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MKLDNN elementwise_mul: Lint changes to UT & integration

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test=develop
panyx0718-patch-1
Michal Gallus 6 years ago
parent 73b7cd0482
commit 08f63c4d12
3 changed files with 50 additions and 40 deletions
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24
paddle/fluid/operators/elementwise/elementwise_op.h
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@ -98,19 +98,19 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
.SetDefault(false);
AddAttr<std::string>(
"x_data_format",
"(string, default NCHW) Only used in mkldnn"
"An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
"Defaults to \"\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("");
"x_data_format",
"(string, default NCHW) Only used in mkldnn"
"An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
"Defaults to \"\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("");
AddAttr<std::string>(
"y_data_format",
"(string, default \"\") Only used in mkldnn"
"An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
"Defaults to \"\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("");
"y_data_format",
"(string, default \"\") Only used in mkldnn"
"An optional string from: \"NHWC\", \"NCHW\", \"NCHW16C\", \"NCHW8C\". "
"Defaults to \"\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("");
AddComment(string::Sprintf(R"DOC(
Elementwise %s Operator

54
paddle/fluid/operators/elementwise_mul_mkldnn_op.cc
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@ -71,13 +71,13 @@ void check(const float* x, const float* y, float* z, int w) {
static mkldnn::memory::format StringToMKLDNNFormat(std::string& format) {
std::transform(format.begin(), format.end(), format.begin(), ::tolower);
if(!format.compare("nchw")) {
if (!format.compare("nchw")) {
return memory::format::nchw;
} else if(!format.compare("nchw16c")) {
} else if (!format.compare("nchw16c")) {
return memory::format::nChw16c;
} else if(!format.compare("nchw8c")) {
} else if (!format.compare("nchw8c")) {
return memory::format::nChw8c;
} else if(!format.compare("nhwc")) {
} else if (!format.compare("nhwc")) {
return memory::format::nhwc;
} else {
return memory::format::any;
@ -85,8 +85,8 @@ static mkldnn::memory::format StringToMKLDNNFormat(std::string& format) {
}
static void UpdateDataFormat(const framework::ExecutionContext& ctx,
framework::Tensor* tensor, const char* attribute) {
if(ctx.op().HasAttr(attribute)) {
framework::Tensor* tensor, const char* attribute) {
if (ctx.op().HasAttr(attribute)) {
auto format_as_string = ctx.Attr<std::string>(attribute);
auto format = StringToMKLDNNFormat(format_as_string);
if (format != memory::format::any) {
@ -98,19 +98,19 @@ static void UpdateDataFormat(const framework::ExecutionContext& ctx,
template <typename T>
static void ReorderInput(framework::Tensor* tensor,
const platform::Place& place,
const mkldnn::engine& engine,
bool isFourDim) {
const mkldnn::engine& engine, bool isFourDim) {
using platform::to_void_cast;
auto dims = paddle::framework::vectorize2int(tensor->dims());
framework::Tensor out_tensor;
out_tensor.Resize(tensor->dims());
out_tensor.set_format(isFourDim ? memory::format::nchw : memory::format::nc);
out_tensor.set_layout(tensor->layout());
mkldnn::memory input_memory = {{{dims, platform::MKLDNNGetDataType<T>(),
tensor->format()}, engine}, to_void_cast<T>(tensor->data<T>())};
mkldnn::memory output_memory = {{{dims, platform::MKLDNNGetDataType<T>(),
out_tensor.format()}, engine},
to_void_cast<T>(out_tensor.mutable_data<T>(place))};
mkldnn::memory input_memory = {
{{dims, platform::MKLDNNGetDataType<T>(), tensor->format()}, engine},
to_void_cast<T>(tensor->data<T>())};
mkldnn::memory output_memory = {
{{dims, platform::MKLDNNGetDataType<T>(), out_tensor.format()}, engine},
to_void_cast<T>(out_tensor.mutable_data<T>(place))};
platform::Reorder(input_memory, output_memory);
tensor->ShareDataWith(out_tensor);
}
@ -163,21 +163,19 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
vector_mul mul;
using mul_func_t =
void (*)(const float *, const float *, float *, int, int);
void (*)(const float*, const float*, float*, int, int);
mul_func_t mul_func = (mul_func_t) mul.getCode();
mul_func_t mul_func = (mul_func_t)mul.getCode();
#pragma omp parallel for collapse(2)
#pragma omp parallel for collapse(2)
for (int ni = 0; ni < n; ni++) {
for (int ci = 0; ci < C; ci++) {
auto ptr_x =
x_data + ni * C * h * w * simd_width +
ci * h * w * simd_width;
x_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
auto ptr_y = y_data + ni * C * simd_width + ci * simd_width;
auto ptr_z =
z_data + ni * C * h * w * simd_width +
ci * h * w * simd_width;
z_data + ni * C * h * w * simd_width + ci * h * w * simd_width;
mul_func(ptr_x, ptr_y, ptr_z, h, w);
}
@ -189,18 +187,20 @@ class ElementwiseMulMKLDNNKernel : public framework::OpKernel<T> {
} else {
// Fallback to naive version:
const bool are_inputs_in_same_format = x->format() == y->format();
const bool is_x_nchw= x->format() == memory::format::nchw;
const bool is_x_nchw = x->format() == memory::format::nchw;
const bool is_x_nc = x->format() == memory::format::nc;
const bool is_y_nchw= y->format() == memory::format::nchw;
const bool is_y_nchw = y->format() == memory::format::nchw;
const bool is_y_nc = y->format() == memory::format::nc;
if(!are_inputs_in_same_format) {
if (!are_inputs_in_same_format) {
using platform::MKLDNNDeviceContext;
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
if(!(is_x_nchw || is_x_nc))
ReorderInput<T>((Tensor*)x, ctx.GetPlace(), mkldnn_engine, x->dims().size() == 4);
if(!(is_y_nchw || is_y_nc))
ReorderInput<T>((Tensor*)y, ctx.GetPlace(), mkldnn_engine, y->dims().size() == 4);
if (!(is_x_nchw || is_x_nc))
ReorderInput<T>((Tensor*)x, ctx.GetPlace(), mkldnn_engine,
x->dims().size() == 4);
if (!(is_y_nchw || is_y_nc))
ReorderInput<T>((Tensor*)y, ctx.GetPlace(), mkldnn_engine,
y->dims().size() == 4);
}
auto mul_func = [](T a, T b) -> T { return a * b; };

