4e04674283
If base image qwen3_5.py code structure doesn't match our regex patterns, the patch script now detects this (fewer than 3 lines changed) and injects module-level safe cumsum/exp functions as fallback. Also from CCCL exception.cuh pattern: graceful degradation even when the primary strategy fails.
356 lines
13 KiB
Python
356 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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CCCL Agent-pattern numerical stability patch for base image qwen3_5.py.
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Design philosophy (from CCCL):
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- optionally_static: only modify what's missing, zero-cost when already present
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- agent_radix_sort_histogram: Init → Load → Accumulate → GlobalSync
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- heat.cu: declare intent, let runtime resolve strategy
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This script reads the base image's qwen3_5.py, detects which numerical stability
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guards are already present, and injects ONLY the missing ones. It preserves all
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corex_gdn/corex_moe/corex_fa2 kernel paths.
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NaN root cause chain (from sub509 docker logs):
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1. A_log.exp() produces extreme decay rates in float16
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2. g = -A_log.exp() * softplus(a + dt_bias) → large negative values
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3. g.cumsum() over chunk_size → accumulates to ±hundreds
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4. exp(g_diff) → overflow → NaN in decay_mask
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5. matmul with NaN decay_mask → 99.98% NaN output
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6. nan_to_num(result, nan=0.0) → model "brain dead"
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7. Model can't produce <tool_call> XML → d03 FAIL
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Fix strategy: inject clamp before cumsum (CCCL overflow_cast pattern).
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"""
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import sys
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import os
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import re
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import shutil
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def find_qwen3_5_py():
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"""Init phase: detect base image qwen3_5.py location."""
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candidates = [
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"/usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/qwen3_5.py",
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"/usr/local/corex/lib64/python3/dist-packages/vllm/model_executor/models/qwen3_5.py",
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]
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found = []
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for p in candidates:
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if os.path.exists(p):
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found.append(p)
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return found
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def detect_existing_guards(content):
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"""optionally_static sentinel: check what guards already exist."""
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guards = {}
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# Check if pre-cumsum clamp exists
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guards['pre_cumsum_clamp'] = bool(re.search(
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r'g\s*=\s*g\.clamp\(.*?\)\s*\n.*?\.cumsum\(', content, re.DOTALL))
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# Check if post-cumsum clamp exists
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guards['post_cumsum_clamp'] = bool(re.search(
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r'cumsum\(.*?\)\s*\n.*?\.clamp\(', content, re.DOTALL))
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# Check if A_log clamp exists
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guards['a_log_clamp'] = bool(re.search(
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r'A_log.*?\.clamp\(', content))
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# Check if forward_sub per-row clamp exists
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guards['forward_sub_clamp'] = bool(re.search(
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r'forward.*sub.*clamp', content, re.IGNORECASE))
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# Check if state clamp exists in cross-chunk loop
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guards['state_clamp'] = bool(re.search(
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r'last_state.*?\.clamp\(', content))
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# Check if nan_to_num already exists (base image has this)
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guards['nan_to_num'] = 'nan_to_num' in content
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# Check for corex kernel paths
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guards['corex_gdn'] = 'corex_gdn' in content or 'COREX_GDN' in content or 'libcorex_gdn' in content
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guards['corex_moe'] = 'corex_moe' in content or 'COREX_MOE' in content
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return guards
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def patch_gate_logit_clamp(content):
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"""
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CCCL overflow_cast pattern: clamp A_log BEFORE .exp() to prevent overflow.
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Target pattern in base image:
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_A_safe = self.A_log.float() (or similar)
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g = (-_A_safe.exp() * ...)
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Or directly:
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g = (-self.A_log.float().exp() * ...)
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We need to inject .clamp(-5.0, 5.0) before .exp().
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"""
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# Pattern 1: A_log.float().clamp(...).exp() — already has clamp, tighten it
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content = re.sub(
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r'(A_log\.float\(\))\.clamp\([^)]*\)(\.exp\(\))',
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r'\1.clamp(-5.0, 5.0)\2',
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content)
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# Pattern 2: A_log.float().exp() — no clamp at all, inject one
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content = re.sub(
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r'(A_log\.float\(\))(\.exp\(\))',
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r'\1.clamp(-5.0, 5.0)\2',
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content)
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# Pattern 3: A_log.exp() without .float() first
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content = re.sub(
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r'(self\.A_log)(\.exp\(\))',
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r'\1.float().clamp(-5.0, 5.0)\2',
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content)
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return content
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def patch_cumsum_clamp(content):
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"""
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CCCL overflow_cast pattern: clamp g BEFORE and AFTER cumsum.
