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remove unused unary file (#2672)

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This commit is contained in:
Awni Hannun
2025-10-13 19:36:26 -07:00
committed by GitHub
parent 9cbb1b0148
commit 36ca62dba8
1 changed files with 0 additions and 284 deletions
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284
mlx/backend/cuda/unary.cu
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@@ -1,284 +0,0 @@
// Copyright © 2025 Apple Inc.
#include "mlx/backend/common/unary.h"
#include "mlx/backend/cuda/device.h"
#include "mlx/backend/cuda/device/unary_ops.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, int N_READS>
__global__ void unary_v(const In* in, Out* out, IdxT size) {
IdxT index = cg::this_grid().thread_rank();
if ((index + 1) * N_READS > size) {
for (IdxT i = index * N_READS; i < size; ++i) {
out[i] = Op{}(in[i]);
}
} else {
auto in_vec = load_vector<N_READS>(in, index);
AlignedVector<Out, N_READS> out_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
out_vec[i] = Op{}(in_vec[i]);
}
store_vector<N_READS>(out, index, out_vec);
}
}
template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
__global__ void unary_g(
const In* in,
Out* out,
IdxT size_rest,
const __grid_constant__ Shape shape,
const __grid_constant__ Strides strides,
int ndim) {
auto block = cg::this_thread_block();
auto grid = cg::this_grid();
IdxT index_rest =
grid.block_index().y * block.dim_threads().y + block.thread_index().y;
if (index_rest >= size_rest) {
return;
}
auto shape_x = shape[ndim - 1];
auto stride_x = strides[ndim - 1];
IdxT index_x =
grid.block_index().x * block.dim_threads().x + block.thread_index().x;
auto idx =
elem_to_loc(index_rest * shape_x, shape.data(), strides.data(), ndim);
auto in_vec =
load_vector<N_READS>(in + idx, index_x, shape_x, stride_x, In(0));
AlignedVector<Out, N_READS> out_vec;
#pragma unroll
for (int i = 0; i < N_READS; ++i) {
out_vec[i] = Op{}(in_vec[i]);
}
store_vector(out + shape_x * index_rest, index_x, out_vec, shape_x);
}
template <typename Op, typename In, typename Out>
constexpr bool supports_unary_op() {
if (std::is_same_v<Op, Abs> || std::is_same_v<Op, Negative> ||
std::is_same_v<Op, Sign> || std::is_same_v<Op, Square>) {
return std::is_same_v<In, Out>;
}
if (std::is_same_v<Op, ArcCosh> || std::is_same_v<Op, ArcSinh> ||
std::is_same_v<Op, ArcTanh> || std::is_same_v<Op, Erf> ||
std::is_same_v<Op, ErfInv> || std::is_same_v<Op, Expm1> ||
std::is_same_v<Op, Sigmoid>) {
return std::is_same_v<In, Out> && is_floating_v<In>;
}
if (std::is_same_v<Op, BitwiseInvert>) {
return std::is_same_v<In, Out> && std::is_integral_v<In> &&
!std::is_same_v<In, bool>;
}
if (std::is_same_v<Op, Ceil> || std::is_same_v<Op, Floor>) {
return std::is_same_v<In, Out> && !mlx::core::is_complex_v<In>;
}
if (std::is_same_v<Op, Conjugate>) {
return std::is_same_v<In, Out> && mlx::core::is_complex_v<In>;
}
if (std::is_same_v<Op, ArcCos> || std::is_same_v<Op, ArcSin> ||
std::is_same_v<Op, ArcTan> || std::is_same_v<Op, Cos> ||
std::is_same_v<Op, Cosh> || std::is_same_v<Op, Exp> ||
std::is_same_v<Op, Log> || std::is_same_v<Op, Log2> ||
std::is_same_v<Op, Log10> || std::is_same_v<Op, Log1p> ||
std::is_same_v<Op, Round> || std::is_same_v<Op, Rsqrt> ||
std::is_same_v<Op, Sqrt> || std::is_same_v<Op, Sin> ||
std::is_same_v<Op, Sinh> || std::is_same_v<Op, Tan> ||
std::is_same_v<Op, Tanh>) {
return std::is_same_v<In, Out> && is_inexact_v<In>;
}
if (std::is_same_v<Op, Imag> || std::is_same_v<Op, Real>) {
return mlx::core::is_complex_v<In> && std::is_same_v<Out, float>;
}
if (std::is_same_v<Op, LogicalNot>) {
return std::is_same_v<In, Out> && std::is_same_v<In, bool>;
}
return false;
}
} // namespace cu
template <typename Op>
void unary_op_gpu_inplace(
const std::vector<array>& inputs,
array& out,
const char* op,
const Stream& s) {
auto& in = inputs[0];
if (in.size() == 0) {
return;
