
int8 da8w8 - symmetrically quantized activation (dynamically) & statically quantized weights -
TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+inductor" TORCHINDUCTOR_FREEZING=1 python test/integration/test_integration.py -v -k test_int8_dynamic_quant_subclass_api_0_cpucc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov