# -*- coding: utf-8 -*- # Owner(s): ["oncall: quantization"] import sys import os import unittest from typing import Set # torch import torch import torch.nn as nn import torch.ao.nn.quantized as nnq import torch.ao.nn.quantized.dynamic as nnqd import torch.nn.intrinsic.quantized as nniq from torch.fx import GraphModule # Testing utils from torch.testing._internal.common_utils import TestCase, IS_AVX512_VNNI_SUPPORTED from torch.testing._internal.common_quantized import override_qengines, qengine_is_fbgemm from torch.testing._internal.common_quantization import skipIfNoFBGEMM from torch.testing._internal.quantization_torch_package_models import LinearReluFunctional from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver import torch.ao.quantization.quantize_fx as quantize_fx def remove_prefix(text, prefix): if text.startswith(prefix): return text[len(prefix):] return text def get_filenames(self, subname): # NB: we take __file__ from the module that defined the test # class, so we place the expect directory where the test script # lives, NOT where test/common_utils.py lives. module_id = self.__class__.__module__ munged_id = remove_prefix(self.id(), module_id + ".") test_file = os.path.realpath(sys.modules[module_id].__file__) base_name = os.path.join(os.path.dirname(test_file), "../serialized", munged_id) subname_output = "" if subname: base_name += "_" + subname subname_output = " ({})".format(subname) input_file = base_name + ".input.pt" state_dict_file = base_name + ".state_dict.pt" scripted_module_file = base_name + ".scripted.pt" traced_module_file = base_name + ".traced.pt" expected_file = base_name + ".expected.pt" package_file = base_name + ".package.pt" get_attr_targets_file = base_name + ".get_attr_targets.pt" return input_file, state_dict_file, scripted_module_file, \ traced_module_file, expected_file, package_file, get_attr_targets_file class TestSerialization(TestCase): """ Test backward compatiblity for serialization and numerics """ # Copy and modified from TestCase.assertExpected def _test_op(self, qmodule, subname=None, input_size=None, input_quantized=True, generate=False, prec=None, new_zipfile_serialization=False): r""" Test quantized modules serialized previously can be loaded with current code, make sure we don't break backward compatibility for the serialization of quantized modules """ input_file, state_dict_file, scripted_module_file, traced_module_file, \ expected_file, _package_file, _get_attr_targets_file = \ get_filenames(self, subname) # only generate once. if generate and qengine_is_fbgemm(): input_tensor = torch.rand(*input_size).float() if input_quantized: input_tensor = torch.quantize_per_tensor(input_tensor, 0.5, 2, torch.quint8) torch.save(input_tensor, input_file) # Temporary fix to use _use_new_zipfile_serialization until #38379 lands. torch.save(qmodule.state_dict(), state_dict_file, _use_new_zipfile_serialization=new_zipfile_serialization) torch.jit.save(torch.jit.script(qmodule), scripted_module_file) torch.jit.save(torch.jit.trace(qmodule, input_tensor), traced_module_file) torch.save(qmodule(input_tensor), expected_file) input_tensor = torch.load(input_file) qmodule.load_state_dict(torch.load(state_dict_file)) qmodule_scripted = torch.jit.load(scripted_module_file) qmodule_traced = torch.jit.load(traced_module_file) expected = torch.load(expected_file) self.assertEqual(qmodule(input_tensor), expected, atol=prec) self.assertEqual(qmodule_scripted(input_tensor), expected, atol=prec) self.assertEqual(qmodule_traced(input_tensor), expected, atol=prec) def _test_op_graph(self, qmodule, subname=None, input_size=None, input_quantized=True, generate=False, prec=None, new_zipfile_serialization=False): r""" Input: a floating point module If generate == True, traces and scripts the module and quantizes the results with PTQ, and saves the results. If generate == False, traces and scripts the module and quantizes the results with PTQ, and compares to saved results. """ input_file, state_dict_file, scripted_module_file, traced_module_file, \ expected_file, _package_file, _get_attr_targets_file = \ get_filenames(self, subname) # only generate once. if generate and qengine_is_fbgemm(): input_tensor = torch.rand(*input_size).float() torch.save(input_tensor, input_file) # convert to TorchScript scripted = torch.jit.script(qmodule) traced = torch.jit.trace(qmodule, input_tensor) # quantize def _eval_fn(model, data): model(data) qconfig_dict = {'': torch.ao.quantization.default_qconfig} scripted_q = torch.ao.quantization.quantize_jit( scripted, qconfig_dict, _eval_fn, [input_tensor]) traced_q = torch.ao.quantization.quantize_jit( traced, qconfig_dict, _eval_fn, [input_tensor]) torch.jit.save(scripted_q, scripted_module_file) torch.jit.save(traced_q, traced_module_file) torch.save(scripted_q(input_tensor), expected_file) input_tensor = torch.load(input_file) qmodule_scripted = torch.jit.load(scripted_module_file) qmodule_traced = torch.jit.load(traced_module_file) expected = torch.load(expected_file) self.assertEqual(qmodule_scripted(input_tensor), expected, atol=prec) self.assertEqual(qmodule_traced(input_tensor), expected, atol=prec) def _test_obs(self, obs, input_size, subname=None, generate=False, check_numerics=True): """ Test observer code can be loaded from state_dict. """ input_file, state_dict_file, _, traced_module_file, expected_file, \ _package_file, _get_attr_targets_file = get_filenames(self, None) if generate: input_tensor = torch.rand(*input_size).float() torch.save(input_tensor, input_file) torch.save(obs(input_tensor), expected_file) torch.save(obs.state_dict(), state_dict_file) input_tensor = torch.load(input_file) obs.load_state_dict(torch.load(state_dict_file)) expected = torch.load(expected_file) if check_numerics: self.assertEqual(obs(input_tensor), expected) def _test_package(self, fp32_module, input_size, generate=False): """ Verifies that files created in the past with torch.package work on today's FX graph mode quantization transforms. """ input_file, state_dict_file, _scripted_module_file, _traced_module_file, \ expected_file, package_file, get_attr_targets_file = \ get_filenames(self, None) package_name = 'test' resource_name_model = 'test.pkl' def _do_quant_transforms( m: torch.nn.Module, input_tensor: torch.Tensor, ) -> torch.nn.Module: example_inputs = (input_tensor,) # do the quantizaton transforms and save result qconfig = torch.quantization.get_default_qconfig('fbgemm') mp = quantize_fx.prepare_fx(m, {'': qconfig}, example_inputs=example_inputs) mp(input_tensor) mq = quantize_fx.convert_fx(mp) return mq def _get_get_attr_target_strings(m: GraphModule) -> Set[str]: results = set() for node in m.graph.nodes: if node.op == 'get_attr': results.add(node.target) return results if generate and qengine_is_fbgemm(): input_tensor = torch.randn(*input_size) torch.save(input_tensor, input_file) # save the model with torch.package with torch.package.PackageExporter(package_file) as exp: exp.intern('torch.testing._internal.quantization_torch_package_models') exp.save_pickle(package_name, resource_name_model, fp32_module) # do the quantization transforms and save the result mq = _do_quant_transforms(fp32_module, input_tensor) get_attrs = _get_get_attr_target_strings(mq) torch.save(get_attrs, get_attr_targets_file) q_result = mq(input_tensor) torch.save(q_result, expected_file) # load input tensor input_tensor = torch.load(input_file) expected_output_tensor = torch.load(expected_file) expected_get_attrs = torch.load(get_attr_targets_file) # load model from package and verify output and get_attr targets match imp = torch.package.PackageImporter(package_file) m = imp.load_pickle(package_name, resource_name_model) mq = _do_quant_transforms(m, input_tensor) get_attrs = _get_get_attr_target_strings(mq) self.assertTrue( get_attrs == expected_get_attrs, f'get_attrs: expected {expected_get_attrs}, got {get_attrs}') output_tensor = mq(input_tensor) self.assertTrue(torch.allclose(output_tensor, expected_output_tensor)) @override_qengines def test_linear(self): module = nnq.Linear(3, 1, bias_=True, dtype=torch.qint8) self._test_op(module, input_size=[1, 3], generate=False) @override_qengines def test_linear_relu(self): module = nniq.LinearReLU(3, 1, bias=True, dtype=torch.qint8) self._test_op(module, input_size=[1, 3], generate=False) @override_qengines def test_linear_dynamic(self): module_qint8 = nnqd.Linear(3, 1, bias_=True, dtype=torch.qint8) self._test_op(module_qint8, "qint8", input_size=[1, 3], input_quantized=False, generate=False) if qengine_is_fbgemm(): module_float16 = nnqd.Linear(3, 1, bias_=True, dtype=torch.float16) self._test_op(module_float16, "float16", input_size=[1, 3], input_quantized=False, generate=False) @override_qengines def test_conv2d(self): module = nnq.