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This commit is contained in:
Dan Saunders
@@ -88,205 +88,189 @@ def _call_grouped_mm(
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def moe_ffn_forward_grouped(
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hidden_states, gate_linear, experts_module, top_k: int
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) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
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"""
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Attempt a grouped GEMM fast path using PyTorch 2.8+.
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If unavailable or fails, returns (None, None) so caller can fallback.
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"""
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try:
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bsz, seqlen, hdim = hidden_states.shape
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x = hidden_states.view(-1, hdim)
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router_logits = gate_linear(x)
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"""Attempt grouped GEMM fast path using PyTorch 2.8+."""
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global LAST_ERROR
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LAST_ERROR = None
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bsz, seqlen, hdim = hidden_states.shape
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x = hidden_states.view(-1, hdim)
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router_logits = gate_linear(x)
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# topk routing in torch (keep simple to avoid dependency cycles)
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
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topk_weight = (topk_weight / topk_weight.sum(dim=-1, keepdim=True)).to(x.dtype)
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# top-k routing executed in torch to avoid extra dependencies
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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topk_weight, topk_idx = torch.topk(routing_weights, top_k, dim=-1, sorted=False)
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topk_weight = (topk_weight / topk_weight.sum(dim=-1, keepdim=True)).to(x.dtype)
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# Build per-expert input lists
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flat_idx = topk_idx.view(-1)
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x_rep = x.repeat_interleave(top_k, dim=0)
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flat_idx = topk_idx.view(-1)
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x_rep = x.repeat_interleave(top_k, dim=0)
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# Cache stacked weights on experts (support Mixtral and Qwen-style layouts)
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E = experts_module.num_experts
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dev, dt = x.device, x.dtype
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first = experts_module[0]
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E = experts_module.num_experts
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dev, dt = x.device, x.dtype
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first = experts_module[0]
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is_mixtral = _is_mixtral_layout(first)
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is_qwen2 = _is_qwen_layout(first)
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nested_attr: Optional[str] = None
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if not (is_mixtral or is_qwen2):
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for candidate in ("mlp", "ffn"):
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nested = getattr(first, candidate, None)
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if nested is None:
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continue
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if _is_mixtral_layout(nested):
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is_mixtral = True
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nested_attr = candidate
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break
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if _is_qwen_layout(nested):
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is_qwen2 = True
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nested_attr = candidate
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break
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if not (is_mixtral or is_qwen2):
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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"torch_grouped: unsupported expert layout; falling back to naive"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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def _resolve_expert(idx: int):
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expert = experts_module[idx]
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if nested_attr is None:
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return expert
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nested_mod = getattr(expert, nested_attr, None)
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if nested_mod is None:
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raise AttributeError(
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f"expert {idx} missing nested module '{nested_attr}'"
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)
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return nested_mod
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try:
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if is_mixtral:
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if (
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not hasattr(experts_module, "_stacked_w1")
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or experts_module._stacked_w1.device != dev
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or experts_module._stacked_w1.dtype != dt
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):
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mods = [_resolve_expert(i) for i in range(E)]
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w1 = [mod.w1.weight.t() for mod in mods]
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w3 = [mod.w3.weight.t() for mod in mods]
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w2 = [mod.w2.weight.t() for mod in mods]
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experts_module._stacked_w1 = (
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torch.stack(w1, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w3 = (
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torch.stack(w3, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w2 = (
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torch.stack(w2, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w13 = torch.cat(
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[experts_module._stacked_w1, experts_module._stacked_w3], dim=-1
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).contiguous()
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W13 = experts_module._stacked_w13
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W2 = experts_module._stacked_w2
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else:
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# Qwen-style MoE: either gate_up_proj (2I x H) or (up_proj + gate_proj), down_proj (H x I)
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if (
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not hasattr(experts_module, "_stacked_w13")
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or experts_module._stacked_w13.device != dev
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or experts_module._stacked_w13.dtype != dt
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):
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w13 = []
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w2 = []
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for i in range(E):
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mod = _resolve_expert(i)
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# prefer fused gate_up_proj if present
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if hasattr(mod, "gate_up_proj"):
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w13.append(mod.gate_up_proj.weight.t())
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elif hasattr(mod, "up_proj") and hasattr(mod, "gate_proj"):
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# concatenate [up | gate] along N
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w13.append(
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torch.cat(
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[mod.up_proj.weight.t(), mod.gate_proj.weight.t()],
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dim=-1,
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)
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)
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else:
