unit test ready

paddle/fluid/operators/distributed/parameter_prefetch.cc

@@ -48,7 +48,7 @@ static size_t GetSectionIndex(int64_t id,
 }
 
 static std::vector<int64_t> ToAbsoluteSection(
-    const std::vector<int64_t>& height_sections) {
+    const std::vector<int>& height_sections) {
   std::vector<int64_t> abs_sections;
   abs_sections.resize(height_sections.size());
   abs_sections[0] = 0;
@@ -59,7 +59,7 @@ static std::vector<int64_t> ToAbsoluteSection(
 }
 
 static std::vector<std::vector<int64_t>> SplitIds(
-    const std::string& id_name, const std::vector<int64_t>& height_section,
+    const std::string& id_name, const std::vector<int>& height_section,
     framework::Scope* scope) {
   auto& id_tensor = scope->FindVar(id_name)->Get<framework::LoDTensor>();
   auto* id_data = id_tensor.data<int64_t>();
@@ -79,7 +79,7 @@ static std::vector<std::vector<int64_t>> SplitIds(
 
 static void SplitIdsIntoMultipleVarsBySection(
     const std::string& id_name, const std::vector<std::string>& in_var_names,
-    const std::vector<int64_t>& height_section,
+    const std::vector<int>& height_section,
     const std::vector<std::vector<int64_t>>& splited_ids,
     framework::Scope* scope) {
   PADDLE_ENFORCE_EQ(in_var_names.size(), height_section.size(), "");
@@ -101,7 +101,7 @@ static void SplitIdsIntoMultipleVarsBySection(
 static void MergeMultipleVarsIntoOneBySection(
     const std::string& id_name, const std::string& out_name,
     const std::vector<std::string>& out_var_names,
-    const std::vector<int64_t>& height_section,
+    const std::vector<int>& height_section,
     const std::vector<std::vector<int64_t>>& splited_ids,
     const framework::ExecutionContext& context, framework::Scope* scope) {
   PADDLE_ENFORCE_EQ(out_var_names.size(), height_section.size(), "");
@@ -154,7 +154,7 @@ static void MergeMultipleVarsIntoOneBySection(
 void prefetch(const std::string& id_name, const std::string& out_name,
               const std::vector<std::string>& table_names,
               const std::vector<std::string>& epmap,
-              const std::vector<int64_t>& height_sections,
+              const std::vector<int>& height_sections,
               const framework::ExecutionContext& context) {
   auto& local_scope = context.scope().NewScope();
 

paddle/fluid/operators/distributed/parameter_prefetch.h

@@ -26,7 +26,7 @@ namespace distributed {
 void prefetch(const std::string& id_name, const std::string& out_name,
               const std::vector<std::string>& table_names,
               const std::vector<std::string>& epmap,
-              const std::vector<int64_t>& height_sections,
+              const std::vector<int>& height_sections,
               const framework::ExecutionContext& context);
 
 };  // namespace distributed

paddle/fluid/operators/lookup_table_op.cc

@@ -91,9 +91,9 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
     // for parameter prefetch
     AddAttr<bool>("remote_prefetch", "").SetDefault(false);
     AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
-    AddAttr<std::vector<int64_t>>("height_sections",
+    AddAttr<std::vector<int>>("height_sections",
                               "Height for each output SelectedRows.")
-        .SetDefault(std::vector<int64_t>({}));
+        .SetDefault(std::vector<int>({}));
     AddAttr<std::vector<std::string>>(
         "epmap",
         "(string vector, default 127.0.0.1:6164)"

paddle/fluid/operators/lookup_table_op.h

@@ -52,8 +52,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
 
     // for remote prefetch
     auto epmap = context.Attr<std::vector<std::string>>("epmap");
-    auto height_sections =
-        context.Attr<std::vector<int64_t>>("height_sections");
+    auto height_sections = context.Attr<std::vector<int>>("height_sections");
     auto table_names = context.Attr<std::vector<std::string>>("table_names");
 
     if (!height_sections.empty()) {

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

+#   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 run_pserver(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.full((10, 8), 5.0).astype("float32")
+            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, use_cuda, sync_mode, pserver_func):
+        p = Process(target=pserver_func, args=(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_lookup_table_op(self, place, port):
+        scope = fluid.core.Scope()
+        program = Program()
+        with fluid.scope_guard(scope):
+            with program_guard(program, startup_program=Program()):
+                # create and initialize Param Variable
+                param = scope.var('W').get_tensor()
+                param_array = np.full((10, 8), 1.0).astype("float32")
+                param.set(param_array, place)
+
+                ids = scope.var('Ids').get_tensor()
+                ids_array = np.array([[1.0], [2.0]]).astype("int64")
+                ids.set(ids_array, place)
+                ids.set_lod([[0, 1, 2]])
+
+                out = scope.var('Out').get_tensor()
+
+                emaps = ['127.0.0.1:' + str(port)]
+                table_names = ['table']
+                height_sections = [10]
+                # create and run sgd operator
+                lookup_table_op = Operator(
+                    "lookup_table",
+                    W='W',
+                    Ids='Ids',
+                    Out='Out',
+                    remote_prefetch=True,
+                    epmap=emaps,
+                    table_names=table_names,
+                    height_sections=height_sections)
+                lookup_table_op.run(scope, place)
+
+                # get and compare result
+                result_array = np.array(out)
+
+                print(result_array)
+
+                self.assertTrue((result_array[0] == 5).all())
+                self.assertTrue((result_array[0] == 5).all())
+
+    def test_lookup_remote_table(self):
+        # run pserver on CPU in sync mode
+        p1 = self._start_pserver(False, True, run_pserver)
+        self._wait_ps_ready(p1.pid)
+        port = 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_lookup_table_op(place, port)
+
+        # raise SIGTERM to pserver
+        os.kill(p1.pid, signal.SIGINT)
+        p1.join()
+
+
+if __name__ == '__main__':
+    unittest.main()