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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
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… unify_ops_yaml_directory
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@huangjiyi
huangjiyi committed May 21, 2024
2 parents 0b72ee0 + 01b4ecb commit 506fad1
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2 changes: 2 additions & 0 deletions paddle/fluid/framework/new_executor/interpreter/static_build.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -876,6 +876,8 @@ void FakeInitializeOutputsForFunctionKernel(
dtype = GetInputDType(runtime_ctx, "X");
} else if (op_type == "dequantize_linear") {
dtype = GetInputDType(runtime_ctx, "Scale");
} else if (op_type == "quantize_linear") {
dtype = GetInputDType(runtime_ctx, "Scale");
} else if (op_type == "lamb") {
bool multi_precision = op.Attr<bool>("multi_precision");
dtype = GetInputDType(runtime_ctx, "Moment1");
Expand Down
14 changes: 13 additions & 1 deletion paddle/fluid/operators/ops_signature/quantize_linear_sig.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -16,16 +16,28 @@ limitations under the License. */

namespace phi {

KernelSignature QuantizeLinearOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature(
"quantize_linear_deprecated",
{"X", "Scale", "ZeroPoint", "InAccum", "InState"},
{"quant_axis", "bit_length", "round_type", "is_test", "only_observer"},
{"Y", "OutState", "OutAccum", "OutScale"});
}

KernelSignature DeQuantizeLinearOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature(
"dequantize_linear",
"dequantize_linear_deprecated",
{"X", "Scale", "ZeroPoint", "InAccum", "InState"},
{"quant_axis", "bit_length", "round_type", "is_test", "only_observer"},
{"Y", "OutState", "OutAccum", "OutScale"});
}

} // namespace phi

PD_REGISTER_ARG_MAPPING_FN(quantize_linear,
phi::QuantizeLinearOpArgumentMapping);

PD_REGISTER_ARG_MAPPING_FN(dequantize_linear,
phi::DeQuantizeLinearOpArgumentMapping);
159 changes: 23 additions & 136 deletions paddle/fluid/operators/quantize_linear_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -9,119 +9,28 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/fluid/operators/quantize_linear_op.h"

#include <algorithm>
#include <string>
#include <vector>

#include "paddle/common/ddim.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/phi/common/transform.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/impl/clip_kernel_impl.h"

#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/binary.h"
#include "paddle/phi/infermeta/multiary.h"

namespace paddle {
namespace operators {

template <typename T>
struct DequantizeFunctor<phi::CPUContext, T> {
void operator()(const phi::CPUContext &dev_ctx,
const phi::DenseTensor *in,
const phi::DenseTensor *scale,
T max_range,
phi::DenseTensor *out) {
auto in_e = phi::EigenVector<T>::Flatten(*in);
const T *scale_factor = scale->data<T>();
auto out_e = phi::EigenVector<T>::Flatten(*out);

auto &dev = *dev_ctx.eigen_device();
out_e.device(dev) = in_e * scale_factor[0] / max_range;
}
};

template <typename T>
struct ChannelDequantizeFunctorV2<phi::CPUContext, T> {
void operator()(const phi::CPUContext &dev_ctx,
const phi::DenseTensor *in,
const phi::DenseTensor *scale,
T max_range,
const int quant_axis,
phi::DenseTensor *out) {
// Dequant op is before quantized op
// Dequantize the weight of quantized op
auto in_dims = in->dims();
const int64_t channel = in_dims[quant_axis];
const T *scale_factor = scale->data<T>();
if (quant_axis == 0) {
for (int64_t i = 0; i < channel; i++) {
T s = scale_factor[i];
phi::DenseTensor one_channel_in = in->Slice(i, i + 1);
phi::DenseTensor one_channel_out = out->Slice(i, i + 1);
auto in_e = phi::EigenVector<T>::Flatten(one_channel_in);
auto out_e = phi::EigenVector<T>::Flatten(one_channel_out);
auto &dev = *dev_ctx.eigen_device();
out_e.device(dev) = in_e * s / max_range;
}
} else if (quant_axis == 1) {
int64_t out_iter = 1;
for (int i = 0; i < quant_axis; i++) {
out_iter *= in_dims[i];
}
int64_t step_i = in->numel() / out_iter;
int64_t step_j = in->numel() / (out_iter * channel);
auto *in_data = in->data<T>();
auto *out_data = dev_ctx.Alloc<T>(out, out->numel() * sizeof(T));
for (int64_t i = 0; i < out_iter; i++) {
for (int64_t j = 0; j < channel; j++) {
auto *cur_in = in_data + i * step_i + j * step_j;
auto *cur_out = out_data + i * step_i + j * step_j;
T s = scale_factor[j];
for (int64_t k = 0; k < step_j; k++) {
*cur_out = (*cur_in) * s / max_range;
++cur_in;
++cur_out;
}
}
}
}
}
};

