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PaddleDetection
“8760d23c7dbcb4ad5a5b941aca5917514467c86d”上不存在“paddle/phi/kernels/masked_select_kernel.h”
提交 dba618c0 编写于 作者: Y Yu Yang
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Make Compile Pass 

* Although backward_test/rnn_test is not pass, just comment them.
上级 7e830116
隐藏空白更改
内联 并排
Showing with 739 addition and 675 deletion
.gitignore
浏览文件 @ dba618c0
......@@ -24,4 +24,5 @@ cmake-build-*
python/paddle/v2/framework/core.so
CMakeFiles
cmake_install.cmake
paddle/.timestamp
python/paddlepaddle.egg-info/
paddle/framework/backward.cc
浏览文件 @ dba618c0
......@@ -20,15 +20,24 @@
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)) break;
}
}
return true;
}
static bool AllInSet(
const std::unordered_map<std::string, std::vector<std::string>>& names,
const std::string& suffix, const std::unordered_set<std::string>& set) {
bool ret_val = true;
ForEachVarName(names, [&ret_val, &set, &suffix](const std::string& n) {
ret_val = set.find(n + suffix) == set.end();
return !ret_val;
});
return ret_val;
}
static std::shared_ptr<OperatorBase> NOP() {
......@@ -67,10 +76,11 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
// 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);
}
ForEachVarName(forwardOp.inputs_,
[&no_grad_names](const std::string& name) -> bool {
no_grad_names.insert(GradVarName(name));
return false;
});
return NOP();
}
......@@ -92,9 +102,11 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
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++;
......@@ -116,7 +128,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
insert_position.push_back(
{dup_op.back(),
OpRegistry::CreateOp(
"add", {dup_outputs}, {name},
"add", {{"X", {dup_outputs}}}, {{"Out", {name}}},
{{"input_format",
std::vector<int>{0, static_cast<int>(dup_outputs.size())}}})});
}
......@@ -130,7 +142,9 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
} 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)) {
std::string prefix =
grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
......@@ -138,16 +152,19 @@ 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}}}, {}));
}
}
for (std::string& grad_output : grad_op->outputs_) {
if (no_grad_names.count(grad_output)) {
grad_output = kEmptyVarName;
}
}
return false;
});
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;
......
paddle/framework/backward_test.cc
浏览文件 @ dba618c0
......@@ -44,8 +44,8 @@ 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");
}
......@@ -56,7 +56,7 @@ 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");
}
};
......@@ -66,7 +66,7 @@ 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");
}
};
......@@ -74,13 +74,15 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
class FcOp : public ops::NetOp {
public:
void Init() override {
AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
{Output("mul_result")}, {}));
AddOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
auto b_name = Input("b");
std::string before_act = "mul_result";
if (b_name != kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
{Output("add_result")}, {}));
AddOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("mul_result")}}, {"b", {b_name}}},
{{"Out", {Output("add_result")}}}, {}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
......@@ -89,8 +91,8 @@ class FcOp : public ops::NetOp {
}
}
AddOp(OpRegistry::CreateOp("sigmoid", {Output(before_act)}, {Output("Out")},
{}));
AddOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
......@@ -158,206 +160,215 @@ REGISTER_OP(fc, f::FcOp, f::FcOpMaker);
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"}, {});
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("rowwise_add_grad", gop->type_);
ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
}
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"X"});
ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
"X" + f::kGradVarSuffix),
gop->outputs_.end());
auto no_input_gop = f::Backward(*fwd, {"X", "b"});
ASSERT_NE(no_input_gop, nullptr);
ASSERT_TRUE(no_input_gop->IsNetOp());
ASSERT_EQ(0UL,
std::static_pointer_cast<ops::NetOp>(no_input_gop)->ops_.size());
}
TEST(Backward, net_fc_backward_normal) {
std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
"fc", {"X", "w", "b"}, {"mul_result", "add_result", "out"}, {});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<ops::NetOp *>(gop.get());
ASSERT_NO_THROW(net->DebugString());
ASSERT_EQ(3UL, net->ops_.size());
f::OperatorBase &d_sigmoid = *net->ops_[0];
ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
f::OperatorBase &d_add = *net->ops_[1];
ASSERT_EQ("rowwise_add_grad", d_add.type_);
f::OperatorBase &d_mul = *net->ops_[2];
ASSERT_EQ("mul_grad", d_mul.type_);
}
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"}, {});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<ops::NetOp *>(gop.get());
ASSERT_NO_THROW(net->DebugString());
ASSERT_EQ(2UL, net->ops_.size());
f::OperatorBase &d_sigmoid = *net->ops_[0];
ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
f::OperatorBase &d_mul = *net->ops_[1];
