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torch_bindings.cpp
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#include "cache.h"
#include "cuda_utils.h"
#include "ops.h"
#include "registration.h"
#include <torch/library.h>
// Note on op signatures:
// The X_meta signatures are for the meta functions corresponding to op X.
// They must be kept in sync with the signature for X. Generally, only
// functions that return Tensors require a meta function.
//
// See the following links for detailed docs on op registration and function
// schemas.
// https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU/edit#heading=h.ptttacy8y1u9
// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/README.md#annotations
TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// vLLM custom ops
// Attention ops
// Compute the attention between an input query and the cached
// keys/values using PagedAttention.
ops.def(
"paged_attention_v1("
" Tensor! out, Tensor query, Tensor key_cache,"
" Tensor value_cache, int num_kv_heads, float scale,"
" Tensor block_tables, Tensor seq_lens, int block_size,"
" int max_seq_len, Tensor? alibi_slopes,"
" str kv_cache_dtype, float kv_scale, int tp_rank,"
" int blocksparse_local_blocks,"
" int blocksparse_vert_stride, int blocksparse_block_size,"
" int blocksparse_head_sliding_step) -> ()");
ops.impl("paged_attention_v1", torch::kCUDA, &paged_attention_v1);
// PagedAttention V2.
ops.def(
"paged_attention_v2("
" Tensor! out, Tensor exp_sums, Tensor max_logits,"
" Tensor tmp_out, Tensor query, Tensor key_cache,"
" Tensor value_cache, int num_kv_heads, float scale,"
" Tensor block_tables, Tensor seq_lens, int block_size,"
" int max_seq_len, Tensor? alibi_slopes,"
" str kv_cache_dtype, float kv_scale, int tp_rank,"
" int blocksparse_local_blocks,"
" int blocksparse_vert_stride, int blocksparse_block_size,"
" int blocksparse_head_sliding_step) -> ()");
ops.impl("paged_attention_v2", torch::kCUDA, &paged_attention_v2);
// Activation ops
// Activation function used in SwiGLU.
ops.def("silu_and_mul(Tensor! out, Tensor input) -> ()");
ops.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
// Activation function used in GeGLU with `none` approximation.
ops.def("gelu_and_mul(Tensor! out, Tensor input) -> ()");
ops.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);
// Activation function used in GeGLU with `tanh` approximation.
ops.def("gelu_tanh_and_mul(Tensor! out, Tensor input) -> ()");
ops.impl("gelu_tanh_and_mul", torch::kCUDA, &gelu_tanh_and_mul);
// GELU implementation used in GPT-2.
ops.def("gelu_new(Tensor! out, Tensor input) -> ()");
ops.impl("gelu_new", torch::kCUDA, &gelu_new);
// Approximate GELU implementation.
ops.def("gelu_fast(Tensor! out, Tensor input) -> ()");
ops.impl("gelu_fast", torch::kCUDA, &gelu_fast);
// Quick GELU implementation.
ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
ops.impl("gelu_quick", torch::kCUDA, &gelu_quick);
// Layernorm
// Apply Root Mean Square (RMS) Normalization to the input tensor.
ops.def(
"rms_norm(Tensor! out, Tensor input, Tensor weight, float epsilon) -> "
"()");
ops.impl("rms_norm", torch::kCUDA, &rms_norm);
// In-place fused Add and RMS Normalization.
ops.def(
"fused_add_rms_norm(Tensor! input, Tensor! residual, Tensor weight, "
"float epsilon) -> ()");
ops.impl("fused_add_rms_norm", torch::kCUDA, &fused_add_rms_norm);
// Rotary embedding
// Apply GPT-NeoX or GPT-J style rotary embedding to query and key.
ops.def(
"rotary_embedding(Tensor positions, Tensor! query,"
" Tensor! key, int head_size,"
" Tensor cos_sin_cache, bool is_neox) -> ()");
ops.impl("rotary_embedding", torch::kCUDA, &rotary_embedding);
// Apply GPT-NeoX or GPT-J style rotary embedding to query and key
// (supports multiple loras).
ops.def(
"batched_rotary_embedding(Tensor positions, Tensor! query,"
" Tensor! key, int head_size,"
" Tensor cos_sin_cache, bool is_neox,"
" int rot_dim,"
" Tensor cos_sin_cache_offsets) -> ()");
ops.impl("batched_rotary_embedding", torch::kCUDA, &batched_rotary_embedding);
// Quantization ops
#ifndef USE_ROCM
// Quantized GEMM for AQLM.
