mirror of
https://github.com/vllm-project/vllm.git
synced 2026-06-06 00:16:14 +00:00
Chris Leonard
59d0236193
Signed-off-by: Chris Leonard <chleonar@redhat.com> Signed-off-by: Shengqi Chen <harry-chen@outlook.com> Co-authored-by: Shengqi Chen <harry-chen@outlook.com>
114 lines
4.3 KiB
C++
114 lines
4.3 KiB
C++
#pragma once
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#include <optional>
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#include <string>
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#include <torch/library.h>
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#include <tuple>
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#include "core/scalar_type.hpp"
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#include <vector>
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torch::Tensor weak_ref_tensor(torch::Tensor& tensor) {
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// Ensure tensor is on CUDA
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if (!tensor.is_cuda()) {
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throw std::runtime_error("Tensor must be on CUDA device");
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}
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// Get the raw data pointer
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void* data_ptr = tensor.data_ptr();
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// Get tensor sizes and strides
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std::vector<int64_t> sizes = tensor.sizes().vec();
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std::vector<int64_t> strides = tensor.strides().vec();
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// Get tensor options (dtype, device)
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auto options = tensor.options();
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// Create a new tensor from the raw data pointer
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auto new_tensor = torch::from_blob(data_ptr, sizes, strides, options);
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return new_tensor;
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}
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// rms_norm and fused_add_rms_norm declarations also exist in
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// csrc/libtorch_stable/ops.h (torch::stable ABI for CUDA). They remain here
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// because the CPU build still uses these torch::Tensor declarations.
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void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
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double epsilon);
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void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
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torch::Tensor& weight, double epsilon);
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void silu_and_mul_per_block_quant(torch::Tensor& out,
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torch::Tensor const& input,
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torch::Tensor& scales, int64_t group_size,
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std::optional<torch::Tensor> scale_ub,
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bool is_scale_transposed);
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// rotary_embedding also exist in csrc/libtorch_stable/ops.h (torch::stable
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// ABI for CUDA). It remains here because the CPU build still uses these
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// torch::Tensor declarations.
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void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
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std::optional<torch::Tensor> key, int64_t head_size,
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torch::Tensor& cos_sin_cache, bool is_neox,
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int64_t rope_dim_offset, bool inverse);
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void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
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void silu_and_mul_clamp(torch::Tensor& out, torch::Tensor& input, double limit);
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void silu_and_mul_quant(torch::Tensor& out, torch::Tensor& input,
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torch::Tensor& scale);
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void persistent_masked_m_silu_mul_quant(
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const at::Tensor& input, // (E, T, 2*H)
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const at::Tensor& counts, // (E)
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at::Tensor& y_q, // (E, T, H) [OUT]
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at::Tensor& y_s, // (E, T, H//group_size) [OUT]
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bool use_ue8m0);
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void gelu_and_mul(torch::Tensor& out, torch::Tensor& input);
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void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);
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void gelu_new(torch::Tensor& out, torch::Tensor& input);
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void gelu_fast(torch::Tensor& out, torch::Tensor& input);
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void gelu_quick(torch::Tensor& out, torch::Tensor& input);
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void cutlass_mla_decode(torch::Tensor const& out, torch::Tensor const& q_nope,
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torch::Tensor const& q_pe,
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torch::Tensor const& kv_c_and_k_pe_cache,
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torch::Tensor const& seq_lens,
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torch::Tensor const& page_table, double scale);
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torch::Tensor get_cuda_view_from_cpu_tensor(torch::Tensor& cpu_tensor);
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void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
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torch::Tensor const& scale,
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std::optional<torch::Tensor> const& azp);
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void dynamic_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
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torch::Tensor& scales,
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std::optional<torch::Tensor> const& azp);
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torch::Tensor dynamic_4bit_int_moe_cpu(
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torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
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torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
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int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
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int64_t activation_kind);
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using fptr_t = int64_t;
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#ifdef USE_ROCM
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fptr_t init_custom_qr(int64_t rank, int64_t world_size,
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std::optional<int64_t> qr_max_size = std::nullopt);
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void qr_destroy(fptr_t _fa);
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torch::Tensor qr_get_handle(fptr_t _fa);
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void qr_open_handles(fptr_t _fa, const std::vector<torch::Tensor>& handles);
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void qr_all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out,
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int64_t quant_level, bool cast_bf2half = false);
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int64_t qr_max_size();
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#endif
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