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提交 7aaf019a 编写于 作者: Z zchen0211
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

上级 a4b7ef10 f80fea8d
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内联 并排
Showing with 1717 addition and 1785 deletion
doc/getstarted/build_and_install/docker_install_cn.rst
浏览文件 @ 7aaf019a
......@@ -74,13 +74,13 @@ PaddlePaddle发布新版本的时候都会发布对应版本的生产镜像以
.. code-block:: bash
docker run -it --rm paddlepaddle/paddle:0.10.0-dev /bin/bash
docker run -it --rm -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /bin/bash
或者，可以以后台进程方式运行容器：
.. code-block:: bash
docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0-dev
docker run -d -p 2202:22 -p 8888:8888 -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /usr/sbin/sshd -D
然后用密码 :code:`root` SSH进入容器：
......
paddle/framework/CMakeLists.txt
浏览文件 @ 7aaf019a
......@@ -7,7 +7,7 @@ cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory device_context)
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc details/lod_tensor.cc DEPS ddim place tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
......@@ -15,23 +15,19 @@ cc_test(variable_test SRCS variable_test.cc)
cc_library(scope SRCS scope.cc)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
proto_library(attribute_proto SRCS attribute.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attribute_proto)
proto_library(op_desc SRCS op_desc.proto DEPS attribute_proto)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
proto_library(framework_proto SRCS framework.proto)
cc_library(attribute SRCS attribute.cc DEPS op_desc op_proto)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope attribute)
cc_library(operator SRCS operator.cc DEPS framework_proto device_context tensor scope attribute)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)
cc_library(op_registry SRCS op_registry.cc DEPS op_desc grad_op_builder)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.proto)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
......
paddle/framework/attribute.cc
浏览文件 @ 7aaf019a
......@@ -44,7 +44,7 @@ AttrType AttrTypeID<std::vector<std::string>>() {
return STRINGS;
}
Attribute GetAttrValue(const AttrDesc& attr_desc) {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
case paddle::framework::AttrType::INT: {
return attr_desc.i();
......
paddle/framework/attribute.h
浏览文件 @ 7aaf019a
......@@ -20,8 +20,7 @@ limitations under the License. */
#include <unordered_set>
#include <vector>
#include "paddle/framework/attribute.pb.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/variant.h"
......@@ -37,7 +36,7 @@ typedef std::unordered_map<std::string, Attribute> AttributeMap;
template <typename T>
AttrType AttrTypeID();
Attribute GetAttrValue(const AttrDesc& attr_desc);
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
// check whether a value(attribute) fit a certain limit
template <typename T>
......
paddle/framework/backward.cc
浏览文件 @ 7aaf019a
......@@ -21,15 +21,25 @@
namespace paddle {
namespace framework {
static bool AllInSet(const std::vector<std::string>& names,
const std::string& suffix,
const std::unordered_set<std::string>& set) {
template <typename Map, typename T>
static void ForEachVarName(Map& names, T callback) {
for (auto& name : names) {
if (set.find(name + suffix) == set.end()) {
return false;
for (auto& n : name.second) {
if (callback(n)) return;
}
}
return true;
}
// return whether all the names + suffixes in the set
static bool AllInSet(
const std::map<std::string, std::vector<std::string>>& names,
const std::string& suffix, const std::unordered_set<std::string>& set) {
bool all_in_set = true;
ForEachVarName(names, [&all_in_set, &set, &suffix](const std::string& n) {
all_in_set = set.find(n + suffix) != set.end();
return !all_in_set;
});
return all_in_set;
}
static std::shared_ptr<OperatorBase> NOP() {
......@@ -39,7 +49,7 @@ static std::shared_ptr<OperatorBase> NOP() {
return net_op;
}
// Get backward operator from a forward operator, recursively implementation.
// Get backward operator from a forward operator, a recursive implementation.
//
// no_grad_names the gradient variable names without gradient calculating.
//
......@@ -47,31 +57,35 @@ static std::shared_ptr<OperatorBase> NOP() {
// BackwardRecursive. use `uid = uniq_id++;` to get the unique index, and
// pass `uniq_id` through recursive calling.
//
// returns The backward operator. For simple situation, it is a simple
// operator. For complex situation, it is a NetOp.
// returns The backward operator. In a simple situation, it may be a simple
// operator, in a complex situation, it maybe a NetOp.
//
// See Backward.h for details
static std::shared_ptr<OperatorBase> BackwardRecursive(
const OperatorBase& forwardOp,
std::unordered_set<std::string>& no_grad_names, size_t& uniq_id);
std::shared_ptr<OperatorBase> BackwardRecursive(
const OperatorBase& forwardOp,
std::unordered_set<std::string>& no_grad_names, size_t& uniq_id) {
// If all input gradients of forwarding operator do not need to calculate,
// just return an NOP. Not return null ptr because NOP does not take
// too much time for calculation, but it is useful for simplifying logic.
if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
// much time for calculation, but it is useful for simplifying logic.
if (AllInSet(forwardOp.inputs_ /*names*/, kGradVarSuffix /*suffix*/,
no_grad_names /*set*/)) {
return NOP();
}
// All output gradients of forwarding operator do not need to calculate.
// Then all input gradients cannot be computed at all, and we put them into
// `no_grad_names` set. Return an NOP.
if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
for (auto& name : forwardOp.inputs_) {
// Mark all input is not need
no_grad_names.insert(name + kGradVarSuffix);
}
if (AllInSet(forwardOp.outputs_ /*names*/, kGradVarSuffix /*suffix*/,
no_grad_names /*set*/)) {
ForEachVarName(forwardOp.inputs_,
[&no_grad_names](const std::string& name) -> bool {
no_grad_names.insert(GradVarName(name));
return false;
});
return NOP();
}
......@@ -83,55 +97,65 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
auto& forwardNet = static_cast<const operators::NetOp&>(forwardOp);
// Map from output gradient variable name to operator's indices in
// backward net. That operator generates that variable.
// backward net's ops_. That operator generates that variable.
std::unordered_map<std::string, std::vector<size_t>> dup_output_ops;
size_t local_op_id = 0;
// reversely travel forwardNet
// reversely travel forwardNet and collect all duplicate outputs.
for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend();
++it, ++local_op_id) {
auto fwd = *it;
auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
net->AddOp(bwd);
for (auto& out : bwd->outputs_) {
dup_output_ops[out].emplace_back(local_op_id);
}
ForEachVarName(bwd->outputs_,
[&dup_output_ops, local_op_id](const std::string& out) {
dup_output_ops[out].emplace_back(local_op_id);
return false;
});
}
// Get unique ID for this method.
auto uid = uniq_id++;
// TODO(dzh): more comment
// multiple operators which have the same output (y for example) may
// overwrite the same y variable when backward, special operations are token
// to handle this case. For each duplicate output, rename it to an alias
// (original name with a offset), append an `add` op for its operator,
// and finally sum all the alias variable to the final output variable y.
using Pos = std::pair<size_t, std::shared_ptr<OperatorBase>>;
std::list<Pos> insert_position;
for (auto& dup_output_op : dup_output_ops) {
const std::string& name = dup_output_op.first;
auto& dup_op = dup_output_op.second;
// no duplicate output
if (dup_op.size() == 1) continue;
std::vector<std::string> dup_outputs;
// process the duplicate outputs
std::vector<std::string> dup_outputs;
for (size_t i = 0; i < dup_op.size(); ++i) {
// rename each duplicate output to an alias
auto op_offset = dup_op[i];
dup_outputs.push_back(name + "@RENAME@" + std::to_string(uid) + "@" +
std::to_string(i));
net->ops_[op_offset]->Rename(name, dup_outputs.back());
}
// collect all the offset to append `add` op for each alias
insert_position.push_back(
{dup_op.back(),
OpRegistry::CreateOp(
"add", {dup_outputs}, {name},
{{"input_format",
std::vector<int>{0, static_cast<int>(dup_outputs.size())}}})});
{dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}},
{{"Out", {name}}}, {})});
}
// make sure the inserted `add` ops follow the BFS order.
insert_position.sort(
[](const Pos& l, const Pos& r) { return l.first > r.first; });
for (auto& pos : insert_position) {
net->InsertOp(pos.first + 1, pos.second);
}
} else {
std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
for (std::string& grad_input : grad_op->inputs_) {
ForEachVarName(grad_op->inputs_, [&no_grad_names,
&net](std::string& grad_input) {
if (no_grad_names.count(grad_input)) {
// +1 for \0
std::string prefix = grad_input.substr(
......@@ -140,16 +164,19 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {prefix},
{grad_input}, {}));
net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {{"Src", {prefix}}},
{{"Dst", {grad_input}}}, {}));
}
}
return false;
});
for (std::string& grad_output : grad_op->outputs_) {
if (no_grad_names.count(grad_output)) {
grad_output = kEmptyVarName;
}
}
ForEachVarName(grad_op->outputs_,
[&no_grad_names](std::string& grad_output) {
if (no_grad_names.count(grad_output)) {
grad_output = kEmptyVarName;
}
return false;
});
if (net->ops_.empty()) { // Current no aux op is added to network
return grad_op;
......@@ -159,7 +186,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
net->type_ = "@GENERATED_BACKWARD@";
net->CompleteAddOp();
return net;
}
} // namespace framework
// See header for comments
std::shared_ptr<OperatorBase> Backward(
......
paddle/framework/backward_test.cc
浏览文件 @ 7aaf019a
......@@ -30,8 +30,7 @@ using DeviceContext = platform::DeviceContext;
class EmptyOp : public OperatorBase {
public:
DEFINE_OPERATOR_CTOR(EmptyOp, OperatorBase)
using OperatorBase::OperatorBase;
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
};
......@@ -40,9 +39,9 @@ class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input X of Add").IgnoreGradient();
AddInput("b", "Bias of Add").IgnoreGradient();
AddOutput("Out", "Out of Add").IgnoreGradient();
AddInput("X", "Input X of Add").AsNoGradient();
AddInput("b", "Bias of Add").AsNoGradient();
AddOutput("Out", "Out of Add").AsNoGradient();
AddComment("Add Op");
}
};
......@@ -51,8 +50,8 @@ class MulOpMaker : public OpProtoAndCheckerMaker {
public:
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("A", "A");
AddInput("B", "B");
AddInput("X", "A");
AddInput("Y", "B");
AddOutput("Out", "Out");
AddComment("Mul");
}
......@@ -63,7 +62,7 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker {
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "X");
AddOutput("Y", "Y");
AddOutput("Out", "Y");
AddComment("Sigmoid");
}
};
......@@ -73,21 +72,25 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
NoGradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "X input");
AddOutput("Y", "Y output");
AddOutput("Out", "Y output");
AddComment("NoGradOp, same input output. no Grad");
}
};
class FcOp : public operators::NetOp {
public:
void Init() override {
AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
{Output("mul_result")}, {}));
auto b_name = Input("b");
FcOp(const std::string &type, const VarNameMap &inputs,
const VarNameMap &outputs, const AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AddOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
auto input_b = Inputs("b");
std::string before_act = "mul_result";
if (b_name != kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
{Output("add_result")}, {}));
if (input_b.size() != 0) {
AddOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
{{"Out", {Output("add_result")}}}, {}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
......@@ -96,8 +99,8 @@ class FcOp : public operators::NetOp {
}
}
AddOp(OpRegistry::CreateOp("sigmoid", {Output(before_act)}, {Output("Out")},
{}));
AddOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
......@@ -109,8 +112,8 @@ class FcOpMaker : public OpProtoAndCheckerMaker {
AddInput("X", "x");
AddInput("W", "w");
AddInput("b", "b");
AddOutput("mul_result", "").SetTemporary();
AddOutput("add_result", "").SetTemporary();
AddOutput("mul_result", "").AsIntermediate();
AddOutput("add_result", "").AsIntermediate();
AddOutput("Out", "");
AddComment("");
}
......@@ -141,7 +144,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
public:
AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x").SetMultiple();
AddInput("X", "x").AsDuplicable();
AddOutput("Y", "y");
AddComment("");
}
......@@ -167,27 +170,24 @@ REGISTER_OP(many_output_op, f::EmptyOp, f::ManyOutputOpMaker);
REGISTER_GRADIENT_OP(many_output_op, many_output_op_grad, f::EmptyOp);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(4UL, gop->inputs_.size());
ASSERT_EQ(f::kEmptyVarName, gop->inputs_[0]);
ASSERT_EQ(1UL, gop->inputs_.size());
ASSERT_EQ("rowwise_add_grad", gop->type_);
ASSERT_EQ(f::GradVarName("X"), gop->outputs_[0]);
ASSERT_EQ(f::GradVarName("b"), gop->outputs_[1]);
ASSERT_EQ(f::GradVarName("X"), gop->Output(f::GradVarName("X")));
ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
}
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"X"});
ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
f::GradVarName("X")),
gop->outputs_.end());
auto gop = f::Backward(*fwd, {"x"});
ASSERT_EQ(gop->Output(f::GradVarName("X")), f::kEmptyVarName);
auto no_input_gop = f::Backward(*fwd, {"X", "b"});
auto no_input_gop = f::Backward(*fwd, {"x", "b"});
ASSERT_NE(no_input_gop, nullptr);
ASSERT_TRUE(no_input_gop->IsNetOp());
ASSERT_EQ(0UL,
......@@ -195,8 +195,12 @@ TEST(Backward, simple_op_not_need_grad) {
}
TEST(Backward, net_fc_backward_normal) {
std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
"fc", {"X", "w", "b"}, {"mul_result", "add_result", "out"}, {});
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_res"}},
{"add_result", {"add_re"}},
{"Out", {"out"}}},
{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
......@@ -218,8 +222,11 @@ TEST(Backward, net_fc_backward_normal) {
TEST(Backward, net_fc_backward_not_have_b) {
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
{"mul_result", "add_result", "tmp"}, {});
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {}}},
{{"mul_result", {"mul_res"}},
{"add_result", {"add_res"}},
{"Out", {"tmp"}}},
{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
......@@ -238,38 +245,49 @@ TEST(Backward, net_fc_backward_not_have_b) {
TEST(Backward, net_input_of_network_not_need_grad) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("fc", {"X", "W1", "b1"},
{"mul_tmp_0", "add_tmp_0", "hidden0"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"hidden0", "W2", "b2"},
{"mul_tmp_1", "add_tmp_1", "hidden1"}, {}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x"}}, {"W", {"W1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_tmp_0"}},
{"add_result", {"add_tmp_0"}},
{"Out", {"hidden0"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_tmp_1"}},
{"add_result", {"add_tmp_1"}},
{"Out", {"hidden1"}}},
{}));
net.CompleteAddOp();
auto bwd = Backward(net, {"X"}); // X@GRAD is not need.
auto bwd = Backward(net, {"x"}); // x@GRAD is not need.
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
bwd_net->outputs_.begin(), bwd_net->outputs_.end());
all_output.erase(f::kEmptyVarName);
auto output_vars = bwd_net->OutputVars(true);
std::unordered_set<std::string> all_outputs =
std::unordered_set<std::string>(output_vars.begin(), output_vars.end());
all_outputs.erase(f::kEmptyVarName);
for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
ASSERT_NE(all_output.find(f::GradVarName(out)), all_output.end());
ASSERT_NE(all_outputs.find(f::GradVarName(out)), all_outputs.end());
}
// Not Generated X
ASSERT_EQ(all_output.find(f::GradVarName("X")), all_output.end());
ASSERT_EQ(all_outputs.find(f::GradVarName("X")), all_outputs.end());
ASSERT_EQ(2UL, bwd_net->ops_.size());
ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
ASSERT_EQ(3UL, first_fc_grad->ops_.size());
ASSERT_EQ(f::kEmptyVarName,
first_fc_grad->ops_[2]->Output(f::GradVarName("A")));
first_fc_grad->ops_[2]->Output(f::GradVarName("X")));
}
TEST(Backward, net_shared_weight) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("mul", {"X", "W"}, {"Out"}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {"Out", "W"}, {"FinalOut"}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
{{"Out", {"out"}}}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
{{"Out", {"FinalOut"}}}, {}));
net.CompleteAddOp();
auto bwd = f::Backward(net, {});
......@@ -280,31 +298,37 @@ TEST(Backward, net_shared_weight) {
}
TEST(Backward, op_register_grad_not_for_network) {
auto fwd = f::OpRegistry::CreateOp(
"fc", {"X", "W", "b"}, {"mul_out", "add_out", "out1"},
{{"temporary_index", std::vector<int>{0, 1}}});
auto fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_out"}},
{"add_result", {"add_out"}},
{"Out", {"out1"}}},
{{"temporary_index", std::vector<int>{0, 1}}});
ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet);
}
TEST(Backward, op_all_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto backward = f::Backward(*fwd, {"X", "b"});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"x", "b"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
TEST(Backward, op_all_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto backward = f::Backward(*fwd, {"Out"});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"out"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
TEST(Backward, op_part_of_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("many_output_op", {"X"}, {"Y", "Z"}, {});
auto fwd = f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
{{"y", {"Y"}}, {"z", {"Z"}}}, {});
auto backward = f::Backward(*fwd, {"Z"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
......@@ -312,10 +336,10 @@ TEST(Backward, op_part_of_output_are_not_need) {
auto &fill_zero = *net->ops_[0];
ASSERT_EQ("fill_zeros_like", fill_zero.type_);
ASSERT_EQ(1UL, fill_zero.inputs_.size());
ASSERT_EQ("Z", fill_zero.inputs_[0]);
ASSERT_EQ(1UL, fill_zero.outputs_.size());
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.outputs_[0]);
ASSERT_EQ(1UL, fill_zero.Inputs("Src").size());
ASSERT_EQ("Z", fill_zero.Input("Src"));
ASSERT_EQ(1UL, fill_zero.Outputs("Dst").size());
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.Output("Dst"));
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.type_);
......@@ -327,44 +351,62 @@ TEST(Backward, op_part_of_output_are_not_need) {
}
TEST(Backward, op_part_of_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("mul", {"a", "b"}, {"out"}, {});
auto fwd = f::OpRegistry::CreateOp("mul", {{"X", {"a"}}, {"Y", {"b"}}},
{{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"a"});
auto &grad_mul = *backward;
ASSERT_EQ(grad_mul.type_, "mul_grad");
ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
ASSERT_EQ(grad_mul.Output(f::GradVarName("A")), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output(f::GradVarName("B")), f::GradVarName("b"));
ASSERT_EQ(grad_mul.Output(f::GradVarName("X")), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output(f::GradVarName("Y")), f::GradVarName("b"));
ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
ASSERT_EQ(grad_mul.Input("A"), "a");
ASSERT_EQ(grad_mul.Input("B"), "b");
ASSERT_EQ(grad_mul.Input("X"), "a");
ASSERT_EQ(grad_mul.Input("Y"), "b");
ASSERT_EQ(grad_mul.Input("Out"), "out");
}
TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("fc", {"x1", "w1", "b1"},
{"mul_out1", "add_out1", "out1"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"out1", "w2", "b2"},
{"mul_out2", "tmp_out2", "out2"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"out2", "w3", "b3"},
{"mul_out3", "tmp_out3", "out3"}, {}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x1"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_out1"}},
{"add_result", {"add_out1"}},
{"Out", {"out1"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_out2"}},
{"add_result", {"tmp_out2"}},
{"Out", {"out2"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
{{"mul_result", {"mul_out3"}},
{"add_result", {"tmp_out3"}},
{"Out", {"out3"}}},
{}));
net.CompleteAddOp();
auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
ASSERT_TRUE(backward->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(bwd_net->ops_.size(), 3UL);
auto &grad_fc = *bwd_net->ops_[0];
EXPECT_EQ(grad_fc.inputs_.size(),
3UL /* external input number */
const char *all = paddle::operators::NetOp::kAll;
EXPECT_EQ(grad_fc.inputs_[all].size(),
2UL /* external input number */
+ 1UL /* external output number*/
+ 1UL /* number of gradient of external output*/
+ 2U /* internal variable number*/);
EXPECT_EQ(grad_fc.outputs_.size(), 2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add */
+ 1UL /* input number of sigmod */);
EXPECT_EQ(bwd_net->ops_[1]->inputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
EXPECT_EQ(grad_fc.outputs_[all].size(),
2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add
*/
+ 1UL /* input number of sigmod */);
EXPECT_EQ(bwd_net->ops_[1]->inputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->outputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_[all].size(), 0UL);
}
paddle/framework/ddim.cc
浏览文件 @ 7aaf019a
......@@ -283,6 +283,5 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
DDim::DDim(std::initializer_list<int> init_list) {
*this = make_ddim(init_list);
}
} // namespace framework
} // namespace paddle
paddle/framework/details/lod_tensor.cc 已删除 100644 → 0
浏览文件 @ a4b7ef10
/* 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/framework/lod_tensor.h"
#include <memory>
namespace paddle {
namespace framework {
namespace details {
using LOD = LODTensor::LOD;
std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level_begin,
size_t level_end) {
auto new_lod = std::make_shared<LOD>();
new_lod->reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
new_lod->emplace_back(lod[i]);
}
return new_lod;
}
std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level, size_t elem_begin,
size_t elem_end, bool tensor_shared) {
// slice the lod.
auto new_lod = std::make_shared<LOD>();
new_lod->reserve(lod.size() - level);
auto start = lod.at(level)[elem_begin];
auto end = lod.at(level)[elem_end];
for (auto it = lod.begin() + level; it != lod.end(); it++) {
auto it_begin = std::find(it->begin(), it->end(), start);
auto it_end = std::find(it_begin, it->end(), end);
PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
new_lod->emplace_back(it_begin, it_end + 1);
if (!tensor_shared) {
// reset offset if tensor is copyed and sliced.
std::transform(new_lod->back().begin(), new_lod->back().end(),
new_lod->back().begin(),
[start](int v) { return v - start; });
PADDLE_ENFORCE(new_lod->back().front() == 0, "error in slice LOD");
}
}
return new_lod;
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/framework/details/lod_tensor.h 已删除 100644 → 0
浏览文件 @ a4b7ef10
/* 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 <memory>
namespace paddle {
namespace framework {
namespace details {
/*
* Slice levels from LOD.
*
* @lod: LOD to slice.
* @level_begin: level to begin slice.
* @level_end: level to end slice.
*/
std::shared_ptr<LODTensor::LOD> SliceLOD(const LODTensor::LOD &lod,
size_t level_begin, size_t level_end);
/*
* Slice elements from a level of LOD.
*
* @lod: LOD to slice.
* @level: which level to slice.
* @elem_begin: element's index to begin slice.
* @elem_end: element's index to end slice.
*/
std::shared_ptr<LODTensor::LOD> SliceLOD(const LODTensor::LOD &lod,
size_t level, size_t elem_begin,
size_t elem_end, bool tensor_shared);
} // namespace details
} // namespace framework
} // namespace paddle
paddle/framework/attribute.proto → paddle/framework/framework.proto
浏览文件 @ 7aaf019a
......@@ -15,9 +15,6 @@ limitations under the License. */
syntax = "proto2";
package paddle.framework;
// Attribute Type for paddle's Op.
// Op contains many attributes. Each type of attributes could be different.
// The AttrType will be shared between AttrDesc and AttrProto.
enum AttrType {
INT = 0;
FLOAT = 1;
......@@ -25,4 +22,61 @@ enum AttrType {
INTS = 3;
FLOATS = 4;
STRINGS = 5;
}
\ No newline at end of file
}
// OpDesc describes an instance of a C++ framework::OperatorBase
// derived class type.
message OpDesc {
message Attr {
required string name = 1;
required AttrType type = 2;
optional int32 i = 3;
optional float f = 4;
optional string s = 5;
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
};
message Var {
required string parameter = 1;
repeated string arguments = 2;
};
required string type = 3;
repeated Var inputs = 1;
repeated Var outputs = 2;
repeated Attr attrs = 4;
};
// OpProto describes a C++ framework::OperatorBase derived class.
message OpProto {
// VarProto describes the C++ type framework::Variable.
message Var {
required string name = 1;
required string comment = 2;
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool no_gradient = 5 [ default = false ];
}
// AttrProto describes the C++ type Attribute.
message Attr {
required string name = 1;
required AttrType type = 2;
required string comment = 3;
// If that attribute is generated, it means the Paddle third
// language binding has responsibility to fill that
// attribute. End-User should not set that attribute.
optional bool generated = 4 [ default = false ];
}
required string type = 1;
repeated Var inputs = 2;
repeated Var outputs = 3;
repeated Attr attrs = 4;
required string comment = 5;
}
paddle/framework/grad_op_builder.cc
浏览文件 @ 7aaf019a
......@@ -13,105 +13,52 @@ express or implied. See the License for the specific language governing
permissions and limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
typedef std::vector<int> Ints;
enum class OpArgType { IN, OUT };
const Ints* AttrFormat(const AttributeMap& attrs, const std::string& key) {
return (attrs.count(key) > 0) ? &boost::get<Ints>(attrs.at(key)) : nullptr;
}
Ints* AttrFormat(AttributeMap& attrs, const std::string& key) {
return (attrs.count(key) > 0) ? &boost::get<Ints>(attrs.at(key)) : nullptr;
}
static void TransOpArg(const OperatorBase* src_op,
std::vector<std::string>& grad_inputs,
std::vector<std::string>& grad_outputs,
AttributeMap& grad_attrs,
std::unordered_map<std::string, int>& grad_idxs,
const std::string& src_type, const std::string& dst_type,
int& idx, bool is_grad) {
const std::vector<std::string>& src_inout =
(src_type == "input_format") ? src_op->inputs_ : src_op->outputs_;
const std::vector<int>* src_format = AttrFormat(src_op->Attrs(), src_type);
std::vector<std::string>& dst_inout =
(dst_type == "input_format") ? grad_inputs : grad_outputs;
std::vector<int>* dst_format = AttrFormat(grad_attrs, dst_type);
const OpProto& proto = OpRegistry::protos().at(src_op->type_);
OperatorBase::VarNameMap* vars,
const OpArgType& src_type, bool is_grad) {
const auto& src_inout =
src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
auto& dst_inout = *vars;
const OpProto& proto = OpProtos().at(src_op->type_);
const auto& src_arg_list =
(src_type == "input_format") ? proto.inputs() : proto.outputs();
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
std::string src_name = arg.name();
std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
grad_idxs[dst_name] = idx++;
int src_arg_idx = src_op->in_out_idxs_->at(src_name);
int src_begin =
src_format == nullptr ? src_arg_idx : src_format->at(src_arg_idx);
int src_end = src_format == nullptr ? src_arg_idx + 1
: src_format->at(src_arg_idx + 1);
for (int i = src_begin; i < src_end; ++i) {
std::string s =
is_grad ? src_inout[i] + kGradVarSuffix
: (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
dst_inout.emplace_back(s);
}
if (dst_format != nullptr) {
dst_format->push_back(dst_inout.size());
if (arg.no_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_inout[dst_name].reserve(src_inout.at(src_name).size());
for (auto& var_name : src_inout.at(src_name)) {
std::string s = is_grad ? GradVarName(var_name) : var_name;
dst_inout[dst_name].emplace_back(s);
}
}
}
OperatorBase* BuildGradOp(const OperatorBase* op) {
const std::string& grad_op_type = OpRegistry::grad_ops().at(op->Type());
AttributeMap grad_attrs(op->Attrs());
grad_attrs.erase("input_format");
grad_attrs.erase("output_format");
if (op->Attrs().count("input_format") > 0) {
grad_attrs["output_format"] = std::vector<int>({0});
}
if (op->Attrs().count("input_format") > 0 ||
op->Attrs().count("output_format") > 0) {
grad_attrs["input_format"] = std::vector<int>({0});
}
std::vector<std::string> grad_inputs, grad_outputs;
using VarIndexMap = std::unordered_map<std::string, int>;
VarIndexMap* grad_idxs = new VarIndexMap;
int in_idx = 0;
int out_idx = 0;
TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
"input_format", "input_format", in_idx, false); // I
TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
"output_format", "input_format", in_idx, false); // G
TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
"output_format", "input_format", in_idx, true); // OG
TransOpArg(op, grad_inputs, grad_outputs, grad_attrs, *grad_idxs,
"input_format", "output_format", out_idx, true); // IG
OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
grad_op->type_ = grad_op_type;
grad_op->inputs_ = grad_inputs;
grad_op->outputs_ = grad_outputs;
grad_op->attrs_ = grad_attrs;
grad_op->in_out_idxs_.reset(grad_idxs);
return grad_op;
auto gop_type_it = OpRegistry::grad_ops().find(op->type_);
PADDLE_ENFORCE(gop_type_it != OpRegistry::grad_ops().end(),
"Operator %s do not register gradient type", op->type_);
auto& grad_op_type = gop_type_it->second;
OperatorBase::VarNameMap inputs;
OperatorBase::VarNameMap outputs;
TransOpArg(op, &inputs, OpArgType::IN, false); // I
TransOpArg(op, &inputs, OpArgType::OUT, false); // O
TransOpArg(op, &inputs, OpArgType::OUT, true); // OG
TransOpArg(op, &outputs, OpArgType::IN, true); // IG
auto gop_it = OpRegistry::op_creators().find(grad_op_type);
PADDLE_ENFORCE(gop_it != OpRegistry::op_creators().end(),
"Operator %s 's Gradient %s's creator cannot be found",
op->type_, grad_op_type);
return gop_it->second(grad_op_type, inputs, outputs, op->attrs_);
}
} // namespace framework
......
paddle/framework/grad_op_builder_test.cc
浏览文件 @ 7aaf019a
......@@ -10,8 +10,7 @@ namespace framework {
class NOP : public OperatorBase {
public:
DEFINE_OPERATOR_CTOR(NOP, OperatorBase)
using OperatorBase::OperatorBase;
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope,
const platform::DeviceContext &dev_ctx) const override {}
......@@ -22,10 +21,10 @@ class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple();
AddInput("In2_mult", "a multiple input").AsDuplicable();
AddInput("In3", "another single input");
AddOutput("Out1", "a single output");
AddOutput("Out2_mult", "a multiple output").SetMultiple();
AddOutput("Out2_mult", "a multiple output").AsDuplicable();
AddComment("test op with multiple inputs and outputs");
}
};
......@@ -35,10 +34,10 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple().IgnoreGradient();
AddInput("In3_mult", "another multiple input").SetMultiple();
AddOutput("Out1_mult", "a multiple output").SetMultiple();
AddOutput("Out2", "a single output").IgnoreGradient();
AddInput("In2_mult", "a multiple input").AsDuplicable().AsNoGradient();
AddInput("In3_mult", "another multiple input").AsDuplicable();
AddOutput("Out1_mult", "a multiple output").AsDuplicable();
AddOutput("Out2", "a single output").AsNoGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
......@@ -49,18 +48,18 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(
f::OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
"add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(static_cast<int>(grad_add_op->inputs_.size()), 4);
EXPECT_EQ(static_cast<int>(grad_add_op->outputs_.size()), 2);
EXPECT_EQ(grad_add_op->inputs_.size(), 4UL);
EXPECT_EQ(grad_add_op->outputs_.size(), 2UL);
EXPECT_EQ(grad_add_op->Input("X"), "x");
EXPECT_EQ(grad_add_op->Input("Y"), "y");
EXPECT_EQ(grad_add_op->Input("Out"), "out");
EXPECT_EQ(grad_add_op->Input("Out@GRAD"), "out@GRAD");
EXPECT_EQ(grad_add_op->Output("X@GRAD"), "x@GRAD");
EXPECT_EQ(grad_add_op->Output("Y@GRAD"), "y@GRAD");
EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker);
......@@ -69,15 +68,15 @@ REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 4, 5}},
{"output_format", std::vector<int>{0, 1, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"mult_io", {"in1", "in2_1", "in2_2", "in2_3", "in3"},
{"out1", "out2_1", "out2_2"}, attrs));
"mult_io", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2", "in2_3"}},
{"In3", {"in3"}}},
{{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
ASSERT_EQ(grad_test_op->inputs_.size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
......@@ -91,7 +90,7 @@ TEST(GradOpBuilder, MutiInOut) {
std::vector<std::string>(
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
......@@ -101,31 +100,28 @@ TEST(GradOpBuilder, MutiInOut) {
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 3, 5}},
{"output_format", std::vector<int>{0, 2, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"io_ignored", {"in1", "in2_1", "in2_2", "in3_1", "in3_2"},
{"out1_1", "out1_2", "out2"}, attrs));
"io_ignored", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2"}},
{"In3_mult", {"in3_1", "in3_2"}}},
{{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
ASSERT_EQ(grad_test_op->inputs_.size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({f::kEmptyVarName, f::kEmptyVarName}));
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Input("Out2"), f::kEmptyVarName);
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
std::vector<std::string>(
{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
f::GradVarName("out2"));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>(
......
paddle/framework/lod_tensor.cc
浏览文件 @ 7aaf019a
......@@ -19,32 +19,59 @@
namespace paddle {
namespace framework {
LODTensor LODTensor::SliceShared(size_t level_begin, size_t level_end) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
// slice levels just need to update LOD info, each level will contains the
// whole tensor_, so no need to modify tensor_.
return LODTensor(tensor_, new_lod);
LODTensor::LOD LODTensor::LOD::SliceLevels(size_t level_begin,
size_t level_end) const {
LOD new_lod;
new_lod.reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
new_lod.emplace_back(at(i));
}
return new_lod;
}
LODTensor LODTensor::SliceShared(size_t level, size_t elem_begin,
size_t elem_end) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
PADDLE_ENFORCE(elem_begin < NumElements(level),
"element begin [%d] out of range [%d]", elem_begin,
NumElements(level));
PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
"element end [%d] out of range [%d]", elem_end,
NumElements(level));
auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
true /*tensor_shared*/);
// slice elements just need to update LOD info, because offsets are not
// changed, so the original tensor_ can be reused.
return LODTensor(tensor_, new_lod);
LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
size_t elem_end) const {
// slice the lod.
LOD new_lod;
new_lod.reserve(size() - level);
auto start = this->at(level)[elem_begin];
auto end = this->at(level)[elem_end];
for (auto it = this->begin() + level; it != this->end(); it++) {
auto it_begin = std::find(it->begin(), it->end(), start);
auto it_end = std::find(it_begin, it->end(), end);
PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
new_lod.emplace_back(it_begin, it_end + 1);
// reset offset if tensor is copyed and sliced.
std::transform(new_lod.back().begin(), new_lod.back().end(),
new_lod.back().begin(),
[start](int v) { return v - start; });
PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD");
}
PADDLE_ENFORCE_LE(new_lod.size(), this->size());
return new_lod;
}
bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto& a_level = a[i];
const auto& b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
}
}
return true;
}
} // namespace framework
......
paddle/framework/lod_tensor.h
浏览文件 @ 7aaf019a
......@@ -15,7 +15,7 @@
#pragma once
#include <memory>
#if (!PADDLE_ONLY_CPU)
#if !defined(PADDLE_ONLY_CPU)
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#endif
......@@ -31,30 +31,29 @@ namespace framework {
* LODTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
class LODTensor {
class LODTensor : public Tensor {
public:
// Level save offsets of each unit.
#ifdef PADDLE_ONLY_CPU
using Level = std::vector<size_t>;
template <typename T>
using Vector = std::vector<T>;
#else
using Level = thrust::device_vector<size_t>;
template <typename T>
using Vector = thrust::host_vector<T>;
#endif
// LOD stores offsets of each level of units, the largest units level first,
// LoD stores offsets of each level of units, the largest units level first,
// then the smaller units level. Each Level stores the offsets of units in
// Tesor.
typedef std::vector<Level> LOD;
class LOD : public std::vector<Vector<size_t>> {
public:
LOD SliceLevels(size_t level_begin, size_t level_end) const;
LOD SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) const;
};
LODTensor() {}
LODTensor(const std::shared_ptr<Tensor> &tensor,
const std::shared_ptr<LOD> &lod) {
Reset(tensor, lod);
}
explicit LODTensor(const LOD &lod) : lod_(lod) {}
void Reset(const std::shared_ptr<Tensor> &tensor,
const std::shared_ptr<LOD> &lod) {
tensor_ = tensor;
lod_start_pos_ = lod;
}
virtual Tensor *Clone() const { return new LODTensor(lod_); }
/*
* Get a element from LOD.
......@@ -65,16 +64,14 @@ class LODTensor {
PADDLE_ENFORCE(elem < NumElements(level),
"element begin [%d] out of range [%d]", elem,
NumElements(level));
return (*lod_start_pos_)[level][elem];
return (lod_)[level][elem];
}
/*
* Number of LODTensor's levels, each level has units of data, for example,
* in the sentence's view, article, paragraph, sentence are 3 levels.
*/
size_t NumLevels() const {
return lod_start_pos_ ? lod_start_pos_->size() : 0UL;
}
size_t NumLevels() const { return lod_.size(); }
/*
* Number of elements in a level.
*/
......@@ -82,64 +79,71 @@ class LODTensor {
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
// the last offset is the end of last element
return lod_start_pos_->at(level).size() - 1;
return lod_[level].size() - 1;
}
/*
* Slice of levels[level_begin:level_end], with tensor copied.
*/
template <typename T>
LODTensor SliceCopied(size_t level_begin, size_t level_end,
const platform::Place &dst_place) const;
/*
* Slice of levels[level_begin:level_end], with tensor shared.
*/
LODTensor SliceShared(size_t level_begin, size_t level_end) const;
/*
* Slice of elements of a level, [elem_begin: elem_end], with tensor copied.
* @note: low performance in slice lod_start_pos_.
*/
template <typename T>
LODTensor SliceCopied(size_t level, size_t elem_begin, size_t elem_end,
const platform::Place &dst_place) const;
LODTensor SliceLevels(size_t level_begin, size_t level_end) const;
/*
* Slice of elements of a level, [elem_begin: elem_end], with tensor shared.
* @note: low performance in slice lod_start_pos_.
*/
LODTensor SliceShared(size_t level, size_t elem_begin, size_t elem_end) const;
/*
* Copy other's lod_start_pos_, to share LOD info.
* @note: the LOD info should not be changed.
* @note: low performance in slice lod_.
*/
void ShareLOD(const LODTensor &other) {
lod_start_pos_ = other.lod_start_pos_;
}
template <typename T>
LODTensor SliceInLevel(size_t level, size_t elem_begin,
size_t elem_end) const;
/*
* Copy other's lod_start_pos_'s content, free to mutate.
* Copy other's lod_'s content, free to mutate.
*/
void CopyLOD(const LODTensor &other) {
lod_start_pos_ = std::make_shared<LOD>(*other.lod_start_pos_);
}
void CopyLOD(const LODTensor &other) { lod_ = other.lod_; }
/*
* Determine whether LODTensor has a valid LOD info.
*/
bool HasLOD() const { return bool(lod_start_pos_); }
LOD *lod() const { return lod_start_pos_.get(); }
const LOD &lod() const { return lod_; }
LOD *mutable_lod() { return &lod_; }
std::shared_ptr<Tensor> &tensor() { return tensor_; }
Tensor *raw_tensor() { return tensor_.get(); }
virtual ~LODTensor() {}
private:
std::shared_ptr<LOD> lod_start_pos_;
std::shared_ptr<Tensor> tensor_;
LOD lod_;
};
bool operator==(const LODTensor::LOD &a, const LODTensor::LOD &b);
template <typename T>
LODTensor LODTensor::SliceLevels(size_t level_begin, size_t level_end) const {
auto new_lod = lod_.SliceLevels(level_begin, level_end);
// slice levels just need to update LOD info, each level will contains the
// whole tensor_, so no need to modify tensor_.
LODTensor new_tensor(new_lod);
new_tensor.ShareDataWith<T>(*this);
return new_tensor;
}
template <typename T>
LODTensor LODTensor::SliceInLevel(size_t level, size_t elem_begin,
size_t elem_end) const {
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
PADDLE_ENFORCE(elem_begin < NumElements(level),
"element begin [%d] out of range [%d]", elem_begin,
NumElements(level));
PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
"element end [%d] out of range [%d]", elem_end,
NumElements(level));
auto new_lod = lod_.SliceInLevel(level, elem_begin, elem_end);
// slice elements just need to update LOD info, because offsets are not
// changed, so the original tensor_ can be reused.
LODTensor new_tensor(new_lod);
new_tensor.ShareDataWith<T>(*this);
return new_tensor;
}
} // namespace framework
} // namespace paddle
#include "paddle/framework/lod_tensor_impl.h"
paddle/framework/lod_tensor_impl.h 已删除 100644 → 0
浏览文件 @ a4b7ef10
/* 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 "paddle/framework/details/lod_tensor.h"
namespace paddle {
namespace framework {
template <typename T>
LODTensor LODTensor::SliceCopied(size_t level_begin, size_t level_end,
const platform::Place &dst_place) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
auto new_tensor = std::make_shared<Tensor>();
new_tensor->CopyFrom<T>(*tensor_, dst_place);
return LODTensor(new_tensor, new_lod);
}
template <typename T>
LODTensor LODTensor::SliceCopied(size_t level, size_t elem_begin,
size_t elem_end,
const platform::Place &dst_place) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
PADDLE_ENFORCE(elem_begin < NumElements(level),
"element begin [%d] out of range [%d]", elem_begin,
NumElements(level));
PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
"element end [%d] out of range [%d]", elem_end,
NumElements(level));
auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
false /*tensor_shared*/);
auto start_idx = new_lod->front().front();
auto end_idx = new_lod->front().back() - 1 /*the next element's start*/;
auto sliced_tensor = tensor_->Slice<T>(start_idx, end_idx);
auto new_tensor = std::make_shared<Tensor>();
new_tensor->CopyFrom<T>(sliced_tensor, dst_place);
return LODTensor(new_tensor, new_lod);
}
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor_test.cc
浏览文件 @ 7aaf019a
......@@ -15,6 +15,7 @@
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <memory>
namespace paddle {
......@@ -29,22 +30,28 @@ class LODTensorTester : public ::testing::Test {
// 0 10 20
// 0 5 10 15 20
// 0 2 5 7 10 12 15 20
auto lod = std::make_shared<LODTensor::LOD>();
lod->push_back(std::vector<size_t>{0, 10, 20});
lod->push_back(std::vector<size_t>{0, 5, 10, 15, 20});
lod->push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
LODTensor::LOD lod;
lod.push_back(std::vector<size_t>{0, 10, 20});
lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
auto tensor = std::make_shared<Tensor>();
tensor->Resize({20 /*batch size*/, 128 /*dim*/});
ASSERT_EQ(lod.size(), 3UL);
tensor.Resize({20 /*batch size*/, 128 /*dim*/});
// malloc memory
tensor->mutable_data<float>(place);
tensor.mutable_data<float>(place);
lod_tensor.reset(new LODTensor(lod));
lod_tensor->Resize({20 /*batch size*/, 128 /*dim*/});
lod_tensor->Reset(tensor, lod);
lod_tensor->ShareDataWith<float>(tensor);
// lod_tensor->ShareDataWith<Tensor>(tensor);
}
protected:
std::unique_ptr<LODTensor> lod_tensor;
platform::CPUPlace place;
Tensor tensor;
};
TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); }
......@@ -55,110 +62,54 @@ TEST_F(LODTensorTester, NumElements) {
ASSERT_EQ(lod_tensor->NumElements(2), 8UL);
}
TEST_F(LODTensorTester, SliceShared_Level) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
auto new_lod_tensor = lod_tensor->SliceShared(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
auto new_lod_tensor = lod_tensor->SliceShared(level, level + 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1),
lod_tensor->NumElements(level + 1));
ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
}
}
TEST_F(LODTensorTester, SliceCopied_Level) {
TEST_F(LODTensorTester, SliceLevels) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
auto new_lod_tensor =
lod_tensor->SliceCopied<float>(level, level + 1, place);
auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
// ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
// TODO(superjom) add tensor comparation here.
// ASSERT_EQ(new_lod_tensor, *lod_tensor);
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
auto new_lod_tensor =
lod_tensor->SliceCopied<float>(level, level + 2, place);
auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1),
lod_tensor->NumElements(level + 1));
// ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
// TODO(superjom) add tensor comparation here.
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
}
}
TEST_F(LODTensorTester, SliceShared_Element) {
size_t level = 0;
auto new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
level = 1;
new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
}
TEST_F(LODTensorTester, SliceCopied_Element) {
TEST_F(LODTensorTester, SliceInLevel) {
size_t level = 0;
auto new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
auto new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
level = 1;
new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
level = 1;
// LOD is
// 0 5 10
// 0 2 5 7 10
new_lod_tensor = lod_tensor->SliceCopied<float>(level, 1, 3, place);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.lod_element(0, 0), 0UL);
ASSERT_EQ(new_lod_tensor.lod_element(0, 1), 5UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 0), 0UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 1), 2UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 2), 5UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 3), 7UL);
// TODO(superjom) compare the content of these tensors
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
}
TEST_F(LODTensorTester, ShareLOD) {
LODTensor new_lod_tensor;
new_lod_tensor.ShareLOD(*lod_tensor);
new_lod_tensor.CopyLOD(*lod_tensor);
ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod());
}
TEST_F(LODTensorTester, CopyLOD) {
LODTensor new_lod_tensor;
new_lod_tensor.CopyLOD(*lod_tensor);
ASSERT_NE(new_lod_tensor.lod(), lod_tensor->lod());
bool equals = std::equal(lod_tensor->lod().begin(), lod_tensor->lod().end(),
new_lod_tensor.lod().begin());
ASSERT_TRUE(equals);
}
} // namespace framework
......
paddle/framework/op_desc.proto 已删除 100644 → 0
浏览文件 @ a4b7ef10
/* 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. */
syntax = "proto2";
package paddle.framework;
import "attribute.proto";
// AttrDesc is used to describe Attributes of an Operator. It contain's
// name, type, and value of Attribute.
//
// e.g, for scale=3.0: name=scala, type=AttrType.FLOAT, value=3.0
message AttrDesc {
required string name = 1;
required AttrType type = 2;
optional int32 i = 3;
optional float f = 4;
optional string s = 5;
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
};
// Protocol Message to describe an Operator.
//
// In PaddlePaddle, Operator is used to do a certain computation such
// as "add", "sub", "cosine", etc.
// (1) Operator needs to know the input and output variable names.
// (2) Some ops may have special attributes such as "scale" in "CosineOp".
//
// 3rd-party language can build this proto message and call
// AddOp(const OpDesc& op_desc) of Paddle core to create an Operator.
message OpDesc {
// input names of this Operator.
repeated string inputs = 1;
// output names of this Operator.
repeated string outputs = 2;
// type of this Operator, such as "add", "sub", "fc".
required string type = 3;
// Attributes of this Operator. e.g., scale=3.0 in cosine op.
repeated AttrDesc attrs = 4;
};
\ No newline at end of file
paddle/framework/op_desc_test.cc 已删除 100644 → 0
浏览文件 @ a4b7ef10
/* 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 <gtest/gtest.h>
#include <paddle/framework/op_desc.pb.h>
TEST(OpDesc, Create) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("add");
op_desc.add_inputs("X");
op_desc.add_inputs("Y");
op_desc.add_outputs("Z");
auto attr = op_desc.mutable_attrs()->Add();
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(3.14);
// required field name is not set, so IsInitialized should be false.
ASSERT_FALSE(op_desc.IsInitialized());
attr->set_name("add");
// after all required fields are set, IsInitialized should be true now.
ASSERT_TRUE(op_desc.IsInitialized());
}
\ No newline at end of file
paddle/framework/op_proto.proto 已删除 100644 → 0
浏览文件 @ a4b7ef10
/* 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. */
// Protocol Message for 3rd-party language binding.
//
// Paddle Python package will use `OpProto` to generate op creation methods.
// The op creation methods take user's input and generate `OpDesc` proto
// message,
// then pass `OpDesc` to C++ side and create Op pointer.
//
syntax = "proto2";
package paddle.framework;
import "attribute.proto";
// Attribute protocol message for 3rd-party language binding.
// It will store the Op support what attribute and what type.
message AttrProto {
// Supported attribute name. e.g. `scale` for cosine op.
required string name = 1;
// Supported attribute type.
required AttrType type = 2;
// Supported attribute comments. It helps 3rd-party language generate
// doc-string.
required string comment = 3;
// If that attribute is generated, it means the Paddle third language
// binding has responsibility to fill that attribute. End-User should
// not set that attribute.
optional bool generated = 4 [ default = false ];
}
// Input or output message for 3rd-party language binding.
// It contains parameter name and its comments.
message VarProto {
// Input or output name in that op creation function.
// e.g. `cos(a, b, output, ...)`, "a", "b", "output" are names.
required string name = 1;
// The comment for that input. It helps 3rd-party language generate
// doc-string.
required string comment = 2;
// Is that input/output could be a list or not.
// If so, that Op should write a attributed named `input_format` or
// `output_format`.
//
// e.g.
// If the op is a fc op, the inputs are `X`, `W`, `b`. The `X` and `W`
// could be multiple, so the multiple of `X` and `W` is True, and OpDesc
// will hold a attribute of them.
//
// The Op desc of same fc could be
// {
// "type": "fc",
// "input": ["X1", "X2", "W1", "W2", "b"],
// "output": "fc.out",
// "attrs" : {
// "input_format": [0, 2, 4, 5]
// }
// }
//
optional bool multiple = 3 [ default = false ];
// It marks that output is a temporary output. That output is not used by
// user, but used by other op internally as input. If other op is not use
// that output, it could be optimized early.
//
// Attribute temporary_index will be set in OpDesc if there is some
// outputs are temporary.
//
// output = [ "xxx.out1", "xxx.tmp", "xxx.out2"],
// attrs = {
// "temporary_index": [1]
// }
optional bool temporary = 4 [ default = false ];
// The gradient of operator can be ignored immediately
// e.g. operator AddOp, y = x1 + x2, the gradient of dy/dx1, dy/dx2
// can be ignored for the future optimized on graph.
optional bool ignore_gradient = 6;
}
// Op protocol message for 3rd-party language binding.
// It contains all information for generating op creation method.
message OpProto {
// The input information to generate op creation method.
repeated VarProto inputs = 1;
// The output information to generate op creation method.
repeated VarProto outputs = 2;
// The attribute information to generate op creation method.
repeated AttrProto attrs = 3;
// The comments for that Op. It helps 3rd-party language generate
// doc-string. The whole documentation of that Op is generated by comment,
// inputs, outputs, attrs together.
required string comment = 4;
// The type of that Op.
required string type = 5;
}
paddle/framework/op_proto_test.cc 已删除 100644 → 0
浏览文件 @ a4b7ef10
#include <gtest/gtest.h>
#include <paddle/framework/op_proto.pb.h>
TEST(TestOpProto, ALL) {
paddle::framework::OpProto proto;
{
auto ipt = proto.mutable_inputs()->Add();
*ipt->mutable_name() = "a";
*ipt->mutable_comment() = "the one input of cosine op";
}
{
auto ipt = proto.mutable_inputs()->Add();
*ipt->mutable_name() = "b";
*ipt->mutable_comment() = "the other input of cosine op";
}
{
auto opt = proto.mutable_outputs()->Add();
*opt->mutable_name() = "output";
*opt->mutable_comment() = "the output of cosine op";
}
{
auto attr = proto.mutable_attrs()->Add();
*attr->mutable_name() = "scale";
attr->set_type(paddle::framework::AttrType::FLOAT);
*attr->mutable_comment() = "the scale attribute of cosine op";
}
proto.set_type("cos");
*proto.mutable_comment() = "cosine op, output = scale * cos(a, b)";
ASSERT_TRUE(proto.IsInitialized());
}
\ No newline at end of file
paddle/framework/op_registry.h
浏览文件 @ 7aaf019a
......@@ -20,8 +20,9 @@ limitations under the License. */
#include <unordered_map>
#include <unordered_set>
#include "paddle/framework/attribute.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
namespace paddle {
......@@ -44,52 +45,48 @@ class OpProtoAndCheckerMaker {
protected:
struct VariableBuilder {
VarProto* var_;
std::function<void()> on_multiple_;
std::function<void()> on_temporary_;
OpProto::Var* var_;
VariableBuilder& SetMultiple() {
var_->set_multiple(true);
on_multiple_();
VariableBuilder& AsDuplicable() {
var_->set_duplicable(true);
return *this;
}
VariableBuilder& SetTemporary() {
PADDLE_ENFORCE(bool(on_temporary_), "Cannot set temporary");
var_->set_temporary(true);
on_temporary_();
VariableBuilder& AsIntermediate() {
var_->set_intermediate(true);
return *this;
}
VariableBuilder& IgnoreGradient() {
var_->set_ignore_gradient(true);
// TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it
// means that input/output is not needed when calculate gradient. It does
// not mean no gradient when backward. It should be changed soon.
VariableBuilder& AsNoGradient() {
var_->set_no_gradient(true);
return *this;
}
};
VariableBuilder AddInput(const std::string& name,
const std::string& comment) {
VarProto* input = proto_->add_inputs();
auto* input = proto_->add_inputs();
input->set_name(name);
input->set_comment(comment);
return VariableBuilder{input, [=] { this->SetHasMultipleInput(); },
nullptr};
return VariableBuilder{input};
}
VariableBuilder AddOutput(const std::string& name,
const std::string& comment) {
VarProto* output = proto_->add_outputs();
auto* output = proto_->add_outputs();
output->set_name(name);
output->set_comment(comment);
return VariableBuilder{output, [=] { this->SetHasMultipleOutput(); },
[=] { this->SetHasTemporaryOutput(); }};
return VariableBuilder{output};
}
template <typename T>
TypedAttrChecker<T>& AddAttr(const std::string& name,
const std::string& comment,
bool generated = false) {
AttrProto* attr = proto_->add_attrs();
auto* attr = proto_->add_attrs();
attr->set_name(name);
attr->set_comment(comment);
attr->set_generated(generated);
......@@ -100,53 +97,6 @@ class OpProtoAndCheckerMaker {
void AddComment(const std::string& comment) { proto_->set_comment(comment); }
private:
void SetHasMultiple(const std::string& in_out, bool* flag) {
if (!*flag) {
AddAttr<std::vector<int>>(in_out + "_format",
"The multiple index of " + in_out +
"\n"
R"DOC(
This attribute is used by Paddle core framework. Paddle's Op support each input
or output could be a list of variable. This attribute is used to show how that
list organized.
e.g.
input = ["a", "b", "c", "d", "e", "f"]
input_format = [0, 4, 5, 6]
means
The number of all input variables this op is six, and they are segmented into
three inputs.
The first input is input[0:4], second is input[4:5], third is input[5:6].
)DOC",
/*generated*/ true);
*flag = true;
}
}
void SetHasMultipleInput() { SetHasMultiple("input", &has_multiple_input_); }
void SetHasMultipleOutput() {
SetHasMultiple("output", &has_multiple_output_);
}
void SetHasTemporaryOutput() {
if (!has_temporary_output_) {
AddAttr<std::vector<int>>("temporary_index",
R"DOC(The temporary index of output.
Not all output of Paddle Op is used by user. For faster computation, each op
could output some its internal state to other op, other op could take that
output to make compute faster.
Add a mark to which output is temporary is helpful for future optimization.
