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Remove test_quantizer.py and reuse one of its test in test_quantization.py (#27269)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27269 Remove `test_quantizer.py`, add and rewrite one of the tests in `test_quantizer` in `test_quantization.py` The conv test is removed for now since conv pattern is still broken, we'll add another test later ghstack-source-id: 92869823 Test Plan: python test/test_quantization.py Imported from OSS Differential Revision: D18182916 fbshipit-source-id: 325b5d8e877228d6a513e3ddf52c974479250d42
This commit is contained in:
Jerry Zhang
Facebook Github Bot
parent
dfe7b25eaf
commit
1c436ded44
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@ -110,7 +110,7 @@ test_python_nn() {
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}
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test_python_all_except_nn() {
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time python test/run_test.py --exclude nn --verbose --bring-to-front quantization quantized quantized_tensor quantized_nn_mods quantizer
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time python test/run_test.py --exclude nn --verbose --bring-to-front quantization quantized quantized_tensor quantized_nn_mods
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assert_git_not_dirty
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}
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@ -47,7 +47,6 @@ TESTS = [
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'quantized',
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'quantized_tensor',
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'quantized_nn_mods',
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'quantizer',
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'sparse',
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'torch',
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'type_info',
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@ -31,6 +31,7 @@ from common_quantization import AnnotatedTwoLayerLinearModel, AnnotatedNestedMod
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AnnotatedSubNestedModel, AnnotatedCustomConfigNestedModel
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from jit_utils import _tmp_donotuse_dont_inline_everything
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from jit_utils import get_forward
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from hypothesis import given
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from hypothesis import strategies as st
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@ -695,10 +696,49 @@ class GraphModePostTrainingQuantTest(QuantizationTestCase):
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[self.calib_data],
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inplace=False)
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result_eager = model_eager(self.calib_data[0][0])
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torch._C._jit_pass_quant_fusion(model_script._c._get_module('fc1')._get_method('forward').graph)
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result_script = model_script._c._get_method('forward')(self.calib_data[0][0])
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result_script = get_forward(model_script._c)(self.calib_data[0][0])
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self.assertEqual(result_eager, result_script)
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@unittest.skip("quantization for inlined linear is not working right now")
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def test_nested(self):
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# Eager mode
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eager_model = AnnotatedNestedModel()
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# default_per_channel_qconfig is not scriptable right now,
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# temporarily change to default_qconfig until default_per_channel_qconfig is fixed
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eager_model.sub2.fc1.qconfig = default_qconfig
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# Graph mode
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script_model = NestedModel()
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# Copy weights for eager_model
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script_model.sub1.fc.weight = torch.nn.Parameter(eager_model.sub1.fc.weight.detach())
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script_model.sub1.fc.bias = torch.nn.Parameter(eager_model.sub1.fc.bias.detach())
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script_model.sub2.fc1.weight = torch.nn.Parameter(eager_model.sub2.fc1.module.weight.detach())
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script_model.sub2.fc1.bias = torch.nn.Parameter(eager_model.sub2.fc1.module.bias.detach())
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script_model.sub2.fc2.weight = torch.nn.Parameter(eager_model.sub2.fc2.weight.detach())
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script_model.sub2.fc2.bias = torch.nn.Parameter(eager_model.sub2.fc2.bias.detach())
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script_model.fc3.weight = torch.nn.Parameter(eager_model.fc3.module.weight.detach())
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script_model.fc3.bias = torch.nn.Parameter(eager_model.fc3.module.bias.detach())
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print(eager_model(self.calib_data[0][0]))
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# Quantize eager module
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quantized_eager_model = quantize(eager_model, test_only_eval_fn, self.calib_data)
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qconfig_dict = {
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'sub2.fc1': default_qconfig,
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'fc3': default_qconfig
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}
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quantized_script_model = quantize_script(
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torch.jit.script(script_model),
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qconfig_dict,
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test_only_eval_fn,
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[self.calib_data],
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inplace=False)
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eager_result = quantized_eager_model(self.calib_data[0][0])
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print(get_forward(quantized_script_model._c._get_module('fc3')).graph)
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script_result = get_forward(quantized_script_model._c)(self.calib_data[0][0])
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print(eager_result, script_result)
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self.assertEqual(eager_result, script_result)
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class FunctionalModuleTest(QuantizationTestCase):
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# Histogram Observers are slow, so have no-deadline to ensure test doesn't time out
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@ -1,164 +0,0 @@
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import torch.jit
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from jit_utils import _tmp_donotuse_dont_inline_everything
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from torch._jit_internal import Optional
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import torch.nn as nn
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from common_utils import TestCase, run_tests
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from common_quantization import NestedModel, AnnotatedNestedModel
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from torch.quantization import QuantStub, DeQuantStub, \
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quantize, default_eval_fn, QConfig
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class Observer(torch.nn.Module):
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__annotations__ = {'scale' : Optional[torch.Tensor], 'zero_point': Optional[torch.Tensor]}
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def __init__(self):
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super(Observer, self).__init__()
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self.dtype = torch.quint8
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self.qscheme = torch.per_tensor_affine
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self.scale, self.zero_point = None, None
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def forward(self, x):
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self.scale = torch.tensor([2.0])
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self.zero_point = torch.tensor([3])
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return x
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@torch.jit.export
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def calculate_qparams(self):
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return self.scale, self.zero_point
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class WeightObserver(Observer):
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def __init__(self):
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super(WeightObserver, self).__init__()
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self.dtype = torch.qint8
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@unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
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" Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
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" with instruction set support avx2 or newer.")
