[model] refactor qwen3.5 GDN to use FLA causal conv1d and cu_seq_lens_q - #176
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- Add FLA causal_conv1d as the conv function for packed sequences - Pass cu_seq_lens_q directly to forward, removing manual padding/unpadding - Derive layer_types from linear_attention_freq in __init__ - Use layer_number-1 to match HF indexing
hjh0119
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Aug 31, 2026
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hjh0119
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Aug 31, 2026
hjh0119
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Sep 7, 2026
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thanks, can you resolve the conflicts? |
Conflict in src/mcore_bridge/model/mm_gpts/qwen3_5.py resolved by keeping the PR's varlen forward path (FLA causal_conv1d + cu_seq_lens_q). The upstream change to this file only replaced packed_seq_params.num_samples with get_num_samples(packed_seq_params) in the manual padding block, which the PR removes entirely in favor of the packed varlen path.
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@hjh0119 Conflicts resolved, ready for another look. Thanks! |
hjh0119
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Sep 7, 2026
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Fix
src/mcore_bridge/model/mm_gpts/qwen3_5.py__init__hybrid-arch adaptation: auto-deriveconfig.layer_typesfromlinear_attention_freq(int → per-layer list); passlayer_number - 1to the HF parent (0-basedlayer_idx).self.causal_conv1d_fn = self._fla_conv_fn, which routes FLAcausal_conv1d(..., cu_seqlens=self._cur_cu_seqlens)— gives the conv sequence-boundary isolation in varlen (solves cross-sample state leakage thatattention_maskcannot fully prevent on GDN's recurrent state).[1, total, D]and passescu_seq_lens_qtosuper().forward(...)instead of un-padding to a padded batch + repacking after. Verified the parentQwen3_5MoeGatedDeltaNet.forward(transformers ≥5.14.1,modeling_qwen3_5_moe.py:546) consumeskwargs["cu_seq_lens_q"]→chunk_gated_delta_rule(cu_seqlens=...), and FLAcausal_conv1dsupportscu_seqlens(signature verified), so both conv and delta-rule run varlen.