Skip to content
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

[XPU]. Add support fp16 to some op. #9254

Merged
merged 2 commits into from
Jul 25, 2022
Merged

[XPU]. Add support fp16 to some op. #9254

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6aa7f70
[XPU]. Add support fp16 to some op.
wbn03 Jul 15, 2022
b6608f1
[XPU]. Add meshgrid op on xpu and bindings of floordiv/min/mod.
wbn03 Jul 18, 2022
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
9 changes: 8 additions & 1 deletion lite/core/optimizer/mir/__xpu__static_kernel_pick_pass.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -327,11 +327,18 @@ class XPUStaticKernelPickPass : public mir::StmtPass {
"conv2d_transpose",
"elementwise_mul",
"elementwise_add",
"elementwise_mod",
"elementwise_floordiv",
"reduce_mean",
"bilinear_interp",
"bilinear_interp_v2",
"nearest_interp",
"nearest_interp_v2"};
"nearest_interp_v2",
"tile",
"transpose",
"pixel_shuffle",
"expand_v2",
"meshgrid"};
const std::set<std::string> xpu_inplace_op_{"reshape",
"reshape2",
"flatten",
Expand Down
1 change: 1 addition & 0 deletions lite/kernels/xpu/CMakeLists.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,7 @@ add_kernel(expand_v2_compute_xpu XPU basic SRCS expand_v2_compute.cc)
add_kernel(range_compute_xpu XPU extra SRCS range_compute.cc)
add_kernel(where_compute_xpu XPU extra SRCS where_compute.cc)
add_kernel(gather_nd_compute_xpu XPU extra SRCS gather_nd_compute.cc)
add_kernel(meshgrid_compute_xpu XPU basic SRCS meshgrid_compute.cc)

# extra
add_kernel(lookup_table_compute_xpu XPU extra SRCS lookup_table_compute.cc)
Expand Down
38 changes: 28 additions & 10 deletions lite/kernels/xpu/activation_compute.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,24 +33,26 @@ void ReluCompute<T, PType>::Run() {
CHECK_EQ(r, 0);
}

void Relu6Compute::Run() {
template <typename T, PrecisionType PType>
void Relu6Compute<T, PType>::Run() {
auto& param = this->template Param<param_t>();
auto& ctx = this->ctx_->template As<XPUContext>();

int r = xdnn::relu6(ctx.GetRawContext(),
param.X->data<float>(),
param.Out->mutable_data<float>(TARGET(kXPU)),
param.X->template data<T>(),
param.Out->template mutable_data<T>(TARGET(kXPU)),
param.X->numel());
CHECK_EQ(r, 0);
}

void GeluCompute::Run() {
template <typename T, PrecisionType PType>
void GeluCompute<T, PType>::Run() {
auto& param = this->template Param<param_t>();
auto& ctx = this->ctx_->template As<XPUContext>();

int r = xdnn::gelu(ctx.GetRawContext(),
param.X->data<float>(),
param.Out->mutable_data<float>(TARGET(kXPU)),
param.X->template data<T>(),
param.Out->template mutable_data<T>(TARGET(kXPU)),
param.X->numel());
CHECK_EQ(r, 0);
}
Expand Down Expand Up @@ -302,17 +304,33 @@ REGISTER_LITE_KERNEL(relu, kXPU, kFP16, kNCHW, reluFP16, reluFP16)
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(
relu6, kXPU, kFloat, kNCHW, paddle::lite::kernels::xpu::Relu6Compute, def)
using relu6FP32 =
paddle::lite::kernels::xpu::Relu6Compute<float, PRECISION(kFloat)>;
using relu6FP16 =
paddle::lite::kernels::xpu::Relu6Compute<float16, PRECISION(kFP16)>;
REGISTER_LITE_KERNEL(relu6, kXPU, kFloat, kNCHW, relu6FP32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();
REGISTER_LITE_KERNEL(relu6, kXPU, kFP16, kNCHW, relu6FP16, relu6FP16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(
gelu, kXPU, kFloat, kNCHW, paddle::lite::kernels::xpu::GeluCompute, def)
using geluFP32 =
paddle::lite::kernels::xpu::GeluCompute<float, PRECISION(kFloat)>;
using geluFP16 =
paddle::lite::kernels::xpu::GeluCompute<float16, PRECISION(kFP16)>;
REGISTER_LITE_KERNEL(gelu, kXPU, kFloat, kNCHW, geluFP32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();
using gelu_fp16 =
paddle::lite::kernels::xpu::GeluCompute<float16, PRECISION(kFP16)>;
REGISTER_LITE_KERNEL(gelu, kXPU, kFP16, kNCHW, geluFP16, geluFP16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

using tanhFP32 =
paddle::lite::kernels::xpu::TanhCompute<float, PRECISION(kFloat)>;
Expand Down
6 changes: 4 additions & 2 deletions lite/kernels/xpu/activation_compute.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,8 @@ class ReluCompute : public KernelLite<TARGET(kXPU), PType> {
virtual ~ReluCompute() = default;
};

class Relu6Compute : public KernelLite<TARGET(kXPU), PRECISION(kFloat)> {
template <typename T, PrecisionType PType>
class Relu6Compute : public KernelLite<TARGET(kXPU), PType> {
public:
using param_t = operators::ActivationParam;

