Add fake_quantize_op. (#11359)

* Add a fake_quantize_op, which quantize an input tensor to a tensor with lower bits.

paddle/fluid/operators/fake_quantize_op.cc

+/* Copyright (c) 2016 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/fake_quantize_op.h"
+#include <string>
+
+namespace paddle {
+namespace operators {
+
+class FakeQuantizeOp : public framework::OperatorWithKernel {
+ public:
+  FakeQuantizeOp(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(ctx->HasInput("X"),
+                   "Input(X) of FakeQuantizeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of FakeQuantizeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("OutMovingScale"),
+                   "OutMovingScale(Out) of FakeQuantizeOp should not be null");
+    // if (ctx->HasInput("InMovingScale")) {
+    ctx->SetOutputDim("OutMovingScale", ctx->GetInputDim("InMovingScale"));
+    //}
+    // if (ctx->HasInput("InScales")) {
+    PADDLE_ENFORCE(ctx->HasOutput("OutScales"),
+                   "OutScales(Out) of FakeQuantizeOp should not be null");
+    ctx->SetOutputDim("OutScales", ctx->GetInputDim("InScales"));
+    // PADDLE_ENFORCE_EQ(ctx->Inputs("InScales")[0],
+    // ctx->Outputs("OutScales")[0],
+    //                  "Mean and MeanOut should share the same memory");
+    //}
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class FakeQuantizeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) Input tensor of scale operator.");
+    AddInput("InScales", "(Tensor) scale buffer, used in static quantization.")
+        .AsDispensable();
+    AddInput("InMovingScale", "Last scale, used in static quantization.")
+        .AsDispensable();
+    AddInput("InCurrentIter",
+             "Last iteration number, used in static quantization.")
+        .AsDispensable();
+    AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
+    AddOutput("OutScales",
+              "(Tensor) scale buffer, used in static quantization.")
+        .AsDispensable();
+    AddOutput("OutMovingScale", " Current scale");
+    AddOutput("OutCurrentIter", "Current iteration number.").AsDispensable();
+    AddAttr<std::string>("quantize_type",
+                         "(string, default abs_max)"
+                         "The scaling tpe of the quantize operator.")
+        .SetDefault("abs_max");
+    AddAttr<int>("window_size", "(int, default 10000)").SetDefault(10000);
+    AddAttr<int>("bit_length", "(int, default 8)")
+        .SetDefault(8)
+        .AddCustomChecker([](const int &bit_length) {
+          PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
+                         "'bit_length' should be between 1 and 16.");
+        });
+    AddAttr<bool>("is_test", "").SetDefault(false);
+    AddComment(R"DOC(
+FakeQuantize operator
+
+quantize_type = abs_max:
+
+    $$scale = max(abs(x))$$ 
+
+quantize_type = range_abs_max:
+
+    $$scale = max(max(abs(x)), history_abs_max)$$ 
+
+quantize_type = moving_average_abs_max:
+
+    $$scale = 0.1*scale+0.9*new_abs_max)$$ 
+
+$$Out = scale*X$$
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(fake_quantize, ops::FakeQuantizeOp, ops::FakeQuantizeOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    fake_quantize,
+    ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/fake_quantize_op.cu

