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PaddleDetection
未验证 提交 df99b16a 编写于 作者: K Kexin Zhao 提交者： GitHub
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Merge pull request #9167 from kexinzhao/pool2d_fp16 

Add float16 support for pool 2d operator
上级 3f5705c3 dfec1df1
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Showing with 152 addition and 102 deletion
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ df99b16a
......@@ -28,6 +28,8 @@ using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
using DataLayout = platform::DataLayout;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
static_cast<size_t>(1024) * 1024 * 1024;
......@@ -134,8 +136,7 @@ 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 ---------------------
typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
beta = 0.0f;
ScalingParamType<T> 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,
......@@ -282,8 +283,7 @@ 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 ---------------------
typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
beta = 0.0f;
ScalingParamType<T> 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.
......
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @ df99b16a
......@@ -24,6 +24,8 @@ using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using ScopedPoolingDescriptor = platform::ScopedPoolingDescriptor;
using DataLayout = platform::DataLayout;
using PoolingMode = platform::PoolingMode;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
template <typename T>
class PoolCUDNNOpKernel : public framework::OpKernel<T> {
......@@ -78,8 +80,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn pool algorithm ---------------------
auto handle = ctx.cuda_device_context().cudnn_handle();
T alpha = 1.0f, beta = 0.0f;
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
PADDLE_ENFORCE(platform::dynload::cudnnPoolingForward(
handle, cudnn_pool_desc, &alpha, cudnn_input_desc, input_data, &beta,
cudnn_output_desc, output_data));
......@@ -144,8 +145,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn pool algorithm ---------------------
auto handle = ctx.cuda_device_context().cudnn_handle();
T alpha = 1.0f, beta = 0.0f;
ScalingParamType<T> 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.
......@@ -162,17 +162,19 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_KERNEL(pool2d, CUDNN, ::paddle::platform::CUDAPlace,
REGISTER_OP_KERNEL(pool2d, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNOpKernel<float>,
ops::PoolCUDNNOpKernel<double>);
REGISTER_OP_KERNEL(pool2d_grad, CUDNN, ::paddle::platform::CUDAPlace,
ops::PoolCUDNNOpKernel<double>,
ops::PoolCUDNNOpKernel<plat::float16>);
REGISTER_OP_KERNEL(pool2d_grad, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNGradOpKernel<float>,
ops::PoolCUDNNGradOpKernel<double>);
REGISTER_OP_KERNEL(pool3d, CUDNN, ::paddle::platform::CUDAPlace,
REGISTER_OP_KERNEL(pool3d, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNOpKernel<float>,
ops::PoolCUDNNOpKernel<double>);
REGISTER_OP_KERNEL(pool3d_grad, CUDNN, ::paddle::platform::CUDAPlace,
REGISTER_OP_KERNEL(pool3d_grad, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNGradOpKernel<float>,
ops::PoolCUDNNGradOpKernel<double>);
paddle/fluid/operators/pool_op.cc
浏览文件 @ df99b16a
......@@ -124,11 +124,15 @@ framework::OpKernelType PoolOpGrad::GetExpectedKernelType(
}
#endif
auto input_data_type = framework::ToDataType(ctx.Input<Tensor>("X")->type());
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");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
layout_, library_);
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
library_);
}
Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ df99b16a
......@@ -483,9 +483,9 @@ class OpTest(unittest.TestCase):
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.
input: The dtype of input will be changed to np.uint16 if
it is originally np.float16, such that the internal memory
of input will be reinterpreted as of dtype np.uint16.
"""
if input.dtype == np.float16:
input.dtype = np.uint16
......
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ df99b16a
......@@ -63,12 +63,13 @@ def conv2d_forward_naive(input, filter, group, conv_param):
class TestConv2dOp(OpTest):
def setUp(self):
self.op_type = "conv2d"
self.use_cudnn = False
self.use_mkldnn = False
self.init_op_type()
self.dtype = np.float32
self.init_kernel_type()
self.init_group()
self.init_dilation()
self.init_data_type()
self.init_test_case()
conv2d_param = {
......@@ -159,17 +160,14 @@ class TestConv2dOp(OpTest):
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]
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv2d"
def init_kernel_type(self):
pass
class TestWithPad(TestConv2dOp):
......@@ -241,13 +239,13 @@ class TestWithInput1x1Filter1x1(TestConv2dOp):
#----------------Conv2dCUDNN----------------
class TestCUDNN(TestConv2dOp):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv2d"
class TestFP16CUDNN(TestCUDNN):
def init_data_type(self):
class TestFP16CUDNN(TestConv2dOp):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
......@@ -258,13 +256,13 @@ class TestFP16CUDNN(TestCUDNN):
class TestCUDNNWithPad(TestWithPad):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv2d"
class TestFP16CUDNNWithPad(TestCUDNNWithPad):
def init_data_type(self):
class TestFP16CUDNNWithPad(TestWithPad):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
......@@ -275,13 +273,13 @@ class TestFP16CUDNNWithPad(TestCUDNNWithPad):
class TestCUDNNWithStride(TestWithStride):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv2d"
class TestFP16CUDNNWithStride(TestCUDNNWithStride):
def init_data_type(self):
class TestFP16CUDNNWithStride(TestWithStride):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
