mirror of
https://github.com/saymrwulf/pytorch.git
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058645acb1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23003 torch.quantization.fuse_module and torch.nn._intrinsic convRelu and LinearRelu Fusion function to combine specific modules: (conv,bn) and (conv,bn,relu). In all cases, replace modules in place. The first module is replaced with the _intrinsic fused module and the remaining modules are replaced by nn.Identity. Support both training and eval. For training, the modules are "fused" with a sequential container. This is to allow for further module swaps for quantization aware training. Also add: torch.nn._intrinsic for convRelu and LinearRelu. TODO: Add tests for _intrinsic modules. Conv BN fusion code is based on DsKhudia's implementation Differential Revision: D16199720 fbshipit-source-id: 95fb9ffe72b361d280313b2ec57de2acd4f9dda2
374 lines
14 KiB
Python
374 lines
14 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import torch
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import torch.nn.quantized as nnq
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from torch.quantization import QConfig, \
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default_qconfig, default_qat_qconfig, default_observer, default_weight_observer, \
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quantize, prepare, convert, prepare_qat, quantize_qat, fuse_modules
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from common_utils import run_tests
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from common_quantization import QuantizationTestCase, SingleLayerLinearModel, \
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TwoLayerLinearModel, NestedModel, WrappedModel, ManualQuantModel, \
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ModForFusion, ManualLinearQATModel, ManualConvLinearQATModel, test_only_eval_fn, test_only_train_fn
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class PostTrainingQuantTest(QuantizationTestCase):
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def test_single_layer(self):
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r"""Quantize SingleLayerLinearModel which has one Linear module, make sure it is swapped
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to nnq.Linear which is the quantized version of the module
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"""
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model = SingleLayerLinearModel().eval()
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qconfig_dict = {
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'': default_qconfig
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}
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model = prepare(model, qconfig_dict)
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# Check if observers and quant/dequant nodes are inserted
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self.checkNoPrepModules(model)
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self.checkHasPrepModules(model.fc1)
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self.checkObservers(model)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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self.checkNoPrepModules(model)
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self.checkHasPrepModules(model.fc1)
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self.checkQuantizedLinear(model.fc1)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(SingleLayerLinearModel().eval(), test_only_eval_fn, self.calib_data, qconfig_dict)
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checkQuantized(model)
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def test_two_layers(self):
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r"""TwoLayerLinearModel has two Linear modules but we only quantize the second one
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`fc2`, and `fc1`is not quantized
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"""
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model = TwoLayerLinearModel().eval()
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qconfig_dict = {
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'fc2': default_qconfig
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}
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model = prepare(model, qconfig_dict)
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self.checkNoPrepModules(model)
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self.checkObservers(model)
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self.checkNoPrepModules(model.fc1)
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self.checkHasPrepModules(model.fc2)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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self.checkNoPrepModules(model)
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self.checkNoPrepModules(model.fc1)
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self.checkHasPrepModules(model.fc2)
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self.assertEqual(type(model.fc1), torch.nn.Linear)
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self.checkQuantizedLinear(model.fc2)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(TwoLayerLinearModel().eval(), test_only_eval_fn, self.calib_data, qconfig_dict)
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checkQuantized(model)
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def test_nested1(self):
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r"""Test quantization for nested model, top level 'fc3' and
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'fc1' of submodule 'sub2', 'sub2.fc2' is not quantized
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"""
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model = NestedModel().eval()
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qconfig_dict = {
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'fc3': default_qconfig,
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'sub2.fc1': default_qconfig
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}
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def checkPrepModules(model, before_calib=False):
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if before_calib:
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self.checkObservers(model)
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self.checkNoPrepModules(model)
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self.checkNoPrepModules(model.sub1)
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self.checkNoPrepModules(model.sub1.fc)
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self.checkNoPrepModules(model.sub1.relu)
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self.checkNoPrepModules(model.sub2)
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self.checkHasPrepModules(model.sub2.fc1)
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self.checkNoPrepModules(model.sub2.fc2)
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self.checkHasPrepModules(model.fc3)
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model = prepare(model, qconfig_dict)
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checkPrepModules(model, True)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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checkPrepModules(model)
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self.checkLinear(model.sub1.fc)
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self.checkQuantizedLinear(model.fc3)
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self.checkQuantizedLinear(model.sub2.fc1)
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self.checkLinear(model.sub2.fc2)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(NestedModel().eval(), test_only_eval_fn, self.calib_data, qconfig_dict)
