mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
45c62a3377
Summary: same function in observer and quantize, consolidated to a single function. Note the definitions were slightly different, I've changed the definition to be maximally inclusive so that the name of the function is more accurate Test Plan: python test/test_public_bindings.py python test/test_quantization.py Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D40709276](https://our.internmc.facebook.com/intern/diff/D40709276) Pull Request resolved: https://github.com/pytorch/pytorch/pull/87520 Approved by: https://github.com/jcaip
520 lines
17 KiB
Python
520 lines
17 KiB
Python
# Owner(s): ["oncall: quantization"]
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from .common import AOMigrationTestCase
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class TestAOMigrationQuantization(AOMigrationTestCase):
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def test_package_import_quantize(self):
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self._test_package_import('quantize')
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def test_function_import_quantize(self):
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function_list = [
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'_convert',
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'_observer_forward_hook',
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'_propagate_qconfig_helper',
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'_remove_activation_post_process',
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'_remove_qconfig',
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'add_observer_',
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'add_quant_dequant',
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'convert',
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'get_observer_dict',
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'get_unique_devices_',
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'_is_activation_post_process',
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'prepare',
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'prepare_qat',
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'propagate_qconfig_',
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'quantize',
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'quantize_dynamic',
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'quantize_qat',
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'register_activation_post_process_hook',
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'swap_module',
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]
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self._test_function_import('quantize', function_list)
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def test_package_import_stubs(self):
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self._test_package_import('stubs')
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def test_function_import_stubs(self):
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function_list = [
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'QuantStub',
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'DeQuantStub',
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'QuantWrapper',
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]
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self._test_function_import('stubs', function_list)
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def test_package_import_quantize_jit(self):
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self._test_package_import('quantize_jit')
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def test_function_import_quantize_jit(self):
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function_list = [
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'_check_is_script_module',
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'_check_forward_method',
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'script_qconfig',
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'script_qconfig_dict',
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'fuse_conv_bn_jit',
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'_prepare_jit',
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'prepare_jit',
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'prepare_dynamic_jit',
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'_convert_jit',
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'convert_jit',
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'convert_dynamic_jit',
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'_quantize_jit',
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'quantize_jit',
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'quantize_dynamic_jit',
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]
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self._test_function_import('quantize_jit', function_list)
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def test_package_import_fake_quantize(self):
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self._test_package_import('fake_quantize')
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def test_function_import_fake_quantize(self):
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function_list = [
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'_is_per_channel',
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'_is_per_tensor',
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'_is_symmetric_quant',
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'FakeQuantizeBase',
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'FakeQuantize',
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'FixedQParamsFakeQuantize',
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'FusedMovingAvgObsFakeQuantize',
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'default_fake_quant',
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'default_weight_fake_quant',
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'default_fixed_qparams_range_neg1to1_fake_quant',
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'default_fixed_qparams_range_0to1_fake_quant',
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'default_per_channel_weight_fake_quant',
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'default_histogram_fake_quant',
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'default_fused_act_fake_quant',
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'default_fused_wt_fake_quant',
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'default_fused_per_channel_wt_fake_quant',
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'_is_fake_quant_script_module',
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'disable_fake_quant',
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'enable_fake_quant',
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'disable_observer',
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'enable_observer',
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]
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self._test_function_import('fake_quantize', function_list)
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class TestAOMigrationNNQuantized(AOMigrationTestCase):
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def test_package_import_nn_quantized_modules(self):
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r"""Tests the migration of the torch.nn.quantized.modules"""
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self._test_package_import('modules', base='nn.quantized')
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self._test_package_import('modules.activation', base='nn.quantized')
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self._test_package_import('modules.batchnorm', base='nn.quantized')
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self._test_package_import('modules.conv', base='nn.quantized')
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self._test_package_import('modules.dropout', base='nn.quantized')
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self._test_package_import('modules.embedding_ops', base='nn.quantized')
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self._test_package_import('modules.functional_modules', base='nn.quantized')
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self._test_package_import('modules.linear', base='nn.quantized')
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self._test_package_import('modules.normalization', base='nn.quantized')
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self._test_package_import('modules.utils', base='nn.quantized')
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def test_package_import_nn_quantized(self):
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skip = [
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# These are added in the `torch.nn.quantized` to allow
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# for the legacy import, s.a. `import torch.nn.quantized.conv`, etc.