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros") self._test_op(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_nobias(self): module = nnq.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=False, padding_mode="zeros") self._test_op(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_graph(self): module = nn.Sequential( torch.ao.quantization.QuantStub(), nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros"), ) self._test_op_graph(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_nobias_graph(self): module = nn.Sequential( torch.ao.quantization.QuantStub(), nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=False, padding_mode="zeros"), ) self._test_op_graph(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_graph_v2(self): # tests the same thing as test_conv2d_graph, but for version 2 of # ConvPackedParams{n}d module = nn.Sequential( torch.ao.quantization.QuantStub(), nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros"), ) self._test_op_graph(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_nobias_graph_v2(self): # tests the same thing as test_conv2d_nobias_graph, but for version 2 of # ConvPackedParams{n}d module = nn.Sequential( torch.ao.quantization.QuantStub(), nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=False, padding_mode="zeros"), ) self._test_op_graph(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_graph_v3(self): # tests the same thing as test_conv2d_graph, but for version 3 of # ConvPackedParams{n}d module = nn.Sequential( torch.ao.quantization.QuantStub(), nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros"), ) self._test_op_graph(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_nobias_graph_v3(self): # tests the same thing as test_conv2d_nobias_graph, but for version 3 of # ConvPackedParams{n}d module = nn.Sequential( torch.ao.quantization.QuantStub(), nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=False, padding_mode="zeros"), ) self._test_op_graph(module, input_size=[1, 3, 6, 6], generate=False) @override_qengines def test_conv2d_relu(self): module = nniq.ConvReLU2d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros") self._test_op(module, input_size=[1, 3, 6, 6], generate=False) # TODO: graph mode quantized conv2d module @override_qengines def test_conv3d(self): if qengine_is_fbgemm(): module = nnq.Conv3d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros") self._test_op(module, input_size=[1, 3, 6, 6, 6], generate=False) # TODO: graph mode quantized conv3d module @override_qengines def test_conv3d_relu(self): if qengine_is_fbgemm(): module = nniq.ConvReLU3d(3, 3, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode="zeros") self._test_op(module, input_size=[1, 3, 6, 6, 6], generate=False) # TODO: graph mode quantized conv3d module @override_qengines @unittest.skipIf(IS_AVX512_VNNI_SUPPORTED, "This test fails on machines with AVX512_VNNI support. Ref: GH Issue 59098") def test_lstm(self): class LSTMModule(torch.nn.Module): def __init__(self): super().__init__() self.lstm = nnqd.LSTM(input_size=3, hidden_size=7, num_layers=1).to(dtype=torch.float) def forward(self, x): x = self.lstm(x) return x if qengine_is_fbgemm(): mod = LSTMModule() self._test_op(mod, input_size=[4, 4, 3], input_quantized=False, generate=False, new_zipfile_serialization=True) def test_per_channel_observer(self): obs = PerChannelMinMaxObserver() self._test_obs(obs, input_size=[5, 5], generate=False) def test_per_tensor_observer(self): obs = MinMaxObserver() self._test_obs(obs, input_size=[5, 5], generate=False) def test_default_qat_qconfig(self): class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.linear = nn.Linear(5, 5) self.relu = nn.ReLU() def forward(self, x): x = self.linear(x) x = self.relu(x) return x model = Model() model.linear.weight = torch.nn.Parameter(torch.randn(5, 5)) model.qconfig = torch.ao.quantization.get_default_qat_qconfig("fbgemm") ref_model = torch.ao.quantization.QuantWrapper(model) ref_model = torch.ao.quantization.prepare_qat(ref_model) self._test_obs(ref_model, input_size=[5, 5], generate=False, check_numerics=False) @skipIfNoFBGEMM def test_linear_relu_package_quantization_transforms(self): m = LinearReluFunctional(4).eval() self._test_package(m, input_size=(1, 1, 4, 4), generate=False)