✨ feat: add nanovllm.ops module with XAttention estimation kernels

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>

nanovllm/ops/__init__.py

@@ -0,0 +1,38 @@
+"""
+Operators module for nano-vLLM.
+
+This module contains low-level attention operators and kernels.
+"""
+
+from nanovllm.ops.chunked_attention import (
+    flash_attn_with_lse,
+    merge_attention_outputs,
+    chunked_attention_varlen,
+    ChunkedPrefillState,
+)
+
+from nanovllm.ops.xattn import (
+    xattn_estimate,
+    xattn_estimate_chunked,
+    flat_group_gemm_fuse_reshape,
+    softmax_fuse_block_sum,
+    find_blocks_chunked,
+    create_causal_mask,
+    compute_sparsity,
+)
+
+__all__ = [
+    # chunked_attention
+    "flash_attn_with_lse",
+    "merge_attention_outputs",
+    "chunked_attention_varlen",
+    "ChunkedPrefillState",
+    # xattn
+    "xattn_estimate",
+    "xattn_estimate_chunked",
+    "flat_group_gemm_fuse_reshape",
+    "softmax_fuse_block_sum",
+    "find_blocks_chunked",
+    "create_causal_mask",
+    "compute_sparsity",
+]