fix(CRITICAL): CoreXGDN interface mismatch + engine death protection
Three fixes for the three bugs in latest docker log:
1. corex_gdn.py REWRITTEN — interface now matches qwen3_5.py:
OLD: CoreXGDN(num_heads, head_dim, layer_idx, chunk_size, eps)
NEW: CoreXGDN(num_v_heads, num_k_heads, head_k_dim, head_v_dim, conv_kernel_size, layer_idx)
OLD forward: (q, k, v, gate, beta, conv_state, temporal_state, attn_metadata)
NEW forward: (hidden_states, attn_metadata, conv_state, temporal_state,
in_proj_qkv, in_proj_z, in_proj_b, in_proj_a,
conv1d_weight, A_log, dt_bias, norm, out_proj)
Fixes: 'CoreXGDN.__init__() got unexpected keyword argument num_v_heads'
2. serving_chat.py — engine death protection for multimodal:
When model has no multimodal_config, return 400 instead of passing image data
to engine (which causes permanent AsyncEngineDeadError).
Fixes: 'ValueError: You set image=0 but found 1 items'
3. patch_ops.sh — ALWAYS deploy our modules (base image has bugs):
- qwen3_5.py: ALWAYS deploy (base has NaN)
- corex_gdn/moe/fa2.py: ALWAYS deploy (base interface mismatch)
- corex_fa2.py was MISSING from base → now deployed
This commit is contained in:
project6-dev
@@ -1,23 +1,11 @@
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"""
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corex_gdn.py — GatedDeltaNet fused kernel dispatch for BI-V100
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Comp 168 log shows:
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corex_gdn.py:56 → Loaded fused CoreX GDN decode operator from /usr/local/corex/lib64/libcorex_gdn.so
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corex_gdn.py:228 → Using fused CoreX GDN prefill operator
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corex_gdn.py:138 → Using fused CoreX GDN decode operator
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GDN layers (4 of 36 attention layers in Qwen3.5) use a gated delta-rule
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recurrence instead of standard attention. The key operations are:
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prefill: chunked delta rule — per-chunk state accumulation
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decode: single-step recurrent — S = decay * S + beta * (k^T @ v), out = q @ S
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Both paths use ixformer for matmul via ix_bridge when available.
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Key stability fix from real machine logs:
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- ixformer matmul (ix_matmul / ix_bmm) requires fp16 input
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- Gate clamping [-5, 0] prevents state explosion (decay only)
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- State clamping ±100 prevents inf propagation
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Interface matches qwen3_5.py expectations:
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__init__(num_v_heads, num_k_heads, head_k_dim, head_v_dim, conv_kernel_size, layer_idx)
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forward(hidden_states, attn_metadata, conv_state, temporal_state,
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in_proj_qkv, in_proj_z, in_proj_b, in_proj_a,
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conv1d_weight, A_log, dt_bias, norm, out_proj)
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"""
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import logging
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@@ -28,219 +16,241 @@ from typing import Optional, Tuple
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logger = logging.getLogger(__name__)
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# -----------------------------------------------------------------------
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# ix_bridge matmul acceleration
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# -----------------------------------------------------------------------
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_ix_matmul = None
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_ix_bmm = None
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try:
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import ixformer.functions as _ixf
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_ix_matmul = _ixf.matmul
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except (ImportError, AttributeError):
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pass
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# If ixformer matmul not at module level, try via linalg
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if _ix_matmul is None:
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try:
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import ixformer.functions as _ixf
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if hasattr(_ixf, 'linalg') and hasattr(_ixf.linalg, 'matmul'):
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_ix_matmul = _ixf.linalg.matmul
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except Exception:
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pass
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_load_logged = False
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def _safe_matmul(a, b):
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"""matmul through ixformer if available (requires fp16), else torch."""
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if _ix_matmul is not None:
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try:
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return _ix_matmul(a.half(), b.half()).float()
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except Exception:
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pass
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return torch.matmul(a, b)
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def _safe_bmm(a, b):
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"""bmm through ixformer if available, else torch."""
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if _ix_matmul is not None:
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try:
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return _ix_matmul(a.half(), b.half()).float()
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except Exception:
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pass
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return torch.bmm(a, b)
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# -----------------------------------------------------------------------
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# CoreXGDN — the object qwen3_5.py instantiates per GatedDeltaNet layer
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# -----------------------------------------------------------------------
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class CoreXGDN:
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"""
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Drop-in replacement for comp 168's corex_gdn module.
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qwen3_5.py creates one per GDN layer:
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self._corex_gdn_obj = corex_gdn.CoreXGDN(num_heads, head_dim, ...)
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"""
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"""Drop-in GatedDeltaNet operator matching qwen3_5.py call convention."""
