9f3ee9279e
Add ops module ported from tzj/minference branch containing: - xattn.py: XAttention block importance estimation with Triton kernels - xattn_estimate(): standard estimation for sparse attention mask - xattn_estimate_chunked(): chunked prefill compatible version - flat_group_gemm_fuse_reshape(): fused stride reshape + GEMM kernel - softmax_fuse_block_sum(): online softmax + block-wise sum kernel - chunked_attention.py: Flash attention with LSE output for chunk merging - test_xattn_estimate_chunked.py: verification test (all seq_lens pass) This prepares the foundation for AttentionPolicy refactoring where XAttentionPolicy.estimate() will call these ops. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
39 lines
842 B
Python
39 lines
842 B
Python
"""
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Operators module for nano-vLLM.
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This module contains low-level attention operators and kernels.
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"""
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from nanovllm.ops.chunked_attention import (
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flash_attn_with_lse,
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merge_attention_outputs,
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chunked_attention_varlen,
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ChunkedPrefillState,
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)
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from nanovllm.ops.xattn import (
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xattn_estimate,
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xattn_estimate_chunked,
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flat_group_gemm_fuse_reshape,
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softmax_fuse_block_sum,
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find_blocks_chunked,
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create_causal_mask,
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compute_sparsity,
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)
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__all__ = [
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# chunked_attention
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"flash_attn_with_lse",
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"merge_attention_outputs",
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"chunked_attention_varlen",
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"ChunkedPrefillState",
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# xattn
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"xattn_estimate",
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"xattn_estimate_chunked",
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"flat_group_gemm_fuse_reshape",
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"softmax_fuse_block_sum",
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"find_blocks_chunked",
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"create_causal_mask",
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"compute_sparsity",
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]
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