Cherry pick pyramid hash support int8 weight and add dequantize_log op (#23371)

* cherry-pick add dequantize_log_op and make pyramid hash support int8 weight  (#22548)

* add dequantize_log_op and make pyramid hash support int8 weight test=develop

* add unittest and update pyramid hash op test=develop

* remove paddle_enforce test=develop

* fix error message test=develop

* remove incorrent commit test=develop

* fix error message in log_dequantize test=develop

* change 2019 to 2020 test=develop

* remove useless check_grad test=develop

* cherry-pick fix compile error in win gpu  (#23196)

* fix compile error in win gpu test=develop

* fix compile error in win gpu test=develop

* fix compile error in win gpu test=develop

paddle/fluid/operators/dequantize_log_op.cc

+/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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/dequantize_log_op.h"
+#include <math.h>
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct DequantizeFunctor<platform::CPUDeviceContext, T> {
+  void operator()(const platform::CPUDeviceContext& dev_ctx,
+                  const framework::Tensor* in, const framework::Tensor* dict,
+                  framework::Tensor* out) {
+    const float* dict_data = dict->data<float>();
+    const T* input_data = in->data<T>();
+    float* output_data = out->mutable_data<float>(dev_ctx.GetPlace());
+    int ind = in->numel();
+    for (size_t i = 0; i < (unsigned)ind; i++) {
+      if (input_data[i] < 0) {
+        output_data[i] = -std::pow(2.0, dict_data[input_data[i] + 128]);
+      } else {
+        output_data[i] = std::pow(2.0, dict_data[input_data[i]]);
+      }
+    }
+  }
+};
+
+template struct DequantizeFunctor<platform::CPUDeviceContext, int8_t>;
+
+class DequantizeLogOp : public framework::OperatorWithKernel {
+ public:
+  DequantizeLogOp(const std::string& type,
+                  const framework::VariableNameMap& inputs,
+                  const framework::VariableNameMap& outputs,
+                  const framework::AttributeMap& attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
+                      platform::errors::NotFound(
+                          "Input(X) of DequantizeLogOp is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
+                      platform::errors::NotFound(
+                          "Output(Out) of DequantizeLogOp is not found."));
+
+    ctx->ShareDim("X", /*->*/ "Out");
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const {
+    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    auto type = framework::OpKernelType(data_type, ctx.device_context());
+    return type;
+  }
+};
+
+class DequantizeLogOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(int8 Tensor) The input with int8 type is the "
+             "low precision tensor.");
+    AddInput("Dict", "(float) The Dict in quantization stage.");
+    AddOutput("Out",
+              "(float32 Tensor) The output is the dequantized high "
+              "precision tensor.");
+    AddComment(R"DOC(
+DequantizeLogOp operator.
+
+This calculation is an opposite operation of QuantizeLogOp:
+
+
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+using CPU = paddle::platform::CPUDeviceContext;
+
+REGISTER_OPERATOR(
+    dequantize_log, ops::DequantizeLogOp, ops::DequantizeLogOpMaker,
+    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OP_CPU_KERNEL(dequantize_log, ops::DequantizeLogKernel<CPU, int8_t>);

paddle/fluid/operators/dequantize_log_op.cu

+/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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/dequantize_log_op.h"
+#include "paddle/fluid/operators/math.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void KeDequantize(const T* in, const float* dict, int num,
+                             float* out) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < num) {
+    if (in[idx] < 0) {
+      out[idx] = -std::pow(static_cast<float>(2.0), dict[in[idx] + 128]);
+    } else {
+      out[idx] = std::pow(static_cast<float>(2.0), dict[in[idx]]);
+    }
+  }
+}
+
+template <typename T>
+struct DequantizeFunctor<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& dev_ctx,
+                  const framework::Tensor* in, const framework::Tensor* dict,
+                  framework::Tensor* out) {
+    const T* in_data = in->data<T>();
+    const float* dict_data = dict->data<float>();
+    float* out_data = out->mutable_data<float>(dev_ctx.GetPlace());
+
+    int num = in->numel();
+    int block = 512;
+    int grid = (num + block - 1) / block;
+
+    KeDequantize<T><<<grid, block, 0, dev_ctx.stream()>>>(in_data, dict_data,
+                                                          num, out_data);
+  }
+};
+
+template struct DequantizeFunctor<platform::CUDADeviceContext, int8_t>;
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+using CUDA = paddle::platform::CUDADeviceContext;
+REGISTER_OP_CUDA_KERNEL(dequantize_log, ops::DequantizeLogKernel<CUDA, int8_t>);

