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Merge pull request #11 from GeeeekExplorer/tp_dev

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Xingkai Yu
2025-06-15 10:37:21 +08:00
committed by GitHub
parent 4a8aa090a7 fc778a4da9
commit ba96387043
16 changed files with 135 additions and 69 deletions
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9
nanovllm/config.py
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@@ -1,14 +1,16 @@
import os
from dataclasses import dataclass
from transformers import AutoConfig
@dataclass
class Config:
model: str = ''
model: str
max_num_batched_tokens: int = 32768
max_num_seqs: int = 512
max_model_len: int = 4096
gpu_memory_utilization: float = 0.9
tensor_parallel_size: int = 1
enforce_eager: bool = False
hf_config: AutoConfig | None = None
eos: int = -1
@@ -16,5 +18,8 @@ class Config:
num_kvcache_blocks: int = -1
def __post_init__(self):
assert self.model
assert os.path.isdir(self.model)
assert self.kvcache_block_size % 256 == 0
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)

2
nanovllm/engine/block_manager.py
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@@ -107,7 +107,7 @@ class BlockManager:
block_table.append(block_id)
elif len(seq) % self.block_size == 0:
assert last_block.hash == -1
token_ids = seq.last_block()
token_ids = seq.block(seq.num_blocks-1)
prefix = self.blocks[block_table[-2]].hash if len(block_table) > 1 else -1
h = compute_hash(token_ids, prefix)
last_block.update(h, token_ids)

31
nanovllm/engine/llm_engine.py
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@@ -1,6 +1,9 @@
import atexit
from dataclasses import fields
from time import perf_counter
from tqdm.auto import tqdm
from transformers import AutoConfig, AutoTokenizer
import torch.multiprocessing as mp
from nanovllm.config import Config
from nanovllm.sampling_params import SamplingParams
@@ -12,17 +15,27 @@ from nanovllm.engine.model_runner import ModelRunner
class LLMEngine:
def __init__(self, model, **kwargs):
config = Config(model)
for k, v in kwargs.items():
if hasattr(config, k):
setattr(config, k, v)
Sequence.block_size = config.kvcache_block_size
config.hf_config = AutoConfig.from_pretrained(config.model)
config.max_model_len = min(config.max_model_len, config.hf_config.max_position_embeddings)
config_fileds = {field.name for field in fields(Config)}
config_kwargs = {k: v for k, v in kwargs.items() if k in config_fileds}
config = Config(model, **config_kwargs)
self.ps = []
self.events = []
for i in range(1, config.tensor_parallel_size):
event = mp.Event()
process = mp.Process(target=ModelRunner, args=(config, i, event))
process.start()
self.ps.append(process)
self.events.append(event)
self.model_runner = ModelRunner(config, 0, self.events)
self.tokenizer = AutoTokenizer.from_pretrained(config.model, use_fast=True)
config.eos = self.tokenizer.eos_token_id
self.model_runner = ModelRunner(config)
self.scheduler = Scheduler(config)
atexit.register(self.exit)
def exit(self):
self.model_runner.call("exit")
for p in self.ps:
p.join()
def add_request(self, prompt: str | list[int], sampling_params: SamplingParams):
if isinstance(prompt, str):
@@ -32,7 +45,7 @@ class LLMEngine:
def step(self):
seqs, is_prefill = self.scheduler.schedule()
token_ids = self.model_runner.run(seqs, is_prefill)
token_ids = self.model_runner.call("run", seqs, is_prefill)
self.scheduler.postprocess(seqs, token_ids)
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)

