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112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests of QuantBatchNorm module."""
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import pytest
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from modelopt.torch.quantization import set_quantizer_attribute, tensor_quant
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from modelopt.torch.quantization.nn import QuantModuleRegistry
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NUM_CHANNELS = 3
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class TestQuantBatchNormND:
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@pytest.mark.parametrize(
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("original_cls", "input_shape"),
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[
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(nn.BatchNorm1d, (2, NUM_CHANNELS, 8)),
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(nn.BatchNorm2d, (2, NUM_CHANNELS, 8, 8)),
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(nn.BatchNorm3d, (2, NUM_CHANNELS, 8, 8, 8)),
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],
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)
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def test_no_quant(self, original_cls, input_shape):
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batchnorm_object = original_cls(NUM_CHANNELS, affine=True)
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quant_batchnorm_object = QuantModuleRegistry.convert(batchnorm_object)
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quant_batchnorm_object.input_quantizer.disable()
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test_input = torch.randn(input_shape)
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out1 = quant_batchnorm_object(test_input)
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out2 = F.batch_norm(
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test_input,
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quant_batchnorm_object.running_mean,
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quant_batchnorm_object.running_var,
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quant_batchnorm_object.weight,
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quant_batchnorm_object.bias,
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training=True,
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)
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assert torch.allclose(out1, out2, rtol=0, atol=0)
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@pytest.mark.parametrize(
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("original_cls", "input_shape"),
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[
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(nn.BatchNorm1d, (2, NUM_CHANNELS, 8)),
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(nn.BatchNorm2d, (2, NUM_CHANNELS, 8, 8)),
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(nn.BatchNorm3d, (2, NUM_CHANNELS, 8, 8, 8)),
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],
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)
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def test_fake_quant_per_tensor(self, original_cls, input_shape):
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batchnorm_object = original_cls(NUM_CHANNELS, affine=True)
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quant_batchnorm_object = QuantModuleRegistry.convert(batchnorm_object)
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test_input = torch.randn(input_shape)
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quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)))
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out1 = quant_batchnorm_object(test_input)
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out2 = F.batch_norm(
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quant_input,
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quant_batchnorm_object.running_mean,
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quant_batchnorm_object.running_var,
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quant_batchnorm_object.weight,
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quant_batchnorm_object.bias,
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training=True,
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)
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assert torch.allclose(out1, out2, rtol=0, atol=0)
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@pytest.mark.parametrize(
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("original_cls", "input_shape"),
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[
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(nn.BatchNorm1d, (2, NUM_CHANNELS, 8)),
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(nn.BatchNorm2d, (2, NUM_CHANNELS, 8, 8)),
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(nn.BatchNorm3d, (2, NUM_CHANNELS, 8, 8, 8)),
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],
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)
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def test_fake_quant_per_channel(self, original_cls, input_shape):
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batchnorm_object = original_cls(NUM_CHANNELS, affine=True)
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quant_batchnorm_object = QuantModuleRegistry.convert(batchnorm_object)
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set_quantizer_attribute(quant_batchnorm_object, lambda name: True, {"axis": (1)})
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test_input = torch.randn(input_shape)
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reduce_dims = list(range(len(test_input.shape)))
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reduce_dims.pop(1)
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quant_input = tensor_quant.fake_tensor_quant(
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test_input, torch.abs(test_input).amax(dim=reduce_dims, keepdim=True)
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)
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out1 = quant_batchnorm_object(test_input)
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out2 = F.batch_norm(
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quant_input,
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quant_batchnorm_object.running_mean,
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quant_batchnorm_object.running_var,
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quant_batchnorm_object.weight,
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quant_batchnorm_object.bias,
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training=True,
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)
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assert torch.allclose(out1, out2, rtol=0, atol=0)
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