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Replace framework::proto::VarType part #64388

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May 20, 2024
Merged

Replace framework::proto::VarType part #64388

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0a23b84
Fix
co63oc May 16, 2024
06cdc9c
Fix
co63oc May 16, 2024
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6 changes: 2 additions & 4 deletions paddle/fluid/operators/fused/onednn/fusion_lstm_onednn_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -398,15 +398,13 @@ class FusionLSTMMKLDNNKernel : public framework::OpKernel<T> {
std::shared_ptr<dnnl::memory> h0_memory_p, weight_h_memory_p,
weight_x_memory_p;

if (framework::TransToProtoVarType(weight_h->dtype()) ==
paddle::framework::proto::VarType_Type_FP32) {
if (weight_h->dtype() == phi::DataType::FLOAT32) {
h0_memory_p = handler.template AcquireH0Memory<float>(h0);
weight_x_memory_p =
handler.template AcquireWeightXMemory<float>(weight_x);
weight_h_memory_p =
handler.template AcquireWeightHMemory<float>(weight_h);
} else if (framework::TransToProtoVarType(weight_h->dtype()) ==
paddle::framework::proto::VarType_Type_BF16) {
} else if (weight_h->dtype() == phi::DataType::BFLOAT16) {
h0_memory_p = handler.template AcquireH0Memory<phi::dtype::bfloat16>(h0);
weight_x_memory_p =
handler.template AcquireWeightXMemory<phi::dtype::bfloat16>(weight_x);
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14 changes: 5 additions & 9 deletions paddle/fluid/operators/fused/resnet_basic_block_op.cc
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Expand Up @@ -225,26 +225,22 @@ class ResNetBasicBlockOp : public framework::OperatorWithKernel {

// By default, the type of the scale, bias, mean,
// and var tensors should be float when input tensor's dtype is float16.
auto bn_param_type = framework::proto::VarType::FP32;
auto bn_param_type = phi::DataType::FLOAT32;
PADDLE_ENFORCE_EQ(
bn_param_type,
framework::TransToProtoVarType(
ctx.Input<phi::DenseTensor>("Scale1")->dtype()),
ctx.Input<phi::DenseTensor>("Scale1")->dtype(),
phi::errors::InvalidArgument("Scale input should be of float type"));
PADDLE_ENFORCE_EQ(
bn_param_type,
framework::TransToProtoVarType(
ctx.Input<phi::DenseTensor>("Bias1")->dtype()),
ctx.Input<phi::DenseTensor>("Bias1")->dtype(),
phi::errors::InvalidArgument("Bias input should be of float type"));
PADDLE_ENFORCE_EQ(
bn_param_type,
framework::TransToProtoVarType(
ctx.Input<phi::DenseTensor>("Scale2")->dtype()),
ctx.Input<phi::DenseTensor>("Scale2")->dtype(),
phi::errors::InvalidArgument("Scale input should be of float type"));
PADDLE_ENFORCE_EQ(
bn_param_type,
framework::TransToProtoVarType(
ctx.Input<phi::DenseTensor>("Bias2")->dtype()),
ctx.Input<phi::DenseTensor>("Bias2")->dtype(),
phi::errors::InvalidArgument("Bias input should be of float type"));

return phi::KernelKey(input_data_type, ctx.GetPlace());
Expand Down
8 changes: 3 additions & 5 deletions paddle/fluid/operators/fused/resnet_unit_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -206,17 +206,15 @@ class ResNetUnitOp : public framework::OperatorWithKernel {
auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
// By default, the type of the scale, bias, mean,
// and var tensors should be float when input tensor's dtype is float16.
auto bn_param_type = framework::proto::VarType::FP32;
auto bn_param_type = phi::DataType::FLOAT32;