72
nanovllm/engine/model_runner.py
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@@ -1,4 +1,8 @@
import pickle
import torch
import torch.distributed as dist
from multiprocess.synchronize import Event
from multiprocess.shared_memory import SharedMemory
from nanovllm.config import Config
from nanovllm.engine.sequence import Sequence
@@ -11,12 +15,17 @@ from nanovllm.utils.loader import load_model
class ModelRunner:
def __init__(self, config: Config):
def __init__(self, config: Config, rank: int, event: Event | list[Event]):
self.config = config
hf_config = config.hf_config
self.block_size = config.kvcache_block_size
self.enforce_eager = config.enforce_eager
self.world_size = config.tensor_parallel_size
self.rank = rank
self.event = event
dist.init_process_group("nccl", "tcp://localhost:2333", world_size=self.world_size, rank=rank)
torch.cuda.set_device(rank)
default_dtype = torch.get_default_dtype()
torch.set_default_dtype(hf_config.torch_dtype)
torch.set_default_device("cuda")
@@ -29,14 +38,63 @@ class ModelRunner:
torch.set_default_device("cpu")
torch.set_default_dtype(default_dtype)
if self.world_size > 1:
if rank == 0:
self.shm = SharedMemory(name="nanovllm", create=True, size=2**20)
dist.barrier()
else:
dist.barrier()
self.shm = SharedMemory(name="nanovllm")
self.loop()
def exit(self):
if self.world_size > 1:
self.shm.close()
if self.rank == 0:
self.shm.unlink()
# dist.destroy_process_group()
def loop(self):
while True:
method_name, args = self.read_shm()
self.call(method_name, *args)
if method_name == "exit":
break
def read_shm(self):
assert self.world_size > 1 and self.rank
self.event.wait()
n = int.from_bytes(self.shm.buf[0:4], "little")
method_name, *args = pickle.loads(self.shm.buf[4:n+4])
self.event.clear()
return method_name, args
def write_shm(self, method_name, *args):
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:
event.set()
def call(self, method_name, *args):
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):
config = self.config
hf_config = config.hf_config
total, used, _ = get_gpu_memory()
free = total * gpu_memory_utilization - used
block_bytes = 2 * hf_config.num_hidden_layers * self.block_size * hf_config.num_key_value_heads * hf_config.head_dim * hf_config.torch_dtype.itemsize
num_kv_heads = hf_config.num_key_value_heads // dist.get_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(free) // block_bytes
self.kv_cache = torch.zeros(2, hf_config.num_hidden_layers, config.num_kvcache_blocks, self.block_size, hf_config.num_key_value_heads, hf_config.head_dim)
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():
if hasattr(module, "k_cache") and hasattr(module, "v_cache"):
@@ -66,7 +124,7 @@ class ModelRunner:
for seq in seqs:
seqlen = len(seq)
input_ids.extend(seq[seq.num_cached_tokens:])
positions.extend(list(range(seq.num_cached_tokens, len(seq))))
positions.extend(list(range(seq.num_cached_tokens, seqlen)))
seqlen_q = seqlen - seq.num_cached_tokens
seqlen_k = seqlen
cu_seqlens_q.append(cu_seqlens_q[-1] + seqlen_q)
@@ -78,7 +136,7 @@ class ModelRunner:
if i != seq.num_blocks - 1:
end = start + self.block_size
else:
end = start + len(seq.last_block())
end = start + seq.last_block_num_tokens
slot_mapping.extend(list(range(start, end)))
assert len(input_ids) == len(slot_mapping)
assert len(input_ids) == cu_seqlens_q[-1]
@@ -102,7 +160,7 @@ class ModelRunner:
input_ids.append(seq.last_token)
positions.append(len(seq))
context_lens.append(len(seq))
slot_mapping.append(seq.block_table[-1] * self.block_size + len(seq.last_block()) - 1)
slot_mapping.append(seq.block_table[-1] * self.block_size + seq.last_block_num_tokens - 1)
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)
slot_mapping = torch.tensor(slot_mapping, dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
@@ -148,7 +206,7 @@ class ModelRunner:
input_ids, positions = self.prepare_prefill(seqs) if is_prefill else self.prepare_decode(seqs)
temperatures = self.prepare_sample(seqs)
logits = self.run_model(input_ids, positions, is_prefill)
token_ids = self.sampler(logits, temperatures).tolist()
token_ids = self.sampler(logits, temperatures).tolist() if self.rank == 0 else None
reset_context()
return token_ids

4
nanovllm/engine/scheduler.py
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@@ -14,8 +14,6 @@ class Scheduler:
self.block_manager = BlockManager(config.num_kvcache_blocks, config.kvcache_block_size)
self.waiting: deque[Sequence] = deque()
self.running: deque[Sequence] = deque()
self.num_finished = 0
self.num_tokens = 0
def is_finished(self):
return not self.waiting and not self.running
@@ -67,11 +65,9 @@ class Scheduler:
self.waiting.appendleft(seq)
def postprocess(self, seqs: list[Sequence], token_ids: list[int]) -> list[bool]:
self.num_tokens += len(token_ids)
for seq, token_id in zip(seqs, token_ids):
seq.append_token(token_id)
if (not seq.ignore_eos and token_id == self.eos) or seq.num_completion_tokens == seq.max_tokens:
seq.status = SequenceStatus.FINISHED
self.block_manager.deallocate(seq)
self.running.remove(seq)
self.num_finished += 1