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'activation',
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'batchnorm',
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'conv',
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'dropout',
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'embedding_ops',
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'functional_modules',
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'linear',
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'normalization',
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'_reference',
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]
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self._test_package_import('quantized', base='nn', skip=skip)
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def test_functional_import(self):
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r"""Tests the migration of the torch.nn.quantized.functional"""
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function_list = [
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'avg_pool2d',
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'avg_pool3d',
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'adaptive_avg_pool2d',
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'adaptive_avg_pool3d',
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'conv1d',
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'conv2d',
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'conv3d',
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'interpolate',
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'linear',
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'max_pool1d',
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'max_pool2d',
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'celu',
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'leaky_relu',
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'hardtanh',
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'hardswish',
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'threshold',
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'elu',
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'hardsigmoid',
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'clamp',
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'upsample',
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'upsample_bilinear',
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'upsample_nearest',
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]
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self._test_function_import('functional', function_list, base='nn.quantized')
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def test_modules_import(self):
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module_list = [
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# Modules
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'BatchNorm2d',
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'BatchNorm3d',
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'Conv1d',
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'Conv2d',
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'Conv3d',
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'ConvTranspose1d',
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'ConvTranspose2d',
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'ConvTranspose3d',
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'DeQuantize',
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'ELU',
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'Embedding',
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'EmbeddingBag',
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'GroupNorm',
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'Hardswish',
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'InstanceNorm1d',
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'InstanceNorm2d',
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'InstanceNorm3d',
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'LayerNorm',
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'LeakyReLU',
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'Linear',
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'MaxPool2d',
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'Quantize',
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'ReLU6',
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'Sigmoid',
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'Softmax',
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'Dropout',
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# Wrapper modules
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'FloatFunctional',
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'FXFloatFunctional',
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'QFunctional',
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]
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self._test_function_import('modules', module_list, base='nn.quantized')
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def test_modules_activation(self):
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function_list = [
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'ReLU6',
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'Hardswish',
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'ELU',
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'LeakyReLU',
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'Sigmoid',
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'Softmax',
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]
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self._test_function_import('activation', function_list,
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base='nn.quantized.modules')
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def test_modules_batchnorm(self):
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function_list = [
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'BatchNorm2d',
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'BatchNorm3d',
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]
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self._test_function_import('batchnorm', function_list,
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base='nn.quantized.modules')
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def test_modules_conv(self):
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function_list = [
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'_reverse_repeat_padding',
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'Conv1d',
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'Conv2d',
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'Conv3d',
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'ConvTranspose1d',
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'ConvTranspose2d',
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'ConvTranspose3d',
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]
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self._test_function_import('conv', function_list,
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base='nn.quantized.modules')
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def test_modules_dropout(self):
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function_list = [
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'Dropout',
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]
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self._test_function_import('dropout', function_list,
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base='nn.quantized.modules')
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def test_modules_embedding_ops(self):
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function_list = [
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'EmbeddingPackedParams',
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'Embedding',
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'EmbeddingBag',
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]
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self._test_function_import('embedding_ops', function_list,
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base='nn.quantized.modules')
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def test_modules_functional_modules(self):
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function_list = [
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'FloatFunctional',
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'FXFloatFunctional',
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'QFunctional',
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]
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self._test_function_import('functional_modules', function_list,
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base='nn.quantized.modules')
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def test_modules_linear(self):
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function_list = [
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'Linear',
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'LinearPackedParams',
