Fix gemma4 core: quantized inference + gguf loading + fused moe gelu_tanh by lesj0610 · Pull Request #40281 · vllm-project/vllm

lesj0610 · 2026-04-19T10:54:47Z

summary

this pr adds gemma4 core support on top of latest main.

scope in this pr:

fused moe gelu_tanh activation support
gemma4 AutoRound support
gemma4 AWQ support
gemma4 GGUF support
gemma4 tokenizer / processor / multimodal glue needed for real serving

wanted to keep this pr focused on gemma4 core support.

why this pr is needed

gemma4 was not in usable state on latest main for important real cases:

AutoRound path had quantized runtime correctness gaps
AWQ path had packed expert loading issues
GGUF path needed local config/tokenizer/processor fallback glue
fused moe path did not support gelu_pytorch_tanh semantics needed by gemma4 moe

this pr only fixes what is needed to make gemma4 usable and stable.

relation to existing prs

#39460 is my earlier pr. this pr replaces that work and #39460 will be closed after this is opened.
there is some related work in #39406 and #39582, but this pr keeps a narrower gemma4 core scope.
#35302 is also related for moe_wna16 activation support direction. this pr carries the gemma4-needed path on current latest main.

main changes

1. fused moe activation support

added shared GELU_TANH / GELU_TANH_NO_MUL support in fused moe stack.

key points:

from_str("gelu_pytorch_tanh") alias support
backend allowlist support
safe fallback when gelu_tanh_and_mul custom op is not available

2. gemma4 quantized inference fixes

main changes:

row-parallel GPTQ-family group-tail fallback for AutoRound path
remove hardcoded SiLU-only assumption in moe_wna16
quantized router weight dequant/load for gemma4
fail-closed handling for unsupported router bits
warning when router quant tensors are incomplete

3. gemma4 gguf support glue

what was added:

local GGUF sibling config.json fallback
gemma4 manual gguf tensor mapping
tokenizer special id patch from GGUF metadata
processor/tokenizer patch consistency
multimodal device/dtype glue

validation

verified on local setup:

CUDA 12.8
transformers 5.5.1
TP=2

runtime validation:

gemma4 AutoRound + TRITON: working
gemma4 AWQ + TRITON: working
gemma4 GGUF: working

pre-commit checks passed on changed files.

example command used:

pre-commit run --files vllm/transformers_utils/processor.py vllm/model_executor/models/gemma4.py vllm/model_executor/layers/quantization/inc.py vllm/model_executor/model_loader/gguf_loader.py

known limitations

gemma4 GGUF needs transformers >= 5.x. confirmed on 5.5.1.
local GGUF config.json fallback is not gemma4-only. it affects all local GGUF loading.
inc tail-shard fallback keeps dequantized weights in memory, so some irregular TP shapes may use more VRAM.
fused moe GELU_TANH allowlisting assumes underlying C++ backend handles this path correctly.

github-actions · 2026-04-19T10:54:57Z

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mergify · 2026-04-19T10:55:32Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @lesj0610.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

gemini-code-assist

Code Review

This pull request introduces support for the GELU_TANH activation function across various MoE kernels and adds comprehensive support for Gemma4 GGUF models, including specialized tensor mapping and tokenizer patching. It also implements a fallback dequantization path (INCGPTQRowParallelTailLinearMethod) for row-parallel GPTQ layers with group-tail shards. Review feedback identified critical bugs in the fallback implementation, specifically an AttributeError when accessing a non-existent tp_rank attribute and an issue with initializing non-persistent buffers using register_buffer(..., None). Additionally, improvements were suggested for importing necessary distributed utilities and vectorizing tensor creation for better performance.

gemini-code-assist · 2026-04-19T10:59:00Z

+        shard_width = getattr(
+            layer, "input_size_per_partition", input_size_per_partition
+        )
+        shard_offset = qweight.tp_rank * shard_width


PackedvLLMParameter does not have a tp_rank attribute. Accessing it will raise an AttributeError during model initialization. Use get_tensor_model_parallel_rank() to retrieve the current tensor parallel rank.

Suggested change

shard_offset = qweight.tp_rank * shard_width

shard_offset = get_tensor_model_parallel_rank() * shard_width

gemini-code-assist · 2026-04-19T10:59:01Z

+            dtype=torch.int32,
+        )
+        layer.register_parameter("g_idx", Parameter(g_idx, requires_grad=False))
+        layer.register_buffer("_inc_tail_dequant_weight", None, persistent=False)


Calling register_buffer with None does not actually create the attribute on the module in a way that allows subsequent access like layer._inc_tail_dequant_weight (it effectively does nothing). This will lead to an AttributeError when the cache is first checked in _get_dequantized_weight. Since this is a non-persistent cache, it should be initialized as a regular attribute.

