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Enhance Op's Error Message (#27455)

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* test=develop, update error message
revert-27520-disable_pr
123malin 5 years ago committed by GitHub
parent 827ac36faa
commit a04524759e
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 64 additions and 47 deletions
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4
paddle/fluid/operators/concat_op.cc
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@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/concat_op.h"
#include <memory>
#include <string>
#include <vector>
@ -78,7 +79,8 @@ class ConcatOp : public framework::OperatorWithKernel {
}
}
if (flag == 0) {
PADDLE_THROW("All Inputs of Concat OP are Empty!");
PADDLE_THROW(platform::errors::InvalidArgument(
"All Inputs of Concat OP are Empty!"));
}
#ifdef PADDLE_WITH_MKLDNN
if (platform::CanMKLDNNBeUsed(ctx)) {

78
paddle/fluid/operators/optimizers/decayed_adagrad_op.cc
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@ -23,46 +23,54 @@ class DecayedAdagradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(Param) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
"Input(Grad) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Moment"),
"Input(Moment) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("LearningRate"),
"Input(LearningRate) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Grad").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
"Output(MomentOut) of DecayedAdagradOp should not be null.");
OP_INOUT_CHECK(ctx->HasInput("Param"), "Input", "Param",
"DecayedAdagradOp");
OP_INOUT_CHECK(ctx->HasInput("Grad"), "Input", "Grad", "DecayedAdagradOp");
OP_INOUT_CHECK(ctx->HasInput("Moment"), "Input", "Moment",
"DecayedAdagradOp");
OP_INOUT_CHECK(ctx->HasInput("LearningRate"), "Input", "LearningRate",
"DecayedAdagradOp");
PADDLE_ENFORCE_EQ(
ctx->GetInputsVarType("Param").front(),
framework::proto::VarType::LOD_TENSOR,
platform::errors::InvalidArgument(
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(),
ctx->GetInputsVarType("Param").front()));
PADDLE_ENFORCE_EQ(
ctx->GetInputsVarType("Grad").front(),
framework::proto::VarType::LOD_TENSOR,
platform::errors::InvalidArgument(
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Grad").front(),
ctx->GetInputsVarType("Grad").front()));
OP_INOUT_CHECK(ctx->HasOutput("ParamOut"), "Output", "ParamOut",
"DecayedAdagradOp");
OP_INOUT_CHECK(ctx->HasOutput("MomentOut"), "Output", "MomentOut",
"DecayedAdagradOp");
auto lr_dims = ctx->GetInputDim("LearningRate");
PADDLE_ENFORCE_NE(framework::product(lr_dims), 0,
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function.");
platform::errors::InvalidArgument(
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."));
PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
"LearningRate should have one element");
platform::errors::InvalidArgument(
"LearningRate should have one element"));
auto param_dims = ctx->GetInputDim("Param");
PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Grad"),
"Param and Grad input of DecayedAdagradOp should have "
"the same dimension.");
PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Moment"),
"Param and Moment input of DecayedAdagradOp should have "
"the same dimension.");
PADDLE_ENFORCE_EQ(
param_dims, ctx->GetInputDim("Grad"),
platform::errors::InvalidArgument(
"Param and Grad input of DecayedAdagradOp should have "
"the same dimension."));
PADDLE_ENFORCE_EQ(
param_dims, ctx->GetInputDim("Moment"),
platform::errors::InvalidArgument(
"Param and Moment input of DecayedAdagradOp should have "
"the same dimension."));
ctx->SetOutputDim("ParamOut", param_dims);
ctx->SetOutputDim("MomentOut", param_dims);

22
paddle/fluid/operators/optimizers/decayed_adagrad_op.h
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@ -24,17 +24,19 @@ class DecayedAdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.InputNames("Param").front(),
framework::ToTypeName(param_var->Type()));
PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(), true,
platform::errors::InvalidArgument(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.InputNames("Param").front(),
framework::ToTypeName(param_var->Type())));
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.InputNames("Grad").front(),
framework::ToTypeName(grad_var->Type()));
PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
platform::errors::InvalidArgument(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.InputNames("Grad").front(),
framework::ToTypeName(grad_var->Type())));
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");

7
paddle/fluid/operators/optimizers/lars_momentum_op.h
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@ -30,7 +30,12 @@ class LarsMomentumOpKernel : public framework::OpKernel<T> {
auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
auto* grad_var = ctx.InputVar("Grad");
// only support dense for now.
PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true);
PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
platform::errors::InvalidArgument(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.InputNames("Grad").front(),
framework::ToTypeName(grad_var->Type())));
auto grad = ctx.Input<framework::LoDTensor>("Grad");
param_out->mutable_data<T>(ctx.GetPlace());

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