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[PTen]Separate origin Kernel and add Kernel for C++ API #39002

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merged 11 commits into from
Jan 21, 2022
Merged

[PTen]Separate origin Kernel and add Kernel for C++ API #39002

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5c5ae2e
add kernel for c++ api
YuanRisheng Jan 17, 2022
84e4328
fix compile bugs
YuanRisheng Jan 17, 2022
7c0a653
fix kunlun compile bugs
YuanRisheng Jan 18, 2022
deee972
perfect cmake
YuanRisheng Jan 18, 2022
f25b3c4
fix compile bugs when run ci-inference
YuanRisheng Jan 20, 2022
5986410
fix compile bugs
YuanRisheng Jan 20, 2022
9ef36c0
merge develop
YuanRisheng Jan 21, 2022
f1f8874
add non-raw kernel for fluid op
YuanRisheng Jan 21, 2022
5aca6fb
fix compile bugs
YuanRisheng Jan 21, 2022
794e60f
fix compile bugs
YuanRisheng Jan 21, 2022
9020218
fix unit test bug
YuanRisheng Jan 21, 2022
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61 changes: 39 additions & 22 deletions cmake/pten_kernel.cmake
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Original file line number Diff line number Diff line change
Expand Up @@ -103,38 +103,55 @@ function(kernel_library TARGET)
list(LENGTH gpu_srcs gpu_srcs_len)
list(LENGTH xpu_srcs xpu_srcs_len)

if (${common_srcs_len} GREATER 0)
# If the kernel has a device independent public implementation,
# we will use this implementation and will not adopt the implementation
# under specific devices
# Build Target according different src organization
if((${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0 OR
${xpu_srcs_len} GREATER 0) AND ${common_srcs_len} GREATER 0)
# If the common_srcs depends on specific device srcs, build target using this rule.
if (WITH_GPU)
if (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0)
nv_library(${TARGET}_part SRCS ${cpu_srcs} ${gpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
nv_library(${TARGET} SRCS ${common_srcs} DEPS ${TARGET}_part)
endif()
elseif (WITH_ROCM)
if (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0)
hip_library(${TARGET}_part SRCS ${cpu_srcs} ${gpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
hip_library(${TARGET} SRCS ${common_srcs} DEPS ${TARGET}_part)
endif()
else()
if (${cpu_srcs_len} GREATER 0 OR ${xpu_srcs_len} GREATER 0)
cc_library(${TARGET}_part SRCS ${cpu_srcs} ${xpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
cc_library(${TARGET} SRCS ${common_srcs} DEPS ${TARGET}_part)
endif()
endif()
elseif (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0 OR ${xpu_srcs_len} GREATER 0)
if (WITH_GPU)
nv_library(${TARGET} SRCS ${common_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
if (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0)
nv_library(${TARGET} SRCS ${cpu_srcs} ${gpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
elseif (WITH_ROCM)
hip_library(${TARGET} SRCS ${common_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
if (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0)
hip_library(${TARGET} SRCS ${cpu_srcs} ${gpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
else()
cc_library(${TARGET} SRCS ${common_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
if (${cpu_srcs_len} GREATER 0 OR ${xpu_srcs_len} GREATER 0)
cc_library(${TARGET} SRCS ${cpu_srcs} ${xpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
endif()
else()
# If the kernel has a header file declaration, but no corresponding
# implementation can be found, this is not allowed
if (${cpu_srcs_len} EQUAL 0 AND ${gpu_srcs_len} EQUAL 0 AND
${xpu_srcs_len} EQUAL 0)
message(FATAL_ERROR "Cannot find any implementation for ${TARGET}")
if (${common_srcs_len} EQUAL 0)
message(FATAL_ERROR "Cannot find any implementation for ${TARGET}")
else()
# If the kernel has a device independent public implementation,
# we will use this implementation and will not adopt the implementation
# under specific devices
if (WITH_GPU)
if (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0)
nv_library(${TARGET} SRCS ${cpu_srcs} ${gpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
nv_library(${TARGET} SRCS ${common_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
elseif (WITH_ROCM)
if (${cpu_srcs_len} GREATER 0 OR ${gpu_srcs_len} GREATER 0)
hip_library(${TARGET} SRCS ${cpu_srcs} ${gpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
hip_library(${TARGET} SRCS ${common_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
else()
if (${cpu_srcs_len} GREATER 0 OR ${xpu_srcs_len} GREATER 0)
cc_library(${TARGET} SRCS ${cpu_srcs} ${xpu_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
cc_library(${TARGET} SRCS ${common_srcs} DEPS ${kernel_library_DEPS} ${kernel_deps})
endif()
endif()
endif()
endif()