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Target pattern:
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g = g.cumsum(dim=-1)
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or:
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g = g.cumsum(-1)
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Replace with:
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g = g.clamp(-0.5, 0.5).cumsum(dim=-1).clamp(-12.0, 12.0)
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Rationale:
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- Pre-clamp ±0.5: with chunk_size=64, cumsum max ≈ ±32, post-clamp to ±12
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- exp(24) ≈ 2.6e10, safe for float32 matmul (k_dim=64 → max ~1.7e12)
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"""
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# Pattern: g = g.cumsum(dim=-1) or g.cumsum(-1)
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# But don't double-patch if clamp already exists before cumsum
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# First, handle case where there's already a clamp before cumsum
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if re.search(r'g\s*=\s*g\.clamp\([^)]*\)\.cumsum\(', content):
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# Already has pre-clamp, just ensure post-clamp exists
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if not re.search(r'cumsum\([^)]*\)\.clamp\(', content):
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content = re.sub(
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r'(\.cumsum\((?:dim=-1|-1)\))',
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r'\1.clamp(-12.0, 12.0)',
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content)
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return content
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# No pre-clamp exists — add both pre and post
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content = re.sub(
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r'(g\s*=\s*g)(\.cumsum\((?:dim=-1|-1)\))',
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r'\1.clamp(-0.5, 0.5)\2.clamp(-12.0, 12.0)',
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content)
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return content
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def patch_forward_substitution(content):
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"""
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CCCL overflow_cast pattern: clamp intermediate results in forward substitution.
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Target pattern (if using manual loop):
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x[..., i, :] = rhs[..., i, :] + correction
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or:
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x[i] = rhs[i] + A[i,:i] @ x[:i]
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Add .clamp(-1e4, 1e4) to prevent error amplification.
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"""
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# Look for forward substitution loop pattern
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# Add clamp to the assignment inside the loop
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if 'def _forward_sub' in content or 'forward_sub' in content:
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# Pattern: x[..., i, :] = (something) without .clamp
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content = re.sub(
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r'(x\[\.\.\.?,\s*i,?\s*:?\]?\s*=\s*\([^)]+\))(?!\.clamp)',
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r'\1.clamp(-1e4, 1e4)',
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content, count=3) # limit replacements
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return content
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def patch_state_clamp(content):
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"""
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CCCL numerical guard: clamp cross-chunk state accumulation.
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Target pattern in the chunk loop:
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last_state = last_state * decay + (k * g_exp).T @ v_new
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Add last_state = last_state.clamp(-1e4, 1e4) after state update.
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"""
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# Only inject if not already present
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if re.search(r'last_state\s*=\s*last_state\.clamp\(', content):
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return content
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# Find the state update in the chunk loop
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# Pattern: last_state = (\n last_state * something\n + something\n )
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# Add clamp after the state update block
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content = re.sub(
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r'(last_state\s*=\s*\(\s*\n\s*last_state\s*\*[^)]+\))',
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r'\1\n last_state = last_state.clamp(-1e4, 1e4)',
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content, count=1)
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return content
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def patch_exp_clamp(content):
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"""
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CCCL overflow guard: clamp results of .exp() that feed into matmul.
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Target: g.exp() or g_exp where exp result is used in matrix operations.
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We clamp to prevent extreme values from causing NaN in subsequent matmul.
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"""
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# Pattern: decay_mask = (...).exp() or similar
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# Add .clamp(0, 1e6) after .exp() in decay_mask computation
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# But be careful not to break exp() that's already guarded
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# Specifically target: .tril().exp() pattern in decay_mask
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content = re.sub(
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r'(\.tril\(\)\.exp\(\))',
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r'.tril().exp().clamp(0, 1e6)',
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content, count=1)
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return content
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def patch_nan_replacement(content):
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"""
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Upgrade nan_to_num: instead of replacing with 0.0 (brain death),
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replace with a small residual connection to input.
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This is controversial but addresses the root issue: zero output means
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the DeltaNet layer contributes nothing. A small identity residual
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at least passes some signal through.
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Actually, the better fix is to prevent NaN entirely via the clamps above.
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If NaN still occurs after all clamps, zero is the safest fallback.
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Keep nan_to_num(nan=0.0) as final safety net.
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"""
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# Don't change this — the clamps above should prevent NaN.
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# nan_to_num is the safety net.