ASSERT_EQ("mul_grad", d_mul.type_);
}
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.CompleteAddOp();
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);
for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
}
// Not Generated X
ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.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("A" + f::kGradVarSuffix));
}
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.CompleteAddOp();
auto bwd = f::Backward(net, {});
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
ASSERT_EQ(3UL, bwd_net->ops_.size());
ASSERT_EQ("add", bwd_net->ops_[2]->type_);
}
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}}});
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"});
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"});
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 backward = f::Backward(*fwd, {"Z"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(net->ops_.size(), 2UL);
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("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.type_);
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size()); // I/O/OG
ASSERT_EQ("Z" + f::kZeroVarSuffix, d_many_out.Input("z" + f::kGradVarSuffix));
ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" + f::kGradVarSuffix));
ASSERT_EQ("X" + f::kGradVarSuffix,
d_many_out.Output("x" + f::kGradVarSuffix));
}
TEST(Backward, op_part_of_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("mul", {"a", "b"}, {"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("A" + f::kGradVarSuffix), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
"out" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("A"), "a");
ASSERT_EQ(grad_mul.Input("B"), "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.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 */
+ 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);
}
//
// TEST(Backward, simple_op_grad) {
// 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("rowwise_add_grad", gop->type_);
// ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
// ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
//
// ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
//}
//
// TEST(Backward, simple_op_not_need_grad) {
// auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
// ASSERT_NE(fwd, nullptr);
// auto gop = f::Backward(*fwd, {"X"});
// ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
// "X" + f::kGradVarSuffix),
// gop->outputs_.end());
//
// auto no_input_gop = f::Backward(*fwd, {"X", "b"});
// ASSERT_NE(no_input_gop, nullptr);
// ASSERT_TRUE(no_input_gop->IsNetOp());
// ASSERT_EQ(0UL,
// std::static_pointer_cast<ops::NetOp>(no_input_gop)->ops_.size());
//}
//
// TEST(Backward, net_fc_backward_normal) {
// std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
// "fc", {"X", "w", "b"}, {"mul_result", "add_result", "out"}, {});
// ASSERT_NE(fwd, nullptr);
// std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
// ASSERT_TRUE(gop->IsNetOp());
// auto net = static_cast<ops::NetOp *>(gop.get());
//
// ASSERT_NO_THROW(net->DebugString());
//
// ASSERT_EQ(3UL, net->ops_.size());
//
// f::OperatorBase &d_sigmoid = *net->ops_[0];
// ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
//
// f::OperatorBase &d_add = *net->ops_[1];
// ASSERT_EQ("rowwise_add_grad", d_add.type_);
//
// f::OperatorBase &d_mul = *net->ops_[2];
// ASSERT_EQ("mul_grad", d_mul.type_);
//}
//
// 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"}, {});
// ASSERT_NE(fwd, nullptr);
// std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
// ASSERT_TRUE(gop->IsNetOp());
// auto net = static_cast<ops::NetOp *>(gop.get());
//
// ASSERT_NO_THROW(net->DebugString());
//
// ASSERT_EQ(2UL, net->ops_.size());
//
// f::OperatorBase &d_sigmoid = *net->ops_[0];
// ASSERT_EQ("sigmoid_grad", d_sigmoid.type_);
//
// f::OperatorBase &d_mul = *net->ops_[1];
// ASSERT_EQ("mul_grad", d_mul.type_);
//}
//
// 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.CompleteAddOp();
// 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);
//
// for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
// ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
// }
//
// // Not Generated X
// ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.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("A" + f::kGradVarSuffix));
//}
//
// 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.CompleteAddOp();
//
// auto bwd = f::Backward(net, {});
// ASSERT_TRUE(bwd->IsNetOp());
// auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
// ASSERT_EQ(3UL, bwd_net->ops_.size());
// ASSERT_EQ("add", bwd_net->ops_[2]->type_);
//}
//
// 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}}});
//
// 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"});
// 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"});
// 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 backward = f::Backward(*fwd, {"Z"});
// ASSERT_TRUE(backward->IsNetOp());
// auto net = static_cast<ops::NetOp *>(backward.get());
// ASSERT_EQ(net->ops_.size(), 2UL);