if (${common_srcs_len} GREATER 0 OR ${cpu_srcs_len} GREATER 0 OR
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2 changes: 1 addition & 1 deletion paddle/fluid/operators/cholesky_solve_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -202,7 +202,7 @@ class CholeskySolveGradKernel : public framework::OpKernel<T> {
commonterm_for_range(commonterm_functor);
commonterm_conj = helper.Transpose(commonterm_conj);

pten::AddKernel<T>(
pten::AddRawKernel<T>(
static_cast<const typename paddle::framework::ConvertToPtenContext<
DeviceContext>::TYPE &>(dev_ctx),
commonterm, commonterm_conj, -1, &commonterm);
Expand Down
2 changes: 1 addition & 1 deletion paddle/fluid/operators/elementwise/elementwise_add_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ class ElementwiseAddKernel : public framework::OpKernel<T> {
auto pt_x = paddle::experimental::MakePtenDenseTensor(*x);
auto pt_y = paddle::experimental::MakePtenDenseTensor(*y);
auto pt_z = paddle::experimental::MakePtenDenseTensor(*z);
pten::AddKernel<T>(
pten::AddRawKernel<T>(
static_cast<const typename framework::ConvertToPtenContext<
DeviceContext>::TYPE &>(dev_ctx),
*pt_x.get(), *pt_y.get(), axis, pt_z.get());
Expand Down
2 changes: 1 addition & 1 deletion paddle/fluid/operators/elementwise/elementwise_div_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@ class ElementwiseDivKernel : public framework::OpKernel<T> {
auto pt_x = paddle::experimental::MakePtenDenseTensor(*x);
auto pt_y = paddle::experimental::MakePtenDenseTensor(*y);
auto pt_z = paddle::experimental::MakePtenDenseTensor(*z);
pten::DivideKernel<T>(
pten::DivideRawKernel<T>(
static_cast<const typename framework::ConvertToPtenContext<
DeviceContext>::TYPE&>(dev_ctx),
*pt_x.get(), *pt_y.get(), axis, pt_z.get());
Expand Down
4 changes: 2 additions & 2 deletions paddle/fluid/operators/elementwise/elementwise_mul_op.cu
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Expand Up @@ -51,8 +51,8 @@ class ElementwiseMulKernel<platform::CUDADeviceContext, T>
auto pt_x = paddle::experimental::MakePtenDenseTensor(*x_lod);
auto pt_y = paddle::experimental::MakePtenDenseTensor(*y_lod);
auto pt_z = paddle::experimental::MakePtenDenseTensor(*z_lod);
pten::MultiplyKernel<T>(cuda_ctx, *pt_x.get(), *pt_y.get(), axis,
pt_z.get());
pten::MultiplyRawKernel<T>(cuda_ctx, *pt_x.get(), *pt_y.get(), axis,
pt_z.get());
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"X's type[%s] is not supported by elementwise_op. X's type should be "
Expand Down
2 changes: 1 addition & 1 deletion paddle/fluid/operators/elementwise/elementwise_mul_op.h
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Expand Up @@ -124,7 +124,7 @@ class ElementwiseMulKernel : public framework::OpKernel<T> {
auto pt_x = paddle::experimental::MakePtenDenseTensor(*x_lod);
auto pt_y = paddle::experimental::MakePtenDenseTensor(*y);
auto pt_z = paddle::experimental::MakePtenDenseTensor(*z_lod);
pten::MultiplyKernel<T>(
pten::MultiplyRawKernel<T>(
static_cast<const typename framework::ConvertToPtenContext<
DeviceContext>::TYPE&>(dev_ctx),
*pt_x.get(), *pt_y.get(), axis, pt_z.get());
Expand Down
24 changes: 20 additions & 4 deletions paddle/fluid/operators/elementwise/elementwise_op.h
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Expand Up @@ -140,26 +140,42 @@ class ElementwiseOp : public framework::OperatorWithKernel {

