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Launch a thread to overlap CPU and GPU (#1687)

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This commit is contained in:
Lianmin Zheng
2024-10-16 11:20:17 -07:00
committed by GitHub
parent e4b367baa8
commit dbec2f1847
3 changed files with 142 additions and 20 deletions
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47
python/sglang/srt/managers/scheduler.py
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@@ -193,16 +193,6 @@ class Scheduler:
self.tree_cache_metrics = {"total": 0, "hit": 0}
self.policy = SchedulePolicy(self.schedule_policy, self.tree_cache)
if self.server_args.enable_overlap_schedule:
def cache_finished_req(req):
free_delta = int(self.running_batch and req in self.cur_batch.reqs)
self.tree_cache.cache_finished_req(req, free_delta=free_delta)
else:
cache_finished_req = self.tree_cache.cache_finished_req
self.cache_finished_req = cache_finished_req
# Init running status
self.waiting_queue: List[Req] = []
self.running_batch: Optional[ScheduleBatch] = None
@@ -245,6 +235,7 @@ class Scheduler:
self.new_token_ratio_decay = global_config.new_token_ratio_decay
self.batch_is_full = False
# Init profiler
if os.getenv("SGLANG_TORCH_PROFILER_DIR", "") == "":
self.profiler = None
else:
@@ -261,6 +252,25 @@ class Scheduler:
with_stack=True,
)
# Init states for overlap schedule
if self.server_args.enable_overlap_schedule:
self.forward_batch_generation = (
self.tp_worker.forward_batch_generation_non_blocking
)
self.resolve_next_token_ids = (
lambda bid, x: self.tp_worker.resolve_future_token_ids(bid)
)
def cache_finished_req(req):
free_delta = int(self.running_batch and req in self.cur_batch.reqs)
self.tree_cache.cache_finished_req(req, free_delta=free_delta)
self.cache_finished_req = cache_finished_req
else:
self.forward_batch_generation = self.tp_worker.forward_batch_generation
self.resolve_next_token_ids = lambda bid, x: x.tolist()
self.cache_finished_req = self.tree_cache.cache_finished_req
@torch.inference_mode()
def event_loop_normal(self):
self.last_batch = None
@@ -712,7 +722,7 @@ class Scheduler:
if self.is_generation:
if batch.forward_mode.is_decode() or batch.extend_num_tokens != 0:
model_worker_batch = batch.get_model_worker_batch()
logits_output, next_token_ids = self.tp_worker.forward_batch_generation(
logits_output, next_token_ids = self.forward_batch_generation(
model_worker_batch
)
else:
@@ -724,12 +734,12 @@ class Scheduler:
else:
next_token_ids = torch.full((batch.batch_size(),), 0)
batch.output_ids = next_token_ids
ret = logits_output, next_token_ids
ret = logits_output, next_token_ids, model_worker_batch.bid
else: # embedding or reward model
assert batch.extend_num_tokens != 0
model_worker_batch = batch.get_model_worker_batch()
embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch)
ret = embeddings
ret = embeddings, model_worker_batch.bid
return ret
def process_batch_result(self, batch: ScheduleBatch, result):
@@ -742,7 +752,7 @@ class Scheduler:
def process_batch_result_prefill(self, batch: ScheduleBatch, result):
if self.is_generation:
logits_output, next_token_ids = result
logits_output, next_token_ids, bid = result
if batch.return_logprob:
# Move logprobs to cpu
if logits_output.next_token_logprobs is not None:
@@ -761,7 +771,7 @@ class Scheduler:
logits_output.normalized_prompt_logprobs.tolist()
)
next_token_ids = next_token_ids.tolist()
next_token_ids = self.resolve_next_token_ids(bid, next_token_ids)
# Check finish conditions
logprob_pt = 0
@@ -790,7 +800,8 @@ class Scheduler:
)
else: # embedding or reward model
assert batch.extend_num_tokens != 0
embeddings = result.tolist()
embeddings, bid = result
embeddings = embeddings.tolist()
# Check finish conditions
for i, req in enumerate(batch.reqs):
@@ -811,7 +822,7 @@ class Scheduler:
self.stream_output(batch.reqs)
def process_batch_result_decode(self, batch: ScheduleBatch, result):
logits_output, next_token_ids = result
logits_output, next_token_ids, bid = result
self.num_generated_tokens += len(batch.reqs)
# Move logprobs to cpu
@@ -821,7 +832,7 @@ class Scheduler:
next_token_ids,
].tolist()
next_token_ids = next_token_ids.tolist()
next_token_ids = self.resolve_next_token_ids(bid, next_token_ids)
# Check finish condition
for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)):

