# Owner(s): ["oncall: quantization"] from .common import AOMigrationTestCase class TestAOMigrationQuantization(AOMigrationTestCase): def test_package_import_quantize(self): self._test_package_import('quantize') def test_function_import_quantize(self): function_list = [ '_convert', '_observer_forward_hook', '_propagate_qconfig_helper', '_remove_activation_post_process', '_remove_qconfig', 'add_observer_', 'add_quant_dequant', 'convert', 'get_observer_dict', 'get_unique_devices_', 'is_activation_post_process', 'prepare', 'prepare_qat', 'propagate_qconfig_', 'quantize', 'quantize_dynamic', 'quantize_qat', 'register_activation_post_process_hook', 'swap_module', ] self._test_function_import('quantize', function_list) def test_package_import_stubs(self): self._test_package_import('stubs') def test_function_import_stubs(self): function_list = [ 'QuantStub', 'DeQuantStub', 'QuantWrapper', ] self._test_function_import('stubs', function_list) def test_package_import_quantize_jit(self): self._test_package_import('quantize_jit') def test_function_import_quantize_jit(self): function_list = [ '_check_is_script_module', '_check_forward_method', 'script_qconfig', 'script_qconfig_dict', 'fuse_conv_bn_jit', '_prepare_jit', 'prepare_jit', 'prepare_dynamic_jit', '_convert_jit', 'convert_jit', 'convert_dynamic_jit', '_quantize_jit', 'quantize_jit', 'quantize_dynamic_jit', ] self._test_function_import('quantize_jit', function_list) def test_package_import_fake_quantize(self): self._test_package_import('fake_quantize') def test_function_import_fake_quantize(self): function_list = [ '_is_per_channel', '_is_per_tensor', '_is_symmetric_quant', 'FakeQuantizeBase', 'FakeQuantize', 'FixedQParamsFakeQuantize', 'FusedMovingAvgObsFakeQuantize', 'default_fake_quant', 'default_weight_fake_quant', 'default_fixed_qparams_range_neg1to1_fake_quant', 'default_fixed_qparams_range_0to1_fake_quant', 'default_per_channel_weight_fake_quant', 'default_histogram_fake_quant', 'default_fused_act_fake_quant', 'default_fused_wt_fake_quant', 'default_fused_per_channel_wt_fake_quant', '_is_fake_quant_script_module', 'disable_fake_quant', 'enable_fake_quant', 'disable_observer', 'enable_observer', ] self._test_function_import('fake_quantize', function_list) class TestAOMigrationNNQuantized(AOMigrationTestCase): def test_package_import_nn_quantized_modules(self): r"""Tests the migration of the torch.nn.quantized.modules""" self._test_package_import('modules', base='nn.quantized') self._test_package_import('modules.activation', base='nn.quantized') self._test_package_import('modules.batchnorm', base='nn.quantized') self._test_package_import('modules.conv', base='nn.quantized') self._test_package_import('modules.dropout', base='nn.quantized') self._test_package_import('modules.embedding_ops', base='nn.quantized') self._test_package_import('modules.functional_modules', base='nn.quantized') self._test_package_import('modules.linear', base='nn.quantized') self._test_package_import('modules.normalization', base='nn.quantized') self._test_package_import('modules.utils', base='nn.quantized') def test_package_import_nn_quantized(self): skip = [ # These are added in the `torch.nn.quantized` to allow # for the legacy import, s.a. `import torch.nn.quantized.conv`, etc. 'activation', 'batchnorm', 'conv', 'dropout', 'embedding_ops', 'functional_modules', 'linear', 'normalization', '_reference', ] self._test_package_import('quantized', base='nn', skip=skip) def test_functional_import(self): r"""Tests the migration of the torch.nn.quantized.functional""" function_list = [ 'avg_pool2d', 'avg_pool3d', 'adaptive_avg_pool2d', 'adaptive_avg_pool3d', 'conv1d', 'conv2d', 'conv3d', 'interpolate', 'linear', 'max_pool1d', 'max_pool2d', 'celu', 'leaky_relu', 'hardtanh', 'hardswish', 'threshold', 'elu', 'hardsigmoid', 'clamp', 'upsample', 'upsample_bilinear', 'upsample_nearest', ] self._test_function_import('functional', function_list, base='nn.quantized') def test_modules_import(self): module_list = [ # Modules 'BatchNorm2d', 'BatchNorm3d', 'Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d', 'DeQuantize', 'ELU', 'Embedding', 'EmbeddingBag', 'GroupNorm', 'Hardswish', 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d', 'LayerNorm', 'LeakyReLU', 'Linear', 'MaxPool2d', 'Quantize', 'ReLU6', 'Sigmoid', 'Softmax', 'Dropout', # Wrapper modules 'FloatFunctional', 'FXFloatFunctional', 'QFunctional', ] self._test_function_import('modules', module_list, base='nn.quantized') def test_modules_activation(self): function_list = [ 'ReLU6', 'Hardswish', 'ELU', 'LeakyReLU', 'Sigmoid', 'Softmax', ] self._test_function_import('activation', function_list, base='nn.quantized.modules') def test_modules_batchnorm(self): function_list = [ 'BatchNorm2d', 'BatchNorm3d', ] self._test_function_import('batchnorm', function_list, base='nn.quantized.modules') def test_modules_conv(self): function_list = [ '_reverse_repeat_padding', 'Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d', ] self._test_function_import('conv', function_list, base='nn.quantized.modules') def test_modules_dropout(self): function_list = [ 