Skip to content

[oneDNN ] disabling more ops caching #34830

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Aug 17, 2021
Merged

[oneDNN ] disabling more ops caching #34830

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
b9c4eb9
- disabled caching of layer norm
jczaja Aug 9, 2021
f53bf6e
- LRN with disabled cache
jczaja Aug 11, 2021
c62e05f
lint fixes
jczaja Aug 16, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
88 changes: 39 additions & 49 deletions paddle/fluid/operators/mkldnn/layer_norm_mkldnn_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,45 +19,36 @@ namespace paddle {
namespace operators {

template <typename T>
class LayerNormMKLDNNHandler
: public platform::MKLDNNHandlerT<T, dnnl::layer_normalization_forward> {
class LayerNormMKLDNNHandler : public platform::MKLDNNHandlerNoCachingT<
T, dnnl::layer_normalization_forward> {
public:
LayerNormMKLDNNHandler(const std::vector<int64_t>& dims, const float& epsilon,
const dnnl::normalization_flags& flags,
const bool& is_test, const MKLDNNMemoryFormat fmt,
const platform::MKLDNNDeviceContext& dev_ctx,
platform::Place cpu_place,
const std::string& uniq_name)
: platform::MKLDNNHandlerT<T, dnnl::layer_normalization_forward>(
dev_ctx, dev_ctx.GetEngine(), cpu_place,
platform::CreateKey(dev_ctx, dims, uniq_name)) {
if (!this->isCached()) {
auto md = dnnl::memory::desc(dims, platform::MKLDNNGetDataType<T>(), fmt);
if (!is_test) {
// TODO(grygielski) Delete forcing stats_md after DNNL 1.2 is introduced
auto stats_md = dnnl::memory::desc(
{begin(dims), end(dims) - 1}, platform::MKLDNNGetDataType<float>(),
platform::MKLDNNFormatForSize(dims.size() - 1,
MKLDNNMemoryFormat::nchw));
this->AcquireForwardPrimitiveDescriptor(
dnnl::prop_kind::forward_training, md, stats_md, epsilon, flags);
} else {
this->AcquireForwardPrimitiveDescriptor(
dnnl::prop_kind::forward_inference, md, epsilon, flags);
}
const mkldnn::engine engine, platform::Place cpu_place)
: platform::MKLDNNHandlerNoCachingT<T, dnnl::layer_normalization_forward>(
engine, cpu_place) {
auto md = dnnl::memory::desc(dims, platform::MKLDNNGetDataType<T>(), fmt);
if (!is_test) {
// TODO(grygielski) Delete forcing stats_md after DNNL 1.2 is introduced
auto stats_md = dnnl::memory::desc(
{begin(dims), end(dims) - 1}, platform::MKLDNNGetDataType<float>(),
platform::MKLDNNFormatForSize(dims.size() - 1,
MKLDNNMemoryFormat::nchw));
this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
md, stats_md, epsilon, flags);
} else {
this->AcquireForwardPrimitiveDescriptor(
dnnl::prop_kind::forward_inference, md, epsilon, flags);
}
}

std::shared_ptr<dnnl::memory> AcquireScaleShiftMemory() {
return this->AcquireMemoryFromPrimitive("@scaleshift_mem_p");
}

std::shared_ptr<dnnl::memory> AcquireScaleShiftMemory(
std::vector<float>& scaleshift_data) {
// scaleshift_data comes from temporary buffer so we need to copy it into
// created memory primitivie
auto scaleshift_mem = this->AcquireMemoryFromPrimitive(
this->fwd_pd_->weights_desc(), "@scaleshift_mem_p");
auto scaleshift_mem =
this->AcquireMemoryFromPrimitive(this->fwd_pd_->weights_desc());
auto data_ptr = scaleshift_mem->get_data_handle();
std::size_t num_bytes = scaleshift_data.size() * sizeof(float);
std::memcpy(data_ptr, scaleshift_data.data(), num_bytes);
Expand All @@ -68,15 +59,15 @@ class LayerNormMKLDNNHandler
T* mean_data = mean->mutable_data<T>(this->place_,
this->fwd_pd_->mean_desc().get_size());
return this->AcquireMemoryFromPrimitive(this->fwd_pd_->mean_desc(),
mean_data, "@mean_mem_p");
mean_data);
}

std::shared_ptr<dnnl::memory> AcquireVarianceMemory(
framework::Tensor* variance) {
T* variance_data = variance->mutable_data<T>(
this->place_, this->fwd_pd_->variance_desc().get_size());
return this->AcquireMemoryFromPrimitive(this->fwd_pd_->variance_desc(),
variance_data, "@variance_mem_p");
variance_data);
}
};

Expand All @@ -95,6 +86,7 @@ class LayerNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {

auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();

auto src_tz = paddle::framework::vectorize(x->dims());
PADDLE_ENFORCE_EQ(begin_norm_axis, (src_tz.size() - 1),
Expand All @@ -112,8 +104,8 @@ class LayerNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
}

