From ca41e552507b87cd202ceaecac79fe6063d38383 Mon Sep 17 00:00:00 2001 From: lilong12 Date: Fri, 15 May 2020 12:06:16 +0800 Subject: [PATCH] [Cherry-pick 1.8] Improving error reporting messages for ops #24438 (#24534) * improve error reporting messages --- paddle/fluid/operators/crop_op.cc | 24 ++-- paddle/fluid/operators/crop_op.h | 55 +++++++-- paddle/fluid/operators/crop_tensor_op.cc | 51 ++++---- paddle/fluid/operators/crop_tensor_op.h | 110 +++++++++++++----- paddle/fluid/operators/expand_op.cc | 48 +++++--- paddle/fluid/operators/expand_op.h | 60 +++++++--- .../fluid/operators/merge_selected_rows_op.cc | 22 ++-- paddle/fluid/operators/shard_index_op.cc | 16 ++- paddle/fluid/operators/shard_index_op.cu | 27 ++++- paddle/fluid/operators/shard_index_op.h | 40 +++++-- python/paddle/fluid/layers/nn.py | 7 +- .../fluid/tests/unittests/test_layers.py | 8 ++ 12 files changed, 324 insertions(+), 144 deletions(-) diff --git a/paddle/fluid/operators/crop_op.cc b/paddle/fluid/operators/crop_op.cc index d0b5e510457..c73b421ae4f 100644 --- a/paddle/fluid/operators/crop_op.cc +++ b/paddle/fluid/operators/crop_op.cc @@ -27,16 +27,18 @@ class CropOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of CropOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of CropOp should not be null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Crop"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Crop"); auto x_dim = ctx->GetInputDim("X"); if (!ctx->HasInput("Y")) { auto shape = ctx->Attrs().Get>("shape"); PADDLE_ENFORCE_EQ( int64_t(shape.size()), x_dim.size(), - "Shape size should be equal to dimension size of input tensor."); + platform::errors::InvalidArgument( + "The number of elements (%d) of CropOp's " + "'shape' attribute should be equal to the number of dimensions " + "(%d) of the Input(X).", + shape.size(), x_dim.size())); std::vector tensor_shape(shape.size()); for (size_t i = 0; i < shape.size(); ++i) { tensor_shape[i] = static_cast(shape[i]); @@ -45,8 +47,10 @@ class CropOp : public framework::OperatorWithKernel { } else { auto y_dim = ctx->GetInputDim("Y"); PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y_dim), - "Tensor rank of both CropOp's " - "inputs must be same."); + platform::errors::InvalidArgument( + "The number of dimensions (%d) of CropOp's input(X)" + " must be equal to that (%d) of input(Y).", + framework::arity(x_dim), framework::arity(y_dim))); ctx->SetOutputDim("Out", y_dim); } } @@ -163,9 +167,9 @@ class CropOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), - "Input(Out@GRAD) should not be null"); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CropGrad"); + OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input", + framework::GradVarName("Out"), "CropGrad"); auto x_dims = ctx->GetInputDim("X"); auto x_grad_name = framework::GradVarName("X"); if (ctx->HasOutput(x_grad_name)) { diff --git a/paddle/fluid/operators/crop_op.h b/paddle/fluid/operators/crop_op.h index cfc2cac7beb..0338495096a 100644 --- a/paddle/fluid/operators/crop_op.h +++ b/paddle/fluid/operators/crop_op.h @@ -31,14 +31,23 @@ static std::vector GetOffsets(const framework::ExecutionContext& ctx) { std::vector res; int rank = ctx.Input("X")->dims().size(); if (ctx.HasInput("Offsets")) { - PADDLE_ENFORCE(ctx.Attr>("offsets").empty(), - "Input 'Offsets' and attribute 'offsets' should not be used " - "at the same time."); + PADDLE_ENFORCE_EQ(ctx.Attr>("offsets").empty(), true, + platform::errors::InvalidArgument( + "Input 'Offsets' and attribute 'offsets' " + "should not be used at the same time for CropOp.")); const auto* offsets_tensor = ctx.Input("Offsets"); - PADDLE_ENFORCE_EQ(offsets_tensor->dims().size(), 1); + PADDLE_ENFORCE_EQ(offsets_tensor->dims().size(), 1, + platform::errors::InvalidArgument( + "The number of dimensions of input 'Offsets' for " + "CropOp must be 1, but the value received is %d.", + offsets_tensor->dims().size())); PADDLE_ENFORCE_EQ( rank, offsets_tensor->dims()[0], - "Offsets size should be equal to dimension size of input tensor."); + platform::errors::InvalidArgument("The