[quant][pt2] Fix custom dtype per channel weight in QAT #112612
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Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/112612
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 7bccb52 with merge base 27e31ab ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
jerryzh168
reviewed
Nov 1, 2023
jerryzh168
reviewed
Nov 1, 2023
| self.assertEqual(dq_dtype, torch.int32) | ||
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| def _get_conv_bn_getitem_nodes(model: torch.fx.GraphModule): |
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oh OK you copied this, then it's probably fine
jerryzh168
approved these changes
Nov 1, 2023
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
andrewor14
added a commit
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Nov 1, 2023
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar ghstack-source-id: 8998a5c Pull Request resolved: #112612
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
andrewor14
added a commit
that referenced
this pull request
Nov 2, 2023
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar ghstack-source-id: c56ddff Pull Request resolved: #112612
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Merge failedReason: 1 jobs have failed, first few of them are: trunk / macos-12-py3-arm64 / test (default, 1, 3, macos-m1-12) Details for Dev Infra teamRaised by workflow job |
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar [ghstack-poisoned]
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Skylion007
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that referenced
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Nov 14, 2023
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar Pull Request resolved: pytorch#112612 Approved by: https://github.com/jerryzh168
desai0007
pushed a commit
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that referenced
this pull request
Feb 26, 2025
Summary: Previously we only copied over q/dq args for the per tensor case. This was because the qparams for `quantize_per_tensor` are literals while the qparams for `quantize_per_channel` are `get_attr` nodes (tensors), which disappear from the original nodes in the graph after subgraph rewriting. However, this is problematic because, in the per channel case, not all q/dq args are tensors. In particular, the args after the qparams (axis, qmin, qmax, dtype) are all literals. For these literal args we simply used the hardcoded ones (0, -127, 127, torch.int8 respectively), even if the user explicitly specified to use a different weight dtype. This commit fixes this by copying over these literal args for the per channel case as well. Test Plan: python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype Reviewers: jerryzh168, kimishpatel Subscribers: jerryzh168, kimishpatel, supriyar ghstack-source-id: df92283 Pull Request resolved: pytorch/pytorch#112612
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Stack from ghstack (oldest at bottom):
Summary: Previously we only copied over q/dq args for the per
tensor case. This was because the qparams for
quantize_per_tensorare literals while the qparams for
quantize_per_channelareget_attrnodes (tensors), which disappear from the originalnodes in the graph after subgraph rewriting.
However, this is problematic because, in the per channel case,
not all q/dq args are tensors. In particular, the args after
the qparams (axis, qmin, qmax, dtype) are all literals. For
these literal args we simply used the hardcoded ones
(0, -127, 127, torch.int8 respectively), even if the user
explicitly specified to use a different weight dtype. This
commit fixes this by copying over these literal args for the
per channel case as well.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT.test_qat_per_channel_weight_custom_dtype
Reviewers: jerryzh168, kimishpatel
Subscribers: jerryzh168, kimishpatel, supriyar