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[bugfix] Fixed the bug in retrieving the quantization method for mlp.experts (e.g., DeepSeek_v3.2_exp w8a8) #4035

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[bugfix] Fixed the bug in retrieving the quantization method for mlp.experts (e.g., DeepSeek_v3.2_exp w8a8) #4035

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11 changes: 11 additions & 0 deletions vllm_ascend/quantization/quant_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,6 +157,17 @@ def is_layer_skipped_ascend(
f"Detected some but not all shards of {prefix} "
"are quantized. All shards of fused layers "
"to have the same precision.")
elif "experts" in prefix:
# For the experts' prefix (e.g., "model.layers.3.mlp.experts")
# Assume all experts within the same MLP use the same quantization method
experts_quant_description = [
self.quant_description[layer]
for layer in self.quant_description if prefix in layer
]
is_skipped = any(
quantization == "FLOAT"
for quantization in experts_quant_description
)
Comment on lines +163 to +170
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@gemini-code-assist gemini-code-assist bot Nov 6, 2025

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high

This logic can be improved for correctness and robustness:

  1. Correctness: Using prefix in layer is less precise than layer.startswith(prefix + '.') for hierarchical module names. It could lead to incorrect matches if another module name contains the expert prefix as a substring.
  2. Robustness: If no expert layers are found for the given prefix, experts_quant_description will be an empty list. any() on an empty list returns False, causing is_skipped to be False. This will likely lead to a RuntimeError later in get_linear_quant_type. It's safer to treat this case as skipped (unquantized).

Here is a suggested improvement that addresses these points.

Suggested change
experts_quant_description = [
self.quant_description[layer]
for layer in self.quant_description if prefix in layer
]
is_skipped = any(
quantization == "FLOAT"
for quantization in experts_quant_description
)
experts_quant_description = [
self.quant_description[layer]
for layer in self.quant_description if layer.startswith(prefix + '.')
]
if not experts_quant_description:
is_skipped = True
else:
is_skipped = any(
quantization == "FLOAT"
for quantization in experts_quant_description)

else:
is_skipped = self.quant_description[prefix + '.weight'] == "FLOAT"

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10 changes: 10 additions & 0 deletions vllm_ascend/quantization/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,16 @@ def get_linear_quant_type(quant_description: Dict[str, Any], prefix: str,
f"Not all shards of {prefix} are quantized with same quant type."
f"Shard {proj_name} uses {shard_quant_type}, but another shard"
f"use {quant_type}. Please check quantization config.")
elif "experts" in prefix:
# For the experts' prefix (e.g., "model.layers.3.mlp.experts")
# Assume all experts within the same MLP use the same quantization method
experts_quant_description = set(
quant_description[layer]
for layer in quant_description if prefix in layer
)
Comment on lines +58 to +61
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@gemini-code-assist gemini-code-assist bot Nov 6, 2025

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high

Using prefix in layer is less precise than layer.startswith(prefix + '.') for hierarchical module names. It could lead to incorrect matches if another module name contains the expert prefix as a substring. Using startswith is safer and more correct. Additionally, using a set comprehension {...} is more idiomatic than set(...).

Suggested change
experts_quant_description = set(
quant_description[layer]
for layer in quant_description if prefix in layer
)
experts_quant_description = {
quant_description[layer]
for layer in quant_description if layer.startswith(prefix + '.')
}

if not len(experts_quant_description) == 1:
raise RuntimeError(f"{prefix} has different quantization type: {experts_quant_description}.")
quant_type = experts_quant_description.pop()
else:
quant_type = quant_description[prefix + '.weight']
return quant_type
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