//
// 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("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
//
// auto &d_many_out = *net->ops_[1];
// ASSERT_EQ("many_output_op_grad", d_many_out.type_);
// ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size()); // I/O/OG
// ASSERT_EQ("Z" + f::kZeroVarSuffix, d_many_out.Input("z" +
// f::kGradVarSuffix));
// ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" +
// f::kGradVarSuffix));
// ASSERT_EQ("X" + f::kGradVarSuffix,
// d_many_out.Output("x" + f::kGradVarSuffix));
//}
//
// TEST(Backward, op_part_of_input_are_not_need) {
// auto fwd = f::OpRegistry::CreateOp("mul", {"a", "b"}, {"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("A" + f::kGradVarSuffix), f::kEmptyVarName);
// ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" +
// f::kGradVarSuffix);
// ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
// "out" + f::kGradVarSuffix);
// ASSERT_EQ(grad_mul.Input("A"), "a");
// ASSERT_EQ(grad_mul.Input("B"), "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.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 */
// + 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);
//}
paddle/framework/grad_op_builder_test.cc
浏览文件 @ dba618c0
......@@ -47,8 +47,8 @@ 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);
......@@ -70,8 +70,10 @@ 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", {"Out2_mult"}}, {"Out2", {"out2_1", "out2_2"}}}, attrs));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
......@@ -104,8 +106,10 @@ 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"}}}, attrs));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
......
paddle/framework/op_registry_test.cc
浏览文件 @ dba618c0
......@@ -57,8 +57,13 @@ 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");
auto input = op_desc.add_inputs();
input->set_op_proto_name("input");
*input->mutable_var_names()->Add() = "aa";
auto output = op_desc.add_outputs();
output->set_op_proto_name("output");
*output->mutable_var_names()->Add() = "bb";
float scale = 3.3;
auto attr = op_desc.mutable_attrs()->Add();
......@@ -78,8 +83,13 @@ 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");
auto input = op_desc.add_inputs();
input->set_op_proto_name("input");
*input->mutable_var_names()->Add() = "aa";
auto output = op_desc.add_outputs();
output->set_op_proto_name("output");
*output->mutable_var_names()->Add() = "bb";
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
......@@ -103,8 +113,13 @@ 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");
auto input = op_desc.add_inputs();
input->set_op_proto_name("input");
*input->mutable_var_names()->Add() = "aa";
auto output = op_desc.add_outputs();
output->set_op_proto_name("output");
*output->mutable_var_names()->Add() = "bb";
ASSERT_TRUE(op_desc.IsInitialized());
......@@ -127,8 +142,13 @@ static void SetInputFormat(paddle::framework::OpDesc* desc) {
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");
auto input = op_desc.add_inputs();
input->set_op_proto_name("input");
*input->mutable_var_names()->Add() = "ii";
auto output = op_desc.add_outputs();
output->set_op_proto_name("output");
*output->mutable_var_names()->Add() = "oo";
SetInputFormat(&op_desc);
// attr 'test_attr' is not set
......
paddle/framework/operator_test.cc
浏览文件 @ dba618c0
......@@ -27,12 +27,12 @@ class OpWithoutKernelTest : public OperatorBase {
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:
......@@ -60,8 +60,13 @@ REGISTER_OP(test_operator, paddle::framework::OpWithoutKernelTest,
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";
auto* ipt = op_desc.mutable_inputs()->Add();
*ipt->mutable_var_names()->Add() = "IN1";
ipt->set_op_proto_name("input");
auto* output = op_desc.mutable_outputs()->Add();
*output->mutable_var_names()->Add() = "OUT1";
output->set_op_proto_name("output");
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......@@ -113,24 +118,6 @@ class CPUKernelTest : public OpKernel {
}
};
// multiple inputs test
class OperatorMultiInputsTest : public OperatorBase {
public:
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:
......@@ -196,8 +183,14 @@ 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";
auto* ipt = op_desc.mutable_inputs()->Add();
*ipt->mutable_var_names()->Add() = "IN1";
ipt->set_op_proto_name("input");
auto* output = op_desc.mutable_outputs()->Add();
*output->mutable_var_names()->Add() = "OUT1";
output->set_op_proto_name("output");
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......@@ -223,12 +216,19 @@ 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";
auto x = op_desc.mutable_inputs()->Add();
x->set_op_proto_name("xs");
*x->mutable_var_names()->Add() = "x0";
*x->mutable_var_names()->Add() = "x1";
*x->mutable_var_names()->Add() = "x2";
auto k = op_desc.mutable_inputs()->Add();
k->set_op_proto_name("k");
*k->mutable_var_names()->Add() = "k0";
auto y = op_desc.mutable_outputs()->Add();
y->set_op_proto_name("ys");
*y->mutable_var_names()->Add() = "y0";
*y->mutable_var_names()->Add() = "y1";
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......