111
python/sglang/srt/managers/tp_worker.py
View File

@@ -17,6 +17,11 @@ limitations under the License.
import json
import logging
import threading
import time
from queue import Queue
import torch
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
@@ -75,6 +80,7 @@ class TpModelWorker:
tokenizer_mode=server_args.tokenizer_mode,
trust_remote_code=server_args.trust_remote_code,
)
self.device = self.model_runner.device
# Profile number of tokens
self.max_total_num_tokens = self.model_runner.max_total_num_tokens
@@ -100,6 +106,9 @@ class TpModelWorker:
)[0]
set_random_seed(self.random_seed)
if server_args.enable_overlap_schedule:
self.init_overlap_status()
def get_token_and_memory_info(self):
return (
self.max_total_num_tokens,
@@ -109,6 +118,83 @@ class TpModelWorker:
self.random_seed,
)
def init_overlap_status(self):
self.future_logits_output_dict = dict()
self.future_logits_output_ct = 0
self.future_token_ids_ct = 0
self.future_token_ids_map = torch.empty(
(self.max_running_requests * 5,), dtype=torch.int32, device=self.device
)
self.future_token_ids_limit = self.max_running_requests * 3
self.future_token_ids_output = dict()
self.future_event_map = dict()
self.forward_queue = Queue()
self.forward_stream = torch.cuda.Stream()
self.forward_thread = threading.Thread(
target=self.forward_thread_func,
)
self.forward_thread.start()
def forward_thread_func(self):
with torch.cuda.stream(self.forward_stream):
self.forward_thread_func_()
@torch.inference_mode()
def forward_thread_func_(self):
while True:
tic1 = time.time()
model_worker_batch, future_logits_output, future_next_token_ids = (
self.forward_queue.get()
)
# Resolve future tokens in the input
# logger.info(f"raw input {model_worker_batch.input_ids=}")
tic2 = time.time()
resolved_input_ids = model_worker_batch.input_ids
future_mask = resolved_input_ids < 0
resolved_input_ids[future_mask] = self.future_token_ids_map[
-resolved_input_ids[future_mask]
]
# logger.info(f"resolved input {model_worker_batch.input_ids=}")
# Run forward
logits_output, next_token_ids = self.forward_batch_generation(
model_worker_batch
)
# Set future values
if model_worker_batch.return_logprob:
self.future_logits_output_dict[future_logits_output] = logits_output
# logger.info(f"set output {future_next_token_ids=}, {next_token_ids=}")
self.future_token_ids_map[-future_next_token_ids] = next_token_ids.to(
torch.int32
)
# logger.info("Set event")
self.future_token_ids_output[model_worker_batch.bid] = (
next_token_ids.tolist()
)
self.future_event_map[model_worker_batch.bid].set()
if False:
tic3 = time.time()
self.acc_time_with_waiting += tic3 - tic1
self.acc_time_without_waiting += tic3 - tic2
if self.forward_queue.qsize() == 0:
logger.info(
f"{self.acc_time_with_waiting=:.3f}, {self.acc_time_without_waiting=:.3f}, {self.forward_queue.qsize()=}"
)
def resolve_future_token_ids(self, bid: int):
self.future_event_map[bid].wait()
ret = self.future_token_ids_output[bid]
del self.future_event_map[bid]
return ret
def resolve_future_logits_output(self, future_obj):
return self.future_logits_output_dict.pop(future_obj)
def forward_batch_generation(self, model_worker_batch: ModelWorkerBatch):
forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
logits_output = self.model_runner.forward(forward_batch)
@@ -121,6 +207,31 @@ class TpModelWorker:
embeddings = logits_output.embeddings
return embeddings
def forward_batch_generation_non_blocking(
self, model_worker_batch: ModelWorkerBatch
):
# Allocate output future objects
future_logits_output = self.future_logits_output_ct
self.future_logits_output_ct += 1
bs = len(model_worker_batch.seq_lens)
future_next_token_ids = -torch.arange(
self.future_token_ids_ct + 1,
self.future_token_ids_ct + 1 + bs,
dtype=torch.int32,
device=self.device,
)
self.future_token_ids_ct = (
self.future_token_ids_ct + bs
) % self.future_token_ids_limit
ret = future_logits_output, future_next_token_ids
self.future_event_map[model_worker_batch.bid] = threading.Event()
self.forward_queue.put(
(model_worker_batch.copy(), future_logits_output, future_next_token_ids)
)
return ret
def update_weights(self, recv_req: UpdateWeightReqInput):
success, message = self.model_runner.update_weights(
recv_req.model_path, recv_req.load_format

4
python/sglang/srt/server.py
View File

@@ -447,7 +447,7 @@ def _set_envs_and_config(server_args: ServerArgs):
os.environ["NCCL_CUMEM_ENABLE"] = "0"
os.environ["NCCL_NVLS_ENABLE"] = "0"
os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"
os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "4"
# Set ulimit
set_ulimit()
@@ -528,7 +528,7 @@ def _wait_and_warmup(server_args, pipe_finish_writer, pid):
kill_child_process(pid, including_parent=False)
return
# print(f"{res.json()=}")
print(f"{res.json()=}")
logger.info("The server is fired up and ready to roll!")
if pipe_finish_writer is not None: