forked from mindspore-Ecosystem/mindspore
123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
# Copyright 2022 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 time
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.functional import vmap
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from mindspore.common.api import ms_function
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class FastGeluNet(nn.Cell):
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"""FastGeluNet"""
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def __init__(self):
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"""Init."""
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super(FastGeluNet, self).__init__()
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self.fast_gelu = P.FastGeLU()
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def construct(self, x):
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"""Construct."""
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return self.fast_gelu(x)
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def fast_gelu_compute(x):
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"""FastGeluCompute."""
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return x * np.exp(0.851 * (x - np.abs(x))) / (1 + np.exp(-1.702 * np.abs(x)))
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def np_all_close_with_loss(out, expect):
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"""np_all_close_with_loss"""
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return np.allclose(out, expect, 0.005, 0.005, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6)])
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@pytest.mark.parametrize('dtype', [np.float32, np.float16])
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def test_fast_gelu_cpu(shape, dtype):
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"""
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Feature: FastGeLU cpu kernel
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Description: test the rightness of FastGeLU cpu kernel.
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Expectation: Success.
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"""
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prop_cpu = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop_cpu).astype(dtype)
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y_np_cpu = fast_gelu_compute(x_np)
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x_ms_cpu = Tensor(x_np)
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net = FastGeluNet()
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y_ms = net(x_ms_cpu)
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assert np_all_close_with_loss(y_np_cpu, y_ms.asnumpy())
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x_ms_cpu = Tensor(x_np)
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y_fun = F.fast_gelu(x_ms_cpu)
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assert np_all_close_with_loss(y_np_cpu, y_fun.asnumpy())
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x_ms_cpu = Tensor(x_np)
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fast_gelu_nn = nn.FastGelu()
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y_nn = fast_gelu_nn(x_ms_cpu)
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assert np_all_close_with_loss(y_np_cpu, y_nn.asnumpy())
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype', [np.float32, np.float16])
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def test_fast_gelu_vmap(dtype, shape=(100, 2)):
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"""
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Feature: FastGeLU cpu kernel
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Description: test the rightness of FastGeLU cpu kernel vmap feature.
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Expectation: Success.
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"""
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def fast_gelu_fun(x):
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"""fast_gelu_fun"""
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return P.FastGeLU()(x)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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x = Tensor(x_np)
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x = F.sub(x, 0)
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start_time = time.perf_counter()
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output_vmap = vmap(fast_gelu_fun, in_axes=(0,))(x)
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vmap_time = time.perf_counter() - start_time
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start_time_manually = time.perf_counter()
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@ms_function
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def manually_batched(xs):
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"""manually_batched"""
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output = []
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for i in range(xs.shape[0]):
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output.append(fast_gelu_fun(xs[i]))
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return F.stack(output)
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output_manually = manually_batched(x)
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manually_time = time.perf_counter() - start_time_manually
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assert np_all_close_with_loss(output_vmap.asnumpy(), output_manually.asnumpy())
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assert vmap_time < manually_time
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