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87 lines
3.1 KiB
87 lines
3.1 KiB
# 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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def format_reorder(out, size):
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in_n = size[0]
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out_h = size[2]
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out_w = size[3]
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out_c = size[1]
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out_tmp = np.zeros((in_n, out_h, out_w, out_c))
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for n in range(in_n):
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for i in range(out_h):
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for j in range(out_w):
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for m in range(out_c):
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out_tmp[n, i, j, m] = out[n, m, i, j]
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return out_tmp.reshape(in_n, out_c, out_h, out_w)
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