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提交 4c283d87 编写于 作者: L luotao1
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Merge branch 'develop' into all_data

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doc/fluid/new_docs/user_guides/howto/debug/visualdl.md
浏览文件 @ 4c283d87
......@@ -104,6 +104,7 @@ visualDL --logdir=scratch_log --port=8080
# 访问 http://127.0.0.1:8080
```
如果出现`TypeError: __init__() got an unexpected keyword argument 'file'`, 是因为protobuf不是3.5以上，运行`pip install --upgrade protobuf`就能解决。
如果在虚拟环境下仍然遇到安装问题，请尝试以下方法。
......
paddle/fluid/API.spec
浏览文件 @ 4c283d87
......@@ -43,6 +43,7 @@ paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list',
paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.Trainer.save_inference_model ArgSpec(args=['self', 'param_path', 'feeded_var_names', 'target_var_indexes'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.test ArgSpec(args=['self', 'reader', 'feed_order'], varargs=None, keywords=None, defaults=None)
......@@ -376,7 +377,7 @@ paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ ArgSpec(args=['self', 'l
paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.FtrlOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power'], varargs=None, keywords='kwargs', defaults=(0.0, 0.0, -0.5))
paddle.fluid.optimizer.FtrlOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum'], varargs=None, keywords='kwargs', defaults=(0.95, 1e-06, 0.0))
paddle.fluid.optimizer.RMSPropOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered'], varargs=None, keywords='kwargs', defaults=(0.95, 1e-06, 0.0, False))
paddle.fluid.optimizer.RMSPropOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdadeltaOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho'], varargs=None, keywords='kwargs', defaults=(1e-06, 0.95))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
浏览文件 @ 4c283d87
......@@ -11,6 +11,7 @@
// 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/framework/ir/fc_lstm_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/lod_tensor.h"
......
paddle/fluid/framework/ir/graph_viz_pass.cc
浏览文件 @ 4c283d87
......@@ -50,20 +50,37 @@ std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
Dot dot;
std::vector<Dot::Attr> op_attrs({Dot::Attr("style", "filled"),
Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "red")});
std::vector<Dot::Attr> var_attrs({Dot::Attr("style", "filled,rounded"),
// Dot::Attr("shape", "diamond"),
const std::vector<Dot::Attr> op_attrs({
Dot::Attr("style", "rounded,filled,bold"), //
Dot::Attr("shape", "box"), //
Dot::Attr("color", "#303A3A"), //
Dot::Attr("fontcolor", "#ffffff"), //
Dot::Attr("width", "1.3"), //
Dot::Attr("height", "0.84"), //
Dot::Attr("fontname", "Arial"), //
});
const std::vector<Dot::Attr> arg_attrs({
Dot::Attr("shape", "box"), //
Dot::Attr("style", "rounded,filled,bold"), //
Dot::Attr("fontname", "Arial"), //
Dot::Attr("fillcolor", "#999999"), //
Dot::Attr("color", "#dddddd"), //
});
const std::vector<Dot::Attr> param_attrs({
Dot::Attr("shape", "box"), //
Dot::Attr("style", "rounded,filled,bold"), //
Dot::Attr("fontname", "Arial"), //
Dot::Attr("color", "#148b97"), //
Dot::Attr("fontcolor", "#ffffff"), //
});
const std::vector<Dot::Attr> marked_op_attrs(
{Dot::Attr("style", "rounded,filled,bold"), Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "yellow")});
const std::vector<Dot::Attr> marked_var_attrs(
{Dot::Attr("style", "filled,rounded"), Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "yellow")});
std::vector<Dot::Attr> marked_op_attrs({Dot::Attr("style", "filled"),
Dot::Attr("shape", "box"),
Dot::Attr("fillcolor", "lightgray")});
std::vector<Dot::Attr> marked_var_attrs(
{Dot::Attr("style", "filled,rounded"),
// Dot::Attr("shape", "diamond"),
Dot::Attr("fillcolor", "lightgray")});
auto marked_nodes = ConsumeMarkedNodes(graph.get());
// Create nodes
......@@ -74,9 +91,17 @@ std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
marked_nodes.count(n) ? marked_op_attrs : op_attrs;
dot.AddNode(node_id, attr, node_id);
} else if (n->IsVar()) {
decltype(op_attrs) attr =
marked_nodes.count(n) ? marked_var_attrs : var_attrs;
dot.AddNode(node_id, attr, node_id);
decltype(op_attrs)* attr;
if (marked_nodes.count(n)) {
attr = &marked_var_attrs;
} else if (const_cast<Node*>(n)->Var() &&
const_cast<Node*>(n)->Var()->Persistable()) {
attr = &param_attrs;
} else {
attr = &arg_attrs;
}
dot.AddNode(node_id, *attr, node_id);
}
node2dot[n] = node_id;
}
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ 4c283d87
......@@ -105,6 +105,6 @@ if (NOT EXISTS ${TEXT_CLASSIFICATION_INSTALL_DIR} AND WITH_TESTING AND WITH_INFE
inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
endif()
inference_analysis_test(test_text_classification SRCS test_text_classification.cc
inference_analysis_test(test_text_classification SRCS analyzer_text_classification_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)
paddle/fluid/inference/analysis/test_text_classification.cc → paddle/fluid/inference/analysis/analyzer_text_classification_tester.cc
浏览文件 @ 4c283d87
......@@ -12,14 +12,16 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/api/timer.h"
DEFINE_string(infer_model, "", "Directory of the inference model.");
DEFINE_string(infer_data, "", "Path of the dataset.");
......@@ -86,10 +88,3 @@ TEST(text_classification, basic) { Main(FLAGS_batch_size); }
} // namespace inference
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(seq_concat_fc_fuse_pass);
USE_PASS(fc_lstm_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
USE_PASS(attention_lstm_fuse_pass);
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 4c283d87
......@@ -44,19 +44,7 @@ function(inference_api_test TARGET_NAME)
endfunction(inference_api_test)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api
analysis
ir_pass_manager
pass
fc_fuse_pass
fc_lstm_fuse_pass
seq_concat_fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detector
infer_clean_graph_pass
attention_lstm_fuse_pass
)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ 4c283d87
......@@ -119,7 +119,8 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
const framework::Tensor& last_scale,
const framework::Tensor& iter, const int window_size,
framework::Tensor* scales_arr, framework::Tensor* out_scale) {
auto& gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
T* scale_arr = scales_arr->mutable_data<T>(gpu_place);
T* out_scale_data = out_scale->mutable_data<T>(gpu_place);
......
paddle/fluid/operators/flatten_op.cc
浏览文件 @ 4c283d87
......@@ -157,6 +157,116 @@ class FlattenGradOp : public framework::OperatorBase {
}
};
// FIXME(zcd): flatten2 adds an intermediate output(XShape) based on flatten,
// the XShape is used to carry the shape and lod of X which will be used in
// flatten_grad, in this way, the framework can reuse the memory of X
// immediately the flatten2_op is finished.
// Considering compatibility issues, we could not fix flatten2_op
class Flatten2OpInferShape : public FlattenOpInferShape {
public:
void operator()(framework::InferShapeContext *ctx) const override {
FlattenOpInferShape::operator()(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output (XShape) of Flatten op should not be null.");
const auto &in_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(in_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < in_dims.size(); ++i) {
xshape_dims[i + 1] = in_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", "XShape");
}
};
class Flatten2Op : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axis = Attr<int>("axis");
auto in_dims =
scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
const auto &out_dims = FlattenOpInferShape::GetOutputShape(axis, in_dims);
framework::AttributeMap attrs;
attrs["shape"] = out_dims;
attrs["inplace"] = false;
// Invoke Reshape Op
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class Flatten2OpMaker : public FlattenOpMaker {
public:
void Make() override {
FlattenOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in FlattenGradOp.")
.AsIntermediate();
}
};
class Flatten2GradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("flatten2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Flatten2GradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("XShape"),
"Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = context->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
context->SetOutputDim(framework::GradVarName("X"), x_dims);
context->ShareLoD("XShape", framework::GradVarName("X"));
}
};
class Flatten2GradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto xshape_name = Input("XShape");
auto xshape_dims =
scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
attrs["inplace"] = false;
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {dout_name}}, {"Shape", {}}},
{{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
......@@ -167,3 +277,8 @@ REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
ops::FlattenOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker);
REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
ops::Flatten2GradInferShape);
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ 4c283d87
......@@ -30,14 +30,7 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
"Input(WeightX) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightH"),
"Input(WeightH) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("XX"), "Output(XX) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
"Output(BatchedOut) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of GRU should not be null.");
......@@ -80,15 +73,20 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
}
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedOut", out_dims);
ctx->ShareLoD("X", "Hidden");
int xx_width;
if (ctx->Attrs().Get<bool>("use_seq")) {
xx_width = wx_dims[1];
} else {
xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
"Output(BatchedOut) of GRU should not be null.");
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedOut", out_dims);
}
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
......
paddle/fluid/operators/layer_norm_op.cu
浏览文件 @ 4c283d87
......@@ -67,27 +67,27 @@ template <typename T, int BlockDim>
__global__ void LayerNormForward(const T *x, const T *scale, const T *bias,
T *y, T *mean, T *var, float epsilon,
int feature_size) {
using BlockReduce = cub::BlockReduce<PairForLayerNorm<T>, BlockDim>;
using BlockReduce = cub::BlockReduce<PairForLayerNorm<double>, BlockDim>;
__shared__ typename BlockReduce::TempStorage temp_storage;
int beg_idx = blockIdx.x * feature_size + threadIdx.x;
int end_idx = (blockIdx.x + 1) * feature_size;
// Step 1: Reduce to calculate mean and var
T mean_val = static_cast<T>(0);
T var_val = static_cast<T>(0);
double mean_val = 0;
double var_val = 0;
for (int i = beg_idx; i < end_idx; i += BlockDim) {
T tmp = x[i];
mean_val += tmp;
var_val += (tmp * tmp);
}
auto pair = BlockReduce(temp_storage)
.Reduce(PairForLayerNorm<T>(mean_val, var_val),
PairForLayerNormAddFunctor<T>());
.Reduce(PairForLayerNorm<double>(mean_val, var_val),
PairForLayerNormAddFunctor<double>());
if (threadIdx.x == 0) {
auto tmp = pair.first_ / feature_size;
mean[blockIdx.x] = tmp;
var[blockIdx.x] = pair.second_ / feature_size - tmp * tmp;
mean[blockIdx.x] = static_cast<T>(tmp);
var[blockIdx.x] = static_cast<T>(pair.second_ / feature_size - tmp * tmp);
}
__syncthreads();
mean_val = mean[blockIdx.x];
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 4c283d87
......@@ -246,6 +246,88 @@ class ReshapeGradKernel {
}
};
// FIXME(zcd): reshape2 adds an intermediate output(XShape) based on reshape,
// the XShape is used to carry the shape and lod of X which will be used in
// reshape_grad, in this way, the framework can reuse the memory of X
// immediately the reshape_op is finished.
// Considering compatibility issues, we could not fix reshape_op
class Reshape2Op : public ReshapeOp {
public:
Reshape2Op(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ReshapeOp(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
ReshapeOp::InferShape(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) of ReshapeOp should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
};
class Reshape2OpMaker : public ReshapeOpMaker {
public:
void Make() override {
ReshapeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in FlattenGradOp.")
.AsIntermediate();
}
};
class Reshape2GradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("reshape2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Reshape2GradOp : public framework::OperatorWithKernel {
public:
Reshape2GradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = ctx->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD("XShape", framework::GradVarName("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
->type()),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
......@@ -261,6 +343,17 @@ REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
REGISTER_OPERATOR(reshape2, ops::Reshape2Op, ops::Reshape2OpMaker,
ops::Reshape2GradMaker);
REGISTER_OPERATOR(reshape2_grad, ops::Reshape2GradOp);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
REGISTER_OP_CPU_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
......@@ -269,4 +362,11 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2, float, ops::ReshapeKernel, double,
ops::ReshapeKernel, int, ops::ReshapeKernel,
int64_t, ops::ReshapeKernel);
REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad, float, ops::ReshapeGradKernel,
double, ops::ReshapeGradKernel, int,
ops::ReshapeGradKernel, int64_t,
ops::ReshapeGradKernel);
#endif
paddle/fluid/operators/rmsprop_op.cc
浏览文件 @ 4c283d87
......@@ -36,9 +36,13 @@ class RmspropOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(param_out) of RmspropOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
"Output(Momentum_out) of RmspropOp should not be null.");
"Output(MomentOut) of RmspropOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MeanSquareOut"),
"Output(MeanSquareOut) of RmspropOp should not be null.");
if (ctx->Attrs().Get<bool>("centered")) {
PADDLE_ENFORCE(ctx->HasOutput("MeanGradOut"),
"Output(MeanGradOut) of RmspropOp should not be null.");
}
auto param_dim = ctx->GetInputDim("Param");
PADDLE_ENFORCE_EQ(
......@@ -58,6 +62,9 @@ class RmspropOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dim);
ctx->SetOutputDim("MomentOut", param_dim);
ctx->SetOutputDim("MeanSquareOut", param_dim);
if (ctx->Attrs().Get<bool>("centered")) {
ctx->SetOutputDim("MeanGradOut", param_dim);
}
}
};
......@@ -70,6 +77,10 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("MeanSquare",
"(Tensor, default Tensor<float>)"
" The mean square value that gets updated.");
AddInput("MeanGrad",
"(Tensor, default Tensor<float>)"
" The moving average of gradient")
.AsDispensable();
AddInput("LearningRate",
"(Tensor, default Tensor<float>) "
"The learning rate should be a tensor of size 1.");
......@@ -82,6 +93,8 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("ParamOut", "(Tensor) Output updated parameter value.");
AddOutput("MomentOut", "(Tensor) Output updated moment.");
AddOutput("MeanSquareOut", "(Tensor) Output Mean squared updated value.");
AddOutput("MeanGradOut",
"(Tensor) Output moving average of gradient updated value.");
AddAttr<float>("epsilon",
"(float, default 1e-10) Constant "
......@@ -93,6 +106,8 @@ class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(0.9f);
AddAttr<float>("momentum", "(float, default 0.0) Constant value.")
.SetDefault(0.0f);
AddAttr<bool>("centered", "(bool, default false) use centered rmsprop.")
.SetDefault(false);
AddComment(R"DOC(
Rmsprop Optimizer.
......@@ -103,6 +118,14 @@ MomentOut = momentum * Moment +
ParamOut = Param - MomentOut
$$
if centered is true:
mean_grad = decay * mean_square{t-1} + (1-decay) * gradient
mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
mom = momentum * mom{t-1} + learning_rate * g_t /
sqrt(mean_square - mean_grad**2 + epsilon)
param -= mom
The original slides that proposed Rmsprop: Slide 29 of
http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
......
paddle/fluid/operators/rmsprop_op.h
浏览文件 @ 4c283d87
......@@ -41,6 +41,7 @@ class RmspropOpKernel : public framework::OpKernel<T> {
float epsilon = ctx.Attr<float>("epsilon");
float rho = ctx.Attr<float>("decay");
float momentum = ctx.Attr<float>("momentum");
bool centered = ctx.Attr<bool>("centered");
auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
auto ms = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanSquare"));
......@@ -53,12 +54,24 @@ class RmspropOpKernel : public framework::OpKernel<T> {
auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
Eigen::DSizes<int, 1> grad_dsize(grad->numel());
Eigen::DSizes<int, 1> grad_dsize(static_cast<int>(grad->numel()));
ms_out.device(place) = rho * ms + (1 - rho) * g * g;
if (centered) {
auto mg = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanGrad"));
auto* mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
mean_grad_out->mutable_data<T>(ctx.GetPlace());
auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
mg_out.device(place) = rho * mg + (1 - rho) * g;
mom_out.device(place) = momentum * mom +
lr.broadcast(grad_dsize) * g /
(ms_out - mg_out.square() + epsilon).sqrt();
} else {
mom_out.device(place) =
momentum * mom +
lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
}
p_out.device(place) = p - mom_out;
}
};
......
paddle/fluid/operators/squeeze_op.cc
浏览文件 @ 4c283d87
......@@ -181,6 +181,113 @@ class SqueezeGradOp : public framework::OperatorBase {
}
};
// FIXME(zcd): squeeze2 adds an intermediate output(XShape) based on squeeze,
// the XShape is used to carry the shape and lod of X which will be used in
// squeeze_grad, in this way, the framework can reuse the memory of X
// immediately the squeeze2_op is finished.
// Considering compatibility issues, we could not fix squeeze2_op
class Squeeze2OpMaker : public SqueezeOpMaker {
public:
void Make() override {
SqueezeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in SqueezeGradOp.")
.AsIntermediate();
}
};
class Squeeze2OpInferShape : public SqueezeOpInferShape {
public:
void operator()(framework::InferShapeContext *ctx) const override {
SqueezeOpInferShape::operator()(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) of Squeeze operator should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
};
class Squeeze2Op : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
// Invoke Reshape Op
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class Squeeze2GradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("squeeze2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Squeeze2GradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("XShape"),
"Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = context->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
context->SetOutputDim(framework::GradVarName("X"), x_dims);
context->ShareLoD("XShape", framework::GradVarName("X"));
}
};
class Squeeze2GradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto xshape_name = Input("XShape");
auto xshape_dims =
scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {dout_name}}, {"Shape", {}}},
{{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
......@@ -192,3 +299,8 @@ REGISTER_OPERATOR(squeeze, ops::SqueezeOp, ops::SqueezeOpMaker,
ops::SqueezeOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(squeeze_grad, ops::SqueezeGradOp, ops::SqueezeGradInferShape);
REGISTER_OPERATOR(squeeze2, ops::Squeeze2Op, ops::Squeeze2OpMaker,
ops::Squeeze2OpInferShape, ops::Squeeze2GradOpMaker);
REGISTER_OPERATOR(squeeze2_grad, ops::Squeeze2GradOp,
ops::Squeeze2GradInferShape);
paddle/fluid/operators/transpose_op.cc
浏览文件 @ 4c283d87
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/transpose_op.h"
#include <string>
#include <vector>
namespace paddle {
......@@ -24,7 +25,7 @@ class TransposeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
auto x_dims = ctx->GetInputDim("X");
......@@ -101,7 +102,7 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
......@@ -113,6 +114,93 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
}
};
// FIXME(zcd): transpose2 adds an intermediate output(XShape) based on
// transpose, the XShape is used to carry the shape and lod of X which
// will be used in transpose_grad, in this way, the framework can reuse
// the memory of X immediately the transpose2_op is finished.
// Considering compatibility issues, we could not fix transpose2_op
class Transpose2Op : public TransposeOp {
public:
Transpose2Op(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: TransposeOp(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
TransposeOp::InferShape(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) should not be null");
const auto &in_dims = ctx->GetInputDim("X");
std::vector<int64_t> x_shape_dim(in_dims.size() + 1);
x_shape_dim[0] = 0;
for (int i = 0; i < in_dims.size(); ++i) {
x_shape_dim[i + 1] = in_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(x_shape_dim));
ctx->ShareLoD("X", /*->*/ "XShape");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class Transpose2OpMaker : public TransposeOpMaker {
public:
void Make() override {
TransposeOpMaker::Make();
AddOutput("XShape", "(Tensor)The output tensor.").AsIntermediate();
}
};
class Transpose2GradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("transpose2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Transpose2OpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("XShape"), "Input(XShape) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
if (ctx->HasOutput(framework::GradVarName("X"))) {
auto xshape_dim = ctx->GetInputDim("XShape");
auto x_shape_dim =
framework::slice_ddim(xshape_dim, 1, xshape_dim.size());
ctx->SetOutputDim(framework::GradVarName("X"), x_shape_dim);
ctx->ShareLoD("XShape", framework::GradVarName("X"));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))
->type()),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
......@@ -120,8 +208,20 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
ops::Transpose2GradMaker);
REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
REGISTER_OP_CPU_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
paddle/fluid/operators/transpose_op.cu.cc
浏览文件 @ 4c283d87
......@@ -21,3 +21,10 @@ REGISTER_OP_CUDA_KERNEL(
REGISTER_OP_CUDA_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/fluid/operators/unsqueeze_op.cc
浏览文件 @ 4c283d87
......@@ -168,6 +168,112 @@ class UnsqueezeGradOp : public framework::OperatorBase {
}
};
// FIXME(zcd): unsqueeze2 adds an intermediate output(XShape) based on
// unsqueeze, the XShape is used to carry the shape and lod of X which
// will be used in unsqueeze_grad, in this way, the framework can reuse
// the memory of X immediately the unsqueeze2_op is finished.
