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未验证 提交 ead83422 编写于 作者: J jakpiase 提交者： GitHub
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Added oneDNN reduce_op GRAD kernel (#32280)

上级 1593ee25
隐藏空白更改
内联 并排
Showing with 329 addition and 128 deletion
paddle/fluid/operators/reduce_ops/mkldnn/reduce_mean_mkldnn_op.cc
浏览文件 @ ead83422
......@@ -25,6 +25,31 @@ class ReduceMeanMKLDNNKernel : public ReduceMKLDNNKernel<T> {
}
};
template <typename T>
class ReduceMeanGradMKLDNNKernel : public ReduceGradMKLDNNKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* input_x = ctx.Input<Tensor>("X");
auto input_dims = framework::vectorize(input_x->dims());
auto reduce_dims = ctx.Attr<std::vector<int>>("dim");
int number_of_elements = 1;
if (!ctx.Attr<bool>("reduce_all")) {
for (size_t i = 0; i < reduce_dims.size(); ++i) {
reduce_dims[i] = (reduce_dims[i] >= 0)
? reduce_dims[i]
: input_dims.size() + reduce_dims[i];
number_of_elements *= input_dims[reduce_dims[i]];
}
} else {
number_of_elements = input_x->numel();
}
this->RunKernel(ctx, dnnl::algorithm::binary_add, 0.0f,
1.0L / number_of_elements);
}
};
} // namespace operators
} // namespace paddle
......@@ -32,3 +57,7 @@ namespace ops = paddle::operators;
REGISTER_OP_KERNEL(reduce_mean, MKLDNN, paddle::platform::CPUPlace,
ops::ReduceMeanMKLDNNKernel<float>,
ops::ReduceMeanMKLDNNKernel<paddle::platform::bfloat16>);
REGISTER_OP_KERNEL(reduce_mean_grad, MKLDNN, paddle::platform::CPUPlace,
ops::ReduceMeanGradMKLDNNKernel<float>,
ops::ReduceMeanGradMKLDNNKernel<paddle::platform::bfloat16>);
paddle/fluid/operators/reduce_ops/mkldnn/reduce_mkldnn_op.h
浏览文件 @ ead83422
......@@ -121,5 +121,65 @@ class ReduceMKLDNNKernel : public framework::OpKernel<T> {
}
};
template <typename T>
class ReduceGradMKLDNNKernel : public framework::OpKernel<T> {
public:
void RunKernel(const framework::ExecutionContext& ctx,
dnnl::algorithm binary_type, float scale_x,
float scale_y) const {
const auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& onednn_engine = dev_ctx.GetEngine();
auto dims = ctx.Attr<std::vector<int>>("dim");
auto* input_dy = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* output_dx = ctx.Output<Tensor>(framework::GradVarName("X"));
output_dx->mutable_data<T>(ctx.GetPlace());
output_dx->set_format(getPlainFormatTag(output_dx));
output_dx->set_layout(input_dy->layout());
platform::BroadcastDataMKLDNNHandler<T> handler(
binary_type, dev_ctx, onednn_engine, ctx.GetPlace(), output_dx,
input_dy, scale_x, scale_y,
ctx.InputName(framework::GradVarName("Out")));
const auto src_dx_memory = handler.AcquireSrcMemory(output_dx);
const auto src_dy_memory = handler.AcquireSecondSrcMemory(input_dy);
const auto binary_prim = handler.AcquireForwardPrimitive();
const std::unordered_map<int, dnnl::memory> args = {
{DNNL_ARG_SRC_0, *src_dx_memory},
{DNNL_ARG_SRC_1, *src_dy_memory},
{DNNL_ARG_DST, *src_dx_memory}};
auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
binary_prim->execute(astream, args);
astream.wait();
}
protected:
mkldnn::memory::format_tag getPlainFormatTag(const Tensor* tensor) const {
auto tensor_dims_size = tensor->dims().size();
PADDLE_ENFORCE_EQ(
tensor_dims_size <= 5 && tensor_dims_size >= 1, true,
platform::errors::InvalidArgument(
"Dims for reduction_grad oneDNN op must be in range <1, 5>"));
switch (tensor_dims_size) {
case 1:
return mkldnn::memory::format_tag::a;
case 2:
return mkldnn::memory::format_tag::ab;
case 3:
return mkldnn::memory::format_tag::abc;
case 4:
return mkldnn::memory::format_tag::abcd;
}
return mkldnn::memory::format_tag::abcde;
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/reduce_ops/mkldnn/reduce_sum_mkldnn_op.cc
