Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into mnist

paddle/memory/memory.cc

@@ -19,8 +19,13 @@ limitations under the License. */
 #include <memory>     // for unique_ptr
 #include <mutex>      // for call_once
 
+#include "glog/logging.h"
+
 #include "paddle/memory/detail/buddy_allocator.h"
 #include "paddle/memory/detail/system_allocator.h"
+#include "paddle/platform/gpu_info.h"
+
+DECLARE_double(fraction_of_gpu_memory_to_use);
 
 namespace paddle {
 namespace memory {
@@ -80,6 +85,11 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
                                                  platform::GpuMinChunkSize(),
                                                  platform::GpuMaxChunkSize()));
     }
+    VLOG(3) << "\n\nNOTE: each GPU device use "
+            << FLAGS_fraction_of_gpu_memory_to_use * 100 << "% of GPU memory.\n"
+            << "You can set environment variable '"
+            << platform::kEnvFractionGpuMemoryToUse
+            << "' to change the fraction of GPU usage.\n\n";
   });
 
   platform::SetDeviceId(gpu_id);

paddle/memory/memory.h

@@ -14,7 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#include "paddle/platform/gpu_info.h"
 #include "paddle/platform/place.h"
 
 namespace paddle {

paddle/operators/math/math_function.cc

@@ -25,8 +25,8 @@ void gemm<platform::CPUPlace, float>(const CBLAS_TRANSPOSE transA,
                                      const float alpha, const float* A,
                                      const float* B, const float beta, float* C,
                                      platform::DeviceContext* context) {
-  int lda = K;
-  int ldb = N;
+  int lda = (transA == CblasNoTrans) ? K : M;
+  int ldb = (transB == CblasNoTrans) ? N : K;
   int ldc = N;
   cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
               beta, C, ldc);
@@ -40,8 +40,8 @@ void gemm<platform::CPUPlace, double>(const CBLAS_TRANSPOSE transA,
                                       const double* B, const double beta,
                                       double* C,
                                       platform::DeviceContext* context) {
-  int lda = K;
-  int ldb = N;
+  int lda = (transA == CblasNoTrans) ? K : M;
+  int ldb = (transB == CblasNoTrans) ? N : K;
   int ldc = N;
   cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
               beta, C, ldc);

paddle/operators/mul_op.cc

@@ -18,6 +18,8 @@
 namespace paddle {
 namespace operators {
 
+using framework::Tensor;
+
 class MulOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -59,10 +61,23 @@ class MulOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(const framework::InferShapeContext &ctx) const override {}
-  std::string DebugString() const override {
-    LOG(INFO) << "MulGrad";
-    return "";
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<Tensor>("X")->dims();
+    auto y_dims = ctx.Input<Tensor>("Y")->dims();
+    auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
+    auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    PADDLE_ENFORCE(x_dims[0] == out_dims[0],
+                   "Out@GRAD M X N must equal to X dims 0, M ");
+    PADDLE_ENFORCE(y_dims[1] == out_dims[1],
+                   "Out@GRAD M X N must equal to Y dims 1, N ");
+
+    x_grad->Resize(x_dims);
+    y_grad->Resize(y_dims);
   }
 };
 
@@ -72,3 +87,5 @@ class MulOpGrad : public framework::OperatorWithKernel {
 namespace ops = paddle::operators;
 REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad);
 REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(mul_grad,
+                       ops::MulGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/mul_op.cu

@@ -17,3 +17,5 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(mul_grad,
+                       ops::MulGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/mul_op.h

@@ -31,18 +31,34 @@ template <typename Place, typename T>
 class MulKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
-        {Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
-    auto* input0 = context.Input<Tensor>("X");
-    auto* input1 = context.Input<Tensor>("Y");
-    auto* output = context.Output<Tensor>("Out");
-    output->mutable_data<T>(context.GetPlace());
-    auto X = EigenMatrix<T>::From(*input0);
-    auto Y = EigenMatrix<T>::From(*input1);
-    auto Z = EigenMatrix<T>::From(*output);
-    auto& place = context.GetEigenDevice<Place>();
-
-    Z.device(place) = X.contract(Y, dim_pair);
+    auto* X = context.Input<Tensor>("X");
+    auto* Y = context.Input<Tensor>("Y");
+    auto* Z = context.Output<Tensor>("Out");
+    Z->mutable_data<T>(context.GetPlace());
+    auto* device_context =
+        const_cast<platform::DeviceContext*>(context.device_context_);
+    math::matmul<Place, T>(*X, false, *Y, false, 1, Z, 0, device_context);
+  }
+};
+
+template <typename Place, typename T>
+class MulGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* X = ctx.Input<Tensor>("X");
+    auto* Y = ctx.Input<Tensor>("Y");
+    auto* dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dY = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    dX->mutable_data<T>(ctx.GetPlace());
+    dY->mutable_data<T>(ctx.GetPlace());
+    auto* device_context =
+        const_cast<platform::DeviceContext*>(ctx.device_context_);
+    // dX = dOut * Y'. dX: M x K, dOut : M x N, Y : K x N
+    math::matmul<Place, T>(*dOut, false, *Y, true, 1, dX, 0, device_context);
+    // dY = X' * dOut. dY: K x N, dOut : M x N, X : M x K
+    math::matmul<Place, T>(*X, true, *dOut, false, 1, dY, 0, device_context);
   }
 };
 

paddle/operators/rowwise_add_op.cc

@@ -17,7 +17,9 @@
 namespace paddle {
 namespace operators {
 
-class RowWiseAddOp : public framework::OperatorWithKernel {
+using framework::Tensor;
+
+class RowwiseAddOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
@@ -34,9 +36,9 @@ class RowWiseAddOp : public framework::OperatorWithKernel {
   }
 };
 
-class RowWiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
+class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  RowWiseAddOpMaker(framework::OpProto *proto,
+  RowwiseAddOpMaker(framework::OpProto *proto,
                     framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The left input of row-wise add op, must be matrix");
@@ -49,12 +51,32 @@ for i in xrange(X.shape[0]):
 )DOC");
   }
 };
+class RowwiseAddGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto dims0 = ctx.Input<Tensor>("X")->dims();
+    auto dims1 = ctx.Input<Tensor>("b")->dims();
+    PADDLE_ENFORCE_EQ(1, dims1.size(), "b dims should be 1")
+    ctx.Output<Tensor>(framework::GradVarName("X"))->Resize(dims0);
+    ctx.Output<Tensor>(framework::GradVarName("b"))->Resize(dims1);
+  }
+};
 
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_WITHOUT_GRADIENT(rowwise_add, ops::RowWiseAddOp,
-                             ops::RowWiseAddOpMaker);
+REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker,
+            rowwise_add_grad, ops::RowwiseAddGradOp);
+REGISTER_OP_CPU_KERNEL(
+    rowwise_add, ops::RowwiseAddKernel<paddle::platform::CPUPlace, float>);
 REGISTER_OP_CPU_KERNEL(
-    rowwise_add, ops::RowWiseAddKernel<paddle::platform::CPUPlace, float>);
+    rowwise_add_grad,
+    ops::RowwiseAddGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/rowwise_add_op.cu

@@ -17,4 +17,4 @@
 
 namespace ops = paddle::operators;
 REGISTER_OP_GPU_KERNEL(
-    rowwise_add, ops::RowWiseAddKernel<paddle::platform::GPUPlace, float>);
+    rowwise_add, ops::RowwiseAddKernel<paddle::platform::GPUPlace, float>);

paddle/operators/rowwise_add_op.h

@@ -28,7 +28,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
 using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename Place, typename T>
-class RowWiseAddKernel : public framework::OpKernel {
+class RowwiseAddKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto out = context.Output<Tensor>("Out");
@@ -47,5 +47,25 @@ class RowWiseAddKernel : public framework::OpKernel {
   }
 };
 