// Considering compatibility issues, we could not fix unsqueeze2_op
class Unsqueeze2OpInferShape : public UnsqueezeOpInferShape {
public:
void operator()(framework::InferShapeContext *ctx) const override {
UnsqueezeOpInferShape::operator()(ctx);
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) of Unsqueeze operator should not be null.");
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
};
class Unsqueeze2OpMaker : public UnsqueezeOpMaker {
public:
void Make() override {
UnsqueezeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in UnsqueezeGradOp.")
.AsIntermediate();
}
};
class Unsqueeze2Op : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = Unsqueeze2OpInferShape::GetOutputShape(axes, x_dims);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
// Invoke Reshape op.
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class Unsqueeze2GradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *grad_op = new framework::OpDesc();
grad_op->SetType("unsqueeze2_grad");
grad_op->SetInput("XShape", Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
class Unsqueeze2GradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("XShape"),
"Input(XShape) shouldn't be null.");
PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto xshape_dims = context->GetInputDim("XShape");
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
context->SetOutputDim(framework::GradVarName("X"), x_dims);
context->ShareLoD("XShape", framework::GradVarName("X"));
}
};
class Unsqueeze2GradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto xshape_name = Input("XShape");
auto xshape_dims =
scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(x_dims);
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape2", {{"X", {dout_name}}, {"Shape", {}}},
{{"Out", {dx_name}}, {"XShape", {xshape_name}}}, attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
......@@ -180,3 +286,8 @@ REGISTER_OPERATOR(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp,
ops::UnsqueezeGradInferShape);
REGISTER_OPERATOR(unsqueeze2, ops::Unsqueeze2Op, ops::Unsqueeze2OpMaker,
ops::Unsqueeze2OpInferShape, ops::Unsqueeze2GradOpMaker);
REGISTER_OPERATOR(unsqueeze2_grad, ops::Unsqueeze2GradOp,
ops::Unsqueeze2GradInferShape);
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 4c283d87
......@@ -121,6 +121,12 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
if (nullptr == dso_handle) {
LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
<< dlerror() << ")";
if (dlPath.find("nccl") != std::string::npos) {
std::cout
<< "You may need to install 'nccl2' from NVIDIA official website: "
<< "https://developer.nvidia.com/nccl/nccl-download"
<< "before install PaddlePaddle" << std::endl;
}
dlPath = dso_name;
dso_handle = GetDsoHandleFromDefaultPath(dlPath, dynload_flags);
}
......
paddle/scripts/paddle_build.sh
浏览文件 @ 4c283d87
......@@ -115,6 +115,7 @@ function cmake_gen() {
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_CONTRIB=${WITH_CONTRIB:-ON}
-DWITH_INFERENCE=${WITH_INFERENCE:-ON}
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
-DPY_VERSION=${PY_VERSION:-2.7}
========================================
......@@ -144,6 +145,7 @@ EOF
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-DWITH_INFERENCE=${WITH_INFERENCE:-ON} \
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
-DPY_VERSION=${PY_VERSION:-2.7}
}
......@@ -498,7 +500,7 @@ EOF
EOF
if [[ ${WITH_GPU} == "ON" ]]; then
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.1.2-1+cuda${CUDA_MAJOR} libnccl-dev=2.1.2-1+cuda${CUDA_MAJOR} &&"
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} &&"
else
NCCL_DEPS=""
fi
......
python/paddle/dataset/image.py
浏览文件 @ 4c283d87
......@@ -104,7 +104,7 @@ def batch_images_from_tar(data_file,
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
protocol=2)
file_id += 1
data = []
labels = []
......@@ -113,9 +113,7 @@ def batch_images_from_tar(data_file,
output['label'] = labels
output['data'] = data
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
output, open('%s/batch_%d' % (out_path, file_id), 'wb'), protocol=2)
with open(meta_file, 'a') as meta:
for file in os.listdir(out_path):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 4c283d87
......@@ -4025,10 +4025,12 @@ def transpose(x, perm, name=None):
helper = LayerHelper('transpose', **locals())
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='transpose',
type='transpose2',
inputs={'X': [x]},
outputs={'Out': [out]},
outputs={'Out': [out],
'XShape': [x_shape]},
attrs={'axis': perm})
return out
......@@ -4520,13 +4522,15 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"Each dimension size given in shape must not be negtive "
"except one unknown dimension.")
helper = LayerHelper("reshape", **locals())
helper = LayerHelper("reshape2", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
x_shape = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reshape",
type="reshape2",
inputs=inputs,
attrs={"shape": shape},
outputs={"Out": out})
outputs={"Out": out,
"XShape": x_shape})
return helper.append_activation(out)
......@@ -4570,11 +4574,13 @@ def squeeze(input, axes, name=None):
"""
helper = LayerHelper("squeeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="squeeze",
type="squeeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out})
outputs={"Out": out,
"XShape": x_shape})
return out
......@@ -4605,11 +4611,13 @@ def unsqueeze(input, axes, name=None):
"""
helper = LayerHelper("unsqueeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type="unsqueeze",
type="unsqueeze2",
inputs={"X": input},
attrs={"axes": axes},
outputs={"Out": out})
outputs={"Out": out,
"XShape": x_shape})
return out
......@@ -5811,10 +5819,12 @@ def flatten(x, axis=1, name=None):
raise ValueError("The axis should be a int, and in range [0, rank(x)]")
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
helper.append_op(
type='flatten',
type='flatten2',
inputs={"X": x},
outputs={'Out': out},
outputs={'Out': out,
'XShape': x_shape},
attrs={"axis": axis})
return out
......
python/paddle/fluid/optimizer.py
浏览文件 @ 4c283d87
......@@ -897,7 +897,20 @@ class RMSPropOptimizer(Optimizer):
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{v(w,t) +
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) +
\\epsilon}} \\nabla Q_{i}(w)
w & = w - v(w, t)
if centered is True:
.. math::
r(w, t) & = \\rho r(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2
g(w, t) & = \\rho g(w, t-1) + (1 - \\rho)\\nabla Q_{i}(w)
v(w, t) & = \\beta v(w, t-1) + \\frac{\\eta} {\\sqrt{r(w,t) - (g(w, t))^2 +
\\epsilon}} \\nabla Q_{i}(w)
w & = w - v(w, t)
......@@ -915,6 +928,10 @@ class RMSPropOptimizer(Optimizer):
avoid division by zero, set 1e-6 by default.
momentum(float): :math:`\\beta` in equation is the momentum term,
set 0.0 by default.
centered(bool): If True, gradients are normalized by the estimated variance of
the gradient; if False, by the uncentered second moment. Setting this to
True may help with training, but is slightly more expensive in terms of
computation and memory. Defaults to False.
Raises:
ValueError: If learning_rate, rho, epsilon, momentum are None.
......@@ -928,12 +945,14 @@ class RMSPropOptimizer(Optimizer):
_momentum_acc_str = "momentum"
_mean_square_acc_str = "mean_square"
_mean_grad_acc_str = "mean_grad"
def __init__(self,
learning_rate,
rho=0.95,
epsilon=1.0e-6,
momentum=0.0,
centered=False,
**kwargs):
super(RMSPropOptimizer, self).__init__(
learning_rate=learning_rate, **kwargs)
......@@ -950,6 +969,7 @@ class RMSPropOptimizer(Optimizer):
self._rho = rho
self._epsilon = epsilon
self._momentum = momentum
self._centered = centered
def _create_accumulators(self, block, parameters):
if not isinstance(block, framework.Block):
......@@ -958,6 +978,7 @@ class RMSPropOptimizer(Optimizer):
for p in parameters:
self._add_accumulator(self._momentum_acc_str, p)
self._add_accumulator(self._mean_square_acc_str, p)
self._add_accumulator(self._mean_grad_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
if not isinstance(block, framework.Block):
......@@ -967,6 +988,8 @@ class RMSPropOptimizer(Optimizer):
param_and_grad[0])
mean_square_acc = self._get_accumulator(self._mean_square_acc_str,
param_and_grad[0])
mean_grad_acc = self._get_accumulator(self._mean_grad_acc_str,
param_and_grad[0])
rmsprop_op = block.append_op(
type=self.type,
inputs={
......@@ -974,17 +997,20 @@ class RMSPropOptimizer(Optimizer):
"Grad": param_and_grad[1],
"Moment": momentum_acc,
"MeanSquare": mean_square_acc,
"MeanGrad": mean_grad_acc,
"LearningRate": self._create_param_lr(param_and_grad),
},
outputs={
"ParamOut": param_and_grad[0],
"MomentOut": momentum_acc,
"MeanSquareOut": mean_square_acc
"MeanSquareOut": mean_square_acc,
"MeanGradOut": mean_grad_acc
},
attrs={
"epsilon": self._epsilon,
"decay": self._rho,
"momentum": self._momentum
"momentum": self._momentum,
"centered": self._centered
})
return rmsprop_op
......
python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py
浏览文件 @ 4c283d87
......@@ -47,14 +47,14 @@ def train_program():
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
return avg_loss
return [avg_loss, y_predict]
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname):
def train(use_cuda, train_program, params_dirname, inference_model_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
......@@ -74,6 +74,8 @@ def train(use_cuda, train_program, params_dirname):
'''
if params_dirname is not None:
trainer.save_params(params_dirname)
trainer.save_inference_model(inference_model_dirname,
['x'], [1])
trainer.stop()
trainer.train(
......@@ -99,15 +101,55 @@ def infer(use_cuda, inference_program, params_dirname=None):
print("infer results: ", results[0])
def infer_by_saved_model(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
test_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=batch_size)
test_data = next(test_reader())
test_feat = numpy.array(
[data[0] for data in test_data]).astype("float32")
test_label = numpy.array(
[data[1] for data in test_data]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: numpy.array(test_feat)},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
print("ground truth: ", test_label)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
params_dirname = "fit_a_line.inference.model"
params_dirname = "fit_a_line.model"
inference_model_dirname = "fit_a_line.inference_model"
train(use_cuda, train_program, params_dirname)
train(use_cuda, train_program, params_dirname, inference_model_dirname)
infer(use_cuda, inference_program, params_dirname)
infer_by_saved_model(use_cuda, inference_model_dirname)
class TestFitALine(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ 4c283d87
......@@ -36,6 +36,7 @@ import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from test_dist_base import TestDistRunnerBase, runtime_main
import paddle.compat as cpt
from paddle.compat import long_type
import hashlib
......@@ -315,7 +316,8 @@ def pad_batch_data(insts,
"""
return_list = []
max_len = max(len(inst) for inst in insts)
num_token = reduce(lambda x, y: x + y,
num_token = six.moves.reduce(
lambda x, y: x + y,
[len(inst) for inst in insts]) if return_num_token else 0
# Any token included in dict can be used to pad, since the paddings' loss
# will be masked out by weights and make no effect on parameter gradients.
......@@ -328,7 +330,7 @@ def pad_batch_data(insts,
return_list += [inst_weight.astype("float32").reshape([-1, 1])]
else: # position data
inst_pos = np.array([
range(1, len(inst) + 1) + [0] * (max_len - len(inst))
list(range(1, len(inst) + 1)) + [0] * (max_len - len(inst))
for inst in insts
])
return_list += [inst_pos.astype("int64").reshape([-1, 1])]
......@@ -385,10 +387,11 @@ def prepare_batch_input(insts, data_input_names, src_pad_idx, trg_pad_idx,
return_num_token=True)
data_input_dict = dict(
list(
zip(data_input_names, [
src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
]))
])))
return data_input_dict, np.asarray([num_token], dtype="float32")
......@@ -561,7 +564,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
np.log(TrainTaskConfig.label_smooth_eps / (
ModelHyperParams.trg_vocab_size - 1) + 1e-20))
init = False
for pass_id in xrange(TrainTaskConfig.pass_num):
for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
if batch_id >= 5:
......@@ -587,11 +590,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
ModelHyperParams.eos_idx, ModelHyperParams.n_head,
ModelHyperParams.d_model)
total_num_token += num_token
feed_kv_pairs = data_input_dict.items()
feed_kv_pairs = list(data_input_dict.items())
if TrainTaskConfig.local:
feed_kv_pairs += {
feed_kv_pairs += list({
lr_scheduler.learning_rate.name: lr_rate
}.items()
}.items())
feed_list.append(dict(feed_kv_pairs))
if not init:
......@@ -873,6 +876,7 @@ class DataReader(object):
f = tarfile.open(fpaths[0], "r")
for line in f.extractfile(tar_fname):
line = cpt.to_text(line)
fields = line.strip("\n").split(self._field_delimiter)
if (not self._only_src and len(fields) == 2) or (
self._only_src and len(fields) == 1):
......@@ -882,8 +886,9 @@ class DataReader(object):
if not os.path.isfile(fpath):
raise IOError("Invalid file: %s" % fpath)
with open(fpath, "r") as f:
with open(fpath, "rb") as f:
for line in f:
line = cpt.to_text(line)
fields = line.strip("\n").split(self._field_delimiter)
if (not self._only_src and len(fields) == 2) or (
self._only_src and len(fields) == 1):
......@@ -892,8 +897,9 @@ class DataReader(object):
@staticmethod
def load_dict(dict_path, reverse=False):
word_dict = {}
with open(dict_path, "r") as fdict:
with open(dict_path, "rb") as fdict:
for idx, line in enumerate(fdict):
line = cpt.to_text(line)
if reverse:
word_dict[idx] = line.strip("\n")
else:
......@@ -1034,7 +1040,7 @@ def multi_head_attention(queries,
# size of the input as the output dimension size.
return layers.reshape(
x=trans_x,
shape=map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]]))
shape=list(map(int, [0, 0, trans_x.shape[2] * trans_x.shape[3]])))
def scaled_dot_product_attention(q, k, v, attn_bias, d_model, dropout_rate):
"""
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 4c283d87
......@@ -249,7 +249,7 @@ class OpTest(unittest.TestCase):
outs, _ = self._calc_output(place)
return outs
def _calc_output(self, place, parallel=False):
def _calc_output(self, place, parallel=False, no_check_set=None):
program = Program()
block = program.global_block()
......@@ -273,6 +273,8 @@ class OpTest(unittest.TestCase):
# if not, fill the fetch_list by the user configured outputs in test.
if len(fetch_list) == 0:
for var_name, var in six.iteritems(outputs):
if no_check_set is not None and var_name in no_check_set:
continue
if isinstance(var, list):
for v in var:
fetch_list.append(v)
......@@ -291,11 +293,17 @@ class OpTest(unittest.TestCase):
return_numpy=False)
return outs, fetch_list
def check_output_with_place(self, place, atol):
outs, fetch_list = self._calc_output(place)
def check_output_with_place(self,
place,
atol,
no_check_set=None,
equal_nan=False):
outs, fetch_list = self._calc_output(place, no_check_set=no_check_set)
for out_name, out_dup in Operator.get_op_outputs(self.op_type):
if out_name not in self.outputs:
continue
if no_check_set is not None and out_name in no_check_set:
continue
def find_actual(target_name, fetch_list):
found = [
......@@ -321,7 +329,7 @@ class OpTest(unittest.TestCase):
if isinstance(expect, tuple) else expect
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + sub_out_name + ") has diff at " +
str(place))
if isinstance(expect, tuple):
......@@ -337,7 +345,7 @@ class OpTest(unittest.TestCase):
expect_t = expect[0] if isinstance(expect, tuple) else expect
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) +
"\nExpect " + str(expect_t) + "\n" + "But Got" +
str(actual_t))
......@@ -360,10 +368,10 @@ class OpTest(unittest.TestCase):
places.append(core.CUDAPlace(0))
return places
def check_output(self, atol=1e-5):
def check_output(self, atol=1e-5, no_check_set=None, equal_nan=False):
places = self._get_places()
for place in places:
self.check_output_with_place(place, atol)
self.check_output_with_place(place, atol, no_check_set, equal_nan)
def check_output_customized(self, checker):
places = self._get_places()
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 4c283d87
......@@ -55,6 +55,7 @@ class TestDistRunnerBase(object):
pserver_prog = t.get_pserver_program(args.current_endpoint)
startup_prog = t.get_startup_program(args.current_endpoint,
pserver_prog)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
......@@ -147,6 +148,8 @@ def runtime_main(test_class):
import paddle.compat as cpt
import socket
from contextlib import closing
class TestDistBase(unittest.TestCase):
......@@ -156,13 +159,19 @@ class TestDistBase(unittest.TestCase):
def setUp(self):
self._trainers = 2
self._pservers = 2
self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._sync_mode = True
self._mem_opt = False
self._use_reduce = False
self._setup_config()
def _find_free_port(self):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(('', 0))
return s.getsockname()[1]
def start_pserver(self, model_file, check_error_log):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
......
python/paddle/fluid/tests/unittests/test_flatten_op.py
浏览文件 @ 4c283d87
......@@ -22,14 +22,17 @@ from op_test import OpTest
class TestFlattenOp(OpTest):
def setUp(self):
self.op_type = "flatten"
self.op_type = "flatten2"
self.init_test_case()
self.inputs = {"X": np.random.random(self.in_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.in_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=["XShape"])
def test_check_grad(self):
self.check_grad(["X"], "Out")
......
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
浏览文件 @ 4c283d87
......@@ -53,12 +53,11 @@ class TestFusionLSTMOp(OpTest):
self.M = 8
self.D = 16
self.has_initial_state = False
self.use_peepholes = False
self.is_reverse = False
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.use_seq = False
self.set_conf()
T = sum(self.lod[0])
......@@ -108,7 +107,6 @@ class TestFusionLSTMOp(OpTest):
}
self.attrs = {
'use_peepholes': self.use_peepholes,
'use_seq': self.use_seq,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
......@@ -178,50 +176,18 @@ class TestFusionLSTMOpPeepholesReverse(TestFusionLSTMOp):
self.is_reverse = True
class TestFusionLSTMOpPoopholesBS1(TestFusionLSTMOp):
class TestFusionLSTMOpPeepholesInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
self.lod = [[3]]
self.D = 16
class TestFusionLSTMOpSeqInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
class TestFusionLSTMOpSeqReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.is_reverse = True
class TestFusionLSTMOpSeqInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
self.is_reverse = True
class TestFusionLSTMOpSeqPeepholes(TestFusionLSTMOp):
class TestFusionLSTMOpPeepholesBS1(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
class TestFusionLSTMOpSeqPeepholesInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.has_initial_state = True
class TestFusionLSTMOpSeqPeepholesReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.is_reverse = True
self.lod = [[2]]
self.D = 8
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_feed.py
浏览文件 @ 4c283d87
......@@ -85,6 +85,7 @@ class TestFetchOp(unittest.TestCase):
assert not math.isnan(np.sum(ret[i])) and \
not math.isinf(np.sum(ret[i]))
@unittest.skip(reason="CI timeout")
def test_fetch_op(self):
tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
tst_reader_iter = tst_reader()
......@@ -139,6 +140,7 @@ class TestFeedParallel(unittest.TestCase):
if batch_id == 2:
break
@unittest.skip(reason="CI timeout")
def test_feed_op(self):
os.environ['CPU_NUM'] = str(4)
if core.is_compiled_with_cuda():
......
