Float8Tensor per row quantization pass bias to fbgemm kernel #2884
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2884
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ✅ No FailuresAs of commit 4d70152 with merge base fbe3df9 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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looks reasonable, can we add a test which covers the new path? |
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/dtypes/test_affine_quantized_float.py -k test_expected_kernels_on_gpu python test/quantization/quantize_/workflows/float8/test_float8_tensor.py ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_kernel_preference_numerical_equivalence python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_expected_gpu_kernel_fbgemm ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_kernel_preference_numerical_equivalence python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_expected_gpu_kernel_fbgemm ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_kernel_preference_numerical_equivalence python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_expected_gpu_kernel_fbgemm ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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drisspg
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Sep 2, 2025
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@drisspg yeah test is this one:
but it's probably better to have a more exhaustive one for all options (auto and torch) as well it is a performance "bug" I think, the effect is that we are not using the most efficient path if there is a bias |
jerryzh168
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Sep 2, 2025
| "torch.ops.triton.quantize_fp8_row.default", 1 | ||
| ).check_count("torch.ops.fbgemm.f8f8bf16_rowwise.default", 1).run(code[0]) | ||
| ).check_count("torch.ops.fbgemm.f8f8bf16_rowwise.default", 1).check_not( | ||
| "triton_poi_fused_add_0" |
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cc @drisspg we explicitly test that triton_poi_fused_add_0 is not generated, to make sure there is no additional res + bias there in this code path
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it's not safe to depend on inductor generated kernel names such as triton_poi_fused_add_0, I think it would be better to ensure that there are no additional kernels called after torch.ops.fbgemm.f8f8bf16_rowwise.default is called.
Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_kernel_preference_numerical_equivalence python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_expected_gpu_kernel_fbgemm ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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drisspg
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Sep 5, 2025
andrewor14
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Summary: Previously bias is not passed to fbgemm kernel for float8 per row quant, this PR adds it. Difference is we should have a faster float8 per row quantized kernel, without changing numerics or other things. Test Plan: ``` python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_kernel_preference_numerical_equivalence python test/quantization/quantize_/workflows/float8/test_float8_tensor.py -k test_expected_gpu_kernel_fbgemm ``` Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2884, branch: jerryzh168/stack/60
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Stacked PRs:
Float8Tensor per row quantization pass bias to fbgemm kernel
Summary:
Previously bias is not passed to fbgemm kernel for float8 per row quant,
this PR adds it.
Difference is we should have a faster float8 per row quantized kernel, without changing
numerics or other things.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags: