de7ee4383e
V1 paged_attention (decode ≤ 8192): Fix: head_mapping int→Tensor conversion. VERIFIED: matches manual attention, max diff < 0.001. Perf: 0.034ms (256 tok), 0.059ms (1K), 0.169ms (4K), 0.272ms (8K). V2 paged_attention (decode > 8192): Native V2 kernel EXISTS (ixf_F.vllm_single_query_cached_kv_attention_v2) but produces INCORRECT output (diff=1.28 vs V1 on same data). Using Python V2 fallback (paged_attention_v2_pytorch.py) for now. The native V2 expects [B,H,bs,d] layout (confirmed) but the output values don't match even with correct layout conversion. Prefill (flash_attn_func): VERIFIED: ixf_F.flash_attn_func(q, k, v, causal=True) works with head_dim=256 and GQA (num_kv_heads=4). Patched into xformers.py as first-attempt before _run_sdpa_fallback. Triton: symlinked /usr/local/lib/ → /usr/local/corex/lib64/ for import.
305 lines
10 KiB
Python
305 lines
10 KiB
Python
"""
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patch_ixformer_native.py — Enable ixformer native kernels on BI-V100
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=====================================================================
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Hardware-verified fixes (2026-07-31):
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1. V1 paged_attention: head_mapping must be Tensor, not int.
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Verified: V1 with Tensor head_mapping matches manual attention (diff < 0.001).
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Performance: 0.034ms (256 tok), 0.272ms (8192 tok).
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2. V2 paged_attention: native kernel EXISTS (vllm_single_query_cached_kv_attention_v2)
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but produces INCORRECT output (diff=1.28 vs V1, norm mismatch).
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The native V2 kernel expects different cache layout [B,H,bs,d] and even with
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correct conversion, the output doesn't match V1 on the same data.
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STATUS: Keep Python V2 fallback (paged_attention_v2_pytorch.py) for seq > 8192.
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TODO: Investigate V2 native kernel parameter semantics.
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3. flash_attn_func: WORKS with head_dim=256, GQA.
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ixf_F.flash_attn_func(q, k, v, causal=True) produces correct output.
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This should replace the Python _run_sdpa_fallback for prefill.
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4. Triton: installed but vllm can't find it (path mismatch).
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Fix: symlink + sys.path insertion.
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"""
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import os
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import sys
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import shutil
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VLLM_ROOT = "/usr/local/corex/lib64/python3/dist-packages/vllm"
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CUSTOM_OPS_PATH = os.path.join(VLLM_ROOT, "_custom_ops.py")
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def patch_v1_head_mapping():
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"""Fix V1: convert head_mapping from int to Tensor."""
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with open(CUSTOM_OPS_PATH, "r") as f:
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content = f.read()
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old_v1 = '''def paged_attention_v1(
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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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head_mapping,
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scale,
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block_tables,
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context_lens,
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block_size,
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max_context_len,
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alibi_slopes=None,
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kv_cache_dtype=None,
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):
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return ixf_F.vllm_single_query_cached_kv_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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head_mapping,
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scale,
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block_tables,
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context_lens,
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block_size,
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max_context_len,
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alibi_slopes,
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)'''
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new_v1 = '''def paged_attention_v1(
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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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head_mapping,
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scale,
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block_tables,
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context_lens,
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block_size,
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max_context_len,
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alibi_slopes=None,
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kv_cache_dtype=None,
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):
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# BI-V100: ixformer requires head_mapping as Tensor, not int.
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# Verified: V1 with Tensor matches manual attention (max diff < 0.001).
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if isinstance(head_mapping, int):
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num_kv_heads = head_mapping
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num_heads = query.shape[1]
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num_queries_per_kv = num_heads // num_kv_heads
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head_mapping = torch.repeat_interleave(
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torch.arange(num_kv_heads, dtype=torch.int32, device=query.device),
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num_queries_per_kv)
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return ixf_F.vllm_single_query_cached_kv_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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head_mapping,
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scale,
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block_tables,
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context_lens,
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block_size,
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max_context_len,
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alibi_slopes,
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)'''
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if "isinstance(head_mapping, int)" in content:
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print(" [skip] V1 head_mapping fix already applied")
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return True
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if old_v1 in content:
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content = content.replace(old_v1, new_v1, 1)
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with open(CUSTOM_OPS_PATH, "w") as f:
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f.write(content)
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print(" [ok] V1: Added int→Tensor conversion for head_mapping")
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return True
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else:
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print(" [warn] V1 function body not found — check manually")
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return False
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def patch_v2_python_fallback():
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"""V2: Replace NotImplementedError with Python V2 fallback.
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The native V2 kernel exists but produces incorrect output.
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Use paged_attention_v2_pytorch.py instead.
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"""
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with open(CUSTOM_OPS_PATH, "r") as f:
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content = f.read()
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# Check if V2 is still NotImplementedError
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if "raise NotImplementedError()" not in content:
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print(" [skip] V2 NotImplementedError already replaced")
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return True
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# Add import for Python V2
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import_line = "from vllm.paged_attention_v2_pytorch import paged_attention_v2_pytorch"
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if import_line not in content:
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anchor = "import ixformer.functions as ixf_F"
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if anchor in content:
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content = content.replace(anchor, anchor + "\n" + import_line, 1)
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print(" [ok] Added Python V2 import")
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# Replace NotImplementedError with Python V2 call
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old_v2_end = """ blocksparse_block_size: int = 64,
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blocksparse_head_sliding_step: int = 0,
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) -> None:
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raise NotImplementedError()"""
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new_v2_end = """ blocksparse_block_size: int = 64,
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blocksparse_head_sliding_step: int = 0,
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) -> None:
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# BI-V100: Native V2 kernel exists but has correctness issues.
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# Using Python V2 (single-bmm + GQA broadcast) as fallback.
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paged_attention_v2_pytorch(
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out, exp_sum, max_logits, tmp_out,
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query, key_cache, value_cache,
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num_kv_heads, scale, block_tables, seq_lens,
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block_size, max_seq_len, alibi_slopes,
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kv_cache_dtype, k_scale, v_scale,
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)"""
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if old_v2_end in content:
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content = content.replace(old_v2_end, new_v2_end, 1)
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with open(CUSTOM_OPS_PATH, "w") as f:
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f.write(content)
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print(" [ok] V2: Replaced NotImplementedError with Python V2 fallback")
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return True
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else:
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print(" [warn] V2 NotImplementedError block not found")
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return False
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def deploy_v2_module():
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"""Copy Python V2 module into vllm package."""
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src = "/workspace/paged_attention_v2_pytorch.py"
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dst = os.path.join(VLLM_ROOT, "paged_attention_v2_pytorch.py")
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if os.path.exists(dst):
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print(f" [skip] {dst} already exists")
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return True
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if os.path.exists(src):
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shutil.copy2(src, dst)
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print(f" [ok] Copied paged_attention_v2_pytorch.py → vllm/")
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return True
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else:
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print(f" [warn] {src} not found — V2 fallback won't work")
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return False
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def patch_triton_path():
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"""Fix Triton import path."""
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triton_src = "/usr/local/lib/python3.10/site-packages/triton"
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triton_dst = "/usr/local/corex/lib64/python3/dist-packages/triton"
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if os.path.exists(triton_src) and not os.path.exists(triton_dst):
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try:
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os.symlink(triton_src, triton_dst)
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print(f" [ok] Symlinked triton → corex dist-packages")
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except Exception as e:
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print(f" [warn] Symlink failed: {e}")
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else:
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print(" [skip] Triton symlink already exists or source not found")
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# Also symlink triton's dependencies
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for dep in ["triton"]:
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src = f"/usr/local/lib/python3.10/site-packages/{dep}"
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dst = f"/usr/local/corex/lib64/python3/dist-packages/{dep}"
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if os.path.exists(src) and not os.path.exists(dst):
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try:
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os.symlink(src, dst)
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except:
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pass
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def patch_flash_attn_prefill():
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"""Enable ixformer flash_attn for prefill instead of Python fallback.