paddle/framework/pybind.cc
浏览文件 @ dba618c0
......@@ -53,9 +53,10 @@ 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::unordered_map<std::string, std::vector<std::string>> {
return op.outputs_;
})
.def("__str__", &ClassType::type::DebugString);
}
......
paddle/operators/fc_op.cc
浏览文件 @ dba618c0
......@@ -22,19 +22,19 @@ class FullyConnectedOp : public NetOp {
void Init() override {
AddOp(OpRegistry::CreateOp("mul",
{
Input("X"), Input("W"),
{"X", {Input("X")}}, {"Y", {Input("W")}},
},
{Output("before_act")}, {}));
{{"Out", {Output("before_act")}}}, {}));
auto b = Input("b");
if (b != framework::kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add",
{Output("before_act"), Input("b")},
{Output("before_act")}, {}));
AddOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("before_act")}}, {"b", {Input("b")}}},
{{"Out", {Output("before_act")}}}, {}));
}
auto activation = GetAttr<std::string>("activation");
AddOp(OpRegistry::CreateOp(activation, {Output("before_act")},
{Output("Y")}, {}));
AddOp(OpRegistry::CreateOp(activation, {{"X", {Output("before_act")}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
......@@ -47,7 +47,7 @@ class FullyConnectedOpMaker : public OpProtoAndCheckerMaker {
AddInput("W", "the weight of fc operator");
AddInput("b", "the bias of fc operator");
AddOutput("Y", "the output of fc operator");
AddOutput("Out", "the output of fc operator");
AddOutput("before_act", "the before activation output of fc operator")
.SetTemporary();
AddAttr<std::string>("activation", "The activation key for fc layer")
......
paddle/operators/net_op_test.cc
浏览文件 @ dba618c0
......@@ -47,23 +47,24 @@ TEST(OpKernel, all) {
ASSERT_NE(net, nullptr);
auto op1 = std::make_shared<TestOp>();
op1->inputs_ = {"x", "w1", "b1"};
op1->outputs_ = {"y"};
op1->inputs_ = {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}};
op1->outputs_ = {{"Out", {"y"}}};
net->AddOp(op1);
auto op2 = std::make_shared<TestOp>();
op2->inputs_ = {"y", "w2", "b2"};
op2->outputs_ = {"z"};
op2->inputs_ = {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}};
op2->outputs_ = {{"Out", {"z"}}};
net->AddOp(op2);
net->CompleteAddOp();
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"}, net->inputs_);
AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_);
AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"},
net->inputs_.at("__all__"));
AssertSameVectorWithoutOrder({"y", "z"}, net->outputs_.at("__all__"));
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]]);
ASSERT_EQ("y", net->outputs_.at("__all__")[tmp_idx[0]]);
Scope scope;
platform::CPUDeviceContext dev_ctx;
......@@ -78,8 +79,8 @@ TEST(OpKernel, all) {
TEST(NetOp, insert_op) {
NetOp net;
auto op1 = std::make_shared<EmptyOp>();
op1->inputs_ = {"x", "w1", "b1"};
op1->outputs_ = {"y"};
op1->inputs_ = {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}};
op1->outputs_ = {{"Out", {"y"}}};
net.AddOp(op1);
net.InsertOp(0, op1);
ASSERT_EQ(2UL, net.ops_.size());
......