LayerNormMKLDNNHandler<T> handler(src_tz, epsilon, flags, is_test,
x->format(), dev_ctx, ctx.GetPlace(),
ctx.OutputName("Y"));
x->format(), mkldnn_engine,
ctx.GetPlace());

auto src_memory = handler.AcquireSrcMemory(x);
auto dst_memory = handler.AcquireDstMemory(y);
Expand All @@ -139,24 +131,22 @@ class LayerNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
args.insert({DNNL_ARG_VARIANCE, *variance_memory});
}

auto scaleshift_memory = handler.AcquireScaleShiftMemory();
std::shared_ptr<mkldnn::memory> scaleshift_memory;
if (with_scaleshift) {
if (scaleshift_memory == nullptr || !is_test) {
auto scale_tz = paddle::framework::vectorize(scale->dims());
const unsigned int C = scale_tz[0];

// MKLDNN requires a single piece of memory for scale and shift/bias
// data
std::vector<float> scaleshift_data;
scaleshift_data.reserve(2 * C);
scaleshift_data.insert(scaleshift_data.begin(), scale->data<float>(),
scale->data<float>() + C);

scaleshift_data.insert(scaleshift_data.end(), bias->data<float>(),
bias->data<float>() + C);

scaleshift_memory = handler.AcquireScaleShiftMemory(scaleshift_data);
}
auto scale_tz = paddle::framework::vectorize(scale->dims());
const unsigned int C = scale_tz[0];

// MKLDNN requires a single piece of memory for scale and shift/bias
// data
std::vector<float> scaleshift_data;
scaleshift_data.reserve(2 * C);
scaleshift_data.insert(scaleshift_data.begin(), scale->data<float>(),
scale->data<float>() + C);

scaleshift_data.insert(scaleshift_data.end(), bias->data<float>(),
bias->data<float>() + C);

scaleshift_memory = handler.AcquireScaleShiftMemory(scaleshift_data);
args.insert({DNNL_ARG_SCALE_SHIFT, *scaleshift_memory});
}

Expand Down
136 changes: 64 additions & 72 deletions paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,94 +21,86 @@ using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;

template <typename T>
class LRNMKLDNNHandler : public platform::MKLDNNHandlerT<T, mkldnn::lrn_forward,
mkldnn::lrn_backward> {
class LRNMKLDNNHandler
: public platform::MKLDNNHandlerNoCachingT<T, mkldnn::lrn_forward,
mkldnn::lrn_backward> {
public:
LRNMKLDNNHandler(const framework::ExecutionContext& ctx,
const MKLDNNDeviceContext& dev_ctx,
const mkldnn::engine mkldnn_engine,
platform::Place cpu_place, const Tensor* input,
const std::string& unique_name)

: platform::MKLDNNHandlerT<T, mkldnn::lrn_forward, mkldnn::lrn_backward>(
dev_ctx, mkldnn_engine, cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(input->dims()),
unique_name)) {
if (!this->isCached()) {
const int n = ctx.Attr<int>("n");
// MKL-DNN implements LRN in a caffe way:
// http://caffe.berkeleyvision.org/tutorial/layers/lrn.html
// Where sum of squares is divided by size of normalization window
// this is not the case for PaddlePaddle LRN.
// Hence we need to compensate for this diffrence by
// multipliing alpha by size of window(n)
const float alpha = ctx.Attr<float>("alpha") * static_cast<float>(n);
const float beta = ctx.Attr<float>("beta");
const float k = ctx.Attr<float>("k");
bool is_test = ctx.Attr<bool>("is_test");

auto dims = framework::vectorize(input->dims());

auto src_md = mkldnn::memory::desc(dims, platform::MKLDNNGetDataType<T>(),
input->format());

this->AcquireForwardPrimitiveDescriptor(
is_test ? mkldnn::prop_kind::forward_inference
: mkldnn::prop_kind::forward_training,
mkldnn::algorithm::lrn_across_channels, src_md, n, alpha, beta, k);
}
platform::Place cpu_place, const Tensor* input)

: platform::MKLDNNHandlerNoCachingT<T, mkldnn::lrn_forward,
mkldnn::lrn_backward>(mkldnn_engine,
cpu_place) {
const int n = ctx.Attr<int>("n");
// MKL-DNN implements LRN in a caffe way:
// http://caffe.berkeleyvision.org/tutorial/layers/lrn.html
// Where sum of squares is divided by size of normalization window
// this is not the case for PaddlePaddle LRN.
// Hence we need to compensate for this diffrence by
// multipliing alpha by size of window(n)
const float alpha = ctx.Attr<float>("alpha") * static_cast<float>(n);
const float beta = ctx.Attr<float>("beta");
const float k = ctx.Attr<float>("k");
bool is_test = ctx.Attr<bool>("is_test");

auto dims = framework::vectorize(input->dims());

auto src_md = mkldnn::memory::desc(dims, platform::MKLDNNGetDataType<T>(),
input->format());

this->AcquireForwardPrimitiveDescriptor(
is_test ? mkldnn::prop_kind::forward_inference
: mkldnn::prop_kind::forward_training,
mkldnn::algorithm::lrn_across_channels, src_md, n, alpha, beta, k);
}