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]
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self._test_function_import('linear', function_list,
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base='nn.quantized.modules')
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def test_modules_normalization(self):
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function_list = [
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'LayerNorm',
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'GroupNorm',
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'InstanceNorm1d',
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'InstanceNorm2d',
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'InstanceNorm3d',
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]
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self._test_function_import('normalization', function_list,
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base='nn.quantized.modules')
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def test_modules_utils(self):
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function_list = [
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'_ntuple_from_first',
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'_pair_from_first',
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'_quantize_weight',
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'hide_packed_params_repr',
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'WeightedQuantizedModule',
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]
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self._test_function_import('utils', function_list,
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base='nn.quantized.modules')
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def test_package_import_nn_quantized_dynamic(self):
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self._test_package_import('dynamic', base='nn.quantized')
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def test_package_import_nn_quantized_dynamic_modules(self):
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r"""Tests the migration of the torch.nn.quantized.modules"""
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self._test_package_import('modules', base='nn.quantized.dynamic')
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self._test_package_import('modules.conv', base='nn.quantized.dynamic')
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self._test_package_import('modules.linear', base='nn.quantized.dynamic')
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self._test_package_import('modules.rnn', base='nn.quantized.dynamic')
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def test_import_nn_quantized_dynamic_import(self):
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module_list = [
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# Modules
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'Linear',
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'LSTM',
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'GRU',
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'LSTMCell',
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'RNNCell',
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'GRUCell',
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'Conv1d',
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'Conv2d',
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'Conv3d',
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'ConvTranspose1d',
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'ConvTranspose2d',
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'ConvTranspose3d',
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]
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self._test_function_import('dynamic', module_list, base='nn.quantized')
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def test_package_import_nn_quantizable(self):
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self._test_package_import('quantizable', base='nn')
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def test_package_import_nn_quantizable_modules(self):
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r"""Tests the migration of the torch.nn.quantizable.modules"""
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self._test_package_import('modules', base='nn.quantizable')
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self._test_package_import('modules.activation', base='nn.quantizable')
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self._test_package_import('modules.rnn', base='nn.quantizable')
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def test_import_nn_quantizable_activation(self):
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module_list = [
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# Modules
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'MultiheadAttention',
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]
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self._test_function_import('activation', module_list, base='nn.quantizable.modules')
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def test_import_nn_quantizable_rnn(self):
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module_list = [
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# Modules
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'LSTM',
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'LSTMCell',
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]
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self._test_function_import('rnn', module_list, base='nn.quantizable.modules')
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# torch.nn.qat and torch.nn.qat.dynamic
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def test_package_import_nn_qat(self):
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self._test_package_import('qat', base='nn')
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def test_package_import_nn_qat_modules(self):
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r"""Tests the migration of the torch.nn.qat.modules"""
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self._test_package_import('modules', base='nn.qat')
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self._test_package_import('modules.conv', base='nn.qat')
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self._test_package_import('modules.embedding_ops', base='nn.qat')
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self._test_package_import('modules.linear', base='nn.qat')
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def test_package_import_nn_qat_dynamic(self):
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r"""Tests the migration of the torch.nn.qat.modules"""
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self._test_package_import('dynamic', base='nn.qat')
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self._test_package_import('dynamic.modules', base='nn.qat')
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self._test_package_import('dynamic.modules.linear', base='nn.qat')
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def test_import_nn_qat_conv(self):
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module_list = [
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'Conv1d',
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'Conv2d',
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'Conv3d',
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]
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self._test_function_import('conv', module_list, base='nn.qat.modules')
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def test_import_nn_qat_embedding_ops(self):
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module_list = [
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'Embedding',
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'EmbeddingBag',
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]
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self._test_function_import('embedding_ops', module_list, base='nn.qat.modules')
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def test_import_nn_qat_linear(self):
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module_list = [
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'Linear',
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]
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self._test_function_import('linear', module_list, base='nn.qat.modules')
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def test_import_nn_qat_dynamic_linear(self):
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module_list = [
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'Linear',
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]
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self._test_function_import('linear', module_list, base='nn.qat.dynamic.modules')
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class TestAOMigrationNNIntrinsic(AOMigrationTestCase):
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def test_package_import_nn_intrinsic_modules(self):