36
nanovllm/engine/sequence.py
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@@ -19,15 +19,17 @@ class Sequence:
self.seq_id = next(Sequence.counter)
self.status = SequenceStatus.WAITING
self.token_ids = copy(token_ids)
self.last_token = token_ids[-1]
self.num_tokens = len(self.token_ids)
self.num_prompt_tokens = len(token_ids)
self._num_cached_tokens = 0
self.num_cached_tokens = 0
self.block_table = []
self.temperature = sampling_params.temperature
self.max_tokens = sampling_params.max_tokens
self.ignore_eos = sampling_params.ignore_eos
def __len__(self):
return len(self.token_ids)
return self.num_tokens
def __lt__(self, other):
return self.seq_id < other.seq_id
@@ -41,7 +43,7 @@ class Sequence:
@property
def num_completion_tokens(self):
return len(self.token_ids) - self.num_prompt_tokens
return self.num_tokens - self.num_prompt_tokens
@property
def prompt_token_ids(self):
@@ -51,33 +53,29 @@ class Sequence:
def completion_token_ids(self):
return self.token_ids[self.num_prompt_tokens:]
@property
def num_cached_tokens(self):
return self._num_cached_tokens
@num_cached_tokens.setter
def num_cached_tokens(self, num_cached_tokens):
assert num_cached_tokens % self.block_size == 0
self._num_cached_tokens = num_cached_tokens
@property
def num_cached_blocks(self):
return self.num_cached_tokens // self.block_size
@property
def num_blocks(self):
return (len(self.token_ids) + self.block_size - 1) // self.block_size
return (self.num_tokens + self.block_size - 1) // self.block_size
@property
def last_token(self):
return self.token_ids[-1]
def last_block_num_tokens(self):
return self.num_tokens - (self.num_blocks - 1) * self.block_size
def block(self, i):
assert 0 <= i < self.num_blocks
return self.token_ids[i*self.block_size: (i+1)*self.block_size]
def last_block(self):
n = self.num_blocks
return self.token_ids[(n-1)*self.block_size:]
def append_token(self, token_id: int):
self.token_ids.append(token_id)
self.last_token = token_id
self.num_tokens += 1
def __getstate__(self):
state = vars(self).copy()
if self.num_completion_tokens:
state.pop("token_ids")
return state

2
nanovllm/layers/activation.py
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@@ -11,4 +11,4 @@ class SiluAndMul(nn.Module):
@torch.compile
def forward(self, x: torch.Tensor) -> torch.Tensor:
x, y = x.chunk(2, -1)
return F.silu(x) * y
return y.mul_(F.silu(x))

16
nanovllm/layers/embed_head.py
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@@ -14,8 +14,8 @@ class VocabParallelEmbedding(nn.Module):
embedding_dim: int,
):
super().__init__()
self.tp_rank = 0 # get_tensor_model_parallel_rank()
self.tp_size = 1 # get_tensor_model_parallel_world_size()
self.tp_rank = dist.get_rank()
self.tp_size = dist.get_world_size()
assert num_embeddings % self.tp_size == 0
self.num_embeddings = num_embeddings
self.num_embeddings_per_partition = self.num_embeddings // self.tp_size
@@ -39,7 +39,7 @@ class VocabParallelEmbedding(nn.Module):
x = mask * (x - self.vocab_start_idx)
y = F.embedding(x, self.weight)
if self.tp_size > 1:
y = mask * y
y = mask.unsqueeze(1) * y
dist.all_reduce(y)
return y
@@ -65,8 +65,8 @@ class ParallelLMHead(VocabParallelEmbedding):
last_indices = context.cu_seqlens_q[1:] - 1
x = x[last_indices].contiguous()
logits = F.linear(x, self.weight, self.bias)
# if self.tp_size > 1:
# all_logits = [torch.empty_like(logits) for _ in range(self.tp_size)]
# dist.gather(logits, all_logits, 0)
# logits = torch.cat(all_logits, -1)
return logits if self.tp_rank == 0 else None
if self.tp_size > 1:
all_logits = [torch.empty_like(logits) for _ in range(self.tp_size)] if self.tp_rank == 0 else None
dist.gather(logits, all_logits, 0)
logits = torch.cat(all_logits, -1) if self.tp_rank == 0 else None
return logits

