[Perf] Refactor tensor disposal logic to reduce memory usage (#966)

### What this PR does / why we need it?
1. In previous PRs #580
#784, I saved GPU memory
by promptly deleting unnecessary tensors. For tensors passed from
upper-layer functions, I used a list container to transfer the parameter
and then popped the tensor from the list within the inner function to
achieve deletion. Recently, I discovered a better implementation in
sglang—the `dispose_tensor` function and I recommend adopting this
approach.
2. Dispose `hidden_states` and `residual` from the previous layer once
they're no longer used.
3. Avoid to generate `self.inputs_embeds` in `ModelRunnerV1` in
non-multimodal scenarios.

With the aforementioned optimizations, using the DeepSeek-R1-W8A8 model
under the conditions of `TP=16` and `max-model-len=32768`, we can save
1.3GB of npu memory.

**Reference**: sgl-project/sglang#6147

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?

---------

Signed-off-by: ApsarasX <apsarax@outlook.com>

Author: ApsarasX

vllm_ascend/models/deepseek_v2.py

@@ -69,6 +69,7 @@
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
 from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
+from vllm_ascend.utils import dispose_tensor
 
 VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
 
@@ -558,8 +559,14 @@ def forward(
             residual = hidden_states
             hidden_states = self.input_layernorm(hidden_states)
         else:
+            previous_hidden_states, previous_residual = hidden_states, residual
             hidden_states, residual = self.input_layernorm(
                 hidden_states, residual)
+            # Dispose hidden_states and residual from the previous layer
+            # to save npu memory because they're no longer used.
+            dispose_tensor(previous_hidden_states)
+            dispose_tensor(previous_residual)
+
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,

vllm_ascend/quantization/w8a8_dynamic.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 #
 
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, Optional
 
 import torch
 import torch.distributed as dist
@@ -26,11 +26,12 @@
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.distributed.parallel_state import get_ep_group
 from vllm_ascend.ops.fused_moe import select_experts
+from vllm_ascend.utils import dispose_tensor
 
 VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
 
 
-def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
+def apply_mlp(hidden_states: torch.Tensor,
               w1: torch.Tensor,
               w1_scale: torch.Tensor,
               w2: torch.Tensor,
@@ -43,7 +44,7 @@ def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
     apply MLP: gate_up_proj -> swiglu -> down_proj
 
     Args:
-        hidden_states_wrapper: wrapper of input hidden states with shape (num_tokens, hidden_size).
+        hidden_states: input hidden states with shape (num_tokens, hidden_size).
         w1: expert weights1 with shape
             (num_experts, hidden_size, intermediate_size * 2)
         w1_scale: weights1 scale with shape (num_experts, intermediate_size * 2)
@@ -62,11 +63,13 @@ def apply_mlp(hidden_states_wrapper: List[torch.Tensor],
         hidden_states: output hidden states after MLP.
     """
 
-    assert len(hidden_states_wrapper) == 1
-    hidden_states = hidden_states_wrapper.pop()
     if dynamic_scale is None:
+        unquantized_hidden_states = hidden_states
         hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
             hidden_states)
+        # Dispose the original unquantized hidden states
+        # to save npu memory because they're no longer used.
+        dispose_tensor(unquantized_hidden_states)
     else:
         pertoken_scale = dynamic_scale
 
@@ -188,11 +191,8 @@ def fused_experts_with_mc2(hidden_states: torch.Tensor,
     if quant_mode == 0:
         dynamic_scale = None
 
-    # place hidden_states in a list to transfer its ownership into the `apply_mlp` function
-    hidden_states_wrapper = [expand_x]
-    del expand_x
-
-    down_out_list = apply_mlp(hidden_states_wrapper,
+    # `expand_x` will be disposed in the `apply_mlp` function
+    down_out_list = apply_mlp(expand_x,
                               w1,
                               w1_scale,
                               w2,
@@ -321,10 +321,8 @@ def fused_experts_with_all2all(
         expert_tokens = expert_tokens.to(torch.int64)
         group_list_type = 0
 
-    hidden_states_wrapper = [hidden_states]
-    del hidden_states
-
-    hidden_states = apply_mlp(hidden_states_wrapper,
+    # `hidden_states` will be disposed in the `apply_mlp` function
+    hidden_states = apply_mlp(hidden_states,
                               w1,
                               w1_scale,
                               w2,
@@ -439,11 +437,8 @@ def fused_experts(hidden_states: torch.Tensor,
         expert_tokens = expert_tokens.to(torch.int64)
         group_list_type = 0
 
-    # place hidden_states in a list to transfer its ownership into the `apply_mlp` function
-    hidden_states_wrapper = [hidden_states]
-    del hidden_states
-
-    hidden_states = apply_mlp(hidden_states_wrapper,
+    # `hidden_states` will be disposed in the `apply_mlp` function
+    hidden_states = apply_mlp(hidden_states,
                               w1,
                               w1_scale,
                               w2,

vllm_ascend/utils.py

@@ -169,3 +169,7 @@ def update_aclgraph_sizes(vllm_config: VllmConfig) -> None:
             "No adjustment needed for ACL graph batch sizes: %s model (layers: %d) with %d sizes",
             vllm_config.model_config.architectures[0], num_hidden_layers,
             len(original_sizes))
+
+
+def dispose_tensor(x: torch.Tensor):
+    x.set_(torch.empty((0, ), device=x.device, dtype=x.dtype))

vllm_ascend/worker/model_runner_v1.py

@@ -240,10 +240,11 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
                 device="cpu",
                 pin_memory=True)
 
-        self.inputs_embeds = torch.zeros(
-            (self.max_num_tokens, self.hidden_size),
-            dtype=self.dtype,
-            device=self.device)
+        if self.is_multimodal_model:
+            self.inputs_embeds = torch.zeros(
+                (self.max_num_tokens, self.hidden_size),
+                dtype=self.dtype,
+                device=self.device)
 
         # OPTIMIZATION: Cache the tensors rather than creating them every step.
         self.arange_np: npt.NDArray[np.int32] = np.arange(max(