浏览文件 @ ead83422
......@@ -25,6 +25,14 @@ class ReduceSumMKLDNNKernel : public ReduceMKLDNNKernel<T> {
}
};
template <typename T>
class ReduceSumGradMKLDNNKernel : public ReduceGradMKLDNNKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
this->RunKernel(ctx, dnnl::algorithm::binary_add, 0.0f, 1.0f);
}
};
} // namespace operators
} // namespace paddle
......@@ -32,3 +40,7 @@ namespace ops = paddle::operators;
REGISTER_OP_KERNEL(reduce_sum, MKLDNN, paddle::platform::CPUPlace,
ops::ReduceSumMKLDNNKernel<float>,
ops::ReduceSumMKLDNNKernel<paddle::platform::bfloat16>);
REGISTER_OP_KERNEL(reduce_sum_grad, MKLDNN, paddle::platform::CPUPlace,
ops::ReduceSumGradMKLDNNKernel<float>,
ops::ReduceSumGradMKLDNNKernel<paddle::platform::bfloat16>);
paddle/fluid/operators/reduce_ops/reduce_op.h
浏览文件 @ ead83422
......@@ -559,15 +559,44 @@ class ReduceGradOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type = OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out"));
#ifdef PADDLE_WITH_MKLDNN
auto CanMKLDNNReduceGradBeUsed = [&]() {
auto dx_dims = ctx.Input<Tensor>("X")->dims();
if (dx_dims.size() > 5) return false; // max 5D tensor is supported
if (ctx.Attr<bool>("reduce_all") ||
((int)ctx.Attr<std::vector<int>>("dim").size() == dx_dims.size()))
return true;
auto dy_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
// Subtensor must be on rightmost part of the bigger tensor
for (int i = 0; i < dy_dims.size(); ++i) {
if (dx_dims[dx_dims.size() - dy_dims.size() + i] != dy_dims[i]) {
return false;
}
}
return true;
};
if (this->CanMKLDNNBeUsed(ctx, input_data_type) &&
CanMKLDNNReduceGradBeUsed()) {
return framework::OpKernelType(input_data_type, ctx.GetPlace(),
framework::DataLayout::kMKLDNN,
framework::LibraryType::kMKLDNN);
}
#endif
int in_dtype = ctx.Attr<int>("in_dtype");
if (in_dtype >= 0) {
return framework::OpKernelType(
static_cast<framework::proto::VarType::Type>(in_dtype),
ctx.GetPlace());
}
return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out")),
ctx.GetPlace());
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ ead83422
......@@ -630,6 +630,78 @@ class BinaryMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::binary> {
}
};
template <typename T>
class BroadcastDataMKLDNNHandler
: public platform::MKLDNNHandlerT<T, dnnl::binary> {
public:
BroadcastDataMKLDNNHandler(const dnnl::algorithm algo,
const MKLDNNDeviceContext& dev_ctx,
const mkldnn::engine engine,
platform::Place cpu_place, const Tensor* x,
const Tensor* y, float scale_x, float scale_y,
const std::string& uniq_name)
: platform::MKLDNNHandlerT<T, dnnl::binary>(
dev_ctx, engine, cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(x->dims()),
uniq_name)) {
if (!this->isCached()) {
PADDLE_ENFORCE_EQ(
x->layout(), DataLayout::kMKLDNN,
platform::errors::InvalidArgument("Wrong layout set for X tensor."));
PADDLE_ENFORCE_NE(
x->format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument("Wrong format set for X tensor."));
PADDLE_ENFORCE_EQ(
y->layout(), DataLayout::kMKLDNN,
platform::errors::InvalidArgument("Wrong layout set for Y tensor."));
PADDLE_ENFORCE_NE(
y->format(), MKLDNNMemoryFormat::undef,
platform::errors::InvalidArgument("Wrong format set for Y tensor."));
auto src1_tz = framework::vectorize(y->dims());
const auto src0_tz = framework::vectorize(x->dims());
// GetExpectedKernelType checks if smaller vector is a subvector with all
// the dims in correct order on the rightmost part of the bigger vector,
// i.e. a correct vector for broadcasting:
// x = 5, 7, 3, 2, 4, 8
// y = 4, 8
src1_tz.reserve(src0_tz.size());
for (size_t i = src1_tz.size(); i < src0_tz.size(); ++i) {
src1_tz.insert(src1_tz.begin(), 1L);