ops.def("aqlm_gemm", &aqlm_gemm);
ops.impl("aqlm_gemm", torch::kCUDA, &aqlm_gemm);
// Decompression method for AQLM.
ops.def("aqlm_dequant", &aqlm_dequant);
ops.impl("aqlm_dequant", torch::kCUDA, &aqlm_dequant);
// Quantized GEMM for AWQ.
ops.def("awq_gemm", &awq_gemm);
ops.impl("awq_gemm", torch::kCUDA, &awq_gemm);
// Dequantization for AWQ.
ops.def("awq_dequantize", &awq_dequantize);
ops.impl("awq_dequantize", torch::kCUDA, &awq_dequantize);
// Marlin (Dense) Optimized Quantized GEMM for GPTQ.
ops.def("marlin_gemm", &marlin_gemm);
ops.impl("marlin_gemm", torch::kCUDA, &marlin_gemm);
// Marlin_24 (Sparse) Optimized Quantized GEMM for GPTQ.
ops.def("gptq_marlin_24_gemm", &gptq_marlin_24_gemm);
ops.impl("gptq_marlin_24_gemm", torch::kCUDA, &gptq_marlin_24_gemm);
// gptq_marlin Optimized Quantized GEMM for GPTQ.
ops.def("gptq_marlin_gemm", &gptq_marlin_gemm);
ops.impl("gptq_marlin_gemm", torch::kCUDA, &gptq_marlin_gemm);
// gptq_marlin repack from GPTQ.
ops.def("gptq_marlin_repack", &gptq_marlin_repack);
ops.impl("gptq_marlin_repack", torch::kCUDA, &gptq_marlin_repack);
// fp8_marlin Optimized Quantized GEMM for FP8 weight-only.
ops.def("fp8_marlin_gemm", &fp8_marlin_gemm);
ops.impl("fp8_marlin_gemm", torch::kCUDA, &fp8_marlin_gemm);
// CUTLASS w8a8 GEMM, supporting symmetric per-tensor or per-row/column
// quantization.
ops.def(
"cutlass_scaled_mm(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor? bias) -> ()");
ops.impl("cutlass_scaled_mm", torch::kCUDA, &cutlass_scaled_mm);
// Check if cutlass scaled_mm is supported for CUDA devices of the given
// capability
ops.def("cutlass_scaled_mm_supports_fp8", &cutlass_scaled_mm_supports_fp8);
ops.impl("cutlass_scaled_mm_supports_fp8", torch::kCUDA,
&cutlass_scaled_mm_supports_fp8);
#endif
// Quantized GEMM for GPTQ.
ops.def("gptq_gemm", &gptq_gemm);
ops.impl("gptq_gemm", torch::kCUDA, &gptq_gemm);
// Post processing for GPTQ.
ops.def("gptq_shuffle(Tensor! q_weight, Tensor q_perm, int bit) -> ()");
ops.impl("gptq_shuffle", torch::kCUDA, &gptq_shuffle);
// Quantized GEMM for SqueezeLLM.
ops.def(
"squeezellm_gemm(Tensor vec, Tensor mat, Tensor! mul, Tensor "
"lookup_table) -> ()");
ops.impl("squeezellm_gemm", torch::kCUDA, &squeezellm_gemm);
// Compute FP8 quantized tensor for given scaling factor.
ops.def(
"static_scaled_fp8_quant(Tensor! out, Tensor input, Tensor scale) -> ()");
ops.impl("static_scaled_fp8_quant", torch::kCUDA, &static_scaled_fp8_quant);
// Compute FP8 quantized tensor and scaling factor.
ops.def(
"dynamic_scaled_fp8_quant(Tensor! out, Tensor input, Tensor! scale) -> "
"()");
ops.impl("dynamic_scaled_fp8_quant", torch::kCUDA, &dynamic_scaled_fp8_quant);
// Aligning the number of tokens to be processed by each expert such
// that it is divisible by the block size.
ops.def(
"moe_align_block_size(Tensor topk_ids, int num_experts,"
" int block_size, Tensor! sorted_token_ids,"
" Tensor! experts_ids,"
" Tensor! num_tokens_post_pad) -> ()");
ops.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);
// Compute int8 quantized tensor for given scaling factor.
