Merge pull request #9143 from kexinzhao/numpy_conv2d_pool2d_fp16

Add float16 support for cudnn conv2d

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -18,6 +18,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/conv_op.h"
 #include "paddle/fluid/platform/assert.h"
 #include "paddle/fluid/platform/cudnn_helper.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -133,7 +134,8 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
     cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- cudnn conv forward ---------------------
-    T alpha = 1.0f, beta = 0.0f;
+    typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
+                                                          beta = 0.0f;
     for (int i = 0; i < groups; i++) {
       PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
           handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
@@ -280,7 +282,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
     cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- cudnn conv backward data ---------------------
-    T alpha = 1.0f, beta = 0.0f;
+    typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
+                                                          beta = 0.0f;
     if (input_grad) {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.
@@ -315,16 +318,18 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OP_KERNEL(conv2d, CUDNN, ::paddle::platform::CUDAPlace,
+namespace plat = paddle::platform;
+REGISTER_OP_KERNEL(conv2d, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvOpKernel<float>,
-                   paddle::operators::CUDNNConvOpKernel<double>);
-REGISTER_OP_KERNEL(conv2d_grad, CUDNN, ::paddle::platform::CUDAPlace,
+                   paddle::operators::CUDNNConvOpKernel<double>,
+                   paddle::operators::CUDNNConvOpKernel<plat::float16>);
+REGISTER_OP_KERNEL(conv2d_grad, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvGradOpKernel<float>,
                    paddle::operators::CUDNNConvGradOpKernel<double>);
 
-REGISTER_OP_KERNEL(conv3d, CUDNN, ::paddle::platform::CUDAPlace,
+REGISTER_OP_KERNEL(conv3d, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvOpKernel<float>,
                    paddle::operators::CUDNNConvOpKernel<double>);
-REGISTER_OP_KERNEL(conv3d_grad, CUDNN, ::paddle::platform::CUDAPlace,
+REGISTER_OP_KERNEL(conv3d_grad, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvGradOpKernel<float>,
                    paddle::operators::CUDNNConvGradOpKernel<double>);

paddle/fluid/operators/conv_op.cc

@@ -83,12 +83,23 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
   }
 #endif
 
+  auto input_data_type =
+      framework::ToDataType(ctx.Input<Tensor>("Input")->type());
+  auto filter_data_type =
+      framework::ToDataType(ctx.Input<Tensor>("Filter")->type());
+  PADDLE_ENFORCE_EQ(input_data_type, filter_data_type,
+                    "input and filter data type should be consistent");
+
+  if (input_data_type == framework::proto::VarType::FP16) {
+    PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
+                      "float16 can only be used when CUDNN is used");
+  }
+
   std::string data_format = ctx.Attr<std::string>("data_format");
   // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
   framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
-  return framework::OpKernelType(
-      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
-      layout_, library_);
+  return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
+                                 library_);
 }
 
 Conv2DOpMaker::Conv2DOpMaker(OpProto* proto, OpAttrChecker* op_checker)

paddle/fluid/platform/cudnn_helper.h

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/platform/dynload/cudnn.h"
 #include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/float16.h"
 #include "paddle/fluid/platform/macros.h"
 
 namespace paddle {
@@ -80,6 +81,22 @@ enum class PoolingMode {
 template <typename T>
 class CudnnDataType;
 
+template <>
+class CudnnDataType<float16> {
+ public:
+  static const cudnnDataType_t type = CUDNN_DATA_HALF;
+  // The scaling param type is float for HALF and FLOAT tensors
+  typedef const float ScalingParamType;
+  static ScalingParamType* kOne() {
+    static ScalingParamType v = 1.0;
+    return &v;
+  }
+  static ScalingParamType* kZero() {
+    static ScalingParamType v = 0.0;
+    return &v;
+  }
+};
+
 template <>
 class CudnnDataType<float> {
  public:

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

@@ -469,6 +469,28 @@ def _numpy_to_lod_tensor(np_value, lod, place):
             tensor.set_lod(lod)
         return tensor
 