}
const auto src0_md = dnnl::memory::desc(
src0_tz, platform::MKLDNNGetDataType<T>(), x->format());
const auto src1_md = dnnl::memory::desc(
src1_tz, platform::MKLDNNGetDataType<T>(), x->format());
dnnl::primitive_attr attributes;
attributes.set_scales(DNNL_ARG_SRC_0, 0, {scale_x});
attributes.set_scales(DNNL_ARG_SRC_1, 0, {scale_y});
this->AcquireForwardPrimitiveDescriptor(attributes, algo, src0_md,
src1_md, src0_md);
}
}
std::shared_ptr<mkldnn::memory> AcquireSrcMemory(framework::Tensor* input) {
T* input_data = input->data<T>();
memset(input_data, 0, this->fwd_pd_->src_desc().get_size());
return this->AcquireMemoryFromPrimitive(
this->fwd_pd_->src_desc(), to_void_cast<T>(input_data), "@src0_mem_p");
}
std::shared_ptr<mkldnn::memory> AcquireSecondSrcMemory(
const framework::Tensor* input) {
const T* input_data = input->data<T>();
return this->AcquireMemoryFromPrimitive(
this->fwd_pd_->src1_desc(), to_void_cast<T>(input_data), "@src1_mem_p");
}
};
template <typename T>
class ReductionMKLDNNHandler
: public platform::MKLDNNHandlerT<T, dnnl::reduction> {
......
python/paddle/fluid/tests/unittests/mkldnn/test_reduce_bf16_mkldnn_op.py
浏览文件 @ ead83422
......@@ -26,158 +26,182 @@ import paddle
"place does not support BF16 evaluation")
@unittest.skipIf(core.is_compiled_with_cuda(),
"core is compiled with CUDA which has no BF implementation")
@skip_check_grad_ci(reason="not implemented")
class TestReduceSumDefaultBF16ONEDNNOp(OpTest):
class TestReduceSumDefaultBF16OneDNNOp(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.random((5, 6, 10)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.outputs = {'Out': x_fp32.sum(axis=0)}
self.x_fp32 = np.random.random((5, 6, 10)).astype("float32")
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.outputs = {'Out': self.x_fp32.sum(axis=0)}
self.attrs = {'use_mkldnn': self.use_mkldnn}
def test_check_output(self):
self.check_output(check_dygraph=False)
def calculate_grads(self):
tmp_tensor = np.zeros(self.x_fp32.shape).astype("float32")
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DBF16ONEDNNOp(TestReduceSumDefaultBF16ONEDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.random((5, 10, 5, 5)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [2]}
self.outputs = {'Out': x_fp32.sum(axis=tuple(self.attrs['dim']))}
prod_of_reduced_dims = self.inputs['X'].shape[0]
axis = 0
if "dim" in self.attrs:
prod_of_reduced_dims = 1
axis = tuple(self.attrs['dim'])
for i in range(len(axis)):
ax = axis[i]
if axis[i] < 0:
ax = len(axis) + axis[i]
prod_of_reduced_dims *= self.inputs['X'].shape[ax]
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DReduceAllWithoutReduceAllAttributeBF16ONEDNNOp(
TestReduceSumDefaultBF16ONEDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.normal(size=(2, 3, 5, 6)).astype('float32')
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [0, 1, 2, 3]}
self.outputs = {'Out': x_fp32.sum(axis=tuple(self.attrs['dim']))}
if 'reduce_all' in self.attrs:
if self.attrs['reduce_all'] is True:
axis = None
prod_of_reduced_dims = np.asarray(self.inputs['X'].shape).prod()
keepdim = False
if 'keep_dim' in self.attrs:
keepdim = True
self.grad_Out = self.x_fp32.sum(axis=axis, keepdims=keepdim)
self.grad_Out = np.atleast_1d(self.grad_Out)
self.grad_X = tmp_tensor + self.grad_Out # broadcast grad
if self.op_type == 'reduce_mean':
self.grad_X /= prod_of_reduced_dims
class TestReduceDefaultWithGradBF16OneDNNOp(TestReduceSumDefaultBF16OneDNNOp):
def test_check_grad(self):
self.calculate_grads()
self.check_grad_with_place(
core.CPUPlace(), ["X"],
"Out",
check_dygraph=False,
user_defined_grads=[self.grad_X],
user_defined_grad_outputs=[convert_float_to_uint16(self.grad_Out)])
