wehub-resource-sync
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# Copyright (c) 2020 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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import unittest
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import numpy as np
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from op_test import OpTest, get_device_place
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import paddle
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from paddle.base import core
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def np_cal_batchfc(input, w, bias):
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slot_pairs_num, batch_size, in_dim = input.shape
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_, _, out_dim = w.shape
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res = np.zeros((slot_pairs_num, batch_size, out_dim))
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for slot in range(slot_pairs_num):
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res[slot, :] = np.dot(input[slot, :], w[slot, :])
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for slot in range(slot_pairs_num):
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for b_index in range(out_dim):
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res[slot, :, b_index] += bias[slot, b_index]
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return res
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def api_wrapper(input, w, bias):
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return paddle._C_ops.batch_fc(input, w, bias)
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class TestBatchFCOp(OpTest):
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def config(self):
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self.slot_pairs_num = 10
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self.batch_size = 5
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self.in_dim = 10
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self.out_dim = 12
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self.dtype = "float64"
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def setUp(self):
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self.config()
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self.input = np.random.random(
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(self.slot_pairs_num, self.batch_size, self.in_dim)
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).astype(self.dtype)
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self.w = np.random.random(
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(self.slot_pairs_num, self.in_dim, self.out_dim)
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).astype(self.dtype)
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self.bias = np.random.random(
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(self.slot_pairs_num, self.out_dim)
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).astype(self.dtype)
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self.op_type = "batch_fc"
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self.python_api = api_wrapper
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np_out = np_cal_batchfc(self.input, self.w, self.bias)
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np_out = np_out.astype(self.dtype)
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self.inputs = {"Input": self.input, "W": self.w, "Bias": self.bias}
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self.outputs = {"Out": np_out}
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def test_check_output_gpu(self):
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self.check_output_with_place(get_device_place())
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def test_check_grad_gpu(self):
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self.check_grad_with_place(
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get_device_place(), ["Bias", "W", "Input"], "Out"
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)
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class TestBatchFCOp1(OpTest):
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def config(self):
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self.slot_pairs_num = 10
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self.batch_size = 5
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self.in_dim = 10
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self.out_dim = 12
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self.dtype = "float64"
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def setUp(self):
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self.config()
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self.input = np.random.random(
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(self.slot_pairs_num, self.batch_size, self.in_dim)
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).astype(self.dtype)
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self.w = np.random.random(
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(self.slot_pairs_num, self.in_dim, self.out_dim)
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).astype(self.dtype)
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self.bias = np.random.random(
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(self.slot_pairs_num, self.out_dim)
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).astype(self.dtype)
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self.op_type = "batch_fc"
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self.python_api = api_wrapper
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np_out = np_cal_batchfc(self.input, self.w, self.bias)
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np_out = np_out.astype(self.dtype)
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self.inputs = {"Input": self.input, "W": self.w, "Bias": self.bias}
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self.outputs = {"Out": np_out}
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def test_check_output_cpu(self):
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try:
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self.check_output_with_place(place=core.CPUPlace())
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except:
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print("do not support cpu test, skip")
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def test_check_grad_cpu(self):
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try:
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self.check_grad_with_place(
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core.CPUPlace(), ["Bias", "W", "Input"], "Out"
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)
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except:
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print("do not support cpu test, skip")
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if __name__ == "__main__":
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unittest.main()
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