[Bugfix][Quantization] Fix Gemma4 AutoRound serving gaps on top of GPTQMarlin row groups#39460
[Bugfix][Quantization] Fix Gemma4 AutoRound serving gaps on top of GPTQMarlin row groups#39460lesj0610 wants to merge 2 commits intovllm-project:mainfrom
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This pull request replaces integer division with ceiling division when calculating quantization scale and zero-point sizes in the GPTQ Marlin implementation, ensuring correct behavior for input sizes that are not multiples of the group size. A new test case is included to verify the fix. Feedback suggests parameterizing the test to cover both desc_act configurations, ensuring that both code paths modified in the PR are properly validated.
| import torch | ||
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| import vllm.model_executor.layers.quantization.gptq_marlin as gptq_marlin_mod | ||
| import vllm.model_executor.parameter as parameter_mod | ||
| from vllm.model_executor.layers.quantization.gptq_marlin import ( | ||
| GPTQMarlinConfig, | ||
| GPTQMarlinLinearMethod, | ||
| ) | ||
| from vllm.model_executor.model_loader.weight_utils import default_weight_loader | ||
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| class _DummyKernel: | ||
| def __init__(self, *args, **kwargs): | ||
| pass | ||
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| def process_weights_after_loading(self, layer): | ||
| return None | ||
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| def apply_weights(self, layer, x, bias): | ||
| raise NotImplementedError | ||
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| def test_gptq_marlin_create_weights_uses_ceil_groups_for_row_parallel( | ||
| monkeypatch, | ||
| ): | ||
| monkeypatch.setattr( | ||
| gptq_marlin_mod, "verify_marlin_supported", lambda **kwargs: None | ||
| ) | ||
| monkeypatch.setattr( | ||
| gptq_marlin_mod, "choose_mp_linear_kernel", lambda _: _DummyKernel | ||
| ) | ||
| monkeypatch.setattr(parameter_mod, "get_tensor_model_parallel_rank", lambda: 0) | ||
| monkeypatch.setattr(parameter_mod, "get_tensor_model_parallel_world_size", lambda: 1) | ||
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| config = GPTQMarlinConfig( | ||
| weight_bits=4, | ||
| group_size=128, | ||
| desc_act=False, | ||
| is_sym=True, | ||
| lm_head_quantized=False, | ||
| dynamic={}, | ||
| full_config={}, | ||
| ) | ||
| method = GPTQMarlinLinearMethod(config) | ||
| layer = torch.nn.Module() | ||
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| method.create_weights( | ||
| layer=layer, | ||
| input_size_per_partition=2112, | ||
| output_partition_sizes=[2816], | ||
| input_size=4224, | ||
| output_size=2816, | ||
| params_dtype=torch.float16, | ||
| weight_loader=default_weight_loader, | ||
| ) | ||
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| assert layer.scales.shape == (17, 2816) | ||
| assert layer.qzeros.shape == (17, 352) |
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This test is great for verifying the bug fix. To make it more robust and cover all the changes in create_weights, I'd suggest parameterizing it to test both desc_act=True and desc_act=False cases. This ensures that the ceiling division logic is correct for both code paths that were modified, preventing potential regressions.
import pytest
import torch
import vllm.model_executor.layers.quantization.gptq_marlin as gptq_marlin_mod
import vllm.model_executor.parameter as parameter_mod
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQMarlinConfig,
GPTQMarlinLinearMethod,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.utils.math_utils import cdiv
class _DummyKernel:
def __init__(self, *args, **kwargs):
pass
def process_weights_after_loading(self, layer):
return None
def apply_weights(self, layer, x, bias):
raise NotImplementedError
@pytest.mark.parametrize("desc_act", [False, True])
def test_gptq_marlin_create_weights_uses_ceil_groups_for_row_parallel(
monkeypatch,
desc_act,
):
monkeypatch.setattr(gptq_marlin_mod, "verify_marlin_supported",
lambda **kwargs: None)
monkeypatch.setattr(gptq_marlin_mod, "choose_mp_linear_kernel",
lambda _: _DummyKernel)
monkeypatch.setattr(parameter_mod, "get_tensor_model_parallel_rank", lambda: 0)
monkeypatch.setattr(parameter_mod, "get_tensor_model_parallel_world_size",
lambda: 1)
input_size_per_partition = 2112
output_partition_sizes = [2816]
input_size = 4224
output_size = 2816
group_size = 128
weight_bits = 4
config = GPTQMarlinConfig(
weight_bits=weight_bits,
group_size=group_size,
desc_act=desc_act,
is_sym=True,
lm_head_quantized=False,
dynamic={},
full_config={},
)
method = GPTQMarlinLinearMethod(config)
layer = torch.nn.Module()
method.create_weights(
layer=layer,
input_size_per_partition=input_size_per_partition,
output_partition_sizes=output_partition_sizes,
input_size=input_size,
output_size=output_size,
params_dtype=torch.float16,
weight_loader=default_weight_loader,
)
# In a row-parallel linear, if desc_act is True, scales are repeated on all
# ranks and sized based on the full input_size. Otherwise, they are sharded
# and sized based on input_size_per_partition.
if desc_act:
expected_groups = cdiv(input_size, group_size)
else:
expected_groups = cdiv(input_size_per_partition, group_size)
assert layer.scales.shape == (expected_groups, output_size)
assert layer.qzeros.shape == (expected_groups,
output_size // (32 // weight_bits))There was a problem hiding this comment.
fixed a bit later in the follow-up commit.
i parameterized that test to cover both desc_act=False and desc_act=True, and also switched the desc_act=True case to a non-divisible shape so it actually exercises the ceil path.
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Hi @mgoin @kylesayrs could you please take a look when you have time? Thanks! |
Signed-off-by: lesj0610 <lesj0610@gmail.com>
Signed-off-by: lesj0610 <lesj0610@gmail.com>
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plz review it. |
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closing this in favor of #40281. the newer pr keeps the gemma4 core scope together on latest main and drops the extra side scope. |
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Intel/gemma-4-26B-A4B-it-int4-AutoRoundwouldn't load after the row-parallel group fix. I traced it to three separate issues — all in the same area, so I kept them in one PR instead of splitting.This is the same branch as the original ceil-division fix, not a new competing PR.
scales/qzerosslicing was wrong when a TP shard starts in the middle of a quant group — global group offset was missing.GateLinearassumed routing weights are always unquantized bf16/fp32, which is not true for AutoRound, took me a while to find this.MoeWNA16had hard assert on SiLU but Gemma4 MoE uses GELU — the fused path actually handles it fine already, just needed to remove the assert.Test Plan
Manual load and short text generation with
Intel/gemma-4-26B-A4B-it-int4-AutoRound. Also tested multimodal input since the model supports it.Test Result
All
test_gptq_marlin.pytests pass. Model loads and generates correctly.Used Codex and Claude Code for implementation assistance. I reviewed all changed lines and ran the tests myself.
Essential Elements of an Effective PR Description Checklist
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