)DOC",
/*generated*/ true)
.SetDefault(std::vector<int>());
has_temporary_output_ = true;
}
}
void CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
......@@ -167,22 +117,24 @@ Add a mark to which output is temporary is helpful for future optimization.
OpProto* proto_;
OpAttrChecker* op_checker_;
bool validated_{false};
bool has_multiple_input_{false};
bool has_multiple_output_{false};
bool has_temporary_output_{false};
};
class OpRegistry {
using OpCreator = std::function<OperatorBase*()>;
using VarIndexMap = std::unordered_map<std::string, int>;
using VarNameList = std::vector<std::string>;
using VarNameMap = OperatorBase::VarNameMap;
using OpCreator = std::function<OperatorBase*(
const std::string& /*type*/, const VarNameMap& /*inputs*/,
const VarNameMap& /*outputs*/, const AttributeMap& /*attrs*/)>;
public:
template <typename OpType, typename ProtoMakerType>
static void RegisterOp(const std::string& op_type) {
op_creators()[op_type] = [] { return new OpType; };
op_creators()[op_type] = [](
const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs) {
return new OpType(type, inputs, outputs, attrs);
};
OpAttrChecker& op_checker = op_checkers()[op_type];
OpProto& op_proto = protos()[op_type];
OpProto& op_proto = OpProtos()[op_type];
auto maker = ProtoMakerType(&op_proto, &op_checker);
maker.Validate();
op_proto.set_type(op_type);
......@@ -190,66 +142,49 @@ class OpRegistry {
op_proto.IsInitialized(),
"Fail to initialize %s's OpProto, because %s is not initialized",
op_type, op_proto.InitializationErrorString());
VarIndexMaps()[op_type].reset(new VarIndexMap());
auto& varmap = *VarIndexMaps()[op_type];
int idx = 0;
for (auto& var : op_proto.inputs()) {
varmap[var.name()] = idx++;
}
idx = 0;
for (auto& var : op_proto.outputs()) {
varmap[var.name()] = idx++;
}
}
template <typename GradOpType>
static void RegisterGradOp(const std::string& op_type,
const std::string& grad_op_type) {
op_creators()[grad_op_type] = [] { return new GradOpType; };
op_creators()[grad_op_type] = [](
const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs) {
return new GradOpType(type, inputs, outputs, attrs);
};
grad_ops()[op_type] = grad_op_type;
}
static std::shared_ptr<OperatorBase> CreateOp(const std::string& type,
const VarNameList& inputs,
const VarNameList& outputs,
const AttributeMap& attrs) {
const VarNameMap& inputs,
const VarNameMap& outputs,
AttributeMap attrs) {
auto op_create_it = op_creators().find(type);
PADDLE_ENFORCE(op_create_it != op_creators().end(),
"Operator %s cannot be found.", type);
op_checkers().at(type).Check(attrs);
auto op = op_create_it->second();
op->type_ = type;
op->inputs_ = inputs;
op->outputs_ = outputs;
auto op = op_create_it->second(type, inputs, outputs, attrs);
op->attrs_ = attrs;
op_checkers().at(type).Check(op->attrs_);
GenerateTempVariableName(op);
return std::shared_ptr<OperatorBase>(op);
}
{
auto var_index_it = VarIndexMaps().find(type);
if (var_index_it != VarIndexMaps().end()) {
op->in_out_idxs_ = var_index_it->second;
}
static VarNameMap ConvertOpDescVarsToVarNameMap(
const google::protobuf::RepeatedPtrField<OpDesc::Var>& op_desc_vars) {
VarNameMap ret_val;
for (auto& var : op_desc_vars) {
auto& var_names = ret_val[var.parameter()];
auto& var_names_in_proto = var.arguments();
var_names.reserve(static_cast<size_t>(var_names_in_proto.size()));
std::copy(var_names_in_proto.begin(), var_names_in_proto.end(),
std::back_inserter(var_names));
}
op->Init();
return std::shared_ptr<OperatorBase>(op);
return ret_val;
}
static std::shared_ptr<OperatorBase> CreateOp(const OpDesc& op_desc) {
std::vector<std::string> inputs;
inputs.reserve((size_t)op_desc.inputs_size());
std::copy(op_desc.inputs().begin(), op_desc.inputs().end(),
std::back_inserter(inputs));
std::vector<std::string> outputs;
outputs.reserve((size_t)op_desc.outputs_size());
std::copy(op_desc.outputs().begin(), op_desc.outputs().end(),
std::back_inserter(outputs));
VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = GetAttrValue(attr);
......@@ -262,26 +197,14 @@ class OpRegistry {
PADDLE_ENFORCE(!op.IsNetOp(),
"Use framework::Backward to get backward ops");
std::shared_ptr<OperatorBase> grad_op(BuildGradOp(&op));
grad_op->Init();
return grad_op;
}
static std::unordered_map<std::string, OpProto>& protos() {
static std::unordered_map<std::string, OpProto> protos_;
return protos_;
}
static std::unordered_map<std::string, std::string>& grad_ops() {
static std::unordered_map<std::string, std::string> grad_ops_;
return grad_ops_;
}
static std::unordered_map<std::string, std::shared_ptr<VarIndexMap>>&
VarIndexMaps() {
static std::unordered_map<std::string, std::shared_ptr<VarIndexMap>> maps_;
return maps_;
}
static std::unordered_map<std::string, OpCreator>& op_creators() {
static std::unordered_map<std::string, OpCreator> op_creators_;
return op_creators_;
......@@ -292,17 +215,6 @@ class OpRegistry {
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
return op_checkers_;
}
static void GenerateTempVariableName(OperatorBase* op) {
static std::atomic<size_t> gUniqId(0UL);
for (auto& outname : op->outputs_) {
if (outname == kTempVarName) {
outname += op->type_;
outname += "@";
outname += std::to_string(gUniqId.fetch_add(1));
}
}
}
};
class Registrar {
......
paddle/framework/op_registry_test.cc
浏览文件 @ 7aaf019a
......@@ -7,8 +7,7 @@ namespace paddle {
namespace framework {
class CosineOp : public OperatorBase {
public:
DEFINE_OPERATOR_CTOR(CosineOp, OperatorBase)
using OperatorBase::OperatorBase;
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
void InferShape(const Scope& scope) const override {}
......@@ -29,8 +28,7 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
class MyTestOp : public OperatorBase {
public:
DEFINE_OPERATOR_CTOR(MyTestOp, OperatorBase)
using OperatorBase::OperatorBase;
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
......@@ -40,8 +38,8 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
MyTestOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op").SetMultiple();
AddOutput("output", "output of cosine op").SetTemporary();
AddInput("input", "input of cosine op").AsDuplicable();
AddOutput("output", "output of cosine op").AsIntermediate();
auto my_checker = [](int i) {
PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
};
......@@ -53,6 +51,15 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
} // namespace framework
} // namespace paddle
static void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::OpDesc::Var* var) {
var->set_parameter(param_name);
for (auto& arg_name : arguments) {
var->add_arguments(arg_name);
}
}
REGISTER_OP(cos_sim, paddle::framework::CosineOp,
paddle::framework::CosineOpProtoAndCheckerMaker);
REGISTER_OP(my_test_op, paddle::framework::MyTestOp,
......@@ -61,8 +68,8 @@ REGISTER_OP(my_test_op, paddle::framework::MyTestOp,
TEST(OpRegistry, CreateOp) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("cos_sim");
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
float scale = 3.3;
auto attr = op_desc.mutable_attrs()->Add();
......@@ -82,8 +89,8 @@ TEST(OpRegistry, CreateOp) {
TEST(OpRegistry, IllegalAttr) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("cos_sim");
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
......@@ -107,8 +114,8 @@ TEST(OpRegistry, IllegalAttr) {
TEST(OpRegistry, DefaultValue) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("cos_sim");
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
ASSERT_TRUE(op_desc.IsInitialized());
......@@ -120,20 +127,11 @@ TEST(OpRegistry, DefaultValue) {
ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
}
static void SetInputFormat(paddle::framework::OpDesc* desc) {
auto attr = desc->add_attrs();
attr->set_name("input_format");
attr->set_type(paddle::framework::INTS);
attr->mutable_ints()->Add(0);
attr->mutable_ints()->Add(1);
}
TEST(OpRegistry, CustomChecker) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("my_test_op");
op_desc.add_inputs("ii");
op_desc.add_outputs("oo");
SetInputFormat(&op_desc);
BuildVar("input", {"ii"}, op_desc.add_inputs());
BuildVar("output", {"oo"}, op_desc.add_outputs());
// attr 'test_attr' is not set
bool caught = false;
......@@ -173,7 +171,6 @@ TEST(OpRegistry, CustomChecker) {
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_i(4);
SetInputFormat(&op_desc);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::platform::CPUDeviceContext dev_ctx;
paddle::framework::Scope scope;
......
paddle/framework/operator.cc
浏览文件 @ 7aaf019a
......@@ -12,9 +12,9 @@ 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 <algorithm>
#include "paddle/framework/operator.h"
#include <algorithm>
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
......@@ -33,84 +33,139 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
const std::string& OperatorBase::Input(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_,
"Input Output Indices could not be nullptr");
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
name);
if (attrs_.count("input_format") == 0) {
return inputs_.at((size_t)it->second);
} else {
const auto& input_format = GetAttr<std::vector<int>>("input_format");
int idx = input_format[it->second];
return inputs_.at((size_t)idx);
static std::unordered_map<std::string, OpProto>* g_op_protos = nullptr;
std::unordered_map<std::string, OpProto>& OpProtos() {
if (g_op_protos == nullptr) {
g_op_protos = new std::unordered_map<std::string, OpProto>();
}
return *g_op_protos;
}
std::vector<std::string> OperatorBase::Inputs(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "IO Idx could not be nullptr");
auto input_format = GetAttr<std::vector<int>>("input_format");
auto offset = in_out_idxs_->at(name);
PADDLE_ENFORCE(input_format.at(static_cast<size_t>(offset) + 1) <=
static_cast<int>(inputs_.size()),
"Input Out Of Range");
return std::vector<std::string>{
inputs_.begin() + input_format.at(offset),
inputs_.begin() + input_format.at(offset + 1)};
const std::string& OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_EQ(ins.size(), 1UL,
"Op %s input %s should contain only one variable", type_,
name);
return ins[0];
}
const std::string& OperatorBase::Output(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "InOut Indice could not be nullptr");
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
const std::vector<std::string>& OperatorBase::Inputs(
const std::string& name) const {
auto it = inputs_.find(name);
PADDLE_ENFORCE(it != inputs_.end(), "Op %s do not have input %s", type_,
name);
if (attrs_.count("output_format") == 0) {
return outputs_.at((size_t)it->second);
} else {
const auto& output_format = GetAttr<std::vector<int>>("output_format");
int idx = output_format[it->second];
return outputs_.at((size_t)idx);
}
return it->second;
}
const std::string& OperatorBase::Output(const std::string& name) const {
auto& outs = Outputs(name);
PADDLE_ENFORCE_EQ(outs.size(), 1UL,
"Op %s output %s should contain only one variable", type_,
name);
return outs[0];
}
std::vector<std::string> OperatorBase::Outputs(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "InOut Indice could not be nullptr");
auto output_format = GetAttr<std::vector<int>>("output_format");
auto offset = in_out_idxs_->at(name);
PADDLE_ENFORCE(output_format.at(static_cast<size_t>(offset) + 1) <=
static_cast<int>(outputs_.size()),
"Output Out of Range");
return std::vector<std::string>{
outputs_.begin() + output_format.at(offset),
outputs_.begin() + output_format.at(offset + 1)};
const std::vector<std::string>& OperatorBase::Outputs(
const std::string& name) const {
auto it = outputs_.find(name);
PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output %s", type_,
name);
return it->second;
}
std::string OperatorBase::DebugString() const {
std::stringstream ss;
ss << "Op(" << type_ << "), inputs:(";
for (size_t i = 0; i < inputs_.size(); ++i) {
ss << inputs_[i];
if (i != inputs_.size() - 1) {
ss << "Op(" << type_ << "), inputs:{";
for (auto it = inputs_.begin(); it != inputs_.end();) {
auto& input = *it;
ss << input.first << "[";
for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i];
if (i != input.second.size() - 1) {
ss << ", ";
}
}
ss << "]";
++it;
if (it != inputs_.end()) {
ss << ", ";
}
}
ss << "), outputs:(";
for (size_t i = 0; i < outputs_.size(); ++i) {
ss << outputs_[i];
if (i != outputs_.size() - 1) {
ss << "}, outputs:{";
for (auto it = outputs_.begin(); it != outputs_.end();) {
auto& output = *it;
ss << output.first << "[";
for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i];
if (i != output.second.size() - 1) {
ss << ", ";
}
}
ss << "]";
++it;
if (it != outputs_.end()) {
ss << ", ";
}
}
ss << ").";
ss << "}.";
return ss.str();
}
void OperatorBase::Rename(const std::string& old_name,
const std::string& new_name) {
std::replace(inputs_.begin(), inputs_.end(), old_name, new_name);
std::replace(outputs_.begin(), outputs_.end(), old_name, new_name);
for (auto& input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
for (auto& output : outputs_) {
std::replace(output.second.begin(), output.second.end(), old_name,
new_name);
}
}
OperatorBase::OperatorBase(const std::string& type,
const OperatorBase::VarNameMap& inputs,
const OperatorBase::VarNameMap& outputs,
const AttributeMap& attrs)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs) {
static std::atomic<size_t> gUniqId(0UL);
for (auto& output : outputs_) {
for (auto& output_name : output.second) {
if (output_name == kTempVarName) {
output_name += type_;
output_name += "@";
output_name += std::to_string(gUniqId.fetch_add(1));
}
}
}
}
std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
std::vector<std::string> ret_val;
if (has_intermediate) {
// push all outputs into ret_val
for (auto& o : outputs_) {
ret_val.reserve(ret_val.size() + o.second.size());
ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
}
return ret_val;
}
auto it = OpProtos().find(type_);
PADDLE_ENFORCE(
it != OpProtos().end(),
"Operator %s not registered, cannot figure out intermediate outputs",
type_);
// get all OpProto::Var for outputs
for (auto& o : it->second.outputs()) {
// ignore all intermediate output
if (o.intermediate()) continue;
auto out = outputs_.find(o.name());
if (out != outputs_.end()) {
ret_val.reserve(ret_val.size() + out->second.size());
ret_val.insert(ret_val.end(), out->second.begin(), out->second.end());
}
}
return ret_val;
}
} // namespace framework
......
paddle/framework/operator.h
浏览文件 @ 7aaf019a
......@@ -20,8 +20,7 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
......@@ -51,6 +50,8 @@ inline std::string GradVarName(const std::string& var_name) {
return var_name + kGradVarSuffix;
}
extern std::unordered_map<std::string, OpProto>& OpProtos();
class OperatorBase;
class InferShapeContext;
class ExecutionContext;
......@@ -63,16 +64,14 @@ class ExecutionContext;
*/
class OperatorBase {
public:
OperatorBase() {} // TODO(yi): This constructor is to be removed.
OperatorBase(const std::string& type, const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs,
const AttributeMap& attrs,
std::unordered_map<std::string, int>* in_out_idxs)
: type_(type),
inputs_(inputs),
outputs_(outputs),
attrs_(attrs),
in_out_idxs_(in_out_idxs) {}
using VarNameMap = std::map<std::string, std::vector<std::string>>;
OperatorBase(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs);
OperatorBase(const OperatorBase& o) = delete;
OperatorBase& operator=(const OperatorBase& o) = delete;
OperatorBase(OperatorBase&& o) = delete;
virtual ~OperatorBase() {}
......@@ -85,10 +84,6 @@ class OperatorBase {
virtual std::string DebugString() const;
/// Init will be called after CreateOperator, you can put some initialization
/// logic here.
virtual void Init() {}
/// InferShape infer the size of Variables used by this Operator with
/// information inside scope
virtual void InferShape(const Scope& scope) const = 0;
......@@ -107,22 +102,18 @@ class OperatorBase {
//! Get a input with argument's name described in `op_proto`
const std::string& Input(const std::string& name) const;
//! Get a input which has multiple variables.
//! TODO add a vector_view to prevent memory copy.
std::vector<std::string> Inputs(const std::string& name) const;
const std::vector<std::string>& Inputs(const std::string& name) const;
//! Get a output with argument's name described in `op_proto`
const std::string& Output(const std::string& name) const;
//! Get an output which has multiple variables.
//! TODO add a vector_view to prevent memory copy.
std::vector<std::string> Outputs(const std::string& name) const;
const std::vector<std::string>& Outputs(const std::string& name) const;
virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
const std::string Type() const { return type_; }
const std::vector<std::string> Inputs() const { return inputs_; }
const std::vector<std::string> Outputs() const { return outputs_; }
std::string Type() const { return type_; }
const AttributeMap& Attrs() const { return attrs_; }
const std::unordered_map<std::string, int>* InOutIdx() const {
return in_out_idxs_.get();
}
public:
std::string type_;
......@@ -130,13 +121,12 @@ class OperatorBase {
// I (Inputs)
// O (Outputs)
// OG (Output Gradients)
std::vector<std::string> inputs_;
VarNameMap inputs_;
// NOTE: in case of OpGrad, outputs_ contains
// IG (Inputs Gradients)
std::vector<std::string> outputs_;
VarNameMap outputs_;
AttributeMap attrs_;
// store the arguments' offset described in op_desc.
std::shared_ptr<std::unordered_map<std::string, int>> in_out_idxs_;
};
class InferShapeContext {
......@@ -144,16 +134,12 @@ class InferShapeContext {
InferShapeContext(const OperatorBase& op, const Scope& scope)
: op_(op), scope_(scope) {}
size_t InputSize() const { return op_.inputs_.size(); }
size_t OutputSize() const { return op_.outputs_.size(); }
const Variable* InputVar(const size_t index) const {
return scope_.FindVar(op_.inputs_.at(index));
size_t InputSize(const std::string& name) const {
return op_.Inputs(name).size();
}
Variable* OutputVar(const size_t index) const {
return scope_.FindVar(op_.outputs_.at(index));
size_t OutputSize(const std::string& name) const {
return op_.Outputs(name).size();
}
const Variable* InputVar(const std::string& name) const {
......@@ -185,27 +171,9 @@ class InferShapeContext {
return res;
}
template <typename T>
const T* Input(const size_t index) const {
auto var = InputVar(index);
PADDLE_ENFORCE_NOT_NULL(var, "Input(%d) should not be nullptr", index);
return &var->Get<T>();
}
template <typename T>
T* Output(const size_t index) const {
auto var = OutputVar(index);
PADDLE_ENFORCE_NOT_NULL(
var,
"Output(%d) not be nullptr, which means variable [%s] does not "
"exist in scope",
index, op_.outputs_[index]);
return var->GetMutable<T>();
}
template <typename T>
const T* Input(const std::string& name) const {
auto var = InputVar(name);
auto* var = InputVar(name);
PADDLE_ENFORCE_NOT_NULL(var, "Input(%s) should not be nullptr", name);
return &var->Get<T>();
}
......@@ -281,6 +249,10 @@ class ExecutionContext : public InferShapeContext {
platform::Place GetPlace() const { return device_context_->GetPlace(); }
const platform::DeviceContext* device_context() const {
return device_context_;
}
const platform::DeviceContext* device_context_;
};
......@@ -300,14 +272,6 @@ class OpKernel {
class OperatorWithKernel : public OperatorBase {
public:
OperatorWithKernel() {} // TODO(yi): This constructor is to be removed.
