support change shuffle and train thread num (#19841)
* support change shuffle thread num * support change train thread num * fix receive shuffle data of each channel * data norm stop gradient * add check thread_tensor type and root_tensor type when merge metric * remove sleep in shuffle, add config * add config of pslib client to client communication * fix xbox str * add data norm op testcase * add flush in trainer finalizeexpand_as_op_1
xujiaqi01
6 years ago
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GitHub
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cedc04775c
@ -0,0 +1,203 @@
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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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"""This is unit test of Test data_norm Op."""
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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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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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from op_test import OpTest
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from paddle.fluid.framework import grad_var_name
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def _reference_testing(x, batch_size, batch_sum, batch_square_sum):
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x_shape = x.shape
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means_arr = batch_sum / batch_size
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scales_arr = np.sqrt(batch_size / batch_square_sum)
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for i in range(x_shape[0]):
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x[i] -= means_arr
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x[i] *= scales_arr
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y = np.array(x)
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return y
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def create_or_get_tensor(scope, var_name, var, place):
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tensor = scope.var(var_name).get_tensor()
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if var is not None:
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assert isinstance(var, np.ndarray)
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tensor.set_recursive_sequence_lengths([])
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tensor.set(var, place)
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return tensor
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class TestDataNormOpInference(unittest.TestCase):
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"""
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test class for data norm op
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test forward
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"""
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def setUp(self):
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"""
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init members of this class
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"""
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self.dtype = np.float32
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self.use_mkldnn = False
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def __assert_close(self, tensor, np_array, msg, atol=1e-4):
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self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
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def check_with_place(self, place, data_layout, dtype, shape):
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"""
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do forward and check
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Args:
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place(Place): CPUPlace
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data_layout(str): NCHW or NWHC
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dtype(dtype): np.float32
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shape(list): input shape
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"""
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epsilon = 0.00001
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if len(shape) == 2:
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x_shape = shape
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c = x_shape[1]
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else:
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ValueError("len(shape) should be equal to 2")
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scale_shape = [c]
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x_val = np.random.random_sample(x_shape).astype(dtype)
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x_val = x_val - 0.5
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batch_size = np.ones(scale_shape).astype(np.float32)
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batch_size *= 1e4
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batch_sum = np.zeros(scale_shape).astype(np.float32)
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batch_square_sum = np.ones(scale_shape).astype(np.float32)
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batch_square_sum *= 1e4
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y_out = _reference_testing(x_val, batch_size, batch_sum,
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batch_square_sum).astype(dtype)
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scope = core.Scope()
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# create input
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x_tensor = create_or_get_tensor(scope, "x_val",
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OpTest.np_dtype_to_fluid_dtype(x_val),
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place)
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batch_size_tensor = create_or_get_tensor(
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scope, "batch_size",
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OpTest.np_dtype_to_fluid_dtype(batch_size), place)
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batch_sum_tensor = create_or_get_tensor(
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scope, "batch_sum",
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OpTest.np_dtype_to_fluid_dtype(batch_sum), place)
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batch_square_sum_tensor = create_or_get_tensor(
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scope, "batch_square_sum",
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OpTest.np_dtype_to_fluid_dtype(batch_square_sum), place)
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# create output
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y_tensor = create_or_get_tensor(scope, "y_out", None, place)
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mean_tensor = create_or_get_tensor(scope, "mean", None, place)
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scales_tensor = create_or_get_tensor(scope, "scales", None, place)
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data_norm_op = Operator(
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"data_norm",
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# inputs
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X="x_val",
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BatchSize="batch_size",
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BatchSum="batch_sum",
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BatchSquareSum="batch_square_sum",
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# outputs
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Y="y_out",
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Means="mean",
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Scales="scales",
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# attrs
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epsilon=epsilon,
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use_mkldnn=self.use_mkldnn)
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data_norm_op.run(scope, place)
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# check inference result
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self.__assert_close(
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y_tensor,
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y_out,
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"inference output are different at " + str(place) + ", " +
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data_layout + ", " + str(np.dtype(dtype)) +
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str(np.array(y_tensor)) + str(y_out),
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atol=1e-3)
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def test_check_output(self):
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"""
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test check forward, check output
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"""
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places = [core.CPUPlace()]
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for place in places:
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for data_format in ["NCHW", "NHWC"]:
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self.check_with_place(place, data_format, self.dtype, [2, 3])
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class TestDataNormOp(OpTest):
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"""
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test class for data norm op
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test forward and backward
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"""
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def setUp(self):
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"""
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init data norm op test env
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"""
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self.op_type = 'data_norm'
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self.use_mkldnn = False
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epsilon = 0.00001
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x_shape = [2, 3]
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scale_shape = [3]
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tp = np.float32
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x_val = np.array([[-0.35702616, -0.42756206, -0.08306625],
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[0.41199666, -0.21719968, -0.10180971]]).astype(tp)
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batch_size = np.ones(scale_shape).astype(tp)
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batch_size *= 1e4
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batch_sum = np.zeros(scale_shape).astype(tp)
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batch_square_sum = np.ones(scale_shape).astype(tp)
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batch_square_sum *= 1e4
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y = np.array(x_val)
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mean = np.array([[0, 0, 0], [0, 0, 0]]).astype(tp)
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scale = np.array([[1, 1, 1], [1, 1, 1]]).astype(tp)
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self.inputs = {
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"X": x_val,
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"BatchSize": batch_size,
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"BatchSum": batch_sum,
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"BatchSquareSum": batch_square_sum
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}
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self.outputs = {"Y": y, "Means": mean, "Scales": scale}
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self.attrs = {"epsilon": epsilon, "use_mkldnn": self.use_mkldnn}
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def test_check_output(self):
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"""
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test check forward, check output
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"""
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self.check_output()
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def test_check_grad(self):
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"""
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test check backward, check grad
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"""
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self.check_grad(['X'], 'Y', no_grad_set=set([]))
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if __name__ == '__main__':
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unittest.main()
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