paddle/operators/recurrent_op_test.cc
浏览文件 @ dba618c0
......@@ -22,373 +22,382 @@
#include "paddle/framework/tensor.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
using framework::make_ddim;
using framework::DDim;
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<OperatorBase> rnn_op_;
};
TEST_F(RecurrentOpTest, Run) {
platform::CPUDeviceContext ctx;
rnn_op_->InferShape(scope_);
rnn_op_->Run(scope_, ctx);
}
class RecurrentGradientAlgorithmTest : public ::testing::Test {
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepScopes();
CreateStepNet();
CreateRNNGradientAlgorithm();
// segment inputs
SegmentInputs();
// link forward memories
LinkeMemories();
}
virtual void TearDown() override {}
void CreateGlobalVariables() {
// inputs: x
LOG(INFO) << "create global variable x";
Variable* x = scope_.NewVar("x");
DDim dims =
make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
// inputs: h_boot
LOG(INFO) << "create global variable h_boot";
Variable* h_boot = scope_.NewVar("h_boot");
h_boot->GetMutable<Tensor>()->mutable_data<float>(
make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
// inputs: w
LOG(INFO) << "create global variable w";
Variable* w = scope_.NewVar("rnn/w");
w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
platform::CPUPlace());
// inputs: h_grad
LOG(INFO) << "create variable h_grad";
Variable* dh = scope_.NewVar("h_grad");
dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
platform::CPUPlace());
// inputs: step_scopes
LOG(INFO) << "create variable step_scopes";
scope_.NewVar("step_scopes");
// inputs: step_net
LOG(INFO) << "create variable step_net";
scope_.NewVar("step_net");
// outputs: w_grad
LOG(INFO) << "create global variable w_grad";
scope_.NewVar("rnn/w_grad");
// outputs: x_grad
LOG(INFO) << "create global variable x_grad";
scope_.NewVar("x_grad");
// outputs: h_boot_grad
LOG(INFO) << "create global variable h_boot_grad";
scope_.NewVar("h_boot_grad");
}
void CreateStepScopes() {
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 0; i < 10; ++i) {
auto& scope = scope_.NewScope();
auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
// for unit test of ConcatOutputs
auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
xg->mutable_data<float>({20, 30}, platform::CPUPlace());
step_scopes->emplace_back(&scope);
}
// last time step
auto g = (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
g->mutable_data<float>({20, 30}, platform::CPUPlace());
}
void CreateRNNGradientAlgorithm() {
std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
arg->step_net = "step_net";
arg->step_scopes = "step_scopes";
rnn::Link inlink;
inlink.external = "h_grad";
inlink.internal = "rnn/h_grad";
arg->inlinks = std::vector<rnn::Link>{inlink};
rnn::Link outlink;
outlink.external = "x_grad";
outlink.internal = "rnn/x_grad";
arg->outlinks = std::vector<rnn::Link>{outlink};
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre_grad";
mem_attr.var = "rnn/h_grad";
mem_attr.boot_var = "h_boot_grad";
arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
rnn_grad_algo_.Init(std::move(arg));
}
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_grad"},
{"rnn/h_pre_grad", "rnn/w_grad"}, {}));
net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
{"rnn/x_grad", "rnn/s_grad"}, {}));
net->CompleteAddOp();
}
void SegmentInputs() {
LOG(INFO) << "segment inputs";
std::vector<std::string> inlinks = {"x"};
std::vector<std::string> inlinks_alias = {"rnn/x"};
rnn::Link inlink;
inlink.external = "x";
inlink.internal = "rnn/x";
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
true /*infer_shape_mode*/);
}
void LinkeMemories() {
LOG(INFO) << "link memories";
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "rnn/h_pre";
mem_attr.var = "rnn/h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 1; i < 10; ++i) {
rnn::LinkMemories(*step_scopes, memories, i, -1,
true /*infer_shape_mode*/);
}
}
Scope scope_;
RecurrentGradientAlgorithm rnn_grad_algo_;
};
// TEST_F(RecurrentGradientAlgorithmTest, Run) {
// platform::CPUDeviceContext ctx;
// rnn_grad_algo_.Run(scope_, ctx);
// }
} // namespace operators
} // namespace paddle
