[Bugfix] use module-level import for patched function in Qwen3Next (#4354)

### What this PR does / why we need it?

**Problem**: The Qwen3Next model implementation currently imports
chunk_gated_delta_rule directly using `from ... import ...`

In frameworks like `verl`, the model file is often imported before
`vllm-ascend` initializes and applies its patches. This causes the model
to permanently hold a reference to the original (unpatched) vLLM kernel,
resulting in execution errors on Ascend devices even if the patch runs
later.

**Solution**: Changed the import style to `from vllm...ops import chunk`
and call `chunk.chunk_gated_delta_rule().`

This ensures that the function lookup happens at runtime (dynamic
dispatch), allowing the model to correctly pick up the patched function
regardless of import order.

- vLLM version: v0.11.0
- vLLM main:
vllm-project/vllm@2918c1b

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

Author: zjchenn

vllm_ascend/models/qwen3_next.py

@@ -16,8 +16,7 @@
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size)
 from vllm.forward_context import get_forward_context
-from vllm.model_executor.layers.fla.ops import RMSNormGated
-from vllm.model_executor.layers.fla.ops.chunk import chunk_gated_delta_rule
+from vllm.model_executor.layers.fla.ops import RMSNormGated, chunk
 from vllm.model_executor.layers.fla.ops.fused_recurrent import \
     fused_recurrent_gated_delta_rule
 from vllm.model_executor.layers.fused_moe import FusedMoE
@@ -35,8 +34,7 @@
     mamba_v2_sharded_weight_loader
 from vllm.model_executor.layers.mamba.mamba_utils import (
     MambaStateDtypeCalculator, MambaStateShapeCalculator)
-from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
-    causal_conv1d_fn, causal_conv1d_update)
+from vllm.model_executor.layers.mamba.ops import causal_conv1d
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
@@ -252,7 +250,7 @@ def _forward(
             mixed_qkv_spec = mixed_qkv_spec.view(
                 attn_metadata.num_spec_decodes, -1, mixed_qkv_spec.size(-1))
             mixed_qkv_spec = rearrange(mixed_qkv_spec, 'b l d -> b d l')
-            mixed_qkv_spec = causal_conv1d_update(
+            mixed_qkv_spec = causal_conv1d.causal_conv1d_update(
                 mixed_qkv_spec,
                 conv_state,
                 conv_weights,
@@ -269,7 +267,7 @@ def _forward(
         if attn_metadata.num_prefills > 0:
             # - "cache_indices" updates the conv_state cache in positions
             #   pointed to by "mamba_cache_params.state_indices_tensor"
-            mixed_qkv_non_spec = causal_conv1d_fn(
+            mixed_qkv_non_spec = causal_conv1d.causal_conv1d_fn(
                 mixed_qkv_non_spec.transpose(0, 1),
                 conv_weights,
                 self.conv1d.bias,
@@ -280,7 +278,7 @@ def _forward(
                 query_start_loc=non_spec_query_start_loc,
             ).transpose(0, 1)
         elif attn_metadata.num_decodes > 0:
-            mixed_qkv_non_spec = causal_conv1d_update(
+            mixed_qkv_non_spec = causal_conv1d.causal_conv1d_update(
                 mixed_qkv_non_spec,
                 conv_state,
                 conv_weights,
@@ -364,7 +362,7 @@ def _forward(
                 (
                     cur_core_attn_out_non_spec,
                     cur_last_recurrent_state,
-                ) = chunk_gated_delta_rule(
+                ) = chunk.chunk_gated_delta_rule(
                     query=cur_q,
                     key=cur_k,
                     value=cur_v,