number of elements (%d) for " + "input 'Offsets' must be equal to " + "the number of dimensions (%d) " + "of the input tensor.", + offsets_tensor->dims()[0], rank)); const int* offsets_data; framework::Tensor cpu_tmp_tensor; if (platform::is_cpu_place(offsets_tensor->place())) { @@ -53,7 +62,11 @@ static std::vector GetOffsets(const framework::ExecutionContext& ctx) { res = ctx.Attr>("offsets"); PADDLE_ENFORCE_EQ( rank, static_cast(res.size()), - "Offsets size should be equal to dimension size of input tensor."); + platform::errors::InvalidArgument("The number of elements (%d) for " + "input 'Offsets' must be equal to " + "the number of dimensions (%d) " + "of the input tensor.", + res.size(), rank)); } return res; } @@ -92,6 +105,18 @@ class CropKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { int rank = context.Input("X")->dims().size(); + PADDLE_ENFORCE_GE( + rank, 1, + platform::errors::InvalidArgument( + "The number of dimensions of the Input(X) for CropOp must be " + "greater than or equal to 1, but the value received is %d.", + rank)); + PADDLE_ENFORCE_LE( + rank, 6, + platform::errors::InvalidArgument( + "The number of dimensions of the Input(X) for CropOp must be " + "less than or equal to 6, but the value received is %d.", + rank)); switch (rank) { case 1: CropFunction(context); @@ -111,9 +136,6 @@ class CropKernel : public framework::OpKernel { case 6: CropFunction(context); break; - default: - PADDLE_THROW( - "CropOp only support tensors with no more than 6 dimensions."); } } }; @@ -145,6 +167,18 @@ class CropGradKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { size_t rank = context.Input(framework::GradVarName("Out"))->dims().size(); + PADDLE_ENFORCE_GE( + rank, 1, platform::errors::InvalidArgument( + "The number of dimensions of the input 'Out@GRAD' for " + "CropGrad must be greater than or equal " + "to 1, but the value received is %d.", + rank)); + PADDLE_ENFORCE_LE( + rank, 6, platform::errors::InvalidArgument( + "The number of dimensions of the input 'Out@GRAD' for " + "CropGrad must be less than or equal " + "to 6, but the value received is %d.", + rank)); switch (rank) { case 1: CropGradFunction(context); @@ -164,9 +198,6 @@ class CropGradKernel : public framework::OpKernel { case 6: CropGradFunction(context); break; - default: - PADDLE_THROW( - "CropOp only support tensors with no more than 6 dimensions."); } } }; diff --git a/paddle/fluid/operators/crop_tensor_op.cc b/paddle/fluid/operators/crop_tensor_op.cc index 336f991f754..514333c57f5 100644 --- a/paddle/fluid/operators/crop_tensor_op.cc +++ b/paddle/fluid/operators/crop_tensor_op.cc @@ -27,10 +27,8 @@ class CropTensorOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext *ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, - "Input(X) of Op(crop_tensor) should not be null."); - PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true, - "Output(Out) of Op(crop_tensor) should not be null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CropTensor"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "CropTensor"); auto x_dim = ctx->GetInputDim("X"); auto shape = ctx->Attrs().Get>("shape"); auto offsets = ctx->Attrs().Get>("offsets"); @@ -39,9 +37,11 @@ class CropTensorOp : public framework::OperatorWithKernel { auto inputs_name = ctx->Inputs("ShapeTensor"); PADDLE_ENFORCE_GT( inputs_name.size(), 0, - "Input(ShapeTensor)'size of Op(crop_tensor) can't be zero. " - "Please check the Attr(shape)'s size of " - "Op(fluid.layers.crop_tensor)."); + platform::errors::InvalidArgument( + "The number of elements of the input 'ShapeTensor' for " + "CropTensor must be greater than zero, " + "but the value received is %d.", + inputs_name.size())); auto out_dims = std::vector(inputs_name.size(), -1); for (size_t i = 0; i < shape.size(); ++i) { if (shape[i] > 0) { @@ -59,16 +59,18 @@ class CropTensorOp : public framework::OperatorWithKernel { if (ctx->HasInput("Shape")) { auto shape_dim = ctx->GetInputDim("Shape"); - PADDLE_ENFORCE_EQ( - shape_dim.size(), 1, - "Input(Shape)'s dimension