mirror of
https://github.com/zebrajr/pytorch.git
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a9b0a921d5
Summary: As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH` All changes but the ones to `.clang-tidy` are generated using following script: ``` for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008 Reviewed By: driazati, r-barnes Differential Revision: D29838584 Pulled By: malfet fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
110 lines
3.3 KiB
C++
110 lines
3.3 KiB
C++
#include "caffe2/core/blob_serialization.h"
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#include "caffe2/core/common.h"
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#include "caffe2/core/context.h"
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#include "caffe2/core/tensor_int8.h"
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#include <c10/util/typeid.h>
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#include "caffe2/core/types.h"
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namespace caffe2 {
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namespace int8 {
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class Int8TensorCPUSerializer : public BlobSerializerBase {
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public:
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void Serialize(
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const void* pointer,
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TypeMeta typeMeta,
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const string& name,
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SerializationAcceptor acceptor) override {
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CAFFE_ENFORCE(typeMeta.Match<Int8TensorCPU>());
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const auto& tensor = *static_cast<const Int8TensorCPU*>(pointer);
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BlobProto blob_proto;
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blob_proto.set_name(name);
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blob_proto.set_type("Int8TensorCPU");
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QTensorProto& proto = *blob_proto.mutable_qtensor();
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proto.set_name(name);
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for (int i = 0; i < tensor.t.dim(); ++i) {
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proto.add_dims(tensor.t.dim32(i));
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}
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proto.set_precision(8);
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proto.set_scale(tensor.scale);
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proto.set_bias(tensor.zero_point);
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proto.set_is_signed(false);
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const TensorProto::DataType data_type =
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TypeMetaToDataType(tensor.t.dtype());
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proto.set_data_type(data_type);
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switch (data_type) {
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case TensorProto_DataType_INT32:
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detail::CopyToProtoAsIs(
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tensor.t.numel(),
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tensor.t.template data<int32_t>(),
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proto.mutable_data(),
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&this->context_);
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break;
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case TensorProto_DataType_UINT8:
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detail::CopyToProtoWithCast(
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tensor.t.numel(),
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tensor.t.template data<uint8_t>(),
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proto.mutable_data(),
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&this->context_);
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break;
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default:
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CAFFE_ENFORCE(false, "Unsupported data type in Int8TensorCPU");
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}
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acceptor(name, SerializeBlobProtoAsString_EnforceCheck(blob_proto));
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}
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private:
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CPUContext context_;
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};
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class Int8TensorCPUDeserializer : public TensorDeserializer {
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public:
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void Deserialize(const BlobProto& blob_proto, Blob* blob) override {
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const QTensorProto& proto = blob_proto.qtensor();
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Int8TensorCPU* tensor = blob->template GetMutable<Int8TensorCPU>();
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// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
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tensor->scale = proto.scale();
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tensor->zero_point = proto.bias();
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vector<int> dims;
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// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
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for (const int d : proto.dims()) {
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dims.push_back(d);
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}
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tensor->t.Resize(dims);
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switch (proto.data_type()) {
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case TensorProto_DataType_INT32:
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detail::CopyFromProtoAsIs(
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tensor->t.numel(),
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proto.data(),
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tensor->t.template mutable_data<int32_t>(),
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&this->context_);
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break;
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case TensorProto_DataType_UINT8:
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detail::CopyFromProtoWithCast(
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tensor->t.numel(),
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proto.data(),
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tensor->t.template mutable_data<uint8_t>(),
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&this->context_);
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break;
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default:
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CAFFE_ENFORCE(false, "Unsupported data type in Int8TensorCPU");
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}
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}
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private:
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CPUContext context_;
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};
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} // namespace int8
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namespace {
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REGISTER_BLOB_SERIALIZER(
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(TypeMeta::Id<int8::Int8TensorCPU>()),
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int8::Int8TensorCPUSerializer);
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REGISTER_BLOB_DESERIALIZER(Int8TensorCPU, int8::Int8TensorCPUDeserializer);
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} // namespace
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} // namespace caffe2
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