ops.def(
"static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale) -> "
"()");
ops.impl("static_scaled_int8_quant", torch::kCUDA, &static_scaled_int8_quant);
// Compute int8 quantized tensor and scaling factor
ops.def(
"dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale) -> "
"()");
ops.impl("dynamic_scaled_int8_quant", torch::kCUDA,
&dynamic_scaled_int8_quant);
}
TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
// Cache ops
// Swap in (out) the cache blocks from src to dst.
cache_ops.def(
"swap_blocks(Tensor src, Tensor! dst, Tensor block_mapping) -> ()");
cache_ops.impl("swap_blocks", torch::kCUDA, &swap_blocks);
// Copy the cache blocks from src to dst.
cache_ops.def(
"copy_blocks(Tensor[]! key_caches, Tensor[]! value_caches, Tensor "
"block_mapping) -> ()");
cache_ops.impl("copy_blocks", torch::kCUDA, ©_blocks);
// Reshape the key and value tensors and cache them.
cache_ops.def(
"reshape_and_cache(Tensor key, Tensor value,"
" Tensor! key_cache, Tensor! value_cache,"
" Tensor slot_mapping,"
" str kv_cache_dtype,"
" float kv_scale) -> ()");
cache_ops.impl("reshape_and_cache", torch::kCUDA, &reshape_and_cache);
// Reshape the key and value tensors and cache them.
cache_ops.def(
"reshape_and_cache_flash(Tensor key, Tensor value,"
" Tensor! key_cache,"
" Tensor! value_cache,"
" Tensor slot_mapping,"
" str kv_cache_dtype) -> ()");
cache_ops.impl("reshape_and_cache_flash", torch::kCUDA,
&reshape_and_cache_flash);
// Convert the key and value cache to fp8 data type.
cache_ops.def(
"convert_fp8(Tensor! dst_cache, Tensor src_cache, float scale, str "
"kv_cache_dtype) -> ()");
cache_ops.impl("convert_fp8", torch::kCUDA, &convert_fp8);
}
TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cuda_utils), cuda_utils) {
// Cuda utils
// Gets the specified device attribute.
cuda_utils.def("get_device_attribute", &get_device_attribute);
cuda_utils.impl("get_device_attribute", torch::kCUDA, &get_device_attribute);
// Gets the maximum shared memory per block device attribute.
cuda_utils.def("get_max_shared_memory_per_block_device_attribute",
&get_max_shared_memory_per_block_device_attribute);
cuda_utils.impl("get_max_shared_memory_per_block_device_attribute",
torch::kCUDA,
&get_max_shared_memory_per_block_device_attribute);
}
#ifndef USE_ROCM
TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _custom_ar), custom_ar) {
// Custom all-reduce kernels
custom_ar.def("init_custom_ar", &init_custom_ar);
custom_ar.impl("init_custom_ar", torch::kCUDA, &init_custom_ar);
custom_ar.def("should_custom_ar", &should_custom_ar);
custom_ar.impl("should_custom_ar", torch::kCUDA, &should_custom_ar);
custom_ar.def("all_reduce_reg(int fa, Tensor inp, Tensor! out) -> ()");
custom_ar.impl("all_reduce_reg", torch::kCUDA, &all_reduce_reg);
custom_ar.def(
"all_reduce_unreg(int fa, Tensor inp, Tensor reg_buffer, Tensor! out) -> "
"()");
custom_ar.impl("all_reduce_unreg", torch::kCUDA, &all_reduce_unreg);
custom_ar.def("dispose", &dispose);
custom_ar.impl("dispose", torch::kCPU, &dispose);
custom_ar.def("meta_size", &meta_size);
custom_ar.impl("meta_size", torch::kCPU, &meta_size);
custom_ar.def("register_buffer", ®ister_buffer);
custom_ar.impl("register_buffer", torch::kCUDA, ®ister_buffer);
custom_ar.def("get_graph_buffer_ipc_meta", &get_graph_buffer_ipc_meta);
custom_ar.impl("get_graph_buffer_ipc_meta", torch::kCPU,
&get_graph_buffer_ipc_meta);
custom_ar.def("register_graph_buffers", ®ister_graph_buffers);
custom_ar.impl("register_graph_buffers", torch::kCPU,
®ister_graph_buffers);
}
#endif
REGISTER_EXTENSION(TORCH_EXTENSION_NAME)