size of Op(crop_tensor) must be 1. " - "Please check the Attr(shape)'s dimension size of " - "Op(fluid.layers.crop_tensor)."); + PADDLE_ENFORCE_EQ(shape_dim.size(), 1, + platform::errors::InvalidArgument( + "The number of dimensions of the input " + "'Shape' for CropTensor must be 1, " + "but the value received is %d.", + shape_dim.size())); PADDLE_ENFORCE_EQ(shape_dim[0], x_dim.size(), - "Input(Shape)'s size of Op(crop_tensor) must be equal " - "to dimension size of input tensor. " - "Please check the Attr(shape)'s size of " - "Op(fluid.layers.crop_tensor)."); + platform::errors::InvalidArgument( + "The number of elements (%d) of the input 'Shape' " + "for CropTensor must be equal to the number of" + " dimensions (%d) of the input.", + shape_dim[0], x_dim.size())); if (ctx->IsRuntime()) { // If true, set the shape of Output(Out) according to Input(Shape) in // CropTensorKernel with ExecutionContext. Also check LoD in @@ -80,9 +82,13 @@ class CropTensorOp : public framework::OperatorWithKernel { } return; } - PADDLE_ENFORCE_EQ(int64_t(shape.size()), x_dim.size(), - "Attr(shape)'size of Op(crop_tensor) should be equal to " - "dimension size of input tensor."); + PADDLE_ENFORCE_EQ( + int64_t(shape.size()), x_dim.size(), + platform::errors::InvalidArgument( + "The number of elements (%d) of attribute 'shape' for " + "CropTensor must be equal to the number of " + "dimensions (%d) of the input.", + shape.size(), x_dim.size())); std::vector out_shape(shape.size(), -1); for (size_t i = 0; i < shape.size(); ++i) { if (shape[i] > 0) { @@ -242,10 +248,9 @@ class CropTensorOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext *ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, - "Input(X) of Op(crop_tensor) should not be null."); - PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true, - "Input(Out@GRAD) of Op(crop_tensor) should not be null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CropTensorGrad"); + OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input", + framework::GradVarName("Out"), "CropTensorGrad"); auto x_dims = ctx->GetInputDim("X"); auto x_grad_name = framework::GradVarName("X"); if (ctx->HasOutput(x_grad_name)) { diff --git a/paddle/fluid/operators/crop_tensor_op.h b/paddle/fluid/operators/crop_tensor_op.h index 4c6b70d889a..7fbcd52715f 100644 --- a/paddle/fluid/operators/crop_tensor_op.h +++ b/paddle/fluid/operators/crop_tensor_op.h @@ -35,7 +35,10 @@ inline std::vector get_new_data( auto tensor = list_new_tensor[i]; PADDLE_ENFORCE_EQ( tensor->dims(), framework::make_ddim({1}), - "The tensor's shape in list of Op(crop_tensor) should be [1]."); + platform::errors::InvalidArgument( + "The tensor's shape in list of Op(crop_tensor) should be [1], " + "but the value received is %d.", + tensor->dims())); if (platform::is_gpu_place(tensor->place())) { framework::Tensor temp; TensorCopySync(*tensor, platform::CPUPlace(), &temp); @@ -56,18 +59,23 @@ static framework::DDim ValidateShape(const std::vector shape, auto shape_size = shape.size(); PADDLE_ENFORCE_EQ( in_dim_size, shape_size, - "Attr(shape)'s size of Op(crop_tensor) should be equal " - "to that of input Tensor. " - "Please check the Attr(shape)'s size of Op(fluid.layers.crop_tensor)."); + platform::errors::InvalidArgument( + "The number of elements (%d) for shape of Op(crop_tensor) should be " + "equal to the number of dimensions (%d) of the input tensor.", + shape_size, in_dim_size)); std::vector output_shape(shape.size(), 0); for (size_t i = 0; i < shape.size(); ++i) { if (shape[i] <= 0 && in_dims[i] > 0) { - PADDLE_ENFORCE_NE( - shape[i], 0, - "The element in Attr(shape) of Op(crop_tensor) should not be zero."); - PADDLE_ENFORCE_EQ(shape[i], -1, - "When the element in Attr(shape) of Op(crop_tensor) is " - "negative, only -1 is supported."); + PADDLE_ENFORCE_NE(shape[i], 0, + platform::errors::InvalidArgument( + "The value (%d) of the %uth element for shape of " + "Op(crop_tensor) should not be zero.", + shape[i], i)); + PADDLE_ENFORCE_EQ(shape[i], -1, platform::errors::InvalidArgument( + "When