vllm/csrc/torch_bindings.cpp

// Provides torch::Tensor for ops.h (previously included transitively via
// cache.h, which is no longer included here after cache ops moved to
// _C_stable_libtorch).
#include <torch/all.h>
#include "cuda_utils.h"
#include "ops.h"
#include "core/registration.h"
#include <torch/library.h>
#include <torch/version.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
  //

  ops.def(
      "persistent_masked_m_silu_mul_quant(Tensor input, Tensor counts, Tensor! "
      "y_q, Tensor! y_s,"
      "bool use_ue8m0) -> ()");
  ops.impl("persistent_masked_m_silu_mul_quant", torch::kCUDA,
           &persistent_masked_m_silu_mul_quant);

  ops.def("weak_ref_tensor(Tensor input) -> Tensor");
  ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);

  ops.def("get_cuda_view_from_cpu_tensor(Tensor cpu_tensor) -> Tensor");
  ops.impl("get_cuda_view_from_cpu_tensor", torch::kCPU,
           &get_cuda_view_from_cpu_tensor);

  // Activation ops (quantized only — basic ops moved to _C_stable_libtorch)
  ops.def(
      "silu_and_mul_quant(Tensor! result, Tensor input, Tensor scale) -> ()");
  ops.impl("silu_and_mul_quant", torch::kCUDA, &silu_and_mul_quant);

  // Fused SiLU+Mul + per-block quantization
  ops.def(
      "silu_and_mul_per_block_quant("
      "Tensor! out, "
      "Tensor input, "
      "Tensor! scales, "
      "int group_size, "
      "Tensor? scale_ub=None, "
      "bool is_scale_transposed=False) -> ()");
  ops.impl("silu_and_mul_per_block_quant", torch::kCUDA,
           &silu_and_mul_per_block_quant);

  // Horizontally-fused DeepseekV4-MLA: per-head RMSNorm + GPT-J RoPE for Q, and
  // GPT-J RoPE + UE8M0 FP8 quant + paged cache insert for KV, all in one
  // kernel launch.
  ops.def(
      "fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert("
      "Tensor q_in, Tensor kv, Tensor! k_cache, "
      "Tensor slot_mapping, Tensor position_ids, Tensor cos_sin_cache, "
      "int q_head_padded, float eps, int cache_block_size) -> Tensor");
  ops.impl("fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert", torch::kCUDA,
           &fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert);

  // Quantization ops
#ifndef USE_ROCM

  // Note about marlin kernel 'workspace' arguments:
  // Technically these should be mutable since they are modified by the kernel.
  // But since they are set back to zero once the kernel is finished we can
  // hand wave and say that they have no net effect.
  //
  // The reason to mark 'workspace' as immutable is so that they don't interfere
  // with using ScalarType arguments in the ops. If they are marked as mutable,
  // pytorch throws an assert in
  // 'torch._higher_order_ops._register_effectful_op' that prevents these
  // kernels from being torch.compile'd.
  // See the following document for more info on custom types and ops that use
  // custom types:
  // https://docs.google.com/document/d/18fBMPuOJ0fY5ZQ6YyrHUppw9FA332CpNtgB6SOIgyuA

  // Machete (Dense) Optimized Mixed Precision GEMM for Hopper.
  ops.def(
      "machete_supported_schedules("
      "   ScalarType a_type,"
      "   int b_type,"
      "   ScalarType? maybe_group_scales_type,"
      "   ScalarType? maybe_group_zeros_type,"
      "   ScalarType? maybe_channel_scales_type,"
      "   ScalarType? maybe_token_scales_type,"
      "   ScalarType? maybe_out_type"
      ") -> str[]");
  ops.def(
      "machete_mm("
      "   Tensor A,"
      "   Tensor B,"
      "   int b_type,"
      "   ScalarType? out_type,"
      "   Tensor? group_scales,"
      "   Tensor? group_zeros,"
      "   int?    group_size,"
      "   Tensor? channel_scales,"
      "   Tensor? token_scales,"
      "   str?    schedule"
      ") -> Tensor");
  ops.def(
      "machete_prepack_B("
      "   Tensor B,"
      "   ScalarType a_type,"
      "   int b_type,"
      "   ScalarType? group_scales_type"
      ") -> Tensor");
  // conditionally compiled so impl registration is in source file

  // Marlin Optimized Quantized GEMM (supports GPTQ, AWQ, FP8, NVFP4, MXFP4).
  ops.def(
      "marlin_gemm(Tensor a, Tensor? c_or_none, Tensor b_q_weight, "
      "Tensor? b_bias_or_none,Tensor b_scales, "
      "Tensor? a_scales, Tensor? global_scale, Tensor? b_zeros_or_none, "
      "Tensor? "
      "g_idx_or_none, Tensor? perm_or_none, Tensor workspace, int b_type_id, "
      "SymInt size_m, SymInt size_n, SymInt size_k, bool is_k_full, "
      "bool use_atomic_add, bool use_fp32_reduce, bool is_zp_float) -> Tensor");
  // conditionally compiled so impl registration is in source file