14
nanovllm/layers/linear.py
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@@ -21,8 +21,8 @@ class LinearBase(nn.Module):
self.input_size = input_size
self.output_size = output_size
self.tp_dim = tp_dim
self.tp_rank = 0 # get_tensor_model_parallel_rank()
self.tp_size = 1 # get_tensor_model_parallel_world_size()
self.tp_rank = dist.get_rank()
self.tp_size = dist.get_world_size()
def forward(self, x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
@@ -65,7 +65,6 @@ class ColumnParallelLinear(LinearBase):
self.input_size_per_partition = input_size
self.output_size_per_partition = divide(output_size, self.tp_size)
self.output_partition_sizes = [self.output_size_per_partition]
# If QKV or MergedColumn, use output size of each partition.
if hasattr(self, "output_sizes"):
self.output_partition_sizes = [
divide(output_size, self.tp_size)
@@ -101,8 +100,6 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
bias: bool = False,
):
self.output_sizes = output_sizes
tp_size = 1 # get_tensor_model_parallel_world_size()
assert all(output_size % tp_size == 0 for output_size in output_sizes)
super().__init__(input_size, sum(output_sizes), bias=bias)
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, loaded_shard_id: int):
@@ -110,7 +107,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
shard_offset = sum(self.output_sizes[:loaded_shard_id]) // self.tp_size
shard_size = self.output_sizes[loaded_shard_id] // self.tp_size
param_data = param_data.narrow(self.tp_dim, shard_offset, shard_size)
# loaded_weight = loaded_weight.narrow(self.tp_dim, self.tp_rank * shard_size, shard_size)
loaded_weight = loaded_weight.chunk(self.tp_size, self.tp_dim)[self.tp_rank]
assert param_data.size() == loaded_weight.size()
param_data.copy_(loaded_weight)
@@ -131,8 +128,7 @@ class QKVParallelLinear(ColumnParallelLinear):
if total_num_kv_heads is None:
total_num_kv_heads = total_num_heads
self.total_num_kv_heads = total_num_kv_heads
# Divide the weight matrix along the last dimension.
tp_size = 1 # get_tensor_model_parallel_world_size()
tp_size = dist.get_world_size()
self.num_heads = divide(self.total_num_heads, tp_size)
self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
input_size = self.hidden_size
@@ -158,7 +154,7 @@ class QKVParallelLinear(ColumnParallelLinear):
shard_size = self.num_kv_heads * self.head_size
shard_offset = self.num_heads * self.head_size + self.num_kv_heads * self.head_size
param_data = param_data.narrow(self.tp_dim, shard_offset, shard_size)
# loaded_weight = loaded_weight.narrow(self.tp_dim, self.tp_rank * shard_size, shard_size)
loaded_weight = loaded_weight.chunk(self.tp_size, self.tp_dim)[self.tp_rank]
assert param_data.size() == loaded_weight.size()
param_data.copy_(loaded_weight)

3
nanovllm/models/qwen3.py
View File

@@ -1,5 +1,6 @@
import torch
from torch import nn
import torch.distributed as dist
from transformers import Qwen3Config
from nanovllm.layers.activation import SiluAndMul
@@ -26,7 +27,7 @@ class Qwen3Attention(nn.Module):
) -> None:
super().__init__()
self.hidden_size = hidden_size
tp_size = 1 # get_tensor_model_parallel_world_size()
tp_size = dist.get_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size

1
nanovllm/utils/loader.py
View File

@@ -10,7 +10,6 @@ def default_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor):
def load_model(model: nn.Module, path: str):
assert os.path.isdir(path)
packed_modules_mapping = getattr(model, "packed_modules_mapping", {})
for file in glob(os.path.join(path, "*.safetensors")):
with safe_open(file, "pt", "cpu") as f:

2
pyproject.toml
View File

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "nano-vllm"
version = "0.1.0"
version = "0.2.0"
authors = [{ name = "Xingkai Yu" }]
license = "MIT"
license-files = ["LICENSE"]