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Call new cudnn batch norm API regardless of data type and data layout (#30157)

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revert-31068-fix_conv3d_windows
AshburnLee 4 years ago committed by GitHub
parent 5c8455d6ea
commit 666efc2336
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 156 additions and 187 deletions
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33
paddle/fluid/operators/batch_norm_op.cu
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@ -114,7 +114,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
<< "CUDNN_BN_MIN_EPSILON instead.";
}
epsilon = std::max(epsilon, CUDNN_BN_MIN_EPSILON);
#if CUDNN_VERSION_MIN(7, 0, 0)
#if CUDNN_VERSION_MIN(7, 0, 1)
if (FLAGS_cudnn_batchnorm_spatial_persistent) {
mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
} else {
@ -122,7 +122,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
}
#else
mode_ = CUDNN_BATCHNORM_SPATIAL;
#endif
#endif // CUDNN_VERSION_MIN(7, 0, 1)
VLOG(3) << "Setting descriptors.";
std::vector<int> dims;
@ -151,7 +151,10 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
auto handle = dev_ctx.cudnn_handle();
// Now, depending on whether we are running test or not, we have two paths.
if (test_mode || use_global_stats) {
// It is training mode when it's not reference AND not using pre-trained
// model.
bool training = !test_mode && !use_global_stats;
if (!training) {
// only when test we use input to do computation.
const auto *est_mean = ctx.Input<Tensor>("Mean");
const auto *est_var = ctx.Input<Tensor>("Variance");
@ -234,7 +237,6 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
bool called = false;
#if CUDNN_VERSION_MIN(7, 4, 1)
if (compute_format == DataLayout::kNHWC) {
called = true;
size_t workspace_size = 0;
size_t reserve_space_size = 0;
@ -281,11 +283,11 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
ctx.GetPlace(), transformed_x.type(), workspace_size);
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationForwardTrainingEx(
handle, mode_, CUDNN_BATCHNORM_OPS_BN,
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
data_desc_, transformed_x.template data<T>(), nullptr,
nullptr, data_desc_, transformed_y.template data<T>(),
bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
handle, mode_, CUDNN_BATCHNORM_OPS_BN, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_,
transformed_x.template data<T>(), nullptr, nullptr, data_desc_,
transformed_y.template data<T>(), bn_param_desc_,
scale->template data<BatchNormParamType<T>>(),
bias->template data<BatchNormParamType<T>>(), this_factor,
mean_out->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
@ -298,8 +300,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
ctx.GetPlace()),
nullptr, workspace_ptr, workspace_size, reserve_space_ptr,
reserve_space_size));
}
#endif
#endif // CUDNN_VERSION_MIN(7, 4, 1)
if (!called) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationForwardTraining(
@ -640,7 +641,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
<< "CUDNN_BN_MIN_EPSILON instead.";
}
epsilon = std::max(epsilon, CUDNN_BN_MIN_EPSILON);
#if CUDNN_VERSION_MIN(7, 0, 0)
#if CUDNN_VERSION_MIN(7, 0, 1)
if (FLAGS_cudnn_batchnorm_spatial_persistent) {
mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
} else {
@ -648,7 +649,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
}
#else
mode_ = CUDNN_BATCHNORM_SPATIAL;
#endif
#endif // CUDNN_VERSION_MIN(7, 0, 1)
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
data_desc_, CudnnDataType<T>::type,
@ -672,10 +673,10 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
num, transformed_x.data<T>(), grid2, block, stream);
}
// This branch calls CUDNN APIs
if (d_scale && d_bias) {
bool called = false;
#if CUDNN_VERSION_MIN(7, 4, 1)
if (compute_format == DataLayout::kNHWC) {
called = true;
size_t workspace_size = 0;
void *workspace_ptr = nullptr;
@ -738,8 +739,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
/*reserveSpace=*/const_cast<T *>(
reserve_space->template data<T>()),
/*reserveSpaceSizeInBytes=*/reserve_space_size));
}
#endif
#endif // CUDNN_VERSION_MIN(7, 4, 1)
if (!called) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationBackward(
@ -764,6 +764,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
ctx, &transformed_d_x, d_x);
}
} else {
// This branch call CUDA kernels
if (compute_format == DataLayout::kNCHW) {
if (d_x) {
BNBackwardData<T, block, framework::DataLayout::kNCHW><<<

