Merge branch 'feature/add_prefech_hs' of https://github.com/JiabinYang/Paddle into feature/add_prefech_hs

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -870,9 +870,21 @@ class TestRemoteNce(TestDistLookupTableBase):
 
     def transpiler_test_impl(self):
         trainer, _ = self.get_trainer()
+
+        out_vars = ["nce_w.block0", "nce_w.block1"]
+        in_vars = ["nce_b.block0", "nce_b.block1"]
+
+        recv_var_names = []
+
         for op in trainer.blocks[0].ops:
             if op.type == "recv":
-                pass
+                for var in op.output("Out"):
+                    recv_var_names.append(var)
+
+        for out_var in out_vars:
+            self.assertFalse(out_var in recv_var_names)
+        for in_var in in_vars:
+            self.assertTrue(in_var in recv_var_names)
 
 
 # test for remote prefetch

python/paddle/fluid/tests/unittests/test_nce_remote_table_op.py

@@ -0,0 +1,238 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import os
+import signal
+import time
+import unittest
+from multiprocessing import Process
+
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
+from paddle.fluid.framework import Program, program_guard
+
+
+def nce(input, weight, bias, sample_weight, labels, num_classes,
+        num_sample_class):
+    samples = []
+    sample_labels = []
+    batch_size = input.shape[0]
+    num_true_class = labels.shape[1]
+    for i in range(batch_size):
+        w = 1 if sample_weight is None else sample_weight[i]
+        for label in labels[i]:
+            samples.append((i, label, True, w))
+            sample_labels.append(label)
+        for num in range(num_sample_class):
+            samples.append((i, num, False, w))
+            sample_labels.append(num)
+    # forward bias
+    sample_out = np.zeros(len(samples)).astype(np.float32)
+    if bias is not None:
+        for i in range(len(samples)):
+            sample_out[i] = bias[samples[i][1]]
+    # forward weight
+    for i in range(len(samples)):
+        sample_out[i] += np.dot(input[samples[i][0]], weight[samples[i][1]])
+
+    # forward activation
+    sample_out = 1.0 / (1.0 + np.exp(-sample_out))
+    # forward cost
+    out = np.zeros(batch_size).astype(np.float32)
+    b = 1.0 / num_classes * num_sample_class
+
+    for i in range(len(samples)):
+        o = sample_out[i]
+        cost = -np.log(o / (o + b)) if samples[i][2] else -np.log(b / (o + b))
+        out[samples[i][0]] += cost * samples[i][3]
+    return (out[:, np.newaxis], np.array(sample_out).reshape(
+        batch_size, num_sample_class + num_true_class),
+            np.array(sample_labels).reshape(batch_size,
+                                            num_sample_class + num_true_class))
+
+
+def run_pserver(pserver_id, use_cuda, sync_mode):
+    scope = fluid.core.Scope()
+    program = Program()
+    with fluid.scope_guard(scope):
+        with program_guard(program, startup_program=Program()):
+            # create table parameter in scope
+            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+            # create and initialize Param Variable
+            param = scope.var('table').get_tensor()
+
+            param_array = np.ones((5, 8)).astype("float32")
+            for i in range(len(param_array)):
+                param_array[i] *= param_array[i] * i + pserver_id * 10 + 1
+            param.set(param_array, place)
+
+            optimize_block = program._create_block(program.global_block().idx)
+            program.global_block().append_op(
+                type="listen_and_serv",
+                inputs={'X': []},
+                outputs={},
+                attrs={
+                    "optimize_blocks": [optimize_block],
+                    "endpoint": '127.0.0.1:0',
+                    "Fanin": 1,
+                    "sync_mode": True,
+                    "grad_to_block_id": []
+                })
+
+            exe = fluid.Executor(place)
+            exe.run(program)
+
+
+class TestListenAndServOp(unittest.TestCase):
+    def setUp(self):
+        self.ps_timeout = 5
+
+    def _start_pserver(self, pserver_id, use_cuda, sync_mode, pserver_func):
+        p = Process(target=pserver_func, args=(pserver_id, use_cuda, sync_mode))
+        p.daemon = True
+        p.start()
+        return p
+
+    def _wait_ps_ready(self, pid):
+        start_left_time = self.ps_timeout
+        sleep_time = 0.5
+        while True:
+            assert start_left_time >= 0, "wait ps ready failed"
+            time.sleep(sleep_time)
+            try:
+                # the listen_and_serv_op would touch a file which contains the listen port
+                # on the /tmp directory until it was ready to process all the RPC call.
+                os.stat("/tmp/paddle.%d.port" % pid)