OperatorWithKernel(const std::string& type,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs,
const AttributeMap& attrs,
std::unordered_map<std::string, int>* in_out_idxs)
: OperatorBase(type, inputs, outputs, attrs, in_out_idxs) {}
struct OpKernelKey {
platform::Place place_;
......@@ -331,6 +295,10 @@ class OperatorWithKernel : public OperatorBase {
using OpKernelMap =
std::unordered_map<OpKernelKey, std::unique_ptr<OpKernel>, OpKernelHash>;
OperatorWithKernel(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(const Scope& scope) const override {
InferShape(InferShapeContext(*this, scope));
}
......@@ -357,15 +325,5 @@ class OperatorWithKernel : public OperatorBase {
virtual void InferShape(const InferShapeContext& ctx) const = 0;
};
#define DEFINE_OPERATOR_CTOR(Class, ParentClass) \
public: \
Class() { /* TODO(yi): This constructor is to be removed. */ \
} \
Class(const std::string& type, const std::vector<std::string>& inputs, \
const std::vector<std::string>& outputs, \
const ::paddle::framework::AttributeMap& attrs, \
std::unordered_map<std::string, int>* in_out_idxs) \
: ParentClass(type, inputs, outputs, attrs, in_out_idxs) {}
} // namespace framework
} // namespace paddle
paddle/framework/operator_test.cc
浏览文件 @ 7aaf019a
......@@ -23,22 +23,22 @@ static int op_run_num = 0;
class OpWithoutKernelTest : public OperatorBase {
public:
DEFINE_OPERATOR_CTOR(OpWithoutKernelTest, OperatorBase)
void Init() override { x = 1; }
OpWithoutKernelTest(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs), x(1) {}
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
op_run_num++;
ASSERT_EQ((int)inputs_.size(), 1);
ASSERT_EQ((int)outputs_.size(), 1);
ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
++op_run_num;
ASSERT_EQ(static_cast<int>(inputs_.size()), 1);
ASSERT_EQ(static_cast<int>(outputs_.size()), 1);
ASSERT_EQ(scope.FindVar(inputs_.at("input")[0]), nullptr);
ASSERT_EQ(x, 1);
ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
ASSERT_NE(scope.FindVar(outputs_.at("output")[0]), nullptr);
}
public:
float x = 0;
int x{0};
};
class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -56,14 +56,24 @@ class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
} // namespace framework
} // namespace paddle
static void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::OpDesc::Var* var) {
var->set_parameter(param_name);
for (auto& arg_name : arguments) {
*var->mutable_arguments()->Add() = arg_name;
}
}
REGISTER_OP(test_operator, paddle::framework::OpWithoutKernelTest,
paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
TEST(OperatorBase, all) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("test_operator");
*op_desc.mutable_inputs()->Add() = "IN1";
*op_desc.mutable_outputs()->Add() = "OUT1";
BuildVar("input", {"IN1"}, op_desc.add_inputs());
BuildVar("output", {"OUT1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......@@ -100,7 +110,8 @@ static int cpu_kernel_run_num = 0;
class OpWithKernelTest : public OperatorWithKernel {
public:
DEFINE_OPERATOR_CTOR(OpWithKernelTest, OperatorWithKernel)
using OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {}
};
......@@ -117,35 +128,15 @@ class CPUKernelTest : public OpKernel {
}
};
// multiple inputs test
class OperatorMultiInputsTest : public OperatorBase {
public:
DEFINE_OPERATOR_CTOR(OperatorMultiInputsTest, OperatorBase)
void Init() override { x = 1; }
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
ASSERT_EQ(x, 1);
ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
ASSERT_EQ(Input("x"), "IN1");
ASSERT_EQ(Input("y"), "OUT1");
}
public:
float x = 0;
};
class OpKernelTestMultiInputsProtoAndCheckerMaker
: public OpProtoAndCheckerMaker {
public:
OpKernelTestMultiInputsProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("xs", "inputs of test op").SetMultiple();
AddInput("xs", "inputs of test op").AsDuplicable();
AddInput("k", "input of test op");
AddOutput("ys", "outputs of test op").SetMultiple();
AddOutput("ys", "outputs of test op").AsDuplicable();
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
......@@ -202,8 +193,9 @@ REGISTER_OP_CPU_KERNEL(op_with_kernel,
TEST(OpKernel, all) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("op_with_kernel");
*op_desc.mutable_inputs()->Add() = "IN1";
*op_desc.mutable_outputs()->Add() = "OUT1";
BuildVar("x", {"IN1"}, op_desc.add_inputs());
BuildVar("y", {"OUT1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......@@ -229,32 +221,15 @@ TEST(OpKernel, multi_inputs) {
OpDesc op_desc;
op_desc.set_type("op_multi_inputs_with_kernel");
*op_desc.mutable_inputs()->Add() = "x0";
*op_desc.mutable_inputs()->Add() = "x1";
*op_desc.mutable_inputs()->Add() = "x2";
*op_desc.mutable_inputs()->Add() = "k0";
*op_desc.mutable_outputs()->Add() = "y0";
*op_desc.mutable_outputs()->Add() = "y1";
BuildVar("xs", {"x0", "x1", "x2"}, op_desc.add_inputs());
BuildVar("k", {"k0"}, op_desc.add_inputs());
BuildVar("ys", {"y0", "y1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(3.14);
auto attr0 = op_desc.mutable_attrs()->Add();
attr0->set_name("input_format");
attr0->set_type(paddle::framework::AttrType::INTS);
auto input_format = attr0->mutable_ints();
input_format->Add(0); // x0
input_format->Add(3); // k
input_format->Add(4); // end
auto attr1 = op_desc.mutable_attrs()->Add();
attr1->set_name("output_format");
attr1->set_type(paddle::framework::AttrType::INTS);
auto output_format = attr1->mutable_ints();
output_format->Add(0); // y0
output_format->Add(2); // y1
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
scope.NewVar("x0")->GetMutable<Tensor>();
......
paddle/framework/pybind.cc
浏览文件 @ 7aaf019a
......@@ -56,30 +56,18 @@ void ExposeOperator(ClassType &m) {
return op.type_;
})
.def("outputs",
[](const typename ClassType::type &op) -> std::vector<std::string> {
return op.outputs_;
})
[](const typename ClassType::type &op)
-> std::map<std::string, std::vector<std::string>> {
return op.outputs_;
})
.def("inputs",
[](const typename ClassType::type &op) -> std::vector<std::string> {
return op.inputs_;
})
.def("support_gpu", &ClassType::type::SupportGPU)
.def("temp_outputs",
[](const typename ClassType::type &op) -> std::vector<std::string> {
auto iter = op.attrs_.find("temporary_index");
std::vector<std::string> ret;
if (iter == op.attrs_.end()) {
return ret;
} else {
auto tmp_idx = boost::get<std::vector<int>>(iter->second);
ret.reserve(tmp_idx.size());
for (auto &index : tmp_idx) {
ret.push_back(op.outputs_.at(index));
}
return ret;
}
[](const typename ClassType::type &op) { return op.inputs_; })
.def("__str__", &ClassType::type::DebugString)
.def("no_intermediate_outputs",
[](const typename ClassType::type &op) {
return op.OutputVars(false);
})
.def("__str__", &ClassType::type::DebugString);
.def("support_gpu", &ClassType::type::SupportGPU);
}
static size_t UniqueIntegerGenerator() {
......@@ -172,7 +160,7 @@ All parameter, weight, gradient are variables in Paddle.
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
auto &protos = OpRegistry::protos();
auto &protos = OpProtos();
std::vector<py::bytes> ret_values;
for (auto it = protos.begin(); it != protos.end(); ++it) {
PADDLE_ENFORCE(it->second.IsInitialized(),
......
paddle/framework/tensor.h
浏览文件 @ 7aaf019a
......@@ -105,6 +105,8 @@ class Tensor {
template <typename T>
inline Tensor Slice(const int& begin_idx, const int& end_idx) const;
platform::Place place() const { return holder_->place(); }
private:
template <typename T>
inline void check_memory_size() const;
......
paddle/gserver/tests/LayerGradUtil.cpp
浏览文件 @ 7aaf019a
......@@ -388,14 +388,23 @@ void initDataLayer(TestConfig testConf,
data.grad->zeroMem();
break;
case INPUT_SELF_DEFINE_DATA: {
size_t height = testConf.inputDefs[i].selfDefinedData->getHeight();
size_t width = testConf.inputDefs[i].selfDefinedData->getWidth();
CHECK_GT(static_cast<int>(height), 0);
CHECK_GT(static_cast<int>(width), 0);
data.value = Matrix::create(height, width, false, useGpu);
data.grad = Matrix::create(height, width, false, useGpu);
data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData);
data.grad->zeroMem();
if (testConf.inputDefs[i].ids.size()) {
data.ids = IVector::create(testConf.inputDefs[i].ids.size(), useGpu);
data.ids->copyFrom(testConf.inputDefs[i].ids.data(),
testConf.inputDefs[i].ids.size());
} else if (testConf.inputDefs[i].selfDefinedData) {
size_t height = testConf.inputDefs[i].selfDefinedData->getHeight();
size_t width = testConf.inputDefs[i].selfDefinedData->getWidth();
CHECK_GT(static_cast<int>(height), 0);
CHECK_GT(static_cast<int>(width), 0);
data.value = Matrix::create(height, width, false, useGpu);
data.grad = Matrix::create(height, width, false, useGpu);
data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData);
data.grad->zeroMem();
} else {
LOG(FATAL) << "No self-defined data are given.";
return;
}
const std::vector<int>& labelSeqStartPositions =
testConf.inputDefs[i].labelSeqStartPositions;
......
paddle/gserver/tests/LayerGradUtil.h
浏览文件 @ 7aaf019a
......@@ -68,6 +68,7 @@ struct InputDef {
std::vector<int> labelInitValue;
std::vector<int> labelSeqStartPositions;
std::vector<int> labelSubSeqStartPositions;
std::vector<int> ids;
MatrixPtr selfDefinedData;
InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) {
......@@ -95,6 +96,23 @@ struct InputDef {
isStatic = false;
}
InputDef(InputType type,
string nameIn,
const std::vector<int>& ids,
const std::vector<int>& selfDefinedSeqStartPos = {},
const std::vector<int>& selfDefinedSubSeqStartPos = {})
: labelSeqStartPositions(selfDefinedSeqStartPos),
labelSubSeqStartPositions(selfDefinedSubSeqStartPos),
ids(ids) {
selfDefinedData = nullptr;
inputType = type;
name = nameIn;
dim = 0;
sparse = {""};
paraSize = 0;
isStatic = false;
}
InputDef(InputType type,
string nameIn,
size_t dimIn,
......
paddle/operators/CMakeLists.txt
浏览文件 @ 7aaf019a
......@@ -41,6 +41,7 @@ function(op_library TARGET)
endif()
endfunction()
add_subdirectory(math)
cc_test(gather_test SRCS gather_test.cc DEPS tensor)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
......@@ -52,7 +53,7 @@ op_library(add_op SRCS add_op.cc add_op.cu)
op_library(mean_op SRCS mean_op.cc mean_op.cu)
op_library(mul_op SRCS mul_op.cc mul_op.cu)
op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function)
op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu)
......@@ -64,7 +65,7 @@ op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu)
op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS op_desc tensor op_registry operator net_op)
DEPS framework_proto tensor op_registry operator net_op)
cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
op_library(uniform_random_op
SRCS uniform_random_op.cc uniform_random_op.cu)
paddle/operators/add_op.cc
浏览文件 @ 7aaf019a
......@@ -18,17 +18,15 @@ namespace paddle {
namespace operators {
class AddOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(AddOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1);
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "Inputs of AddOp must all be set");
PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr,
"Outputs of AddOp must all be set");
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims() == ctx.Input<Tensor>(1)->dims(),
"Two input of Add Op's dimension must be same.");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Two input of Add Op's dimension must be same.");
ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......@@ -48,7 +46,9 @@ The equation is: Out = X + Y
};
class AddOpGrad : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(AddOpGrad, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {}
};
......
paddle/operators/add_op.h
浏览文件 @ 7aaf019a
......@@ -28,9 +28,9 @@ template <typename Place, typename T>
class AddKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto input0 = context.Input<Tensor>(0);
auto input1 = context.Input<Tensor>(1);
auto output = context.Output<Tensor>(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());
......
paddle/operators/cross_entropy_op.cc
浏览文件 @ 7aaf019a
......@@ -18,29 +18,25 @@ namespace paddle {
namespace operators {
class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(OnehotCrossEntropyOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 2,
"Input size of OnehotCrossEntropyOp must be two");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1,
"Output size of OnehotCrossEntropyOp must be one");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0),
"0-th input of OnehotCrossEntropyOp should be set");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(1),
"1-th input of OnehotCrossEntropyOp should be set");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0),
"Outputs of OnehotCrossEntropyOp must all be set");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>(0)->dims().size(), 2);
PADDLE_ENFORCE_EQ(ctx.Output<Tensor>(0)->dims().size(), 1,
"label's dimension must be 1.");
ctx.Output<Tensor>(0)->Resize({ctx.Input<Tensor>(0)->dims()[0]});
auto *X = ctx.Input<Tensor>("X");
auto *label = ctx.Input<Tensor>("label");
PADDLE_ENFORCE_EQ(X->dims().size(), 2, "X's dimension must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label's dimension must be 1.");
PADDLE_ENFORCE_EQ(X->dims()[0], label->dims()[0]);
ctx.Output<Tensor>("Y")->Resize({X->dims()[0]});
}
};
class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(OnehotCrossEntropyGradientOp,
framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
......
paddle/operators/cross_entropy_op.h
浏览文件 @ 7aaf019a
......@@ -45,7 +45,7 @@ class OnehotCrossEntropyOpKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& ctx) const override {
auto X = ctx.Input<Tensor>("X");
const T* Xdata = X->data<T>();
const int* label_data = ctx.Input<Tensor>(1)->data<int>();
const int* label_data = ctx.Input<Tensor>("label")->data<int>();
auto Y = ctx.Output<Tensor>("Y");
Y->mutable_data<T>(ctx.GetPlace());
......
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 7aaf019a
......@@ -18,19 +18,13 @@ namespace paddle {
namespace operators {
class FillZerosLikeOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(FillZerosLikeOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
"Input size of FillZerosLikeOp must be one.");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
"Output size of AddOp must be one.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0),
"Input of FillZerosLikeOp must be set.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0),
"Output of FillZerosLikeOp must be set.");
ctx.Output<framework::Tensor>(0)->Resize(
ctx.Input<framework::Tensor>(0)->dims());
ctx.Output<framework::Tensor>("Dst")->Resize(
ctx.Input<framework::Tensor>("Src")->dims());
}
};
......
paddle/operators/fill_zeros_like_op.h
浏览文件 @ 7aaf019a
......@@ -23,7 +23,7 @@ template <typename Place, typename T>
class FillZerosLikeKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* output = context.Output<framework::Tensor>(0);
auto* output = context.Output<framework::Tensor>("Dst");
output->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*output);
t.device(context.GetEigenDevice<Place>()) = t.constant(T(0));
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ 7aaf019a
......@@ -43,7 +43,9 @@ class GaussianRandomKernel : public framework::OpKernel {
};
class GaussianRandomOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(GaussianRandomOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& context) const override {
auto* tensor = context.Output<framework::Tensor>(0);
......
