[float8] all-reduce amax on dp mesh instead of global pg #933
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Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/933
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 7365341 with merge base 26e790d ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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weifengpy
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Sep 24, 2024
| ), | ||
| gemm_input_role=GemmInputRole.WEIGHT, | ||
| reduce_amax=True, | ||
| device_mesh=dp_mesh |
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without the PR, the unit test will throw NCCL timeout error because rank 2 and 4 did not participate in amax all-reduce
weifengpy
commented
Sep 24, 2024
| @@ -36,6 +36,7 @@ def hp_tensor_to_float8_dynamic( | |||
| linear_mm_config: LinearMMConfig, | |||
| reduce_amax: bool = False, | |||
| gemm_input_role: GemmInputRole = GemmInputRole.INPUT, | |||
| device_mesh = None, | |||
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DeviceMesh only exists in newer pytorch (2.4+). I tried to annotate device_mesh with string "torch.distributed.DeviceMesh". but some CI run torch older pytorch 2.2 and complain about not being able to find DeviceMesh. I gave up on type hint. hopefully device_mesh itself is a strong indicator of DeviceMesh
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for now, we are intending to only test float8 code on the latest nightly to move fast and minimize maintenance load. Do you know which CI is giving you issues? We have disabled CI on older PT versions per-test-file using version checks.
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I belive it's the following one. My guess - the function signature is checked at import time. For torch==2.2.2, if one of the test imported float8_utils.py or float8_scaling_utils.py, it will throw error
[test (CUDA 2.2.2, linux.g5.12xlarge.nvidia.gpu, torch==2.2.2 "numpy<2" , cuda, 12.1) / linux-job](https://github.com/pytorch/ao/actions/runs/11023026757/job/30613550360?pr=933#logs)
vkuzo
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Sep 25, 2024
weifengpy
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Sep 26, 2024
* [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:
weifengpy
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Oct 1, 2024
…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 <[email protected]> * 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 <[email protected]> Co-authored-by: Vaishnavi Gupta <[email protected]> Co-authored-by: Apurva Jain <[email protected]> Co-authored-by: Jerry Zhang <[email protected]> Co-authored-by: Ke Wen <[email protected]> Co-authored-by: Mark Saroufim <[email protected]> Co-authored-by: Vasiliy Kuznetsov <[email protected]> Co-authored-by: Thien Tran <[email protected]> Co-authored-by: Tobias van der Werff <[email protected]> Co-authored-by: Shuqi Yang <[email protected]> Co-authored-by: Scott Roy <[email protected]> Co-authored-by: Jesse Cai <[email protected]> Co-authored-by: HDCharles <[email protected]>
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Oct 3, 2024
* [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:
melvinebenezer
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Oct 7, 2024
…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 <[email protected]> * 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 <[email protected]> Co-authored-by: Vaishnavi Gupta <[email protected]> Co-authored-by: Apurva Jain <[email protected]> Co-authored-by: Jerry Zhang <[email protected]> Co-authored-by: Ke Wen <[email protected]> Co-authored-by: Mark Saroufim <[email protected]> Co-authored-by: Vasiliy Kuznetsov <[email protected]> Co-authored-by: Thien Tran <[email protected]> Co-authored-by: Tobias van der Werff <[email protected]> Co-authored-by: Shuqi Yang <[email protected]> Co-authored-by: Scott Roy <[email protected]> Co-authored-by: Jesse Cai <[email protected]> Co-authored-by: HDCharles <[email protected]>
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this PR makes sure FSDP2 + pipeline parallel work. For amax all-reduce, we should do allreduce on dp mesh, instead of global pg. otherwise it causes NCCL hanging