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DReduceAllWithoutReduceAllAttributeNegativeDimsBF16ONEDNNOp(
TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceSum4DReduceAllDimAttributeBF16OneDNNOp(
TestReduceDefaultWithGradBF16OneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.normal(size=(2, 7, 3, 5)).astype('float32')
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [-1, -2, -3, -4]}
self.outputs = {'Out': x_fp32.sum(axis=tuple(self.attrs['dim']))}
self.x_fp32 = np.random.normal(size=(2, 3, 5, 6)).astype('float32')
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [0, 1, 2, 3]}
self.outputs = {'Out': self.x_fp32.sum(axis=tuple(self.attrs['dim']))}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum5DKeepDimsONEDNNOp(TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceSum4DReduceAllWithoutReduceAllAttributeNegativeDimsBF16OneDNNOp(
TestReduceDefaultWithGradBF16OneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.random((2, 5, 3, 2, 2)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.attrs = {'dim': (2, 3, 4), 'keep_dim': True, 'use_mkldnn': True}
self.outputs = {
'Out': x_fp32.sum(axis=tuple(self.attrs['dim']),
keepdims=self.attrs['keep_dim'])
}
self.x_fp32 = np.random.normal(size=(4, 7, 6, 6)).astype('float32')
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [-1, -2, -3, -4]}
self.outputs = {'Out': self.x_fp32.sum(axis=tuple(self.attrs['dim']))}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum5DReduceAllKeepDimsBF16ONEDNNOp(
TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceSum5DReduceAllKeepDimsBF16OneDNNOp(
TestReduceDefaultWithGradBF16OneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.normal(size=(2, 5, 3, 2, 4)).astype('float32')
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.x_fp32 = np.random.normal(size=(2, 5, 3, 2, 5)).astype('float32')
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'reduce_all': True, 'keep_dim': True, 'use_mkldnn': True}
self.outputs = {'Out': x_fp32.sum(keepdims=self.attrs['keep_dim'])}
self.outputs = {'Out': self.x_fp32.sum(keepdims=self.attrs['keep_dim'])}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DReduceAllBF16ONEDNNOp(TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceSum4DReduceAllBF16OneDNNOp(
TestReduceDefaultWithGradBF16OneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
x_fp32 = np.random.normal(size=(4, 3, 2, 3)).astype('float32')
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.x_fp32 = np.random.normal(size=(4, 5, 4, 5)).astype('float32')
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'reduce_all': True, 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': x_fp32.sum()}
self.outputs = {'Out': self.x_fp32.sum()}
@skip_check_grad_ci(
reason="reduce_max is discontinuous non-derivable function,"
" its gradient check is not supported by unittest framework.")
class TestReduceMax3DBF16ONEDNNOp(TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceMax3DBF16OneDNNOp(TestReduceSumDefaultBF16OneDNNOp):
"""Remove Max with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
self.op_type = "reduce_max"
self.use_mkldnn = True
x_fp32 = np.random.random((5, 6, 10)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.x_fp32 = np.random.random((5, 6, 10)).astype("float32")
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'dim': [-1], 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': x_fp32.max(axis=tuple(self.attrs['dim']))}
self.outputs = {'Out': self.x_fp32.max(axis=tuple(self.attrs['dim']))}
@skip_check_grad_ci(
reason="reduce_max is discontinuous non-derivable function,"
" its gradient check is not supported by unittest framework.")