paddle/fluid/operators/dequantize_log_op.h

+/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+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. */
+
+#pragma once
+
+#include <vector>
+#include "paddle/fluid/framework/ddim.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+struct DequantizeFunctor {
+  void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
+                  const framework::Tensor* dict, framework::Tensor* out);
+};
+
+template <typename DeviceContext, typename T>
+class DequantizeLogKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& ctx) const {
+    auto* in = ctx.Input<framework::Tensor>("X");
+    auto* dict = ctx.Input<framework::Tensor>("Dict");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    out->mutable_data<float>(dev_ctx.GetPlace());
+
+    DequantizeFunctor<DeviceContext, T>()(dev_ctx, in, dict, out);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/pyramid_hash_op.cc

@@ -84,52 +84,111 @@ class PyramidHashOP : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "X(Input) should not be null.");
-    PADDLE_ENFORCE_EQ(ctx->HasInput("W"), true, "W(Input) should not be null.");
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X"), true,
+        platform::errors::NotFound("Input(X) of PyramidHashOP is not found."));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("W"), true,
+        platform::errors::NotFound("Input(W) of PyramidHashOP is not found."));
     PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
-                      "Out(Output) should not be null.");
+                      platform::errors::NotFound(
+                          "Output(Out) of PyramidHashOP is not found."));
     PADDLE_ENFORCE_EQ(ctx->HasOutput("DropPos"), true,
-                      "DropPos(TMP Output) should not be null.");
+                      platform::errors::NotFound(
+                          "Output(DropPos) of PyramidHashOP is not found."));
 
     auto x_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "The rank of X(Input) should be 2.");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2,
+                      platform::errors::InvalidArgument(
+                          "The rank of Input(X) of PyramidHashOP is invalid. "
+                          "It should be 2, but got %d",
+                          x_dims.size()));
 
     auto w_dims = ctx->GetInputDim("W");
-    PADDLE_ENFORCE_EQ(w_dims.size(), 2, "W should be 2-D tensor");
+    PADDLE_ENFORCE_EQ(w_dims.size(), 2,
+                      platform::errors::InvalidArgument(
+                          "The rank of Input(W) of PyramidHashOP is invalid. "
+                          "It should be 2, but got %d",
+                          w_dims.size()));
 
     int space_len = ctx->Attrs().Get<int>("space_len");
     int rand_len = ctx->Attrs().Get<int>("rand_len");
 
-    PADDLE_ENFORCE_EQ(w_dims[0], space_len + rand_len,
-                      "w_dims[0] should be equal to (space_len + rand_len)");
-    PADDLE_ENFORCE_EQ(w_dims[1], 1, "w_dims[1] should be equal to 1");
+    PADDLE_ENFORCE_EQ(
+        w_dims[0], space_len + rand_len,
+        platform::errors::InvalidArgument(
+            "The first dimension of Input(W) of PyramidHashOP is invalid. "
+            "It should be space_len + rand_len, but now %d != %d + %d",
+            w_dims[0], space_len, rand_len));
+    PADDLE_ENFORCE_EQ(
+        w_dims[1], 1,
+        platform::errors::InvalidArgument(
+            "The second dimension of Input(W) of PyramidHashOP is invalid."
+            " It should be 1, but got %d",
+            w_dims[1]));
 
