Merge branch 'windows/build' into windows/online

test=develop

cmake/operators.cmake

@@ -109,7 +109,8 @@ function(op_library TARGET)
 
     # Define operators that don't need pybind here.
     foreach(manual_pybind_op "compare_op" "logical_op" "nccl_op"
-"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op")
+"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op"
+"fusion_transpose_flatten_concat_op")
         if ("${TARGET}" STREQUAL "${manual_pybind_op}")
             set(pybind_flag 1)
         endif()

paddle/fluid/framework/CMakeLists.txt

@@ -116,7 +116,7 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
 cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
 
-cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto)
+cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto device_context)
 cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
     shape_inference data_transform lod_tensor profiler transfer_scope_cache)
 

paddle/fluid/framework/transfer_scope_cache.cc

@@ -17,16 +17,28 @@
 namespace paddle {
 namespace framework {
 
+// Holds all the transfer scope across the process.
 std::unordered_map<size_t, Scope*>& global_transfer_data_cache() {
-  thread_local auto* x = new std::unordered_map<size_t, Scope*>;
+  typedef std::unordered_map<size_t, Scope*> map_t;
+  thread_local std::unique_ptr<map_t> x(new map_t);
   return *x;
 }
 
+// Holds all the transfer scope for this thread.
 std::unordered_set<Scope*>& global_transfer_scope_cache() {
-  thread_local auto* x = new std::unordered_set<Scope*>;
+  typedef std::unordered_set<Scope*> set_t;
+  thread_local std::unique_ptr<set_t> x(new set_t);
   return *x;
 }
 
+// Try to create a transfer scope. If one cached scope has match the
+// requirement, just return that one.
+// Inputs:
+// @type0: the source kernel type.
+// @type1: the target kernel type.
+// @scope: the execution scope of this op.
+// Returns: A scope used to hold the transfer data across the different kernel
+// type.
 Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
                               const Scope* scope) {
   Scope* new_scope{nullptr};
@@ -46,27 +58,5 @@ Scope* TryCreateTransferScope(OpKernelType type0, OpKernelType type1,
   return new_scope;
 }
 
-void RemoveKidsFromTransferScopeCache(Scope* scope) {
-  auto it = global_transfer_scope_cache().find(scope);
-  if (it != global_transfer_scope_cache().end()) {
-    global_transfer_scope_cache().erase(it);
-  }
-  for (auto* s : scope->kids()) {
-    auto it = global_transfer_scope_cache().find(s);
-    if (it != global_transfer_scope_cache().end()) {
-      global_transfer_scope_cache().erase(it);
-    }
-  }
-
-  // remove global transfer data cache
-  auto& cache = global_transfer_data_cache();
-  for (auto it = cache.begin(); it != cache.end();) {
-    if (it->second == scope)
-      it = cache.erase(it);
-    else
-      it++;
-  }
-}
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/memory/allocation/retry_allocator_test.cc

@@ -41,7 +41,7 @@ TEST(RetryAllocator, RetryAllocator) {
 
   size_t thread_num = 32;
   size_t sleep_time = 40;
-  size_t extra_time = 2;
+  size_t extra_time = 10;
 
   // Reserve to perform more tests in the future
   std::vector<std::shared_ptr<Allocator>> allocators;

paddle/fluid/operators/fused/CMakeLists.txt

 include(operators)
-register_operators()
+register_operators(EXCLUDES fusion_transpose_flatten_concat_op)
+if (WITH_GPU)
+  op_library(fusion_transpose_flatten_concat_op)
+  file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(fusion_transpose_flatten_concat);\n")
+endif()

paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.cc

+/* Copyright (c) 2016 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. */
+
+#include "paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class TransposeFlattenConcatFusionOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL,
+                      "Inputs(X) of ConcatOp should be empty.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of ConcatOp should not be null.");
+
+    auto ins = ctx->GetInputsDim("X");
+    const size_t n = ins.size();
+    PADDLE_ENFORCE_GT(n, 0, "Input tensors count should > 0.");
+
+    std::vector<int> trans_axis =
+        ctx->Attrs().Get<std::vector<int>>("trans_axis");
+    int flatten_axis = ctx->Attrs().Get<int>("flatten_axis");
+    int concat_axis = ctx->Attrs().Get<int>("concat_axis");
+
+    size_t x_rank = ins[0].size();
+    size_t trans_axis_size = trans_axis.size();
+    PADDLE_ENFORCE_EQ(x_rank, trans_axis_size,
+                      "The input tensor's rank(%d) "
+                      "should be equal to the permutation axis's size(%d)",
+                      x_rank, trans_axis_size);
+
+    auto dims0 =
+        GetFlattenShape(flatten_axis, GetPermuteShape(trans_axis, ins[0]));
+    std::vector<int> out_dims(dims0);
+    for (size_t i = 1; i < n; i++) {
+      auto dimsi =
+          GetFlattenShape(flatten_axis, GetPermuteShape(trans_axis, ins[i]));
+      for (int j = 0; j < static_cast<int>(dims0.size()); j++) {
+        if (j == concat_axis) {
+          out_dims[concat_axis] += dimsi[j];
+        } else {
+          PADDLE_ENFORCE_EQ(out_dims[j], dimsi[j],
+                            "After flatting, the %d-th dim should be save "
+                            "except the specify axis.",
+                            j);
+        }
+      }
+    }
+    if (out_dims[concat_axis] < 0) {
+      out_dims[concat_axis] = -1;
+    }
+    ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
+  }
+};
+
+class TransposeFlattenConcatFusionOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput(
+        "X",
+        "(Tensor) The input tensor, tensors with rank up to 6 are supported.")
+        .AsDuplicable();
+    AddOutput("Out", "(Tensor)The output tensor.");
+    AddAttr<std::vector<int>>(
+        "trans_axis",
+        "(vector<int>) A list of values, and the size of the list should be "
+        "the same with the input tensor rank. This operator permutes the input "
+        "tensor's axes according to the values given.");
+    AddAttr<int>("flatten_axis",
+                 "(int)"
+                 "Indicate up to which input dimensions (exclusive) should be"
+                 "flattened to the outer dimension of the output. The value"
+                 "for axis must be in the range [0, R], where R is the rank of"
+                 "the input tensor. When axis = 0, the shape of the output"
+                 "tensor is (1, (d_0 X d_1 ... d_n), where the shape of the"
+                 "input tensor is (d_0, d_1, ... d_n).");
+    AddAttr<int>("concat_axis",
+                 "The axis along which the input tensors will be concatenated. "
+                 "It should be 0 or 1, since the tensor is 2D after flatting.");
+    AddComment(R"DOC(
+
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fusion_transpose_flatten_concat,
+                  ops::TransposeFlattenConcatFusionOp,
+                  ops::TransposeFlattenConcatFusionOpMaker,
+                  paddle::framework::EmptyGradOpMaker);

paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.cu.cc

+/* Copyright (c) 2016 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. */
+
+#include "paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.h"
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/cudnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+using CudnnDataType = platform::CudnnDataType<T>;
+
+template <typename T>
+class TransposeFlattenConcatFusionKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto ins = ctx.MultiInput<framework::Tensor>("X");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
+    auto odims = out->dims();
+
+    std::vector<int> trans_axis = ctx.Attr<std::vector<int>>("trans_axis");
+    int flatten_axis = ctx.Attr<int>("flatten_axis");
+    int concat_axis = ctx.Attr<int>("concat_axis");
+
+    int rank = ins[0]->dims().size();
+    // use at least 4D in cudnnTransformTensor
+    int max_dim = rank < 4 ? 4 : rank;
+    std::vector<int> stride_x(max_dim, 0);
+    std::vector<int> stride_y(max_dim, 0);
+    std::vector<int> dims_y(max_dim, 0);
+
+    cudnnTensorDescriptor_t in_desc;
+    cudnnTensorDescriptor_t out_desc;
+    CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&in_desc));
+    CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&out_desc));
+    cudnnDataType_t cudnn_dtype = CudnnDataType<T>::type;
+
+    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
+    auto handle = dev_ctx.cudnn_handle();
+
+    T* odata = out->data<T>();
+    for (size_t k = 0; k < ins.size(); ++k) {
+      auto perm_shape = GetPermuteShape(trans_axis, ins[k]->dims());
+      int osize = 1;
+      auto idims = ins[k]->dims();
+      for (int i = 0; i < rank; i++) {
+        stride_x[i] = 1;
+        for (int j = trans_axis[i] + 1; j < rank; j++) {
+          stride_x[i] *= idims[j];
+        }
+        dims_y[i] = perm_shape[i];
+        osize *= perm_shape[i];
+      }
+      stride_y[rank - 1] = 1;
+      for (int i = rank - 2; i >= 0; i--) {
+        if (((i + 1) == flatten_axis) && (concat_axis == 1)) {
+          stride_y[i] = odims[1];
+        } else {
+          stride_y[i] = stride_y[i + 1] * perm_shape[i + 1];
+        }
+      }
+
+      // Since concat is aftern flatten, the output is 2D tensor.
+      // If concat_axis is 0, each input's permutated tensor is continuous.
+      // If concat_axis is 1, the stride of 0-th dim of each input's
+      // permutated tensor is odims()[1].
+
+      for (int i = rank; i < max_dim; i++) {
+        stride_x[i] = 1;
+        stride_y[i] = 1;
+        dims_y[i] = 1;
+      }
+
+      CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
+          in_desc, cudnn_dtype, max_dim, dims_y.data(), stride_x.data()));
+      CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
+          out_desc, cudnn_dtype, max_dim, dims_y.data(), stride_y.data()));
+
+      CUDNN_ENFORCE(platform::dynload::cudnnTransformTensor(
+          handle, CudnnDataType<T>::kOne(), in_desc,
+          static_cast<const void*>(ins[k]->data<T>()),
+          CudnnDataType<T>::kZero(), out_desc, static_cast<void*>(odata)));
+      if (concat_axis == 0) {
+        odata += osize;
+      } else {
+        auto flat_shape = GetFlattenShape(flatten_axis, perm_shape);
+        odata += flat_shape[1];
+      }
+    }
+    CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(in_desc));
+    CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(out_desc));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(fusion_transpose_flatten_concat,
+                        ops::TransposeFlattenConcatFusionKernel<float>,
+                        ops::TransposeFlattenConcatFusionKernel<double>);

paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.h

+/* Copyright (c) 2016 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. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/ddim.h"
+
+namespace paddle {
+namespace operators {
+
+inline std::vector<int32_t> GetPermuteShape(const std::vector<int>& axis,
+                                            const framework::DDim& in_dims) {
+  std::vector<int32_t> out_dims(in_dims.size());
+  for (size_t i = 0; i < axis.size(); i++) {
+    out_dims[i] = in_dims[axis[i]];
+  }
+  return out_dims;
+}
+
+inline std::vector<int32_t> GetFlattenShape(const int axis,
+                                            const std::vector<int>& in_dims) {
+  int64_t outer = 1, inner = 1;
+  for (int i = 0; i < static_cast<int>(in_dims.size()); ++i) {
+    if (i < axis) {
+      outer *= in_dims[i];
+    } else {
+      inner *= in_dims[i];
+    }
+  }
+  std::vector<int32_t> out_shape(2);
+  out_shape[0] = outer;
+  out_shape[1] = inner;
+  return out_shape;
+}
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/lookup_sparse_table_op.cc

@@ -67,6 +67,7 @@ class LookupSparseTableOp : public framework::OperatorBase {
                       framework::proto::VarType::FP32,
                       "The sparse table only support FP32");
     w_t->Get(ids_t, out_t, true, is_test);
+    out_t->set_lod(ids_t.lod());
   }
 };
 

paddle/fluid/operators/sum_op.h

@@ -127,6 +127,9 @@ class SumKernel : public framework::OpKernel<T> {
         math::scatter::MergeAdd<DeviceContext, T> merge_add;
         merge_add(context.template device_context<DeviceContext>(), inputs,
                   out);
+
+        out->SyncIndex();
+
       } else {
         // no data, just set a empty out tensor.
         out->mutable_value()->mutable_data<T>(framework::make_ddim({0}),

paddle/testing/paddle_gtest_main.cc

@@ -31,6 +31,11 @@ int main(int argc, char** argv) {
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
   new_argv.push_back(
       strdup("--tryfromenv=fraction_of_gpu_memory_to_use,allocator_strategy"));
+#elif __clang__
+  new_argv.push_back(
+      strdup("--tryfromenv=use_mkldnn,initial_cpu_memory_in_"
+             "mb,allocator_strategy"));
+  new_argv.push_back(strdup("--undefok=use_mkldnn,initial_cpu_memory_in_mb"));
 #else
   new_argv.push_back(
       strdup("--tryfromenv=use_pinned_memory,use_mkldnn,initial_cpu_memory_in_"

python/paddle/fluid/__init__.py

@@ -91,6 +91,7 @@ def __bootstrap__():
     """
     import sys
     import os
+    import platform
     from . import core
 
     in_test = 'unittest' in sys.modules
@@ -110,14 +111,17 @@ def __bootstrap__():
         print('PLEASE USE OMP_NUM_THREADS WISELY.', file=sys.stderr)
 