Suggested change

layer.register_buffer("_inc_tail_dequant_weight", None, persistent=False)

layer._inc_tail_dequant_weight = None

gemini-code-assist · 2026-04-19T10:59:01Z

+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    check_marlin_supports_layer,
+)


The get_tensor_model_parallel_rank function is required to correctly calculate the shard_offset in INCGPTQRowParallelTailLinearMethod.create_weights. It should be imported from vllm.distributed.

Suggested change

from vllm.model_executor.layers.quantization.utils.marlin_utils import (

check_marlin_supports_layer,

)

from vllm.distributed import (

get_tensor_model_parallel_rank,

)

from vllm.model_executor.layers.quantization.utils.marlin_utils import (

check_marlin_supports_layer,

)

gemini-code-assist · 2026-04-19T10:59:01Z

+        g_idx = torch.tensor(
+            [
+                (shard_offset + i) // self.group_size
+                for i in range(input_size_per_partition)
+            ],
+            dtype=torch.int32,
+        )


Creating the g_idx tensor using a list comprehension is inefficient, especially for large models. A vectorized approach using torch.arange is significantly faster and more idiomatic.

g_idx = (torch.arange(input_size_per_partition, dtype=torch.int32) + shard_offset) // self.group_size

Co-authored-by: OpenAI Codex Signed-off-by: lesj0610 <lesj0610@example.com> Signed-off-by: lesj0610 <lesj0610@gmail.com>

Signed-off-by: lesj0610 <lesj0610@gmail.com>

Co-authored-by: OpenAI Codex Signed-off-by: lesj0610 <lesj0610@gmail.com>

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chatgpt-codex-connector · 2026-04-19T11:24:28Z

+                quant_params = router_quant_params[router_name]
+                if len(quant_params) == 3:
+                    weight_name = f"{router_name}.weight"
+                    param = params_dict[weight_name]


Skip off-stage router tensors before dequantizing router weight

In Gemma4Model.load_weights, the new router quant branch dequantizes as soon as it has qweight/qzeros/scales and then does params_dict[weight_name] directly. During pipeline-parallel loading (PP>1), checkpoint weights for layers not owned by this stage still pass through this function, so weight_name is absent and this path raises KeyError before the existing is_pp_missing_parameter safeguards can run. This can break loading quantized Gemma4 checkpoints in multi-stage deployments.

Useful? React with 👍 / 👎.

chatgpt-codex-connector · 2026-04-19T11:24:28Z

+        token = tokenizer.convert_ids_to_tokens(token_id)
+        if token is None:
+            continue
+        setattr(tokenizer, token_attr, token)


Avoid mutating shared cached tokenizer for GGUF id patching

cached_get_tokenizer returns an lru_cache-shared tokenizer instance, but _maybe_patch_gemma4_gguf_tokenizer mutates it in place via setattr(..., pad/bos/eos/unk). Because the cache key does not include GGUF file path or embedded special IDs, a later model load that reuses the same cached tokenizer can inherit special-token settings from a different Gemma4 GGUF file, causing cross-model tokenizer state leakage in long-lived processes/tests.

Useful? React with 👍 / 👎.

Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: lesj0610 <lesj0610@gmail.com>

lesj0610 · 2026-04-21T04:02:07Z

@mgoin @22quinn please check this when you have time.

lesj0610 · 2026-05-04T01:08:36Z

Closing this monolithic PR in favor of independent, non-stacked replacement PRs.

Replacement / related scopes:

[Bugfix][Gemma4] Fix AutoRound GPTQ router and INC tail shards #41588 — Gemma4 AutoRound GPTQ router dequantization + INC row-parallel tail-shard fallback.
[Model][GGUF] Add Gemma4 GGUF serving glue #41589 — Gemma4 GGUF/tokenizer/processor serving glue.
[Model] Fix Gemma4 MoE activation mismatch #41574 — Gemma4 MoE gelu_tanh activation wiring is already covered separately.
[Bugfix][Gemma4] Fix quantized MoE weight loading and KV cache spec merge #39582 — quantized Gemma4 MoE packed/scale expert loading and KV-cache spec merge is already covered separately.