     int num_emb = ctx->Attrs().Get<int>("num_emb");
-    PADDLE_ENFORCE_EQ(num_emb % rand_len, 0,
-                      "random length should mod embedding size");
+    PADDLE_ENFORCE_EQ(
+        num_emb % rand_len, 0,
+        platform::errors::InvalidArgument(
+            "The PyramidHashOP's Attr(num_emb) should mod Attr(rand_len), "
+            "but num_emb is %d, rand_len is %d",
+            num_emb, rand_len));
 
     int white_list_len = ctx->Attrs().Get<int>("white_list_len");
     if (white_list_len > 0) {
       PADDLE_ENFORCE_EQ(
           ctx->HasInput("WhiteList"), true,
-          "WhiteList(Input) should not be null when white_list_len > 0");
+          platform::errors::NotFound("Input(WhiteList) of PyramidHashOP is not "
+                                     "found but white_list_len > 0."));
       auto wl_dims = ctx->GetInputDim("WhiteList");
-      PADDLE_ENFORCE_EQ(wl_dims.size(), 2, "WhiteList should be 2-D tensor");
+      PADDLE_ENFORCE_EQ(
+          wl_dims.size(), 2,
+          platform::errors::InvalidArgument(
+              "The rank of Input(WhiteList) of PyramidHashOP is invalid."
+              " It should be 2, but got %d",
+              wl_dims.size()));
       PADDLE_ENFORCE_EQ(wl_dims[0], white_list_len,
-                        "wl_dims[0] should be equal to white_list_len");
-      PADDLE_ENFORCE_EQ(wl_dims[1], 1, "wl_dims[1] should be equal to 1");
+                        platform::errors::InvalidArgument(
+                            "The first dimension of Input(WhiteList) of "
+                            "PyramidHashOP is invalid."
+                            " It should be equal to Attr(white_list_len) "
+                            ", but first dimension is %d, white_list_len is %d",
+                            wl_dims[0], white_list_len));
+      PADDLE_ENFORCE_EQ(wl_dims[1], 1,
+                        platform::errors::InvalidArgument(
+                            "The second dimension of Input(WhiteList) of "
+                            "PyramidHashOP is invalid."
+                            " It should be 1, but got %d",
+                            wl_dims[1]));
     }
 
     int black_list_len = ctx->Attrs().Get<int>("black_list_len");
     if (black_list_len > 0) {
       PADDLE_ENFORCE_EQ(
           ctx->HasInput("BlackList"), true,
-          "BlackList(Input) should not be null when black_list_len > 0");
+          platform::errors::NotFound("Input(BlackList) of PyramidHashOP is not "
+                                     "found but black_list_len > 0."));
       auto bl_dims = ctx->GetInputDim("BlackList");
-      PADDLE_ENFORCE_EQ(bl_dims.size(), 2, "BlackList should be 2-D tensor");
+      PADDLE_ENFORCE_EQ(
+          bl_dims.size(), 2,
+          platform::errors::InvalidArgument(
+              "The rank of Input(BlackList) of PyramidHashOP is invalid."
+              " It should be 2, but got %d",
+              bl_dims.size()));
       PADDLE_ENFORCE_EQ(bl_dims[0], black_list_len,
-                        "bl_dims[0] should be equal to black_list_len");
-      PADDLE_ENFORCE_EQ(bl_dims[1], 1, "bl_dims[1] should be equal to 1");
+                        platform::errors::InvalidArgument(
+                            "The first dimension of Input(BlackList) of "
+                            "PyramidHashOP is invalid."
+                            " It should be equal to Attr(black_list_len)"
+                            ", but first dimension is %d, black_list_len is %d",
+                            bl_dims[0], black_list_len));
+      PADDLE_ENFORCE_EQ(bl_dims[1], 1,
+                        platform::errors::InvalidArgument(
+                            "The second dimension of Input(BlackList) of "
+                            "PyramidHashOP is invalid."
+                            " It should be 1, but got %d",
+                            bl_dims[1]));
     }
 
     if (ctx->IsRuntime()) {
@@ -154,20 +213,22 @@ template <typename DeviceContext, typename T>
 class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
  public:
   bool should_use_term(math::bloomfilter* _filter,
-                       math::bloomfilter* _black_filter, const T* word_repr,
+                       math::bloomfilter* _black_filter, const float* word_repr,
                        int len) const {
     return (!_filter ||
-            1 == math::bloomfilter_get(_filter, word_repr, len * sizeof(T))) &&
+            1 == math::bloomfilter_get(_filter, word_repr,
+                                       len * sizeof(float))) &&
            (!_black_filter ||
             0 == math::bloomfilter_get(_black_filter, word_repr,
-                                       len * sizeof(T)));
+                                       len * sizeof(float)));
   }
 
