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🐛 put decode back in warmup #293

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Merged
yannicks1 merged 2 commits into from
Jul 9, 2025
Merged

🐛 put decode back in warmup #293

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cf7cb06
:bug: put decode back in warmup
joerunde Jul 9, 2025
fd95c78
:art: fmt
joerunde Jul 9, 2025
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76 changes: 39 additions & 37 deletions vllm_spyre/v1/worker/spyre_worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -357,44 +357,46 @@ def _warmup_spyre_dynamic_size(self, special_token_ids):
logger.info("[WARMUP] Prefill %d/%d...", i + 1, batch_size)
self.execute_model(scheduler_output)

# one decode iteration across all sequences
req_ids = []
new_token_ids = []
new_block_ids = []
num_computed_tokens = []
for req in dummy_requests:
req_ids.append(req.req_id)
new_token_ids.append([
valid_token_ids_tensor[torch.randint(
0, len(valid_token_ids_tensor), (1, )).item()]
]) # placeholder token
new_block_ids.append([req.block_ids])
num_computed_tokens.append(prompt_len)
cached_request_data = CachedRequestData(
req_ids=req_ids,
resumed_from_preemption=False,
new_token_ids=new_token_ids,
new_block_ids=new_block_ids,
num_computed_tokens=num_computed_tokens,
)
# one decode iteration across all sequences
req_ids = []
new_token_ids = []
new_block_ids = []
num_computed_tokens = []
for req in dummy_requests:
req_ids.append(req.req_id)
new_token_ids.append([
valid_token_ids_tensor[torch.randint(
0, len(valid_token_ids_tensor), (1, )).item()]
]) # placeholder token
new_block_ids.append([req.block_ids])
num_computed_tokens.append(prompt_len)
cached_request_data = CachedRequestData(
req_ids=req_ids,
resumed_from_preemption=False,
new_token_ids=new_token_ids,
new_block_ids=new_block_ids,
num_computed_tokens=num_computed_tokens,
)

scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=cached_request_data,
num_scheduled_tokens={f"warmup-{i}": 1
for i in range(batch_size)},
total_num_scheduled_tokens=batch_size,
scheduled_spec_decode_tokens={},
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
logger.info("[WARMUP] Decode...")
self.execute_model(scheduler_output)
self._cleanup_model_runner(request=dummy_requests)
scheduler_output = SchedulerOutput(
scheduled_new_reqs=[],
scheduled_cached_reqs=cached_request_data,
num_scheduled_tokens={
f"warmup-{i}": 1
for i in range(batch_size)
},
total_num_scheduled_tokens=batch_size,
scheduled_spec_decode_tokens={},
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
logger.info("[WARMUP] Decode...")
self.execute_model(scheduler_output)
self._cleanup_model_runner(request=dummy_requests)

# warmup_mode completes the graph compilation, but we need to do
# one additional prefill to deploy the compiled program to the device,
Expand Down