'Dropout', ] self._test_function_import('dropout', function_list, base='nn.quantized.modules') def test_modules_embedding_ops(self): function_list = [ 'EmbeddingPackedParams', 'Embedding', 'EmbeddingBag', ] self._test_function_import('embedding_ops', function_list, base='nn.quantized.modules') def test_modules_functional_modules(self): function_list = [ 'FloatFunctional', 'FXFloatFunctional', 'QFunctional', ] self._test_function_import('functional_modules', function_list, base='nn.quantized.modules') def test_modules_linear(self): function_list = [ 'Linear', 'LinearPackedParams', ] self._test_function_import('linear', function_list, base='nn.quantized.modules') def test_modules_normalization(self): function_list = [ 'LayerNorm', 'GroupNorm', 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d', ] self._test_function_import('normalization', function_list, base='nn.quantized.modules') def test_modules_utils(self): function_list = [ '_ntuple_from_first', '_pair_from_first', '_quantize_weight', 'hide_packed_params_repr', 'WeightedQuantizedModule', ] self._test_function_import('utils', function_list, base='nn.quantized.modules') def test_package_import_nn_quantized_dynamic(self): self._test_package_import('dynamic', base='nn.quantized') def test_package_import_nn_quantized_dynamic_modules(self): r"""Tests the migration of the torch.nn.quantized.modules""" self._test_package_import('modules', base='nn.quantized.dynamic') self._test_package_import('modules.conv', base='nn.quantized.dynamic') self._test_package_import('modules.linear', base='nn.quantized.dynamic') self._test_package_import('modules.rnn', base='nn.quantized.dynamic') def test_import_nn_quantized_dynamic_import(self): module_list = [ # Modules 'Linear', 'LSTM', 'GRU', 'LSTMCell', 'RNNCell', 'GRUCell', 'Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d', ] self._test_function_import('dynamic', module_list, base='nn.quantized') def test_package_import_nn_quantizable(self): self._test_package_import('quantizable', base='nn') def test_package_import_nn_quantizable_modules(self): r"""Tests the migration of the torch.nn.quantizable.modules""" self._test_package_import('modules', base='nn.quantizable') self._test_package_import('modules.activation', base='nn.quantizable') self._test_package_import('modules.rnn', base='nn.quantizable') def test_import_nn_quantizable_activation(self): module_list = [ # Modules 'MultiheadAttention', ] self._test_function_import('activation', module_list, base='nn.quantizable.modules') def test_import_nn_quantizable_rnn(self): module_list = [ # Modules 'LSTM', 'LSTMCell', ] self._test_function_import('rnn', module_list, base='nn.quantizable.modules') # torch.nn.qat and torch.nn.qat.dynamic def test_package_import_nn_qat(self): self._test_package_import('qat', base='nn') def test_package_import_nn_qat_modules(self): r"""Tests the migration of the torch.nn.qat.modules""" self._test_package_import('modules', base='nn.qat') self._test_package_import('modules.conv', base='nn.qat') self._test_package_import('modules.embedding_ops', base='nn.qat') self._test_package_import('modules.linear', base='nn.qat') def test_package_import_nn_qat_dynamic(self): r"""Tests the migration of the torch.nn.qat.modules""" self._test_package_import('dynamic', base='nn.qat') self._test_package_import('dynamic.modules', base='nn.qat') self._test_package_import('dynamic.modules.linear', base='nn.qat') def test_import_nn_qat_conv(self): module_list = [ 'Conv1d', 'Conv2d', 'Conv3d', ] self._test_function_import('conv', module_list, base='nn.qat.modules') def test_import_nn_qat_embedding_ops(self): module_list = [ 'Embedding', 'EmbeddingBag', ] self._test_function_import('embedding_ops', module_list, base='nn.qat.modules') def test_import_nn_qat_linear(self): module_list = [ 'Linear', ] self._test_function_import('linear', module_list, base='nn.qat.modules') def test_import_nn_qat_dynamic_linear(self): module_list = [ 'Linear', ] self._test_function_import('linear', module_list, base='nn.qat.dynamic.modules') class TestAOMigrationNNIntrinsic(AOMigrationTestCase): def test_package_import_nn_intrinsic_modules(self): r"""Tests the migration of the torch.nn.intrinsic.modules""" self._test_package_import('modules', base='nn.intrinsic') self._test_package_import('modules.fused', base='nn.intrinsic') def test_package_import_nn_intrinsic(self): skip = [ 'qat', 'quantized', ] self._test_package_import('intrinsic', base='nn', skip=skip) def test_modules_import_nn_intrinsic(self): module_list = [ # Modules '_FusedModule', 'ConvBn1d', 'ConvBn2d', 'ConvBn3d', 'ConvBnReLU1d', 'ConvBnReLU2d', 'ConvBnReLU3d', 'ConvReLU1d', 'ConvReLU2d', 'ConvReLU3d', 'LinearReLU', 'BNReLU2d', 'BNReLU3d', 'LinearBn1d', ] self._test_function_import('intrinsic', module_list, base='nn') def test_modules_fused(self): function_list = [ '_FusedModule', 'ConvBn1d', 'ConvBn2d', 'ConvBn3d', 'ConvBnReLU1d', 'ConvBnReLU2d', 'ConvBnReLU3d', 'ConvReLU1d', 'ConvReLU2d', 'ConvReLU3d', 'LinearReLU', 'BNReLU2d', 'BNReLU3d', 'LinearBn1d', ] self._test_function_import('fused', function_list, base='nn.intrinsic.modules')