  // gptq_marlin repack from GPTQ.
  ops.def(
      "gptq_marlin_repack(Tensor b_q_weight, Tensor perm, "
      "SymInt size_k, SymInt size_n, int num_bits, bool is_a_8bit) -> Tensor");
  // conditionally compiled so impl registrations are in source file

  // awq_marlin repack from AWQ.
  ops.def(
      "awq_marlin_repack(Tensor b_q_weight, SymInt size_k, "
      "SymInt size_n, int num_bits, bool is_a_8bit) -> Tensor");
  // conditionally compiled so impl registrations are in source file

  // preprocess W-int4A-fp8 weight for marlin kernel
  ops.def(
      "marlin_int4_fp8_preprocess(Tensor qweight, "
      "Tensor? qzeros_or_none, bool inplace) -> Tensor");
  // conditionally compiled so impl registrations are in source file

#endif

#ifndef USE_ROCM
  // Expert-specialization mxfp8 blockscaled grouped quantization (SM100+).
  ops.def(
      "mxfp8_experts_quant("
      " Tensor input, Tensor problem_sizes, Tensor expert_offsets,"
      " Tensor blockscale_offsets, Tensor! quant_output, Tensor! scale_factor)"
      " -> ()");
  // conditionally compiled so impl registration is in source file

  // Expert-specialization mxfp8 blockscaled grouped GEMM (SM100+).
  ops.def(
      "cutlass_mxfp8_grouped_mm("
      " Tensor a, Tensor b, Tensor sfa, Tensor sfb, Tensor! out,"
      " Tensor problem_sizes, Tensor expert_offsets, Tensor blockscale_offsets)"
      " -> ()");
  // conditionally compiled so impl registration is in source file

#endif

#ifndef USE_ROCM
  ops.def(
      "minimax_allreduce_rms("
      "Tensor input,"
      "Tensor norm_weight,"
      "Tensor workspace,"
      "int rank,"
      "int nranks,"
      "float eps) -> Tensor");
  ops.impl("minimax_allreduce_rms", torch::kCUDA, &minimax_allreduce_rms);
  ops.def(
      "minimax_allreduce_rms_qk("
      "Tensor qkv,"
      "Tensor norm_weight_q,"
      "Tensor norm_weight_k,"
      "Tensor workspace,"
      "int q_size,"
      "int kv_size,"
      "int rank,"
      "int nranks,"
      "float eps) -> (Tensor, Tensor)");
  ops.impl("minimax_allreduce_rms_qk", torch::kCUDA, &minimax_allreduce_rms_qk);

  //  conditionally compiled so impl in source file
#endif
}

TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cuda_utils), cuda_utils) {
  // Cuda utils

  // Gets the specified device attribute.
  cuda_utils.def("get_device_attribute(int attribute, int device_id) -> int");
  cuda_utils.impl("get_device_attribute", &get_device_attribute);

  // Gets the maximum shared memory per block device attribute.
  cuda_utils.def(
      "get_max_shared_memory_per_block_device_attribute(int device_id) -> int");
  cuda_utils.impl("get_max_shared_memory_per_block_device_attribute",
                  &get_max_shared_memory_per_block_device_attribute);
}

TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _custom_ar), custom_ar) {
  // Custom all-reduce kernels
  custom_ar.def(
      "init_custom_ar(int[] ipc_tensors, Tensor rank_data, "
      "int rank, bool fully_connected) -> int");
  custom_ar.impl("init_custom_ar", torch::kCUDA, &init_custom_ar);
  custom_ar.def(
      "all_reduce(int fa, Tensor inp, Tensor! out, int reg_buffer, "
      "int reg_buffer_sz_bytes) -> ()");
  custom_ar.impl("all_reduce", torch::kCUDA, &all_reduce);

  custom_ar.def("dispose", &dispose);
  custom_ar.def("meta_size", &meta_size);

  custom_ar.def("register_buffer", &register_buffer);
  custom_ar.def("get_graph_buffer_ipc_meta", &get_graph_buffer_ipc_meta);
  custom_ar.def("register_graph_buffers", &register_graph_buffers);

  custom_ar.def("allocate_shared_buffer_and_handle",
                &allocate_shared_buffer_and_handle);
  custom_ar.def("open_mem_handle(Tensor mem_handle) -> int", &open_mem_handle);
  custom_ar.impl("open_mem_handle", torch::kCPU, &open_mem_handle);

  custom_ar.def("free_shared_buffer", &free_shared_buffer);
#ifdef USE_ROCM
  // Quick Reduce all-reduce kernels
  custom_ar.def(
      "qr_all_reduce(int fa, Tensor inp, Tensor out, int quant_level, bool "
      "cast_bf2half) -> ()");
  custom_ar.impl("qr_all_reduce", torch::kCUDA, &qr_all_reduce);

  custom_ar.def("init_custom_qr", &init_custom_qr);
  custom_ar.def("qr_destroy", &qr_destroy);

  custom_ar.def("qr_get_handle", &qr_get_handle);

  custom_ar.def("qr_open_handles(int _fa, Tensor[](b!) handles) -> ()");
  custom_ar.impl("qr_open_handles", torch::kCPU, &qr_open_handles);

  // Max input size in bytes
  custom_ar.def("qr_max_size", &qr_max_size);
#endif
}

REGISTER_EXTENSION(TORCH_EXTENSION_NAME)