PADDLE_ENFORCE_EQ(
bn_param_type,
framework::TransToProtoVarType(
ctx.Input<phi::DenseTensor>("ScaleX")->dtype()),
ctx.Input<phi::DenseTensor>("ScaleX")->dtype(),
phi::errors::InvalidArgument("Scale input should be of float type"));
PADDLE_ENFORCE_EQ(
bn_param_type,
framework::TransToProtoVarType(
ctx.Input<phi::DenseTensor>("BiasX")->dtype()),
ctx.Input<phi::DenseTensor>("BiasX")->dtype(),
phi::errors::InvalidArgument("Bias input should be of float type"));
return phi::KernelKey(input_data_type, ctx.GetPlace());
}
Expand Down
3 changes: 1 addition & 2 deletions paddle/fluid/operators/lod_tensor_to_array_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -80,8 +80,7 @@ struct LoDTensorToArrayFunctor {
LoDTensorToArrayFunctorImpl<DeviceContext> func;
func.prev_functor_ = this;
func.dev_ctx_ = dev_ctx;
framework::VisitDataType(framework::TransToProtoVarType(input_.dtype()),
func);
phi::VisitDataType(input_.dtype(), func);
}
};

Expand Down
15 changes: 7 additions & 8 deletions paddle/fluid/operators/lookup_table_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -90,8 +90,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
int64_t row_width = table_t.value().dims()[1];
const auto *table = table_t.value().data<T>();
auto *output = output_t->mutable_data<T>(context.GetPlace());
auto input_data_type =
framework::TransToProtoVarType(table_t.value().dtype());
auto input_data_type = table_t.value().dtype();
for (int64_t i = 0; i < ids_numel; ++i) {
if (padding_idx != kNoPadding && ids[i] == padding_idx) {
memset(output + i * row_width, 0, row_width * sizeof(T));
Expand All @@ -107,9 +106,9 @@ class LookupTableKernel : public framework::OpKernel<T> {
auto id_index = table_t.GetIndexFromId(ids[i]);

if (id_index != -1) {
if (input_data_type == framework::proto::VarType::INT8 ||
input_data_type == framework::proto::VarType::INT16 ||
input_data_type == framework::proto::VarType::BF16) {
if (input_data_type == phi::DataType::INT8 ||
input_data_type == phi::DataType::INT16 ||
input_data_type == phi::DataType::BFLOAT16) {
memcpy(output + i * row_width,
table + id_index * row_width,
row_width * sizeof(T));
Expand Down Expand Up @@ -140,9 +139,9 @@ class LookupTableKernel : public framework::OpKernel<T> {
"the input key should be exists. But received %d.",
id_index));

if (input_data_type == framework::proto::VarType::INT8 ||
input_data_type == framework::proto::VarType::INT16 ||
input_data_type == framework::proto::VarType::BF16) {
if (input_data_type == phi::DataType::INT8 ||
input_data_type == phi::DataType::INT16 ||
input_data_type == phi::DataType::BFLOAT16) {
memcpy(output + i * row_width,
table + id_index * row_width,
row_width * sizeof(T));
Expand Down
17 changes: 8 additions & 9 deletions paddle/fluid/operators/optimizers/sparse_momentum_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -304,11 +304,11 @@ class SparseMomentumOpKernel : public framework::OpKernel<T> {
const bool multi_precision = ctx.Attr<bool>("multi_precision");
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
auto index = ctx.Input<phi::DenseTensor>("Index");
const auto& index_type = framework::TransToProtoVarType(index->dtype());
const auto& index_type = index->dtype();
if (multi_precision) {
if (use_nesterov) {
auto update_method = UseNesterov<MPDType>();
if (index_type == framework::proto::VarType::INT32) {
if (index_type == phi::DataType::INT32) {
InnerCompute<MPDType, int, UseNesterov<MPDType>>(
ctx, multi_precision, update_method);
} else {
Expand All @@ -317,7 +317,7 @@ class SparseMomentumOpKernel : public framework::OpKernel<T> {
}
} else {
auto update_method = NoNesterov<MPDType>();
if (index_type == framework::proto::VarType::INT32) {
if (index_type == phi::DataType::INT32) {
InnerCompute<MPDType, int, NoNesterov<MPDType>>(
ctx, multi_precision, update_method);
} else {
Expand All @@ -328,7 +328,7 @@ class SparseMomentumOpKernel : public framework::OpKernel<T> {
} else {
if (use_nesterov) {
auto update_method = UseNesterov<T>();
if (index_type == framework::proto::VarType::INT32) {
if (index_type == phi::DataType::INT32) {
InnerCompute<T, int, UseNesterov<T>>(
ctx, multi_precision, update_method);
} else {
Expand All @@ -337,7 +337,7 @@ class SparseMomentumOpKernel : public framework::OpKernel<T> {
}
} else {
auto update_method = NoNesterov<T>();
if (index_type == framework::proto::VarType::INT32) {
if (index_type == phi::DataType::INT32) {
InnerCompute<T, int, NoNesterov<T>>(
ctx, multi_precision, update_method);
} else {
Expand Down Expand Up @@ -371,11 +371,10 @@ class SparseMomentumOpKernel : public framework::OpKernel<T> {
phi::DenseTensor cpu_axis;
const phi::DenseTensor* axis_tensor = ctx.Input<phi::DenseTensor>("Axis");
framework::TensorCopy(*axis_tensor, phi::CPUPlace(), &cpu_axis);
const auto& axis_type =
framework::TransToProtoVarType(axis_tensor->dtype());
if (axis_type == framework::proto::VarType::INT32) {
const auto& axis_type = axis_tensor->dtype();
if (axis_type == phi::DataType::INT32) {
axis = static_cast<int>(cpu_axis.data<int32_t>()[0]);
} else if (axis_type == framework::proto::VarType::INT64) {
} else if (axis_type == phi::DataType::INT64) {
axis = static_cast<int>(cpu_axis.data<int64_t>()[0]);
}
} else {
Expand Down
15 changes: 5 additions & 10 deletions paddle/fluid/operators/uniform_random_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -33,8 +33,7 @@ namespace operators {