the value (%d) of the %uth " + "element for shape of Op(crop_tensor)" + " is negative, only -1 is supported.", + shape[i], i)); output_shape[i] = in_dims[i] - offsets[i]; } else { output_shape[i] = static_cast(shape[i]); @@ -83,9 +91,13 @@ static std::vector GetShape(const framework::ExecutionContext& ctx) { auto list_new_shape_tensor = ctx.MultiInput("ShapeTensor"); if (list_new_shape_tensor.size() > 0) { // have offsets tensor list - PADDLE_ENFORCE_EQ(list_new_shape_tensor.size(), rank, - "Input(ShapeTensor)'s length of Op(crop_tensor) should " - "be equal to dimension size of input tensor."); + PADDLE_ENFORCE_EQ( + list_new_shape_tensor.size(), rank, + platform::errors::InvalidArgument( + "The number of tensors (%d) for the input ShapeTensor of " + "Op(crop_tensor) must be equal to the number of " + "dimensions (%d) of the input.", + list_new_shape_tensor.size(), rank)); res = get_new_data(list_new_shape_tensor); return res; @@ -122,13 +134,21 @@ static std::vector GetOffsets(const framework::ExecutionContext& ctx) { if (ctx.HasInput("Offsets")) { PADDLE_ENFORCE_EQ( ctx.Attr>("offsets").empty(), true, - "Input 'Offsets' and attribute 'offsets' should not be used " - "at the same time."); + platform::errors::InvalidArgument( + "Input 'Offsets' and attribute 'offsets' for Op(crop_tensor) " + "cannot be used at the same time.")); const auto* offsets_tensor = ctx.Input("Offsets"); - PADDLE_ENFORCE_EQ(offsets_tensor->dims().size(), 1); - PADDLE_ENFORCE_EQ( - rank, offsets_tensor->dims()[0], - "Offsets size should be equal to dimension size of input tensor."); + PADDLE_ENFORCE_EQ(offsets_tensor->dims().size(), 1, + platform::errors::InvalidArgument( + "The number of dimensions of input 'Offsets' must " + "be 1, but the value received is: %d.", + offsets_tensor->dims().size())); + PADDLE_ENFORCE_EQ(rank, offsets_tensor->dims()[0], + platform::errors::InvalidArgument( + "The number of elements (%d) for " + "input 'Offsets' must be equal to " + "the number of dimensions (%d) of the input tensor.", + offsets_tensor->dims()[0], rank)); const int* offsets_data; framework::Tensor cpu_tmp_tensor; if (platform::is_cpu_place(offsets_tensor->place())) { @@ -143,7 +163,11 @@ static std::vector GetOffsets(const framework::ExecutionContext& ctx) { res = ctx.Attr>("offsets"); PADDLE_ENFORCE_EQ( rank, static_cast(res.size()), - "Offsets size should be equal to dimension size of input tensor."); + platform::errors::InvalidArgument("The number of elements (%d) for " + "input 'Offsets' must be equal to " + "the number of dimensions (%d) " + "of the input tensor.", + static_cast(res.size()), rank)); } return res; } @@ -168,10 +192,13 @@ void CropTensorFunction(const framework::ExecutionContext& context) { out_dims = ValidateShape(shape, offsets, x->dims()); out->mutable_data(out_dims, context.GetPlace()); for (size_t i = 0; i < offsets.size(); ++i) { - PADDLE_ENFORCE_LE( - offsets[i] + shape[i], x_dims[i], - "The sum of the Attr(offsets) and Attr(shape) of Op(crop_tensor) " - "should be less than or equal to corresponding input dimension size."); + PADDLE_ENFORCE_LE(offsets[i] + shape[i], x_dims[i], + platform::errors::InvalidArgument( + "The sum of the %uth elements of " + "offsets (%d) and shape (%d) of Op(crop_tensor) " + "should be less than or " + "equal to the size of %uth dimension of the input.", + i, offsets[i], shape[i], i)); } auto x_tensor = EigenTensor::From(*x); @@ -192,6 +219,19 @@ class CropTensorKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { int rank = context.Input("X")->dims().size(); + PADDLE_ENFORCE_GE( + rank, 1, + platform::errors::InvalidArgument( + "The number of dimensions of the input 'x' for " + "Op(crop_tensor) must be greater than or equal to 1, but the " + "value received is %d.", + rank)); + PADDLE_ENFORCE_LE( + rank, 6, platform::errors::InvalidArgument( + "The number of dimensions of the input 'x' for " + "Op(crop_tensor) must be less than or equal to 6, but the " + "value received is %d.", + rank)); switch (rank) { case 1: CropTensorFunction(context); @@ -211,10 +251,6 @@ class CropTensorKernel : public framework::OpKernel { case 6: CropTensorFunction(context); break; - default: - PADDLE_THROW( - "CropTensorOp only support tensors with no more than 6 " - "dimensions."); } } }; @@ -246,6 +282,20 @@ class CropTensorGradKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { size_t rank = context.Input(framework::GradVarName("Out"))->dims().size(); + PADDLE_ENFORCE_GE( + rank, 1, + platform::errors::InvalidArgument( + "The number of dimensions of the input 'Out@GRAD' for " + "Op(crop_tensor_grad) must be greater than or equal to 1, but the " + "value received is %d.", + rank)); + PADDLE_ENFORCE_LE( + rank, 6, + platform::errors::InvalidArgument( + "The number of dimensions of the input 'Out@GRAD' for " + "Op(crop_tensor_grad) must be less than or equal to 6, but the " + "value received is %d.", + rank)); switch (rank) { case 1: CropTensorGradFunction(context); @@ -265,10 +315,6 @@ class CropTensorGradKernel : public framework::OpKernel { case 6: CropTensorGradFunction(context); break; - default: - PADDLE_THROW( - "CropTensorOp only support tensors with no more than 6 " - "dimensions."); } } }; diff --git a/paddle/fluid/operators/expand_op.cc b/paddle/fluid/operators/expand_op.cc index e86fd06c6f3..3c898ac29f0 100644 --- a/paddle/fluid/operators/expand_op.cc +++ b/paddle/fluid/operators/expand_op.cc @@ -28,9 +28,8 @@ class ExpandOp : public framework::OperatorWithKernel { protected: void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) should not be null."); - PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true, - "Output(Out) should not be null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Expand"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Expand"); auto x_dims = ctx->GetInputDim("X"); auto expand_times = ctx->Attrs().Get>("expand_times"); @@ -38,11 +37,19 @@ class ExpandOp : public framework::OperatorWithKernel { expand_times = std::vector(x_dims.size(), -1); } - PADDLE_ENFORCE_EQ(static_cast(x_dims.size()), expand_times.size(), - "The number of Attr(expand_times)'s value must be equal " - "to the rank of Input(X)."); - PADDLE_ENFORCE_LE(x_dims.size(), 6, - "The rank of Input(X) must not be greater than 6."); + PADDLE_ENFORCE_EQ( + static_cast(x_dims.size()), expand_times.size(), + platform::errors::InvalidArgument( + "The number of elements (%d) of 'expand_times' for " + "Op(expand) must be equal to the number of dimensions " + "(%d) of the input.", + expand_times.size(), static_cast(x_dims.size()))); + PADDLE_ENFORCE_LE( + x_dims.size(), 6, + platform::errors::InvalidArgument( + "The number of dimensions of the input for Op(expand) " + "must not be greater than 6, but the value received is %d.", + x_dims.size())); std::vector out_shape(x_dims.size()); for (size_t i = 0; i < expand_times.size(); ++i) { @@ -51,7 +58,10 @@ class ExpandOp : public framework::OperatorWithKernel { } else { PADDLE_ENFORCE_GT( expand_times[i], 0, - "The element of Attr(expand_times) must greater than 0."); + platform::errors::InvalidArgument( + "The %uth element of 'expand_times' for Op(expand) must be " + "greater than 0, but the value given is %d.", + i, expand_times[i])); out_shape[i] = x_dims[i] * expand_times[i]; } } @@ -139,9 +149,9 @@ class ExpandGradOp : public framework::OperatorWithKernel { protected: void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) should not be null."); - PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true, - "Input(Out@GRAD) should not be null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ExpandGrad"); + OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input", + framework::GradVarName("Out"), "ExpandGrad"); auto x_dims = ctx->GetInputDim("X"); std::vector expand_times = @@ -153,8 +163,10 @@ class ExpandGradOp : public framework::OperatorWithKernel { if (!ctx->IsRuntime() && x_dims[0] < 0) { PADDLE_ENFORCE_EQ( x_dims[0], out_dims[0], - "The first dimension size of Input(Out@GRAD) should be " - "equal to the crroresponding dimension size of Input(X)"); + platform::errors::InvalidArgument( + "The