-  void hash_embedding_ff(const T* hash_id, int len, T* top_pos,
+  void hash_embedding_ff(const float* hash_id, int len, T* top_pos,
                          const T* weights, int _num_emb, int _rand_len,
                          int _space_len) const {
-    unsigned int pos1 = XXH32(hash_id, len * sizeof(T), 0) % _space_len;
-    unsigned int pos2 = XXH32(hash_id, len * sizeof(T), _rand_len) % _space_len;
+    unsigned int pos1 = XXH32(hash_id, len * sizeof(float), 0) % _space_len;
+    unsigned int pos2 =
+        XXH32(hash_id, len * sizeof(float), _rand_len) % _space_len;
 
     for (int j = 0; j != _num_emb; j += _rand_len) {
       if (j + _rand_len < _num_emb) {
@@ -176,8 +237,8 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
       }
 
       unsigned int pos3 =
-          XXH32(hash_id, len * sizeof(T), j + 2 * _rand_len) % _space_len;
-      memcpy(top_pos + j, const_cast<float*>(weights + pos1),
+          XXH32(hash_id, len * sizeof(float), j + 2 * _rand_len) % _space_len;
+      memcpy(top_pos + j, const_cast<T*>(weights + pos1),
              _rand_len * sizeof(T));
       pos1 = pos2;
       pos2 = pos3;
@@ -208,7 +269,7 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
     const auto* bottom_data_ori = bottom->data<int32_t>();
     auto* buff = ctx.Output<LoDTensor>("X_Temp_Out");
     buff->Resize(framework::make_ddim({bottom->dims()[0], bottom->dims()[1]}));
-    T* bottom_data = buff->mutable_data<T>(ctx.GetPlace());
+    float* bottom_data = buff->mutable_data<float>(ctx.GetPlace());
     for (int i = 0; i < bottom->dims()[0]; i++) {
       bottom_data[i] = bottom_data_ori[i];
     }
@@ -223,12 +284,12 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
     math::bloomfilter* _black_filter = NULL;
     if (use_filter) {
       if (white_list_len != 0) {
-        _filter = (math::bloomfilter*)_blobs_1->data<T>();
+        _filter = (math::bloomfilter*)_blobs_1->data<float>();
         PADDLE_ENFORCE_EQ(math::bloomfilter_check(_filter), 1,
                           "white filter not load");
       }
       if (black_list_len != 0) {
-        _black_filter = (math::bloomfilter*)_blobs_2->data<T>();
+        _black_filter = (math::bloomfilter*)_blobs_2->data<float>();
         PADDLE_ENFORCE_EQ(math::bloomfilter_check(_black_filter), 1,
                           "black filter not load");
       }
@@ -251,11 +312,11 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
         for (int ilayer = 1; ilayer < _pyramid_layer && ilayer < w; ++ilayer) {
           for (int l = 0; l < w - ilayer; ++l) {
             if (should_use_term(_filter, _black_filter,
-                                (const T*)(bottom_data + offset[i] + l),
+                                (const float*)(bottom_data + offset[i] + l),
                                 ilayer + 1)) {
               if (_is_training != 0) {
                 unsigned int rand_val = rand_r(&_seed);
-                T rate = static_cast<T>(rand_val) / (RAND_MAX);
+                float rate = static_cast<float>(rand_val) / (RAND_MAX);
                 *(iter_end++) = (rate < _drop_out_percent ? 0 : 1);
               } else {
                 *(iter_end++) = 1;
@@ -311,7 +372,7 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
               // do nothing
             } else {
               auto* top_pos = top_data + top_counter++ * _num_emb;
-              hash_embedding_ff((const T*)(bottom_data + offset[i] + l),
+              hash_embedding_ff((const float*)(bottom_data + offset[i] + l),
                                 ilayer + 1, top_pos, weights, _num_emb,
                                 _rand_len, _space_len);
             }
@@ -322,7 +383,8 @@ class CPUPyramidHashOPKernel : public framework::OpKernel<T> {
     if (iter != iter_end) {
       exit(1);
     }
-    if (_is_training == 0) {
+    auto weight_type = _blobs_0->type();
+    if (_is_training == 0 && weight_type != framework::proto::VarType::INT8) {
       avx_axpy_noadd(top_data, top_data, top->dims()[0] * top->dims()[1],
                      _drop_out_percent);
     }
@@ -334,15 +396,23 @@ class PyramidHashOpGrad : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   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("W"), true, "Input(W) should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
+                      platform::errors::NotFound(
+                          "Input(X) of PyramidHashOpGrad is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("W"), true,
+                      platform::errors::NotFound(
+                          "Input(W) of PyramidHashOpGrad is not found."));
     PADDLE_ENFORCE_EQ(ctx->HasInput("DropPos"), true,
-                      "Input(DropPos) should not be null.");
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X_Temp_Out"), true,
-                      "Input(X_Temp_Out) should not be null.");
+                      platform::errors::NotFound(
+                          "Input(DropPos) of PyramidHashOpGrad is not found."));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X_Temp_Out"), true,
+        platform::errors::NotFound(
+            "Input(X_Temp_Out) of PyramidHashOpGrad is not found."));
     PADDLE_ENFORCE_EQ(
         ctx->HasInput(framework::GradVarName("Out")), true,
-        "Input(Out@GRAD) of PyramidHashGradOp should not be null.");
+        platform::errors::NotFound(
+            "Input(Out@Grad) of PyramidHashOpGrad is not found."));
   }
 
