[Fix]: Handle None scales in generate_zero_point for mixed-format layers

[lingyezhixing]

Motivation

Fix crash when loading compressed-tensors quantized Qwen3.5 models (e.g., from llm-compressor) in TurboMind backend.

Qwen3.5 mixes linear_attention (24 layers) and full_attention (8 layers). For linear_attention layers that lack self_attn weights, the reader returns None for scales. When compressed_tensors=True and has_zero_point=False (symmetric quantization), generate_zero_point(scales) is called unconditionally, crashing on None.

Models with standard AWQ format (quant_method="awq") are unaffected because they take a different code path that never calls generate_zero_point.

Modification

Guard generate_zero_point(scales) with a None check in lmdeploy/turbomind/deploy/parameter.py:

if self.compressed_tensors and not self.has_zero_point:
-    zeros = generate_zero_point(scales)
+    if scales is not None and all(s is not None for s in scales):
+        zeros = generate_zero_point(scales)
+    else:
+        zeros = scales

BC-breaking (Optional)

No.

Use cases (Optional)

Crash reproduction (before fix):

lmdeploy chat cyankiwi/Qwen3.5-4B-AWQ-4bit --backend turbomind

Works correctly (standard AWQ, unaffected):

lmdeploy chat QuantTrio/Qwen3.5-4B-AWQ --backend turbomind

[Copilot]

Pull request overview

Fixes a TurboMind export-time crash when converting/loading compressed-tensors symmetric-int4 weights for Qwen3.5 models that mix linear_attention and full_attention, where some layers may not have self_attn weights and thus produce None scale entries.

Changes:

• Add a None-aware guard around generate_zero_point(scales) for compressed-tensors symmetric quantization.
• Fall back to passing through scales as zeros when scales are missing (None) to avoid crashing.

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[Copilot]

The new branch that skips generate_zero_point when scales (or any element within it) is None isn’t covered by the existing compressed-tensors tests. Please add a unit test that exercises QuantWeightOnly with compressed-tensors keys where weight_scale is a tuple containing None entries (e.g., all None for a missing self_attn layer) and asserts the call does not crash and that zeros is passed through consistently.