Add Float8Tensor #2463
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Add Float8Tensor #2463
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2463
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: ❌ 1 New FailureAs of commit b0c2cf3 with merge base b757fb9 ( NEW FAILURE - The following job has failed:
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jerryzh168
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Jun 30, 2025
Summary: Splits out the float8 rowwise quantized path (both act and weight) of AQT to Float8RowwiseTensor Next: could potentially incorporate the per tensor activation path there as well Next: we can split the per tensor weight path to another Tensor as well, so we can deprecate AQT path for float8 Test Plan: python test/dtypes/test_affine_quantized_float.py python test/quantization/quantize_/test_float8_rowwise_tensor.py Reviewers: Subscribers: Tasks: Tags: stack-info: PR: #2463, branch: jerryzh168/stack/9
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Summary:
* Added Float8Tensor that's using fbgemm kernels and scaled_mm:
* per row activation + per row weight linear calling torch._scaled_mm op (for compatibilty with SM 8.9)
* per tensor activation + per tensor weight quant linear calling torch._scaled_mm op (for compatibilty with SM 8.9)
* per row activation + per row weight bmm calling torch.ops.fbgemm.f8f8bf16_rowwise_batched kernel (only works for SM 9.0+) can use batched scaled mm from torch when it's supported: pytorch/pytorch#157950
* dynamic quantization kwargs is added to the Float8Tensor directly
* Added QuantizeTensorKwargs and QuantizeTensorToFloat8Kwargs to store key word args for Float8Tensor.to_float8
* Updated Float8DynamicActivationFloat8WeightConfig and Float8WeightOnlyConfig to use Float8Tensor
Test Plan:
python test/dtypes/test_affine_quantized_float.py
python test/quantization/quantize_/workflows/float8/test_float8_tensor.py
Reviewers:
Subscribers:
Tasks:
Tags:
stack-info: PR: #2463, branch: jerryzh168/stack/9
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jerryzh168
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Aug 8, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
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Aug 8, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
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Aug 8, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
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to jerryzh168/ao
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Aug 11, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
added a commit
to jerryzh168/ao
that referenced
this pull request
Aug 11, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
added a commit
to jerryzh168/ao
that referenced
this pull request
Aug 11, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
added a commit
to jerryzh168/ao
that referenced
this pull request
Aug 12, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
added a commit
to jerryzh168/ao
that referenced
this pull request
Aug 12, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: pytorch#2463, pytorch#2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
jerryzh168
added a commit
that referenced
this pull request
Aug 12, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: #2463, #2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
liangel-02
pushed a commit
that referenced
this pull request
Aug 25, 2025
Summary:
* Added Float8Tensor that's using fbgemm kernels and scaled_mm:
* per row activation + per row weight linear calling torch._scaled_mm op (for compatibilty with SM 8.9)
* per tensor activation + per tensor weight quant linear calling torch._scaled_mm op (for compatibilty with SM 8.9)
* per row activation + per row weight bmm calling torch.ops.fbgemm.f8f8bf16_rowwise_batched kernel (only works for SM 9.0+) can use batched scaled mm from torch when it's supported: pytorch/pytorch#157950
* dynamic quantization kwargs is added to the Float8Tensor directly
* Added QuantizeTensorKwargs and QuantizeTensorToFloat8Kwargs to store key word args for Float8Tensor.to_float8
* Updated Float8DynamicActivationFloat8WeightConfig and Float8WeightOnlyConfig to use Float8Tensor
Test Plan:
python test/dtypes/test_affine_quantized_float.py
python test/quantization/quantize_/workflows/float8/test_float8_tensor.py
Reviewers:
Subscribers:
Tasks:
Tags:
liangel-02
pushed a commit
that referenced
this pull request
Aug 25, 2025
Summary: We have recently updated our design for structuring tensor subclasses in torchao to remove unnecessary abstractions and reduce indirections and having a structuring that aligns better with people's intuitive understanding of different quantization use cases, examples using the new design are: #2463, #2687 Test Plan: check generated doc Reviewers: Subscribers: Tasks: Tags:
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Stacked PRs:
Add Float8Tensor
Summary:
Test Plan:
python test/dtypes/test_affine_quantized_float.py
python test/quantization/quantize_/workflows/float8/test_float8_tensor.py
Reviewers:
Subscribers:
Tasks:
Tags: