| import torch |
| import torch.nn as nn |
| import torch.nn.intrinsic as nni |
| import torch.nn.intrinsic.quantized as nnq_fused |
| import torch.nn.quantized as nnq |
| import torch.nn.quantized.dynamic as nnqd |
| import torch.quantization |
| |
| from torch.testing._internal.common_quantization import ( |
| QuantizationTestCase, |
| prepare_dynamic, |
| _make_conv_test_input, |
| skipIfNoFBGEMM, |
| ) |
| from torch.testing._internal.common_quantized import ( |
| _calculate_dynamic_qparams, |
| override_quantized_engine, |
| override_qengines, |
| ) |
| from hypothesis import assume, given |
| from hypothesis import strategies as st |
| import torch.testing._internal.hypothesis_utils as hu |
| hu.assert_deadline_disabled() |
| |
| import io |
| import numpy as np |
| |
| ''' |
| Note that tests in this file are just API test, to make sure we wrapped the |
| quantized operator implementations correctly in the user facing APIs, these are |
| not correctness test for the underlying quantized operators. For correctness |
| test please see `caffe2/test/test_quantized_op.py`. |
| ''' |
| |
| class TestStaticQuantizedModule(QuantizationTestCase): |
| def test_relu(self): |
| relu_module = nnq.ReLU() |
| relu6_module = nnq.ReLU6() |
| |
| x = torch.arange(-10, 10, dtype=torch.float) |
| y_ref = torch.relu(x) |
| y6_ref = torch.nn.modules.ReLU6()(x) |
| |
| qx = torch.quantize_per_tensor(x, 1.0, 0, dtype=torch.qint32) |
| qy = relu_module(qx) |
| qy6 = relu6_module(qx) |
| |
| self.assertEqual(y_ref, qy.dequantize(), |
| message="ReLU module API failed") |
| self.assertEqual(y6_ref, qy6.dequantize(), |
| message="ReLU6 module API failed") |
| |
| |
| @given( |
| batch_size=st.integers(1, 5), |
| in_features=st.integers(16, 32), |
| out_features=st.integers(4, 8), |
| use_bias=st.booleans(), |
| use_fused=st.booleans(), |
| per_channel=st.booleans() |
| ) |
| @override_qengines |
| def test_linear_api(self, batch_size, in_features, out_features, use_bias, use_fused, per_channel): |
| """test API functionality for nn.quantized.linear and nn.intrinsic.quantized.linear_relu""" |
| if torch.backends.quantized.engine == 'qnnpack': |
| per_channel = False |
| W = torch.rand(out_features, in_features).float() |
| if per_channel: |
| scale_tensor = torch.ones(out_features, dtype=torch.double) |
| zero_point_tensor = torch.zeros(out_features, dtype=torch.long) |
| for i in range(len(scale_tensor)): |
| scale_tensor[i] = (i + 1.0) / 255.0 |
| W_q = torch.quantize_per_channel(W, scales=scale_tensor, |
| zero_points=zero_point_tensor, |
| axis=0, dtype=torch.qint8) |
| else: |
| W_q = torch.quantize_per_tensor(W, 0.1, 4, torch.qint8) |
| |
| X = torch.rand(batch_size, in_features).float() |
| X_q = torch.quantize_per_tensor(X, 0.2, 10, torch.quint8) |
| B = torch.rand(out_features).float() if use_bias else None |
| scale = 0.5 |
| zero_point = 3 |
| if use_fused: |
| qlinear = nnq_fused.LinearReLU(in_features, out_features) |
| else: |
| qlinear = nnq.Linear(in_features, out_features) |
| |
| # Run module with default-initialized parameters. |
| # This tests that the constructor is correct. |
| qlinear(X_q) |
| |
| qlinear.set_weight_bias(W_q, B) |
| # Simple round-trip test to ensure weight()/set_weight() API |
| self.assertEqual(qlinear.weight(), W_q, atol=1e-5) |
| W_pack = qlinear._packed_params._packed_params |
| |
| qlinear.scale = float(scale) |
| qlinear.zero_point = int(zero_point) |
| Z_q = qlinear(X_q) |
| # Check if the module implementation matches calling the |
| # ops directly |
| if use_fused: |
| Z_ref = torch.ops.quantized.linear_relu(X_q, W_pack, scale, zero_point) |
| |
| self.assertTrue('QuantizedLinearReLU' in str(qlinear)) |
| else: |
| Z_ref = torch.ops.quantized.linear(X_q, W_pack, scale, zero_point) |
| |
| self.assertTrue('QuantizedLinear' in str(qlinear)) |
| self.assertEqual(Z_ref, Z_q) |
| |
| # Test serialization of quantized Linear Module using state_dict |
| model_dict = qlinear.state_dict() |
| b = io.BytesIO() |
| torch.save(model_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| for key in model_dict: |
| if isinstance(model_dict[key], torch._C.ScriptObject): |
| assert isinstance(loaded_dict[key], torch._C.ScriptObject) |
| w_model, b_model = torch.ops.quantized.linear_unpack(model_dict[key]) |
| w_loaded, b_loaded = torch.ops.quantized.linear_unpack(loaded_dict[key]) |
| self.assertEqual(w_model, w_loaded) |
| self.assertEqual(b_model, b_loaded) |
| else: |
| self.assertEqual(model_dict[key], loaded_dict[key]) |
| if use_fused: |
| loaded_qlinear = nnq_fused.LinearReLU(in_features, out_features) |
| else: |
| loaded_qlinear = nnq.Linear(in_features, out_features) |
| loaded_qlinear.load_state_dict(loaded_dict) |
| |
| linear_unpack = torch.ops.quantized.linear_unpack |
| self.assertEqual(linear_unpack(qlinear._packed_params._packed_params), |
| linear_unpack(loaded_qlinear._packed_params._packed_params)) |
| if use_bias: |
| self.assertEqual(qlinear.bias(), loaded_qlinear.bias()) |
| self.assertEqual(qlinear.scale, loaded_qlinear.scale) |
| self.assertEqual(qlinear.zero_point, loaded_qlinear.zero_point) |
| self.assertTrue(dir(qlinear) == dir(loaded_qlinear)) |
| self.assertTrue(hasattr(qlinear, '_packed_params')) |
| self.assertTrue(hasattr(loaded_qlinear, '_packed_params')) |
| self.assertTrue(hasattr(qlinear, '_weight_bias')) |
| self.assertTrue(hasattr(loaded_qlinear, '_weight_bias')) |
| self.assertEqual(qlinear._weight_bias(), loaded_qlinear._weight_bias()) |
| self.assertEqual(qlinear._weight_bias(), torch.ops.quantized.linear_unpack(qlinear._packed_params._packed_params)) |
| Z_q2 = loaded_qlinear(X_q) |
| self.assertEqual(Z_q, Z_q2) |
| |
| # The below check is meant to ensure that `torch.save` and `torch.load` |
| # serialization works, however it is currently broken by the following: |
| # https://github.com/pytorch/pytorch/issues/24045 |
| # |
| # Instead, we currently check that the proper exception is thrown on save. |
| # <start code> |
| # b = io.BytesIO() |
| # torch.save(qlinear, b) |
| # b.seek(0) |
| # loaded = torch.load(b) |
| # self.assertEqual(qlinear.weight(), loaded.weight()) |
| # self.assertEqual(qlinear.scale, loaded.scale) |
| # self.assertEqual(qlinear.zero_point, loaded.zero_point) |
| # <end code> |
| # |
| # Currently disabled after TorchBind PR |
| # with self.assertRaisesRegex(RuntimeError, r'torch.save\(\) is not currently supported'): |
| # b = io.BytesIO() |
| # torch.save(qlinear, b) |
| |
| # Test JIT |
| self.checkScriptable(qlinear, list(zip([X_q], [Z_ref])), check_save_load=True) |
| |
| # Test from_float. |
| float_linear = torch.nn.Linear(in_features, out_features).float() |
| float_linear.qconfig = torch.quantization.default_qconfig |
| torch.quantization.prepare(float_linear, inplace=True) |
| float_linear(X.float()) |
| # Sequential allows swapping using "convert". |
| quantized_float_linear = torch.nn.Sequential(float_linear) |
| quantized_float_linear = torch.quantization.convert(quantized_float_linear, inplace=True) |
| |
| # Smoke test to make sure the module actually runs |
| quantized_float_linear(X_q) |
| |
| # Smoke test extra_repr |
| self.assertTrue('QuantizedLinear' in str(quantized_float_linear)) |
| |
| def test_quant_dequant_api(self): |
| r = torch.tensor([[1., -1.], [1., -1.]], dtype=torch.float) |
| scale, zero_point, dtype = 1.0, 2, torch.qint8 |
| # testing Quantize API |
| qr = torch.quantize_per_tensor(r, scale, zero_point, dtype) |
| quant_m = nnq.Quantize(scale, zero_point, dtype) |
| qr2 = quant_m(r) |
| self.assertEqual(qr, qr2) |
| # testing Dequantize API |
| rqr = qr.dequantize() |
| dequant_m = nnq.DeQuantize() |
| rqr2 = dequant_m(qr2) |
| self.assertEqual(rqr, rqr2) |
| |
| def _test_conv_api_impl( |
| self, module_name, qconv_module, conv_module, batch_size, |
| in_channels_per_group, input_feature_map_size, out_channels_per_group, |
| groups, kernel_size, stride, padding, dilation, X_scale, X_zero_point, |
| W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, use_fused, |
| use_channelwise, |
| ): |
| for i in range(len(kernel_size)): |
| assume(input_feature_map_size[i] + 2 * padding[i] |
| >= dilation[i] * (kernel_size[i] - 1) + 1) |
| |
| in_channels = in_channels_per_group * groups |
| out_channels = out_channels_per_group * groups |
| (X, X_q, W, W_q, b) = _make_conv_test_input( |
| batch_size, in_channels_per_group, input_feature_map_size, |
| out_channels_per_group, groups, kernel_size, X_scale, X_zero_point, |
| W_scale, W_zero_point, use_bias, use_channelwise) |
| |
| qconv_module.set_weight_bias(W_q, b) |
| qconv_module.scale = Y_scale |
| qconv_module.zero_point = Y_zero_point |
| |
| if use_fused: |
| conv_module[0].weight.data = W |
| if use_bias: |
| conv_module[0].bias.data = b |
| else: |
| conv_module.weight.data = W |
| if use_bias: |
| conv_module.bias.data = b |
| |
| # Test members |
| self.assertTrue(module_name in str(qconv_module)) |
| self.assertTrue(hasattr(qconv_module, '_packed_params')) |
| self.assertTrue(hasattr(qconv_module, 'scale')) |
| self.assertTrue(hasattr(qconv_module, 'zero_point')) |
| |
| # Test properties |
| self.assertEqual(W_q, qconv_module.weight()) |
| if use_bias: |
| self.assertEqual(b, qconv_module.bias()) |
| self.assertEqual(Y_scale, qconv_module.scale) |
| self.assertEqual(Y_zero_point, qconv_module.zero_point) |
| |
| # Test forward |
| Y_exp = conv_module(X) |
| Y_exp = torch.quantize_per_tensor( |
| Y_exp, scale=Y_scale, zero_point=Y_zero_point, dtype=torch.quint8) |
| Y_act = qconv_module(X_q) |
| |
| # Make sure the results match |
| # assert_array_almost_equal compares using the following formula: |
| # abs(desired-actual) < 1.5 * 10**(-decimal) |
| # (https://docs.scipy.org/doc/numpy/reference/generated/numpy.testing.assert_almost_equal.html) |
| # We use decimal = 0 to ignore off-by-1 differences between reference |
| # and test. Off-by-1 differences arise due to the order of round and |
| # zero_point addition operation, i.e., if addition followed by round is |
| # used by reference and round followed by addition is used by test, the |
| # results may differ by 1. |
| # For example, the result of round(2.5) + 1 is 3 while round(2.5 + 1) is |
| # 4 assuming the rounding mode is round-to-nearest, ties-to-even. |
| np.testing.assert_array_almost_equal( |
| Y_exp.int_repr().numpy(), Y_act.int_repr().numpy(), decimal=0) |
| |
| # Test serialization of quantized Conv Module using state_dict |
| model_dict = qconv_module.state_dict() |
| self.assertEqual(model_dict['weight'], W_q) |
| if use_bias: |
| self.assertEqual(model_dict['bias'], b) |
| bytes_io = io.BytesIO() |
| torch.save(model_dict, bytes_io) |
| bytes_io.seek(0) |
| loaded_dict = torch.load(bytes_io) |
| for key in loaded_dict: |
| self.assertEqual(model_dict[key], loaded_dict[key]) |
| loaded_qconv_module = type(qconv_module)( |
| in_channels, out_channels, kernel_size, stride, padding, dilation, |
| groups, use_bias, padding_mode="zeros") |
| loaded_qconv_module.load_state_dict(loaded_dict) |
| |
| self.assertTrue(dir(loaded_qconv_module) == dir(qconv_module)) |
| self.assertTrue(module_name in str(loaded_qconv_module)) |
| self.assertTrue(hasattr(loaded_qconv_module, '_packed_params')) |
| self.assertTrue(hasattr(loaded_qconv_module, '_weight_bias')) |
| |
| self.assertEqual(qconv_module.weight(), loaded_qconv_module.weight()) |
| if use_bias: |
| self.assertEqual(qconv_module.bias(), loaded_qconv_module.bias()) |
| self.assertEqual(qconv_module.scale, loaded_qconv_module.scale) |
| self.assertEqual(qconv_module.zero_point, |
| loaded_qconv_module.zero_point) |
| Y_loaded = loaded_qconv_module(X_q) |
| np.testing.assert_array_almost_equal( |
| Y_exp.int_repr().numpy(), Y_loaded.int_repr().numpy(), decimal=0) |
| |
| # The below check is meant to ensure that `torch.save` and `torch.load` |
| # serialization works, however it is currently broken by the following: |
| # https://github.com/pytorch/pytorch/issues/24045 |
| # |
| # Instead, we currently check that the proper exception is thrown on |
| # save. |
| # <start code> |
| # b = io.BytesIO() |
| # torch.save(conv_under_test, b) |
| # b.seek(0) |
| # loaded_conv = torch.load(b) |
| # |
| # self.assertEqual(loaded_qconv_module.bias(), qconv_module.bias()) |
| # self.assertEqual(loaded_qconv_module.scale, qconv_module.scale) |
| # self.assertEqual(loaded_qconv_module.zero_point, |
| # qconv_module.zero_point) |
| # <end code> |
| with self.assertRaisesRegex( |
| RuntimeError, r'torch.save\(\) is not currently supported' |
| ): |
| bytes_io = io.BytesIO() |
| torch.save(qconv_module, bytes_io) |
| |
| # JIT testing |
| self.checkScriptable( |
| qconv_module, list(zip([X_q], [Y_exp])), |
| check_save_load=True) |
| |
| # Test from_float |
| conv_module.qconfig = torch.quantization.default_qconfig |
| torch.quantization.prepare(conv_module, inplace=True) |
| conv_module(X.float()) |
| converted_qconv_module = torch.nn.Sequential(conv_module) |
| torch.quantization.convert(converted_qconv_module, inplace=True) |
| |
| # Smoke test to make sure the module actually runs |
| if use_bias: |
| if use_fused: |
| self.assertEqual(conv_module[0].bias, |
| converted_qconv_module[0].bias()) |
| else: |
| self.assertEqual(conv_module.bias, |
| converted_qconv_module[0].bias()) |
| # Smoke test extra_repr |
| self.assertTrue(module_name in str(converted_qconv_module)) |
| |
| @given(batch_size=st.integers(1, 3), |
| in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]), |
| H=st.integers(4, 16), |
| W=st.integers(4, 16), |
| out_channels_per_group=st.sampled_from([2, 4, 5, 8, 16, 32]), |
| groups=st.integers(1, 4), |
| kernel_h=st.integers(1, 7), |
| kernel_w=st.integers(1, 7), |
| stride_h=st.integers(1, 2), |
| stride_w=st.integers(1, 2), |
| pad_h=st.integers(0, 2), |
| pad_w=st.integers(0, 2), |
| dilation=st.integers(1, 2), |
| X_scale=st.floats(1.2, 1.6), |
| X_zero_point=st.integers(0, 4), |
| W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2), |
| W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2), |
| Y_scale=st.floats(4.2, 5.6), |
| Y_zero_point=st.integers(0, 4), |
| use_bias=st.booleans(), |
| use_fused=st.booleans(), |
| use_channelwise=st.booleans()) |
| @override_qengines |
| def test_conv2d_api( |
| self, batch_size, in_channels_per_group, H, W, out_channels_per_group, |
| groups, kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation, |
| X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, |
| use_bias, use_fused, use_channelwise |
| ): |
| # Tests the correctness of the conv2d module. |
| in_channels = in_channels_per_group * groups |
| out_channels = out_channels_per_group * groups |
| input_feature_map_size = (H, W) |
| kernel_size = (kernel_h, kernel_w) |
| stride = (stride_h, stride_w) |
| padding = (pad_h, pad_w) |
| dilation = (dilation, dilation) |
| if torch.backends.quantized.engine == 'qnnpack': |
| use_channelwise = False |
| if use_fused: |
| module_name = "QuantizedConvReLU2d" |
| qconv_module = nnq_fused.ConvReLU2d( |
| in_channels, out_channels, kernel_size, stride, padding, |
| dilation, groups, use_bias, padding_mode="zeros") |
| else: |
| module_name = "QuantizedConv2d" |
| qconv_module = nnq.Conv2d( |
| in_channels, out_channels, kernel_size, stride, padding, |
| dilation, groups, use_bias, padding_mode="zeros") |
| |
| conv_module = nn.Conv2d( |
| in_channels, out_channels, kernel_size, stride, padding, |
| dilation, groups, use_bias, padding_mode="zeros") |
| if use_fused: |
| relu_module = nn.ReLU() |
| conv_module = nni.ConvReLU2d(conv_module, relu_module) |
| conv_module = conv_module.float() |
| |
| self._test_conv_api_impl( |
| module_name, qconv_module, conv_module, batch_size, |
| in_channels_per_group, input_feature_map_size, |
| out_channels_per_group, groups, kernel_size, stride, padding, |
| dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, |
| Y_zero_point, use_bias, use_fused, use_channelwise) |
| |
| @skipIfNoFBGEMM |
| @given(batch_size=st.integers(1, 3), |
| in_channels_per_group=st.sampled_from([2, 4, 5, 8, 16]), |
| D=st.integers(3, 6), |
| H=st.integers(3, 6), |
| W=st.integers(3, 6), |
| out_channels_per_group=st.sampled_from([2, 4, 5, 8, 16]), |
| groups=st.integers(1, 4), |
| kernel_d=st.integers(1, 3), |
| kernel_h=st.integers(1, 3), |
| kernel_w=st.integers(1, 3), |
| stride_d=st.integers(1, 2), |
| stride_h=st.integers(1, 2), |
| stride_w=st.integers(1, 2), |
| pad_d=st.integers(0, 1), |
| pad_h=st.integers(0, 1), |
| pad_w=st.integers(0, 1), |
| dilation=st.integers(1, 2), |
| X_scale=st.floats(1.2, 1.6), |
| X_zero_point=st.integers(0, 4), |
| W_scale=st.lists(st.floats(0.2, 1.6), min_size=1, max_size=2), |
| W_zero_point=st.lists(st.integers(-5, 5), min_size=1, max_size=2), |
| Y_scale=st.floats(4.2, 5.6), |
| Y_zero_point=st.integers(0, 4), |
| use_bias=st.booleans(), |
| use_fused=st.booleans(), |
| use_channelwise=st.booleans()) |
| def test_conv3d_api( |
| self, batch_size, in_channels_per_group, D, H, W, |
| out_channels_per_group, groups, kernel_d, kernel_h, kernel_w, |
| stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, dilation, X_scale, |
| X_zero_point, W_scale, W_zero_point, Y_scale, Y_zero_point, use_bias, |
| use_channelwise, use_fused, |
| ): |
| # Tests the correctness of the conv3d module. |
| in_channels = in_channels_per_group * groups |
| out_channels = out_channels_per_group * groups |
| input_feature_map_size = (D, H, W) |
| kernel_size = (kernel_d, kernel_h, kernel_w) |
| stride = (stride_d, stride_h, stride_w) |
| padding = (pad_d, pad_h, pad_w) |
| dilation = (dilation, dilation, dilation) |
| with override_quantized_engine('fbgemm'): |
| if use_fused: |
| module_name = "QuantizedConvReLU3d" |
| qconv_module = nnq_fused.ConvReLU3d( |
| in_channels, out_channels, kernel_size, stride, padding, |
| dilation, groups, use_bias, padding_mode="zeros") |
| else: |
| module_name = "QuantizedConv3d" |
| qconv_module = nnq.Conv3d( |
| in_channels, out_channels, kernel_size, stride, padding, |
| dilation, groups, use_bias, padding_mode="zeros") |
| |
| conv_module = nn.Conv3d( |
| in_channels, out_channels, kernel_size, stride, padding, |
| dilation, groups, use_bias, padding_mode="zeros") |
| if use_fused: |
| relu_module = nn.ReLU() |
| conv_module = nni.ConvReLU3d(conv_module, relu_module) |
| conv_module = conv_module.float() |
| |
| self._test_conv_api_impl( |
| module_name, qconv_module, conv_module, batch_size, |
| in_channels_per_group, input_feature_map_size, |
| out_channels_per_group, groups, kernel_size, stride, padding, |
| dilation, X_scale, X_zero_point, W_scale, W_zero_point, Y_scale, |
| Y_zero_point, use_bias, use_fused, use_channelwise) |
| |
| def test_pool_api(self): |
| """Tests the correctness of the pool module. |
| The correctness is defined against the functional implementation. |
| """ |
| N, C, H, W = 10, 10, 10, 3 |
| kwargs = { |
| 'kernel_size': 2, |
| 'stride': None, |
| 'padding': 0, |
| 'dilation': 1 |
| } |
| |
| scale, zero_point = 1.0 / 255, 128 |
| |
| X = torch.randn(N, C, H, W, dtype=torch.float32) |
| qX = torch.quantize_per_tensor(X, scale=scale, zero_point=zero_point, |
| dtype=torch.quint8) |
| qX_expect = torch.nn.functional.max_pool2d(qX, **kwargs) |
| |
| pool_under_test = torch.nn.quantized.MaxPool2d(**kwargs) |
| qX_hat = pool_under_test(qX) |
| self.assertEqual(qX_expect, qX_hat) |
| |
| # JIT Testing |
| self.checkScriptable(pool_under_test, list(zip([X], [qX_expect]))) |
| |
| def test_batch_norm2d(self): |
| """Tests the correctness of the batchnorm2d module. |
