v1 hand-wrote set_aux_hidden_state_layers/get_eagle3_default_aux_hidden_state_layers and the boot reproduced the identical 'Model does not support EAGLE3 interface'. Reason: supports_eagle3() is an isinstance check against a runtime-checkable Protocol whose members include a required ClassVar flag (interfaces.py:1663 supports_eagle3: ClassVar[Literal[True]] = True), so methods alone do not satisfy it. Inheriting SupportsEagle3 -- the idiom every model in this image uses, including the fork's own DFlashLagunaForCausalLM -- supplies the flag, SupportsEagleBase's has_own_lm_head/has_own_embed_tokens, and working default implementations of both methods; the hand-written methods are deleted. base 008eeaa2... -> patched 5eea89c5..., apply-verified against the pinned image. Runbook: corrected method name, recorded the Protocol trap as the generalizable lesson for future spec-decode ports.
120 lines
5.6 KiB
Diff
120 lines
5.6 KiB
Diff
# glm53-dflash-01-aux-hidden-states.patch — give the GLM trunk the EAGLE3 / DFlash2
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# auxiliary-hidden-state interface.
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#
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# STATUS: WIP, and INDEPENDENT of the compile changelist (01-05). It patches the same
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# file as glm53-compile-01-model-decorator.patch, so if both are ever applied, apply 01
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# first. Not part of the deployed shape.
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#
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# WHY. `--speculative-config '{"method":"dflash","model":"/path/to/drafter", ...}'` with
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# incoai/GLM-5.3-Flash-DFlash2 is accepted -- the config parses, the drafter loads -- and
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# then the worker dies at init with:
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#
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# RuntimeError: Model does not support EAGLE3 interface
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#
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# raised by v1/worker/gpu/spec_decode/eagle/eagle3_utils.py:20 through
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# `supports_eagle3(model)` == `isinstance(model, SupportsEagle3)` (a runtime-checkable
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# Protocol). Nothing about the drafter, the method name or the payload is wrong:
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# `glm5next` implements neither half of the contract.
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#
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# THE CONTRACT (read out of this image, not assumed):
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# * interfaces.py:1663 -- `supports_eagle3: ClassVar[Literal[True]] = True`. THE FLAG
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# IS A REQUIRED MEMBER FOR `isinstance`: satisfying the Protocol with methods alone
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# still raises the same RuntimeError -- v1 of this patch did exactly that and the boot
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# failed identically. INHERIT the Protocol instead. That is the idiom in this image
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# (LagunaForCausalLM, MiMoV2FlashForCausalLM and DFlashLagunaForCausalLM all list
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# SupportsEagle3 among their bases) and it brings the flag, SupportsEagleBase's
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# has_own_lm_head/has_own_embed_tokens, AND working non-stub defaults for both methods.
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# * interfaces.py:1673-1719 -- the inherited defaults unwrap
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# `holder = getattr(parent_ref, "model", parent_ref)`, assert
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# `isinstance(holder, EagleModelMixin)`, set `holder._set_aux_hidden_state_layers`, and
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# default to (2, L // 2, L - 3). So the INNER trunk must inherit EagleModelMixin,
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# which supplies `aux_hidden_state_layers` and the slot helpers.
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# * eagle3_utils.py:26-31 -- layers come from the spec config when it carries them
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# (this drafter ships target_layer_ids [5, 14, 24, 33, 42]), else from the default.
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# * deepseek_v2.py:1551-1585 and v1/worker/gpu/cudagraph_utils.py:621 -- the trunk
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# appends one tensor per aux layer while looping and returns the TUPLE
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# (hidden_states, aux_hidden_states) only when that list is non-empty.
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#
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# WHAT IT ADDS for GLM: the mixin on Glm5NextModel; aux capture in the layer loop, where
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# the aux state is the single-stream `hidden_states` ENTERING that layer (GLM's loop
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# carries the already-summed single-stream state -- its non-mHC branch re-splits
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# `residual = hidden_states` inside the layer -- so this is the equivalent of
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# deepseek_v2's `hidden_states + residual`); the conditional tuple return; and
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# SupportsEagle3 among Glm5NextForCausalLM's bases.
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#
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# HONEST SCOPE: this makes the interface PRESENT, which is exactly what the engine
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# checks -- it does not make drafts GOOD, and per the measured profile it is not a
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# decode-speed fix (a verification pass pays pointwise cost proportional to its token
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# count, which is why MTP4 measured flat). It is the prerequisite for driving DFlash2
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# on this endpoint at all.
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#
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# TARGET: vllm/models/glm5next/common/model.py (inside the pinned image)
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# base sha256 008eeaa2a07e091465319f86900b5a321d921238723ccbe22e6b9e971e1d2b28
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# patched sha256 5eea89c51d4ab37ef823526286b5103e39996802ca08aa1934f6373fc4728062
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#
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# APPLY: `patch -p1` from the vllm package root, or bind-mount the patched file over
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# site-packages (which is what a scratch-launcher DFLASH_PATCH=1 does).
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--- a/vllm/models/glm5next/common/model.py
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+++ b/vllm/models/glm5next/common/model.py
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@@ -95,6 +95,12 @@
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logger = init_logger(__name__)
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+from vllm.model_executor.models.interfaces import (
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+ EagleModelMixin,
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+ SupportsEagle3,
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+)
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+
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+
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class Glm5NextMLP(nn.Module):
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def __init__(
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self,
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@@ -623,7 +629,7 @@
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)
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-class Glm5NextModel(nn.Module):
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+class Glm5NextModel(nn.Module, EagleModelMixin):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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@@ -730,7 +736,14 @@
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if self.is_sequence_parallel:
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hidden_states = sp_shard(hidden_states)
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- for layer in self._active_layers:
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+ aux_hidden_states: list[torch.Tensor] = []
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+ for local_idx, layer in enumerate(self._active_layers):
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+ if (getattr(self, "start_layer", 0) + local_idx) in self.aux_hidden_state_layers:
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+ # Auxiliary hidden state for EAGLE-3 / DFlash2 drafts: the
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+ # single-stream state ENTERING this layer, before its input norm --
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+ # the same semantic deepseek_v2 implements by appending
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+ # hidden_states + residual at this point in its loop.
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+ aux_hidden_states.append(hidden_states)
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hidden_states, residual, post, comb = layer(
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positions, hidden_states, residual, post, comb
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)
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@@ -749,6 +762,8 @@
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hidden_states = sp_all_gather(hidden_states)[:full_num_tokens]
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hidden_states = self.norm(hidden_states)
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+ if len(aux_hidden_states) > 0:
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+ return hidden_states, aux_hidden_states
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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@@ -935,7 +950,12 @@
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class Glm5NextForCausalLM(
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- nn.Module, HasInnerState, SupportsPP, MixtureOfExperts, IsHybrid
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+ nn.Module,
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+ HasInnerState,
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+ SupportsPP,
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+ MixtureOfExperts,
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+ IsHybrid,
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+ SupportsEagle3,
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):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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