paddle/operators/math/CMakeLists.txt 0 → 100644
浏览文件 @ 7aaf019a
if(WITH_MKLML)
set(BLAS_LIB mklml)
else()
set(BLAS_LIB cblas)
endif()
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu DEPS ${BLAS_LIB} device_context)
else()
cc_library(math_function SRCS math_function.cc DEPS ${BLAS_LIB} device_context)
endif()
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
paddle/operators/math/math_function.cc 0 → 100644
浏览文件 @ 7aaf019a
/* 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/operators/math/math_function.h"
namespace paddle {
namespace operators {
namespace math {
template <>
void gemm<platform::CPUPlace, float>(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M,
const int N, const int K,
const float alpha, const float* A,
const float* B, const float beta, float* C,
platform::DeviceContext* context) {
int lda = K;
int ldb = N;
int ldc = N;
cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <>
void gemm<platform::CPUPlace, double>(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M,
const int N, const int K,
const double alpha, const double* A,
const double* B, const double beta,
double* C,
platform::DeviceContext* context) {
int lda = K;
int ldb = N;
int ldc = N;
cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <>
void matmul<platform::CPUPlace, float>(const framework::Tensor& matrix_a,
bool trans_a,
const framework::Tensor& matrix_b,
bool trans_b, float alpha,
framework::Tensor* matrix_out,
float beta,
platform::DeviceContext* context) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
"The input and output of matmul be matrix");
PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
platform::is_cpu_place(matrix_b.place()) &&
platform::is_cpu_place(matrix_out->place()),
"Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<platform::CPUPlace, float>(
transA, transB, M, N, K, alpha, matrix_a.data<float>(),
matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
}
template <>
void matmul<platform::CPUPlace, double>(const framework::Tensor& matrix_a,
bool trans_a,
const framework::Tensor& matrix_b,
bool trans_b, double alpha,
framework::Tensor* matrix_out,
double beta,
platform::DeviceContext* context) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
"The input and output of matmul be matrix");
PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
platform::is_cpu_place(matrix_b.place()) &&
platform::is_cpu_place(matrix_out->place()),
"Matrix must all be in CPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<platform::CPUPlace, double>(
transA, transB, M, N, K, alpha, matrix_a.data<double>(),
matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/math_function.cu 0 → 100644
浏览文件 @ 7aaf019a
/* 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/operators/math/math_function.h"
namespace paddle {
namespace operators {
namespace math {
template <>
void gemm<platform::GPUPlace, float>(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M,
const int N, const int K,
const float alpha, const float* A,
const float* B, const float beta, float* C,
platform::DeviceContext* context) {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
cublasOperation_t cuTransA =
(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
PADDLE_ENFORCE(platform::dynload::cublasSgemm(
reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
}
template <>
void gemm<platform::GPUPlace, double>(const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M,
const int N, const int K,
const double alpha, const double* A,
const double* B, const double beta,
double* C,
platform::DeviceContext* context) {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
cublasOperation_t cuTransA =
(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
PADDLE_ENFORCE(platform::dynload::cublasDgemm(
reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
}
template <>
void matmul<platform::GPUPlace, float>(const framework::Tensor& matrix_a,
bool trans_a,
const framework::Tensor& matrix_b,
bool trans_b, float alpha,
framework::Tensor* matrix_out,
float beta,
platform::DeviceContext* context) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
"The input and output of matmul be matrix");
PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
platform::is_gpu_place(matrix_b.place()) &&
platform::is_gpu_place(matrix_out->place()),
"Matrix must all be in GPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<platform::GPUPlace, float>(
transA, transB, M, N, K, alpha, matrix_a.data<float>(),
matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
}
template <>
void matmul<platform::GPUPlace, double>(const framework::Tensor& matrix_a,
bool trans_a,
const framework::Tensor& matrix_b,
bool trans_b, double alpha,
framework::Tensor* matrix_out,
double beta,
platform::DeviceContext* context) {
auto dim_a = matrix_a.dims();
auto dim_b = matrix_b.dims();
auto dim_out = matrix_out->dims();
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
"The input and output of matmul be matrix");
PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
platform::is_gpu_place(matrix_b.place()) &&
platform::is_gpu_place(matrix_out->place()),
"Matrix must all be in GPUPlace");
int M = dim_out[0];
int N = dim_out[1];
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
gemm<platform::GPUPlace, double>(
transA, transB, M, N, K, alpha, matrix_a.data<double>(),
matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/math_function.h 0 → 100644
浏览文件 @ 7aaf019a
/* 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
#ifdef PADDLE_USE_MKLML
#include <mkl_cblas.h>
#include <mkl_lapacke.h>
#include <mkl_vml_functions.h>
#endif
#ifdef PADDLE_USE_MKL
#include <mkl.h>
#include <mkl_lapacke.h>
#endif
#ifdef PADDLE_USE_ATLAS
extern "C" {
#include <cblas.h>
#include <clapack.h>
}
#endif
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
#include <lapacke.h>
#endif
#ifndef LAPACK_FOUND
extern "C" {
#include <cblas.h>
int LAPACKE_sgetrf(int matrix_layout, int m, int n, float* a, int lda,
int* ipiv);
int LAPACKE_dgetrf(int matrix_layout, int m, int n, double* a, int lda,
int* ipiv);
int LAPACKE_sgetri(int matrix_layout, int n, float* a, int lda,
const int* ipiv);
int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda,
const int* ipiv);
}
#endif
#include <cmath>
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/enforce.h"
namespace paddle {
namespace operators {
namespace math {
// Support continuous memory now
// If transA = N, and transB = N
// Then matrixA: M * K, matrixB: K * N matrixC : M * N
// For more detailed info, please refer to
// http://www.netlib.org/lapack/explore-html/d4/de2/sgemm_8f.html
template <typename Place, typename T>
void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
const int M, const int N, const int K, const T alpha, const T* A,
const T* B, const T beta, T* C, platform::DeviceContext* context);
// matrix multiply with continuous memory
template <typename Place, typename T>
void matmul(const framework::Tensor& matrix_a, bool trans_a,
const framework::Tensor& matrix_b, bool trans_b, T alpha,
framework::Tensor* matrix_out, T beta,
platform::DeviceContext* context);
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/math_function_test.cc 0 → 100644
浏览文件 @ 7aaf019a
#include "paddle/operators/math/math_function.h"
#include "gtest/gtest.h"
#ifndef PADDLE_ONLY_CPU
TEST(math_function, notrans_mul_trans) {
paddle::framework::Tensor input1;
paddle::framework::Tensor input1_gpu;
paddle::framework::Tensor input2_gpu;
paddle::framework::Tensor out_gpu;
paddle::framework::Tensor out;
auto* cpu_place = new paddle::platform::CPUPlace();
float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
float arr[6] = {0, 1, 2, 3, 4, 5};
memcpy(input1_ptr, arr, 6 * sizeof(float));
auto* gpu_place = new paddle::platform::GPUPlace(0);
paddle::platform::DeviceContext* context =
new paddle::platform::CUDADeviceContext(*gpu_place);
input1_gpu.CopyFrom<float>(input1, *gpu_place);
input2_gpu.CopyFrom<float>(input1, *gpu_place);
out_gpu.mutable_data<float>({2, 2}, *gpu_place);
paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0, context);
out.CopyFrom<float>(out_gpu, *cpu_place);
float* out_ptr = out.data<float>();
EXPECT_EQ(out_ptr[0], 5);
EXPECT_EQ(out_ptr[1], 14);
EXPECT_EQ(out_ptr[2], 14);
EXPECT_EQ(out_ptr[3], 50);
}
TEST(math_function, trans_mul_notrans) {
paddle::framework::Tensor input1;
paddle::framework::Tensor input1_gpu;
paddle::framework::Tensor input2_gpu;
paddle::framework::Tensor out_gpu;
paddle::framework::Tensor out;
auto* cpu_place = new paddle::platform::CPUPlace();
float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
float arr[6] = {0, 1, 2, 3, 4, 5};
memcpy(input1_ptr, arr, 6 * sizeof(float));
auto* gpu_place = new paddle::platform::GPUPlace(0);
paddle::platform::DeviceContext* context =
new paddle::platform::CUDADeviceContext(*gpu_place);
input1_gpu.CopyFrom<float>(input1, *gpu_place);
input2_gpu.CopyFrom<float>(input1, *gpu_place);
out_gpu.mutable_data<float>({3, 3}, *gpu_place);
paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0, context);
out.CopyFrom<float>(out_gpu, *cpu_place);
float* out_ptr = out.data<float>();
EXPECT_EQ(out_ptr[0], 9);
EXPECT_EQ(out_ptr[1], 12);
EXPECT_EQ(out_ptr[2], 15);
EXPECT_EQ(out_ptr[3], 12);
EXPECT_EQ(out_ptr[4], 17);
EXPECT_EQ(out_ptr[5], 22);
EXPECT_EQ(out_ptr[6], 15);
EXPECT_EQ(out_ptr[7], 22);
EXPECT_EQ(out_ptr[8], 29);
}
#endif
paddle/operators/mean_op.cc
浏览文件 @ 7aaf019a
......@@ -18,14 +18,14 @@ namespace paddle {
namespace operators {
class MeanOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(MeanOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 1, "Input size of AddOp must be one");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of AddOp must be one");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "input should be set");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0), "output should be set");
ctx.Output<Tensor>(0)->Resize(framework::make_ddim({1}));
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of MeanOp must be initialized.");
ctx.Output<Tensor>("Out")->Resize({1});
}
};
......@@ -34,13 +34,15 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").IgnoreGradient();
AddOutput("Out", "The output of mean op").AsNoGradient();
AddComment("Mean Operator");
}
};
class MeanGradOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(MeanGradOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(framework::GradVarName("X"))
......
paddle/operators/mean_op.h
浏览文件 @ 7aaf019a
......@@ -31,14 +31,14 @@ template <typename Place, typename T>
class MeanKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
auto* input = context.Input<Tensor>("X");
auto* output = context.Output<Tensor>("Out");
output->mutable_data<T>(context.GetPlace());
auto X = EigenVector<T>::Flatten(*input);
auto y = EigenScalar<T>::From(*output);
auto place = context.GetEigenDevice<Place>();
auto& place = context.GetEigenDevice<Place>();
y.device(place) = X.mean();
}
......
paddle/operators/mul_op.cc
浏览文件 @ 7aaf019a
......@@ -13,17 +13,19 @@
limitations under the License. */
#include "paddle/operators/mul_op.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
class MulOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(MulOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
auto dim0 = ctx.Input<Tensor>(0)->dims();
auto dim1 = ctx.Input<Tensor>(1)->dims();
auto dim0 = ctx.Input<Tensor>("X")->dims();
auto dim1 = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_EQ(dim0.size(), 2,
"input X(%s) should be a tensor with 2 dims, a matrix",
ctx.op_.Input("X"));
......@@ -33,8 +35,7 @@ class MulOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
dim0[1], dim1[0],
"First matrix's width must be equal with second matrix's height.");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "The mul op takes only one output");
ctx.Output<Tensor>(0)->Resize({dim0[0], dim1[1]});
ctx.Output<Tensor>("Out")->Resize({dim0[0], dim1[1]});
}
};
......@@ -54,7 +55,9 @@ The equation is: Out = X * Y
};
class MulOpGrad : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(MulOpGrad, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {}
std::string DebugString() const override {
......
paddle/operators/mul_op.cu
浏览文件 @ 7aaf019a
......@@ -16,5 +16,4 @@
#include "paddle/operators/mul_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
paddle/operators/mul_op.h
浏览文件 @ 7aaf019a
......@@ -13,6 +13,9 @@
limitations under the License. */
#pragma once
#include "paddle/operators/math/math_function.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
......@@ -30,17 +33,14 @@ class MulKernel : public framework::OpKernel {
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>(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>();
auto& place = context.GetEigenDevice<Place>();
Z.device(place) = X.contract(Y, dim_pair);
}
......
paddle/operators/net_op.cc
浏览文件 @ 7aaf019a
......@@ -15,48 +15,42 @@
*/
#include "paddle/operators/net_op.h"
#include <set>
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
const char NetOp::kAll[] = "all";
void NetOp::CompleteAddOp(bool calc) {
add_op_done_ = true;
if (!calc) return;
std::unordered_set<std::string> input_set;
std::unordered_set<std::string> output_set;
std::unordered_set<std::string> temp_output;
std::set<std::string> input_set;
std::set<std::string> output_set;
for (auto& op : ops_) {
for (auto& ipt : op->inputs_) {
if (!Contains(output_set, ipt)) { // Not other op's output
input_set.insert(ipt);
} else {
temp_output.insert(ipt);
for (auto& var_name : ipt.second) {
if (!Contains(output_set, var_name)) { // Not other op's output
input_set.insert(var_name);
} else {
intermediate_outputs_.insert(var_name);
}
}
}
for (auto& opt : op->outputs_) {
output_set.insert(opt);
}
}
inputs_.reserve(input_set.size());
std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs_));
std::sort(inputs_.begin(), inputs_.end());
outputs_.reserve(output_set.size());
std::copy(output_set.begin(), output_set.end(), std::back_inserter(outputs_));
std::sort(outputs_.begin(), outputs_.end());
std::vector<int> tmp_index;
tmp_index.reserve(temp_output.size());
int output_len = static_cast<int>(outputs_.size());
for (int i = 0; i < output_len; ++i) {
if (Contains(temp_output, outputs_[i])) {
tmp_index.push_back(i);
for (auto& var_name : opt.second) {
output_set.insert(var_name);
}
}
}
attrs_["temporary_index"] = tmp_index;
auto& inputs = inputs_[kAll];
inputs.reserve(input_set.size());
std::copy(input_set.begin(), input_set.end(), std::back_inserter(inputs));
auto& outputs = outputs_[kAll];
outputs.reserve(output_set.size());
std::copy(output_set.begin(), output_set.end(), std::back_inserter(outputs));
}
std::string NetOp::DebugString() const {
......@@ -73,5 +67,25 @@ std::string NetOp::DebugString() const {
bool NetOp::IsNetOp() const { return true; }
std::vector<std::string> NetOp::OutputVars(bool has_intermediate) const {
if (has_intermediate) {
return this->outputs_.at(kAll);
}
auto& all = this->outputs_.at(kAll);
std::vector<std::string> ret_val;
for (auto& each : all) {
if (!Contains(intermediate_outputs_, each)) {
ret_val.push_back(each);
}
}
return ret_val;
}
NetOp::NetOp(const std::string& type,
const framework::OperatorBase::VarNameMap& inputs,
const framework::OperatorBase::VarNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
} // namespace operators
} // namespace paddle
paddle/operators/net_op.h
浏览文件 @ 7aaf019a
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
......@@ -35,7 +36,10 @@ namespace operators {
*/
class NetOp : public framework::OperatorBase {
public:
DEFINE_OPERATOR_CTOR(NetOp, framework::OperatorBase)
static const char kAll[];
NetOp() : framework::OperatorBase("plain_net", {}, {}, {}) {}
NetOp(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const framework::AttributeMap& attrs);
/**
* Infer all the operators' input and output variables' shapes, will be called
......@@ -92,11 +96,13 @@ class NetOp : public framework::OperatorBase {
std::string DebugString() const override;
bool IsNetOp() const override;
std::vector<std::string> OutputVars(bool has_intermediate) const override;
std::vector<std::shared_ptr<OperatorBase>> ops_;
private:
bool add_op_done_{false};
std::set<std::string> intermediate_outputs_;
template <typename T, typename KeyType>
static bool Contains(T container, KeyType key) {
......
paddle/operators/net_op_test.cc
浏览文件 @ 7aaf019a
......@@ -12,8 +12,7 @@ static int run_cnt = 0;
class TestOp : public framework::OperatorBase {
public:
DEFINE_OPERATOR_CTOR(TestOp, framework::OperatorBase)
using framework::OperatorBase::OperatorBase;
void InferShape(const Scope& scope) const override { ++infer_shape_cnt; }
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
......@@ -23,8 +22,7 @@ class TestOp : public framework::OperatorBase {
class EmptyOp : public framework::OperatorBase {
public:
DEFINE_OPERATOR_CTOR(EmptyOp, framework::OperatorBase)
using framework::OperatorBase::OperatorBase;
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {}
};
......@@ -46,40 +44,32 @@ TEST(OpKernel, all) {
auto net = std::make_shared<NetOp>();
ASSERT_NE(net, nullptr);
auto op1 = std::make_shared<TestOp>();
op1->inputs_ = {"x", "w1", "b1"};
op1->outputs_ = {"y"};
auto op1 = std::shared_ptr<TestOp>(
new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {}));
net->AddOp(op1);
auto op2 = std::make_shared<TestOp>();
op2->inputs_ = {"y", "w2", "b2"};
op2->outputs_ = {"z"};
auto op2 = std::shared_ptr<TestOp>(
new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"Out", {"z"}}}, {}));
net->AddOp(op2);
net->CompleteAddOp();
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"}, net->inputs_);
AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_);
auto tmp_idx_iter = net->attrs_.find("temporary_index");
ASSERT_NE(net->attrs_.end(), tmp_idx_iter);
auto& tmp_idx = boost::get<std::vector<int>>(tmp_idx_iter->second);
ASSERT_EQ(1UL, tmp_idx.size());
ASSERT_EQ("y", net->outputs_[tmp_idx[0]]);
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
net->inputs_.at(NetOp::kAll));
AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_.at(NetOp::kAll));
Scope scope;
platform::CPUDeviceContext dev_ctx;
auto final_outs = net->OutputVars(false);
net->InferShape(scope);
net->Run(scope, dev_ctx);
ASSERT_EQ(2, infer_shape_cnt);
ASSERT_EQ(2, run_cnt);
ASSERT_THROW(net->AddOp(op2), platform::EnforceNotMet);
ASSERT_EQ(final_outs.size(), 1UL);
ASSERT_EQ(final_outs[0], "z");
}
TEST(NetOp, insert_op) {
NetOp net;
auto op1 = std::make_shared<EmptyOp>();
op1->inputs_ = {"x", "w1", "b1"};
op1->outputs_ = {"y"};
auto op1 = std::shared_ptr<EmptyOp>(
new EmptyOp("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"Out", {"y"}}}, {}));
net.AddOp(op1);
net.InsertOp(0, op1);
ASSERT_EQ(2UL, net.ops_.size());
......
paddle/operators/recurrent_op.cc
浏览文件 @ 7aaf019a
......@@ -91,12 +91,17 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// create step net's temp inputs
for (auto& input : net_op->inputs_) {
// the weight are located in parent scope
if (!step_scope.FindVar(input))
step_scope.NewVar(input)->GetMutable<Tensor>();
for (auto& var_name : input.second) {
if (!step_scope.FindVar(var_name)) {
step_scope.NewVar(var_name)->GetMutable<Tensor>();
}
}
}
// create stepnet's outputs
for (const auto& output : net_op->outputs_) {
step_scope.NewVar(output);
for (auto& var_name : output.second) {
step_scope.NewVar(var_name);
}
}
step_scopes->emplace_back(&step_scope);
}
......@@ -130,8 +135,11 @@ const rnn::ArgumentName RecurrentGradientOp::kArgName{
"inlink@grad", "inlink_alias", "outlink_alias",
"memories", "pre_memories", "boot_memories@grad"};
void RecurrentOp::Init() {
OperatorBase::Init();
RecurrentOp::RecurrentOp(const std::string& type,
const framework::OperatorBase::VarNameMap& inputs,
const framework::OperatorBase::VarNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
rnn::InitArgument(kArgName, arg.get(), *this);
alg_.Init(std::move(arg));
......@@ -147,13 +155,13 @@ class RecurrentAlgorithmProtoAndCheckerMaker
// inputs and outputs stored in proto
AddInput(name.inlinks,
"the inputs that need to be segmented for each step.")
.SetMultiple();
.AsDuplicable();
AddInput(name.boot_memories, "variables to initialize memories.")
.SetMultiple();
.AsDuplicable();
AddInput(name.step_net, "network shared by all steps.");
AddOutput(name.outlinks, "the outputs that need to concated for all steps.")
.SetMultiple();
.AsDuplicable();
AddOutput(name.step_scopes, "step scopes");
// Attributes stored in AttributeMap
......@@ -225,8 +233,11 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
}
void RecurrentGradientOp::Init() {
OperatorBase::Init();
RecurrentGradientOp::RecurrentGradientOp(
const std::string& type, const framework::OperatorBase::VarNameMap& inputs,
const framework::OperatorBase::VarNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
rnn::InitArgument(kArgName, arg.get(), *this);
alg_.Init(std::move(arg));
......
paddle/operators/recurrent_op.h
浏览文件 @ 7aaf019a
......@@ -100,13 +100,12 @@ class RecurrentGradientAlgorithm {
};
class RecurrentOp final : public framework::OperatorBase {
DEFINE_OPERATOR_CTOR(RecurrentOp, framework::OperatorBase)
public:
void Init() override;
RecurrentOp(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const framework::AttributeMap& attrs);
/**
* InferShape must be called before Run.