     os.environ['OMP_NUM_THREADS'] = str(num_threads)
-
+    sysstr = platform.system()
     read_env_flags = [
-        'use_pinned_memory', 'check_nan_inf', 'benchmark', 'eager_delete_scope',
-        'use_mkldnn', 'use_ngraph', 'initial_cpu_memory_in_mb',
-        'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
-        "dist_threadpool_size", 'eager_delete_tensor_gb', 'allocator_strategy',
+        'check_nan_inf', 'benchmark', 'eager_delete_scope', 'use_mkldnn',
+        'use_ngraph', 'initial_cpu_memory_in_mb', 'init_allocated_mem',
+        'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size",
+        'eager_delete_tensor_gb', 'allocator_strategy',
         'reader_queue_speed_test_mode', 'print_sub_graph_dir'
     ]
+    if 'Darwin' not in sysstr:
+        read_env_flags.append('use_pinned_memory')
+
     if os.name != 'nt':
         read_env_flags.append('warpctc_dir')
         read_env_flags.append('cpu_deterministic')

python/paddle/fluid/contrib/utils/__init__.py

@@ -13,8 +13,10 @@
 # limitations under the License.
 
 from __future__ import print_function
-
+from . import lookup_table_utils
+from .lookup_table_utils import *
 from . import hdfs_utils
 from .hdfs_utils import *
 