python/paddle/fluid/tests/unittests/test_prelu_op.py
浏览文件 @ 4c283d87
......@@ -16,6 +16,7 @@ from __future__ import print_function
import unittest
import numpy as np
import six
from op_test import OpTest
......@@ -62,17 +63,20 @@ class PReluTest(OpTest):
# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
# class TestCase1(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "all"}
if six.PY2:
# class TestCase2(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "channel"}
class TestCase1(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "all"}
class TestCase2(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "channel"}
class TestCase3(PReluTest):
def initTestCase(self):
self.attrs = {'mode': "element"}
# class TestCase3(PReluTest):
# def initTestCase(self):
# self.attrs = {'mode': "element"}
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_reshape_op.py
浏览文件 @ 4c283d87
......@@ -22,106 +22,39 @@ from op_test import OpTest
class TestReshapeOp(OpTest):
def setUp(self):
ori_shape = (2, 25)
new_shape = (5, 10)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer1(OpTest):
def setUp(self):
ori_shape = (5, 10)
new_shape = (5, -1, 5)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(self.attrs["shape"])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer2(OpTest):
def setUp(self):
ori_shape = (2, 2, 6)
new_shape = (2, 0, 3, -1)
infered_shape = (2, 2, 3, -1)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpInplace(OpTest):
def setUp(self):
ori_shape = (2, 25)
new_shape = (5, 10)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInferInplace1(OpTest):
def setUp(self):
ori_shape = (5, 10)
new_shape = (5, -1, 5)
self.init_data()
self.op_type = "reshape2"
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.attrs = {"shape": self.new_shape}
self.outputs = {
"Out": self.inputs["X"].reshape(self.infered_shape),
'XShape': np.random.random(self.ori_shape).astype("float32")
}
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def init_data(self):
self.ori_shape = (2, 25)
self.new_shape = (5, 10)
self.infered_shape = (5, 10)
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInferInplace2(OpTest):
def setUp(self):
ori_shape = (2, 2, 6)
new_shape = (2, 0, 3, -1)
infered_shape = (2, 2, 3, -1)
self.op_type = "reshape"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)}
class TestReshapeOpDimInfer1(TestReshapeOp):
def init_data(self):
self.ori_shape = (5, 10)
self.new_shape = (5, -1, 5)
self.infered_shape = (5, -1, 5)
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestReshapeOpDimInfer2(TestReshapeOp):
def init_data(self):
self.ori_shape = (2, 2, 6)
self.new_shape = (2, 0, 3, -1)
self.infered_shape = (2, 2, 3, -1)
class TestReshapeOpWithInputShape(OpTest):
......@@ -130,20 +63,23 @@ class TestReshapeOpWithInputShape(OpTest):
new_shape = (0, -1, 5)
actual_shape = (2, 3, 5)
self.op_type = "reshape"
self.op_type = "reshape2"
self.inputs = {
"X": np.random.random(ori_shape).astype("float32"),
"Shape": np.array(
actual_shape, dtype="int32")
}
self.attrs = {"shape": new_shape}
self.outputs = {"Out": self.inputs["X"].reshape(actual_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(actual_shape),
'XShape': np.random.random(ori_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(["X"], "Out")
self.check_grad(["X"], "Out", sum_outputs=["Out"])
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_rmsprop_op.py
浏览文件 @ 4c283d87
......@@ -15,90 +15,164 @@
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
class TestRmspropOp1(OpTest):
''' Test RMSProp with explicit inputs
'''
def setUp(self):
self.op_type = "rmsprop"
param = np.random.random((123, 321)).astype("float32")
mean_square = np.random.random((123, 321)).astype("float32")
learning_rate = np.array([0.01]).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
moment = np.zeros((123, 321)).astype("float32")
epsilon = 1e-6
decay = 0.9
momentum = 0.0
self.inputs = {
'Param': param,
'MeanSquare': mean_square,
'LearningRate': learning_rate,
'Grad': grad,
'Moment': moment,
}
self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum}
ms_out = decay * mean_square + (1 - decay) * grad * grad
moment_out = momentum * moment + \
learning_rate * grad / np.sqrt(ms_out + epsilon)
param_out = param - moment_out
self.outputs = {
'ParamOut': param_out,
'MomentOut': moment_out,
'MeanSquareOut': ms_out
}
def test_check_output(self):
self.check_output()
class TestRmspropOp2(OpTest):
'''Test RMSProp with default values for attributes
'''
def setUp(self):
self.op_type = "rmsprop"
param = np.random.random((123, 321)).astype("float32")
mean_square = np.random.random((123, 321)).astype("float32")
learning_rate = np.array([0.01]).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
moment = np.zeros((123, 321)).astype("float32")
epsilon = 1.0e-10
decay = 0.9
momentum = 0.0
self.inputs = {
'Param': param,
'MeanSquare': mean_square,
'LearningRate': learning_rate,
'Grad': grad,
'Moment': moment,
}
ms_out = decay * mean_square + (1 - decay) * grad * grad
moment_out = momentum * moment + \
learning_rate * grad / np.sqrt(ms_out + epsilon)
param_out = param - moment_out
self.outputs = {
'ParamOut': param_out,
'MomentOut': moment_out,
'MeanSquareOut': ms_out
}
def test_check_output(self):
self.check_output()
import paddle.fluid.core as core
from paddle.fluid.op import Operator
class TestBase(unittest.TestCase):
def setup(self, centered, epsilon=1e-6):
np.random.seed(5) # fix seed
self.param_name = "param"
self.param = np.random.random((123, 321)).astype("float32")
self.mean_square_name = "mean_square"
self.mean_square = np.random.random((123, 321)).astype("float32")
self.mean_grad_name = "mean_grad"
self.mean_grad = np.random.random((123, 321)).astype("float32")
self.lr_name = "lr"
self.learning_rate = np.array([0.01]).astype("float32")
self.grad_name = "grad"
self.grad = np.random.random((123, 321)).astype("float32")
self.moment_name = "moment"
self.moment = np.zeros((123, 321)).astype("float32")
self.epsilon = epsilon
self.decay = 0.9
self.momentum = 0.0
self.centered = centered
self.ms_out = self.decay * self.mean_square + (1 - self.decay
) * self.grad * self.grad
if centered:
self.mg_out = self.decay * self.mean_grad + (1 - self.decay
) * self.grad
self.moment_out = self.momentum * self.moment + \
