fix

README.md

@@ -5,8 +5,8 @@ A lightweight vLLM implementation built from scratch.
 ## Key Features
 
 * 🚀 **Fast offline inference** - Comparable inference speeds to vLLM
-* 📖 **Readable codebase** - Clean implementation under 1,200 lines of Python code
-* ⚡ **Optimization Suite** - Prefix caching, Torch compilation, CUDA graph, etc
+* 📖 **Readable codebase** - Clean implementation in ~ 1,200 lines of Python code
+* ⚡ **Optimization Suite** - Prefix caching, Torch compilation, CUDA graph, etc.
 
 ## Installation
 

nanovllm/engine/llm_engine.py

@@ -4,7 +4,7 @@ from transformers import AutoConfig, AutoTokenizer
 
 from nanovllm.config import Config
 from nanovllm.sampling_params import SamplingParams
-from nanovllm.engine.sequence import Sequence, SequenceStatus
+from nanovllm.engine.sequence import Sequence
 from nanovllm.engine.scheduler import Scheduler
 from nanovllm.engine.model_runner import ModelRunner
 
@@ -34,7 +34,7 @@ class LLMEngine:
         seqs, is_prefill = self.scheduler.schedule()
         token_ids = self.model_runner.run(seqs, is_prefill)
         self.scheduler.postprocess(seqs, token_ids)
-        outputs = [(seq.seq_id, seq[seq.num_prompt_tokens:]) for seq in seqs if seq.status == SequenceStatus.FINISHED]
+        outputs = [(seq.seq_id, seq.completion_token_ids) for seq in seqs if seq.is_finished]
         num_tokens = sum(len(seq) for seq in seqs) if is_prefill else -len(seqs)
         return outputs, num_tokens
 

nanovllm/engine/model_runner.py

@@ -44,7 +44,7 @@ class ModelRunner:
                 module.v_cache = self.kv_cache[1, layer_id]
                 layer_id += 1
 
-    def preare_block_tables(self, seqs: list[Sequence]):
+    def prepare_block_tables(self, seqs: list[Sequence]):
         max_len = max(len(seq.block_table) for seq in seqs)
         block_tables = [
             seq.block_table + [-1] * (max_len - len(seq.block_table))
@@ -84,7 +84,7 @@ class ModelRunner:
         assert len(input_ids) == cu_seqlens_q[-1]
         if cu_seqlens_k[-1] > cu_seqlens_q[-1]:    # prefix cache
             context_lens = torch.tensor([len(seq) for seq in seqs], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
-            block_tables = self.preare_block_tables(seqs)
+            block_tables = self.prepare_block_tables(seqs)
         input_ids = torch.tensor(input_ids, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
         positions = torch.tensor(positions, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
         cu_seqlens_q = torch.tensor(cu_seqlens_q, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
@@ -107,7 +107,7 @@ class ModelRunner:
         positions = torch.tensor(positions, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
         slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
         context_lens = torch.tensor(context_lens, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
-        block_tables = self.preare_block_tables(seqs)
+        block_tables = self.prepare_block_tables(seqs)
         set_context(False, slot_mapping=slot_mapping, context_lens=context_lens, block_tables=block_tables)
         return input_ids, positions
 

nanovllm/engine/sequence.py

@@ -35,10 +35,22 @@ class Sequence:
     def __getitem__(self, key):
         return self.token_ids[key]
 
+    @property
+    def is_finished(self):
+        return self.status == SequenceStatus.FINISHED
+
     @property
     def num_completion_tokens(self):
         return len(self.token_ids) - self.num_prompt_tokens
 
+    @property
+    def prompt_token_ids(self):
+        return self.token_ids[:self.num_prompt_tokens]
+
+    @property
+    def completion_token_ids(self):
+        return self.token_ids[self.num_prompt_tokens:]
+
     @property
     def num_cached_tokens(self):
         return self._num_cached_tokens

pyproject.toml

@@ -9,7 +9,7 @@ authors = [{ name = "Xingkai Yu" }]
 license = "MIT"
 license-files = ["LICENSE"]
 readme = "README.md"
-description = "a mimic VLLM implementation from scratch"
+description = "a lightweight vLLM implementation built from scratch"
 requires-python = ">=3.10,<3.13"
 dependencies = [
     "torch>=2.4.0",