+    @staticmethod
+    def np_dtype_to_fluid_dtype(input):
+        """Change the dtype of float16 numpy array
+
+        numpy float16 is binded to paddle::platform::float16 
+        in tensor_py.h via the help of uint16 data type since
+        the internal memory representation of float16 is 
+        uint16_t in paddle and np.uint16 in numpy, which are
+        themselves binded together by pybind.
+
+        Args:
+            input: input numpy array
+
+        Returns:
+            input: if the dtype of input is np.float16, its dtype will be
+                changed to np.uint16 so that the internal memory will be 
+                reinterpreted input as of dtype np.uint16. 
+        """
+        if input.dtype == np.float16:
+            input.dtype = np.uint16
+        return input
+
     def _get_gradient(self, input_to_check, place, output_names, no_grad_set):
         prog = Program()
         block = prog.global_block()

python/paddle/fluid/tests/unittests/test_conv2d_op.py

@@ -68,19 +68,24 @@ def setUp(self):
         self.init_op_type()
         self.init_group()
         self.init_dilation()
+        self.init_data_type()
         self.init_test_case()
 
         conv2d_param = {
             'stride': self.stride,
             'pad': self.pad,
             'dilation': self.dilations
         }
-        input = np.random.random(self.input_size).astype("float32")
-        filter = np.random.random(self.filter_size).astype("float32")
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        filter = np.random.random(self.filter_size).astype(self.dtype)
         output = conv2d_forward_naive(input, filter, self.groups,
-                                      conv2d_param).astype('float32')
+                                      conv2d_param).astype(self.dtype)
 
-        self.inputs = {'Input': input, 'Filter': filter}
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
         self.attrs = {
             'strides': self.stride,
             'paddings': self.pad,
@@ -99,6 +104,8 @@ def test_check_output(self):
             self.check_output()
 
     def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
         if self.use_cudnn:
             place = core.CUDAPlace(0)
             self.check_grad_with_place(
@@ -111,6 +118,8 @@ def test_check_grad(self):
                 set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
 
     def test_check_grad_no_filter(self):
+        if self.dtype == np.float16:
+            return
         if self.use_cudnn:
             place = core.CUDAPlace(0)
             self.check_grad_with_place(
@@ -126,6 +135,8 @@ def test_check_grad_no_filter(self):
                 no_grad_set=set(['Filter']))
 
     def test_check_grad_no_input(self):
+        if self.dtype == np.float16:
+            return
         if self.use_cudnn:
             place = core.CUDAPlace(0)
             self.check_grad_with_place(
@@ -148,6 +159,9 @@ def init_test_case(self):
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 3, 3]
 
+    def init_data_type(self):
+        self.dtype = np.float32
+
     def init_dilation(self):
         self.dilations = [1, 1]
 
@@ -232,36 +246,102 @@ def init_op_type(self):
         self.op_type = "conv2d"
 
 
+class TestFP16CUDNN(TestCUDNN):
+    def init_data_type(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=2e-2)
+
+
 class TestCUDNNWithPad(TestWithPad):
     def init_op_type(self):
         self.use_cudnn = True
         self.op_type = "conv2d"
 
 
+class TestFP16CUDNNWithPad(TestCUDNNWithPad):
+    def init_data_type(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=2e-2)
+
+
 class TestCUDNNWithStride(TestWithStride):
     def init_op_type(self):
         self.use_cudnn = True
         self.op_type = "conv2d"
 
 
+class TestFP16CUDNNWithStride(TestCUDNNWithStride):
+    def init_data_type(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=2e-2)
+
+
 class TestCUDNNWithGroup(TestWithGroup):
     def init_op_type(self):
         self.use_cudnn = True
         self.op_type = "conv2d"
 
 
+class TestFP16CUDNNWithGroup(TestCUDNNWithGroup):
+    def init_data_type(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=2e-2)
+
+
 class TestCUDNNWith1x1(TestWith1x1):
     def init_op_type(self):
         self.use_cudnn = True
         self.op_type = "conv2d"
 
 
+class TestFP16CUDNNWith1x1(TestCUDNNWith1x1):
+    def init_data_type(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=2e-2)
+
+
 class TestCUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
     def init_op_type(self):
         self.use_cudnn = True
         self.op_type = "conv2d"
 
 
+class TestFP16CUDNNWithInput1x1Filter1x1(TestCUDNNWithInput1x1Filter1x1):
+    def init_data_type(self):
+        self.dtype = np.float16
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                self.check_output_with_place(place, atol=2e-2)
+
+
 class TestDepthwiseConv(TestConv2dOp):
     def init_test_case(self):
         self.pad = [1, 1]