Expand All @@ -39,7 +40,8 @@ class Relu6Compute : public KernelLite<TARGET(kXPU), PRECISION(kFloat)> {
virtual ~Relu6Compute() = default;
};

class GeluCompute : public KernelLite<TARGET(kXPU), PRECISION(kFloat)> {
template <typename T, PrecisionType PType>
class GeluCompute : public KernelLite<TARGET(kXPU), PType> {
public:
using param_t = operators::ActivationParam;

Expand Down
203 changes: 188 additions & 15 deletions lite/kernels/xpu/elementwise_compute.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -87,8 +87,44 @@ struct MaxFunctor {
}
};

template <class T, class Functor>
void ElementwiseCompute<T, Functor>::Run() {
template <typename T>
struct MinFunctor {
inline int operator()(xdnn::Context* ctx,
const T* x,
const T* y,
T* z,
const std::vector<int>& xshape,
const std::vector<int>& yshape) const {
return xdnn::broadcast_min<T>(ctx, x, y, z, xshape, yshape);
}
};

template <typename T>
struct ModFunctor {
inline int operator()(xdnn::Context* ctx,
const T* x,
const T* y,
T* z,
const std::vector<int>& xshape,
const std::vector<int>& yshape) const {
return xdnn::broadcast_mod<T>(ctx, x, y, z, xshape, yshape);
}
};

template <typename T>
struct FloordivFunctor {
inline int operator()(xdnn::Context* ctx,
const T* x,
const T* y,
T* z,
const std::vector<int>& xshape,
const std::vector<int>& yshape) const {
return xdnn::broadcast_floordiv<T>(ctx, x, y, z, xshape, yshape);
}
};

template <class T, class Functor, PrecisionType PType>
void ElementwiseCompute<T, Functor, PType>::Run() {
auto& param = this->template Param<param_t>();
auto& ctx = this->ctx_->template As<XPUContext>();
const Tensor* x = param.X;
Expand Down Expand Up @@ -131,20 +167,73 @@ void ElementwiseCompute<T, Functor>::Run() {
} // namespace paddle

namespace xpu = paddle::lite::kernels::xpu;
using AddFloat32 = xpu::ElementwiseCompute<float, xpu::AddFunctor<float>>;
using AddFloat16 = xpu::ElementwiseCompute<float16, xpu::AddFunctor<float16>>;
using AddInt32 = xpu::ElementwiseCompute<int, xpu::AddFunctor<int>>;
using AddInt64 = xpu::ElementwiseCompute<int64_t, xpu::AddFunctor<int64_t>>;
using AddFloat32 =
xpu::ElementwiseCompute<float, xpu::AddFunctor<float>, PRECISION(kFloat)>;
using AddFloat16 = xpu::ElementwiseCompute<float16,
xpu::AddFunctor<float16>,
PRECISION(kFP16)>;
using AddInt32 =
xpu::ElementwiseCompute<int, xpu::AddFunctor<int>, PRECISION(kFloat)>;
using AddInt64 = xpu::ElementwiseCompute<int64_t,
xpu::AddFunctor<int64_t>,
PRECISION(kFloat)>;

using SubFloat32 =
xpu::ElementwiseCompute<float, xpu::SubFunctor<float>, PRECISION(kFloat)>;
using SubFloat16 = xpu::ElementwiseCompute<float16,
xpu::SubFunctor<float16>,
PRECISION(kFP16)>;

using MulFloat32 =
xpu::ElementwiseCompute<float, xpu::MulFunctor<float>, PRECISION(kFloat)>;
using MulFloat16 = xpu::ElementwiseCompute<float16,
xpu::MulFunctor<float16>,
PRECISION(kFP16)>;
using MulInt64 = xpu::ElementwiseCompute<int64_t,
xpu::MulFunctor<int64_t>,
PRECISION(kFloat)>;

using DivFloat32 =
xpu::ElementwiseCompute<float, xpu::DivFunctor<float>, PRECISION(kFloat)>;
using DivFloat16 = xpu::ElementwiseCompute<float16,
xpu::DivFunctor<float16>,
PRECISION(kFP16)>;

using SubFloat32 = xpu::ElementwiseCompute<float, xpu::SubFunctor<float>>;
using MaxFloat32 =
xpu::ElementwiseCompute<float, xpu::MaxFunctor<float>, PRECISION(kFloat)>;
using MaxFloat16 = xpu::ElementwiseCompute<float16,
xpu::MaxFunctor<float16>,
PRECISION(kFP16)>;
using MaxInt32 =
xpu::ElementwiseCompute<int, xpu::MaxFunctor<int>, PRECISION(kFloat)>;

