zijie-tian/nano-vllm
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #60 from GeeeekExplorer/warmup

Browse Source
This commit is contained in:
Xingkai Yu
2025-06-27 22:52:11 +08:00
committed by GitHub
parent 6802cb2f42 cb0b3dec3f
commit 2de882a395
5 changed files with 30 additions and 29 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@@ -14,9 +14,9 @@ A lightweight vLLM implementation built from scratch.
pip install git+https://github.com/GeeeekExplorer/nano-vllm.git
```
## Manual download
## Manual Download
If you’d rather fetch the model weights yourself, you can use:
If you prefer to download the model weights manually, use the following command:
```bash
huggingface-cli download --resume-download Qwen/Qwen3-0.6B \
--local-dir ~/huggingface/Qwen3-0.6B/ \
@@ -25,7 +25,7 @@ huggingface-cli download --resume-download Qwen/Qwen3-0.6B \
## Quick Start
See `example.py` for usage. The API mirrors vLLM's interface with minor differences in the `LLM.generate` method.
See `example.py` for usage. The API mirrors vLLM's interface with minor differences in the `LLM.generate` method:
```python
from nanovllm import LLM, SamplingParams
llm = LLM("/YOUR/MODEL/PATH", enforce_eager=True, tensor_parallel_size=1)

3
nanovllm/config.py
View File

@@ -6,7 +6,7 @@ from transformers import AutoConfig
@dataclass
class Config:
model: str
max_num_batched_tokens: int = 32768
max_num_batched_tokens: int = 16384
max_num_seqs: int = 512
max_model_len: int = 4096
gpu_memory_utilization: float = 0.9
@@ -23,3 +23,4 @@ class Config:
assert 1 <= self.tensor_parallel_size <= 8
self.hf_config = AutoConfig.from_pretrained(self.model)
self.max_model_len = min(self.max_model_len, self.hf_config.max_position_embeddings)
assert self.max_num_batched_tokens >= self.max_model_len

40
nanovllm/engine/model_runner.py
View File

@@ -31,7 +31,8 @@ class ModelRunner:
self.model = Qwen3ForCausalLM(hf_config)
load_model(self.model, config.model)
self.sampler = Sampler()
self.allocate_kv_cache(config.gpu_memory_utilization)
self.warmup_model()
self.allocate_kv_cache()
if not self.enforce_eager:
self.capture_cudagraph()
torch.set_default_device("cpu")
@@ -76,7 +77,6 @@ class ModelRunner:
assert self.world_size > 1 and not self.rank
data = pickle.dumps([method_name, *args])
n = len(data)
assert n + 4 <= self.shm.size
self.shm.buf[0:4] = n.to_bytes(4, "little")
self.shm.buf[4:n+4] = data
for event in self.event:
@@ -86,17 +86,28 @@ class ModelRunner:
if self.world_size > 1 and self.rank == 0:
self.write_shm(method_name, *args)
method = getattr(self, method_name, None)
assert callable(method)
return method(*args)
def allocate_kv_cache(self, gpu_memory_utilization):
def warmup_model(self):
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
max_num_batched_tokens, max_model_len = self.config.max_num_batched_tokens, self.config.max_model_len
num_seqs = min(max_num_batched_tokens // max_model_len, self.config.max_num_seqs)
seqs = [Sequence([0] * max_model_len) for _ in range(num_seqs)]
self.run(seqs, True)
torch.cuda.empty_cache()
def allocate_kv_cache(self):
config = self.config
hf_config = config.hf_config
free, total = torch.cuda.mem_get_info()
used = total - free
peak = torch.cuda.memory_stats()["allocated_bytes.all.peak"]
current = torch.cuda.memory_stats()["allocated_bytes.all.current"]
num_kv_heads = hf_config.num_key_value_heads // self.world_size