*/
* InferShape must be called before Run.
*/
void InferShape(const framework::Scope& scope) const override {
alg_.InferShape(scope);
}
......@@ -124,7 +123,9 @@ class RecurrentOp final : public framework::OperatorBase {
class RecurrentGradientOp final : public framework::OperatorBase {
public:
void Init() override;
RecurrentGradientOp(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs,
const framework::AttributeMap& attrs);
/**
* InferShape must be called before Run.
......
paddle/operators/recurrent_op_test.cc
浏览文件 @ 7aaf019a
......@@ -25,157 +25,7 @@
namespace paddle {
namespace operators {
using framework::make_ddim;
using framework::DDim;
using framework::Tensor;
using framework::Variable;
using framework::Scope;
using framework::OpRegistry;
class RecurrentOpTest : public ::testing::Test {
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepNet();
CreateRNNOp();
}
virtual void TearDown() override {}
void CreateGlobalVariables() {
// create input, and init content
LOG(INFO) << "create global variable x";
for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
Variable* x = scope_.NewVar(inlink);
DDim dims = make_ddim(std::vector<int>{
10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
}
// create output alias just for test
for (auto inlink : std::vector<std::string>{"h@alias"}) {
Variable* x = scope_.NewVar(inlink);
DDim dims =
make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
}
LOG(INFO) << "create global variable w";
Variable* w = scope_.NewVar("rnn/w");
w->GetMutable<Tensor>()->mutable_data<float>(
make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
for (auto boot : std::vector<std::string>{"h_boot"}) {
LOG(INFO) << "create global variable " << boot;
Variable* h_boot = scope_.NewVar(boot);
h_boot->GetMutable<Tensor>()->mutable_data<float>(
make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
platform::CPUPlace());
}
LOG(INFO) << "create variable step_scopes";
scope_.NewVar("step_scopes");
LOG(INFO) << "create variable h";
scope_.NewVar("h");
}
void CreateRNNOp() {
framework::OpDesc op_desc;
op_desc.set_type("recurrent_op");
// inlinks 0
op_desc.add_inputs("x");
op_desc.add_inputs("x0");
op_desc.add_inputs("x1");
// boot_memories 3
op_desc.add_inputs("h_boot");
// step net 5
op_desc.add_inputs("step_net");
// outlinks 6
op_desc.add_outputs("h");
// step scopes 7
op_desc.add_outputs("step_scopes");
auto _input_format = std::vector<int>{
0, // in_link
3, // memories
4 // step_net
};
auto input_format = op_desc.add_attrs();
input_format->set_name("input_format");
input_format->set_type(paddle::framework::AttrType::INTS);
for (auto i : _input_format) {
input_format->add_ints(i);
}
auto output_format = op_desc.add_attrs();
output_format->set_name("output_format");
output_format->set_type(paddle::framework::AttrType::INTS);
for (auto i : std::vector<int>{0, 1, 2}) {
output_format->add_ints(i);
}
auto inlink_alias = op_desc.add_attrs();
inlink_alias->set_name("inlink_alias");
inlink_alias->set_type(paddle::framework::AttrType::STRINGS);
auto outlink_alias = op_desc.add_attrs();
outlink_alias->set_name("outlink_alias");
outlink_alias->set_type(paddle::framework::AttrType::STRINGS);
auto pre_memories = op_desc.add_attrs();
pre_memories->set_name("pre_memories");
pre_memories->set_type(paddle::framework::AttrType::STRINGS);
auto memories = op_desc.add_attrs();
memories->set_name("memories");
memories->set_type(paddle::framework::AttrType::STRINGS);
// create inlink_alias
for (const auto& item :
std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
inlink_alias->add_strings(item);
}
// pre memories
for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
pre_memories->add_strings(item);
}
// memories
for (const auto& item : std::vector<std::string>{"rnn/h"}) {
memories->add_strings(item);
}
// output alias
for (const auto& item : std::vector<std::string>{"h@alias"}) {
outlink_alias->add_strings(item);
}
rnn_op_ = OpRegistry::CreateOp(op_desc);
LOG(INFO) << "rnn_op finish init";
}
void CreateStepNet() {
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
net->AddOp(
OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
net->AddOp(
OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
net->CompleteAddOp();
}
// father scope
Scope scope_;
std::shared_ptr<framework::OperatorBase> rnn_op_;
};
TEST_F(RecurrentOpTest, Run) {
platform::CPUDeviceContext ctx;
rnn_op_->InferShape(scope_);
rnn_op_->Run(scope_, ctx);
}
using namespace paddle::framework;
class RecurrentGradientAlgorithmTest : public ::testing::Test {
protected:
......@@ -281,11 +131,13 @@ class RecurrentGradientAlgorithmTest : public ::testing::Test {
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
net->AddOp(OpRegistry::CreateOp("mul", {"rnn/h_pre", "rnn/w", "rnn/s_grad"},
{"rnn/h_pre_grad", "rnn/w_grad"}, {}));
// TODO(qingqing) modify backward op create for RNNOp unit test
// and the unit test will be removed to Python.
// net->AddOp(OpRegistry::CreateOp("mul", {"X", {"rnn/h_pre", "rnn/w",
// "rnn/s_grad"}}, {"Y", {"rnn/h_pre_grad", "rnn/w_grad"}}, {}));
net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
{"rnn/x_grad", "rnn/s_grad"}, {}));
// net->AddOp(OpRegistry::CreateOp("add_two", {"X", {"rnn/h_grad"}},
// {"Y", {"rnn/x_grad"}}, {"Out", "rnn/s_grad"}}, {}));
net->CompleteAddOp();
}
......@@ -359,7 +211,8 @@ TEST(RecurrentOp, LinkMemories) {
memories.push_back(mem_attr);
for (size_t i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, memories, i, -1, false
/*infer_shape_mode*/);
}
// check
for (size_t i = 0; i < len - 1; ++i) {
......@@ -375,7 +228,8 @@ TEST(RecurrentOp, LinkMemories) {
}
for (int i = len - 2; i >= 0; --i) {
rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, memories, i, 1, false
/*infer_shape_mode*/);
}
// check
for (int i = len - 2; i >= 0; --i) {
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ 7aaf019a
......@@ -18,19 +18,19 @@ namespace paddle {
namespace operators {
class RowWiseAddOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(RowWiseAddOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2UL,
"Two inputs is needed by rowwise add");
auto dim0 = ctx.Input<Tensor>(0)->dims();
auto dim1 = ctx.Input<Tensor>(1)->dims();
auto dim0 = ctx.Input<Tensor>("X")->dims();
auto dim1 = ctx.Input<Tensor>("b")->dims();
PADDLE_ENFORCE(dim0.size() == 2, "Input 0 must be matrix");
PADDLE_ENFORCE(dim1.size() == 1, "The second input must be vector");
PADDLE_ENFORCE(dim0[1] == dim1[0], "The width of two input must be same");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "The output size must be 1");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
PADDLE_ENFORCE(ctx.OutputSize("Out") == 1, "The output size must be 1");
ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......
paddle/operators/rowwise_add_op.h
浏览文件 @ 7aaf019a
......@@ -31,11 +31,11 @@ template <typename Place, typename T>
class RowWiseAddKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto out = context.Output<Tensor>(0);
auto out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto input = EigenMatrix<T>::From(*context.Input<Tensor>(0));
auto bias = EigenVector<T>::From(*context.Input<Tensor>(1));
auto input = EigenMatrix<T>::From(*context.Input<Tensor>("X"));
auto bias = EigenVector<T>::From(*context.Input<Tensor>("b"));
auto output = EigenMatrix<T>::From(*out);
const int bias_size = bias.dimension(0);
......
paddle/operators/sgd_op.cc
浏览文件 @ 7aaf019a
......@@ -18,17 +18,15 @@ namespace paddle {
namespace operators {
class SGDOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(SGDOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 2, "Input size of SGDOp must be two");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "Output size of SGDOp must be one");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), "inputs[0] mast be set");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(1), "inputs[1] mast be set");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(0), "outputs[0] mast be set");
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims() == ctx.Input<Tensor>(1)->dims(),
"Two input of SGD Op's dimension must be same.");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
PADDLE_ENFORCE(
ctx.Input<Tensor>("param")->dims() == ctx.Input<Tensor>("grad")->dims(),
"Two input of SGD Op's dimension must be same.");
ctx.Output<Tensor>("param_out")->Resize(ctx.Input<Tensor>("param")->dims());
}
};
......
paddle/operators/sigmoid_op.cc
浏览文件 @ 7aaf019a
......@@ -18,12 +18,12 @@ namespace paddle {
namespace operators {
class SigmoidOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(SigmoidOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Sigmoid Op only have one output");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......@@ -39,7 +39,9 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
};
class SigmoidOpGrad : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(SigmoidOpGrad, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
......
paddle/operators/sigmoid_op.h
浏览文件 @ 7aaf019a
......@@ -28,8 +28,8 @@ template <typename Place, typename T>
class SigmoidKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
auto input = context.Input<Tensor>("X");
auto output = context.Output<Tensor>("Y");
output->mutable_data<T>(context.GetPlace());
// The clipping is used in Paddle's raw implenmention
......
paddle/operators/softmax_op.cc
浏览文件 @ 7aaf019a
......@@ -18,15 +18,13 @@ namespace paddle {
namespace operators {
class SoftmaxOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(SoftmaxOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 1UL,
"Only one input is need for softmax");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims().size(), 2UL,
"The input of softmax op must be matrix");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
"Only one output is need for softmax");
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
"The input of softmax op must be matrix");
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......@@ -43,14 +41,12 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
};
class SoftmaxOpGrad : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(SoftmaxOpGrad, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL,
"Input of SoftmaxOpGrad should be 3, X, Y, YG");
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1UL,
"Output of SoftmaxOpGrad should be 1");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
"Input(Y@GRAD) should not be null");
PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
......
paddle/operators/uniform_random_op.cc
浏览文件 @ 7aaf019a
......@@ -27,7 +27,7 @@ template <typename T>
class CPUUniformRandomKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>(0);
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed =
static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
......@@ -46,12 +46,14 @@ class CPUUniformRandomKernel : public framework::OpKernel {
};
class UniformRandomOp : public framework::OperatorWithKernel {
DEFINE_OPERATOR_CTOR(UniformRandomOp, framework::OperatorWithKernel)
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE(GetAttr<float>("min") < GetAttr<float>("max"),
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>(0);
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = GetAttr<std::vector<int>>("dims");
tensor->Resize(framework::make_ddim(dims));
}
......
paddle/operators/uniform_random_op.cu
浏览文件 @ 7aaf019a
......@@ -46,7 +46,7 @@ template <typename T>
class GPUUniformRandomKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>(0);
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed =
static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
......
paddle/platform/dynload/cublas.h
浏览文件 @ 7aaf019a
......@@ -62,12 +62,12 @@ extern void *cublas_dso_handle;
DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name)
#define CUBLAS_BLAS_ROUTINE_EACH(__macro) \
__macro(cublasSgemv); \
__macro(cublasDgemv); \
__macro(cublasSgemm); \
__macro(cublasDgemm); \
__macro(cublasSgeam); \
__macro(cublasDgeam); \
__macro(cublasSgemv_v2); \
__macro(cublasDgemv_v2); \
__macro(cublasSgemm_v2); \
__macro(cublasDgemm_v2); \
__macro(cublasSgeam_v2); \
__macro(cublasDgeam_v2); \
__macro(cublasCreate_v2); \
__macro(cublasDestroy_v2); \
__macro(cublasSetStream_v2); \
......
paddle/platform/enforce.h
浏览文件 @ 7aaf019a
......@@ -14,14 +14,21 @@ limitations under the License. */
#pragma once
#include <execinfo.h>
#include <dlfcn.h> // for dladdr
#include <execinfo.h> // for backtrace
#include <iomanip>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include "paddle/string/printf.h"
#include "paddle/string/to_string.h"
#ifdef __GNUC__
#include <cxxabi.h> // for __cxa_demangle
#endif
#ifndef PADDLE_ONLY_CPU
#include "paddle/platform/dynload/cublas.h"
......@@ -39,6 +46,19 @@ limitations under the License. */
namespace paddle {
namespace platform {
namespace {
#ifdef __GNUC__
inline std::string demangle(std::string name) {
int status = -4; // some arbitrary value to eliminate the compiler warning
std::unique_ptr<char, void (*)(void*)> res{
abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
return (status == 0) ? res.get() : name;
}
#else
inline std::string demangle(std::string name) { return name; }
#endif
}
struct EnforceNotMet : public std::exception {
std::exception_ptr exp_;
std::string err_str_;
......@@ -48,15 +68,29 @@ struct EnforceNotMet : public std::exception {
std::rethrow_exception(exp_);
} catch (const std::exception& exp) {
std::ostringstream sout;
sout << string::Sprintf("%s at [%s:%d]", exp.what(), f, l) << std::endl;
sout << "Call Stacks: " << std::endl;
sout << "PaddlePaddle Call Stacks: " << std::endl;
void* call_stack[TRACE_STACK_LIMIT];
int sz = backtrace(call_stack, TRACE_STACK_LIMIT);
auto line = backtrace_symbols(call_stack, sz);
for (int i = 0; i < sz; ++i) {
sout << line[i] << std::endl;
auto size = backtrace(call_stack, TRACE_STACK_LIMIT);
auto symbols = backtrace_symbols(call_stack, size);
Dl_info info;
for (int i = 0; i < size; ++i) {
if (dladdr(call_stack[i], &info)) {
auto demangled = demangle(info.dli_sname);
auto addr_offset = static_cast<char*>(call_stack[i]) -
static_cast<char*>(info.dli_saddr);
sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
2 + sizeof(void*) * 2, call_stack[i],
demangled, addr_offset);
} else {
sout << string::Sprintf("%-3d %*0p %s\n", i, 2 + sizeof(void*) * 2,
call_stack[i]);
}
}
free(line);
free(symbols);
err_str_ = sout.str();
}
}
......@@ -170,7 +204,7 @@ inline void throw_on_error(T e) {
* PADDLE_ENFORCE_EQ(a, b);
*
* will raise an expression described as follows:
* "enforce a == b failed, 1 != 2" with detailed stack infomation.
* "enforce a == b failed, 1 != 2" with detailed stack information.
*
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
......
python/paddle/trainer_config_helpers/evaluators.py
浏览文件 @ 7aaf019a
......@@ -298,8 +298,8 @@ def pnpair_evaluator(
input,
label,
info,
name=None,
weight=None, ):
weight=None,
name=None, ):
"""
Positive-negative pair rate Evaluator which adapts to rank task like
learning to rank. This evaluator must contain at least three layers.
......@@ -308,27 +308,31 @@ def pnpair_evaluator(
.. code-block:: python
eval = pnpair_evaluator(input, info, label)
eval = pnpair_evaluator(input, label, info)
:param name: Evaluator name.
:type name: None|basestring
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: LayerOutput
:param info: Label layer name. (TODO, explaination)
:param info: Info layer name. (TODO, explaination)
:type info: LayerOutput
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1]. (TODO, explaination)
:type weight: LayerOutput
:param name: Evaluator name.
:type name: None|basestring
"""
if not isinstance(input, list):
input = [input]
if label:
input.append(label)
if info:
input.append(info)
evaluator_base(
name=name,
type="pnpair",
input=input,
label=label,
info=info,
weight=weight)
type="pnpair",
weight=weight,
name=name, )
@evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
......@@ -429,12 +433,12 @@ def chunk_evaluator(
.. code-block:: text
Scheme Description
Scheme Description
plain Use the same label for the whole chunk.
IOB Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
IOB Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
IOE Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
IOBES Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
IOBES Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
To make it clear, let's illustrate by an NER example.
Assuming that there are three named entity types including ORG, PER and LOC which are called 'chunk type' here,
if 'IOB' scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
......@@ -451,7 +455,7 @@ def chunk_evaluator(
tagType = label % numTagType
chunkType = label / numTagType
otherChunkType = numChunkTypes
The following table shows the mapping rule between tagType and tag type in each scheme.
.. code-block:: text
......@@ -475,7 +479,7 @@ def chunk_evaluator(
O 6
In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
"IOB" so tagType has two values: 0 for B and 1 for I.
"IOB" so tagType has two values: 0 for B and 1 for I.
Here we will use I-LOC to explain the above mapping rules in detail.
For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
and the tag is I.
......@@ -486,7 +490,7 @@ def chunk_evaluator(
eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
:param input: The input layers.
:type input: LayerOutput
:param label: An input layer containing the ground truth label.
