mirror of
https://github.com/ml-explore/mlx.git
synced 2025-06-26 18:51:14 +08:00
249 lines
8.1 KiB
Plaintext
249 lines
8.1 KiB
Plaintext
// Copyright © 2025 Apple Inc.
|
|
|
|
#include "mlx/backend/common/binary.h"
|
|
#include "mlx/backend/cuda/device.h"
|
|
#include "mlx/backend/cuda/device/binary_ops.cuh"
|
|
#include "mlx/backend/cuda/device/cucomplex_math.cuh"
|
|
#include "mlx/backend/cuda/kernel_utils.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_a, Out* out_b, IdxT size) {
|
|
IdxT index = cg::this_grid().thread_rank();
|
|
if (index < size) {
|
|
auto out = Op{}(a[0], b[0]);
|
|
out_a[0] = out[0];
|
|
out_b[0] = out[1];
|
|
}
|
|
}
|
|
|
|
template <typename Op, typename In, typename Out, typename IdxT>
|
|
__global__ void
|
|
binary_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
|
|
IdxT index = cg::this_grid().thread_rank();
|
|
if (index < size) {
|
|
auto out = Op{}(a[0], b[index]);
|
|
out_a[index] = out[0];
|
|
out_b[index] = out[1];
|
|
}
|
|
}
|
|
|
|
template <typename Op, typename In, typename Out, typename IdxT>
|
|
__global__ void
|
|
binary_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
|
|
IdxT index = cg::this_grid().thread_rank();
|
|
if (index < size) {
|
|
auto out = Op{}(a[index], b[0]);
|
|
out_a[index] = out[0];
|
|
out_b[index] = out[1];
|
|
}
|
|
}
|
|
|
|
template <typename Op, typename In, typename Out, typename IdxT>
|
|
__global__ void
|
|
binary_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
|
|
IdxT index = cg::this_grid().thread_rank();
|
|
if (index < size) {
|
|
auto out = Op{}(a[index], b[index]);
|
|
out_a[index] = out[0];
|
|
out_b[index] = out[1];
|
|
}
|
|
}
|
|
|
|
template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
|
|
__global__ void binary_g_nd(
|
|
const In* a,
|
|
const In* b,
|
|
Out* out_a,
|
|
Out* out_b,
|
|
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());
|
|
auto out = Op{}(a[a_idx], b[b_idx]);
|
|
out_a[index] = out[0];
|
|
out_b[index] = out[1];
|
|
}
|
|
}
|
|
|
|
template <typename Op, typename In, typename Out, typename IdxT>
|
|
__global__ void binary_g(
|
|
const In* a,
|
|
const In* b,
|
|
Out* out_a,
|
|
Out* out_b,
|
|
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);
|
|
auto out = Op{}(a[a_idx], b[b_idx]);
|
|
out_a[index] = out[0];
|
|
out_b[index] = out[1];
|
|
}
|
|
}
|
|
|
|
template <typename Op, typename In, typename Out>
|
|
constexpr bool supports_binary_op() {
|
|
if (std::is_same_v<Op, DivMod>) {
|
|
return std::is_same_v<In, Out> &&
|
|
(std::is_integral_v<Out> || is_floating_v<Out>);
|
|
}
|
|
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_a = outputs[0];
|
|
auto& out_b = outputs[1];
|
|
auto bopt = get_binary_op_type(a, b);
|
|
set_binary_op_output_data(a, b, out_a, bopt);
|
|
set_binary_op_output_data(a, b, out_b, bopt);
|
|
|
|
if (out_a.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_a);
|
|
encoder.set_output_array(out_b);
|
|
encoder.launch_kernel([&](cudaStream_t stream) {
|
|
MLX_SWITCH_ALL_TYPES(a.dtype(), CTYPE_IN, {
|
|
MLX_SWITCH_ALL_TYPES(out_a.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_a);
|
|
auto& a_strides = strides[0];
|
|
auto& b_strides = strides[1];
|
|
bool large = a.data_size() > INT32_MAX ||
|
|
b.data_size() > INT32_MAX || out_a.data_size() > INT32_MAX;
|
|
MLX_SWITCH_BOOL(large, LARGE, {
|
|
using IdxT = std::conditional_t<LARGE, int64_t, int32_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_a, large);
|
|
kernel<<<num_blocks, block_dims, 0, stream>>>(
|
|
a.data<InType>(),
|
|
b.data<InType>(),
|
|
out_a.data<OutType>(),
|
|
out_b.data<OutType>(),
|
|
out_a.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_a, large);
|
|
kernel<<<num_blocks, block_dims, 0, stream>>>(
|
|
a.data<InType>(),
|
|
b.data<InType>(),
|
|
out_a.data<OutType>(),
|
|
out_b.data<OutType>(),
|
|
out_a.size(),
|
|
const_param(shape),
|
|
const_param(a_strides),
|
|
const_param(b_strides),
|
|
ndim);
|
|
}
|
|
});
|
|
} else {
|
|
MLX_SWITCH_BOOL(out_a.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_a.data_size(),
|
|
out_a.shape(),
|
|
out_a.strides(),
|
|
LARGE);
|
|
kernel<<<num_blocks, block_dims, 0, stream>>>(
|
|
a.data<InType>(),
|
|
b.data<InType>(),
|
|
out_a.data<OutType>(),
|
|
out_b.data<OutType>(),
|
|
out_a.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_a.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);
|
|
}
|
|
|
|
void DivMod::eval_gpu(
|
|
const std::vector<array>& inputs,
|
|
std::vector<array>& outputs) {
|
|
nvtx3::scoped_range r("DivMod::eval_gpu");
|
|
auto& s = outputs[0].primitive().stream();
|
|
binary_op_gpu<cu::DivMod>(inputs, outputs, get_primitive_string(this), s);
|
|
}
|
|
|
|
} // namespace mlx::core
|