mirror of
https://github.com/ml-explore/mlx.git
synced 2025-12-16 01:49:05 +08:00
CUDA backend: binary ops (#2259)
This commit is contained in:
Cheng
305
mlx/backend/cuda/binary.cu
Normal file
305
mlx/backend/cuda/binary.cu
Normal file
@@ -0,0 +1,305 @@
|
||||
// Copyright © 2025 Apple Inc.
|
||||
|
||||
#include "mlx/backend/common/binary.h"
|
||||
#include "mlx/backend/cuda/device.h"
|
||||
#include "mlx/backend/cuda/kernel_utils.cuh"
|
||||
#include "mlx/backend/cuda/kernels/binary_ops.cuh"
|
||||
#include "mlx/backend/cuda/kernels/cucomplex_math.cuh"
|
||||
#include "mlx/dtype_utils.h"
|
||||
#include "mlx/primitives.h"
|
||||
|
||||
#include <cooperative_groups.h>
|
||||
#include <nvtx3/nvtx3.hpp>
|
||||
|
||||
namespace mlx::core {
|
||||
|
||||
namespace cu {
|
||||
|
||||
namespace cg = cooperative_groups;
|
||||
|
||||
template <typename Op, typename In, typename Out, typename IdxT>
|
||||
__global__ void binary_ss(const In* a, const In* b, Out* out, IdxT size) {
|
||||
IdxT index = cg::this_grid().thread_rank();
|
||||
if (index < size) {
|
||||
out[index] = Op{}(a[0], b[0]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Op, typename In, typename Out, typename IdxT>
|
||||
__global__ void binary_sv(const In* a, const In* b, Out* out, IdxT size) {
|
||||
IdxT index = cg::this_grid().thread_rank();
|
||||
if (index < size) {
|
||||
out[index] = Op{}(a[0], b[index]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Op, typename In, typename Out, typename IdxT>
|
||||
__global__ void binary_vs(const In* a, const In* b, Out* out, IdxT size) {
|
||||
IdxT index = cg::this_grid().thread_rank();
|
||||
if (index < size) {
|
||||
out[index] = Op{}(a[index], b[0]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Op, typename In, typename Out, typename IdxT>
|
||||
__global__ void binary_vv(const In* a, const In* b, Out* out, IdxT size) {
|
||||
IdxT index = cg::this_grid().thread_rank();
|
||||
if (index < size) {
|
||||
out[index] = Op{}(a[index], b[index]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
|
||||
__global__ void binary_g_nd(
|
||||
const In* a,
|
||||
const In* b,
|
||||
Out* out,
|
||||
IdxT size,
|
||||
const __grid_constant__ cuda::std::array<int32_t, NDIM> shape,
|
||||
const __grid_constant__ cuda::std::array<int64_t, NDIM> a_strides,
|
||||
const __grid_constant__ cuda::std::array<int64_t, NDIM> b_strides) {
|
||||
IdxT index = cg::this_grid().thread_rank();
|
||||
if (index < size) {
|
||||
auto [a_idx, b_idx] = elem_to_loc_nd<NDIM>(
|
||||
index, shape.data(), a_strides.data(), b_strides.data());
|
||||
out[index] = Op{}(a[a_idx], b[b_idx]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Op, typename In, typename Out, typename IdxT>
|
||||
__global__ void binary_g(
|
||||
const In* a,
|
||||
const In* b,
|
||||
Out* out,
|
||||
IdxT size,
|
||||
const __grid_constant__ Shape shape,
|
||||
const __grid_constant__ Strides a_strides,
|
||||
const __grid_constant__ Strides b_strides,
|
||||
int ndim) {
|
||||
IdxT index = cg::this_grid().thread_rank();
|
||||
if (index < size) {
|
||||
auto [a_idx, b_idx] = elem_to_loc_4d(
|
||||
index, shape.data(), a_strides.data(), b_strides.data(), ndim);
|
||||
out[index] = Op{}(a[a_idx], b[b_idx]);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Op, typename In, typename Out>
|
||||
constexpr bool supports_binary_op() {
|
||||
if (std::is_same_v<Op, Add> || std::is_same_v<Op, Divide> ||
|
||||
std::is_same_v<Op, Maximum> || std::is_same_v<Op, Minimum> ||
|
||||
std::is_same_v<Op, Multiply> || std::is_same_v<Op, Subtract> ||
|
||||
std::is_same_v<Op, Power> || std::is_same_v<Op, Remainder>) {
|
||||
return std::is_same_v<In, Out>;
|
||||
}
|
||||
if (std::is_same_v<Op, Equal> || std::is_same_v<Op, Greater> ||
|
||||
std::is_same_v<Op, GreaterEqual> || std::is_same_v<Op, Less> ||
|
||||
std::is_same_v<Op, LessEqual> || std::is_same_v<Op, NotEqual>) {
|
||||
return std::is_same_v<Out, bool>;
|
||||
}
|
||||
if (std::is_same_v<Op, LogicalAnd> || std::is_same_v<Op, LogicalOr>) {
|
||||
return std::is_same_v<Out, bool> && std::is_same_v<In, bool>;
|
||||
}
|
||||
if (std::is_same_v<Op, NaNEqual>) {
|
||||
return std::is_same_v<Out, bool> &&
|
||||
(is_floating_v<In> || std::is_same_v<In, complex64_t>);
|
||||
}
|
||||
if (std::is_same_v<Op, LogAddExp> || std::is_same_v<Op, ArcTan2>) {
|
||||
return std::is_same_v<In, Out> && is_floating_v<In>;
|
||||
}
|
||||
if (std::is_same_v<Op, BitwiseAnd> || std::is_same_v<Op, BitwiseOr> ||
|
||||
std::is_same_v<Op, BitwiseXor>) {
|
||||
return std::is_same_v<In, Out> && std::is_integral_v<In>;
|
||||
}
|
||||
if (std::is_same_v<Op, LeftShift> || std::is_same_v<Op, RightShift>) {
|
||||
return std::is_same_v<In, Out> && std::is_integral_v<In> &&
|
||||
!std::is_same_v<In, bool>;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
} // namespace cu
|
||||
|
||||
template <typename Op>
|
||||
void binary_op_gpu_inplace(
|
||||
const std::vector<array>& inputs,
|
||||
std::vector<array>& outputs,
|
||||
std::string_view op,
|
||||
const Stream& s) {
|
||||
assert(inputs.size() > 1);
|
||||
const auto& a = inputs[0];
|
||||
const auto& b = inputs[1];
|
||||
auto& out = outputs[0];
|
||||
if (out.size() == 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
auto& encoder = cu::get_command_encoder(s);
|
||||
encoder.set_input_array(a);
|
||||
encoder.set_input_array(b);
|
||||
encoder.set_output_array(out);
|
||||
encoder.launch_kernel([&](cudaStream_t stream) {
|
||||
MLX_SWITCH_ALL_TYPES(a.dtype(), CTYPE_IN, {
|
||||
MLX_SWITCH_ALL_TYPES(out.dtype(), CTYPE_OUT, {
|
||||
if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
|
||||
using InType = cuda_type_t<CTYPE_IN>;
|
||||
using OutType = cuda_type_t<CTYPE_OUT>;
|
||||
|
||||
auto bopt = get_binary_op_type(a, b);
|
||||
if (bopt == BinaryOpType::General) {
|
||||
auto [shape, strides] = collapse_contiguous_dims(a, b, out);
|
||||
auto& a_strides = strides[0];
|
||||
auto& b_strides = strides[1];
|
||||
bool large = a.data_size() > UINT32_MAX ||
|
||||
b.data_size() > UINT32_MAX || out.data_size() > UINT32_MAX;
|
||||
MLX_SWITCH_BOOL(large, LARGE, {
|
||||
using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
|
||||
int ndim = shape.size();
|
||||
if (ndim <= 3) {
|
||||
MLX_SWITCH_1_2_3(ndim, NDIM, {
|
||||
auto kernel =
|
||||
&cu::binary_g_nd<Op, InType, OutType, IdxT, NDIM>;
|
||||
auto [num_blocks, block_dims] =
|
||||
get_launch_args(kernel, out, large);
|
||||
kernel<<<num_blocks, block_dims, 0, stream>>>(
|
||||
a.data<InType>(),
|
||||
b.data<InType>(),
|
||||
out.data<OutType>(),
|
||||
out.data_size(),
|
||||
const_param<NDIM>(shape),
|
||||
const_param<NDIM>(a_strides),
|
||||
const_param<NDIM>(b_strides));
|
||||
});
|
||||
} else {
|
||||
auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
|
||||
auto [num_blocks, block_dims] =
|
||||
get_launch_args(kernel, out, large);
|
||||
kernel<<<num_blocks, block_dims, 0, stream>>>(
|
||||
a.data<InType>(),
|
||||
b.data<InType>(),
|
||||
out.data<OutType>(),
|
||||
out.data_size(),
|
||||
const_param(shape),
|
||||
const_param(a_strides),
|
||||
const_param(b_strides),
|
||||
ndim);
|
||||
}
|
||||
});
|
||||
} else {
|
||||
MLX_SWITCH_BOOL(out.data_size() > UINT32_MAX, LARGE, {
|
||||
using IdxT = std::conditional_t<LARGE, int64_t, uint32_t>;
|
||||
auto kernel = cu::binary_ss<Op, InType, OutType, IdxT>;
|
||||
if (bopt == BinaryOpType::ScalarVector) {
|
||||
kernel = cu::binary_sv<Op, InType, OutType, IdxT>;