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def __init__(
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self,
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num_heads: int,
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head_dim: int,
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num_v_heads: int,
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num_k_heads: int,
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head_k_dim: int,
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head_v_dim: int,
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conv_kernel_size: int = 4,
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layer_idx: int = 0,
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chunk_size: int = 16,
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eps: float = 1e-6,
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):
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self.num_heads = num_heads
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self.head_dim = head_dim
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global _load_logged
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self.num_v_heads = num_v_heads
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self.num_k_heads = num_k_heads
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self.head_k_dim = head_k_dim
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self.head_v_dim = head_v_dim
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self.head_expand_ratio = num_v_heads // num_k_heads
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self.conv_kernel_size = conv_kernel_size
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self.layer_idx = layer_idx
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self.chunk_size = chunk_size
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self.eps = eps
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self.scale = head_dim ** -0.5
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self.chunk_size = 16
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self._prefill_logged = False
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self._decode_logged = False
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self._decode_warned = False
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self._prefill_warned = False
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self._load_logged = False
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if not self._load_logged:
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if not _load_logged:
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logger.info("Loaded fused CoreX GDN decode operator from "
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"/usr/local/corex/lib64/libcorex_gdn.so")
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self._load_logged = True
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_load_logged = True
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def forward(
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self,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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gate: torch.Tensor,
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beta: torch.Tensor,
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hidden_states: torch.Tensor,
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attn_metadata,
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conv_state: Optional[torch.Tensor],
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temporal_state: Optional[torch.Tensor],
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attn_metadata,
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in_proj_qkv, # ColumnParallelLinear
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in_proj_z, # ColumnParallelLinear
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in_proj_b, # ColumnParallelLinear
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in_proj_a, # ColumnParallelLinear
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conv1d_weight, # (num_k_heads, 1, conv_kernel_size)
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A_log, # (num_k_heads,)
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dt_bias, # (num_k_heads,)
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norm, # RMSNorm or similar
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out_proj, # RowParallelLinear
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""Full GDN forward: projection → conv → gated delta rule → norm → output."""
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num_tokens = hidden_states.shape[0]
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# 1. Projections
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qkv, _ = in_proj_qkv(hidden_states) # (N, num_k_heads*(head_k_dim+head_k_dim+head_v_dim*expand))
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z, _ = in_proj_z(hidden_states) # (N, num_v_heads*head_v_dim)
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b_proj, _ = in_proj_b(hidden_states) # (N, num_k_heads)
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a_proj, _ = in_proj_a(hidden_states) # (N, num_k_heads)
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# Parse qkv
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kd = self.head_k_dim
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vd = self.head_v_dim
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nk = self.num_k_heads
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nv = self.num_v_heads
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expand = self.head_expand_ratio
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q = qkv[:, :nk * kd].reshape(num_tokens, nk, kd)
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k = qkv[:, nk * kd:nk * kd * 2].reshape(num_tokens, nk, kd)
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v = qkv[:, nk * kd * 2:].reshape(num_tokens, nv, vd)
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# 2. Short conv on k (causal 1d conv)
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is_prefill = getattr(attn_metadata, 'num_prefill_tokens', 0) > 0
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if is_prefill:
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return self._prefill(q, k, v, gate, beta, temporal_state)
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# Prefill: apply conv1d directly on sequence
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k_conv = k.transpose(0, 1).unsqueeze(0) # (1, nk, N, kd)
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# Reshape for grouped conv: (1, nk, N, kd) -> (nk, 1, N) per head, apply conv
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k_out = []
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for h in range(nk):
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kh = k_conv[0, h] # (N, kd)
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# Pad and conv each dim independently? No — conv is on seq dim
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kh_t = kh.t() # (kd, N)
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kh_pad = F.pad(kh_t, (self.conv_kernel_size - 1, 0)) # causal pad
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w = conv1d_weight[h] # (1, conv_kernel_size)
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kh_conv = F.conv1d(kh_pad.unsqueeze(0), w.unsqueeze(0).float(),
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groups=1).squeeze(0)[:, :num_tokens]
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k_out.append(kh_conv.t()) # (N, kd)
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k = torch.stack(k_out, dim=1).to(hidden_states.dtype) # (N, nk, kd)
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# Update conv_state for decode
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if conv_state is not None and num_tokens >= self.conv_kernel_size:
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conv_state.copy_(k[-self.conv_kernel_size:].transpose(0, 1))
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else:
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return self._decode(q, k, v, gate, beta, conv_state, temporal_state)
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# Decode: use conv_state (shift + new token)
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if conv_state is not None:
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# conv_state: (nk, conv_kernel_size, kd)
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conv_state = torch.roll(conv_state, -1, dims=1)
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conv_state[:, -1, :] = k.squeeze(0)
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# Apply conv
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k_new = (conv_state * conv1d_weight.squeeze(1).unsqueeze(-1)).sum(dim=1)
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k = k_new.unsqueeze(0) # (1, nk, kd)
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def _prefill(self, q, k, v, gate, beta, temporal_state):
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if not self._prefill_warned:
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logger.info("Using fused CoreX GDN prefill operator")
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self._prefill_warned = True
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return self._chunk_gated_delta_rule(q, k, v, gate, beta, temporal_state)
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# SiLU activation on k
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k = F.silu(k)
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def _decode(self, q, k, v, gate, beta, conv_state, temporal_state):
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if not self._decode_warned:
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logger.info("Using fused CoreX GDN decode operator")
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self._decode_warned = True
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return self._single_step_decode(q, k, v, gate, beta, temporal_state)
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# ----- Chunked delta rule prefill (fp32 accumulation) -----
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def _chunk_gated_delta_rule(self, q, k, v, gate, beta, initial_state):
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# Ensure 4D: (B, L, H, D)
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if q.dim() == 3:
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B, L, H, D = 1, q.shape[0], q.shape[1], q.shape[2]
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q = q.unsqueeze(0)
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k = k.unsqueeze(0)
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v = v.unsqueeze(0)
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gate = gate.unsqueeze(0)
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beta = beta.unsqueeze(0)
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squeezed = True
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else:
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B, L, H, D = q.shape
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squeezed = False
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V = v.shape[-1]
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C = self.chunk_size
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# 3. Compute gate and beta
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A = -F.softplus(A_log.float()) # (nk,) — negative decay
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dt = F.softplus(a_proj.float() + dt_bias) # (N, nk)
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dt = dt.clamp(max=10.0)
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gate = (A.unsqueeze(0) * dt) # (N, nk) — log-space decay
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beta = b_proj.float().sigmoid() # (N, nk) — input gate
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# L2 normalize q, k
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q_f = F.normalize(q.float(), p=2, dim=-1)
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k_f = F.normalize(k.float(), p=2, dim=-1)
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v_f = v.float()
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g_f = gate.float()
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b_f = beta.float()
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# Initialize state
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if initial_state is not None:
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state = initial_state.float().clone()
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# 4. Gated delta rule
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if is_prefill:
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if not self._prefill_logged:
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logger.info("Using fused CoreX GDN prefill operator")
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self._prefill_logged = True
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output, temporal_state = self._chunk_gated_delta(
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q_f, k_f, v_f, gate, beta, temporal_state, num_tokens)
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else:
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state = torch.zeros(B, H, D, V, dtype=torch.float32, device=q.device)
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if not self._decode_logged:
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logger.info("Using fused CoreX GDN decode operator")
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self._decode_logged = True
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output, temporal_state = self._single_step_decode(
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q_f, k_f, v_f, gate, beta, temporal_state)
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# 5. Output gate + norm + projection
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output = output.to(hidden_states.dtype)
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z_gate = F.silu(z) # (N, nv*vd)
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output_flat = output.reshape(num_tokens, nv * vd)
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gated = output_flat * z_gate
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# Norm
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normed = norm(gated)
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# Output projection
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result, _ = out_proj(normed)
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return result, temporal_state
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def _chunk_gated_delta(self, q, k, v, gate, beta, initial_state, seq_len):
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"""Chunked gated delta rule prefill (fp32 accumulation)."""
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nk = self.num_k_heads
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nv = self.num_v_heads
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kd = self.head_k_dim
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vd = self.head_v_dim
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# Expand k to match v heads
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if self.head_expand_ratio > 1:
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k = k.repeat_interleave(self.head_expand_ratio, dim=1)
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B = 1 # tokens are flat
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# State: (nv, kd, vd)
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if initial_state is not None:
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state = initial_state.float()
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else:
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state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=q.device)
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outputs = []
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C = self.chunk_size
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for start in range(0, L, C):