| The correctness is defined against the functional implementation. |
| """ |
| x = torch.randn((2, 4, 6, 8), dtype=torch.float) |
| float_mod = torch.nn.BatchNorm2d(4) |
| float_mod.training = False |
| |
| y_ref = float_mod(x) |
| quant_ref = torch.quantize_per_tensor(y_ref, 1.0, 0, dtype=torch.quint8) |
| |
| quant_mod = nnq.BatchNorm2d(4) |
| qx = torch.quantize_per_tensor(x, 1.0, 0, dtype=torch.quint8) |
| qy = quant_mod(qx) |
| |
| self.assertEqual(quant_ref.int_repr().numpy(), qy.int_repr().numpy(), |
| message="BatchNorm2d module API failed") |
| |
| def test_batch_norm3d(self): |
| """Tests the correctness of the batchnorm3d module. |
| The correctness is defined against the functional implementation. |
| """ |
| x = torch.randn((2, 4, 6, 8, 10), dtype=torch.float) |
| float_mod = torch.nn.BatchNorm3d(4) |
| float_mod.training = False |
| |
| y_ref = float_mod(x) |
| quant_ref = torch.quantize_per_tensor(y_ref, 1.0, 0, dtype=torch.quint8) |
| |
| quant_mod = nnq.BatchNorm3d(4) |
| qx = torch.quantize_per_tensor(x, 1.0, 0, dtype=torch.quint8) |
| qy = quant_mod(qx) |
| |
| self.assertEqual(quant_ref.int_repr().numpy(), qy.int_repr().numpy(), |
| message="BatchNorm3d module API failed") |
| |
| def test_layer_norm(self): |
| """Tests the correctness of the layernorm module. |
| The correctness is defined against the functional implementation. |
| """ |
| x_scale = 10.0 / 256 |
| x_zero_point = 0 |
| y_scale = 5.0 / 256 |
| y_zero_point = 127 |
| |
| dims = (1, 4, 8) |
| |
| X = (torch.randn(dims, dtype=torch.float) - 0.5) * 10 |
| qX = torch.quantize_per_tensor(X, x_scale, x_zero_point, dtype=torch.quint8) |
| dqX = qX.dequantize() |
| |
| float_mod = torch.nn.LayerNorm(dqX.size()[1:]).float() |
| float_mod.weight = torch.nn.Parameter(torch.rand(*dims[1:])) |
| float_mod.bias = torch.nn.Parameter(torch.rand(*dims[1:])) |
| |
| dqY_ref = float_mod(dqX) |
| qY_ref = torch.quantize_per_tensor( |
| dqY_ref, y_scale, y_zero_point, dtype=torch.quint8) |
| |
| quant_mod = nnq.LayerNorm( |
| qX.size()[1:], float_mod.weight, float_mod.bias, y_scale, y_zero_point) |
| qY = quant_mod(qX) |
| |
| self.assertEqual(qY_ref.int_repr().numpy(), qY.int_repr().numpy(), |
| message="LayerNorm module API failed, qY_ref\n{} vs qY\n{}" |
| .format(qY_ref, qY)) |
| |
| |
| class TestDynamicQuantizedModule(QuantizationTestCase): |
| @skipIfNoFBGEMM |
| @given( |
| batch_size=st.integers(1, 5), |
| in_features=st.integers(16, 32), |
| out_features=st.integers(4, 8), |
| use_bias=st.booleans(), |
| use_default_observer=st.booleans(), |
| ) |
| @override_qengines |
| def test_linear_api(self, batch_size, in_features, out_features, use_bias, use_default_observer): |
| """test API functionality for nn.quantized.dynamic.Linear""" |
| W = torch.rand(out_features, in_features).float() |
| W_scale, W_zp = _calculate_dynamic_qparams(W, torch.qint8) |
| W_q = torch.quantize_per_tensor(W, W_scale, W_zp, torch.qint8) |
| X = torch.rand(batch_size, in_features).float() |
| B = torch.rand(out_features).float() if use_bias else None |
| qlinear = nnqd.Linear(in_features, out_features) |
| # Run module with default-initialized parameters. |
| # This tests that the constructor is correct. |
| qlinear.set_weight_bias(W_q, B) |
| qlinear(X) |
