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https://github.com/pytorch/pytorch.git
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954ce94950
This PR is part of a series attempting to re-submit https://github.com/pytorch/pytorch/pull/134592 as smaller PRs. In quantization tests: - Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run. - Raise a RuntimeError on tests which have been disabled (not run) Pull Request resolved: https://github.com/pytorch/pytorch/pull/154728 Approved by: https://github.com/ezyang
368 lines
10 KiB
Python
368 lines
10 KiB
Python
# Owner(s): ["oncall: quantization"]
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from torch.testing._internal.common_utils import raise_on_run_directly
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from .common import AOMigrationTestCase
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class TestAOMigrationNNQuantized(AOMigrationTestCase):
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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(
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"activation", function_list, base="nn.quantized.modules"
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)
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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(
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"batchnorm", function_list, base="nn.quantized.modules"
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)
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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, 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(
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"dropout", function_list, base="nn.quantized.modules"
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)
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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(
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"embedding_ops", function_list, base="nn.quantized.modules"
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)
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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(
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"functional_modules", function_list, base="nn.quantized.modules"
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)
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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, 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(
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"normalization", function_list, base="nn.quantized.modules"
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)
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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, base="nn.quantized.modules")
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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_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(
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"activation", module_list, base="nn.quantizable.modules"
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)
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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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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_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, base="nn.intrinsic.modules")
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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(
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"conv_fused", function_list, base="nn.intrinsic.qat.modules"
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)
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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(
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"linear_fused", function_list, base="nn.intrinsic.qat.modules"
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)
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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(
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"linear_relu", function_list, base="nn.intrinsic.qat.modules"
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)
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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(
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"bn_relu", function_list, base="nn.intrinsic.quantized.modules"
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)
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def test_modules_intrinsic_quantized_conv_relu(self):
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function_list = [
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"ConvReLU1d",
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"ConvReLU2d",
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"ConvReLU3d",
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]
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self._test_function_import(
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"conv_relu", function_list, base="nn.intrinsic.quantized.modules"
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)
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def test_modules_intrinsic_quantized_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(
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"linear_relu", function_list, base="nn.intrinsic.quantized.modules"
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)
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def test_modules_no_import_nn_intrinsic_quantized_dynamic(self):
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# TODO(future PR): generalize this
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import torch
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_ = torch.ao.nn.intrinsic.quantized.dynamic
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_ = torch.nn.intrinsic.quantized.dynamic
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if __name__ == "__main__":
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raise_on_run_directly("test/test_quantization.py")
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