forked from mindspore-Ecosystem/mindspore
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
# Copyright 2021 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 mindspore.context as context
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from mindspore import Tensor
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import mindspore.nn as nn
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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import mindspore.ops.functional as F
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import pytest
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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# enable graph kernel optimization.
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context.set_context(enable_graph_kernel=True)
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class BertAttentionPiece(Cell):
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def __init__(self):
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super(BertAttentionPiece, self).__init__()
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self.add = P.Add()
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self.dropout = nn.Dropout(1 - 0.1)
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self.softmax = nn.Softmax()
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self.multiply_data = -10000.0
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self.sub = P.Sub()
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self.multiply = P.Mul()
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self.get_dtype = P.DType()
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self.cast = P.Cast()
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def construct(self, attention_mask, attention_scores):
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multiply_out = self.sub(self.cast(F.tuple_to_array((1.0,)), self.get_dtype(attention_scores)),
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self.cast(attention_mask, self.get_dtype(attention_scores)))
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adder = self.multiply(multiply_out, self.multiply_data)
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attention_scores = self.add(adder, attention_scores)
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attention_probs = self.softmax(attention_scores)
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attention_probs = self.dropout(attention_probs)
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return attention_probs
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def get_rtol_atol(dtype):
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if dtype == np.float16:
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return 1.e-3, 1.e-3
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return 1.e-4, 1.e-4
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def compare_result(expect, output, dtype):
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rtol, atol = get_rtol_atol(dtype)
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if isinstance(expect, (list, tuple)):
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assert isinstance(output, (list, tuple)) and len(expect) == len(output)
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expect_list = list(expect)
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output_list = list(output)
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for e, o in zip(expect_list, output_list):
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assert np.allclose(e.asnumpy(), o.asnumpy(), rtol, atol, equal_nan=True)
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else:
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assert np.allclose(expect.asnumpy(), output.asnumpy(), rtol, atol, equal_nan=True)
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def get_softmax_output(x, y, enable_stitch_fusion):
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# enable graph kernel stitch fusion.
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if enable_stitch_fusion:
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context.set_context(graph_kernel_flags="--enable_stitch_fusion=true")
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net = BertAttentionPiece()
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result = net(x, y)
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return result
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def test_softmax(shape, dtype):
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np.random.seed(0)
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x = Tensor(np.random.normal(0, 1, shape).astype(dtype))
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y = Tensor(np.random.normal(0, 1, shape).astype(dtype))
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expect = get_softmax_output(x, y, False)
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output = get_softmax_output(x, y, True)
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compare_result(expect, output, dtype)
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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_softmax_gpu():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_softmax([64, 12, 128, 128], np.float16)
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