test=developrevert-15774-anakin_subgraph_engine
Krzysztof Binias
6 years ago
parent
a6e3cd5eb7
commit
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import print_function
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import numpy as np
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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def check_if_mkldnn_primitives_exist_in_bwd(test_case, op_type, x, out,
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out_grad, x_grad):
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def __assert_close(tensor, np_array, msg, atol=1e-4):
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test_case.assertTrue(
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np.allclose(
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np.array(tensor), np_array, atol=atol), msg)
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place = core.CPUPlace()
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var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad}
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var_names = list(var_dict.keys())
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ground_truth = {name: var_dict[name] for name in var_names}
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program = fluid.Program()
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with fluid.program_guard(program):
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block = program.global_block()
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for name in ground_truth:
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block.create_var(
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name=name, dtype=np.float32, shape=ground_truth[name].shape)
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op = block.append_op(
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type=op_type,
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inputs={'X': block.var('x'), },
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outputs={'Out': block.var('out')},
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attrs={'use_mkldnn': True})
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# Generate backward op_desc
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grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(op.desc,
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set(), [])
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grad_op_desc = grad_op_desc_list[0]
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new_op_desc = block.desc.append_op()
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new_op_desc.copy_from(grad_op_desc)
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for var_name in grad_op_desc.output_arg_names():
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block.desc.var(var_name.encode('ascii'))
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grad_op_desc.infer_var_type(block.desc)
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grad_op_desc.infer_shape(block.desc)
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for arg in grad_op_desc.output_arg_names():
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grad_var = block.desc.find_var(arg.encode('ascii'))
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grad_var.set_dtype(core.VarDesc.VarType.FP32)
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exe = fluid.Executor(place)
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# Do at least 2 iterations
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for i in range(2):
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out = exe.run(
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program,
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feed={name: var_dict[name]
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for name in ['x', 'out@GRAD']},
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fetch_list=['x@GRAD', 'out'])
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__assert_close(x_grad, out[0], 'x@GRAD')
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@ -0,0 +1,57 @@
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import print_function
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import unittest
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import numpy as np
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from paddle.fluid.tests.unittests.op_test import OpTest
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import paddle.fluid.core as core
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from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp, stable_softmax
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from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
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class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
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def init_kernel_type(self):
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self.use_mkldnn = True
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class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
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def get_x_shape(self):
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return [2, 3, 4, 5]
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# Check if primitives already exist in backward
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class TestSoftmaxMKLDNNPrimitivesAlreadyExist(unittest.TestCase):
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def setUp(self):
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super(TestSoftmaxMKLDNNPrimitivesAlreadyExist, self).setUp()
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np.random.seed(123)
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self.op_type = 'softmax'
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self.x = np.random.uniform(-1, 1, 2).astype(np.float32)
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self.out = stable_softmax(self.x)
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self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
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self.x_grad = self.__softmax_bwd(self.out, self.out_grad)
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# Softmax grad calculation
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def __softmax_bwd(self, out, out_grad):
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return out * (out_grad - np.dot(out, out_grad))
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def test_check(self):
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check_if_mkldnn_primitives_exist_in_bwd(
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self, self.op_type, self.x, self.out, self.out_grad, self.x_grad)
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if __name__ == '__main__':
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unittest.main()
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