framework::KernelSignature GetExpectedPtenKernelArgs(
const framework::ExecutionContext &ctx) const override {
int axis = ctx.Attr<int>("axis");
if (Type() == "elementwise_add") {
if (ctx.InputVar("X")->IsType<framework::LoDTensor>()) {
return framework::KernelSignature("add", {"X", "Y"}, {"axis"}, {"Out"});
if (axis == -1) {
return framework::KernelSignature("add", {"X", "Y"}, {}, {"Out"});
}
return framework::KernelSignature("add_raw", {"X", "Y"}, {"axis"},
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这里要既能找到raw也能找到非raw的吧，axis值无效的时候，直接使用非raw，raw是用来保底的

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done

{"Out"});
}
}
if (Type() == "elementwise_sub") {
if (ctx.InputVar("X")->IsType<framework::LoDTensor>()) {
return framework::KernelSignature("subtract", {"X", "Y"}, {"axis"},
if (axis == -1) {
return framework::KernelSignature("subtract", {"X", "Y"}, {},
{"Out"});
}
return framework::KernelSignature("subtract_raw", {"X", "Y"}, {"axis"},
{"Out"});
}
}
if (Type() == "elementwise_div") {
if (ctx.InputVar("X")->IsType<framework::LoDTensor>()) {
return framework::KernelSignature("divide", {"X", "Y"}, {"axis"},
if (axis == -1) {
return framework::KernelSignature("divide", {"X", "Y"}, {}, {"Out"});
}
return framework::KernelSignature("divide_raw", {"X", "Y"}, {"axis"},
{"Out"});
}
}
if (Type() == "elementwise_mul") {
if (ctx.InputVar("X")->IsType<framework::LoDTensor>()) {
return framework::KernelSignature("multiply", {"X", "Y"}, {"axis"},
if (axis == -1) {
return framework::KernelSignature("multiply", {"X", "Y"}, {},
{"Out"});
}
return framework::KernelSignature("multiply_raw", {"X", "Y"}, {"axis"},
{"Out"});
}
}
Expand Down
2 changes: 1 addition & 1 deletion paddle/fluid/operators/elementwise/elementwise_sub_op.h
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Expand Up @@ -51,7 +51,7 @@ class ElementwiseSubKernel : public framework::OpKernel<T> {
auto pt_x = paddle::experimental::MakePtenDenseTensor(*x);
auto pt_y = paddle::experimental::MakePtenDenseTensor(*y);
auto pt_z = paddle::experimental::MakePtenDenseTensor(*z);
pten::SubtractKernel<T>(
pten::SubtractRawKernel<T>(
static_cast<const typename framework::ConvertToPtenContext<
DeviceContext>::TYPE&>(dev_ctx),
*pt_x.get(), *pt_y.get(), axis, pt_z.get());
Expand Down
4 changes: 2 additions & 2 deletions paddle/fluid/operators/lu_op.h
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Expand Up @@ -221,7 +221,7 @@ void Tensor_Add(const DeviceContext& dev_ctx, const framework::Tensor& src1,
out->Resize(src1.dims());
out->mutable_data<T>(dev_ctx.GetPlace());

pten::AddKernel<
pten::AddRawKernel<
T, typename paddle::framework::ConvertToPtenContext<DeviceContext>::TYPE>(
static_cast<const typename paddle::framework::ConvertToPtenContext<
DeviceContext>::TYPE&>(dev_ctx),
Expand All @@ -234,7 +234,7 @@ void Tensor_Sub(const DeviceContext& dev_ctx, const framework::Tensor& src1,
out->Resize(src1.dims());
out->mutable_data<T>(dev_ctx.GetPlace());

pten::SubtractKernel<
pten::SubtractRawKernel<
T, typename paddle::framework::ConvertToPtenContext<DeviceContext>::TYPE>(
static_cast<const typename paddle::framework::ConvertToPtenContext<
DeviceContext>::TYPE&>(dev_ctx),
Expand Down
13 changes: 11 additions & 2 deletions paddle/fluid/operators/reduce_ops/reduce_op.h
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Expand Up @@ -551,17 +551,26 @@ class ReduceOp : public framework::OperatorWithKernel {