template struct DequantizeFunctor<phi::CPUContext, phi::dtype::float16>;
template struct DequantizeFunctor<phi::CPUContext, float>;
template struct DequantizeFunctor<phi::CPUContext, double>;
template struct ChannelDequantizeFunctorV2<phi::CPUContext,
phi::dtype::float16>;
template struct ChannelDequantizeFunctorV2<phi::CPUContext, float>;
template struct ChannelDequantizeFunctorV2<phi::CPUContext, double>;

class QuantizeLinearOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "QuantizeLinear");
OP_INOUT_CHECK(ctx->HasInput("Scale"), "Input", "Scale", "QuantizeLinear");
OP_INOUT_CHECK(
ctx->HasInput("ZeroPoint"), "Input", "ZeroPoint", "QuantizeLinear");
OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "QuantizeLinear");
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
int quant_axis = ctx->Attrs().Get<int>("quant_axis");
if (ctx->HasOutput("OutScale")) {
if (quant_axis < 0) {
ctx->SetOutputDim("OutScale", {1});
} else {
ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[quant_axis]});
}
}
if (ctx->HasOutput("OutState")) {
ctx->SetOutputDim("OutState", {1});
}
if (ctx->HasOutput("OutAccum")) {
ctx->SetOutputDim("OutAccum", {1});
}
ctx->ShareLoD("X", /*->*/ "Y");
}

protected:
phi::KernelKey GetExpectedKernelType(
Expand All @@ -130,7 +39,6 @@ class QuantizeLinearOp : public framework::OperatorWithKernel {
ctx.GetPlace());
}
};

class QuantizeLinearOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
Expand Down Expand Up @@ -159,42 +67,16 @@ class QuantizeLinearOpMaker : public framework::OpProtoAndCheckerMaker {
"(int, default 0) The axis for quantization. "
"For conv2d, depthwise_conv2d, conv2d_transpose "
"and mul, the quant_axis is equal to the cout axis.")
.SetDefault(0)
.AddCustomChecker([](const int &quant_axis) {
PADDLE_ENFORCE_EQ(
quant_axis == 0 || quant_axis == 1 || quant_axis == -1,
true,
phi::errors::InvalidArgument("'quant_axis' should be 0 or 1, but "
"the received is %d",
quant_axis));
});
AddAttr<int>("bit_length", "(int, default 8)")
.SetDefault(8)
.AddCustomChecker([](const int &bit_length) {
PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16,
true,
phi::errors::InvalidArgument(
"'bit_length' should be between 1 and 16, but "
"the received is %d",
bit_length));
});
.SetDefault(0);
AddAttr<int>("bit_length", "(int, default 8)").SetDefault(8);
AddAttr<int>(
"round_type",
"(int, default 0) The round type of fp32 to int."
"0: rounding to nearest ties to even. Eg: round(1.5)=2, round(2.5)=2"
"0: rounding to nearest ties to even. Eg: round(1.5)=2, "
"round(2.5)=2"
"1: rounding to nearest ties away from zero. Eg: round(1.5)=2, "
"round(2.5)=3")
.SetDefault(0)
.AddCustomChecker([](const int &round_type) {
PADDLE_ENFORCE_EQ(
round_type == 0 || round_type == 1,
true,
phi::errors::InvalidArgument(
"'round_type' should be 0 or 1, 0 rounding to "
"nearest ties to even and 1 is rounding to nearest "
"ties away from zero.but the received is %d",
round_type));
});
.SetDefault(0);
AddAttr<bool>("is_test",
"(bool, default false) Set to true for inference only, false "
"for training. Some layers may run faster when this is true.")
Expand All @@ -216,25 +98,30 @@ In above three formulas, the range value of c is as follow:
)DOC");
}
};

} // namespace operators
} // namespace paddle

namespace ops = paddle::operators;

DECLARE_INFER_SHAPE_FUNCTOR(quantize_linear,
QuantizeLinearInferShapeFunctor,
PD_INFER_META(phi::QuantizeLinearInferMeta));

DECLARE_INFER_SHAPE_FUNCTOR(dequantize_linear,
DeQuantizeLinearInferShapeFunctor,
PD_INFER_META(phi::QuantizeLinearInferMeta));
REGISTER_OPERATOR(
quantize_linear,
ops::QuantizeLinearOp,
ops::QuantizeLinearOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);

PD_REGISTER_STRUCT_KERNEL(
quantize_linear, CPU, ALL_LAYOUT, ops::QuantizeLinearKernel, float) {}
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
QuantizeLinearInferShapeFunctor);

REGISTER_OPERATOR(
dequantize_linear,
ops::QuantizeLinearOp,
ops::QuantizeLinearOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
DeQuantizeLinearInferShapeFunctor);
131 changes: 0 additions & 131 deletions paddle/fluid/operators/quantize_linear_op.cu
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