  protected:
@@ -412,6 +482,7 @@ class CPUPyramidHashOPGradKernel : public framework::OpKernel<T> {
     auto& drop_pos_offset = drop_pos->lod()[0];
 
     const auto* top_diff = top->data<T>();
+    // in-place update weight, so need const_cast
     T* weights = const_cast<T*>(_blobs->data<T>());
     T mlr = -1.0 * _lr;
 
@@ -455,7 +526,10 @@ REGISTER_OPERATOR(pyramid_hash, ops::PyramidHashOP, ops::PyramidHashOpMaker,
 REGISTER_OPERATOR(pyramid_hash_grad, ops::PyramidHashOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
-    pyramid_hash, ops::CPUPyramidHashOPKernel<plt::CPUDeviceContext, float>);
+    pyramid_hash, ops::CPUPyramidHashOPKernel<plt::CPUDeviceContext, float>,
+    ops::CPUPyramidHashOPKernel<plt::CPUDeviceContext, double>,
+    ops::CPUPyramidHashOPKernel<plt::CPUDeviceContext, int8_t>);
 REGISTER_OP_CPU_KERNEL(
     pyramid_hash_grad,
-    ops::CPUPyramidHashOPGradKernel<plt::CPUDeviceContext, float>);
+    ops::CPUPyramidHashOPGradKernel<plt::CPUDeviceContext, float>,
+    ops::CPUPyramidHashOPGradKernel<plt::CPUDeviceContext, double>);

paddle/fluid/operators/search_compute.h

@@ -83,8 +83,13 @@ static const unsigned int AVX_CUT_LEN_MASK = 7U;
 #define _mm256_store_px _mm256_storeu_ps
 #define _mm256_broadcast_sx _mm256_broadcast_ss
 
