87a19d2d00
来源:
1. Chranos/ixformer (GitHub) → ixformer_sdk/ (230 files, 70K lines)
- inference/functions/vllm.py: vllm_moe_topk_softmax 完整实现 (2033 lines)
- inference/functions/moe.py: MoE ops 完整实现 (1380 lines)
- contrib/vllm_flash_attn/: FA2 Python 接口 (1018 lines)
- contrib/tgi/fused_moe.py: TGI fused MoE (429 lines)
- csrc/include/ixformer/: C++ kernel headers + cmake
2. Deep-Spark/xllm (GitHub) → upstream_ref/xllm_latest/ (+15 files)
- npu_torch/qwen3_5_decoder_layer_impl.cpp/.h
- npu_torch/qwen3_5_gated_delta_net.cpp/.h
- npu_torch/qwen3_next_*.cpp/.h (6 files)
- npu_torch/attention.cpp/.h + fused_moe.cpp/.h + CMakeLists.txt
- models/llm/qwen3_5.h + qwen3_5_mtp.h + qwen3_next.h
- models/vlm/qwen3_5.h
调用链完整性:
ixformer_sdk/inference/functions/vllm.py
→ ops.infer.moe_topk_softmax() (C++ 层)
→ 这就是 base 镜像 libixformer.so 里的实现
upstream_ref/xllm_latest/core/layers/ilu/fused_moe.cpp
→ ixformer::infer::topk_softmax() (直接 C++ 调用)
→ ixformer::infer::group_gemm() → 完整 7-step MoE pipeline
144 lines
3.9 KiB
Python
144 lines
3.9 KiB
Python
from typing import Union
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import ixformer._C as ops
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import torch
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__all__ = [
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"paged_attention",
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"paged_attention_flashinfer",
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"paged_attention_cache_appended",
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]
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# paged_attention_cache_append
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def paged_attention_cache_appended(
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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slot_mapping: torch.Tensor,
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kv_cache_format: str = "HND", # STD NHD HND
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key_cache_scales: torch.Tensor = None,
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value_cache_scales: torch.Tensor = None,
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):
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if isinstance(key, torch.Tensor):
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ops.infer.paged_attention_cache_appended(
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key,
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value,
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key_cache,
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value_cache,
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slot_mapping,
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key.stride(0),
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value.stride(0),
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key_cache.stride(0),
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value_cache.stride(0),
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kv_cache_format,
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key_cache_scales,
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value_cache_scales,
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)
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else:
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raise NotImplementedError()
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def paged_attention(
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output: torch.Tensor,
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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num_kv_heads: int,
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scale: float,
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block_tables: torch.Tensor,
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seq_lens: torch.Tensor,
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block_size: int,
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max_seq_len: int,
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alibi_slopes: torch.Tensor = None,
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use_sqrt_alibi: bool = False,
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key_cache_scales: torch.Tensor = None,
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value_cache_scales: torch.Tensor = None,
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kv_cache_format: str = "HND",
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algo: int = -1,
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):
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"""
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kv_cache_format
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STD : k/v format as same as vllm
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NHD : k/v format is [block_size, num_kv_heads, head_dim] in one page
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HND : k/v format is [num_kv_heads, block_size, head_dim] in one page
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algo
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-1 : auto chooes algorithm according to kv_cache_format
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0 : use the first algorithm
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1 : use the second algorithm
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"""
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if isinstance(query, torch.Tensor):
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ops.infer.paged_attention(
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output,
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query,
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key_cache,
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value_cache,
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num_kv_heads,
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scale,
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block_tables,
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seq_lens,
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block_size,
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max_seq_len,
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use_sqrt_alibi,
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alibi_slopes,
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key_cache_scales,
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value_cache_scales,
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kv_cache_format,
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algo,
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)
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else:
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raise NotImplementedError()
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def paged_attention_flashinfer(
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output: torch.Tensor,
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query: torch.Tensor,
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paged_kv_data,
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paged_kv_indptr: torch.Tensor,
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paged_kv_indices: torch.Tensor,
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paged_kv_last_page_len: torch.Tensor,
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scale: float,
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max_seq_len: int = -1,
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use_sqrt_alibi: bool = False,
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alibi_slopes: torch.Tensor = None,
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kv_cache_format: str = "HND",
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# key_cache_scales: torch.Tensor = None,
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# value_cache_scales: torch.Tensor = None,
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):
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"""
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out / query : [num_seqs, num_qo_heads, head_size]
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paged_kv_data
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Tensor:
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NHD [max_num_pages, 2, page_size, num_kv_heads, head_size]
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HND [max_num_pages, 2, num_kv_heads, page_size, head_size]
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tuple(k_data, v_data)
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NHD [max_num_pages, page_size, num_kv_heads, head_size]
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HND [max_num_pages, num_kv_heads, page_size, head_size]
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paged_kv_indptr int32 : [num_seqs + 1]
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paged_kv_indices int32 : [max_num_pages]
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paged_kv_last_page_len int32 : [num_seqs]
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"""
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if isinstance(paged_kv_data, tuple):
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k_data, v_data = paged_kv_data
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pack_kv_data = (None, k_data, v_data)
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else:
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pack_kv_data = (paged_kv_data, None, None)
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if isinstance(query, torch.Tensor):
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ops.infer.paged_attention_flashinfer(
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output,
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query,
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*pack_kv_data,
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paged_kv_indptr,
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paged_kv_indices,
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paged_kv_last_page_len,
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scale,
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max_seq_len,
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use_sqrt_alibi,
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alibi_slopes,
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kv_cache_format,
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)
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else:
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raise NotImplementedError()
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