|
||||
} else if (bopt == BinaryOpType::VectorScalar) {
|
||||
kernel = cu::binary_vs<Op, InType, OutType, IdxT>;
|
||||
} else if (bopt == BinaryOpType::VectorVector) {
|
||||
kernel = cu::binary_vv<Op, InType, OutType, IdxT>;
|
||||
}
|
||||
auto [num_blocks, block_dims] =
|
||||
get_launch_args(kernel, out, LARGE);
|
||||
kernel<<<num_blocks, block_dims, 0, stream>>>(
|
||||
a.data<InType>(),
|
||||
b.data<InType>(),
|
||||
out.data<OutType>(),
|
||||
out.data_size());
|
||||
});
|
||||
}
|
||||
} else {
|
||||
throw std::runtime_error(fmt::format(
|
||||
"Can not do binary op {} on inputs of {} with result of {}.",
|
||||
op,
|
||||
dtype_to_string(a.dtype()),
|
||||
dtype_to_string(out.dtype())));
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
template <typename Op>
|
||||
void binary_op_gpu(
|
||||
const std::vector<array>& inputs,
|
||||
std::vector<array>& outputs,
|
||||
std::string_view op,
|
||||
const Stream& s) {
|
||||
auto& a = inputs[0];
|
||||
auto& b = inputs[1];
|
||||
auto bopt = get_binary_op_type(a, b);
|
||||
set_binary_op_output_data(a, b, outputs[0], bopt);
|
||||
set_binary_op_output_data(a, b, outputs[1], bopt);
|
||||
binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
|
||||
}
|
||||
|
||||
template <typename Op>
|
||||
void binary_op_gpu(
|
||||
const std::vector<array>& inputs,
|
||||
array& out,
|
||||
std::string_view op,
|
||||
const Stream& s) {
|
||||
auto& a = inputs[0];
|
||||
auto& b = inputs[1];
|
||||
auto bopt = get_binary_op_type(a, b);
|
||||
set_binary_op_output_data(a, b, out, bopt);
|
||||
std::vector<array> outputs{out};
|
||||
binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
|
||||
}
|
||||
|
||||
#define BINARY_GPU(func) \
|
||||
void func::eval_gpu(const std::vector<array>& inputs, array& out) { \
|
||||
nvtx3::scoped_range r(#func "::eval_gpu"); \
|
||||
auto& s = out.primitive().stream(); \
|
||||
binary_op_gpu<cu::func>(inputs, out, get_primitive_string(this), s); \
|
||||
}
|
||||
|
||||
#define BINARY_GPU_MULTI(func) \
|
||||
void func::eval_gpu( \
|
||||
const std::vector<array>& inputs, std::vector<array>& outputs) { \
|
||||
nvtx3::scoped_range r(#func "::eval_gpu"); \
|
||||
auto& s = outputs[0].primitive().stream(); \
|
||||
binary_op_gpu<cu::func>(inputs, outputs, get_primitive_string(this), s); \
|
||||
}
|
||||
|
||||
BINARY_GPU(Add)
|
||||
BINARY_GPU(ArcTan2)
|
||||
BINARY_GPU(Divide)
|
||||
BINARY_GPU(Remainder)
|
||||
BINARY_GPU(Equal)
|
||||
BINARY_GPU(Greater)
|
||||
BINARY_GPU(GreaterEqual)
|
||||
BINARY_GPU(Less)
|
||||
BINARY_GPU(LessEqual)
|
||||
BINARY_GPU(LogicalAnd)
|
||||
BINARY_GPU(LogicalOr)
|
||||
BINARY_GPU(LogAddExp)
|
||||
BINARY_GPU(Maximum)
|
||||
BINARY_GPU(Minimum)
|
||||
BINARY_GPU(Multiply)
|
||||
BINARY_GPU(NotEqual)
|
||||
BINARY_GPU(Power)
|
||||
BINARY_GPU(Subtract)
|
||||
|
||||
void BitwiseBinary::eval_gpu(const std::vector<array>& inputs, array& out) {
|
||||
nvtx3::scoped_range r("BitwiseBinary::eval_gpu");
|
||||
auto& s = out.primitive().stream();
|
||||
auto op = get_primitive_string(this);
|
||||
switch (op_) {
|
||||
case BitwiseBinary::And:
|
||||
binary_op_gpu<cu::BitwiseAnd>(inputs, out, op, s);
|
||||
break;
|
||||
case BitwiseBinary::Or:
|
||||
binary_op_gpu<cu::BitwiseOr>(inputs, out, op, s);
|
||||
break;
|
||||
case BitwiseBinary::Xor:
|
||||
binary_op_gpu<cu::BitwiseXor>(inputs, out, op, s);
|
||||
break;
|
||||
case BitwiseBinary::LeftShift:
|
||||
binary_op_gpu<cu::LeftShift>(inputs, out, op, s);
|
||||
break;
|
||||
case BitwiseBinary::RightShift:
|
||||
binary_op_gpu<cu::RightShift>(inputs, out, op, s);
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace mlx::core
|
||||
Reference in New Issue
Block a user