+/* Copyright (c) 2016 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 <string>
+#include "paddle/fluid/operators/fake_quantize_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void FindAbsMaxKernel(const int n, const T* in, T* out) {
+  int bid = threadIdx.x + blockIdx.x * blockDim.x;
+  int tid = threadIdx.x;
+
+  extern __shared__ T shared_max_data[];
+  if (gridDim.x > 1) {
+    shared_max_data[tid] = T(0);
+    for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
+      T tmp = fabs(in[i]);
+      if (tmp > shared_max_data[tid]) {
+        shared_max_data[tid] = tmp;
+      }
+    }
+  } else {
+    if (bid < n) {
+      shared_max_data[tid] = fabs(in[bid]);
+    } else {
+      shared_max_data[tid] = T(0);
+    }
+  }
+  __syncthreads();
+
+  for (int i = blockDim.x / 2; i > 0; i >>= 1) {
+    if (tid < i && shared_max_data[tid] < shared_max_data[tid + i]) {
+      shared_max_data[tid] = shared_max_data[tid + i];
+    }
+    __syncthreads();
+  }
+  if (tid == 0) {
+    out[blockIdx.x] = shared_max_data[0];
+  }
+}
+
+float FindAbsMaxGpu(const platform::CUDADeviceContext& ctx, const float* array,
+                    int length) {
+  float host_max;
+  int kNumTheads = 1024;
+  int gridDimx = (kNumTheads - 1 + length) / kNumTheads;
+  gridDimx = (gridDimx > kNumTheads) ? kNumTheads : gridDimx;
+  framework::Tensor t;
+  float* device_max = t.mutable_data<float>(framework::make_ddim({gridDimx}),
+                                            platform::CUDAPlace());
+  FindAbsMaxKernel<float><<<gridDimx, kNumTheads, kNumTheads * sizeof(float),
+                            ctx.stream()>>>(length, array, device_max);
+  FindAbsMaxKernel<
+      float><<<1, kNumTheads, kNumTheads * sizeof(float), ctx.stream()>>>(
+      gridDimx, device_max, device_max);
+  PADDLE_ENFORCE_EQ(
+      cudaMemcpy(&host_max, device_max, sizeof(float), cudaMemcpyDeviceToHost),
+      cudaSuccess, "cudaMemcpy failed");
+  return host_max;
+}
+
+template <typename T>
+__global__ void ApplySaturateKernel(const int n, const T* in, T* out,
+                                    int* num_saturate, const T min,
+                                    const T max) {
+  int bid = threadIdx.x + blockIdx.x * blockDim.x;
+  int tid = threadIdx.x;
+
+  extern __shared__ int shared_count[];
+  shared_count[tid] = 0;
+  for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
+    if (in[i] > max) {
+      out[i] = max;
+      shared_count[tid] += 1;
+    } else if (in[i] < min) {
+      out[i] = min;
+      shared_count[tid] += 1;
+    } else {
+      out[i] = in[i];
+    }
+  }
+  __syncthreads();
+
+  for (int i = blockDim.x / 2; i > 0; i >>= 1) {
+    if (tid < i) {
+      shared_count[tid] += shared_count[tid + i];
+    }
+    __syncthreads();
+  }
+  if (tid == 0) {
+    num_saturate[blockIdx.x] = shared_count[0];
+  }
+}
+
+template <typename T>
+__global__ void ReduceKernel(const int n, const T* in, T* out) {
+  int tid = threadIdx.x;
+  extern __shared__ T shared_sum[];
+  if (tid < n) {
+    shared_sum[tid] = in[tid];
+  } else {
+    shared_sum[tid] = T(0);
+  }
+  __syncthreads();
+  // blockDim.x must >= n
+  for (int i = (n + 1) / 2; i > 0; i >>= 1) {
+    if (tid < i) {
+      shared_sum[tid] += shared_sum[tid + i];
+    }
+    __syncthreads();
+  }
+  if (tid == 0) {
+    out[0] = shared_sum[0];
+  }
+}
+
+template <typename T>
+int ApplySaturateGpu(const platform::CUDADeviceContext& ctx, const int n,
+                     const T* in, T* out, const T min, const T max) {
+  int host_num_saturate;
+  int kNumTheads = 1024;
+  int gridDimx = (n + kNumTheads - 1) / kNumTheads;
+  gridDimx = (gridDimx > kNumTheads) ? kNumTheads : gridDimx;
+  framework::Tensor t;
+  int* device_num_saturate = t.mutable_data<int>(
+      framework::make_ddim({gridDimx}), platform::CUDAPlace());
+  ApplySaturateKernel<
+      T><<<gridDimx, kNumTheads, kNumTheads * sizeof(T), ctx.stream()>>>(
+      n, in, out, device_num_saturate, min, max);
+  ReduceKernel<int><<<1, kNumTheads, kNumTheads * sizeof(T), ctx.stream()>>>(
+      gridDimx, device_num_saturate, device_num_saturate);
+  PADDLE_ENFORCE_EQ(cudaSuccess,
+                    cudaMemcpy(&host_num_saturate, device_num_saturate,
+                               sizeof(int), cudaMemcpyDeviceToHost),
+                    "cudaMemcpy failed");
+  return host_num_saturate;
+}
+
+template <typename DeviceContext, typename T>
+class FakeQuantizeCUDAKernel : public framework::OpKernel<T> {
+ public:
+  T FindRangeAbsMax(const platform::CUDADeviceContext& ctx,
+                    framework::Tensor* scale_list, framework::Tensor* out_scale,
+                    const T& cur_scale, int window_size,
+                    int current_iter) const {
+    T* sl = scale_list->mutable_data<T>(platform::CPUPlace());
+    T remove_tmp = sl[current_iter];
+    sl[current_iter] = cur_scale;
+    T& max_scale = out_scale->mutable_data<T>(platform::CPUPlace())[0];
+    if (max_scale < cur_scale) {
+      max_scale = cur_scale;
+    } else if (fabs(remove_tmp - max_scale) < 1e-6) {