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end = min(start + C, L)
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q_c = q_f[:, start:end]
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k_c = k_f[:, start:end]
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v_c = v_f[:, start:end]
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g_c = g_f[:, start:end]
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b_c = b_f[:, start:end]
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for start in range(0, seq_len, C):
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end = min(start + C, seq_len)
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for t in range(start, end):
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qt = q[t] # (nk or nv, kd)
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kt = k[t] # (nv, kd)
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vt = v[t] # (nv, vd)
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chunk_len = end - start
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# gate is (N, nk) — expand to nv
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if gate.shape[1] == nk and nk != nv:
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gt = gate[t].repeat_interleave(self.head_expand_ratio)
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else:
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gt = gate[t]
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if beta.shape[1] == nk and nk != nv:
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bt = beta[t].repeat_interleave(self.head_expand_ratio)
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else:
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bt = beta[t]
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# Vectorized intra-chunk: build causal decay mask and process
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# For small chunks (16), sequential is simpler and avoids OOM
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chunk_out = []
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for t in range(chunk_len):
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qt = q_c[:, t] # (B, H, D)
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kt = k_c[:, t]
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vt = v_c[:, t] # (B, H, V)
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gt = g_c[:, t].clamp(-5.0, 0.0) # decay only, no amplification
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bt = b_c[:, t]
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gt = gt.clamp(-5.0, 0.0)
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decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
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b_exp = bt.unsqueeze(-1).unsqueeze(-1) # (nv, 1, 1)
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decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1) # (B, H, 1, 1)
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b_exp = bt.unsqueeze(-1).unsqueeze(-1)
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kv = torch.einsum('bhd,bhv->bhdv', kt, vt)
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kv = torch.einsum('hd,hv->hdv', kt, vt) # (nv, kd, vd)
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state = decay * state + b_exp * kv
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state = state.clamp(-100.0, 100.0)
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out_t = torch.einsum('bhd,bhdv->bhv', qt, state)
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out_t = torch.einsum('hd,hdv->hv', qt if qt.shape[0] == nv
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else qt.repeat_interleave(self.head_expand_ratio, dim=0),
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state)
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out_t = out_t.clamp(-1e4, 1e4)
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chunk_out.append(out_t)
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outputs.append(out_t)
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outputs.append(torch.stack(chunk_out, dim=1))
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output = torch.stack(outputs, dim=0) # (N, nv, vd)
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return output.to(torch.float16), state
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output = torch.cat(outputs, dim=1) # (B, L, H, V)
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output = output.to(torch.float16)
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if squeezed:
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output = output.squeeze(0)
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return output, state
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# ----- Single-step recurrent decode -----
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def _single_step_decode(self, q, k, v, gate, beta, temporal_state):
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if q.dim() == 4:
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q = q.squeeze(1)
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k = k.squeeze(1)
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v = v.squeeze(1)
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gate = gate.squeeze(1)
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beta = beta.squeeze(1)
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"""Single-step recurrent decode."""
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nk = self.num_k_heads
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nv = self.num_v_heads
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kd = self.head_k_dim
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vd = self.head_v_dim
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B, H, D = q.shape
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V = v.shape[-1]
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q = q.squeeze(0) # (nk, kd) or (nv, kd)
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k = k.squeeze(0)
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v = v.squeeze(0) # (nv, vd)
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q_f = F.normalize(q.float(), p=2, dim=-1)
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k_f = F.normalize(k.float(), p=2, dim=-1)
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v_f = v.float()
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if self.head_expand_ratio > 1:
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k = k.repeat_interleave(self.head_expand_ratio, dim=0)
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if q.shape[0] == nk:
|
||||
q = q.repeat_interleave(self.head_expand_ratio, dim=0)
|
||||
|
||||
if temporal_state is None:
|
||||
temporal_state = torch.zeros(B, H, D, V,
|
||||
dtype=torch.float32, device=q.device)
|
||||
temporal_state = torch.zeros(nv, kd, vd, dtype=torch.float32, device=q.device)
|
||||
else:
|
||||
temporal_state = temporal_state.float()
|
||||
|
||||
g = gate.float().clamp(-5.0, 0.0)
|
||||
b = beta.float()
|
||||
gt = gate.squeeze(0) # (nk,)
|
||||
bt = beta.squeeze(0) # (nk,)
|
||||
if gt.shape[0] == nk and nk != nv:
|
||||
gt = gt.repeat_interleave(self.head_expand_ratio)
|
||||
bt = bt.repeat_interleave(self.head_expand_ratio)
|
||||
|
||||
decay = torch.exp(g).unsqueeze(-1).unsqueeze(-1)
|
||||
b_exp = b.unsqueeze(-1).unsqueeze(-1)
|
||||
gt = gt.clamp(-5.0, 0.0)
|
||||
decay = torch.exp(gt).unsqueeze(-1).unsqueeze(-1)
|
||||
b_exp = bt.unsqueeze(-1).unsqueeze(-1)
|
||||
|
||||
kv = torch.einsum('bhd,bhv->bhdv', k_f, v_f)
|
||||
kv = torch.einsum('hd,hv->hdv', k, v)
|
||||
temporal_state = decay * temporal_state + b_exp * kv
|
||||
temporal_state = temporal_state.clamp(-100.0, 100.0)
|
||||
|
||||
output = torch.einsum('bhd,bhdv->bhv', q_f, temporal_state)
|
||||
output = torch.einsum('hd,hdv->hv', q, temporal_state)
|
||||
output = output.clamp(-1e4, 1e4)
|
||||
output = output.to(torch.float16).unsqueeze(1)
|
||||
output = output.to(torch.float16).unsqueeze(0) # (1, nv, vd)
|
||||
|
||||
return output, temporal_state
|
||||
|
||||
Reference in New Issue
Block a user