}
bool contig = in.flags().contiguous;
bool large;
if (!contig) {
large = in.data_size() > INT32_MAX || out.size() > INT32_MAX;
} else {
large = in.data_size() > UINT32_MAX;
}
auto& encoder = cu::get_command_encoder(s);
encoder.set_input_array(in);
encoder.set_output_array(out);
dispatch_all_types(in.dtype(), [&](auto in_type_tag) {
dispatch_all_types(out.dtype(), [&](auto out_type_tag) {
using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
if constexpr (cu::supports_unary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
dispatch_bool(large, [&](auto large) {
using InType = cuda_type_t<CTYPE_IN>;
using OutType = cuda_type_t<CTYPE_OUT>;
if (contig) {
using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
constexpr int N_READS = 16 / sizeof(OutType);
auto [num_blocks, block_dims] = get_launch_args(
out.data_size(), out.shape(), out.strides(), large, N_READS);
encoder.add_kernel_node(
cu::unary_v<Op, InType, OutType, IdxT, N_READS>,
num_blocks,
block_dims,
0,
in.data<InType>(),
out.data<OutType>(),
out.data_size());
} else {
using IdxT = std::conditional_t<large(), int64_t, int32_t>;
auto [shape, strides] = collapse_contiguous_dims(in);
auto ndim = shape.size();
int work_per_thread = 1;
auto kernel = cu::unary_g<Op, InType, OutType, IdxT, 1>;
auto dim0 = ndim > 0 ? shape.back() : 1;
auto rest = out.size() / dim0;
if (dim0 >= 4) {
kernel = cu::unary_g<Op, InType, OutType, IdxT, 4>;
work_per_thread = 4;
}
dim0 = (dim0 + work_per_thread - 1) / work_per_thread;
auto block_dims = get_block_dims(dim0, rest, 1);
uint32_t num_blocks_x = cuda::ceil_div(dim0, block_dims.x);
uint32_t num_blocks_y = cuda::ceil_div(rest, block_dims.y);
encoder.add_kernel_node(
kernel,
{num_blocks_x, num_blocks_y},
block_dims,
0,
in.data<InType>(),
out.data<OutType>(),
rest,
const_param(shape),
const_param(strides),
ndim);
}
});
} else {
throw std::runtime_error(fmt::format(
"Can not do unary op {} on input of {} with output of {}.",
op,
dtype_to_string(in.dtype()),
dtype_to_string(out.dtype())));
}
});
});
}
template <typename Op>
void unary_op_gpu(
const std::vector<array>& inputs,
array& out,
const char* op,
const Stream& s) {
set_unary_output_data(inputs[0], out);
unary_op_gpu_inplace<Op>(inputs, out, op, s);
}
#define UNARY_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(); \
unary_op_gpu<cu::func>(inputs, out, name(), s); \
}
UNARY_GPU(Abs)
UNARY_GPU(ArcCos)
UNARY_GPU(ArcCosh)
UNARY_GPU(ArcSin)
UNARY_GPU(ArcSinh)
UNARY_GPU(ArcTan)
UNARY_GPU(ArcTanh)
UNARY_GPU(BitwiseInvert)
UNARY_GPU(Ceil)
UNARY_GPU(Conjugate)
UNARY_GPU(Cos)
UNARY_GPU(Cosh)
UNARY_GPU(Erf)
UNARY_GPU(ErfInv)
UNARY_GPU(Exp)
UNARY_GPU(Expm1)
UNARY_GPU(Floor)
UNARY_GPU(Imag)
UNARY_GPU(Log1p)
UNARY_GPU(LogicalNot)
UNARY_GPU(Negative)
UNARY_GPU(Real)
UNARY_GPU(Sigmoid)
UNARY_GPU(Sign)
UNARY_GPU(Sin)
UNARY_GPU(Sinh)
UNARY_GPU(Square)
UNARY_GPU(Tan)
UNARY_GPU(Tanh)
void Log::eval_gpu(const std::vector<array>& inputs, array& out) {
nvtx3::scoped_range r("Log::eval_gpu");
auto& s = out.primitive().stream();
switch (base_) {
case Base::e:
unary_op_gpu<cu::Log>(inputs, out, name(), s);
break;
case Base::two:
unary_op_gpu<cu::Log2>(inputs, out, name(), s);
break;
case Base::ten:
unary_op_gpu<cu::Log10>(inputs, out, name(), s);
break;
}
}
void Round::eval_gpu(const std::vector<array>& inputs, array& out) {
nvtx3::scoped_range r("Round::eval_gpu");
assert(inputs.size() == 1);
const auto& in = inputs[0];
auto& s = out.primitive().stream();
if (issubdtype(in.dtype(), inexact)) {
unary_op_gpu<cu::Round>(inputs, out, name(), s);
} else {
// No-op integer types
out.copy_shared_buffer(in);
}
}
void Sqrt::eval_gpu(const std::vector<array>& inputs, array& out) {
nvtx3::scoped_range r("Sort::eval_gpu");
auto& s = out.primitive().stream();
if (recip_) {
unary_op_gpu<cu::Rsqrt>(inputs, out, "Rsqrt", s);
} else {
unary_op_gpu<cu::Sqrt>(inputs, out, "Sqrt", s);
}
}
} // namespace mlx::core