+      int size = (current_iter > window_size) ? window_size : current_iter;
+      max_scale = T(FindAbsMaxGpu(ctx, scale_list->data<float>(), size));
+    }
+    return max_scale;
+  }
+
+  T FindMovingAverageAbsMmax(framework::Tensor* in_scale,
+                             framework::Tensor* out_scale,
+                             const T& cur_scale) const {
+    T* ins = in_scale->mutable_data<T>(platform::CPUPlace());
+    T* outs = out_scale->mutable_data<T>(platform::CPUPlace());
+    outs[0] = 0.9 * cur_scale + 0.1 * ins[0];
+    return T(outs[0]);
+  }
+
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
+                   "This kernel only runs on GPU device.");
+    auto& device_ctx = context.cuda_device_context();
+    auto* tensor = context.Output<framework::Tensor>("Out");
+    auto* in = context.Input<framework::Tensor>("X");
+    const bool is_test = context.Attr<bool>("is_test");
+    tensor->mutable_data<T>(in->place());
+    context.Output<framework::Tensor>("OutMovingScale")
+        ->mutable_data<T>(
+            context.Input<framework::Tensor>("InMovingScale")->place());
+    auto quantize_type =
+        static_cast<std::string>(context.Attr<std::string>("quantize_type"));
+    if (quantize_type == std::string("range_abs_max")) {
+      context.Output<framework::Tensor>("OutScales")
+          ->mutable_data<T>(
+              context.Input<framework::Tensor>("InScales")->place());
+      context.Output<framework::Tensor>("OutCurrentIter")
+          ->mutable_data<T>(
+              context.Input<framework::Tensor>("InCurrentIter")->place());
+    }
+
+    T scale = T(1);
+    int window_size = context.Attr<int>("window_size");
+    T bin_cnt = (T)((1 << (context.Attr<int>("bit_length") - 1)) - 1);
+    if (quantize_type == std::string("abs_max")) {
+      auto* saving_scale = context.Output<framework::Tensor>("OutMovingScale");
+      scale = (T)FindAbsMaxGpu(device_ctx, in->data<float>(), in->numel());
+      saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
+
+      auto& device_ctx = context.template device_context<DeviceContext>();
+      auto* scale_list = context.Output<framework::Tensor>("OutScales");
+      math::SetConstant<DeviceContext, T> scalar;
+      scale_list->mutable_data<T>(context.GetPlace());
+      scalar(device_ctx, scale_list, static_cast<T>(0));
+      auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
+      iter->mutable_data<T>(context.GetPlace());
+      scalar(device_ctx, iter, static_cast<T>(0));
+    } else if (quantize_type == std::string("range_abs_max")) {
+      auto* moving_scale = const_cast<framework::Tensor*>(
+          context.Input<framework::Tensor>("InMovingScale"));
+      if (is_test) {
+        scale = moving_scale->mutable_data<T>(platform::CPUPlace())[0];
+      } else {
+        auto* it = const_cast<framework::Tensor*>(
+            context.Input<framework::Tensor>("InCurrentIter"));
+        auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
+        int* last_iter = it->mutable_data<int>(platform::CPUPlace());
+        int* current_iter = iter->mutable_data<int>(platform::CPUPlace());
+        auto* scale_list = context.Output<framework::Tensor>("OutScales");
+        auto* saving_scale =
+            context.Output<framework::Tensor>("OutMovingScale");
+        scale = (T)FindAbsMaxGpu(device_ctx, in->data<float>(), in->numel());
+        scale = FindRangeAbsMax(device_ctx, scale_list, saving_scale, scale,
+                                window_size, current_iter[0]);
+        (*current_iter) = (*last_iter) + 1;
+      }
+    } else if (quantize_type == std::string("moving_average_abs_max")) {
+      auto* moving_scale = const_cast<framework::Tensor*>(
+          context.Input<framework::Tensor>("InMovingScale"));
+      if (is_test) {
+        scale = moving_scale->mutable_data<T>(platform::CPUPlace())[0];
+      } else {
+        scale = (T)FindAbsMaxGpu(device_ctx, in->data<float>(), in->numel());
+        auto* saving_scale =
+            context.Output<framework::Tensor>("OutMovingScale");
+        scale = FindMovingAverageAbsMmax(
+            const_cast<framework::Tensor*>(moving_scale), saving_scale, scale);
+      }
+    }
+
+    ApplySaturateGpu<T>(device_ctx, in->numel(), in->data<T>(),
+                        tensor->mutable_data<T>(in->place()), -scale, scale);
+    scale = bin_cnt / scale;
+
+    auto& dev =
+        *context.template device_context<DeviceContext>().eigen_device();
+    auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
+    auto eigen_in = framework::EigenVector<T>::Flatten(*tensor);
+    eigen_out.device(dev) = (scale * eigen_in).round();
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_CUDA_KERNEL(fake_quantize,
+                        paddle::operators::FakeQuantizeCUDAKernel<
+                            paddle::platform::CUDADeviceContext, float>,
+                        paddle::operators::FakeQuantizeCUDAKernel<
+                            paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/fake_quantize_op.h