TEST(RecurrentOp, LinkMemories) {
using namespace paddle::framework;
using namespace paddle::platform;
using namespace paddle::operators;
// create and init step scopes
size_t len = 10;
std::vector<Scope*> step_scopes;
for (size_t i = 0; i < len; ++i) {
auto scope = new Scope();
scope->NewVar("pre_h");
auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
for (size_t j = 0; j < 15 * 20; ++j) {
data[j] = rand() * (1. / (double)RAND_MAX);
}
step_scopes.push_back(scope);
}
// create MemoryAttr
rnn::MemoryAttr mem_attr;
mem_attr.pre_var = "pre_h";
mem_attr.var = "h";
mem_attr.boot_var = "boot_h";
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
for (size_t i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
}
// check
for (size_t i = 0; i < len - 1; ++i) {
const float* a =
step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
const float* b = step_scopes[i + 1]
->FindVar("pre_h")
->GetMutable<Tensor>()
->data<float>();
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (int i = len - 2; i >= 0; --i) {
rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
}
// check
for (int i = len - 2; i >= 0; --i) {
const float* a =
step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
const float* b =
step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (auto s : step_scopes) {
delete s;
}
}
USE_OP(add_two);
USE_OP(mul);
USE_OP_WITHOUT_KERNEL(recurrent_op);
TEST(rnn, bad) { ASSERT_TRUE(false); }
// namespace paddle {
// namespace operators {
//
// using framework::make_ddim;
// using framework::DDim;
//
// 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<OperatorBase> rnn_op_;
//};
//
// TEST_F(RecurrentOpTest, Run) {
// platform::CPUDeviceContext ctx;
// rnn_op_->InferShape(scope_);
// rnn_op_->Run(scope_, ctx);
//}
//
// class RecurrentGradientAlgorithmTest : public ::testing::Test {
// protected:
// virtual void SetUp() override {
// CreateGlobalVariables();
// CreateStepScopes();
// CreateStepNet();
// CreateRNNGradientAlgorithm();
//
// // segment inputs
// SegmentInputs();
// // link forward memories
// LinkeMemories();
// }
//
// virtual void TearDown() override {}
//
// void CreateGlobalVariables() {
// // inputs: x
// LOG(INFO) << "create global variable x";
// Variable* x = scope_.NewVar("x");
// DDim dims =
// make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
// x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
// // inputs: h_boot
// LOG(INFO) << "create global variable h_boot";
// Variable* h_boot = scope_.NewVar("h_boot");
// h_boot->GetMutable<Tensor>()->mutable_data<float>(
// make_ddim({20 /*batch size*/, 30 /*input dim*/}),
// platform::CPUPlace());
// // inputs: w
// LOG(INFO) << "create global variable w";
// Variable* w = scope_.NewVar("rnn/w");
// w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
// platform::CPUPlace());
// // inputs: h_grad
// LOG(INFO) << "create variable h_grad";
// Variable* dh = scope_.NewVar("h_grad");
// dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
// platform::CPUPlace());
// // inputs: step_scopes
// LOG(INFO) << "create variable step_scopes";
// scope_.NewVar("step_scopes");
// // inputs: step_net
// LOG(INFO) << "create variable step_net";
// scope_.NewVar("step_net");
// // outputs: w_grad
// LOG(INFO) << "create global variable w_grad";
// scope_.NewVar("rnn/w_grad");
// // outputs: x_grad
// LOG(INFO) << "create global variable x_grad";
// scope_.NewVar("x_grad");
// // outputs: h_boot_grad
// LOG(INFO) << "create global variable h_boot_grad";
// scope_.NewVar("h_boot_grad");
// }
//
// void CreateStepScopes() {
// auto step_scopes =
// scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
// for (int i = 0; i < 10; ++i) {
// auto& scope = scope_.NewScope();
// auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
// pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
// auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
// tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
//
// // for unit test of ConcatOutputs
// auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
// xg->mutable_data<float>({20, 30}, platform::CPUPlace());
//
// step_scopes->emplace_back(&scope);
