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[CUDA] Switch to CUDA graphs (#2317)

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* cuda graph prototype

fix signal bug + start to add dependencies

capture more

capture more ops

remaining ops

fix reduce and rope deps

add concurrent context

try update, but not working

cosistent topology order

use node api

use node api directly to reduce overhead

fix bug

use kernels in unary

cache graph

format

fix synchronization

format

* comment
This commit is contained in:
Awni Hannun
2025-07-02 15:59:13 -07:00
committed by GitHub
parent e76e9b87f0
commit ec0d5db67b
36 changed files with 1461 additions and 1212 deletions
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127
mlx/backend/cuda/ternary.cu
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@@ -91,73 +91,80 @@ void ternary_op_gpu_inplace(
encoder.set_input_array(b);
encoder.set_input_array(c);
encoder.set_output_array(out);
encoder.launch_kernel([&](cudaStream_t stream) {
dispatch_all_types(out.dtype(), [&](auto type_tag) {
using DType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
dispatch_all_types(out.dtype(), [&](auto type_tag) {
using DType = cuda_type_t<MLX_GET_TYPE(type_tag)>;
auto topt = get_ternary_op_type(a, b, c);
if (topt == TernaryOpType::General) {
dispatch_bool(
a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
c.data_size() > INT32_MAX || out.data_size() > INT32_MAX,
[&](auto large) {
using IdxT = std::conditional_t<large(), int64_t, int32_t>;
Shape shape;
std::vector<Strides> strides;
std::tie(shape, strides) = collapse_contiguous_dims(a, b, c, out);
auto& a_strides = strides[0];
auto& b_strides = strides[1];
auto& c_strides = strides[2];
int ndim = shape.size();
if (ndim <= 3) {
dispatch_1_2_3(ndim, [&](auto dims_constant) {
auto kernel =
cu::ternary_g_nd<Op, DType, IdxT, dims_constant()>;
auto [num_blocks, block_dims] =
get_launch_args(kernel, out, large());
kernel<<<num_blocks, block_dims, 0, stream>>>(
a.data<bool>(),
b.data<DType>(),
c.data<DType>(),
out.data<DType>(),
out.size(),
const_param<dims_constant()>(shape),
const_param<dims_constant()>(a_strides),
const_param<dims_constant()>(b_strides),
const_param<dims_constant()>(c_strides));
});
} else {
auto kernel = cu::ternary_g<Op, DType, IdxT>;
auto topt = get_ternary_op_type(a, b, c);
if (topt == TernaryOpType::General) {
dispatch_bool(
a.data_size() > INT32_MAX || b.data_size() > INT32_MAX ||
c.data_size() > INT32_MAX || out.data_size() > INT32_MAX,
[&](auto large) {
using IdxT = std::conditional_t<large(), int64_t, int32_t>;
Shape shape;
std::vector<Strides> strides;
std::tie(shape, strides) = collapse_contiguous_dims(a, b, c, out);
auto& a_strides = strides[0];
auto& b_strides = strides[1];
auto& c_strides = strides[2];
int ndim = shape.size();
if (ndim <= 3) {
dispatch_1_2_3(ndim, [&](auto dims_constant) {
auto kernel =
cu::ternary_g_nd<Op, DType, IdxT, dims_constant()>;
auto [num_blocks, block_dims] =
get_launch_args(kernel, out, large());
kernel<<<num_blocks, block_dims, 0, stream>>>(
encoder.add_kernel_node(
kernel,
num_blocks,
block_dims,
a.data<bool>(),
b.data<DType>(),
c.data<DType>(),
out.data<DType>(),
out.data_size(),
const_param(shape),
const_param(a_strides),
const_param(b_strides),
const_param(c_strides),
ndim);
}
});
} else {
dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
auto kernel = cu::ternary_v<Op, DType, IdxT>;
auto [num_blocks, block_dims] = get_launch_args(
kernel, out.data_size(), out.shape(), out.strides(), large());
kernel<<<num_blocks, block_dims, 0, stream>>>(
a.data<bool>(),
b.data<DType>(),
c.data<DType>(),
out.data<DType>(),
out.data_size());
});
}
});
out.size(),
const_param<dims_constant()>(shape),
const_param<dims_constant()>(a_strides),
const_param<dims_constant()>(b_strides),
const_param<dims_constant()>(c_strides));
});
} else {
auto kernel = cu::ternary_g<Op, DType, IdxT>;
auto [num_blocks, block_dims] =
get_launch_args(kernel, out, large());
encoder.add_kernel_node(
kernel,
num_blocks,
block_dims,
a.data<bool>(),
b.data<DType>(),
c.data<DType>(),
out.data<DType>(),
out.data_size(),
const_param(shape),
const_param(a_strides),
const_param(b_strides),
const_param(c_strides),
ndim);
}
});
} else {
dispatch_bool(out.data_size() > INT32_MAX, [&](auto large) {
using IdxT = std::conditional_t<large(), int64_t, uint32_t>;
auto kernel = cu::ternary_v<Op, DType, IdxT>;
auto [num_blocks, block_dims] = get_launch_args(
kernel, out.data_size(), out.shape(), out.strides(), large());
encoder.add_kernel_node(
kernel,
num_blocks,
block_dims,
a.data<bool>(),
b.data<DType>(),
c.data<DType>(),
out.data<DType>(),
out.data_size());
});
}
});
}