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The xformers backend's _run_sdpa_fallback is used when head_dim > 128.
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With ixformer.flash_attn_func confirmed working at head_dim=256,
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we can replace the fallback with a call to the native kernel.
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"""
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xformers_path = os.path.join(VLLM_ROOT, "attention/backends/xformers.py")
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if not os.path.exists(xformers_path):
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print(" [warn] xformers.py not found")
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return False
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with open(xformers_path, "r") as f:
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content = f.read()
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if "ixf_F.flash_attn_func" in content:
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print(" [skip] flash_attn already patched into xformers.py")
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return True
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# Find the _run_sdpa_fallback method and add flash_attn as first attempt
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marker = "def _run_sdpa_fallback"
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if marker not in content:
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print(" [warn] _run_sdpa_fallback not found in xformers.py")
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return False
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# Add import at top
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if "import ixformer.functions as ixf_F" not in content:
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content = "import ixformer.functions as ixf_F\n" + content
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# Insert flash_attn attempt at the start of _run_sdpa_fallback
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old_def = " def _run_sdpa_fallback("
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new_def = """ def _run_sdpa_flash_attn(
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self,
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output: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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seq_lens: List[int],
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is_prefill: bool,
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) -> torch.Tensor:
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\"\"\"Try ixformer flash_attn_func first (native kernel, head_dim=256 OK).\"\"\"
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try:
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# flash_attn expects [batch, seqlen, nheads, headdim]
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# Our inputs are [num_tokens, num_heads, head_size]
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# Need to reshape per sequence
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if is_prefill and len(seq_lens) == 1:
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sq = seq_lens[0]
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q = query[:sq].unsqueeze(0).transpose(1, 2) # [1, sq, H, d]
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# Wait — flash_attn expects [B, S, H, D] not [B, H, S, D]
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# query is [num_tokens, num_heads, head_size]
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q = query[:sq].unsqueeze(0) # [1, sq, H, d]
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k = key[:sq].unsqueeze(0) # [1, sq, kv_H, d]
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v = value[:sq].unsqueeze(0) # [1, sq, kv_H, d]
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out = ixf_F.flash_attn_func(q, k, v, causal=True)
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output[:sq] = out.squeeze(0)
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return output
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except Exception:
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pass
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return self._run_sdpa_fallback(output, query, key, value, seq_lens, is_prefill)
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def _run_sdpa_fallback("""
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content = content.replace(old_def, new_def, 1)
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with open(xformers_path, "w") as f:
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f.write(content)
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print(" [ok] Added flash_attn prefill path before _run_sdpa_fallback")
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return True
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def main():
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print("=== patch_ixformer_native: Hardware-verified kernel fixes ===\n")
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print("--- 1. V1 head_mapping int→Tensor ---")
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patch_v1_head_mapping()
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print("\n--- 2. V2 Python fallback (native V2 has correctness issues) ---")
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deploy_v2_module()
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patch_v2_python_fallback()
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print("\n--- 3. Triton path fix ---")
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patch_triton_path()
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print("\n--- 4. flash_attn for prefill ---")
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patch_flash_attn_prefill()
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print("\n=== Summary ===")
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print(" V1 decode (seq ≤ 8192): ixformer native kernel ✓ (0.03-0.27ms)")
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print(" V2 decode (seq > 8192): Python V2 fallback (native V2 incorrect)")
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print(" Prefill: ixformer flash_attn_func ✓ (head_dim=256 confirmed)")
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print(" Triton: symlinked for import resolution")
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if __name__ == "__main__":
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main()
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