LRNMKLDNNHandler(const framework::ExecutionContext& ctx,
const MKLDNNDeviceContext& dev_ctx,
const mkldnn::engine mkldnn_engine,
platform::Place cpu_place, const Tensor* in_x,
const Tensor* out_grad, Tensor* in_x_grad,
const std::string& unique_name)
: platform::MKLDNNHandlerT<T, mkldnn::lrn_forward, mkldnn::lrn_backward>(
dev_ctx, dev_ctx.GetEngine(), cpu_place,
platform::CreateKey(dev_ctx, framework::vectorize(in_x->dims()),
unique_name)) {
if (!this->isBwdCached()) {
PADDLE_ENFORCE_EQ(
ctx.Attr<bool>("is_test"), false,
platform::errors::PreconditionNotMet(
"is_test attribute should be set to False in training phase."));

const int n = ctx.Attr<int>("n");
const float alpha = ctx.Attr<float>("alpha") * static_cast<float>(n);
const float beta = ctx.Attr<float>("beta");
const float k = ctx.Attr<float>("k");

auto dims = framework::vectorize<int64_t>(in_x->dims());

auto src_md = mkldnn::memory::desc(dims, platform::MKLDNNGetDataType<T>(),
in_x->format());
auto diff_md = mkldnn::memory::desc(
dims, platform::MKLDNNGetDataType<T>(), out_grad->format());

this->AcquireForwardPrimitiveDescriptor(
mkldnn::prop_kind::forward_training,
mkldnn::algorithm::lrn_across_channels, src_md, n, alpha, beta, k);

this->AcquireBackwardPrimitiveDescriptor(
mkldnn::algorithm::lrn_across_channels, src_md, diff_md, n, alpha,
beta, k);
}
const Tensor* out_grad, Tensor* in_x_grad)
: platform::MKLDNNHandlerNoCachingT<T, mkldnn::lrn_forward,
mkldnn::lrn_backward>(mkldnn_engine,
cpu_place) {
PADDLE_ENFORCE_EQ(
ctx.Attr<bool>("is_test"), false,
platform::errors::PreconditionNotMet(
"is_test attribute should be set to False in training phase."));

const int n = ctx.Attr<int>("n");
const float alpha = ctx.Attr<float>("alpha") * static_cast<float>(n);
const float beta = ctx.Attr<float>("beta");
const float k = ctx.Attr<float>("k");

auto dims = framework::vectorize<int64_t>(in_x->dims());

auto src_md = mkldnn::memory::desc(dims, platform::MKLDNNGetDataType<T>(),
in_x->format());
auto diff_md = mkldnn::memory::desc(dims, platform::MKLDNNGetDataType<T>(),
out_grad->format());

this->AcquireForwardPrimitiveDescriptor(
mkldnn::prop_kind::forward_training,
mkldnn::algorithm::lrn_across_channels, src_md, n, alpha, beta, k);

this->AcquireBackwardPrimitiveDescriptor(
mkldnn::algorithm::lrn_across_channels, src_md, diff_md, n, alpha, beta,
k);
}

std::shared_ptr<mkldnn::memory> AcquireWorkspaceMemory(Tensor* workspace) {
T* ptr = workspace->mutable_data<T>(
this->place_, this->fwd_pd_->workspace_desc().get_size());
return this->AcquireMemoryFromPrimitive(this->fwd_pd_->workspace_desc(),
ptr, "@wrk_mem_p");
ptr);
}

std::shared_ptr<mkldnn::memory> AcquireBackwardWorkspaceMemory(
const Tensor* workspace) {
const T* workspace_data = workspace->data<T>();
return this->AcquireMemoryFromPrimitive(
this->fwd_pd_->workspace_desc(),
platform::to_void_cast<T>(workspace_data), "@bwd-wrk_mem_p");
platform::to_void_cast<T>(workspace_data));
}
};

Expand All @@ -131,8 +123,7 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto out = ctx.Output<Tensor>("Out");
auto mid = ctx.Output<Tensor>("MidOut");

LRNMKLDNNHandler<T> handler(ctx, dev_ctx, mkldnn_engine, ctx.GetPlace(), x,
ctx.OutputName("Out"));
LRNMKLDNNHandler<T> handler(ctx, mkldnn_engine, ctx.GetPlace(), x);

auto src_memory = handler.AcquireSrcMemory(x);
auto dst_memory = handler.AcquireDstMemory(out);
Expand Down Expand Up @@ -178,9 +169,10 @@ class LRNMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
auto in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));

auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();

LRNMKLDNNHandler<T> handler(ctx, dev_ctx, ctx.GetPlace(), in_x, out_grad,
in_x_grad, ctx.InputName("Out"));
LRNMKLDNNHandler<T> handler(ctx, mkldnn_engine, ctx.GetPlace(), in_x,
out_grad, in_x_grad);

auto src_memory = handler.AcquireSrcMemory(in_x);
auto workspace = handler.AcquireBackwardWorkspaceMemory(mid);
Expand Down
Loading