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@unittest.skip("temporarily disable the test")
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class QuantizerTestCase(TestCase):
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@_tmp_donotuse_dont_inline_everything
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def test_default(self):
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class TestM(nn.Module):
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def __init__(self, qconfig):
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super(TestM, self).__init__()
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self.conv = nn.Conv2d(3, 1, 3).float()
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self.conv.weight.data.fill_(1.0)
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self.conv.bias.data.fill_(0.01)
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self.qconfig = qconfig
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self.quant = QuantStub()
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self.dequant = DeQuantStub()
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def forward(self, x):
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return self.dequant(self.conv(self.quant(x)))
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class TestScriptM(torch.jit.ScriptModule):
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def __init__(self):
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super(TestScriptM, self).__init__()
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self.conv = nn.Conv2d(3, 1, 3).float()
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self.conv.bias.data.fill_(0.01)
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@torch.jit.script_method
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def forward(self, x):
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y = self.conv(x)
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return y
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# Test Data
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data = [(torch.randn(10, 3, 10, 10, dtype=torch.float), 1)]
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# Eager mode
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fake_qconfig = QConfig(activation=Observer, weight=WeightObserver)
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eager_module = TestM(fake_qconfig)
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# Script mode
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script_module = TestScriptM()
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script_module.conv.weight = torch.nn.Parameter(eager_module.conv.weight.detach())
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quantized_eager_module = quantize(eager_module, default_eval_fn, data)
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def get_forward(m):
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return m._c._get_method('forward')
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# TODO: test jit.script as well
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ScriptedObserver = torch.jit.script(Observer())
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ScriptedWeightObserver = torch.jit.script(WeightObserver())
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qconfig_dict = {
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'':
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QConfig(
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activation=ScriptedObserver._c,
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weight=ScriptedWeightObserver._c)
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}
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torch._C._jit_pass_insert_observers(script_module._c,
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"forward",
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qconfig_dict)
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# Run ScriptM Model and Collect statistics
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get_forward(script_module)(data[0][0])
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# Insert quantize and dequantize calls
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script_module._c = torch._C._jit_pass_insert_quant_dequant(script_module._c, "forward")
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# Note that observer modules are not removed right now
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torch._C._jit_pass_quant_fusion(script_module._c._get_method('forward').graph)
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get_forward(script_module)(data[0][0])
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eager_result = quantized_eager_module(data[0][0])
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script_result = get_forward(script_module)(data[0][0])
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self.assertEqual(eager_result, script_result)
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@_tmp_donotuse_dont_inline_everything
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def test_qconfig_dict(self):
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data = [(torch.randn(10, 5, dtype=torch.float) * 20, 1)]
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# Eager mode
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qconfig = QConfig(activation=Observer, weight=WeightObserver)
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eager_module = AnnotatedNestedModel()
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eager_module.fc3.qconfig = qconfig
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eager_module.sub2.fc1.qconfig = qconfig
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# Assign weights
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eager_module.sub1.fc.weight.data.fill_(1.0)
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eager_module.sub2.fc1.module.weight.data.fill_(1.0)
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eager_module.sub2.fc2.weight.data.fill_(1.0)
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eager_module.fc3.module.weight.data.fill_(1.0)
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script_module = torch.jit.script(NestedModel())
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# Copy weights for eager_module
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script_module.sub1.fc.weight = eager_module.sub1.fc.weight
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script_module.sub2.fc1.weight = eager_module.sub2.fc1.module.weight
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script_module.sub2.fc2.weight = eager_module.sub2.fc2.weight
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script_module.fc3.weight = eager_module.fc3.module.weight
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# Quantize eager module
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quantized_eager_module = quantize(eager_module, default_eval_fn, data)
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def get_forward(m):
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return m._c._get_method('forward')
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# Quantize script_module
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torch._C._jit_pass_constant_propagation(get_forward(script_module).graph)
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ScriptedObserver = torch.jit.script(Observer())
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ScriptedWeightObserver = torch.jit.script(WeightObserver())
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scripted_qconfig = QConfig(
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activation=ScriptedObserver._c,
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weight=ScriptedWeightObserver._c)
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qconfig_dict = {
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'sub2.fc1': scripted_qconfig,
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'fc3': scripted_qconfig
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}
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torch._C._jit_pass_insert_observers(script_module._c,
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"forward",
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qconfig_dict)
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# Run script_module and Collect statistics
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get_forward(script_module)(data[0][0])
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# Insert quantize and dequantize calls
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script_module._c = torch._C._jit_pass_insert_quant_dequant(script_module._c, "forward")
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# Note that observer modules are not removed right now
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torch._C._jit_pass_quant_fusion(script_module._c._get_method('forward').graph)
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get_forward(script_module)(data[0][0])
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eager_result = quantized_eager_module(data[0][0])
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script_result = get_forward(script_module)(data[0][0])
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self.assertEqual(eager_result, script_result)
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if __name__ == '__main__':
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run_tests()
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