[XNNPACK] resolve ambiguity around 2d affine quantized tensors#8958
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facebook-github-bot merged 1 commit intopytorch:mainfrom Mar 15, 2025
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[XNNPACK] resolve ambiguity around 2d affine quantized tensors#8958facebook-github-bot merged 1 commit intopytorch:mainfrom
facebook-github-bot merged 1 commit intopytorch:mainfrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/8958
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jackzhxng
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Mar 6, 2025
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This is needed to resolve numerical inaccuracies introduced by switching to the quantize_ API here: #8772. Will leave to @digantdesai / @kimishpatel for final stamp
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@mcr229 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
GregoryComer
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Mar 7, 2025
Summary: There is some ambiguity around deriving per_token and per_channel_group semantics from quantize affine. Specifically when rank is two. Take for example the following input shape with the following group size: - input_shape (5, 2048) - group_size (1, 2048) This tensor has 5 scales, and can be seen has having one scale per batch dimension. Or one scale per token. However, this can also be seen as a single weight in which each group is the size of a channel, effectively giving per_channel semantics. This ambiguity does not play well within the XNNPACK Backend as we are must parse these differing quantization types. For now we rely on the fact that per_token quantization happens dynamically. Meaning that the scales and zero points are dynamically choosen. As a result, we check that the scales come from getitem and is dynamically chosen. We further ensure that per_channel_group checks are not per_token. Test Plan: ``` python -m unittest backends.xnnpack.test.ops.test_linear.TestLinear.test_linear_qd8_f32_per_token_weight_per_channel_group_int4 ``` Reviewed By: GregoryComer Differential Revision: D70719546 Pulled By: mcr229
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This pull request was exported from Phabricator. Differential Revision: D70719546 |
This was referenced Mar 17, 2025
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Differential Revision: D70719546 Pull Request resolved: pytorch#8958
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Summary
There is some ambiguity around deriving per_token and per_channel_group semantics from quantize affine. Specifically when rank is two. Take for example the following input shape with the following group size:
This tensor has 5 scales, and can be seen has having one scale per batch dimension. Or one scale per token. However, this can also be seen as a single weight in which each group is the size of a channel, effectively giving per_channel semantics. This ambiguity does not play well within the XNNPACK Backend as we are must parse these differing quantization types. For now we rely on the fact that per_token quantization happens dynamically. Meaning that the scales and zero points are dynamically choosen. As a result, we check that the scales come from getitem and is dynamically chosen. We further ensure that per_channel_group checks are not per_token.
Test plan