chenzomi
5 years ago
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a834a6308e
commit
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import pytest
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from mindspore import Tensor
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.ops.operations import _quant_ops as Q
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context.set_context(device_target='GPU', device_id=0)
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class Net(nn.Cell):
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def __init__(self, num_bits=8, narrow_range=False):
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super(Net, self).__init__()
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self.op = Q.FakeQuantPerLayerGrad(num_bits=num_bits, narrow_range=narrow_range)
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def construct(self, dout, x, minq, maxq):
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return self.op(dout, x, minq, maxq)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad1():
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# WithArgsGradient RegularRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.26, -0.25, -0.24, 0.0, 63.5, 63.6]).astype(np.float32)
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min_val = np.array([-0.125]).reshape(1).astype(np.float32)
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max_val = np.array([63.625]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad2():
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# WithArgsGradient NarrowRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.26, -0.25, -0.24, 0.0, 63.25, 63.3]).astype(np.float32)
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min_val = np.array([-0.125]).reshape(1).astype(np.float32)
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max_val = np.array([63.375]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad3():
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# WithArgsGradient_4Bits_RegularRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.6, -0.5, -0.4, 0.0, 7.0, 7.1]).astype(np.float32)
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min_val = np.array([-0.4]).reshape(1).astype(np.float32)
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max_val = np.array([7.1]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=False)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad4():
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# WithArgsGradient_4Bits_NarrowRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.6, -0.5, -0.4, 0.0, 6.5, 6.6]).astype(np.float32)
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min_val = np.array([-0.4]).reshape(1).astype(np.float32)
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max_val = np.array([6.6]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=True)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad5():
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# FakeQuantWithMinMaxVarsGradient
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).astype(np.float32)
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min_val = np.array([0.0]).reshape(1).astype(np.float32)
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max_val = np.array([0.0]).reshape(1).astype(np.float32)
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expect = dout
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad6():
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# WithVarsGradient_RegularRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.26, -0.25, -0.24, 0.0, 63.5, 63.6]).astype(np.float32)
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min_val = np.array([-0.125]).reshape(1).astype(np.float32)
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max_val = np.array([63.625]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=False)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad7():
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# WithVarsGradient_NarrowRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.26, -0.25, -0.24, 0.0, 63.25, 63.3]).astype(np.float32)
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min_val = np.array([-0.125]).reshape(1).astype(np.float32)
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max_val = np.array([63.375]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=8, narrow_range=True)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad8():
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# WithVarsGradient_4Bits_RegularRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.6, -0.5, -0.4, 0.0, 7.0, 7.1]).astype(np.float32)
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min_val = np.array([-0.4]).reshape(1).astype(np.float32)
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max_val = np.array([7.1]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=False)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_fake_quant_grad9():
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# WithVarsGradient_4Bits_NarrowRange
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dout = np.random.uniform(-1, 1, size=[6]).astype('float32')
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x = np.array([-0.6, -0.5, -0.4, 0.0, 6.5, 6.6]).astype(np.float32)
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min_val = np.array([-0.4]).reshape(1).astype(np.float32)
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max_val = np.array([6.6]).reshape(1).astype(np.float32)
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expect = np.array([0.0, dout[1], dout[2], dout[3], dout[4], 0.0]).astype(np.float32)
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net = Net(num_bits=4, narrow_range=True)
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output = net(Tensor(dout), Tensor(x), Tensor(min_val), Tensor(max_val))
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error = np.ones(shape=expect.shape) * 1.0e-5
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diff = output.asnumpy().flatten() - expect
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print("output: ", output)
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print("expect: ", expect)
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assert np.all(np.abs(diff) < error)
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