+__all__ = lookup_table_utils.__all__
 __all__ = hdfs_utils.__all__

python/paddle/fluid/contrib/utils/lookup_table_utils.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 time
+import logging
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid import io
+from paddle.fluid import Program
+
+__all__ = [
+    "load_inference_model", "load_persistable_vars",
+    "convert_dist_to_sparse_program"
+]
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+_logger = logging.getLogger("lookup_table_utils")
+_logger.setLevel(logging.INFO)
+
+model_filename = "__model__"
+lookup_table_dir = "__lookup_table__"
+
+
+def __insert_lookup_sparse_table_op(main_program, idx, ids, w, out):
+    main_program.global_block()._insert_op(
+        index=idx,
+        type="lookup_sparse_table",
+        inputs={"Ids": [ids],
+                "W": [w]},
+        outputs={"Out": [out]},
+        attrs={
+            "is_distributed": False,
+            "is_sparse": True,
+            "grad_inplace": False
+        })
+
+
+def __get_prefetch_op_tuples(main_program):
+    # current lookup tables op is split_ids->prefetch->merge_ids
+    prefetch_op_tuples = None
+    op_types = [op.type for op in main_program.global_block().ops]
+
+    for i in range(len(op_types)):
+        if op_types[i] == "prefetch":
+            if op_types[i - 1] == "split_ids" and op_types[i +
+                                                           1] == "merge_ids":
+                split_ids_op_id = i - 1
+                split_ids_inputs = main_program.global_block().ops[i - 1].input(
+                    "Ids")
+                prefetch_op_inputs = main_program.global_block().ops[i].input(
+                    "X")
+                prefetch_op_outputs = main_program.global_block().ops[i].output(
+                    "Out")
+                merge_ids_outputs = main_program.global_block().ops[
+                    i + 1].output("Out")
+
+                need_delete_vars = []
+                need_delete_vars.extend(prefetch_op_inputs)
+                need_delete_vars.extend(prefetch_op_outputs)
+
+                prefetch_op_tuples = (split_ids_op_id, split_ids_inputs,
+                                      merge_ids_outputs, need_delete_vars)
+                break
+    return prefetch_op_tuples
+
+
+def convert_dist_to_sparse_program(main_program):
+    if not main_program._distributed_lookup_table:
+        _logger.warn(
+            "There are no distributed lookup tables need to be converted")
+        return
+
+    # create table param and grad var in pserver program
+    origin_emb_var = "{}.origin".format(main_program._distributed_lookup_table)
+    emb_var = main_program._distributed_lookup_table
+    main_program.global_block()._rename_var(emb_var, origin_emb_var)
+    origin_param_var = main_program.global_block().vars[origin_emb_var]
+
+    param_var = main_program.global_block().create_var(
+        name=emb_var,
+        shape=origin_param_var.shape,
+        dtype=origin_param_var.dtype,
+        type=core.VarDesc.VarType.SELECTED_ROWS,
+        persistable=True)
+    # parameter must be selected rows
+    param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+    main_program._sync_with_cpp()
+
+    prefetch_op_tuples = __get_prefetch_op_tuples(main_program)
+
+    split_ids_id = prefetch_op_tuples[0]
+
+    for idx in range(split_ids_id + 2, split_ids_id - 1, -1):
+        main_program.global_block()._remove_op(idx)
+    main_program.desc.flush()
+
+    in_out_pairs = zip(prefetch_op_tuples[1], prefetch_op_tuples[2])
+
+    for in_out_pair in in_out_pairs:
+        idx = split_ids_id
+        ids = main_program.global_block().vars[in_out_pair[0]]
+        out = main_program.global_block().vars[in_out_pair[1]]
+        __insert_lookup_sparse_table_op(main_program, idx, ids, param_var, out)
+        main_program.desc.flush()
+    return main_program
+
+
+def load_persistable_vars(executor, dirname, program, lookup_table_var):
+    def _is_checkpoint_var(exclude_fluid_vars=None):
+        """
+        the checkpoint will not save or load all the variables.
+        var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
+
+        : param var(Variable)
+        """
+
+        if exclude_fluid_vars is None:
+            exclude_fluid_vars = []
+
+        def is_valid(var):
+            if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
+                    var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
+                    var.desc.type() == core.VarDesc.VarType.RAW:
+                return False
+            # @GRAD are named for gradient variables, checkpoint will not save it.
+            if "@GRAD" in var.name:
+                return False
+            # .trainer_ are named for distribute train variables, checkpoint will not save it.
+            if ".trainer_" in var.name:
+                return False
+
+            # .block is named for distribute train variables, checkpoint will not save it.
+            if ".block" in var.name:
+                return False
+
+            if "tmp_" in var.name:
+                return False
+
+            if var.name in exclude_fluid_vars:
+                return False
+
+            return var.persistable
+
+        return is_valid
+
+    def _load_lookup_table_vars(executor, dirname, main_program,
+                                lookup_table_vars):
+        if not os.path.isdir(dirname):
+            raise ValueError("There is no directory named '%s'", dirname)
+
+        lookup_table_dirname = os.path.join(dirname, lookup_table_dir)
+
+        emb_var_name = lookup_table_vars[0]
+        emb_var = main_program.global_block().var(emb_var_name)
+
+        emb_files = []
+        for emb_name in os.listdir(lookup_table_dirname):
+            if emb_var_name in emb_name:
+                emb_files.append(emb_name)
+
+        convert_program = Program()
+        global_block = convert_program.global_block()
+
+        emb_var = global_block.create_var(
+            name=emb_var.name,
+            shape=emb_var.shape,
+            dtype=emb_var.dtype,
+            type=core.VarDesc.VarType.SELECTED_ROWS,
+            persistable=True)
+        emb_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+
+        sums = []
+
+        for i, emb_file in enumerate(emb_files):
+            var_name = "{}_{}".format(emb_var.name, i)
+            param_var = global_block.create_var(
+                name=var_name,
+                shape=emb_var.shape,
+                dtype=emb_var.dtype,
+                type=core.VarDesc.VarType.SELECTED_ROWS,
+                persistable=True)
+            param_var.desc.set_type(core.VarDesc.VarType.SELECTED_ROWS)
+            global_block.append_op(
+                type='load',
+                inputs={},
+                outputs={'Out': [param_var]},
+                attrs={
+                    'file_path': os.path.join(lookup_table_dirname, var_name)
+                })
+            sums.append(param_var)
+        global_block.append_op(
+            type='sum', inputs={"X": sums}, outputs={'Out': emb_var}, attrs={})
+        global_block.append_op(type='delete_var', inputs={'X': sums})
+        executor.run(convert_program)
+
+    _logger.info("Start Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+    lookup_table_vars = [lookup_table_var]
+
+    io.load_vars(
+        executor,
+        dirname=dirname,
+        main_program=program,
+        predicate=_is_checkpoint_var(lookup_table_vars),
+        filename=None)
+
+    _load_lookup_table_vars(executor, dirname, program, lookup_table_vars)
+
+    _logger.info("Finish Load Sparse Program With "
+                 "Distributed Lookup Table Vars from {}, time = {}".format(
+                     dirname, time.ctime()))
+
+
+def load_inference_model(dirname, executor, lookup_table_var_name):
+    if not os.path.isdir(dirname):
+        raise ValueError("There is no directory named '%s'", dirname)
+
+    local_model = os.path.join(dirname, model_filename)
+
+    with open(local_model, "rb") as f:
+        program_desc_str = f.read()
+
+    program = Program.parse_from_string(program_desc_str)
+
+    if not core._is_program_version_supported(program._version()):
+        raise ValueError("Unsupported program version: %d\n" %
+                         program._version())
+
+    # Binary data also need version.
+    load_persistable_vars(executor, dirname, program, lookup_table_var_name)
+
+    feed_target_names = program.desc.get_feed_target_names()
+    fetch_target_names = program.desc.get_fetch_target_names()
+    fetch_targets = [
+        program.global_block().var(name) for name in fetch_target_names
+    ]
+
+    return [program, feed_target_names, fetch_targets]

python/paddle/fluid/framework.py

@@ -1698,6 +1698,7 @@ class Program(object):
 
         p._copy_param_info_from(self)
         p._copy_data_info_from(self)
+        p._copy_dist_param_info_from(self)
         return p
 
     def _prune(self, targets):
@@ -1938,6 +1939,25 @@ class Program(object):
                              "program, with represent the same topology")
         self.global_block()._copy_param_info_from(other.global_block())
 