+/* Copyright (c) 2016 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 <string>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/clip_op.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+using platform::Transform;
+
+template <typename DeviceContext, typename T>
+class FakeQuantizeKernel : public framework::OpKernel<T> {
+ public:
+  T FindAbsMax(framework::Tensor* in, int n) const {
+    T* p = in->mutable_data<T>(platform::CPUPlace());
+    T abs_max = (T)0.00000001;
+    for (int i = 0; i < n; i++) {
+      T tmp = fabs(p[i]);
+      if (tmp > abs_max) abs_max = tmp;
+    }
+    return T(abs_max);
+  }
+  T FindRangeAbsMax(framework::Tensor* scale_list, framework::Tensor* out_scale,
+                    const T& cur_scale, int window_size,
+                    int current_iter) const {
+    T* sl = scale_list->mutable_data<T>(platform::CPUPlace());
+    T remove_tmp = sl[current_iter];
+    sl[current_iter] = cur_scale;
+    T& max_scale = out_scale->mutable_data<T>(platform::CPUPlace())[0];
+    if (max_scale < cur_scale) {
+      max_scale = cur_scale;
+    } else if (fabs(remove_tmp - max_scale) < 1e-6) {
+      int size = (current_iter > window_size) ? window_size : current_iter;
+      max_scale = T(FindAbsMax(scale_list, size));
+    }
+    return max_scale;
+  }
+
+  T FindMovingAverageAbsMmax(framework::Tensor* in_scale,
+                             framework::Tensor* out_scale,
+                             const T& cur_scale) const {
+    T* ins = in_scale->mutable_data<T>(platform::CPUPlace());
+    T* outs = out_scale->mutable_data<T>(platform::CPUPlace());
+    outs[0] = 0.9 * cur_scale + 0.1 * ins[0];
+    return T(outs[0]);
+  }
+
+  virtual void Compute(const framework::ExecutionContext& context) const {
+    auto* tensor = context.Output<framework::Tensor>("Out");
+    auto* in = context.Input<framework::Tensor>("X");
+    const bool is_test = context.Attr<bool>("is_test");
+    tensor->mutable_data<T>(in->place());
+
+    auto* oms_tensor = context.Output<framework::Tensor>("OutMovingScale");
+    oms_tensor->mutable_data<T>(in->place());
+
+    auto quantize_type =
+        static_cast<std::string>(context.Attr<std::string>("quantize_type"));
+    if (quantize_type == std::string("range_abs_max")) {
+      auto* oss_tensor = context.Output<framework::Tensor>("OutScales");
+      oss_tensor->mutable_data<T>(
+          context.Input<framework::Tensor>("InScales")->place());
+      auto* oci_tensor = context.Output<framework::Tensor>("OutCurrentIter");
+      oci_tensor->mutable_data<T>(
+          context.Input<framework::Tensor>("InCurrentIter")->place());
+    }
+
+    T scale = static_cast<T>(1);
+    int window_size = context.Attr<int>("window_size");
+    int bit_length = context.Attr<int>("bit_length");
+    int bin_cnt = std::pow(2, bit_length - 1) - 1;
+
+    auto& dev =
+        *context.template device_context<DeviceContext>().eigen_device();
+    auto raw_in = framework::EigenVector<T>::Flatten(*in);
+    if (quantize_type == std::string("abs_max")) {
+      auto* saving_scale = context.Output<framework::Tensor>("OutMovingScale");
+      auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
+      scale_out.device(dev) = raw_in.abs().maximum();
+      scale = scale_out(0);
+
+      auto& device_ctx = context.template device_context<DeviceContext>();
+      auto* scale_list = context.Output<framework::Tensor>("OutScales");
+      math::SetConstant<DeviceContext, T> scalar;
+      scale_list->mutable_data<T>(context.GetPlace());
+      scalar(device_ctx, scale_list, static_cast<T>(0));
+      auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
+      iter->mutable_data<T>(context.GetPlace());
+      scalar(device_ctx, iter, static_cast<T>(0));
+    } else if (quantize_type == std::string("range_abs_max")) {
+      auto* moving_scale = context.Input<framework::Tensor>("InMovingScale");
+      if (is_test) {
+        scale = moving_scale->data<T>()[0];
+      } else {
+        auto* it = context.Input<framework::Tensor>("InCurrentIter");
+        auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
+        const int* last_iter = it->data<int>();
+        int* current_iter = iter->mutable_data<int>(platform::CPUPlace());
+        auto* scale_list = context.Output<framework::Tensor>("OutScales");
+        auto* saving_scale =
+            context.Output<framework::Tensor>("OutMovingScale");
+        auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
+        scale_out.device(dev) = raw_in.abs().maximum();
+        scale = saving_scale->mutable_data<T>(platform::CPUPlace())[0];
+        scale = FindRangeAbsMax(scale_list, saving_scale, scale, window_size,
+                                current_iter[0]);
+        saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
+        (*current_iter) = (*last_iter) + 1;
+      }
+    } else if (quantize_type == std::string("moving_average_abs_max")) {
+      auto* moving_scale = context.Input<framework::Tensor>("InMovingScale");
+      if (is_test) {
+        scale = moving_scale->data<T>()[0];
+      } else {
+        auto* saving_scale =
+            context.Output<framework::Tensor>("OutMovingScale");
+        auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
+        scale_out.device(dev) = raw_in.abs().maximum();
+        scale = saving_scale->mutable_data<T>(platform::CPUPlace())[0];
+        scale = FindMovingAverageAbsMmax(
+            const_cast<framework::Tensor*>(moving_scale), saving_scale, scale);
+        saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
+      }
+    }
+
+    Transform<DeviceContext> trans;
+    trans(context.template device_context<DeviceContext>(), in->data<T>(),
+          in->data<T>() + in->numel(), tensor->mutable_data<T>(in->place()),
+          ClipFunctor<T>(-scale, scale));
+    auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
+    auto eigen_in = framework::EigenVector<T>::Flatten(*tensor);
+    eigen_out.device(dev) = (bin_cnt / scale * eigen_in).round();
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/legacy/capi/examples/model_inference/multi_thread/convert_protobin.sh