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r"""Tests the migration of the torch.nn.intrinsic.modules"""
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self._test_package_import('modules', base='nn.intrinsic')
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self._test_package_import('modules.fused', base='nn.intrinsic')
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def test_package_import_nn_intrinsic(self):
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skip = []
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self._test_package_import('intrinsic', base='nn', skip=skip)
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def test_modules_import_nn_intrinsic(self):
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module_list = [
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# Modules
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'_FusedModule',
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'ConvBn1d',
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'ConvBn2d',
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'ConvBn3d',
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'ConvBnReLU1d',
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'ConvBnReLU2d',
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'ConvBnReLU3d',
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'ConvReLU1d',
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'ConvReLU2d',
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'ConvReLU3d',
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'LinearReLU',
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'BNReLU2d',
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'BNReLU3d',
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'LinearBn1d',
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]
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self._test_function_import('intrinsic', module_list, base='nn')
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def test_modules_nn_intrinsic_fused(self):
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function_list = [
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'_FusedModule',
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'ConvBn1d',
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'ConvBn2d',
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'ConvBn3d',
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'ConvBnReLU1d',
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'ConvBnReLU2d',
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'ConvBnReLU3d',
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'ConvReLU1d',
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'ConvReLU2d',
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'ConvReLU3d',
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'LinearReLU',
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'BNReLU2d',
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'BNReLU3d',
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'LinearBn1d',
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]
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self._test_function_import('fused', function_list,
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base='nn.intrinsic.modules')
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def test_package_import_nn_intrinsic_qat(self):
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r"""Tests the migration of the torch.nn.intrinsic.modules"""
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self._test_package_import('qat', base='nn.intrinsic')
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self._test_package_import('qat.modules', base='nn.intrinsic')
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def test_modules_import_nn_intrinsic_qat(self):
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module_list = [
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"LinearReLU",
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"LinearBn1d",
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"ConvReLU1d",
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"ConvReLU2d",
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"ConvReLU3d",
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"ConvBn1d",
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"ConvBn2d",
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"ConvBn3d",
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"ConvBnReLU1d",
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"ConvBnReLU2d",
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"ConvBnReLU3d",
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"update_bn_stats",
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"freeze_bn_stats",
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]
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self._test_function_import('qat', module_list, base='nn.intrinsic')
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def test_modules_intrinsic_qat_conv_fused(self):
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function_list = [
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'ConvBn1d',
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'ConvBnReLU1d',
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'ConvReLU1d',
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'ConvBn2d',
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'ConvBnReLU2d',
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'ConvReLU2d',
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'ConvBn3d',
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'ConvBnReLU3d',
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'ConvReLU3d',
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'update_bn_stats',
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'freeze_bn_stats'
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]
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self._test_function_import('conv_fused', function_list,
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base='nn.intrinsic.qat.modules')
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def test_modules_intrinsic_qat_linear_fused(self):
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function_list = [
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'LinearBn1d',
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]
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self._test_function_import('linear_fused', function_list,
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base='nn.intrinsic.qat.modules')
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def test_modules_intrinsic_qat_linear_relu(self):
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function_list = [
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'LinearReLU',
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]
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self._test_function_import('linear_relu', function_list,
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base='nn.intrinsic.qat.modules')
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def test_package_import_nn_intrinsic_quantized(self):
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r"""Tests the migration of the torch.nn.intrinsic.quantized"""
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self._test_package_import('quantized', base='nn.intrinsic')
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self._test_package_import('quantized.modules', base='nn.intrinsic')
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def test_modules_import_nn_intrinsic_quantized(self):
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module_list = [
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'BNReLU2d',
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'BNReLU3d',
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'ConvReLU1d',
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'ConvReLU2d',
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'ConvReLU3d',
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'LinearReLU',
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]
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self._test_function_import('quantized', module_list, base='nn.intrinsic')
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def test_modules_intrinsic_quantized_bn_relu(self):
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function_list = [
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'BNReLU2d',
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'BNReLU3d',
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]
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self._test_function_import('bn_relu', function_list,
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base='nn.intrinsic.quantized.modules')
|
|
|
|
def test_modules_intrinsic_quantized_conv_relu(self):
|
|
function_list = [
|
|
'ConvReLU1d',
|
|
'ConvReLU2d',
|
|
'ConvReLU3d',
|
|
]
|
|
self._test_function_import('conv_relu', function_list,
|
|
base='nn.intrinsic.quantized.modules')
|
|
|
|
def test_modules_intrinsic_quantized_linear_relu(self):
|
|
function_list = [
|
|
'LinearReLU',
|
|
]
|
|
self._test_function_import('linear_relu', function_list,
|
|
base='nn.intrinsic.quantized.modules')
|