test=developrevert-15207-remove_op_handle_lock_and_fix_var
tensor-tang
6 years ago
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# Copyright (c) 2018 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 os
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import signal
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import time
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import unittest
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from multiprocessing import Process
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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from paddle.fluid.framework import Program, program_guard
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def nce(input, weight, bias, sample_weight, labels, num_classes,
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num_sample_class):
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samples = []
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sample_labels = []
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batch_size = input.shape[0]
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num_true_class = labels.shape[1]
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for i in range(batch_size):
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w = 1 if sample_weight is None else sample_weight[i]
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for label in labels[i]:
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samples.append((i, label, True, w))
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sample_labels.append(label)
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for num in range(num_sample_class):
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samples.append((i, num, False, w))
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sample_labels.append(num)
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# forward bias
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sample_out = np.zeros(len(samples)).astype(np.float32)
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if bias is not None:
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for i in range(len(samples)):
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sample_out[i] = bias[samples[i][1]]
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# forward weight
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for i in range(len(samples)):
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sample_out[i] += np.dot(input[samples[i][0]], weight[samples[i][1]])
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# forward activation
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sample_out = 1.0 / (1.0 + np.exp(-sample_out))
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# forward cost
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out = np.zeros(batch_size).astype(np.float32)
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b = 1.0 / num_classes * num_sample_class
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for i in range(len(samples)):
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o = sample_out[i]
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cost = -np.log(o / (o + b)) if samples[i][2] else -np.log(b / (o + b))
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out[samples[i][0]] += cost * samples[i][3]
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return (out[:, np.newaxis], np.array(sample_out).reshape(
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batch_size, num_sample_class + num_true_class),
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np.array(sample_labels).reshape(batch_size,
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num_sample_class + num_true_class))
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def run_pserver(pserver_id, use_cuda, sync_mode):
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scope = fluid.core.Scope()
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program = Program()
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with fluid.scope_guard(scope):
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with program_guard(program, startup_program=Program()):
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# create table parameter in scope
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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# create and initialize Param Variable
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param = scope.var('table').get_tensor()
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param_array = np.ones((5, 8)).astype("float32")
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for i in range(len(param_array)):
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param_array[i] *= param_array[i] * i + pserver_id * 10 + 1
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param.set(param_array, place)
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optimize_block = program._create_block(program.global_block().idx)
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program.global_block().append_op(
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type="listen_and_serv",
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inputs={'X': []},
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outputs={},
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attrs={
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"optimize_blocks": [optimize_block],
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"endpoint": '127.0.0.1:0',
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"Fanin": 1,
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"sync_mode": True,
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"grad_to_block_id": []
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})
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exe = fluid.Executor(place)
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exe.run(program)
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class TestListenAndServOp(unittest.TestCase):
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def setUp(self):
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self.ps_timeout = 5
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def _start_pserver(self, pserver_id, use_cuda, sync_mode, pserver_func):
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p = Process(target=pserver_func, args=(pserver_id, use_cuda, sync_mode))
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p.daemon = True
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p.start()
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return p
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def _wait_ps_ready(self, pid):
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start_left_time = self.ps_timeout
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sleep_time = 0.5
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while True:
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assert start_left_time >= 0, "wait ps ready failed"
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time.sleep(sleep_time)
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try:
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# the listen_and_serv_op would touch a file which contains the listen port
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# on the /tmp directory until it was ready to process all the RPC call.
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os.stat("/tmp/paddle.%d.port" % pid)
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return
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except os.error:
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start_left_time -= sleep_time
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def _get_pserver_port(self, pid):
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with open("/tmp/paddle.%d.port" % pid, 'r') as f:
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port = int(f.read().strip())
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return port
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def _run_nce_op_two_pserver(self, place, port0, port1):
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scope = fluid.core.Scope()
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program = Program()
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with fluid.scope_guard(scope):
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with program_guard(program, startup_program=Program()):
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x = scope.var('Input').get_tensor()
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x_array = np.random.random((4, 8)).astype("float32")
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x.set(x_array, place)
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# create and initialize Param Variable
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param = scope.var('Weight').get_tensor()
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param_array = np.zeros((5, 8)).astype("float32")
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param.set(param_array, place)
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bias = scope.var('Bias').get_tensor()
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bias_array = np.random.random((5, 1)).astype("float32")
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bias.set(bias_array, place)
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sample_w = scope.var('SampleWeight').get_tensor()
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sample_weight = np.random.random((4, 1)).astype("float32")
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sample_w.set(sample_weight, place)
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label = scope.var('Label').get_tensor()
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label_array = np.array([[0], [1], [4], [3]])
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label.set(label_array, place)
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cost = scope.var('Cost').get_tensor()
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cost_w = np.zeros((4, 1)).astype("float32")
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cost.set(cost_w, place)
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sample_l = scope.var('SampleLogits').get_tensor()
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sample_l_w = np.zeros((4, 3)).astype("float32")
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sample_l.set(sample_l_w, place)
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sample_la = scope.var('SampleLabels').get_tensor()
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sample_la_w = np.zeros((4, 3)).astype("int")
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sample_la.set(sample_la_w, place)
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emaps = ['127.0.0.1:' + str(port0), '127.0.0.1:' + str(port1)]
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table_names = ['table', 'table']
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height_sections = [2, 3]
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# create and run nce operator
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nce_op = Operator(
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"nce",
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Input='Input',
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Weight='Weight',
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Label='Label',
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Bias='Bias',
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Cost='Cost',
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SampleLogits='SampleLogits',
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SampleLabels='SampleLabels',
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SampleWeight='SampleWeight',
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num_total_classes=5,
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num_neg_samples=2,
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custom_neg_classes=list(range(2)),
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sampler=0,
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seed=0,
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is_sparse=True,
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remote_prefetch=True,
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epmap=emaps,
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table_names=table_names,
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height_sections=height_sections)
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nce_op.run(scope, place)
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# get and compare result
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o_cost = np.array(scope.var('Cost').get_tensor())
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o_logits = np.array(scope.var('SampleLogits').get_tensor())
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o_labels = np.array(scope.var('SampleLabels').get_tensor())
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param_array = np.ones((5, 8)).astype("float32")
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for i in range(2):
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param_array[i] *= param_array[i] * i + 0 * 10 + 1
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for i in range(2, 5):
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param_array[i] *= param_array[i] * i + 1 * 10 + 1
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out = nce(x_array, param_array, bias_array, sample_weight,
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label_array, 5, 2)
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self.assertAlmostEqual(o_cost.all(), out[0].all(), delta=1e-6)
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self.assertAlmostEqual(o_logits.all(), out[1].all(), delta=1e-6)
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self.assertAlmostEqual(o_labels.all(), out[2].all(), delta=1e-6)
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def test_nce_op_remote(self):
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os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
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# run pserver on CPU in sync mode
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p0 = self._start_pserver(0, False, True, run_pserver)
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self._wait_ps_ready(p0.pid)
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port0 = self._get_pserver_port(p0.pid)
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p1 = self._start_pserver(1, False, True, run_pserver)
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self._wait_ps_ready(p1.pid)
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port1 = self._get_pserver_port(p1.pid)
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places = [core.CPUPlace()]
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for place in places:
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self._run_nce_op_two_pserver(place, port0, port1)
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# raise SIGTERM to pserver
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os.kill(p0.pid, signal.SIGINT)
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p0.join()
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os.kill(p1.pid, signal.SIGINT)
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p1.join()
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if __name__ == '__main__':
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unittest.main()
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