using MulFloat32 = xpu::ElementwiseCompute<float, xpu::MulFunctor<float>>;
using MulFloat16 = xpu::ElementwiseCompute<float16, xpu::MulFunctor<float16>>;
using MinFloat32 =
xpu::ElementwiseCompute<float, xpu::MinFunctor<float>, PRECISION(kFloat)>;

using MulInt64 = xpu::ElementwiseCompute<int64_t, xpu::MulFunctor<int64_t>>;
using DivFloat32 = xpu::ElementwiseCompute<float, xpu::DivFunctor<float>>;
using MaxFloat32 = xpu::ElementwiseCompute<float, xpu::MaxFunctor<float>>;
using MaxInt32 = xpu::ElementwiseCompute<int, xpu::MaxFunctor<int>>;
using MinFloat16 = xpu::ElementwiseCompute<float16,
xpu::MinFunctor<float16>,
PRECISION(kFP16)>;
using MinInt32 =
xpu::ElementwiseCompute<int, xpu::MinFunctor<int>, PRECISION(kFloat)>;

using ModFloat32 =
xpu::ElementwiseCompute<float, xpu::ModFunctor<float>, PRECISION(kFloat)>;

using ModFloat16 = xpu::ElementwiseCompute<float16,
xpu::ModFunctor<float16>,
PRECISION(kFP16)>;
using ModInt32 =
xpu::ElementwiseCompute<int, xpu::ModFunctor<int>, PRECISION(kFloat)>;

using FloordivFloat32 = xpu::ElementwiseCompute<float,
xpu::FloordivFunctor<float>,
PRECISION(kFloat)>;

using FloordivFloat16 = xpu::ElementwiseCompute<float16,
xpu::FloordivFunctor<float16>,
PRECISION(kFP16)>;
using FloordivInt32 =
xpu::ElementwiseCompute<int, xpu::FloordivFunctor<int>, PRECISION(kFloat)>;

REGISTER_LITE_KERNEL(elementwise_add, kXPU, kFloat, kNCHW, AddFloat32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
Expand All @@ -153,7 +242,7 @@ REGISTER_LITE_KERNEL(elementwise_add, kXPU, kFloat, kNCHW, AddFloat32, def)
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_add, kXPU, kFloat, kNCHW, AddFloat16, DISABLE_XPU1_AddFloat16)
elementwise_add, kXPU, kFP16, kNCHW, AddFloat16, DISABLE_XPU1_AddFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
Expand All @@ -177,14 +266,21 @@ REGISTER_LITE_KERNEL(elementwise_sub, kXPU, kFloat, kNCHW, SubFloat32, def)
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_sub, kXPU, kFP16, kNCHW, SubFloat16, DISABLE_XPU1_SubFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_mul, kXPU, kFloat, kNCHW, MulFloat32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_mul, kXPU, kFloat, kNCHW, MulFloat16, DISABLE_XPU1_MulFloat16)
elementwise_mul, kXPU, kFP16, kNCHW, MulFloat16, DISABLE_XPU1_MulFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
Expand All @@ -202,14 +298,91 @@ REGISTER_LITE_KERNEL(elementwise_div, kXPU, kFloat, kNCHW, DivFloat32, def)
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_div, kXPU, kFP16, kNCHW, DivFloat16, DISABLE_XPU1_DivFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_max, kXPU, kFloat, kNCHW, MaxFloat32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_max, kXPU, kFP16, kNCHW, MaxFloat16, DISABLE_XPU1_MaxFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_max, kXPU, kFloat, kNCHW, MaxInt32, int32)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_min, kXPU, kFloat, kNCHW, MinFloat32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_min, kXPU, kFP16, kNCHW, MinFloat16, DISABLE_XPU1_MinFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_min, kXPU, kFloat, kNCHW, MinInt32, int32)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_mod, kXPU, kFloat, kNCHW, ModFloat32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_mod, kXPU, kFP16, kNCHW, ModFloat16, DISABLE_XPU1_ModFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_mod, kXPU, kFloat, kNCHW, ModInt32, int32)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_floordiv, kXPU, kFloat, kNCHW, FloordivFloat32, def)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU))})
.Finalize();

REGISTER_LITE_KERNEL(elementwise_floordiv,
kXPU,
kFP16,
kNCHW,
FloordivFloat16,
DISABLE_XPU1_FloordivFloat16)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kFP16))})
.Finalize();

REGISTER_LITE_KERNEL(
elementwise_floordiv, kXPU, kFloat, kNCHW, FloordivInt32, int32)
.BindInput("X", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindInput("Y", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.BindOutput("Out", {LiteType::GetTensorTy(TARGET(kXPU), PRECISION(kInt32))})
.Finalize();
4 changes: 2 additions & 2 deletions lite/kernels/xpu/elementwise_compute.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,8 @@ namespace lite {
namespace kernels {
namespace xpu {

template <class T, class Functor>
class ElementwiseCompute : public KernelLite<TARGET(kXPU), PRECISION(kFloat)> {
template <class T, class Functor, PrecisionType PType>
class ElementwiseCompute : public KernelLite<TARGET(kXPU), PType> {
public:
using param_t = operators::ElementwiseParam;

Expand Down
Loading