12
python/paddle/fluid/tests/unittests/test_elementwise_mul_mkldnn_op.py
Unescape View File

@ -20,6 +20,7 @@ import paddle.fluid.core as core
from paddle.fluid.op import Operator
from test_elementwise_mul_op import *
class TestElementwiseMulMKLDNNOp_BroadcastNCHW16c(ElementwiseMulOp):
def init_input_output(self):
x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
@ -49,7 +50,9 @@ class TestElementwiseMulMKLDNNOp_BroadcastNCHW16c(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
@unittest.skip("Not implemented yet.") # TODO(mgallus): enable when implemented.
@unittest.skip(
"Not implemented yet.") # TODO(mgallus): enable when implemented.
class TestElementwiseMulMKLDNNOp_BroadcastNCHW8c(ElementwiseMulOp):
def init_input_output(self):
x = np.random.rand(1, 8, 2, 2).astype(self.dtype)
@ -79,6 +82,7 @@ class TestElementwiseMulMKLDNNOp_BroadcastNCHW8c(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
class TestElementwiseMulMKLDNNOp_FallbackNCHW(ElementwiseMulOp):
def init_input_output(self):
self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
@ -101,6 +105,7 @@ class TestElementwiseMulMKLDNNOp_FallbackNCHW(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
class TestElementwiseMulMKLDNNOp_FallbackNCHW16C(ElementwiseMulOp):
def init_input_output(self):
x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
@ -130,6 +135,7 @@ class TestElementwiseMulMKLDNNOp_FallbackNCHW16C(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
class TestElementwiseMulMKLDNNOp_FallbackNoReorders(ElementwiseMulOp):
def init_input_output(self):
x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
@ -159,6 +165,7 @@ class TestElementwiseMulMKLDNNOp_FallbackNoReorders(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
class TestElementwiseMulMKLDNNOp_FallbackWithReorder1(ElementwiseMulOp):
def init_input_output(self):
self.x = np.random.rand(1, 16, 2, 2).astype(self.dtype)
@ -187,6 +194,7 @@ class TestElementwiseMulMKLDNNOp_FallbackWithReorder1(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
class TestElementwiseMulMKLDNNOp_FallbackWithReorder2(ElementwiseMulOp):
def init_input_output(self):
self.y = np.random.rand(1, 16, 2, 2).astype(self.dtype)
@ -215,6 +223,7 @@ class TestElementwiseMulMKLDNNOp_FallbackWithReorder2(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
class TestElementwiseMulMKLDNNOp_FallbackNoReorders2(ElementwiseMulOp):
def init_input_output(self):
self.x = np.random.rand(1, 16).astype(self.dtype)
@ -242,5 +251,6 @@ class TestElementwiseMulMKLDNNOp_FallbackNoReorders2(ElementwiseMulOp):
def test_check_grad_ingore_y(self):
pass
if __name__ == '__main__':
unittest.main()

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