class TestReduceMax4DNegativeAndPositiveDimsBF16ONEDNNOp(
TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceMax4DNegativeAndPositiveDimsBF16OneDNNOp(
TestReduceSumDefaultBF16OneDNNOp):
"""Remove Max with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
self.op_type = "reduce_max"
self.use_mkldnn = True
x_fp32 = np.random.random((5, 6, 10, 9)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.x_fp32 = np.random.random((5, 6, 10, 9)).astype("float32")
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'dim': [-1, 0, 1], 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': x_fp32.max(axis=tuple(self.attrs['dim']))}
self.outputs = {'Out': self.x_fp32.max(axis=tuple(self.attrs['dim']))}
@skip_check_grad_ci(
reason="reduce_min is discontinuous non-derivable function,"
" its gradient check is not supported by unittest framework.")
class TestReduceMin3DBF16ONEDNNOp(TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceMin3DBF16OneDNNOp(TestReduceSumDefaultBF16OneDNNOp):
"""Remove Min with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
self.op_type = "reduce_min"
self.use_mkldnn = True
x_fp32 = np.random.random((5, 6, 10)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.x_fp32 = np.random.random((5, 6, 10)).astype("float32")
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'dim': [2], 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': x_fp32.min(axis=tuple(self.attrs['dim']))}
self.outputs = {'Out': self.x_fp32.min(axis=tuple(self.attrs['dim']))}
@skip_check_grad_ci(reason="not implemented")
class TestReduceMean3DBF16ONEDNNOp(TestReduceSumDefaultBF16ONEDNNOp):
class TestReduceMean3DBF16OneDNNOp(TestReduceDefaultWithGradBF16OneDNNOp):
def setUp(self):
self.op_type = "reduce_mean"
self.use_mkldnn = True
x_fp32 = np.random.random((5, 6, 10)).astype("float32")
x_bf16 = convert_float_to_uint16(x_fp32)
self.inputs = {'X': x_bf16}
self.x_fp32 = np.random.random((5, 6, 10)).astype("float32")
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': x_fp32.sum(axis=0) / x_fp32.shape[0]}
self.outputs = {'Out': self.x_fp32.sum(axis=0) / self.x_fp32.shape[0]}
class TestReduceMean4DBF16OneDNNOp(TestReduceDefaultWithGradBF16OneDNNOp):
def setUp(self):
self.op_type = "reduce_mean"
self.use_mkldnn = True
self.x_fp32 = np.random.random((5, 6, 3, 5)).astype("float32")
self.x_bf16 = convert_float_to_uint16(self.x_fp32)
self.inputs = {'X': self.x_bf16}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [0, 1]}
self.outputs = {
'Out': self.x_fp32.sum(axis=tuple(self.attrs['dim'])) /
(self.x_fp32.shape[0] * self.x_fp32.shape[1])
}
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/mkldnn/test_reduce_mkldnn_op.py
浏览文件 @ ead83422
......@@ -19,8 +19,7 @@ import paddle.fluid as fluid
import paddle
@skip_check_grad_ci(reason="not implemented")
class TestReduceSumDefaultONEDNNOp(OpTest):
class TestReduceSumDefaultOneDNNOp(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
......@@ -32,46 +31,35 @@ class TestReduceSumDefaultONEDNNOp(OpTest):
self.check_output()
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DONEDNNOp(TestReduceSumDefaultONEDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
self.inputs = {'X': np.random.random((5, 10, 5, 5)).astype("float32")}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [2]}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
class TestReduceDefaultWithGradOneDNNOp(TestReduceSumDefaultOneDNNOp):
def test_check_grad(self):
self.check_grad(['X'], 'Out')
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DReduceAllWithoutReduceAllAttributeONEDNNOp(
TestReduceSumDefaultONEDNNOp):
class TestReduceSum4DOneDNNOp(TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
self.inputs = {'X': np.random.random((5, 10, 5, 5)).astype("float32")}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [0, 1, 2, 3]}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [2]}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DReduceAllWithoutReduceAllAttributeNegativeDimsONEDNNOp(