first dimension size (%d) of Input(Out@GRAD) should be " + "equal to the crroresponding dimension size (%d) of Input(X)", + out_dims[0], x_dims[0])); start_pos = 1u; } @@ -165,9 +177,11 @@ class ExpandGradOp : public framework::OperatorWithKernel { if (ctx->IsRuntime()) { PADDLE_ENFORCE_EQ( x_dims[i] * expand_times[i], out_dims[i], - "Each dimension size of Input(Out@GRAD) should be " - "equal to multiplication of crroresponding dimension " - "size of Input(X) and Attr(expand_times) value."); + platform::errors::InvalidArgument( + "The %uth dimension size (%d) of Input(Out@GRAD) should be " + "equal to the multiplication of the crroresponding dimension " + "sizes of Input(X) (%d) and expand_times (%d).", + i, out_dims[i], x_dims[i], expand_times[i])); } } } diff --git a/paddle/fluid/operators/expand_op.h b/paddle/fluid/operators/expand_op.h index 4f167468ebd..7ba9e98e1fb 100644 --- a/paddle/fluid/operators/expand_op.h +++ b/paddle/fluid/operators/expand_op.h @@ -96,12 +96,19 @@ class ExpandKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto rank = context.Input("X")->dims().size(); - switch (rank) { - REP_EXPAND_TEMPLATE(MAX_RANK_SUPPORTED) - default: - PADDLE_ENFORCE(false, - "Only support tensor with rank being between 1 and 6."); - } + PADDLE_ENFORCE_GE( + rank, 1, + platform::errors::InvalidArgument( + "The number of dimensions of the input 'x' for Op(expand) " + "must be greater than or equal to 1, but the value received is %d.", + rank)); + PADDLE_ENFORCE_LE( + rank, MAX_RANK_SUPPORTED, + platform::errors::InvalidArgument( + "The number of dimensions of the input 'x' for Op(expand) " + "must be less than or equal to %d, but the value received is %d.", + MAX_RANK_SUPPORTED, rank)); + switch (rank) { REP_EXPAND_TEMPLATE(MAX_RANK_SUPPORTED) } } protected: @@ -111,9 +118,13 @@ class ExpandKernel : public framework::OpKernel { auto in_dims = in0->dims(); auto expand_times = get_expand_times(context); - PADDLE_ENFORCE_EQ(static_cast(in_dims.size()), expand_times.size(), - "The number of Attr(expand_times)'s value must be equal " - "to the rank of Input(X)."); + PADDLE_ENFORCE_EQ( + static_cast(in_dims.size()), expand_times.size(), + platform::errors::InvalidArgument( + "The number of elements (%d) of 'expand_times' for " + "Op(expand) must be equal to the number " + "of dimensions (%d) of the input.", + expand_times.size(), static_cast(in_dims.size()))); auto* out0 = context.Output("Out"); Eigen::DSizes bcast_dims; for (size_t i = 0; i < expand_times.size(); ++i) { @@ -172,12 +183,19 @@ class ExpandGradKernel : public framework::OpKernel { framework::TensorCopy(*in0, context.GetPlace(), context.device_context(), out0); } else { - switch (dims) { - REP_EXPAND_GRAD_TEMPLATE(MAX_RANK_SUPPORTED) - default: - PADDLE_ENFORCE( - false, "Only support tensor with rank being between 1 and 6."); - } + PADDLE_ENFORCE_GE(dims, 1, platform::errors::InvalidArgument( + "The number of dimensions of the input " + "'Out@GRAD' for Op(expand_grad)" + " must be greater than or equal to 1, but " + "the value received is %d.", + dims)); + PADDLE_ENFORCE_LE(dims, MAX_RANK_SUPPORTED, + platform::errors::InvalidArgument( + "The number of dimensions of the input 'Out@GRAD' " + "for Op(expand_grad) must be less than or equal " + "to %d, but the value received is %d.", + MAX_RANK_SUPPORTED, dims)); + switch (dims) { REP_EXPAND_GRAD_TEMPLATE(MAX_RANK_SUPPORTED) } } } @@ -189,11 +207,15 @@ class ExpandGradKernel : public framework::OpKernel { size_t reshape_size = reshape_dims_vec.size(); size_t reduce_size = reduce_dims_vec.size(); PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(), - "Inconsistent size between template Dims and " - "reshape dimensions."); + platform::errors::InvalidArgument( + "Inconsistent size between template Dims (%d) and " + "reshape dimensions (%d).", + reshape_size, reshape_dims_vec.size())); PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(), - "Inconsistent size between template Dims and " - "reduce dimensions."); + platform::errors::InvalidArgument( + "Inconsistent