block_bytes = 2 * hf_config.num_hidden_layers * self.block_size * num_kv_heads * hf_config.head_dim * hf_config.torch_dtype.itemsize
config.num_kvcache_blocks = int(total * gpu_memory_utilization - used) // block_bytes
config.num_kvcache_blocks = int(total * config.gpu_memory_utilization - used - peak + current) // block_bytes
assert config.num_kvcache_blocks > 0
self.kv_cache = torch.zeros(2, hf_config.num_hidden_layers, config.num_kvcache_blocks, self.block_size, num_kv_heads, hf_config.head_dim)
layer_id = 0
for module in self.model.modules():
@@ -107,10 +118,7 @@ class ModelRunner:
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))
for seq in seqs
]
block_tables = [seq.block_table + [-1] * (max_len - len(seq.block_table)) for seq in seqs]
block_tables = torch.tensor(block_tables, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
return block_tables
@@ -133,6 +141,8 @@ class ModelRunner:
cu_seqlens_k.append(cu_seqlens_k[-1] + seqlen_k)
max_seqlen_q = max(seqlen_q, max_seqlen_q)
max_seqlen_k = max(seqlen_k, max_seqlen_k)
if not seq.block_table:
continue
for i in range(seq.num_cached_blocks, seq.num_blocks):
start = seq.block_table[i] * self.block_size
if i != seq.num_blocks - 1:
@@ -140,7 +150,6 @@ class ModelRunner:
else:
end = start + seq.last_block_num_tokens
slot_mapping.extend(list(range(start, end)))
assert len(input_ids) == len(slot_mapping)
if cu_seqlens_k[-1] > cu_seqlens_q[-1]: # prefix cache
block_tables = self.prepare_block_tables(seqs)
input_ids = torch.tensor(input_ids, dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
@@ -177,7 +186,7 @@ class ModelRunner:
return temperatures
@torch.inference_mode()
def run_model(self, input_ids: torch.Tensor, positions: torch.Tensor, is_prefill):
def run_model(self, input_ids: torch.Tensor, positions: torch.Tensor, is_prefill: bool):
if is_prefill or self.enforce_eager or input_ids.size(0) > 512:
return self.model.compute_logits(self.model(input_ids, positions))
else:
@@ -206,12 +215,6 @@ class ModelRunner:
@torch.inference_mode()
def capture_cudagraph(self):
get_rng_state = torch.cuda.get_rng_state
set_rng_state = torch.cuda.set_rng_state
rng_state = torch.cuda.get_rng_state()
torch.cuda.get_rng_state = lambda: rng_state
torch.cuda.set_rng_state = lambda _: None
config = self.config
hf_config = config.hf_config
max_bs = min(self.config.max_num_seqs, 512)
@@ -246,6 +249,3 @@ class ModelRunner:
block_tables=block_tables,
outputs=outputs,
)
torch.cuda.get_rng_state = get_rng_state
torch.cuda.set_rng_state = set_rng_state

2
nanovllm/engine/sequence.py
View File

@@ -15,7 +15,7 @@ class Sequence:
block_size = 256
counter = count()
def __init__(self, token_ids: list[int], sampling_params: SamplingParams):
def __init__(self, token_ids: list[int], sampling_params = SamplingParams()):
self.seq_id = next(Sequence.counter)
self.status = SequenceStatus.WAITING
self.token_ids = copy(token_ids)

4
nanovllm/layers/attention.py
View File

@@ -61,8 +61,8 @@ class Attention(nn.Module):
k = k.view(-1, self.num_kv_heads, self.head_dim)
v = v.view(-1, self.num_kv_heads, self.head_dim)
context = get_context()
k_cache = self.k_cache
v_cache = self.v_cache
k_cache, v_cache = self.k_cache, self.v_cache
if k_cache.numel() and v_cache.numel():
store_kvcache(k, v, k_cache, v_cache, context.slot_mapping)
if context.is_prefill:
if context.block_tables is not None: # prefix cache