Add torchchat quantizer #897
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/897
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Summary: Pull Request resolved: pytorch#897 This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api). See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API. I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat. They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper. jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you. I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion. We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel). From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further. Reviewed By: digantdesai Differential Revision: D62394341
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Summary: Pull Request resolved: pytorch#897 This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api). See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API. I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat. They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper. jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you. I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion. We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel). From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further. Reviewed By: digantdesai Differential Revision: D62394341
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nit: the file name sounds like an interface but the content looks like all different kinds of implementation....
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Summary: Pull Request resolved: pytorch#897 This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api). See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API. I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat. They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper. jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you. I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion. We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel). From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further. Differential Revision: D62394341
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Summary: Pull Request resolved: pytorch#897 This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api). See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API. I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat. They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper. jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you. I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion. We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel). From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further. Differential Revision: D62394341
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Summary: Pull Request resolved: pytorch#897 This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api). See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API. I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat. They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper. jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you. I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion. We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel). From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further. Differential Revision: D62394341
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Differential Revision: D62394341 Pull Request resolved: pytorch#897
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…th torch.compile (#904) * [float8] improve eager numerics for dynamic scales * leave torch.linalg.vector_norm for another PR Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * cuda Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove _data and investigate Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove _data comment Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * upcast to float32 is enough Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * explain why float32 Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * _data parity Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * handle sm8.9 Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix transformer unit test Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * print if error Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * Add tutorial for trainable tensor subclass (#908) Summary: The new tutorial provides an example of how to implement a trainable tensor subclass that wraps quantized data. This extends the existing `MyDTypeTensor` with a few necessary steps to ensure proper gradient updates, namely: 1. Define a differentiable constructor 2. Define backward pass for ops of interest (e.g. torch.nn.functional.linear) 3. Handle special ops used by the optimizer (e.g. aten.add, aten.add_) Test Plan: python tutorials/developer_api_guide/my_trainable_tensor_subclass.py * Introducing 1-bit quantization for Llama in torchchat (#910) Differential Revision: D63052325 Pull Request resolved: #911 * Rename Floating point to fp8 (#909) * [float8] fix typo in bitwise_identical unit test (#918) Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * Adding example for quantized tensor + tensor parallelism (#785) * [WIP] Adding example for quantized tensor + tensor parallelism Summary: This PR adds an example of how quantized tensor subclass can work with DTensor: https://github.com/pytorch/pytorch/blob/main/torch/distributed/_tensor/README.md End goal is to rewrite https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/llama2.py with normal llama2 implementation and show case with DTensor + AffineQuantizedTensor + torch.compile we can get on par performance with the custom tensor parallel implementation Test Plan: torchrun --standalone --nnodes=1 --nproc-per-node=4 tutorials/developer_api_guide/tensor_parallel.py Reviewers: Subscribers: Tasks: Tags: * tensor parallel file * Use DTensor.from instead of distribute_tensor * implementing aten.slice.Tensor (WIP) * working * some shape fix and use more quant primitive ops * Add rowwise test * make rowwise sharding work * compile still not working yet * fake tensor didn't pick up shape changes from transpose * backend='eager' * change transpose to non-inplace op * add error message * works now with torch nightly * remove print * ruff * Clean up * Fix device id --------- Co-authored-by: Ke Wen <kw2501@meta.com> * rename cuda mode -> gpu mode (#925) * Add workaround to recover the perf for quantized vit in torch.compile (#926) Add temporary workaround to recover the perf for quantized vit under torch.compile Summary: Recently we found a perf drop in quantized vit due to #898 (comment) This PR add a temp fix until we figure out the longer term fix. I think ideally we should figure out why the tensor subclass check failed in torch.compile (https://github.com/pytorch/pytorch/blob/e4d294221b140fdbb49a64f297bc60c9fcc2f80e/torch/nn/modules/activation.py#L1286) and fix that Test Plan: python tutorials/quantize_vit/run_vit_b_quant.py Reviewers: Subscribers: Tasks: Tags: * clean up device checks in float8 unit test files (#923) Summary: While working on rowwise scaling I noticed that some of the CUDA device capability checks we had in the test files did not make sense, cleaning this up. Test Plan: tests pass on my H100 CI, it should skip less tests now since CI only has CUDA capability 8, 9 Reviewers: Subscribers: Tasks: Tags: * [low-bit optim] Change 8-bit and FP8 optim block size from 2048 to 256 to match new bnb v0.44 (#927) * Float8 autoquant weight only (#866) * Fix failing FP6 benchmark (#931) * Remove two if statements in fp8 padding (#935) Reviewed By: vkuzo Differential Revision: D63051205 Pull Request resolved: #935 Approved by: https://github.com/vkuzo * [Distributed] Improve sharding example (#937) * [Distributed] Improve sharding example * Add comment * Add composable QAT quantizer (#938) Summary: This is a utility for users who wish to apply multiple QAT quantizers to their models. In the near future, we expect to add an embedding QAT quantizer that composes with the existing linear QAT quantizers. Test Plan: python test/quantization/test_qat.py -k test_composable_qat_quantizer * resolve conflict with latest main Differential Revision: D63048850 Pull Request resolved: #912 * Add torchchat quantizer Differential Revision: D62394341 Pull Request resolved: #897 * Add compile tests to test suite (#906) * Add compile tests to test suite Summary: This is a follow up PR addressing #839 (comment) We can add more compiler related tests in the future. Next * refactor a bit to use quantize_ API directly * use the test suite in existing API tests Test Plan: python torchao/testing/utils.py Reviewers: Subscribers: Tasks: Tags: * rename * add result check * Fix up CMakeLists and reorganize some code locations Differential Revision: D62711903 Pull Request resolved: #948 * [float8] all-reduce amax on dp mesh instead of global pg (#933) * [float8] all-reduce amax on dp mesh instead of global pg Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * liner Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * improve comments Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * move hp tensor inside if Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * int8 dynamic quant + bsr support (#821) This PR, adds in int8 dynamicquant + bsr support. Changes: * Use i8i8 -> bf16 matmul to maintain accuracy * Added a block sparse layout type to AffineQuantizedTensor + check/impl. * Cleaned up benchmark.py script and add a single line `benchmark.sh` file for acceleration numbers * Updated eval.py and added a single line `evaluate.sh` file for accuracy numbers * Lots of lint formatting and README updates * torch.compile now working and is correct * fixing some issues with our support for 70/405B models (#941) Summary: download and convert scripts needed to be updated alongside model.py config files Test Plan: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3.1-70B/model.pth Reviewers: Subscribers: Tasks: Tags: * Update INT8 mixed-precision training test to be less flaky (#950) * Add executorch parallel Differential Revision: D62711909 Pull Request resolved: #953 * test CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * better comment on why upcasting Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * control seed Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * move unit test to test_compile Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix typo Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * float64 upcasting after allreduce Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * use LinearMMConfig Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --------- Co-authored-by: andrewor14 <andrewor14@gmail.com> Co-authored-by: Vaishnavi Gupta <vaishnavi10367@gmail.com> Co-authored-by: Apurva Jain <apurvajain.kota@gmail.com> Co-authored-by: Jerry Zhang <jerryzh168@gmail.com> Co-authored-by: Ke Wen <kw2501@meta.com> Co-authored-by: Mark Saroufim <marksaroufim@meta.com> Co-authored-by: Vasiliy Kuznetsov <vkuzo@users.noreply.github.com> Co-authored-by: Thien Tran <gau.nernst@yahoo.com.sg> Co-authored-by: Tobias van der Werff <33268192+tobiasvanderwerff@users.noreply.github.com> Co-authored-by: Shuqi Yang <shuqiyang@meta.com> Co-authored-by: Scott Roy <161522778+metascroy@users.noreply.github.com> Co-authored-by: Jesse Cai <jessecai@meta.com> Co-authored-by: HDCharles <39544797+HDCharles@users.noreply.github.com>
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Differential Revision: D62394341 Pull Request resolved: pytorch#897
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…th torch.compile (pytorch#904) * [float8] improve eager numerics for dynamic scales * leave torch.linalg.vector_norm for another PR Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * cuda Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove _data and investigate Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove _data comment Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * upcast to float32 is enough Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * explain why float32 Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * _data parity Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * handle sm8.9 Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix transformer unit test Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * print if error Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * Add tutorial for trainable tensor subclass (pytorch#908) Summary: The new tutorial provides an example of how to implement a trainable tensor subclass that wraps quantized data. This extends the existing `MyDTypeTensor` with a few necessary steps to ensure proper gradient updates, namely: 1. Define a differentiable constructor 2. Define backward pass for ops of interest (e.g. torch.nn.functional.linear) 3. Handle special ops used by the optimizer (e.g. aten.add, aten.add_) Test Plan: python tutorials/developer_api_guide/my_trainable_tensor_subclass.py * Introducing 1-bit quantization for Llama in torchchat (pytorch#910) Differential Revision: D63052325 Pull Request resolved: pytorch#911 * Rename Floating point to fp8 (pytorch#909) * [float8] fix typo in bitwise_identical unit test (pytorch#918) Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * Adding example for quantized tensor + tensor parallelism (pytorch#785) * [WIP] Adding example for quantized tensor + tensor parallelism Summary: This PR adds an example of how quantized tensor subclass can work with DTensor: https://github.com/pytorch/pytorch/blob/main/torch/distributed/_tensor/README.md End goal is to rewrite https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/llama2.py with normal llama2 implementation and show case with DTensor + AffineQuantizedTensor + torch.compile we can get on par performance with the custom tensor parallel implementation Test Plan: torchrun --standalone --nnodes=1 --nproc-per-node=4 tutorials/developer_api_guide/tensor_parallel.py Reviewers: Subscribers: Tasks: Tags: * tensor parallel file * Use DTensor.from instead of distribute_tensor * implementing aten.slice.Tensor (WIP) * working * some shape fix and use more quant primitive ops * Add rowwise test * make rowwise sharding work * compile still not working yet * fake tensor didn't pick up shape changes from transpose * backend='eager' * change transpose to non-inplace op * add error message * works now with torch nightly * remove print * ruff * Clean up * Fix device id --------- Co-authored-by: Ke Wen <kw2501@meta.com> * rename cuda mode -> gpu mode (pytorch#925) * Add workaround to recover the perf for quantized vit in torch.compile (pytorch#926) Add temporary workaround to recover the perf for quantized vit under torch.compile Summary: Recently we found a perf drop in quantized vit due to pytorch#898 (comment) This PR add a temp fix until we figure out the longer term fix. I think ideally we should figure out why the tensor subclass check failed in torch.compile (https://github.com/pytorch/pytorch/blob/e4d294221b140fdbb49a64f297bc60c9fcc2f80e/torch/nn/modules/activation.py#L1286) and fix that Test Plan: python tutorials/quantize_vit/run_vit_b_quant.py Reviewers: Subscribers: Tasks: Tags: * clean up device checks in float8 unit test files (pytorch#923) Summary: While working on rowwise scaling I noticed that some of the CUDA device capability checks we had in the test files did not make sense, cleaning this up. Test Plan: tests pass on my H100 CI, it should skip less tests now since CI only has CUDA capability 8, 9 Reviewers: Subscribers: Tasks: Tags: * [low-bit optim] Change 8-bit and FP8 optim block size from 2048 to 256 to match new bnb v0.44 (pytorch#927) * Float8 autoquant