size between template Dims (%d) and " + "reduce dimensions (%d).", + reduce_size, reduce_dims_vec.size())); auto* in0 = context.Input(framework::GradVarName("Out")); auto* out0 = context.Output(framework::GradVarName("X")); out0->mutable_data(context.GetPlace()); diff --git a/paddle/fluid/operators/merge_selected_rows_op.cc b/paddle/fluid/operators/merge_selected_rows_op.cc index e758c2bb654..ea223ad1b32 100644 --- a/paddle/fluid/operators/merge_selected_rows_op.cc +++ b/paddle/fluid/operators/merge_selected_rows_op.cc @@ -23,16 +23,18 @@ class MergeSelectedRowsOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of MergeSelectedRowsOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of MergeSelectedRowsOp should not be null."); - PADDLE_ENFORCE_EQ(ctx->GetInputsVarType("X").front(), - framework::proto::VarType::SELECTED_ROWS, - "Input X only should be SelectedRows."); - PADDLE_ENFORCE_EQ(ctx->GetOutputsVarType("Out").front(), - framework::proto::VarType::SELECTED_ROWS, - "Output Y only should be SelectedRows."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "MergeSelectedRows"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "MergeSelectedRows"); + PADDLE_ENFORCE_EQ( + ctx->GetInputsVarType("X").front(), + framework::proto::VarType::SELECTED_ROWS, + platform::errors::InvalidArgument("Input(X) of MergeSelectedRowsOp " + "should be of type SelectedRows.")); + PADDLE_ENFORCE_EQ( + ctx->GetOutputsVarType("Out").front(), + framework::proto::VarType::SELECTED_ROWS, + platform::errors::InvalidArgument("Output(Out) of MergeSelectedRowsOp " + "should be of type SelectedRows.")); ctx->ShareDim("X", /*->*/ "Out"); } diff --git a/paddle/fluid/operators/shard_index_op.cc b/paddle/fluid/operators/shard_index_op.cc index 3c1de753acf..3e5e2ad3d8c 100644 --- a/paddle/fluid/operators/shard_index_op.cc +++ b/paddle/fluid/operators/shard_index_op.cc @@ -21,17 +21,21 @@ class ShardIndexOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of ShardIndexOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of ShardIndexOp should not be null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ShardIndex"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ShardIndex"); auto x_dims = ctx->GetInputDim("X"); PADDLE_ENFORCE_GE(x_dims.size(), 2, - "Rank of Input(X) should be at least 2."); + platform::errors::InvalidArgument( + "Rank of Input(X) should be at least 2, " + "but the value given is %d.", + x_dims.size())); if (ctx->IsRuntime() || x_dims[x_dims.size() - 1] > 0) { PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U, - "Last dimension of Input(X) should be 1."); + platform::errors::InvalidArgument( + "The last dimension of Input(X) should be 1, " + "but the value given is %d.", + x_dims[x_dims.size() - 1])); } ctx->SetOutputDim("Out", x_dims); diff --git a/paddle/fluid/operators/shard_index_op.cu b/paddle/fluid/operators/shard_index_op.cu index db29b73f9ea..f2800c60c33 100644 --- a/paddle/fluid/operators/shard_index_op.cu +++ b/paddle/fluid/operators/shard_index_op.cu @@ -50,10 +50,29 @@ class ShardIndexCUDAKernel : public framework::OpKernel { int nshards = context.Attr("nshards"); int shard_id = context.Attr("shard_id"); int ignore_value = context.Attr("ignore_value"); - PADDLE_ENFORCE_GT(index_num, 0); - PADDLE_ENFORCE_GT(nshards, 0); - PADDLE_ENFORCE(shard_id >= 0 && shard_id < nshards, - "shard_id(%d) is not in range [0, %d)", shard_id, nshards); + PADDLE_ENFORCE_GT( + index_num, 0, + platform::errors::InvalidArgument( + "The value 'index_num' for Op(shard_index) must be greater than 0, " + "but the value given is %d.", + index_num)); + PADDLE_ENFORCE_GT(nshards, 0, + platform::errors::InvalidArgument( + "The value 'nshard' for Op(shard_index) must be " + "greater than 0, but the value given is %d.", + nshards)); + PADDLE_ENFORCE_GE( + shard_id, 0, + platform::errors::InvalidArgument( + "The value 'shard_id' for Op(shard_index) must be greater or " + "equal to 0, but the value given is %d.", + shard_id)); + PADDLE_ENFORCE_LT( + shard_id, nshards, + platform::errors::InvalidArgument( + "The