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https://github.com/ml-explore/mlx.git
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CUDA backend: binary ops (#2259)
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@@ -13,9 +13,40 @@
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#include <cuda_bf16.h>
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#include <cuda_fp16.h>
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#include <fmt/format.h>
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#include <cuda/cmath>
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namespace mlx::core {
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// Convert a number between 1~3 to constexpr.
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#define MLX_SWITCH_1_2_3(N, NDIM, ...) \
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switch (N) { \
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case 1: { \
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constexpr int NDIM = 1; \
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__VA_ARGS__; \
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break; \
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} \
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case 2: { \
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constexpr int NDIM = 2; \
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__VA_ARGS__; \
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break; \
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} \
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case 3: { \
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constexpr int NDIM = 3; \
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__VA_ARGS__; \
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break; \
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} \
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}
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// Like MLX_SWITCH_ALL_TYPES but for booleans.
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#define MLX_SWITCH_BOOL(BOOL, BOOL_ALIAS, ...) \
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if (BOOL) { \
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constexpr bool BOOL_ALIAS = true; \
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__VA_ARGS__; \
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} else { \
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constexpr bool BOOL_ALIAS = false; \
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__VA_ARGS__; \
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}
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// Maps CPU types to CUDA types.
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template <typename T>
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struct CTypeToCudaType {
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@@ -66,4 +97,35 @@ dim3 get_2d_grid_dims(
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const Strides& strides,
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size_t divisor);
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// Return a block size that achieves maximum potential occupancy for kernel.
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template <typename T>
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inline uint max_occupancy_block_dim(T kernel) {
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int _, block_dim;
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CHECK_CUDA_ERROR(cudaOccupancyMaxPotentialBlockSize(&_, &block_dim, kernel));
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return block_dim;
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}
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// Get the num_blocks and block_dims that maximize occupancy for |kernel|,
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// assuming each thread handles |work_per_thread| elements of |arr|.
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template <typename T>
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inline std::tuple<dim3, uint> get_launch_args(
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T kernel,
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const array& arr,
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bool large,
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int work_per_thread = 1) {
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size_t nthreads = cuda::ceil_div(arr.size(), work_per_thread);
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uint block_dim = max_occupancy_block_dim(kernel);
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if (block_dim > nthreads) {
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block_dim = nthreads;
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}
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dim3 num_blocks;
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if (large) {
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num_blocks = get_2d_grid_dims(arr.shape(), arr.strides(), work_per_thread);
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num_blocks.x = cuda::ceil_div(num_blocks.x, block_dim);
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} else {
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num_blocks.x = cuda::ceil_div(nthreads, block_dim);
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}
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return std::make_tuple(num_blocks, block_dim);
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}
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} // namespace mlx::core
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