+    def _copy_dist_param_info_from(self, other):
+        """
+        Copy the information of distributed information from other program.
+
+        Args:
+            other(Program): Other program
+
+        Returns:
+            None
+        """
+        if not isinstance(other, Program):
+            raise TypeError("_copy_dist_param_info_from should be invoked with "
+                            "Program")
+        self._is_distributed = other._is_distributed
+        self._is_chief = other._is_chief
+        self._slice_vars_and_attrs = other._slice_vars_and_attrs
+        self._endpoints = other._endpoints
+        self._distributed_lookup_table = other._distributed_lookup_table
+
     def _copy_data_info_from(self, other):
         """
         Copy the information of data variables from other program.

python/paddle/fluid/io.py

@@ -165,6 +165,7 @@ def save_vars(executor,
 
         save_vars(
             executor,
+            main_program=main_program,
             dirname=dirname,
             vars=list(filter(predicate, main_program.list_vars())),
             filename=filename)
@@ -172,11 +173,18 @@ def save_vars(executor,
         save_program = Program()
         save_block = save_program.global_block()
 
+        if main_program is None:
+            main_program = default_main_program()
+        if not isinstance(main_program, Program):
+            raise TypeError("program should be as Program type or None")
+
         save_var_map = {}
         for each_var in vars:
             # NOTE: don't save the variable which type is RAW
             if each_var.type == core.VarDesc.VarType.RAW:
                 continue
+            if each_var.name == main_program._distributed_lookup_table:
+                continue
             new_var = _clone_var_in_block_(save_block, each_var)
             if filename is None:
                 save_block.append_op(
@@ -198,6 +206,16 @@ def save_vars(executor,
                 outputs={},
                 attrs={'file_path': os.path.join(dirname, filename)})
 
+        # if there is lookup table, the trainer 0 will notify all pserver to save.
+        if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
+            lookup_table_filename = os.path.join(dirname, "__lookup_table__")
+            attrs = {}
+            attrs['epmap'] = main_program._endpoints
+            attrs['dir'] = lookup_table_filename
+            attrs['lookup_table'] = main_program._distributed_lookup_table
+            save_block.append_op(
+                type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
+
         executor.run(save_program)
 
 
@@ -379,11 +397,22 @@ def load_vars(executor,
         load_prog = Program()
         load_block = load_prog.global_block()
 
+        if main_program is None:
+            main_program = default_main_program()
+        if not isinstance(main_program, Program):
+            raise TypeError("program should be as Program type or None")
+
+        load_slice_vars = []
+        for each_var in main_program._slice_vars_and_attrs:
+            load_slice_vars.append(each_var[2].name)
+
         load_var_map = {}
         for each_var in vars:
             assert isinstance(each_var, Variable)
             if each_var.type == core.VarDesc.VarType.RAW:
                 continue
+            if each_var.name in load_slice_vars:
+                continue
             new_var = _clone_var_in_block_(load_block, each_var)
             if filename is None:
                 load_block.append_op(
@@ -406,9 +435,6 @@ def load_vars(executor,
                 attrs={'file_path': os.path.join(dirname, filename)})
         executor.run(load_prog)
 
-        if main_program is None:
-            main_program = default_main_program()
-
         # load slice vars on pserver, if have it.
         _load_slice_up_vars(executor, dirname,
                             main_program._slice_vars_and_attrs)
@@ -618,13 +644,6 @@ def save_inference_model(dirname,
     if main_program is None:
         main_program = default_main_program()
 
-    # if there is lookup table, the trainer 0 will notify all pserver to save.
-    if main_program._is_distributed and main_program._is_chief and main_program._distributed_lookup_table:
-        lookup_table_filename = os.path.join(dirname, "__lookup_table__")
-        _save_lookup_tables_by_notify(executor, lookup_table_filename,
-                                      main_program._distributed_lookup_table,
-                                      main_program._endpoints)
-
     # when a pserver and a trainer running on the same machine, mkdir may conflict
     try:
         os.makedirs(dirname)
@@ -642,6 +661,9 @@ def save_inference_model(dirname,
     # it can only be loaded for inference directly. If it's false, the whole
     # original program and related meta are saved so that future usage can be
     # more flexible.
+
+    origin_program = main_program.clone()
+
     if export_for_deployment:
         main_program = main_program.clone()
         global_block = main_program.global_block()
@@ -666,8 +688,11 @@ def save_inference_model(dirname,
         with open(model_basename + ".main_program", "wb") as f:
             f.write(main_program.desc.serialize_to_string())
 