| |
| # Simple round-trip test to ensure weight()/set_weight() API |
| self.assertEqual(qlinear.weight(), W_q) |
| W_pack = qlinear._packed_params._packed_params |
| Z_dq = qlinear(X) |
| |
| # Check if the module implementation matches calling the |
| # ops directly |
| Z_ref = torch.ops.quantized.linear_dynamic(X, W_pack) |
| self.assertEqual(Z_ref, Z_dq) |
| |
| # Test serialization of dynamic quantized Linear Module using state_dict |
| model_dict = qlinear.state_dict() |
| b = io.BytesIO() |
| torch.save(model_dict, b) |
| b.seek(0) |
| loaded_dict = torch.load(b) |
| for key in model_dict: |
| if isinstance(model_dict[key], torch._C.ScriptObject): |
| assert isinstance(loaded_dict[key], torch._C.ScriptObject) |
| w_model, b_model = torch.ops.quantized.linear_unpack(model_dict[key]) |
| w_loaded, b_loaded = torch.ops.quantized.linear_unpack(loaded_dict[key]) |
| self.assertEqual(w_model, w_loaded) |
| self.assertEqual(b_model, b_loaded) |
| else: |
| self.assertEqual(model_dict[key], loaded_dict[key]) |
| loaded_qlinear = nnqd.Linear(in_features, out_features) |
| loaded_qlinear.load_state_dict(loaded_dict) |
| |
| linear_unpack = torch.ops.quantized.linear_unpack |
| self.assertEqual(linear_unpack(qlinear._packed_params._packed_params), |
| linear_unpack(loaded_qlinear._packed_params._packed_params)) |
| if use_bias: |
| self.assertEqual(qlinear.bias(), loaded_qlinear.bias()) |
| self.assertTrue(dir(qlinear) == dir(loaded_qlinear)) |
| self.assertTrue(hasattr(qlinear, '_packed_params')) |
| self.assertTrue(hasattr(loaded_qlinear, '_packed_params')) |
| self.assertTrue(hasattr(qlinear, '_weight_bias')) |
| self.assertTrue(hasattr(loaded_qlinear, '_weight_bias')) |
| |
| self.assertEqual(qlinear._weight_bias(), loaded_qlinear._weight_bias()) |
| self.assertEqual(qlinear._weight_bias(), torch.ops.quantized.linear_unpack(qlinear._packed_params._packed_params)) |
| Z_dq2 = qlinear(X) |
| self.assertEqual(Z_dq, Z_dq2) |
| |
| # The below check is meant to ensure that `torch.save` and `torch.load` |
| # serialization works, however it is currently broken by the following: |
| # https://github.com/pytorch/pytorch/issues/24045 |
| # |
| # Instead, we currently check that the proper exception is thrown on save. |
| # <start code> |
| # b = io.BytesIO() |
| # torch.save(qlinear, b) |
| # b.seek(0) |
| # loaded = torch.load(b) |
| # self.assertEqual(qlinear.weight(), loaded.weight()) |
| # self.assertEqual(qlinear.zero_point, loaded.zero_point) |
| # <end code> |
| # with self.assertRaisesRegex(RuntimeError, r'torch.save\(\) is not currently supported'): |
| # b = io.BytesIO() |
| # torch.save(qlinear, b) |
| |
| # Test JIT |
| self.checkScriptable(qlinear, list(zip([X], [Z_ref])), check_save_load=True) |
| |
| # Test from_float |
| float_linear = torch.nn.Linear(in_features, out_features).float() |
| if use_default_observer: |
| float_linear.qconfig = torch.quantization.default_dynamic_qconfig |
| prepare_dynamic(float_linear) |
| float_linear(X.float()) |
| quantized_float_linear = nnqd.Linear.from_float(float_linear) |
| |
| # Smoke test to make sure the module actually runs |
| quantized_float_linear(X) |
| |
| # Smoke test extra_repr |
| self.assertTrue('QuantizedLinear' in str(quantized_float_linear)) |
