minor simplify

nanovllm/engine/block_manager.py

@@ -46,7 +46,7 @@ class BlockManager:
         block.reset()
         self.free_block_ids.remove(block_id)
         self.used_block_ids.add(block_id)
-        return self.blocks[block_id]
+        return block
 
     def _deallocate_block(self, block_id: int) -> Block:
         assert self.blocks[block_id].ref_count == 0

nanovllm/engine/llm_engine.py

@@ -63,8 +63,7 @@ class LLMEngine:
         sampling_params: SamplingParams | list[SamplingParams],
         use_tqdm: bool = True,
     ) -> list[str]:
-        if use_tqdm:
-            pbar = tqdm(total=len(prompts), desc="Generating", dynamic_ncols=True)
+        pbar = tqdm(total=len(prompts), desc="Generating", dynamic_ncols=True, disable=not use_tqdm)
         if not isinstance(sampling_params, list):
             sampling_params = [sampling_params] * len(prompts)
         for prompt, sp in zip(prompts, sampling_params):
@@ -74,7 +73,6 @@ class LLMEngine:
         while not self.is_finished():
             t = perf_counter()
             output, num_tokens = self.step()
-            if use_tqdm:
             if num_tokens > 0:
                 prefill_throughput = num_tokens / (perf_counter() - t)
             else:
@@ -85,10 +83,8 @@ class LLMEngine:
             })
             for seq_id, token_ids in output:
                 outputs[seq_id] = token_ids
-                if use_tqdm:
                 pbar.update(1)
+        pbar.close()
         outputs = [outputs[seq_id] for seq_id in sorted(outputs.keys())]
         outputs = [{"text": self.tokenizer.decode(token_ids), "token_ids": token_ids} for token_ids in outputs]
-        if use_tqdm:
-            pbar.close()
         return outputs

nanovllm/engine/model_runner.py

@@ -180,9 +180,7 @@ class ModelRunner:
         return input_ids, positions
 
     def prepare_sample(self, seqs: list[Sequence]):
-        temperatures = []
-        for seq in seqs:
-            temperatures.append(seq.temperature)
+        temperatures = [seq.temperature for seq in seqs]
         temperatures = torch.tensor(temperatures, dtype=torch.float32, pin_memory=True).cuda(non_blocking=True)
         return temperatures
 

nanovllm/layers/activation.py

@@ -5,9 +5,6 @@ import torch.nn.functional as F
 
 class SiluAndMul(nn.Module):
 
-    def __init__(self):
-        super().__init__()
-
     @torch.compile
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x, y = x.chunk(2, -1)

nanovllm/layers/linear.py

@@ -141,8 +141,7 @@ class RowParallelLinear(LinearBase):
 
     def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
         param_data = param.data
-        if param_data.dim() == 1:
-            # bias is not sharded in RowParallelLinear
+        if param_data.ndim == 1:
             param_data.copy_(loaded_weight)
             return
         shard_size = param_data.size(self.tp_dim)

nanovllm/layers/sampler.py

@@ -4,9 +4,6 @@ from torch import nn
 
 class Sampler(nn.Module):
 
-    def __init__(self):
-        super().__init__()
-
     @torch.compile
     def forward(self, logits: torch.Tensor, temperatures: torch.Tensor):
         logits = logits.float().div_(temperatures.unsqueeze(dim=1))