framework::KernelSignature GetExpectedPtenKernelArgs(
const framework::ExecutionContext& ctx) const override {
bool reduce_all = ctx.Attr<bool>("reduce_all");
if (Type() == "reduce_sum") {
if (ctx.InputVar("X")->IsType<framework::LoDTensor>()) {
if (!reduce_all) {
return framework::KernelSignature(
"sum", {"X"}, {"dim", "keep_dim", "out_dtype"}, {"Out"});
}
return framework::KernelSignature(
"sum", {"X"}, {"dim", "keep_dim", "reduce_all", "out_dtype"},
"sum_raw", {"X"}, {"dim", "keep_dim", "reduce_all", "out_dtype"},
{"Out"});
}
}
if (Type() == "reduce_mean") {
if (ctx.InputVar("X")->IsType<framework::LoDTensor>()) {
if (!reduce_all) {
return framework::KernelSignature("mean", {"X"}, {"dim", "keep_dim"},
{"Out"});
}
return framework::KernelSignature(
"mean", {"X"}, {"dim", "keep_dim", "reduce_all"}, {"Out"});
"mean_raw", {"X"}, {"dim", "keep_dim", "reduce_all"}, {"Out"});
}
}
// TODO(chentianyu03): support other cases after selected rows added
Expand Down
6 changes: 0 additions & 6 deletions paddle/pten/api/include/kernel_signature.h
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Expand Up @@ -30,7 +30,6 @@ using DeviceContext = paddle::platform::DeviceContext;
using add_kernel = void (*)(const DeviceContext&,
const DenseTensor&,
const DenseTensor&,
int,
DenseTensor*);

using cast_kernel = void (*)(const DeviceContext&,
Expand All @@ -41,7 +40,6 @@ using cast_kernel = void (*)(const DeviceContext&,
using divide_kernel = void (*)(const DeviceContext&,
const DenseTensor&,
const DenseTensor&,
int,
DenseTensor*);

using dot_kernel = void (*)(const DeviceContext&,
Expand Down Expand Up @@ -77,13 +75,11 @@ using mean_kernel = void (*)(const DeviceContext&,
const DenseTensor&,
const std::vector<int64_t>&,
bool,
bool,
DenseTensor*);

using multiply_kernel = void (*)(const DeviceContext&,
const DenseTensor&,
const DenseTensor&,
int,
DenseTensor*);

using reshape_kernel = void (*)(const DeviceContext&,
Expand All @@ -102,14 +98,12 @@ using sum_kernel = void (*)(const DeviceContext&,
const DenseTensor&,
const std::vector<int64_t>&,
bool,
bool,
DataType,
DenseTensor*);

using subtract_kernel = void (*)(const DeviceContext&,
const DenseTensor&,
const DenseTensor&,
int,
DenseTensor*);

using conj_kernel = void (*)(const DeviceContext&,
Expand Down
12 changes: 6 additions & 6 deletions paddle/pten/core/kernel_alias_name.h
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Expand Up @@ -20,10 +20,10 @@ namespace pten {
// the key is kernel_name in fluid, the value is the kernel_name in pten
// the key is sorted by key's alphabet
const std::unordered_map<std::string, std::string> kernel_alias_name_map = {
{"elementwise_add", "add"},
{"elementwise_div", "divide"},
{"elementwise_mul", "muliply"},
{"elementwise_sub", "subtract"},
{"elementwise_add", "add_raw"},
{"elementwise_div", "divide_raw"},
{"elementwise_mul", "muliply_raw"},
{"elementwise_sub", "subtract_raw"},
{"fill_any_like", "full_like"},
{"fill_constant", "full"},
{"flatten_contiguous_range", "flatten"},
Expand All @@ -32,8 +32,8 @@ const std::unordered_map<std::string, std::string> kernel_alias_name_map = {
{"matmul_v2_grad", "matmul_grad"},
{"matmul_v2_grad_grad", "matmul_double_grad"},
{"matmul_v2_triple_grad", "matmul_triple_grad"},
{"reduce_mean", "mean"},
{"reduce_sum", "sum"},
{"reduce_mean", "mean_raw"},
{"reduce_sum", "sum_raw"},
{"reshape2", "reshape"},
{"reshape2_grad", "reshape_grad"},
{"reshape2_grad_grad", "reshape_double_grad"},
Expand Down
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