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LAST_ERROR = "unrecognized Qwen MoE expert weight layout"
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if not getattr(
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experts_module, "_ax_grouped_logged_fail", False
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):
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_LOGGER.warning(
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"torch_grouped: could not resolve Qwen MoE expert weights; fallback to naive"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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w2.append(mod.down_proj.weight.t())
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experts_module._stacked_w13 = (
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torch.stack(w13, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w2 = (
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torch.stack(w2, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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W13 = experts_module._stacked_w13
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W2 = experts_module._stacked_w2
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except AttributeError as err:
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LAST_ERROR = str(err)
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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"torch_grouped: expert weights missing expected attributes; falling back to naive"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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# Grouped GEMM for up+gate
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As: List[torch.Tensor] = []
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Bs: List[torch.Tensor] = []
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expert_slices = []
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for i in range(E):
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sel = flat_idx == i
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if sel.any():
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Xi = x_rep[sel]
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As.append(Xi)
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Bs.append(W13[i])
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expert_slices.append((i, sel))
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if not As:
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# no tokens routed — edge case
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out = torch.zeros_like(x)
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return out.view(bsz, seqlen, hdim), router_logits
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Y_list = _call_grouped_mm(As, Bs)
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if Y_list is None:
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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f"torch_grouped: grouped_mm up+gate failed; falling back to naive. Reason: {LAST_ERROR}"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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# SwiGLU on each expert block and prepare for down projection
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As2: List[torch.Tensor] = []
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Bs2: List[torch.Tensor] = []
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y_buf = torch.empty_like(x_rep)
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# split Y into (I, I)
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for Yi in Y_list:
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I2 = Yi.shape[-1] // 2
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Yi_hidden = F.silu(Yi[:, :I2]) * Yi[:, I2:]
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As2.append(Yi_hidden)
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Bs2.append(W2[i])
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Y2_list = _call_grouped_mm(As2, Bs2)
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if Y2_list is None:
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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f"torch_grouped: grouped_mm down failed; falling back to naive. Reason: {LAST_ERROR}"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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# Write back, apply per-token weighting, and reduce over top_k
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for (_, sel), Out_i in zip(expert_slices, Y2_list, strict=False):
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y_buf[sel] = Out_i
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y = (y_buf.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
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if not getattr(experts_module, "_ax_grouped_logged_ok", False):
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_LOGGER.info(
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f"torch_grouped: engaged grouped GEMM path (experts={E}, top_k={top_k})"
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is_mixtral = _is_mixtral_layout(first)
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is_qwen2 = _is_qwen_layout(first)
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nested_attr: Optional[str] = None
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if not (is_mixtral or is_qwen2):
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for candidate in ("mlp", "ffn"):
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nested = getattr(first, candidate, None)
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if nested is None:
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continue
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if _is_mixtral_layout(nested):
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is_mixtral = True
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nested_attr = candidate
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break
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if _is_qwen_layout(nested):
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is_qwen2 = True
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nested_attr = candidate
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break
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if not (is_mixtral or is_qwen2):
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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"torch_grouped: unsupported expert layout; falling back to naive"
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)
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experts_module._ax_grouped_logged_ok = True
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return y.view(bsz, seqlen, hdim), router_logits
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except Exception:
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experts_module._ax_grouped_logged_fail = True
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LAST_ERROR = "unsupported expert layout"
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return None, None
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def _resolve_expert(idx: int):
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expert = experts_module[idx]
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if nested_attr is None:
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return expert
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nested_mod = getattr(expert, nested_attr, None)
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if nested_mod is None:
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raise AttributeError(f"expert {idx} missing nested module '{nested_attr}'")
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return nested_mod
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try:
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if is_mixtral:
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if (
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not hasattr(experts_module, "_stacked_w1")
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or experts_module._stacked_w1.device != dev
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or experts_module._stacked_w1.dtype != dt
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):
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mods = [_resolve_expert(i) for i in range(E)]
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w1 = [mod.w1.weight.t() for mod in mods]
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w3 = [mod.w3.weight.t() for mod in mods]
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w2 = [mod.w2.weight.t() for mod in mods]
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experts_module._stacked_w1 = (
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torch.stack(w1, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w3 = (
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torch.stack(w3, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w2 = (
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torch.stack(w2, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w13 = torch.cat(