3
paddle/fluid/operators/inplace_abn_op.cc
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@ -178,6 +178,9 @@ class InplaceABNOpGradMaker : public framework::SingleGradOpMaker<T> {
op->SetInput("Bias", this->Input("Bias"));
op->SetInput("SavedMean", this->Output("SavedMean"));
op->SetInput("SavedVariance", this->Output("SavedVariance"));
if (this->HasOutput("ReserveSpace")) {
op->SetInput("ReserveSpace", this->Output("ReserveSpace"));
}
// used when setting use_global_stats True during training
if (BOOST_GET_CONST(bool, this->GetAttr("use_global_stats"))) {

14
python/paddle/fluid/dygraph/nn.py
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@ -1309,12 +1309,6 @@ class BatchNorm(layers.Layer):
dtype=self._dtype)
self._variance.stop_gradient = True
self._has_reserve_space = False
if data_layout == 'NHWC':
flag = os.environ.get('FLAGS_cudnn_batchnorm_spatial_persistent')
if flag is not None and flag.lower() in ['true', '1']:
self._has_reserve_space = True
self._in_place = in_place
self._data_layout = data_layout
self._momentum = momentum
@ -1341,7 +1335,6 @@ class BatchNorm(layers.Layer):
batch_norm_out, _, _, _, _, _ = core.ops.batch_norm(
input, self.weight, self.bias, self._mean, self._variance,
mean_out, variance_out, *attrs)
return dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=self._act, use_mkldnn=self._use_mkldnn)
@ -1371,11 +1364,8 @@ class BatchNorm(layers.Layer):
dtype=self._dtype, stop_gradient=True)
saved_variance = self._helper.create_variable_for_type_inference(
dtype=self._dtype, stop_gradient=True)
reserve_space = None
if self._has_reserve_space:
reserve_space = self._helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.FP16, stop_gradient=True)
dtype=self._helper.input_dtype(input), stop_gradient=True)
batch_norm_out = input if self._in_place else self._helper.create_variable_for_type_inference(
self._dtype)
@ -1388,7 +1378,7 @@ class BatchNorm(layers.Layer):
"SavedVariance": [saved_variance]
}
if reserve_space is not None:
outputs["ReserveSpace"] = reserve_space
outputs["ReserveSpace"] = [reserve_space]
self._helper.append_op(
type="batch_norm", inputs=inputs, outputs=outputs, attrs=attrs)

26
python/paddle/fluid/layers/nn.py
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@ -2792,12 +2792,6 @@ def batch_norm(input,
'batch_norm')
dtype = helper.input_dtype()
has_reserve_space = False
if data_layout == 'NHWC':
flag = os.environ.get('FLAGS_cudnn_batchnorm_spatial_persistent')
if flag is not None and flag.lower() in ['true', '1']:
has_reserve_space = True
# use fp32 for bn parameter
if dtype == core.VarDesc.VarType.FP16:
dtype = core.VarDesc.VarType.FP32
@ -2845,17 +2839,16 @@ def batch_norm(input,
# create output
# mean and mean_out share the same memory
mean_out = mean
# variance and variance out share the same memory
# variance and variance_out share the same memory
variance_out = variance
saved_mean = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
reserve_space = None
if has_reserve_space:
if not is_test:
reserve_space = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.FP16, stop_gradient=True)
dtype=helper.input_dtype(), stop_gradient=True)
batch_norm_out = input if in_place else \
helper.create_variable_for_type_inference(dtype)
@ -2998,12 +2991,6 @@ def inplace_abn(input,
'inplace_abn')
dtype = helper.input_dtype()
has_reserve_space = False
if data_layout == 'NHWC':
flag = os.environ.get('FLAGS_cudnn_batchnorm_spatial_persistent')
if flag is not None and flag.lower() in ['true', '1']:
has_reserve_space = True
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
@ -3053,12 +3040,8 @@ def inplace_abn(input,
dtype=dtype, stop_gradient=True)
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
reserve_space = None
if has_reserve_space:
reserve_space = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.FP16, stop_gradient=True)
dtype=dtype, stop_gradient=True)
batch_norm_out = input
inputs = {
@ -3082,7 +3065,6 @@ def inplace_abn(input,
inputs['MomemtumTensor'] = momentum
else:
attrs['momentum'] = momentum
outputs = {
"Y": batch_norm_out,
"MeanOut": mean_out,

