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chore: add _prepare_and_schedule_batch function in PyExecutor #6365

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Merged
QiJune merged 4 commits into from
Jul 28, 2025
Merged

chore: add _prepare_and_schedule_batch function in PyExecutor #6365

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725e206
add _prepare_and_schedule_batch function in PyExecutor
QiJune Jul 25, 2025
7503c03
fix ci
QiJune Jul 28, 2025
1280cf3
Merge branch 'main' into refactor_loop
QiJune Jul 28, 2025
6bff883
Merge branch 'main' into refactor_loop
QiJune Jul 28, 2025
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131 changes: 50 additions & 81 deletions tensorrt_llm/_torch/pyexecutor/py_executor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -800,6 +800,50 @@ def _executor_loop_pp(self):
self.active_requests,
previous_batch)

def _prepare_and_schedule_batch(self):
new_requests = self._fetch_new_requests()
if self.should_stop_processing:
return None, None

if self.kv_cache_transceiver:
self._check_disagg_gen_transfer_status()

iter_stats = None
if self.enable_iter_perf_stats:
iter_stats = self._get_init_iter_stats(
len(new_requests),
self.executor_request_queue.
get_new_active_requests_queue_latency())

self._pad_attention_dp_dummy_request()

if self.drafter is not None:
self._prepare_draft_requests(self.active_requests)

scheduled_batch, fitting_disagg_gen_init_requests, num_fitting_reqs = self._schedule(
)

if self.kv_cache_transceiver:
# For requests that are fitting disagg gen init, also prepare resources for KV cache manager
self._prepare_disagg_gen_init(fitting_disagg_gen_init_requests)

if num_fitting_reqs == 0 and not fitting_disagg_gen_init_requests:
logger.warning(
"num_fitting_reqs=0 and fitting_disagg_gen_init_requests is empty, may not have enough kvCache"
)
self.kv_cache_transceiver.check_context_transfer_status(1)
else:
assert scheduled_batch.batch_size > 0, (
"fail to schedule any pending request, "
"probably run out of resource.")

self.num_scheduled_requests = scheduled_batch.batch_size
logger.debug(
f'has {len(self.active_requests)} active_request, '
f'scheduled {len(scheduled_batch.context_requests)} context requests and '
f'{len(scheduled_batch.generation_requests)} generation requests')
return scheduled_batch, iter_stats

def _executor_loop(self):
torch.cuda.set_device(self.device_id)
with self._profiler() as profile_step:
Expand All @@ -810,48 +854,10 @@ def _executor_loop(self):
profile_step()
if self.enable_iter_perf_stats:
iter_start_time = time.time()
new_requests = self._fetch_new_requests()
if self.should_stop_processing:
break

if self.kv_cache_transceiver:
self._check_disagg_gen_transfer_status()

if self.enable_iter_perf_stats:
iter_stats = self._get_init_iter_stats(
len(new_requests),
self.executor_request_queue.
get_new_active_requests_queue_latency())

self._pad_attention_dp_dummy_request()

if self.drafter is not None:
self._prepare_draft_requests(self.active_requests)

scheduled_batch, fitting_disagg_gen_init_requests, num_fitting_reqs = self._schedule(
)

if self.kv_cache_transceiver:
# For requests that are fitting disagg gen init, also prepare resources for KV cache manager
self._prepare_disagg_gen_init(
fitting_disagg_gen_init_requests)
if num_fitting_reqs == 0 and not fitting_disagg_gen_init_requests:
logger.warning(
"num_fitting_reqs=0 and fitting_disagg_gen_init_requests is empty, may not have enough kvCache"
)
self.kv_cache_transceiver.check_context_transfer_status(
1)
else:
assert scheduled_batch.batch_size > 0, (
"fail to schedule any pending request, "
"probably run out of resource.")

self.num_scheduled_requests = scheduled_batch.batch_size
logger.debug(
f'has {len(self.active_requests)} active_request, '
f'scheduled {len(scheduled_batch.context_requests)} context requests and '
f'{len(scheduled_batch.generation_requests)} generation requests'
)
scheduled_batch, iter_stats = self._prepare_and_schedule_batch()
if scheduled_batch is None:
break

self._pause_requests(scheduled_batch.paused_requests)

Expand Down Expand Up @@ -954,47 +960,10 @@ def _executor_loop_overlap(self):
profile_step()
if self.enable_iter_perf_stats:
iter_start_time = time.time()
new_requests = self._fetch_new_requests()
if self.should_stop_processing:
break

if self.kv_cache_transceiver:
self._check_disagg_gen_transfer_status()

if self.enable_iter_perf_stats:
iter_stats = self._get_init_iter_stats(
len(new_requests),
self.executor_request_queue.
get_new_active_requests_queue_latency())

self._pad_attention_dp_dummy_request()

scheduled_batch, fitting_disagg_gen_init_requests, num_fitting_reqs = self._schedule(
)

if self.kv_cache_transceiver:

# For requests that are fitting disagg gen init, also prepare resources for KV cache manager
self._prepare_disagg_gen_init(
fitting_disagg_gen_init_requests)

if num_fitting_reqs == 0 and not fitting_disagg_gen_init_requests:
logger.warning(
"num_fitting_reqs=0 and fitting_disagg_gen_init_requests is empty, may not have enough kvCache"
)
self.kv_cache_transceiver.check_context_transfer_status(
1)
else:
assert scheduled_batch.batch_size > 0, (
"fail to schedule any pending request, "
"probably run out of resource.")

self.num_scheduled_requests = scheduled_batch.batch_size
logger.debug(
f'has {len(self.active_requests)} active_request, '
f'scheduled {len(scheduled_batch.context_requests)} context requests and '
f'{len(scheduled_batch.generation_requests)} generation requests'
)
scheduled_batch, iter_stats = self._prepare_and_schedule_batch()
if scheduled_batch is None:
break

self._pause_requests(scheduled_batch.paused_requests)

Expand Down