+    main_program._copy_dist_param_info_from(origin_program)
+
     if params_filename is not None:
         params_filename = os.path.basename(params_filename)
+
     save_persistables(executor, dirname, main_program, params_filename)
 
 
@@ -897,6 +922,9 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
         slice_var = var_tuple[2]
         end = start + slice_var.shape[0]
 
+        orig_var_name = orig_var.name
+        orig_var.name = "{}.origin".format(orig_var_name)
+
         clone_orig_var = load_block.create_var(
             name=orig_var.name,
             type=orig_var.type,
@@ -915,7 +943,7 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
             type='load',
             inputs={},
             outputs={'Out': [clone_orig_var]},
-            attrs={'file_path': os.path.join(dirname, clone_orig_var.name)})
+            attrs={'file_path': os.path.join(dirname, orig_var_name)})
         load_block.append_op(
             type="slice",
             inputs={'Input': clone_orig_var},
@@ -924,6 +952,7 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
                    'starts': [start],
                    'ends': [end]})
         need_delete_vars.append(clone_orig_var)
+
     load_block.append_op(
         type='delete_var',
         inputs={'X': need_delete_vars}, )

python/paddle/fluid/layers/nn.py

@@ -6972,18 +6972,18 @@ def prelu(x, mode, param_attr=None, name=None):
     """
     Equation:
 
-        y = \max(0, x) + alpha \min(0, x)
+        y = \max(0, x) + alpha * \min(0, x)
 
     Args:
         x (Variable): The input tensor.
-	  param_attr(ParamAttr|None): The parameter attribute for the learnable
-                                    weight (alpha).
-        mode (string): The mode for weight sharing
-		       all: all elements share same weight
- 		       channel:elements in a channel share same weight
- 		       element:each element has a weight
-	name(str|None): A name for this layer(optional). If set None, the layer
-                        will be named automatically.
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
+                       weight (alpha).
+        mode (string): The mode for weight sharing. It supports all, channel
+                       and element. all: all elements share same weight
+                       channel:elements in a channel share same weight
+                       element:each element has a weight
+        name(str|None): A name for this layer(optional). If set None, the layer
+                       will be named automatically.
 
     Returns:
         Variable: The output tensor with the same shape as input.
@@ -6992,7 +6992,7 @@ def prelu(x, mode, param_attr=None, name=None):
 
         .. code-block:: python
 
-         x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
+            x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
             mode = 'channel'
             output = fluid.layers.prelu(x,mode)
     """

python/paddle/fluid/tests/unittests/test_fusion_transpose_flatten_concat_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 unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid.core as core
+
+
+class TestFusionTransposeFlattenConcationOp(OpTest):
+    def setUp(self):
+        self.init_test_case()
+        self.op_type = "fusion_transpose_flatten_concat"
+
+        ins = []
+        flats = []
+        for i in range(len(self.shapes)):
+            in_shape = self.shapes[i]
+            a = np.random.random(in_shape).astype("float32")
+            ins.append(("x%d" % i, a))
+
+            b = a.transpose(self.trans_axis)
+            flat_shape = (np.prod(b.shape[:self.flatten_axis]),
+                          np.prod(b.shape[self.flatten_axis:]))
+            c = b.reshape(flat_shape)
+            flats.append(c)
+        out = np.concatenate(flats, axis=self.concat_axis)
+
+        self.inputs = {'X': ins}
+        self.attrs = {
+            'trans_axis': list(self.trans_axis),
+            'flatten_axis': self.flatten_axis,
+            'concat_axis': self.concat_axis
+        }
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            self.check_output_with_place(place, 1e-6)
+        else:
+            pass
+
+    def init_test_case(self):
+        self.shapes = [(3, 4, 17, 17), (3, 8, 7, 7), (3, 12, 5, 5)]
+        self.trans_axis = (0, 2, 3, 1)
+        self.flatten_axis = 1
+        self.concat_axis = 1
+
+
+class TestCase1(TestFusionTransposeFlattenConcationOp):
+    def init_test_case(self):
+        self.shapes = [(3, 4, 18, 17), (3, 8, 18, 7), (6, 12, 9, 5)]
+        self.trans_axis = (0, 2, 3, 1)
+        self.flatten_axis = 2
+        self.concat_axis = 1
+
+
+class TestCase2(TestFusionTransposeFlattenConcationOp):
+    def init_test_case(self):
+        self.shapes = [(3, 8, 20, 17), (3, 8, 19, 17), (3, 8, 40, 17)]
+        self.trans_axis = (0, 2, 3, 1)
+        self.flatten_axis = 2
+        self.concat_axis = 0
+
+
+class TestCase3(TestFusionTransposeFlattenConcationOp):
+    def init_test_case(self):
+        self.shapes = [(3, 8, 20, 17), (3, 8, 19, 17), (3, 8, 40, 17)]
+        self.trans_axis = (0, 3, 2, 1)
+        self.flatten_axis = 1
+        self.concat_axis = 1
+
+
+class TestCase4(TestFusionTransposeFlattenConcationOp):
+    def init_test_case(self):
+        self.shapes = [(3, 8, 9, 17), (8, 3, 9, 17), (4, 6, 9, 17)]
+        self.trans_axis = (0, 2, 1, 3)
+        self.flatten_axis = 3
+        self.concat_axis = 1
+
+
+class TestCase5(TestFusionTransposeFlattenConcationOp):
+    def init_test_case(self):
+        self.shapes = [(3, 8, 9, 17, 2), (3, 8, 2, 17, 9), (3, 17, 9, 8, 2)]
+        self.trans_axis = (0, 2, 1, 4, 3)
+        self.flatten_axis = 1
+        self.concat_axis = 1
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -644,6 +644,9 @@ in a single call.")
             else:
                 recv_inputs.append(single_trainer_var)
 