10
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
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@ -440,16 +440,8 @@ class TestBatchNormOpTraining(unittest.TestCase):
"SavedMean": block.var('saved_mean'),
"SavedVariance": block.var('saved_variance')
}
has_reserve_space = False
if data_format == 'NHWC':
flag = os.environ.get(
'FLAGS_cudnn_batchnorm_spatial_persistent')
if flag is not None and flag.lower() in ['true', '1']:
has_reserve_space = True
if has_reserve_space:
block.create_var(name="reserve_space", dtype='float16')
block.create_var(name="reserve_space", dtype='float32')
outputs["ReserveSpace"] = block.var('reserve_space')
del os.environ['FLAGS_cudnn_batchnorm_spatial_persistent']
bn_op = block.append_op(
type="batch_norm",
inputs=inputs,

4
python/paddle/fluid/tests/unittests/test_sync_batch_norm_op.py
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@ -122,7 +122,7 @@ class TestSyncBatchNormOpTraining(unittest.TestCase):
if not only_forward:
others = [
'batch_norm_0.tmp_0', 'batch_norm_0.tmp_1', 'bn_scale@GRAD',
'bn_bias@GRAD', 'batch_norm_0.tmp_2@GRAD', 'conv2d_0.tmp_0@GRAD'
'bn_bias@GRAD', 'batch_norm_0.tmp_3@GRAD', 'conv2d_0.tmp_0@GRAD'
]
fetch_names += others
bn_fetches = exe.run(program=main,
@ -142,7 +142,7 @@ class TestSyncBatchNormOpTraining(unittest.TestCase):
if not only_forward:
others = [
'batch_norm_0.tmp_0', 'batch_norm_0.tmp_1', 'bn_scale@GRAD',
'bn_bias@GRAD', 'batch_norm_0.tmp_2@GRAD', 'conv2d_0.tmp_0@GRAD'
'bn_bias@GRAD', 'batch_norm_0.tmp_3@GRAD', 'conv2d_0.tmp_0@GRAD'
]
fetch_names += others
for nm in fetch_names:

7
python/paddle/nn/functional/norm.py
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@ -166,7 +166,6 @@ def batch_norm(x,
batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv, w, b)
print(batch_norm_out)
"""
assert len(x.shape) >= 2, "input dim must be larger than 1"
# input ad out must share the memory
@ -196,7 +195,6 @@ def batch_norm(x,
batch_norm_out, _, _, _, _, _ = core.ops.batch_norm(
x, weight, bias, running_mean, running_var, mean_out, variance_out,
*attrs)
return dygraph_utils._append_activation_in_dygraph(
batch_norm_out, act=None)
@ -230,13 +228,16 @@ def batch_norm(x,
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
batch_norm_out = helper.create_variable_for_type_inference(dtype)
reserve_space = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=True)
outputs = {
"Y": [batch_norm_out],
"MeanOut": [running_mean],
"VarianceOut": [running_var],
"SavedMean": [saved_mean],
"SavedVariance": [saved_variance]
"SavedVariance": [saved_variance],
"ReserveSpace": [reserve_space]
}
helper.append_op(

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