This avoids asking maintainers to review a broad mixed-scope PR and keeps each remaining change reviewable from main.

lesj0610 mentioned this pull request Apr 19, 2026

[Bugfix][Quantization] Fix Gemma4 AutoRound serving gaps on top of GPTQMarlin row groups #39460

Closed

5 tasks

mergify Bot added nvidia cpu Related to CPU backends labels Apr 19, 2026

github-project-automation Bot added this to NVIDIA Apr 19, 2026

mergify Bot added the needs-rebase label Apr 19, 2026

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lesj0610 added 5 commits April 19, 2026 20:02

Fix Gemma4 quantized runtime paths on latest main

64c4011

Co-authored-by: OpenAI Codex Signed-off-by: lesj0610 <lesj0610@example.com> Signed-off-by: lesj0610 <lesj0610@gmail.com>

Split out non-TurboQuant Gemma4 changes

c0fda2d

Signed-off-by: lesj0610 <lesj0610@gmail.com>

Tighten Gemma4 core review follow-ups

32842a4

Signed-off-by: lesj0610 <lesj0610@gmail.com>

Clarify Gemma4 core assumptions in comments

8fecb86

Signed-off-by: lesj0610 <lesj0610@gmail.com>

Assign patched Gemma4 GGUF tokenizer back to processor

44ea194

Signed-off-by: lesj0610 <lesj0610@gmail.com>

lesj0610 force-pushed the lesj/gemma4-core-pr branch from 33a36b0 to 44ea194 Compare April 19, 2026 11:04

mergify Bot removed the needs-rebase label Apr 19, 2026

Fix INC tail-shard review issues

98e0eff

Co-authored-by: OpenAI Codex Signed-off-by: lesj0610 <lesj0610@gmail.com>

lesj0610 marked this pull request as ready for review April 19, 2026 11:15

lesj0610 requested review from 22quinn, DarkLight1337, mgoin, njhill, pavanimajety, robertgshaw2-redhat, tlrmchlsmth and yewentao256 as code owners April 19, 2026 11:15

claude Bot reviewed Apr 19, 2026

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chatgpt-codex-connector Bot reviewed Apr 19, 2026

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Address Codex review issues

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Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: lesj0610 <lesj0610@gmail.com>

lesj0610 changed the title ~~[Model] gemma4 core: quantized inference + gguf loading + fused moe gelu_tanh~~ fix gemma4 core: quantized inference + gguf loading + fused moe gelu_tanh Apr 20, 2026

lesj0610 changed the title ~~fix gemma4 core: quantized inference + gguf loading + fused moe gelu_tanh~~ Fix gemma4 core: quantized inference + gguf loading + fused moe gelu_tanh Apr 20, 2026

Merge branch 'main' into lesj/gemma4-core-pr

82f21cf

lesj0610 added 3 commits April 20, 2026 17:26

Merge branch 'main' into lesj/gemma4-core-pr

74c135c

Merge branch 'main' into lesj/gemma4-core-pr

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Merge branch 'main' into lesj/gemma4-core-pr

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Merge branch 'main' into lesj/gemma4-core-pr

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lucianommartins mentioned this pull request Apr 23, 2026

[Bug]: Regression in 0.19.1 - Gemma 4 26B MoE fails to load packed experts (KeyError: down_proj_packed). Worked in dev6. #40591

Open

Merge branch 'main' into lesj/gemma4-core-pr

65ff541

hnt2601 mentioned this pull request Apr 25, 2026

[Bugfix][Gemma 4] Clamp soft-token estimate to max_soft_tokens #40796

Merged

This was referenced May 4, 2026

[Bugfix][Gemma4] Fix AutoRound GPTQ router and INC tail shards #41588

Closed

[Model][GGUF] Add Gemma4 GGUF serving glue #41589

Closed

lesj0610 closed this May 4, 2026

github-project-automation Bot moved this to Done in NVIDIA May 4, 2026

teemow mentioned this pull request May 5, 2026

Gemma 4 26B-A4B AWQ4 fails to load on all eugr-tf5 builds (upstream loader bug) giantswarm/vllm#16

Open

4 tasks

	shard_offset = qweight.tp_rank * shard_width
	shard_offset = get_tensor_model_parallel_rank() * shard_width

	layer.register_buffer("_inc_tail_dequant_weight", None, persistent=False)
	layer._inc_tail_dequant_weight = None

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summary

why this pr is needed

relation to existing prs

main changes

1. fused moe activation support

2. gemma4 quantized inference fixes

3. gemma4 gguf support glue

validation

known limitations

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lesj0610 commented Apr 19, 2026 •

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