inline std::vector<int64_t> GetNewDataFromShapeTensor(
const phi::DenseTensor* new_data_tensor) {
if (framework::TransToProtoVarType(new_data_tensor->dtype()) ==
framework::proto::VarType::INT64) {
if (new_data_tensor->dtype() == phi::DataType::INT64) {
auto* new_data = new_data_tensor->data<int64_t>();
phi::DenseTensor cpu_starts_tensor;
if (new_data_tensor->place().GetType() == phi::AllocationType::GPU) {
Expand All @@ -45,8 +44,7 @@ inline std::vector<int64_t> GetNewDataFromShapeTensor(
std::vector<int64_t> vec_new_data(new_data,
new_data + new_data_tensor->numel());
return vec_new_data;
} else if (framework::TransToProtoVarType(new_data_tensor->dtype()) ==
framework::proto::VarType::INT32) {
} else if (new_data_tensor->dtype() == phi::DataType::INT32) {
auto* new_data = new_data_tensor->data<int32_t>();
std::vector<int64_t> vec_new_data;
phi::DenseTensor cpu_starts_tensor;
Expand Down Expand Up @@ -81,17 +79,15 @@ inline std::vector<int64_t> GetNewDataFromShapeTensorList(
"But received tensor's dim=%s.",
tensor->dims()));

if (framework::TransToProtoVarType(tensor->dtype()) ==
framework::proto::VarType::INT32) {
if (tensor->dtype() == phi::DataType::INT32) {
if (tensor->place().GetType() == phi::AllocationType::GPU) {
phi::DenseTensor temp;
paddle::framework::TensorCopySync(*tensor, phi::CPUPlace(), &temp);
vec_new_shape.push_back(static_cast<int64_t>(*temp.data<int32_t>()));
} else {
vec_new_shape.push_back(static_cast<int64_t>(*tensor->data<int32_t>()));
}
} else if (framework::TransToProtoVarType(tensor->dtype()) ==
framework::proto::VarType::INT64) {
} else if (tensor->dtype() == phi::DataType::INT64) {
if (tensor->place().GetType() == phi::AllocationType::GPU) {
phi::DenseTensor temp;
paddle::framework::TensorCopySync(*tensor, phi::CPUPlace(), &temp);
Expand All @@ -105,8 +101,7 @@ inline std::vector<int64_t> GetNewDataFromShapeTensorList(
"But got "
"unsupport dtype: %s.",
i,
paddle::framework::DataTypeToString(
framework::TransToProtoVarType(tensor->dtype()))));
phi::DataTypeToString(tensor->dtype())));
}
}

Expand Down