......@@ -292,13 +290,13 @@ class TestFP16CUDNNWithStride(TestCUDNNWithStride):
class TestCUDNNWithGroup(TestWithGroup):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv2d"
class TestFP16CUDNNWithGroup(TestCUDNNWithGroup):
def init_data_type(self):
class TestFP16CUDNNWithGroup(TestWithGroup):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
......@@ -309,13 +307,13 @@ class TestFP16CUDNNWithGroup(TestCUDNNWithGroup):
class TestCUDNNWith1x1(TestWith1x1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv2d"
class TestFP16CUDNNWith1x1(TestCUDNNWith1x1):
def init_data_type(self):
class TestFP16CUDNNWith1x1(TestWith1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
......@@ -326,13 +324,13 @@ class TestFP16CUDNNWith1x1(TestCUDNNWith1x1):
class TestCUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "conv2d"
class TestFP16CUDNNWithInput1x1Filter1x1(TestCUDNNWithInput1x1Filter1x1):
def init_data_type(self):
class TestFP16CUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
......@@ -375,21 +373,18 @@ class TestDepthwiseConv2(TestConv2dOp):
#----------------Conv2dMKLDNN----------------
class TestMKLDNN(TestConv2dOp):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "conv2d"
class TestMKLDNNWithPad(TestWithPad):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "conv2d"
class TestMKLDNNWithStride(TestWithStride):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "conv2d"
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @ df99b16a
......@@ -78,20 +78,22 @@ def avg_pool2D_forward_naive(x,
class TestPool2d_Op(OpTest):
def setUp(self):
self.op_type = "pool2d"
self.use_cudnn = False
self.use_mkldnn = False
self.dtype = np.float32
self.init_test_case()
self.init_global_pool()
self.init_op_type()
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype("float32")
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool2D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool,
self.ceil_mode).astype("float32")
self.inputs = {'X': input}
self.ceil_mode).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
'strides': self.strides,
......@@ -105,7 +107,7 @@ class TestPool2d_Op(OpTest):
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}
self.outputs = {'Out': output.astype('float32')}
self.outputs = {'Out': output}
def test_check_output(self):
if self.use_cudnn:
......@@ -115,6 +117,8 @@ class TestPool2d_Op(OpTest):
self.check_output()
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.use_cudnn and self.pool_type != "max":
place = core.CUDAPlace(0)
self.check_grad_with_place(
......@@ -128,8 +132,8 @@ class TestPool2d_Op(OpTest):
self.strides = [1, 1]
self.paddings = [0, 0]
def init_op_type(self):
self.op_type = "pool2d"
def init_kernel_type(self):
pass
def init_pool_type(self):
self.pool_type = "avg"
......@@ -149,9 +153,6 @@ class TestCase1(TestPool2d_Op):
self.strides = [1, 1]
self.paddings = [0, 0]
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
......@@ -167,9 +168,6 @@ class TestCase2(TestPool2d_Op):
self.strides = [1, 1]
self.paddings = [1, 1]
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "avg"
self.pool2D_forward_naive = avg_pool2D_forward_naive
......@@ -179,27 +177,18 @@ class TestCase2(TestPool2d_Op):
class TestCase3(TestPool2d_Op):
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
class TestCase4(TestCase1):
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
class TestCase5(TestCase2):
def init_op_type(self):
self.op_type = "pool2d"
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
......@@ -207,39 +196,105 @@ class TestCase5(TestCase2):
#--------------------test pool2d--------------------
class TestCUDNNCase1(TestPool2d_Op):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool2d"
class TestFP16CUDNNCase1(TestPool2d_Op):
def init_kernel_type(self):
self.use_cudnn = True
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=1e-3)
class TestCUDNNCase2(TestCase1):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool2d"
class TestFP16CUDNNCase2(TestCase1):
def init_kernel_type(self):
self.use_cudnn = True
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=1e-3)
class TestCUDNNCase3(TestCase2):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool2d"
class TestFP16CUDNNCase3(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
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=1e-3)
class TestCUDNNCase4(TestCase3):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool2d"
class TestFP16CUDNNCase4(TestCase3):
def init_kernel_type(self):
self.use_cudnn = True
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=1e-3)
class TestCUDNNCase5(TestCase4):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool2d"
class TestFP16CUDNNCase5(TestCase4):
def init_kernel_type(self):
self.use_cudnn = True
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=1e-3)
class TestCUDNNCase6(TestCase5):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
self.op_type = "pool2d"
class TestFP16CUDNNCase6(TestCase5):
def init_kernel_type(self):
self.use_cudnn = True
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=1e-3)
class TestCeilModeCase1(TestCUDNNCase1):
......@@ -264,39 +319,33 @@ class TestCeilModeCase4(TestCase2):
#--------------------test pool2d MKLDNN--------------------
class TestMKLDNNCase1(TestPool2d_Op):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase2(TestCase1):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase3(TestCase2):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase4(TestCase3):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase5(TestCase4):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
class TestMKLDNNCase6(TestCase5):
def init_op_type(self):
def init_kernel_type(self):
self.use_mkldnn = True
self.op_type = "pool2d"
if __name__ == '__main__':
......
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