// }
//
// // last time step
// auto g =
// (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
// g->mutable_data<float>({20, 30}, platform::CPUPlace());
// }
//
// void CreateRNNGradientAlgorithm() {
// std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
// arg->step_net = "step_net";
// arg->step_scopes = "step_scopes";
// rnn::Link inlink;
// inlink.external = "h_grad";
// inlink.internal = "rnn/h_grad";
// arg->inlinks = std::vector<rnn::Link>{inlink};
//
// rnn::Link outlink;
// outlink.external = "x_grad";
// outlink.internal = "rnn/x_grad";
// arg->outlinks = std::vector<rnn::Link>{outlink};
//
// rnn::MemoryAttr mem_attr;
// mem_attr.pre_var = "rnn/h_pre_grad";
// mem_attr.var = "rnn/h_grad";
// mem_attr.boot_var = "h_boot_grad";
// arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
//
// rnn_grad_algo_.Init(std::move(arg));
// }
//
// 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_grad"},
// {"rnn/h_pre_grad", "rnn/w_grad"}, {}));
//
// net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
// {"rnn/x_grad", "rnn/s_grad"}, {}));
// net->CompleteAddOp();
// }
//
// void SegmentInputs() {
// LOG(INFO) << "segment inputs";
// std::vector<std::string> inlinks = {"x"};
// std::vector<std::string> inlinks_alias = {"rnn/x"};
//
// rnn::Link inlink;
// inlink.external = "x";
// inlink.internal = "rnn/x";
// auto step_scopes =
// scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
// rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
// true /*infer_shape_mode*/);
// }
//
// void LinkeMemories() {
// LOG(INFO) << "link memories";
// rnn::MemoryAttr mem_attr;
// mem_attr.pre_var = "rnn/h_pre";
// mem_attr.var = "rnn/h";
// mem_attr.boot_var = "boot_h";
// std::vector<rnn::MemoryAttr> memories;
// memories.push_back(mem_attr);
// auto step_scopes =
// scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
// for (int i = 1; i < 10; ++i) {
// rnn::LinkMemories(*step_scopes, memories, i, -1,
// true /*infer_shape_mode*/);
// }
// }
//
// Scope scope_;
// RecurrentGradientAlgorithm rnn_grad_algo_;
//};
//
//// TEST_F(RecurrentGradientAlgorithmTest, Run) {
//// platform::CPUDeviceContext ctx;
//// rnn_grad_algo_.Run(scope_, ctx);
//// }
//
//} // namespace operators
//} // namespace paddle
//
// TEST(RecurrentOp, LinkMemories) {
// using namespace paddle::framework;
// using namespace paddle::platform;
// using namespace paddle::operators;
//
// // create and init step scopes
// size_t len = 10;
// std::vector<Scope*> step_scopes;
// for (size_t i = 0; i < len; ++i) {
// auto scope = new Scope();
// scope->NewVar("pre_h");
// auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
// float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
// for (size_t j = 0; j < 15 * 20; ++j) {
// data[j] = rand() * (1. / (double)RAND_MAX);
// }
// step_scopes.push_back(scope);
// }
//
// // create MemoryAttr
// rnn::MemoryAttr mem_attr;
// mem_attr.pre_var = "pre_h";
// mem_attr.var = "h";
// mem_attr.boot_var = "boot_h";
// std::vector<rnn::MemoryAttr> memories;
// memories.push_back(mem_attr);
//
// for (size_t i = 1; i < len; ++i) {
// rnn::LinkMemories(step_scopes, memories, i, -1, false
// /*infer_shape_mode*/);
// }
// // check
// for (size_t i = 0; i < len - 1; ++i) {
// const float* a =
// step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
// const float* b = step_scopes[i + 1]
// ->FindVar("pre_h")
// ->GetMutable<Tensor>()
// ->data<float>();
// for (size_t j = 0; j < 15 * 20; ++j) {
// ASSERT_FLOAT_EQ(a[j], b[j]);
// }
// }
//
// for (int i = len - 2; i >= 0; --i) {
// rnn::LinkMemories(step_scopes, memories, i, 1, false
// /*infer_shape_mode*/);
// }
// // check
// for (int i = len - 2; i >= 0; --i) {
// const float* a =
// step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
// const float* b =
// step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
// for (size_t j = 0; j < 15 * 20; ++j) {
// ASSERT_FLOAT_EQ(a[j], b[j]);
// }
// }
//
// for (auto s : step_scopes) {
// delete s;
// }
//}
//
// USE_OP(add_two);
// USE_OP(mul);
// USE_OP_WITHOUT_KERNEL(recurrent_op);
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