+                return
+            except os.error:
+                start_left_time -= sleep_time
+
+    def _get_pserver_port(self, pid):
+        with open("/tmp/paddle.%d.port" % pid, 'r') as f:
+            port = int(f.read().strip())
+        return port
+
+    def _run_nce_op_two_pserver(self, place, port0, port1):
+        scope = fluid.core.Scope()
+        program = Program()
+        with fluid.scope_guard(scope):
+            with program_guard(program, startup_program=Program()):
+                x = scope.var('Input').get_tensor()
+                x_array = np.random.random((4, 8)).astype("float32")
+                x.set(x_array, place)
+                # create and initialize Param Variable
+                param = scope.var('Weight').get_tensor()
+                param_array = np.zeros((5, 8)).astype("float32")
+                param.set(param_array, place)
+
+                bias = scope.var('Bias').get_tensor()
+                bias_array = np.random.random((5, 1)).astype("float32")
+                bias.set(bias_array, place)
+
+                sample_w = scope.var('SampleWeight').get_tensor()
+                sample_weight = np.random.random((4, 1)).astype("float32")
+                sample_w.set(sample_weight, place)
+
+                label = scope.var('Label').get_tensor()
+                label_array = np.array([[0], [1], [4], [3]])
+                label.set(label_array, place)
+
+                cost = scope.var('Cost').get_tensor()
+                cost_w = np.zeros((4, 1)).astype("float32")
+                cost.set(cost_w, place)
+
+                sample_l = scope.var('SampleLogits').get_tensor()
+                sample_l_w = np.zeros((4, 3)).astype("float32")
+                sample_l.set(sample_l_w, place)
+
+                sample_la = scope.var('SampleLabels').get_tensor()
+                sample_la_w = np.zeros((4, 3)).astype("int")
+                sample_la.set(sample_la_w, place)
+
+                emaps = ['127.0.0.1:' + str(port0), '127.0.0.1:' + str(port1)]
+                table_names = ['table', 'table']
+                height_sections = [2, 3]
+
+                # create and run nce operator
+                nce_op = Operator(
+                    "nce",
+                    Input='Input',
+                    Weight='Weight',
+                    Label='Label',
+                    Bias='Bias',
+                    Cost='Cost',
+                    SampleLogits='SampleLogits',
+                    SampleLabels='SampleLabels',
+                    SampleWeight='SampleWeight',
+                    num_total_classes=5,
+                    num_neg_samples=2,
+                    custom_neg_classes=list(range(2)),
+                    sampler=0,
+                    seed=0,
+                    is_sparse=True,
+                    remote_prefetch=True,
+                    epmap=emaps,
+                    table_names=table_names,
+                    height_sections=height_sections)
+
+                nce_op.run(scope, place)
+
+                # get and compare result
+                o_cost = np.array(scope.var('Cost').get_tensor())
+                o_logits = np.array(scope.var('SampleLogits').get_tensor())
+                o_labels = np.array(scope.var('SampleLabels').get_tensor())
+
+                param_array = np.ones((5, 8)).astype("float32")
+                for i in range(2):
+                    param_array[i] *= param_array[i] * i + 0 * 10 + 1
+                for i in range(2, 5):
+                    param_array[i] *= param_array[i] * i + 1 * 10 + 1
+                out = nce(x_array, param_array, bias_array, sample_weight,
+                          label_array, 5, 2)
+
+                self.assertAlmostEqual(o_cost.all(), out[0].all(), delta=1e-6)
+                self.assertAlmostEqual(o_logits.all(), out[1].all(), delta=1e-6)
+                self.assertAlmostEqual(o_labels.all(), out[2].all(), delta=1e-6)
+
+    def test_nce_op_remote(self):
+        os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1"
+        # run pserver on CPU in sync mode
+        p0 = self._start_pserver(0, False, True, run_pserver)
+        self._wait_ps_ready(p0.pid)
+        port0 = self._get_pserver_port(p0.pid)
+
+        p1 = self._start_pserver(1, False, True, run_pserver)
+        self._wait_ps_ready(p1.pid)
+        port1 = self._get_pserver_port(p1.pid)
+
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+
+        for place in places:
+            self._run_nce_op_two_pserver(place, port0, port1)
+
+        # raise SIGTERM to pserver
+        os.kill(p0.pid, signal.SIGINT)
+        p0.join()
+        os.kill(p1.pid, signal.SIGINT)
+        p1.join()
+
+
+if __name__ == '__main__':
+    unittest.main()