[DO NOT REVIEW] Check dequantized q_proj value #1756
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1756
Note: Links to docs will display an error until the docs builds have been completed. ❌ 13 New FailuresAs of commit a1cd570 with merge base 878ec7a ( NEW FAILURES - The following jobs have failed:
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Summary: Perform quantization on the weights expressed in their original dtype (from the checkpoint) by performing source transformations before dtype cast. Previously the model was being converted to the `dtype_override` arg's dtype and then quantized. This eliminates supposedly eliminates quantization noise. Note - no need to worry about https://github.com/pytorch/ao/blob/main/torchao/quantization/GPTQ.py#L1168, precision is passed in with the checkpoint dtype ### Comparison of arbitrary q_proj tensor from sample Llama checkpoint: Before: ``` Mismatched elements: 3260378 / 4194304 (77.7%) Greatest absolute difference: 0.08802086114883423 at index (1129, 604) (up to 1e-05 allowed) Greatest relative difference: 1.0 at index (0, 1350) (up to 1.3e-06 allowed) Signal-to-noise: 32.8974 dB ``` After: no difference Test Plan: ### Manual testing ``` python -m examples.models.llama.export_llama \ -v -c xl_consolidated/consolidated_renamed.pth \ -p xl_consolidated/et_params.json -kv -d fp32 \ -qmode 8da4w --group_size 32 -X \ --use_sdpa_with_kv_cache \ --output_name quantized_baseline.pte \ --max_context_length 4096 -E 4,32 ``` With the following inserted after the quantization: ``` edge_manager.model( torch.tensor([[2, 3, 4]], dtype=torch.long), {"input_pos": torch.tensor([0], dtype=torch.long)}, ) ``` And the following modifications to GPTQ.py in torchao: pytorch/ao#1756 for testing. ### Automated testing + existing CI tests ### Regression testing TBD Differential Revision: D70184325 Pulled By: jackzhxng
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Mar 19, 2025
Summary: Perform quantization on the weights expressed in their original dtype (from the checkpoint) by performing source transformations before dtype cast. Previously the model was being converted to the `dtype_override` arg's dtype and then quantized. This eliminates supposedly eliminates quantization noise. Note - no need to worry about https://github.com/pytorch/ao/blob/main/torchao/quantization/GPTQ.py#L1168, precision is passed in with the checkpoint dtype ### Comparison of arbitrary q_proj tensor from sample Llama checkpoint: Before: ``` Mismatched elements: 3260378 / 4194304 (77.7%) Greatest absolute difference: 0.08802086114883423 at index (1129, 604) (up to 1e-05 allowed) Greatest relative difference: 1.0 at index (0, 1350) (up to 1.3e-06 allowed) Signal-to-noise: 32.8974 dB ``` After: no difference Test Plan: ### Manual testing ``` python -m examples.models.llama.export_llama \ -v -c xl_consolidated/consolidated_renamed.pth \ -p xl_consolidated/et_params.json -kv -d fp32 \ -qmode 8da4w --group_size 32 -X \ --use_sdpa_with_kv_cache \ --output_name quantized_baseline.pte \ --max_context_length 4096 -E 4,32 ``` With the following inserted after the quantization: ``` edge_manager.model( torch.tensor([[2, 3, 4]], dtype=torch.long), {"input_pos": torch.tensor([0], dtype=torch.long)}, ) ``` And the following modifications to GPTQ.py in torchao: pytorch/ao#1756 for testing. ### Automated testing + existing CI tests ### Regression testing TBD Differential Revision: D70184325 Pulled By: jackzhxng
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Mar 21, 2025
Summary: Perform quantization on the weights expressed in their original dtype (from the checkpoint) by passing in the checkpoint dtype to the quantization source transformation and modifying the computation dtype (the result dtype of the dequant, the dtype that the ops are actually computed in) to the dtype override. We must do it this way since the checkpoint and computation dtype are coupled into a single `precision` parameter in the torchao api, and that is something that we cannot change. Note - no need to worry about https://github.com/pytorch/ao/blob/main/torchao/quantization/GPTQ.py#L1168, precision is passed in with the checkpoint dtype ### Comparison of arbitrary q_proj tensor from sample Llama checkpoint: Before: ``` Mismatched elements: 3260378 / 4194304 (77.7%) Greatest absolute difference: 0.08802086114883423 at index (1129, 604) (up to 1e-05 allowed) Greatest relative difference: 1.0 at index (0, 1350) (up to 1.3e-06 allowed) Signal-to-noise: 32.8974 dB ``` After: no difference Test Plan: ### Manual testing ``` python -m examples.models.llama.export_llama \ -v -c xl_consolidated/consolidated_renamed.pth \ -p xl_consolidated/et_params.json -kv -d fp32 \ -qmode 8da4w --group_size 32 -X \ --use_sdpa_with_kv_cache \ --output_name quantized_baseline.pte \ --max_context_length 4096 -E 4,32 ``` With the following inserted after the quantization: ``` edge_manager.model( torch.tensor([[2, 3, 4]], dtype=torch.long), {"input_pos": torch.tensor([0], dtype=torch.long)}, ) ``` And the following modifications to GPTQ.py in torchao: pytorch/ao#1756 for testing. ### Automated testing + existing CI tests ### Regression testing TBD Reviewed By: kimishpatel Differential Revision: D70184325 Pulled By: jackzhxng
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Mar 22, 2025
Summary: Perform quantization on the weights expressed in their original dtype (from the checkpoint) by passing in the checkpoint dtype to the quantization source transformation and modifying the computation dtype (the result dtype of the dequant, the dtype that the ops are actually computed in) to the dtype override. We must do it this way since the checkpoint and computation dtype are coupled into a single `precision` parameter in the torchao api, and that is something that we cannot change. Note - no need to worry about https://github.com/pytorch/ao/blob/main/torchao/quantization/GPTQ.py#L1168, precision is passed in with the checkpoint dtype ### Comparison of arbitrary q_proj tensor from sample Llama checkpoint: Before: ``` Mismatched elements: 3260378 / 4194304 (77.7%) Greatest absolute difference: 0.08802086114883423 at index (1129, 604) (up to 1e-05 allowed) Greatest relative difference: 1.0 at index (0, 1350) (up to 1.3e-06 allowed) Signal-to-noise: 32.8974 dB ``` After: no difference Test Plan: ### Manual testing ``` python -m examples.models.llama.export_llama \ -v -c xl_consolidated/consolidated_renamed.pth \ -p xl_consolidated/et_params.json -kv -d fp32 \ -qmode 8da4w --group_size 32 -X \ --use_sdpa_with_kv_cache \ --output_name quantized_baseline.pte \ --max_context_length 4096 -E 4,32 ``` With the following inserted after the quantization: ``` edge_manager.model( torch.tensor([[2, 3, 4]], dtype=torch.long), {"input_pos": torch.tensor([0], dtype=torch.long)}, ) ``` And the following modifications to GPTQ.py in torchao: pytorch/ao#1756 for testing. ### Automated testing + existing CI tests ### Regression testing TBD Reviewed By: kimishpatel Differential Revision: D70184325 Pulled By: jackzhxng
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