45 lines
1.8 KiB
C++
45 lines
1.8 KiB
C++
#include "cache.h"
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#include "cuda_utils.h"
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#include "ops.h"
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#include <torch/extension.h>
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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// vLLM custom ops
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pybind11::module ops = m.def_submodule("ops", "vLLM custom operators");
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// Attention ops
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ops.def("paged_attention_v1", &paged_attention_v1,
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"Compute the attention between an input query and the cached "
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"keys/values using PagedAttention.");
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ops.def("paged_attention_v2", &paged_attention_v2, "PagedAttention V2.");
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// Activation ops
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ops.def("silu_and_mul", &silu_and_mul, "Activation function used in SwiGLU.");
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ops.def("gelu_and_mul", &gelu_and_mul,
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"Activation function used in GeGLU with `none` approximation.");
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ops.def("gelu_tanh_and_mul", &gelu_tanh_and_mul,
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"Activation function used in GeGLU with `tanh` approximation.");
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ops.def("gelu_new", &gelu_new, "GELU implementation used in GPT-2.");
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ops.def("gelu_fast", &gelu_fast, "Approximate GELU implementation.");
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// Layernorm
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ops.def("rms_norm", &rms_norm,
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"Apply Root Mean Square (RMS) Normalization to the input tensor.");
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ops.def("fused_add_rms_norm", &fused_add_rms_norm,
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"In-place fused Add and RMS Normalization");
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// Rotary embedding
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ops.def("rotary_embedding", &rotary_embedding,
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"Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
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// Cache ops
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pybind11::module cache_ops = m.def_submodule("cache_ops", "vLLM cache ops");
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cache_ops.def("swap_blocks", &swap_blocks,
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"Swap in (out) the cache blocks from src to dst");
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cache_ops.def("copy_blocks", ©_blocks,
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"Copy the cache blocks from src to dst");
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cache_ops.def("reshape_and_cache", &reshape_and_cache,
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"Reshape the key and value tensors and cache them");
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}
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