Improve fake_dequantize_op.

paddle/fluid/operators/fake_dequantize_op.cc

@@ -18,15 +18,32 @@ limitations under the License. */
 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* scale,
+                  T max_range, framework::Tensor* out) {
+    auto in_e = framework::EigenVector<T>::Flatten(*in);
+    const T* scale_factor = scale->data<T>();
+    auto out_e = framework::EigenVector<T>::Flatten(*out);
+
+    auto& dev = *dev_ctx.eigen_device();
+    out_e.device(dev) = (scale_factor[0] / max_range) * in_e;
+  }
+};
+
+template struct DequantizeFunctor<platform::CPUDeviceContext, float>;
+template struct DequantizeFunctor<platform::CPUDeviceContext, double>;
+
 class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
  public:
-  FakeDequantizeMaxAbsOp(const std::string &type,
-                         const framework::VariableNameMap &inputs,
-                         const framework::VariableNameMap &outputs,
-                         const framework::AttributeMap &attrs)
+  FakeDequantizeMaxAbsOp(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 {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of FakeDequantizeMaxAbsOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
@@ -42,21 +59,17 @@ class FakeDequantizeMaxAbsOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "(Tensor) The input with float-32/64 type is the "
              "low precision tensor.");
+    AddInput("Scale", "(float) The scale in quantization stage.");
     AddOutput("Out",
               "(Tensor) The output is the dequantized high "
               "precision tensor.");
-    AddAttr<int>("num_bits",
-                 "(int) `num_bits` is the quantization level bits, "
-                 "such as 2, 5, 8.");
-    AddAttr<float>("scale",
-                   "(float) The maximum absolute value of low precision tensor."
-                   "It is usually calculated by the fake_quantize_max_abs_op.");
+    AddAttr<float>("max_range", "(float) The max range in quantization stage.");
     AddComment(R"DOC(
 FakeDequantizeMaxAbsOp operator.
 
 This calculation is an opposite operation of FakeQuantizeMaxAbsOp:
 
-$$Out = \frac{scale*X}{2^{num_bits} - 1}$$
+$$Out = \frac{scale*X}{ max_range }$$
 
 )DOC");
   }

paddle/fluid/operators/fake_dequantize_op.cu

@@ -14,6 +14,42 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/fake_dequantize_op.h"
 
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void KeDequantize(const T* in, const T* scale, T max_range, int num,
+                             T* out) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < num) {
+    out[idx] = in[idx] * scale[0] / max_range;
+  }
+}
+
+template <typename T>
+struct DequantizeFunctor<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& dev_ctx,
+                  const framework::Tensor* in, const framework::Tensor* scale,
+                  T max_range, framework::Tensor* out) {
+    const T* in_data = in->data<T>();
+    const T* scale_factor = scale->data<T>();
+    T* out_data = out->mutable_data<T>(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, scale_factor, max_range, num, out_data);
+  }
+};
+
+template struct DequantizeFunctor<platform::CUDADeviceContext, float>;
+template struct DequantizeFunctor<platform::CUDADeviceContext, double>;
+
+}  // namespace operators
+}  // namespace paddle
+
 namespace ops = paddle::operators;
 using CUDA = paddle::platform::CUDADeviceContext;
 REGISTER_OP_CUDA_KERNEL(fake_dequantize_max_abs,

paddle/fluid/operators/fake_dequantize_op.h

@@ -19,22 +19,29 @@ limitations under the License. */
 
 namespace paddle {
 namespace operators {
+
+template <typename DeviceContext, typename T>
+struct DequantizeFunctor {
+  void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
+                  const framework::Tensor* scale, T max_range,
+                  framework::Tensor* out);
+};
+
 template <typename DeviceContext, typename T>
 class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
  public:
   virtual void Compute(const framework::ExecutionContext& ctx) const {
     auto* in = ctx.Input<framework::Tensor>("X");
+    auto* scale = ctx.Input<framework::Tensor>("Scale");
     auto* out = ctx.Output<framework::Tensor>("Out");
-    out->mutable_data<T>(in->place());
 
-    int num_bits = ctx.Attr<int>("num_bits");
-    T scale = static_cast<T>(ctx.Attr<float>("scale"));
-    int range = std::pow(2, num_bits) - 1;
+    float max_range = ctx.Attr<float>("max_range");
+
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    out->mutable_data<T>(dev_ctx.GetPlace());
 
-    auto eigen_out = framework::EigenVector<T>::Flatten(*out);
-    auto eigen_in = framework::EigenVector<T>::Flatten(*in);
-    auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
-    eigen_out.device(dev) = (scale / range) * eigen_in;
+    DequantizeFunctor<DeviceContext, T>()(dev_ctx, in, scale,
+                                          static_cast<T>(max_range), out);
   }
 };
 

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

@@ -20,41 +20,50 @@
 from op_test import OpTest
 
 
-def quantize_max_abs(x, num_bits):
-    range = math.pow(2, num_bits) - 1
+def quantize_max_abs(x, max_range):
     scale = np.max(np.abs(x).flatten())
-    y = np.round(x / scale * range)
+    y = np.round(x / scale * max_range)
     return y, scale
 
 
-def dequantize_max_abs(x, num_bits, scale):
-    range = math.pow(2, num_bits) - 1
-    y = (scale / range) * x
+def dequantize_max_abs(x, scale, max_range):
+    y = (scale / max_range) * x
     return y
 
 
 class TestFakeDequantizeMaxAbsOp(OpTest):
     def set_args(self):
         self.num_bits = 8
+        self.max_range = math.pow(2, self.num_bits - 1) - 1
+        self.data_type = "float32"
 
     def setUp(self):
         self.set_args()
         self.op_type = "fake_dequantize_max_abs"
-        x = np.random.randn(31, 65).astype("float32")
-        yq, scale = quantize_max_abs(x, self.num_bits)
-        ydq = dequantize_max_abs(yq, self.num_bits, scale)
+        x = np.random.randn(31, 65).astype(self.data_type)
+        yq, scale = quantize_max_abs(x, self.max_range)
+        ydq = dequantize_max_abs(yq, scale, self.max_range)
 
-        self.inputs = {'X': yq}
-        self.attrs = {'num_bits': self.num_bits, 'scale': float(scale)}
+        self.inputs = {'X': yq, 'Scale': np.array(scale).astype(self.data_type)}
+        self.attrs = {'max_range': self.max_range}
         self.outputs = {'Out': ydq}
 
     def test_check_output(self):
         self.check_output()
 
 
-class TestFakeDequantizeMaxAbsOp5Bits(OpTest):
+class TestFakeDequantizeMaxAbsOpDouble(TestFakeDequantizeMaxAbsOp):
+    def set_args(self):
+        self.num_bits = 8
+        self.max_range = math.pow(2, self.num_bits - 1) - 1
+        self.data_type = "float64"
+
+
+class TestFakeDequantizeMaxAbsOp5Bits(TestFakeDequantizeMaxAbsOp):
     def set_args(self):
         self.num_bits = 5
+        self.max_range = math.pow(2, self.num_bits - 1) - 1
+        self.data_type = "float32"
 
 
 if __name__ == "__main__":