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checkQuantized(model)
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def test_nested2(self):
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r"""Another test case for quantized, we will quantize all submodules
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of submodule sub2, this will include redundant quant/dequant, to
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remove them we need to manually call QuantWrapper or insert
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QuantStub/DeQuantStub, see `test_quant_dequant_wrapper` and
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`test_manual`
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"""
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model = NestedModel().eval()
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qconfig_dict = {
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'fc3': default_qconfig,
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'sub2': default_qconfig
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}
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model = prepare(model, qconfig_dict)
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def checkPrepModules(model, before_calib=False):
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if before_calib:
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self.checkObservers(model)
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self.checkNoPrepModules(model)
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self.checkNoPrepModules(model.sub1)
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self.checkNoPrepModules(model.sub1.fc)
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self.checkNoPrepModules(model.sub1.relu)
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self.checkNoPrepModules(model.sub2)
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self.checkHasPrepModules(model.sub2.fc1)
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self.checkHasPrepModules(model.sub2.fc2)
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self.checkHasPrepModules(model.fc3)
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checkPrepModules(model, True)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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checkPrepModules(model)
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self.checkLinear(model.sub1.fc)
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self.assertEqual(type(model.sub1.relu), torch.nn.ReLU)
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self.checkQuantizedLinear(model.sub2.fc1)
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self.checkQuantizedLinear(model.sub2.fc2)
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self.checkQuantizedLinear(model.fc3)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(NestedModel().eval(), test_only_eval_fn, self.calib_data, qconfig_dict)
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checkQuantized(model)
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def test_nested3(self):
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r"""More complicated nested test case with child qconfig overrides
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parent qconfig
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"""
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model = NestedModel().eval()
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custum_options = {
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'dtype': torch.quint8,
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'qscheme': torch.per_tensor_affine
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}
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custom_qconfig = QConfig(weight=default_weight_observer(),
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activation=default_observer(**custum_options))
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qconfig_dict = {
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'fc3': default_qconfig,
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'sub2': default_qconfig,
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'sub2.fc1': custom_qconfig
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}
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model = prepare(model, qconfig_dict)
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def checkPrepModules(model, before_calib=False):
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if before_calib:
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self.checkObservers(model)
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self.checkNoPrepModules(model)
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self.checkNoPrepModules(model.sub1)
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self.checkNoPrepModules(model.sub1.fc)
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self.checkNoPrepModules(model.sub1.relu)
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self.checkNoPrepModules(model.sub2)
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self.checkHasPrepModules(model.sub2.fc1)
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self.checkHasPrepModules(model.sub2.fc2)
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self.checkHasPrepModules(model.fc3)
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checkPrepModules(model, True)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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checkPrepModules(model)
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self.checkQuantizedLinear(model.sub2.fc1)
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self.checkQuantizedLinear(model.sub2.fc2)
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self.checkQuantizedLinear(model.fc3)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(NestedModel().eval(), test_only_eval_fn, self.calib_data, qconfig_dict)
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checkQuantized(model)
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def test_quant_wrapper(self):
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r"""User need to modify the original code with QuantWrapper,
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and call the quantization utility functions.
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"""
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model = WrappedModel().eval()
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# since we didn't provide qconfig_dict, the model is modified inplace
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# but we can do `model = prepare(model)` as well
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prepare(model)
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self.checkObservers(model)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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self.checkLinear(model.fc)
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self.checkQuantDequant(model.sub)
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self.assertEqual(type(model.sub.module.fc1), nnq.Linear)
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self.assertEqual(type(model.sub.module.fc2), nnq.Linear)
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self.assertEqual(type(model.sub.module.relu), nnq.ReLU)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(WrappedModel().eval(), test_only_eval_fn, self.calib_data, {})
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checkQuantized(model)
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def test_manual(self):
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r"""User inserts QuantStub and DeQuantStub in model code
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and call the quantization utility functions.