......
python/paddle/v2/framework/op.py
浏览文件 @ 7aaf019a
import paddle.v2.framework.core as core
import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2
import paddle.v2.framework.proto.framework_pb2 as framework_pb2
def get_all_op_protos():
......@@ -12,11 +10,15 @@ def get_all_op_protos():
protostrs = core.get_all_op_protos()
ret_values = []
for pbstr in protostrs:
op_proto = op_proto_pb2.OpProto.FromString(str(pbstr))
op_proto = framework_pb2.OpProto.FromString(str(pbstr))
ret_values.append(op_proto)
return ret_values
def is_str(s):
return isinstance(s, str) or isinstance(s, unicode)
class OpDescCreationMethod(object):
"""
A Functor object to convert user input(use key word args) to OpDesc based on
......@@ -27,7 +29,7 @@ class OpDescCreationMethod(object):
"""
def __init__(self, op_proto):
if not isinstance(op_proto, op_proto_pb2.OpProto):
if not isinstance(op_proto, framework_pb2.OpProto):
raise TypeError("Argument should be OpProto")
self.__op_proto__ = op_proto
......@@ -39,26 +41,34 @@ class OpDescCreationMethod(object):
"""
if len(args) != 0:
raise ValueError("Only keyword arguments is supported by Paddle")
op_desc = op_desc_pb2.OpDesc()
# Inputs
ipts, ipt_format, _ = OpDescCreationMethod.extract_input_or_output(
"input", kwargs, self.__op_proto__.inputs)
op_desc.inputs.extend(ipts)
if ipt_format is not None:
op_desc.attrs.extend([ipt_format])
# Outputs
outs, out_format, tmp_index = OpDescCreationMethod.extract_input_or_output(
"output", kwargs, self.__op_proto__.outputs)
op_desc.outputs.extend(outs)
if out_format is not None:
op_desc.attrs.extend([out_format])
if len(tmp_index) != 0:
tmp_index_attr = op_desc.attrs.add()
tmp_index_attr.type = attribute_pb2.INTS
tmp_index_attr.name = "temporary_index"
tmp_index_attr.ints.extend(tmp_index)
op_desc = framework_pb2.OpDesc()
for input_parameter in self.__op_proto__.inputs:
input_arguments = kwargs.get(input_parameter.name, [])
if is_str(input_arguments):
input_arguments = [input_arguments]
if not input_parameter.duplicable and len(input_arguments) > 1:
raise ValueError("Input %s only accepts one input, but give %d"
% (input_parameter.name, len(input_arguments)))
ipt = op_desc.inputs.add()
ipt.parameter = input_parameter.name
ipt.arguments.extend(input_arguments)
for output_parameter in self.__op_proto__.outputs:
output_arguments = kwargs.get(output_parameter.name, [])
if is_str(output_arguments):
output_arguments = [output_arguments]
if not output_parameter.duplicable and len(output_arguments) > 1:
raise ValueError(
"Output %s only accepts one output, but give %d" %
(output_parameter.name, len(output_arguments)))
out = op_desc.outputs.add()
out.parameter = output_parameter.name
out.arguments.extend(output_arguments)
# Types
op_desc.type = self.__op_proto__.type
......@@ -72,17 +82,17 @@ class OpDescCreationMethod(object):
new_attr = op_desc.attrs.add()
new_attr.name = attr.name
new_attr.type = attr.type
if attr.type == attribute_pb2.INT:
if attr.type == framework_pb2.INT:
new_attr.i = user_defined_attr
elif attr.type == attribute_pb2.FLOAT:
elif attr.type == framework_pb2.FLOAT:
new_attr.f = user_defined_attr
elif attr.type == attribute_pb2.STRING:
elif attr.type == framework_pb2.STRING:
new_attr.s = user_defined_attr
elif attr.type == attribute_pb2.INTS:
elif attr.type == framework_pb2.INTS:
new_attr.ints.extend(user_defined_attr)
elif attr.type == attribute_pb2.FLOATS:
elif attr.type == framework_pb2.FLOATS:
new_attr.floats.extend(user_defined_attr)
elif attr.type == attribute_pb2.STRINGS:
elif attr.type == framework_pb2.STRINGS:
new_attr.strings.extend(user_defined_attr)
else:
raise NotImplementedError("Not support attribute type " +
......@@ -90,50 +100,6 @@ class OpDescCreationMethod(object):
return op_desc
@staticmethod
def extract_input_or_output(in_out, kwargs, meta):
"""
Extract input variable names or output variable names from key-word
arguments, which base on VarProtos.
:param in_out: "input" or "output"
:param kwargs: key-word arguments that user inputted.
:param meta: a list of VarProto
:return: The three object will be return. The variable names. The
input_format or output_format attribute(None if the input or output is
not multiple). The temporary variable index list.
"""
multiple = OpDescCreationMethod.any_is_true((m.multiple for m in meta))
tmp_index = []
retv = []
if multiple:
var_format = op_desc_pb2.AttrDesc()
var_format.type = attribute_pb2.INTS
var_format.name = "%s_format" % in_out
var_format.ints.append(0)
for var in meta:
var_name = var.name
if var.temporary:
var_name = [core.var_names.temp()]
tmp_index.append(len(retv))
else:
var_name = kwargs.get(var_name, [])
if not isinstance(var_name, list):
var_name = [var_name]
retv.extend(var_name)
var_format.ints.append(len(var_name) + var_format.ints[-1])
return retv, var_format, tmp_index
else:
for var in meta:
if var.temporary:
retv.append(kwargs.get(var.name, core.var_names.temp()))
tmp_index.append(len(retv))
else:
retv.append(kwargs.get(var.name, core.var_names.empty()))
return retv, None, tmp_index
@staticmethod
def any_is_true(generator):
"""
......@@ -146,13 +112,12 @@ class OpDescCreationMethod(object):
class OpInfo(object):
def __init__(self, name, method, inputs, outputs, attrs, no_temp_outputs):
def __init__(self, name, method, inputs, outputs, attrs):
self.name = name
self.method = method
self.inputs = inputs
self.outputs = outputs
self.attrs = attrs
self.no_temp_outputs = no_temp_outputs
def create_op_creation_method(op_proto):
......@@ -170,10 +135,7 @@ def create_op_creation_method(op_proto):
name=op_proto.type,
inputs=[var.name for var in op_proto.inputs],
outputs=[var.name for var in op_proto.outputs],
attrs=[attr.name for attr in op_proto.attrs],
no_temp_outputs=[
var.name for var in op_proto.outputs if not var.temporary
])
attrs=[attr.name for attr in op_proto.attrs])
class OperatorFactory(object):
......@@ -214,8 +176,5 @@ class OperatorFactory(object):
def get_op_attr_names(self, type):
return self.get_op_info(type).attrs
def get_op_no_temp_output_names(self, type):
return self.get_op_info(type).no_temp_outputs
Operator = OperatorFactory() # Default global factory
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ 7aaf019a
......@@ -24,3 +24,4 @@ py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py)
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)
python/paddle/v2/framework/tests/gradient_checker.py
浏览文件 @ 7aaf019a
......@@ -53,15 +53,18 @@ def get_numeric_gradient(op,
tensor.set(input_values[var_name], core.CPUPlace())
# Create all output variable in local_scope
for output in op.outputs():
if local_scope.find_var(output) is None:
local_scope.new_var(output).get_tensor()
opts = op.outputs()
for key in opts:
for output in opts[key]:
if local_scope.find_var(output) is None:
local_scope.new_var(output).get_tensor()
op.infer_shape(local_scope)
# allocate output memory
for output in op.outputs():
local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace())
for key in opts:
for output in opts[key]:
local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace(
))
# TODO(yuyang18): Only CPU is support now.
cpu_ctx = core.DeviceContext.create(core.CPUPlace())
......@@ -150,19 +153,24 @@ class GradientChecker(unittest.TestCase):
if no_grad_set is None:
no_grad_set = set()
tmp_outs = forward_op.temp_outputs()
no_tmp_out = filter(lambda name: name not in tmp_outs,
forward_op.outputs())
no_tmp_out = forward_op.no_intermediate_outputs()
if len(no_tmp_out) != 1:
raise ValueError("non temp out_names should be 1")
in_names = forward_op.inputs()
inputs = forward_op.inputs()
in_names = [item for k in inputs for item in inputs[k]]
outputs = forward_op.outputs()
out_names = [item for k in outputs for item in outputs[k]]
for no_grad in no_grad_set:
if no_grad not in in_names:
raise ValueError("no_grad should be in in_names")
backward_op = core.Operator.backward(forward_op, no_grad_set)
bwd_outputs = backward_op.outputs()
bwd_out_names = [item for k in bwd_outputs for item in bwd_outputs[k]]
places = [core.CPUPlace()]
if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu():
places.append(core.GPUPlace(0))
......@@ -188,7 +196,7 @@ class GradientChecker(unittest.TestCase):
var.set(value, place)
# create output var
for out_name in forward_op.outputs():
for out_name in out_names:
scope.new_var(out_name).get_tensor()
# infer the shape of output var and compute/set value of output var
......@@ -198,7 +206,7 @@ class GradientChecker(unittest.TestCase):
# create output grad var
# set shape as the output var
# set value of this grad to ones
for name in forward_op.outputs():
for name in out_names:
out_tensor = scope.find_var(name).get_tensor()
grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
grad_tensor.set_dims(out_tensor.shape())
......@@ -206,7 +214,7 @@ class GradientChecker(unittest.TestCase):
grad_tensor.set(data, place)
# create input grad var
for name in backward_op.outputs():
for name in bwd_out_names:
scope.new_var(name).get_tensor()
# infer the shape of input gradient var and compute/set it's value
......
python/paddle/v2/framework/tests/test_add_two_op.py
浏览文件 @ 7aaf019a
......@@ -19,14 +19,5 @@ class TestAddOp(unittest.TestCase):
self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
class TestAddGradOp(unittest.TestCase):
def test_add_grad(self):
op = Operator('add_two', X="X", Y="Y", Out="Out")
backward_op = core.Operator.backward(op, set())
self.assertEqual(backward_op.type(), "add_two_grad")
expected = '''Op(add_two_grad), inputs:(X, Y, Out, Out@GRAD), outputs:(X@GRAD, Y@GRAD).'''
self.assertEqual(expected, str(backward_op))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_net.py
浏览文件 @ 7aaf019a
......@@ -25,12 +25,12 @@ class TestNet(unittest.TestCase):
net.complete_add_op(True)
expected = '''
Op(plain_net), inputs:(W, X, Y), outputs:(Out, fc.out, pre_activation).
Op(add_two), inputs:(X, Y), outputs:(Out).
Op(plain_net), inputs:(W, X), outputs:(fc.out, pre_activation).
Op(plain_net), inputs:(W, X), outputs:(fc.out, pre_activation).
Op(mul), inputs:(X, W), outputs:(pre_activation).
Op(sigmoid), inputs:(pre_activation), outputs:(fc.out).
Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}.
Op(add_two), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{Y[fc.out]}.
'''
self.assertEqual(expected, "\n" + str(net))
......
python/paddle/v2/framework/tests/test_operator.py
浏览文件 @ 7aaf019a
import unittest
import paddle.v2.framework.op as op
import paddle.v2.framework.core as core
import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2
import paddle.v2.framework.proto.framework_pb2 as framework_pb2
class TestGetAllProtos(unittest.TestCase):
......@@ -17,7 +15,7 @@ class TestGetAllProtos(unittest.TestCase):
class TestOpDescCreationMethod(unittest.TestCase):
def test_plain_input_output(self):
op_proto = op_proto_pb2.OpProto()
op_proto = framework_pb2.OpProto()
op_proto.type = "test"
ipt = op_proto.inputs.add()
ipt.name = "X"
......@@ -37,25 +35,32 @@ class TestOpDescCreationMethod(unittest.TestCase):
method = op.OpDescCreationMethod(op_proto)
output = method(X="a", Y="b", Z="c")
expected = op_desc_pb2.OpDesc()
expected = framework_pb2.OpDesc()
expected.type = "test"
expected.inputs.extend(["a", "b"])
expected.outputs.append("c")
ipt_0 = expected.inputs.add()
ipt_0.parameter = "X"
ipt_0.arguments.extend(["a"])
ipt_1 = expected.inputs.add()
ipt_1.parameter = 'Y'
ipt_1.arguments.extend(['b'])
opt = expected.outputs.add()
opt.parameter = "Z"
opt.arguments.extend(["c"])
self.assertEqual(expected, output)
def test_multiple_input_plain_output(self):
op_proto = op_proto_pb2.OpProto()
op_proto = framework_pb2.OpProto()
op_proto.type = "fc"
ipt = op_proto.inputs.add()
ipt.name = "X"
ipt.comment = ""
ipt.multiple = True
ipt.duplicable = True
ipt = op_proto.inputs.add()
ipt.name = "W"
ipt.comment = ""
ipt.multiple = True
ipt.duplicable = True
ipt = op_proto.inputs.add()
ipt.name = "b"
......@@ -70,30 +75,50 @@ class TestOpDescCreationMethod(unittest.TestCase):
method = op.OpDescCreationMethod(op_proto)
generated1 = method(X="x", W="w", b="b", Y="y")
expected1 = op_desc_pb2.OpDesc()
expected1.inputs.extend(['x', 'w', 'b'])
expected1.outputs.extend(['y'])
expected1 = framework_pb2.OpDesc()
tmp = expected1.inputs.add()
tmp.parameter = "X"
tmp.arguments.extend(['x'])
tmp = expected1.inputs.add()
tmp.parameter = 'W'
tmp.arguments.extend(['w'])
tmp = expected1.inputs.add()
tmp.parameter = 'b'
tmp.arguments.extend(['b'])
tmp = expected1.outputs.add()
tmp.parameter = 'Y'
tmp.arguments.extend(['y'])
expected1.type = 'fc'
attr = expected1.attrs.add()
attr.name = 'input_format'
attr.type = attribute_pb2.INTS
attr.ints.extend([0, 1, 2, 3])
self.assertEqual(expected1, generated1)
generated2 = method(
X=['x1', 'x2', 'x3'], b='b', W=['w1', 'w2', 'w3'], Y='y')
expected2 = op_desc_pb2.OpDesc()
expected2.inputs.extend(['x1', 'x2', 'x3', 'w1', 'w2', 'w3', 'b'])
expected2.outputs.extend(['y'])
expected2 = framework_pb2.OpDesc()
tmp = expected2.inputs.add()
tmp.parameter = "X"
tmp.arguments.extend(['x1', 'x2', 'x3'])
tmp = expected2.inputs.add()
tmp.parameter = 'W'
tmp.arguments.extend(['w1', 'w2', 'w3'])
tmp = expected2.inputs.add()
tmp.parameter = 'b'
tmp.arguments.extend(['b'])
tmp = expected2.outputs.add()
tmp.parameter = 'Y'
tmp.arguments.extend(['y'])
expected2.type = 'fc'
attr = expected2.attrs.add()
attr.name = 'input_format'
attr.type = attribute_pb2.INTS
attr.ints.extend([0, 3, 6, 7])
self.assertEqual(expected2, generated2)
def test_attrs(self):
op_proto = op_proto_pb2.OpProto()
op_proto = framework_pb2.OpProto()
op_proto.type = "test"
ipt = op_proto.inputs.add()
ipt.name = 'X'
......@@ -105,12 +130,12 @@ class TestOpDescCreationMethod(unittest.TestCase):
attr.comment = ""
attr.type = type
__add_attr__("int_attr", attribute_pb2.INT)
__add_attr__("float_attr", attribute_pb2.FLOAT)
__add_attr__("string_attr", attribute_pb2.STRING)
__add_attr__("ints_attr", attribute_pb2.INTS)
__add_attr__("floats_attr", attribute_pb2.FLOATS)
__add_attr__("strings_attr", attribute_pb2.STRINGS)
__add_attr__("int_attr", framework_pb2.INT)
__add_attr__("float_attr", framework_pb2.FLOAT)
__add_attr__("string_attr", framework_pb2.STRING)
__add_attr__("ints_attr", framework_pb2.INTS)
__add_attr__("floats_attr", framework_pb2.FLOATS)
__add_attr__("strings_attr", framework_pb2.STRINGS)
op_proto.comment = ""
self.assertTrue(op_proto.IsInitialized())
......@@ -126,76 +151,52 @@ class TestOpDescCreationMethod(unittest.TestCase):
floats_attr=[0.2, 3.2, 4.5],
strings_attr=["a", "b", "c"])
expected = op_desc_pb2.OpDesc()
expected = framework_pb2.OpDesc()
expected.type = "test"
expected.inputs.extend(['a'])
ipt = expected.inputs.add()
ipt.parameter = "X"
ipt.arguments.extend(['a'])
attr = expected.attrs.add()
attr.name = "int_attr"
attr.type = attribute_pb2.INT
attr.type = framework_pb2.INT
attr.i = 10
attr = expected.attrs.add()
attr.name = "float_attr"
attr.type = attribute_pb2.FLOAT
attr.type = framework_pb2.FLOAT
attr.f = 3.2
attr = expected.attrs.add()
attr.name = "string_attr"
attr.type = attribute_pb2.STRING
attr.type = framework_pb2.STRING
attr.s = "test_str"
attr = expected.attrs.add()
attr.name = "ints_attr"
attr.type = attribute_pb2.INTS
attr.type = framework_pb2.INTS
attr.ints.extend([0, 1, 2, 3, 4])
attr = expected.attrs.add()
attr.name = 'floats_attr'
attr.type = attribute_pb2.FLOATS
attr.type = framework_pb2.FLOATS
attr.floats.extend([0.2, 3.2, 4.5])
attr = expected.attrs.add()
attr.name = 'strings_attr'
attr.type = attribute_pb2.STRINGS
attr.type = framework_pb2.STRINGS
attr.strings.extend(['a', 'b', 'c'])
self.assertEqual(expected, generated)
def test_input_temporary_output(self):
op_proto = op_proto_pb2.OpProto()
op_proto.type = "test"
out = op_proto.outputs.add()
out.name = "OUT"
out.comment = ""
out = op_proto.outputs.add()
out.name = "TMP"
out.comment = ""
out.temporary = True
out = op_proto.outputs.add()
out.name = "OUT2"
out.comment = ""
op_proto.comment = ""
method = op.OpDescCreationMethod(op_proto)
generated = method(OUT="a", OUT2="b")
desc = op_desc_pb2.OpDesc()
desc.outputs.extend(["a", core.var_names.temp(), "b"])
desc.type = "test"
attr = desc.attrs.add()
attr.name = "temporary_index"
attr.type = attribute_pb2.INTS
attr.ints.append(2)
self.assertEqual(generated, desc)
class TestOpCreations(unittest.TestCase):
def test_all(self):
add_op = op.Operator("add_two", X="a", Y="b", Out="z")
self.assertIsNotNone(add_op)
# Invoke C++ DebugString()
self.assertEqual('Op(add_two), inputs:(a, b), outputs:(z).',
self.assertEqual('Op(add_two), inputs:{X[a], Y[b]}, outputs:{Out[z]}.',
str(add_op))
......
python/paddle/v2/framework/tests/test_protobuf.py
浏览文件 @ 7aaf019a
import paddle.v2.framework.proto.op_proto_pb2 as op_proto_lib
import paddle.v2.framework.proto.attribute_pb2 as attr_type_lib
import paddle.v2.framework.proto.framework_pb2 as framework_pb2
import unittest
class TestFrameworkProto(unittest.TestCase):
def test_all(self):
op_proto = op_proto_lib.OpProto()
op_proto = framework_pb2.OpProto()
ipt0 = op_proto.inputs.add()
ipt0.name = "a"
ipt0.comment = "the input of cosine op"
......@@ -19,7 +18,7 @@ class TestFrameworkProto(unittest.TestCase):
attr = op_proto.attrs.add()
attr.name = "scale"
attr.comment = "scale of cosine op"
attr.type = attr_type_lib.FLOAT
attr.type = framework_pb2.FLOAT
op_proto.type = "cos"
self.assertTrue(op_proto.IsInitialized())
......
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