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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/73274 As noticed in https://discuss.pytorch.org/t/calibration-of-model-in-post-training-static-quantization-using-fx-api/143661/6 and related to https://github.com/pytorch/pytorch/issues/72698 when using fx quantizaiton, if an op like view was used in a model and the index parameters were passed in to the ops with a variable rather than hard coded, fx would mistakenly insert observers for them, leading to an error when the observer tried to do tensor only operations on a non-tensor. To fix this, an API was added to specify non tensor arguments for various ops to enable better dtype propagation. NON_TENSOR_ARG_DICT is a nested dict whose first key is a named tuple which contains matching parameters for ops with nontensor args, the inner dict's keys are dtypes and the values are a list of those arg indices that take use such dtypes. Alternatively, instead of a list, the inner dict value can also be a function that takes the node as an argument and returns the list of arg indices. Theoretically this api can support arbitrary functions but the current implmentation is limited to simpler functions given the particular issue this fixes seems to be rare. Note: although torch.unsqueeze and torch.transpose are listed in quantization_patterns.py, those ops appear to be untraceable by fx. I've included tests for their cases but fixing this issue is beyond the scope of this PR Test Plan: python test/test_quantization.py test_non_reference_size ... python test/test_quantization.py test_non_reference_<op> Imported from OSS Reviewed By: jerryzh168 Differential Revision: D34410122 fbshipit-source-id: fc09949ca8a2d6473876a4b6c214eb91e9a9dae2 (cherry picked from commit 3a1375d677b7c98d62b1f5c839645698c39b32b9)
28 lines
892 B
Python
28 lines
892 B
Python
# flake8: noqa: F401
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r"""
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This file is in the process of migration to `torch/ao/quantization`, and
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is kept here for compatibility while the migration process is ongoing.
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If you are adding a new entry/functionality, please, add it to the
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appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
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here.
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"""
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from torch.ao.quantization.fx.utils import (
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graph_pretty_str,
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get_per_tensor_qparams,
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quantize_node,
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get_custom_module_class_keys,
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get_linear_prepack_op_for_dtype,
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get_qconv_prepack_op,
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get_qconv_op,
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get_new_attr_name_with_prefix,
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graph_module_from_producer_nodes,
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assert_and_get_unique_device,
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create_getattr_from_value,
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create_qparam_nodes,
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all_node_args_have_no_tensors,
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node_return_type_is_int,
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get_non_observable_arg_indexes_and_types,
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is_get_tensor_info_node,
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maybe_get_next_module
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)
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