Files
serverAppConfigs/scripts/patches/glm53-dflash-01-aux-hidden-states.patch
T
Frank 9c5c2494da patches: glm53-dflash-01 v2 -- satisfy SupportsEagle3 by inheriting the Protocol
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.
2026-09-30 16:53:24 -07:00

120 lines
5.6 KiB
Diff

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