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[experts_module._stacked_w1, experts_module._stacked_w3], dim=-1
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).contiguous()
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W13 = experts_module._stacked_w13
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W2 = experts_module._stacked_w2
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else:
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if (
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not hasattr(experts_module, "_stacked_w13")
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or experts_module._stacked_w13.device != dev
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or experts_module._stacked_w13.dtype != dt
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):
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w13 = []
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w2 = []
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for i in range(E):
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mod = _resolve_expert(i)
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if hasattr(mod, "gate_up_proj"):
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w13.append(mod.gate_up_proj.weight.t())
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elif hasattr(mod, "up_proj") and hasattr(mod, "gate_proj"):
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w13.append(
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torch.cat(
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[mod.up_proj.weight.t(), mod.gate_proj.weight.t()],
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dim=-1,
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)
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)
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else:
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LAST_ERROR = "unrecognized Qwen MoE expert weight layout"
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if not getattr(
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experts_module, "_ax_grouped_logged_fail", False
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):
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_LOGGER.warning(
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"torch_grouped: could not resolve Qwen MoE expert weights; fallback to naive"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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w2.append(mod.down_proj.weight.t())
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experts_module._stacked_w13 = (
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torch.stack(w13, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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experts_module._stacked_w2 = (
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torch.stack(w2, dim=0)
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.to(device=dev, dtype=dt, non_blocking=True)
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.contiguous()
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)
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W13 = experts_module._stacked_w13
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W2 = experts_module._stacked_w2
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except AttributeError as err:
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LAST_ERROR = str(err)
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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"torch_grouped: expert weights missing expected attributes; falling back to naive"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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As: List[torch.Tensor] = []
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Bs: List[torch.Tensor] = []
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expert_slices: List[Tuple[int, torch.Tensor]] = []
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for i in range(E):
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sel = flat_idx == i
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if sel.any():
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Xi = x_rep[sel]
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As.append(Xi)
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Bs.append(W13[i])
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expert_slices.append((i, sel))
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if not As:
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out = torch.zeros_like(x)
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return out.view(bsz, seqlen, hdim), router_logits
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Y_list = _call_grouped_mm(As, Bs)
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if Y_list is None:
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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f"torch_grouped: grouped_mm up+gate failed; falling back to naive. Reason: {LAST_ERROR}"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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As2: List[torch.Tensor] = []
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Bs2: List[torch.Tensor] = []
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y_buf = torch.empty_like(x_rep)
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for (i, _sel), Yi in zip(expert_slices, Y_list, strict=False):
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I2 = Yi.shape[-1] // 2
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Yi_hidden = F.silu(Yi[:, :I2]) * Yi[:, I2:]
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As2.append(Yi_hidden)
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Bs2.append(W2[i])
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Y2_list = _call_grouped_mm(As2, Bs2)
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if Y2_list is None:
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if not getattr(experts_module, "_ax_grouped_logged_fail", False):
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_LOGGER.warning(
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f"torch_grouped: grouped_mm down failed; falling back to naive. Reason: {LAST_ERROR}"
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)
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experts_module._ax_grouped_logged_fail = True
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return None, None
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for (_i, sel), Out_i in zip(expert_slices, Y2_list, strict=False):
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y_buf[sel] = Out_i
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y = (y_buf.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
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if not getattr(experts_module, "_ax_grouped_logged_ok", False):
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_LOGGER.info(
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f"torch_grouped: engaged grouped GEMM path (experts={E}, top_k={top_k})"
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)
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experts_module._ax_grouped_logged_ok = True
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return y.view(bsz, seqlen, hdim), router_logits
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@@ -82,9 +82,18 @@ def apply_grouped_to_moe_blocks(cfg=None) -> None:
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# One-time log per block instance indicating whether grouped engaged or fallback occurred
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if not getattr(self, "_ax_grouped_wrapper_logged", False):
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if y is None:
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_LOG.warning(
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f"Grouped wrapper active but fell back to naive for {self.__class__.__name__}"
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)
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reason = getattr(_tg, "LAST_ERROR", None)
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if reason:
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_LOG.warning(
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"Grouped wrapper fell back to naive for %s (reason=%s)",
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self.__class__.__name__,
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reason,
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)
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else:
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_LOG.warning(
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"Grouped wrapper active but fell back to naive for %s",
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self.__class__.__name__,
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)
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else:
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_LOG.info(
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f"Grouped wrapper engaged for {self.__class__.__name__} (top_k={self.top_k})"
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