self.learning_rate * self.grad / np.sqrt(self.ms_out - np.square(self.mg_out) + self.epsilon)
else:
self.moment_out = self.momentum * self.moment + \
self.learning_rate * self.grad / np.sqrt(self.ms_out + self.epsilon)
self.param_out = self.param - self.moment_out
def check(self,
actual_t,
expect_t,
place,
out_name,
atol=1e-5,
equal_nan=False):
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
+ str(expect_t) + "\n" + "But Got" + str(actual_t))
class TestRmspropOp(TestBase):
def check_with_place(self, place, centered, epsilon):
self.setup(centered, epsilon)
scope = core.Scope()
# create and initialize Param Variable
param = scope.var(self.param_name).get_tensor()
param.set(self.param, place)
mean_square = scope.var(self.mean_square_name).get_tensor()
mean_square.set(self.mean_square, place)
lr = scope.var(self.lr_name).get_tensor()
lr.set(self.learning_rate, place)
grad = scope.var(self.grad_name).get_tensor()
grad.set(self.grad, place)
moment = scope.var(self.moment_name).get_tensor()
moment.set(self.moment, place)
# create and run sgd operator
if self.centered:
mean_grad = scope.var(self.mean_grad_name).get_tensor()
mean_grad.set(self.mean_grad, place)
rmsprop_op = Operator(
"rmsprop",
Param=self.param_name,
Grad=self.grad_name,
MeanSquare=self.mean_square_name,
MeanGrad=self.mean_grad_name,
Moment=self.moment_name,
LearningRate=self.lr_name,
ParamOut=self.param_name,
MeanSquareOut=self.mean_square_name,
MomentOut=self.moment_name,
MeanGradOut=self.mean_grad_name,
epsilon=self.epsilon,
decay=self.decay,
momentum=self.momentum,
centered=True)
else:
rmsprop_op = Operator(
"rmsprop",
Param=self.param_name,
Grad=self.grad_name,
MeanSquare=self.mean_square_name,
Moment=self.moment_name,
LearningRate=self.lr_name,
ParamOut=self.param_name,
MeanSquareOut=self.mean_square_name,
MomentOut=self.moment_name,
epsilon=self.epsilon,
decay=self.decay,
momentum=self.momentum,
centered=False)
rmsprop_op.run(scope, place)
atol = 1e-5
equal_nan = False
if self.centered:
atol = 1e-3
equal_nan = True
self.check(
np.array(mean_square), self.ms_out, place, self.mean_square_name)
self.check(
np.array(moment),
self.moment_out,
place,
self.moment_name,
atol=atol,
equal_nan=equal_nan)
self.check(
np.array(param),
self.param_out,
place,
self.param_name,
atol=atol,
equal_nan=equal_nan)
if self.centered:
self.check(
np.array(mean_grad), self.mg_out, place, self.mean_grad_name)
def test_rmsprop(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place, False, 1e-6)
self.check_with_place(place, False, 1e-10)
self.check_with_place(place, True, 1e-6)
self.check_with_place(place, True, 1e-10)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_squeeze_op.py
浏览文件 @ 4c283d87
......@@ -23,14 +23,17 @@ from op_test import OpTest
# Correct: General.
class TestSqueezeOp(OpTest):
def setUp(self):
self.op_type = "squeeze"
self.op_type = "squeeze2"
self.init_test_case()
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.ori_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(["X"], "Out")
......
python/paddle/fluid/tests/unittests/test_transpose_op.py
浏览文件 @ 4c283d87
......@@ -22,16 +22,19 @@ from op_test import OpTest
class TestTransposeOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "transpose"
self.op_type = "transpose2"
self.inputs = {'X': np.random.random(self.shape).astype("float32")}
self.attrs = {'axis': list(self.axis)}
self.outputs = {'Out': self.inputs['X'].transpose(self.axis)}
self.outputs = {
'XShape': np.random.random(self.shape).astype("float32"),
'Out': self.inputs['X'].transpose(self.axis)
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=['XShape'])
def test_check_grad(self):
self.check_grad(['X'], 'Out')
self.check_grad(['X'], 'Out', sum_outputs=['Out'])
def initTestCase(self):
self.shape = (3, 4)
......
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
浏览文件 @ 4c283d87
......@@ -24,13 +24,16 @@ from op_test import OpTest
class TestUnsqueezeOp(OpTest):
def setUp(self):
self.init_test_case()
self.op_type = "unsqueeze"
self.op_type = "unsqueeze2"
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
self.outputs = {
"Out": self.inputs["X"].reshape(self.new_shape),
"XShape": np.random.random(self.ori_shape).astype("float32")
}
def test_check_output(self):
self.check_output()
self.check_output(no_check_set=["XShape"])
def test_check_grad(self):
self.check_grad(["X"], "Out")
......
python/paddle/fluid/trainer.py
浏览文件 @ 4c283d87
......@@ -431,6 +431,28 @@ class Trainer(object):
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, param_path, feeded_var_names,
target_var_indexes):
"""
Save model for cpp inference into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
feeded_var_names(list(str)): The name of the vars that you
need to feed in before run program.
target_var_indexes(list(int)): the index of target var that
you need to return in trainer.train_func.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
target_vars = [
self.train_func_outputs[index] for index in target_var_indexes
]
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
......
python/paddle/fluid/transpiler/details/program_utils.py
浏览文件 @ 4c283d87
......@@ -153,7 +153,7 @@ def block_to_code(block, block_idx):
indent += 1
# sort all vars
all_vars = sorted(block.vars.iteritems(), key=lambda x: x[0])
all_vars = sorted(six.iteritems(block.vars), key=lambda x: x[0])
for var in all_vars:
print("{}{}".format(get_indent_space(indent), variable_to_code(var[1])))
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 4c283d87
......@@ -300,7 +300,7 @@ class DistributeTranspiler(object):
input_deps = grad_name_to_send_dummy_out.values()
program.global_block().append_op(
type="send_barrier",
inputs={"X": input_deps},
inputs={"X": list(input_deps)},
outputs={"Out": send_barrier_out},
attrs={
"endpoints": pserver_endpoints,
......@@ -455,7 +455,7 @@ class DistributeTranspiler(object):
if len(splited_var) <= 1:
continue
# NOTE: if enable memory optimization, origin vars maybe removed.
if startup_program.global_block().vars.has_key(varname):
if varname in startup_program.global_block().vars:
orig_param = startup_program.global_block().vars[varname]
else:
origin_param_var = self.origin_program.global_block().vars[
......
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