TestReduceSumDefaultONEDNNOp):
class TestReduceSum4DReduceAllDimAttributeBF16OneDNNOp(
TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
self.inputs = {'X': np.random.random((5, 10, 5, 5)).astype("float32")}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [-1, -2, -3, -4]}
self.inputs = {'X': np.random.random((5, 10, 5, 3)).astype("float32")}
self.attrs = {'use_mkldnn': self.use_mkldnn, 'dim': [0, 1, 2, 3]}
self.outputs = {
'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']))
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum5DKeepDimsONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceSum5DKeepDimsOneDNNOp(TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
......@@ -83,8 +71,8 @@ class TestReduceSum5DKeepDimsONEDNNOp(TestReduceSumDefaultONEDNNOp):
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum5DReduceAllKeepDimsONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceSum5DReduceAllKeepDimsOneDNNOp(
TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
......@@ -95,8 +83,7 @@ class TestReduceSum5DReduceAllKeepDimsONEDNNOp(TestReduceSumDefaultONEDNNOp):
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceSum4DReduceAllONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceSum4DReduceAllOneDNNOp(TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_sum"
self.use_mkldnn = True
......@@ -108,7 +95,7 @@ class TestReduceSum4DReduceAllONEDNNOp(TestReduceSumDefaultONEDNNOp):
@skip_check_grad_ci(
reason="reduce_max is discontinuous non-derivable function,"
" its gradient check is not supported by unittest framework.")
class TestReduceMax3DONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceMax3DOneDNNOp(TestReduceSumDefaultOneDNNOp):
"""Remove Max with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
......@@ -124,8 +111,8 @@ class TestReduceMax3DONEDNNOp(TestReduceSumDefaultONEDNNOp):
@skip_check_grad_ci(
reason="reduce_max is discontinuous non-derivable function,"
" its gradient check is not supported by unittest framework.")
class TestReduceMax4DNegativeAndPositiveDimsONEDNNOp(
TestReduceSumDefaultONEDNNOp):
class TestReduceMax4DNegativeAndPositiveDimsOneDNNOp(
TestReduceSumDefaultOneDNNOp):
"""Remove Max with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
......@@ -141,7 +128,7 @@ class TestReduceMax4DNegativeAndPositiveDimsONEDNNOp(
@skip_check_grad_ci(
reason="reduce_min is discontinuous non-derivable function,"
" its gradient check is not supported by unittest framework.")
class TestReduceMin3DONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceMin3DOneDNNOp(TestReduceSumDefaultOneDNNOp):
"""Remove Min with subgradient from gradient check to confirm the success of CI."""
def setUp(self):
......@@ -154,8 +141,7 @@ class TestReduceMin3DONEDNNOp(TestReduceSumDefaultONEDNNOp):
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceMean3DONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceMean3DOneDNNOp(TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_mean"
self.use_mkldnn = True
......@@ -166,8 +152,7 @@ class TestReduceMean3DONEDNNOp(TestReduceSumDefaultONEDNNOp):
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceMean4DReduceAllONEDNNOp(TestReduceSumDefaultONEDNNOp):
class TestReduceMean4DReduceAllOneDNNOp(TestReduceDefaultWithGradOneDNNOp):
def setUp(self):
self.op_type = "reduce_mean"
self.use_mkldnn = True
......@@ -179,16 +164,6 @@ class TestReduceMean4DReduceAllONEDNNOp(TestReduceSumDefaultONEDNNOp):
}
@skip_check_grad_ci(reason="not implemented")
class TestReduceMeanNoReduce1DOp(TestReduceSumDefaultONEDNNOp):
def setUp(self):
self.op_type = "reduce_mean"
self.use_mkldnn = True
self.inputs = {'X': np.random.random((1)).astype("float32")}
self.attrs = {'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': self.inputs['X']}
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
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