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"""
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model = ManualQuantModel().eval()
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# propagate the qconfig of parents to children, model is changed
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# inplace
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prepare(model)
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self.checkObservers(model)
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test_only_eval_fn(model, self.calib_data)
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convert(model)
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def checkQuantized(model):
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self.assertEqual(type(model.fc), nnq.Linear)
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test_only_eval_fn(model, self.calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(ManualQuantModel().eval(), test_only_eval_fn, self.calib_data)
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checkQuantized(model)
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class QuantizationAwareTrainingTest(QuantizationTestCase):
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def test_manual(self):
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model = ManualLinearQATModel()
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model.qconfig = default_qat_qconfig
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model = prepare_qat(model)
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self.checkObservers(model)
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test_only_train_fn(model, self.train_data)
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convert(model)
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def checkQuantized(model):
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self.assertEqual(type(model.fc1), nnq.Linear)
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self.assertEqual(type(model.fc2), nnq.Linear)
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test_only_eval_fn(model, self.calib_data)
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model = ManualLinearQATModel()
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model.qconfig = default_qat_qconfig
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model = quantize_qat(model, test_only_train_fn, self.train_data)
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checkQuantized(model)
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def test_eval_only_fake_quant(self):
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r"""Using FakeQuant in evaluation only mode,
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this is useful for estimating accuracy loss when we quantize the
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network
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"""
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model = ManualLinearQATModel()
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model.qconfig = default_qat_qconfig
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model = prepare_qat(model)
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self.checkObservers(model)
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model.eval()
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test_only_eval_fn(model, self.calib_data)
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def test_conv_linear(self):
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model = ManualConvLinearQATModel()
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model.qconfig = default_qat_qconfig
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model = prepare_qat(model)
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self.checkObservers(model)
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test_only_train_fn(model, self.img_data)
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convert(model)
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def checkQuantized(model):
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self.assertEqual(type(model.conv), nnq.Conv2d)
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self.assertEqual(type(model.fc1), nnq.Linear)
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self.assertEqual(type(model.fc2), nnq.Linear)
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test_only_eval_fn(model, self.img_data)
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checkQuantized(model)
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model = ManualConvLinearQATModel()
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model.qconfig = default_qat_qconfig
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model = quantize_qat(model, test_only_train_fn, self.img_data)
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checkQuantized(model)
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class FusionTest(QuantizationTestCase):
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def test_fuse_module_train(self):
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import torch.nn._intrinsic.modules.fused as torch_fused
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testMod = ModForFusion()
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testMod.train()
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fuse_modules(testMod, [['conv1', 'bn1', 'relu1'],
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['sub1.conv', 'sub1.bn']])
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self.assertEqual(type(testMod.conv1), torch_fused.ConvBnReLU2d,
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"Fused Conv + BN + Relu first layer")
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self.assertEqual(type(testMod.bn1), torch.nn.Identity,
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"Fused Conv + BN + Relu (skipped BN)")
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self.assertEqual(type(testMod.relu1), torch.nn.Identity,
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"Fused Conv + BN + Relu (skipped Relu)")
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self.assertEqual(type(testMod.sub1.conv), torch_fused.ConvBn2d,
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"Fused submodule Conv + BN")
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self.assertEqual(type(testMod.sub1.bn), torch.nn.Identity,
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"Fused submodule Conv + BN (skipped BN)")
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self.assertEqual(type(testMod.sub2.conv), torch.nn.Conv2d,
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"Non-fused submodule Conv")
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self.assertEqual(type(testMod.sub2.bn), torch.nn.BatchNorm2d,
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"Non-fused submodule BN")
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def test_fuse_module_eval(self):
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import torch.nn._intrinsic.modules.fused as torch_fused
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testMod = ModForFusion()
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testMod.eval()
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fuse_modules(testMod, [['conv1', 'bn1', 'relu1'] ,
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['sub1.conv', 'sub1.bn']])
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self.assertEqual(type(testMod.conv1), torch_fused.ConvReLU2d,
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"Fused Conv + BN + Relu first layer (BN is folded)")
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self.assertEqual(type(testMod.conv1[0]), torch.nn.Conv2d,
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"Fused Conv + BN + Relu (Conv + folded BN only)")
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self.assertEqual(type(testMod.conv1[1]), torch.nn.ReLU,
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"Fused Conv + BN + Relu second layer (Relu only)")
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self.assertEqual(type(testMod.bn1), torch.nn.Identity,
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"Fused Conv + BN + Relu second layer (Skipped BN)")
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self.assertEqual(type(testMod.relu1), torch.nn.Identity,
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"Fused Conv + BN + Relu second layer (Skipped Relu)")
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self.assertEqual(type(testMod.sub1.conv), torch.nn.Conv2d,
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"Fused submodule Conv + folded BN")
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self.assertEqual(type(testMod.sub1.bn), torch.nn.Identity,
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"Fused submodule (skipped BN)")
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self.assertEqual(type(testMod.sub2.conv), torch.nn.Conv2d,
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"Non-fused submodule Conv")
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self.assertEqual(type(testMod.sub2.bn), torch.nn.BatchNorm2d,
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"Non-fused submodule BN")
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if __name__ == '__main__':
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run_tests()
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