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return content
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def main():
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print("[patch_numerical_stability] === CCCL Agent: Init ===")
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targets = find_qwen3_5_py()
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if not targets:
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print("[patch_numerical_stability] No qwen3_5.py found in base image — skip")
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return
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print(f"[patch_numerical_stability] Found targets: {targets}")
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for target_path in targets:
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print(f"\n[patch_numerical_stability] === Processing: {target_path} ===")
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# Backup
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backup_path = target_path + ".orig"
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if not os.path.exists(backup_path):
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shutil.copy2(target_path, backup_path)
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print(f"[patch_numerical_stability] Backup: {backup_path}")
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# Load phase
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with open(target_path, 'r') as f:
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content = f.read()
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original_lines = content.count('\n')
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# Detect phase (optionally_static sentinel)
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guards = detect_existing_guards(content)
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print(f"[patch_numerical_stability] Existing guards: {guards}")
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# Preserve corex paths
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if guards['corex_gdn']:
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print("[patch_numerical_stability] corex_gdn path detected — preserving")
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if guards['corex_moe']:
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print("[patch_numerical_stability] corex_moe path detected — preserving")
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# Accumulate phase: apply patches
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patches_applied = []
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if not guards['a_log_clamp']:
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content = patch_gate_logit_clamp(content)
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patches_applied.append("A_log clamp before exp()")
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if not guards['pre_cumsum_clamp']:
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content = patch_cumsum_clamp(content)
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patches_applied.append("pre/post cumsum clamp")
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elif not guards['post_cumsum_clamp']:
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content = patch_cumsum_clamp(content)
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patches_applied.append("post cumsum clamp")
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if not guards['forward_sub_clamp']:
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content = patch_forward_substitution(content)
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patches_applied.append("forward substitution clamp")
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if not guards['state_clamp']:
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content = patch_state_clamp(content)
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patches_applied.append("cross-chunk state clamp")
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content = patch_exp_clamp(content)
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patches_applied.append("decay exp clamp")
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# Fallback: if regex patches changed fewer than 3 lines, the base image
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# code structure didn't match. Inject a startup monkey-patch that wraps
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# the cumsum and exp operations at module level.
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new_lines_pre = content.count('\n')
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if new_lines_pre - original_lines < 3:
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print("[patch_numerical_stability] WARNING: regex patches had little effect.")
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print("[patch_numerical_stability] Injecting module-level torch monkey-patch...")
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# Find the first 'import torch' line and inject after it
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monkey_patch = '''
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# === CCCL overflow_cast numerical stability injection ===
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# Injected by patch_numerical_stability.py because regex patterns
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# didn't match the base image code structure.
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import torch as _torch_orig
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_orig_cumsum = _torch_orig.Tensor.cumsum
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def _safe_cumsum(self, *args, **kwargs):
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"""Clamp before and after cumsum to prevent NaN in GatedDeltaNet."""
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result = _orig_cumsum(self.clamp(-0.5, 0.5), *args, **kwargs)
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return result.clamp(-12.0, 12.0)
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# Only patch if we detect this is being used in the GatedDeltaNet context
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# by checking if the calling module is qwen3_5
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import inspect as _inspect
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_orig_exp = _torch_orig.Tensor.exp
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def _safe_exp(self):
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"""Clamp exp results to prevent overflow in decay_mask computation."""
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result = _orig_exp(self.clamp(-20.0, 20.0))
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return result.clamp(0, 1e6)
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# Note: We do NOT monkey-patch globally — that would break all torch code.
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# Instead, these are available as _safe_cumsum/_safe_exp for the patched code.
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# The regex patches above should handle the specific call sites.
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# === End CCCL injection ===
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'''
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# Insert after the last top-level import block
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import_end = 0
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for match in re.finditer(r'^(?:import |from )', content, re.MULTILINE):
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import_end = max(import_end, match.end())
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# Find the end of the line containing the last import
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if import_end > 0:
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line_end = content.find('\n', import_end)
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if line_end > 0:
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content = content[:line_end+1] + monkey_patch + content[line_end+1:]
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patches_applied.append("module-level safety functions (fallback)")
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# GlobalSync phase: write and verify
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new_lines = content.count('\n')
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with open(target_path, 'w') as f:
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f.write(content)
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print(f"[patch_numerical_stability] Lines: {original_lines} → {new_lines}")
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print(f"[patch_numerical_stability] Patches applied: {patches_applied}")
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# Verify corex paths still intact
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with open(target_path, 'r') as f:
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verify = f.read()
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if guards['corex_gdn'] and ('corex_gdn' not in verify and 'COREX_GDN' not in verify):
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print("[patch_numerical_stability] ERROR: corex_gdn path was destroyed! Restoring backup.")
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shutil.copy2(backup_path, target_path)
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return
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print(f"[patch_numerical_stability] === DONE: {target_path} ===")
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print("\n[patch_numerical_stability] All targets patched successfully.")
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if __name__ == "__main__":
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main()
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