diff --git a/vllm/model_executor/warmup/qwen_triton_warmup.py b/vllm/model_executor/warmup/qwen_triton_warmup.py
index 62e94f93c09..b6ed0aa4d4f 100644
--- a/vllm/model_executor/warmup/qwen_triton_warmup.py
+++ b/vllm/model_executor/warmup/qwen_triton_warmup.py
@@ -8,19 +8,6 @@ from typing import TYPE_CHECKING
 import torch
 
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fla.ops.fused_gdn_prefill_post_conv import (
-    fused_post_conv_prep,
-)
-from vllm.model_executor.layers.fla.ops.fused_sigmoid_gating import (
-    fused_sigmoid_gating_delta_rule_update,
-)
-from vllm.model_executor.layers.mamba.mamba_utils import is_conv_state_dim_first
-from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
-    causal_conv1d_fn,
-)
-from vllm.v1.attention.backends.utils import NULL_BLOCK_ID, PAD_SLOT_ID
-from vllm.v1.worker.block_table import BlockTable
-from vllm.v1.worker.utils import _zero_kv_blocks_kernel
 
 if TYPE_CHECKING:
     from vllm.v1.worker.gpu_model_runner import GPUModelRunner
@@ -128,6 +115,10 @@ def _qwen_gdn_warmup_config(
             continue
 
         conv_cache, ssm_state = cache_tensors
+        from vllm.model_executor.layers.mamba.mamba_utils import (
+            is_conv_state_dim_first,
+        )
+
         conv_state = (
             conv_cache if is_conv_state_dim_first() else conv_cache.transpose(-1, -2)
         )
@@ -191,6 +182,8 @@ def _warm_zero_kv_blocks_with_runner_zeroer(runner: object) -> bool:
 def _warm_zero_kv_blocks_kernel(
     device: torch.device, config: _ZeroKvWarmupConfig
 ) -> None:
+    from vllm.v1.worker.utils import _zero_kv_blocks_kernel
+
     max_n_blocks = max(_ZERO_KV_N_BLOCKS)
     scratch = torch.empty(
         max_n_blocks * config.page_size_el,
@@ -219,6 +212,8 @@ def _warm_zero_kv_blocks_kernel(
 
 
 def _warm_compute_slot_mapping_kernel(device: torch.device) -> None:
+    from vllm.v1.worker.block_table import BlockTable
+
     # num_tokens/max_num_tokens are do_not_specialize; keep the launch tiny.
     num_tokens = 1
     query_start_loc = torch.tensor([0, num_tokens], dtype=torch.int32, device=device)
@@ -244,6 +239,11 @@ def _warm_compute_slot_mapping_kernel(device: torch.device) -> None:
 def _warm_causal_conv1d_fwd_kernel(
     device: torch.device, config: _QwenGDNWarmupConfig
 ) -> None:
+    from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
+        causal_conv1d_fn,
+    )
+    from vllm.v1.attention.backends.utils import NULL_BLOCK_ID, PAD_SLOT_ID
+
     x_storage = torch.empty(
         (1, config.conv_dim), dtype=config.conv_dtype, device=device
     )
@@ -276,6 +276,10 @@ def _warm_causal_conv1d_fwd_kernel(
 def _warm_fused_post_conv_kernel(
     device: torch.device, config: _QwenGDNWarmupConfig
 ) -> None:
+    from vllm.model_executor.layers.fla.ops.fused_gdn_prefill_post_conv import (
+        fused_post_conv_prep,
+    )
+
     qkv_dim = 2 * config.h * config.k + config.hv * config.v
     for length in _FLA_POST_CONV_WARMUP_LENGTHS:
         conv_output = torch.empty(
@@ -302,6 +306,10 @@ def _warm_fused_sigmoid_gating_delta_rule_update_kernel(
     device: torch.device,
     config: _QwenGDNWarmupConfig,
 ) -> None:
+    from vllm.model_executor.layers.fla.ops.fused_sigmoid_gating import (
+        fused_sigmoid_gating_delta_rule_update,
+    )
+
     q = torch.empty((1, 1, config.h, config.k), dtype=config.conv_dtype, device=device)
     k = torch.empty_like(q)
     v = torch.empty((1, 1, config.hv, config.v), dtype=config.conv_dtype, device=device)