+template <typename Place, typename T>
+class RowwiseAddGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* db = context.Output<Tensor>(framework::GradVarName("b"));
+    dX->mutable_data<T>(context.GetPlace());
+    db->mutable_data<T>(context.GetPlace());
+
+    auto OutGrad = EigenMatrix<T>::From(*dOut);
+    auto place = context.GetEigenDevice<Place>();
+    EigenMatrix<T>::From(*dX).device(place) = OutGrad;
+
+    // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html
+    // colwise add
+    Eigen::array<int, 1> dims{{1}}; /* dimension to reduce */
+    EigenVector<T>::Flatten(*db).device(place) = OutGrad.sum(dims);
+  }
+};
 }  // namespace operators
 }  // namespace paddle

paddle/operators/sgd_op.h

@@ -30,7 +30,7 @@ class SGDOpKernel : public framework::OpKernel {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto param = ctx.Input<Tensor>("param");
     auto grad = ctx.Input<Tensor>("grad");
-    auto param_out = ctx.Output<Tensor>(0);
+    auto param_out = ctx.Output<Tensor>("param_out");
     float lr = ctx.op_.GetAttr<float>("learning_rate");
 
     param_out->mutable_data<T>(ctx.GetPlace());

paddle/platform/CMakeLists.txt

 cc_library(cpu_info SRCS cpu_info.cc DEPS gflags glog)
 cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info)
 
-nv_library(gpu_info SRCS gpu_info.cc DEPS gflags)
+nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog)
 
 cc_library(place SRCS place.cc)
 cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
@@ -9,6 +9,7 @@ cc_test(place_test SRCS place_test.cc DEPS place glog gflags)
 add_subdirectory(dynload)
 
 cc_test(enforce_test SRCS enforce_test.cc DEPS stringpiece)
+cc_test(environment_test SRCS environment_test.cc DEPS stringpiece)
 
 IF(WITH_GPU)
     set(GPU_CTX_DEPS dynload_cuda dynamic_loader)

paddle/platform/environment.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 <stdlib.h>
+#include <unistd.h>
+#include <vector>
+
+#include "paddle/platform/enforce.h"
+#include "paddle/string/piece.h"
+
+extern char** environ;  // for environment variables
+
+namespace paddle {
+namespace platform {
+
+inline void SetEnvVariable(const std::string& name, const std::string& value) {
+  PADDLE_ENFORCE_NE(setenv(name.c_str(), value.c_str(), 1), -1,
+                    "Failed to set environment variable %s=%s", name, value);
+}
+
+inline void UnsetEnvVariable(const std::string& name) {
+  PADDLE_ENFORCE_NE(unsetenv(name.c_str()), -1,
+                    "Failed to unset environment variable %s", name);
+}
+
+inline bool IsEnvVarDefined(const std::string& name) {
+  return std::getenv(name.c_str()) != nullptr;
+}
+
+inline std::string GetEnvValue(const std::string& name) {
+  PADDLE_ENFORCE(IsEnvVarDefined(name),
+                 "Tried to access undefined environment variable %s", name);
+  return std::getenv(name.c_str());
+}
+
+inline std::vector<std::string> GetAllEnvVariables() {
+  std::vector<std::string> vars;
+  for (auto var = environ; *var != nullptr; ++var) {
+    auto tail = string::Index(*var, "=");
+    auto name = string::SubStr(*var, 0, tail).ToString();
+    vars.push_back(name);
+  }
+  return vars;
+}
+
+}  // namespace platform
+}  // namespace paddle

paddle/platform/environment_test.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+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/platform/environment.h"
+
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+
+TEST(ENVIRONMENT, ACCESS) {
+  namespace platform = paddle::platform;
+  namespace string = paddle::string;
+
+  platform::SetEnvVariable("PADDLE_USE_ENV", "TRUE");
+
+  EXPECT_TRUE(platform::IsEnvVarDefined("PADDLE_USE_ENV"));
+  EXPECT_EQ(platform::GetEnvValue("PADDLE_USE_ENV"), "TRUE");
+
+  platform::UnsetEnvVariable("PADDLE_USE_ENV");
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV"));
+
+  platform::SetEnvVariable("PADDLE_USE_ENV1", "Hello ");
+  platform::SetEnvVariable("PADDLE_USE_ENV2", "World, ");
+  platform::SetEnvVariable("PADDLE_USE_ENV3", "PaddlePaddle!");
+
+  std::string env_info;
+  auto vars = platform::GetAllEnvVariables();
+  for_each(vars.begin(), vars.end(), [&](const std::string& var) {
+    env_info += platform::GetEnvValue(var);
+  });
+
+  EXPECT_TRUE(string::Contains(env_info, "Hello World, PaddlePaddle!"));
+  platform::UnsetEnvVariable("PADDLE_USE_ENV1");
+  platform::UnsetEnvVariable("PADDLE_USE_ENV2");
+  platform::UnsetEnvVariable("PADDLE_USE_ENV3");
+
+  env_info.clear();
+  vars = platform::GetAllEnvVariables();
+  for_each(vars.begin(), vars.end(), [&](const std::string& var) {
+    env_info += platform::GetEnvValue(var);
+  });
+
+  EXPECT_FALSE(string::Contains(env_info, "Hello World, PaddlePaddle!"));
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV1"));
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV2"));
+  EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV3"));
+}