+        self._slice_params_and_optimizes = self._get_slice_vars_and_attrs(
+            endpoint)
+
         # step 3
         # Create a union-find data structure from optimize ops,
         # If two ops are connected, we could add these two ops
@@ -766,7 +769,7 @@ in a single call.")
                                                grad_to_block_id, merged_var,
                                                lr_ops)
 
-# dedup grad to ids list
+        # dedup grad to ids list
         grad_to_block_id = list(set(grad_to_block_id))
         # append global ops
         if global_ops:
@@ -827,8 +830,8 @@ in a single call.")
             attrs=attrs)
 
         # add distributed attrs
-        pserver_program._slice_vars_and_attrs = self._get_slice_vars_and_attrs(
-            endpoint)
+        pserver_program._slice_vars_and_attrs = list(
+            self._slice_params_and_optimizes.values())
 
         pserver_program._sync_with_cpp()
         # save pserver program to generate pserver side startup relatively.
@@ -941,12 +944,12 @@ to transpile() call.")
                     outputs={"Out": startup_tmpvar})
 
         # add slice vars
-        s_prog._slice_vars_and_attrs = self._get_slice_vars_and_attrs(endpoint)
+        s_prog._slice_vars_and_attrs = pserver_program._slice_vars_and_attrs
 
         return s_prog
 
     def _get_slice_vars_and_attrs(self, endpoint):
-        slice_vars_and_attrs = []
+        slice_vars_and_attrs = {}
         block_suffix = "block"
         for param in self.param_grad_ep_mapping[endpoint]["params"]:
             orig_var_name, block_name, _ = self._get_varname_parts(param.name)
@@ -960,8 +963,7 @@ to transpile() call.")
             slice_vars = self.param_var_mapping[orig_var_name]
             for slice_var in slice_vars[:block_idx]:
                 skip_dim0 += slice_var.shape[0]
-            slice_vars_and_attrs.append([orig_var, skip_dim0, param])
-
+            slice_vars_and_attrs[param.name] = [orig_var, skip_dim0, param]
         return slice_vars_and_attrs
 
     # ====================== private transpiler functions =====================
@@ -1662,10 +1664,10 @@ to transpile() call.")
             if key in ["Param", "Grad", "LearningRate"]:
                 continue
             var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
+            param_var = new_inputs["Param"]
             # update accumulator variable shape
-            param_shape = new_inputs["Param"].shape
-            new_shape = self._get_optimizer_input_shape(opt_op.type, key,
-                                                        var.shape, param_shape)
+            new_shape = self._get_optimizer_input_shape(
+                opt_op.type, key, var.shape, param_var.shape)
             tmpvar = pserver_block.create_var(
                 name=var.name,
                 persistable=var.persistable,
@@ -1673,6 +1675,13 @@ to transpile() call.")
                 shape=new_shape)
             new_inputs[key] = tmpvar
 
+            # var shape been changed
+            if new_shape != var.shape:
+                slice_var_args = self._slice_params_and_optimizes[
+                    param_var.name]
+                self._slice_params_and_optimizes[
+                    var.name] = [var, slice_var_args[1], tmpvar]
+
         # change output's ParamOut variable
         outputs = self._get_output_map_from_op(
             self.origin_program.global_block().vars, opt_op)