-template <typename T>
-inline void avx_axpy(const T* x, T* y, size_t len, const T alpha) {
+#define _mm256_mul_pd _mm256_mul_pd
+#define _mm256_add_pd _mm256_add_pd
+#define _mm256_load_pd _mm256_loadu_pd
+#define _mm256_store_pd _mm256_storeu_pd
+#define _mm256_broadcast_sd _mm256_broadcast_sd
+
+inline void avx_axpy(const float* x, float* y, size_t len, const float alpha) {
   unsigned int jjj, lll;
   jjj = lll = 0;
 
@@ -102,8 +107,43 @@ inline void avx_axpy(const T* x, T* y, size_t len, const T alpha) {
   }
 }
 
-template <typename T>
-inline void avx_axpy_noadd(const T* x, T* y, size_t len, const T alpha) {
+inline void avx_axpy(const double* x, double* y, size_t len,
+                     const float alpha) {
+  unsigned int jjj, lll;
+  jjj = lll = 0;
+
+  lll = len & ~AVX_CUT_LEN_MASK;
+  double alpha_d = static_cast<double>(alpha);
+
+  __m256d mm_alpha = _mm256_broadcast_sd(&alpha_d);
+  for (jjj = 0; jjj < lll; jjj += AVX_STEP_SIZE) {
+    _mm256_store_pd(
+        y + jjj,
+        _mm256_add_pd(_mm256_load_pd(y + jjj),
+                      _mm256_mul_pd(mm_alpha, _mm256_load_pd(x + jjj))));
+  }
+
+  for (; jjj < len; jjj++) {
+    y[jjj] += alpha * x[jjj];
+  }
+}
+inline void avx_axpy_noadd(const double* x, double* y, size_t len,
+                           const float alpha) {
+  unsigned int jjj, lll;
+  jjj = lll = 0;
+  double alpha_d = static_cast<double>(alpha);
+  lll = len & ~AVX_CUT_LEN_MASK;
+  __m256d mm_alpha = _mm256_broadcast_sd(&alpha_d);
+  for (jjj = 0; jjj < lll; jjj += AVX_STEP_SIZE) {
+    _mm256_store_pd(y + jjj, _mm256_mul_pd(mm_alpha, _mm256_load_pd(x + jjj)));
+  }
+
+  for (; jjj < len; jjj++) {
+    y[jjj] = alpha * x[jjj];
+  }
+}
+inline void avx_axpy_noadd(const float* x, float* y, size_t len,
+                           const float alpha) {
   unsigned int jjj, lll;
   jjj = lll = 0;
 
@@ -117,6 +157,11 @@ inline void avx_axpy_noadd(const T* x, T* y, size_t len, const T alpha) {
     y[jjj] = alpha * x[jjj];
   }
 }
+inline void avx_axpy_noadd(const int8_t* x, int8_t* y, size_t len,
+                           const float alpha) {
+  PADDLE_THROW(platform::errors::Unimplemented(
+      "int8_t input of avx_axpy_noadd is  not supported"));
+}
 
 }  // namespace operators
 }  // namespace paddle

python/paddle/fluid/tests/unittests/test_dequantize_log_op.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import math
+from op_test import OpTest
+
+
+def dequantize_log(x, dict_data):
+    output_data = np.zeros_like(x).astype('float32')
+    x_f = x.flatten()
+    output_data_f = output_data.flatten()
+    for i in range(x_f.size):
+        if x_f[i] < 0:
+            output_data_f[i] = -np.power(2, dict_data[x_f[i] + 128])
+        else:
+            output_data_f[i] = np.power(2, dict_data[x_f[i]])
+    return output_data_f.reshape(x.shape)
+
+
+class TestDequantizeLogOp(OpTest):
+    def setUp(self):
+        self.op_type = "dequantize_log"
+        x = np.random.randint(low=-128, high=127, size=(20, 10)).astype('int8')
+        dict_data = np.random.random(128).astype('float32')
+        xdq = dequantize_log(x, dict_data)
+
+        self.inputs = {
+            'X': np.array(x).astype('int8'),
+            'Dict': np.array(dict_data).astype('float32')
+        }
+        self.outputs = {'Out': xdq}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()