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https://github.com/zebrajr/pytorch.git
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84c2c89e2c
Differential Revision: D16199356 Original commit changeset: 62aeaf47c12c fbshipit-source-id: d06a96b0a617ae38029ffb246173ec065454b666
268 lines
9.1 KiB
Python
268 lines
9.1 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 default_eval_fn, QConfig, default_qconfig, \
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default_observer, quantize, prepare, convert
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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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calib_data = [torch.rand(20, 5, dtype=torch.float) for _ in range(20)]
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class ModelQuantizeAPITest(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()
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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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(SingleLayerLinearModel(), default_eval_fn, 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()
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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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(TwoLayerLinearModel(), default_eval_fn, 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()
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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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(NestedModel(), default_eval_fn, 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()
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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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(NestedModel(), default_eval_fn, 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()
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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_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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(NestedModel(), default_eval_fn, 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()
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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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(WrappedModel(), default_eval_fn, 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()
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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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default_eval_fn(model, 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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default_eval_fn(model, calib_data)
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checkQuantized(model)
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# test one line API
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model = quantize(ManualQuantModel(), default_eval_fn, calib_data)
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checkQuantized(model)
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if __name__ == '__main__':
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run_tests()
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