weight only (pytorch#866) * Fix failing FP6 benchmark (pytorch#931) * Remove two if statements in fp8 padding (pytorch#935) Reviewed By: vkuzo Differential Revision: D63051205 Pull Request resolved: pytorch#935 Approved by: https://github.com/vkuzo * [Distributed] Improve sharding example (pytorch#937) * [Distributed] Improve sharding example * Add comment * Add composable QAT quantizer (pytorch#938) Summary: This is a utility for users who wish to apply multiple QAT quantizers to their models. In the near future, we expect to add an embedding QAT quantizer that composes with the existing linear QAT quantizers. Test Plan: python test/quantization/test_qat.py -k test_composable_qat_quantizer * resolve conflict with latest main Differential Revision: D63048850 Pull Request resolved: pytorch#912 * Add torchchat quantizer Differential Revision: D62394341 Pull Request resolved: pytorch#897 * Add compile tests to test suite (pytorch#906) * Add compile tests to test suite Summary: This is a follow up PR addressing pytorch#839 (comment) We can add more compiler related tests in the future. Next * refactor a bit to use quantize_ API directly * use the test suite in existing API tests Test Plan: python torchao/testing/utils.py Reviewers: Subscribers: Tasks: Tags: * rename * add result check * Fix up CMakeLists and reorganize some code locations Differential Revision: D62711903 Pull Request resolved: pytorch#948 * [float8] all-reduce amax on dp mesh instead of global pg (pytorch#933) * [float8] all-reduce amax on dp mesh instead of global pg Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * liner Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * improve comments Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * move hp tensor inside if Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * linter Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * int8 dynamic quant + bsr support (pytorch#821) This PR, adds in int8 dynamicquant + bsr support. Changes: * Use i8i8 -> bf16 matmul to maintain accuracy * Added a block sparse layout type to AffineQuantizedTensor + check/impl. * Cleaned up benchmark.py script and add a single line `benchmark.sh` file for acceleration numbers * Updated eval.py and added a single line `evaluate.sh` file for accuracy numbers * Lots of lint formatting and README updates * torch.compile now working and is correct * fixing some issues with our support for 70/405B models (pytorch#941) Summary: download and convert scripts needed to be updated alongside model.py config files Test Plan: python generate.py --checkpoint_path ../../../checkpoints/meta-llama/Meta-Llama-3.1-70B/model.pth Reviewers: Subscribers: Tasks: Tags: * Update INT8 mixed-precision training test to be less flaky (pytorch#950) * Add executorch parallel Differential Revision: D62711909 Pull Request resolved: pytorch#953 * test CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * better comment on why upcasting Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * control seed Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * move unit test to test_compile Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix typo Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * float64 upcasting after allreduce Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * use LinearMMConfig Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --------- Co-authored-by: andrewor14 <andrewor14@gmail.com> Co-authored-by: Vaishnavi Gupta <vaishnavi10367@gmail.com> Co-authored-by: Apurva Jain <apurvajain.kota@gmail.com> Co-authored-by: Jerry Zhang <jerryzh168@gmail.com> Co-authored-by: Ke Wen <kw2501@meta.com> Co-authored-by: Mark Saroufim <marksaroufim@meta.com> Co-authored-by: Vasiliy Kuznetsov <vkuzo@users.noreply.github.com> Co-authored-by: Thien Tran <gau.nernst@yahoo.com.sg> Co-authored-by: Tobias van der Werff <33268192+tobiasvanderwerff@users.noreply.github.com> Co-authored-by: Shuqi Yang <shuqiyang@meta.com> Co-authored-by: Scott Roy <161522778+metascroy@users.noreply.github.com> Co-authored-by: Jesse Cai <jessecai@meta.com> Co-authored-by: HDCharles <39544797+HDCharles@users.noreply.github.com>
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Summary:
This diff adds a quantizer for the new torchao kernels that is similar to the Int8DynActInt4WeightQuantizer quantizer in torchchat (imported from from torchao.quantization.quant_api). See the draft torchchat PR (pytorch/torchchat#1070) for how this can integrate with torchchat's quantization API.
I confirmed that models quantized with this are compatible with eager, compile, AOTI, and export to ExecuTorch in torchchat. They do not run on ExecuTorch because we still have not written an ExecuTorch kernel wrapper.
jerryzh168 this does not use the new subclass API, and this is something I'd like to discuss further with you. I'll set up a sync with you this week, but I wanted to have some API on the table to ground the discussion.
We do not currently have the required C++ methods implemented to support the new subclass API (e.g., we cannot unpack the packed weights from python; they are instead unpacked inline in the kernel). From a torchchat user's perspective, I do not think this is important, but I'd like to discuss further.
Differential Revision: D62394341