-../dense/convert_protobin.sh
\ No newline at end of file

paddle/legacy/capi/examples/model_inference/multi_thread/convert_protobin.sh

+../dense/convert_protobin.sh

paddle/legacy/capi/examples/model_inference/sequence/convert_protobin.sh

-../dense/convert_protobin.sh
\ No newline at end of file

paddle/legacy/capi/examples/model_inference/sequence/convert_protobin.sh

+../dense/convert_protobin.sh

paddle/legacy/capi/examples/model_inference/sparse_binary/convert_protobin.sh

-../dense/convert_protobin.sh
\ No newline at end of file

paddle/legacy/capi/examples/model_inference/sparse_binary/convert_protobin.sh

+../dense/convert_protobin.sh

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

+#   Copyright (c) 2018 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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestFakeQuantizeOp(OpTest):
+    def setUp(self):
+        self.op_type = "fake_quantize"
+        self.attrs = {
+            'bit_length': 8,
+            'quantize_type': 'abs_max',
+            'window_size': 10000
+        }
+        self.inputs = {
+            'X': np.random.random((10, 10)).astype("float32"),
+            'InScales': np.zeros(self.attrs['window_size']).astype("float32"),
+            'InCurrentIter': np.zeros(1).astype("float32"),
+            'InMovingScale': np.zeros(1).astype("float32")
+        }
+        self.scale = {
+            'abs_max': np.max(np.abs(self.inputs['X'])).astype("float32")
+        }
+        self.outputs = {
+            'Out': np.round(self.inputs['X'] / self.scale['abs_max'] * (
+                (1 << (self.attrs['bit_length'] - 1)) - 1)),
+            'OutScales': np.zeros(self.attrs['window_size']).astype("float32"),
+            'OutMovingScale':
+            np.array([self.scale['abs_max']]).astype("float32"),
+            'OutCurrentIter': np.zeros(1).astype("float32")
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == "__main__":
+    unittest.main()