value 'shard_id' for Op(shard_index) must be less than " + "nshards (%d), but the value given is %d.", + nshards, shard_id)); out->Resize(in->dims()); out->set_lod(in->lod()); diff --git a/paddle/fluid/operators/shard_index_op.h b/paddle/fluid/operators/shard_index_op.h index f943de586bc..c2fe3711686 100644 --- a/paddle/fluid/operators/shard_index_op.h +++ b/paddle/fluid/operators/shard_index_op.h @@ -29,10 +29,29 @@ class ShardIndexCPUKernel : public framework::OpKernel { int nshards = context.Attr("nshards"); int shard_id = context.Attr("shard_id"); int ignore_value = context.Attr("ignore_value"); - PADDLE_ENFORCE_GT(index_num, 0); - PADDLE_ENFORCE_GT(nshards, 0); - PADDLE_ENFORCE(shard_id >= 0 && shard_id < nshards, - "shard_id(%d) is not in range [0, %d)", shard_id, nshards); + PADDLE_ENFORCE_GT( + index_num, 0, + platform::errors::InvalidArgument( + "The value 'index_num' for Op(shard_index) must be greater than 0, " + "but the value given is %d.", + index_num)); + PADDLE_ENFORCE_GT(nshards, 0, + platform::errors::InvalidArgument( + "The value 'nshard' for Op(shard_index) must be " + "greater than 0, but the value given is %d.", + nshards)); + PADDLE_ENFORCE_GE( + shard_id, 0, + platform::errors::InvalidArgument( + "The value 'shard_id' for Op(shard_index) must be greater or " + "equal to 0, but the value given is %d.", + shard_id)); + PADDLE_ENFORCE_LT( + shard_id, nshards, + platform::errors::InvalidArgument( + "The value 'shard_id' for Op(shard_index) must be less than " + "nshards (%d), but the value given is %d.", + nshards, shard_id)); int shard_size = (index_num + nshards - 1) / nshards; @@ -42,9 +61,16 @@ class ShardIndexCPUKernel : public framework::OpKernel { auto* out_data = out->mutable_data(context.GetPlace()); int64_t numel = in->numel(); for (int64_t i = 0; i < numel; ++i) { - PADDLE_ENFORCE(in_data[i] >= 0 && in_data[i] < index_num, - "Input index(%d) is out of range [0,%d)", in_data[i], - index_num); + PADDLE_ENFORCE_GE(in_data[i], 0, + platform::errors::InvalidArgument( + "The input_index for Op(shard_index) must be " + "greater or equal to 0, but the value given is %d.", + in_data[i])); + PADDLE_ENFORCE_LT(in_data[i], index_num, + platform::errors::InvalidArgument( + "The input_index for Op(shard_index) must be less " + "than index_num (%d), but the value given is %d.", + index_num, in_data[i])); if (in_data[i] / shard_size == shard_id) { out_data[i] = in_data[i] % shard_size; } else { diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 5a71320e584..8c60bd584bc 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -9913,12 +9913,10 @@ def crop(x, shape=None, offsets=None, name=None): crop = fluid.layers.crop(z, shape=[2, 2, 3]) """ + check_variable_and_dtype(x, 'x', ['float32'], 'crop') + check_type(shape, 'shape', (list, tuple, Variable), 'crop') helper = LayerHelper('crop', **locals()) - if not (isinstance(shape, list) or isinstance(shape, tuple) or \ - isinstance(shape, Variable)): - raise ValueError("The shape should be a list, tuple or Variable.") - if offsets is None: offsets = [0] * len(x.shape) @@ -15580,6 +15578,7 @@ def shard_index(input, index_num, nshards, shard_id, ignore_value=-1): nshards=2, shard_id=0) """ + check_variable_and_dtype(input, 'input', ['int64'], 'shard_index') op_type = 'shard_index' helper = LayerHelper(op_type, **locals()) if shard_id < 0 or shard_id >= nshards: diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index 18624c3b695..ff95dd7cf83 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/python/paddle/fluid/tests/unittests/test_layers.py @@ -1840,6 +1840,14 @@ class TestLayer(LayerTest): self.assertIsNotNone(out2) self.assertIsNotNone(out3) + def test_shard_index(self): + with self.static_graph(): + x = fluid.layers.data(name="label", shape=[4, 1], dtype='int64') + shard_label = fluid.layers.shard_index( + input=x, index_num=20, nshards=2, shard_id=0) + + self.assertIsNotNone(shard_label) + def test_accuracy(self): x = np.random.rand(3, 32, 32).astype("float32") y = np.array([[1], [0], [1]]) -- GitLab