paddle/platform/gpu_info.cc

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/platform/gpu_info.h"
+
 #include "gflags/gflags.h"
+
 #include "paddle/platform/enforce.h"
+#include "paddle/platform/environment.h"
 
 DEFINE_double(fraction_of_gpu_memory_to_use, 0.95,
               "Default use 95% of GPU memory for PaddlePaddle,"
@@ -70,6 +73,13 @@ size_t GpuMaxChunkSize() {
 
   GpuMemoryUsage(available, total);
 
+  if (IsEnvVarDefined(kEnvFractionGpuMemoryToUse)) {
+    auto val = std::stod(GetEnvValue(kEnvFractionGpuMemoryToUse));
+    PADDLE_ENFORCE_GT(val, 0.0);
+    PADDLE_ENFORCE_LE(val, 1.0);
+    FLAGS_fraction_of_gpu_memory_to_use = val;
+  }
+
   // Reserving the rest memory for page tables, etc.
   size_t reserving = (1 - FLAGS_fraction_of_gpu_memory_to_use) * total;
 

paddle/platform/gpu_info.h

@@ -18,10 +18,15 @@ limitations under the License. */
 
 #include <cuda_runtime.h>
 #include <stddef.h>
+#include <string>
 
 namespace paddle {
 namespace platform {
 
+//! Environment variable: fraction of GPU memory to use on each device.
+const std::string kEnvFractionGpuMemoryToUse =
+    "PADDLE_FRACTION_GPU_MEMORY_TO_USE";
+
 //! Get the total number of GPU devices in system.
 int GetDeviceCount();
 

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -25,4 +25,5 @@ py_test(test_operator SRCS test_operator.py)
 # py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
 py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
 py_test(test_recurrent_op SRCS test_recurrent_op.py)
+py_test(test_sgd_op SRCS test_sgd_op.py)
 py_test(test_gradient_checker SRCS test_gradient_checker.py)

python/paddle/v2/framework/tests/test_mul_op.py

 import unittest
-from op_test_util import OpTestMeta
 import numpy as np
+from gradient_checker import GradientChecker, create_op
+from op_test_util import OpTestMeta
 
 
 class TestMulOp(unittest.TestCase):
@@ -15,5 +16,19 @@ class TestMulOp(unittest.TestCase):
         self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
 
 
+class MulGradOpTest(GradientChecker):
+    def test_mul(self):
+        op = create_op("mul")
+        inputs = {
+            'X': np.random.random((32, 84)).astype("float32"),
+            'Y': np.random.random((84, 100)).astype("float32")
+        }
+        # mul op will enlarge the relative error
+        self.check_grad(
+            op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+
+
+# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_rowwise_add_op.py

 import unittest
-from op_test_util import OpTestMeta
 import numpy as np
+from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
 
 
 class TestRowwiseAddOp(unittest.TestCase):
@@ -15,5 +16,15 @@ class TestRowwiseAddOp(unittest.TestCase):
         self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])}
 
 
+class RowwiseAddGradOpTest(GradientChecker):
+    def test_rowwise_add(self):
+        op = create_op("rowwise_add")
+        inputs = {
+            "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"),
+            "b": np.random.uniform(0.1, 1, [10]).astype("float32")
+        }
+        self.check_grad(op, inputs, set(["X", "b"]), "Out")
+
+
 if __name__ == '__main__':
     unittest.main()