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Clean up v0.9.1 code (#1672)

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vllm has released 0.9.2. This PR drop 0.9.1 support.

- vLLM version: v0.9.1
- vLLM main:
b942c094e3

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-07-09 08:52:24 +08:00
committed by GitHub
parent 0d4bc03946
commit 830332ebfc
23 changed files with 205 additions and 846 deletions
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279
vllm_ascend/worker/model_runner_v1.py
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@@ -44,6 +44,7 @@ from vllm.logger import logger
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
from vllm.model_executor.model_loader import get_model
from vllm.model_executor.models.interfaces import has_step_pooler
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
from vllm.multimodal.utils import group_mm_inputs_by_modality
@@ -79,7 +80,7 @@ from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
ProfileExecuteDuration,
check_torchair_cache_exist, is_310p,
maybe_converting_weight_acl_format,
vllm_version_is, write_kv_cache_bytes_to_file)
write_kv_cache_bytes_to_file)
from vllm_ascend.worker.eagle_proposer_v1 import EagleProposer
from vllm_ascend.worker.mtp_proposer_v1 import MtpProposer
from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
@@ -95,9 +96,6 @@ import vllm.envs as envs_vllm
import vllm_ascend.envs as envs_ascend
if vllm_version_is("0.9.1"):
from vllm.v1.spec_decode.utils import is_spec_decode_supported
if is_310p():
torch_npu.npu.set_compile_mode(jit_compile=False)
@@ -408,16 +406,13 @@ class NPUModelRunner(LoRAModelRunnerMixin):
else:
generator = None
# For vllm v0.9.1 version compatibility, we check if
# `pooling_params` is present in the new request data.
pooling_params = getattr(new_req_data, "pooling_params", None)
self.requests[req_id] = CachedRequestState(
req_id=req_id,
prompt_token_ids=new_req_data.prompt_token_ids,
mm_inputs=new_req_data.mm_inputs,
mm_positions=new_req_data.mm_positions,
sampling_params=sampling_params,
pooling_params=pooling_params,
pooling_params=new_req_data.pooling_params,
generator=generator,
block_ids=new_req_data.block_ids,
num_computed_tokens=new_req_data.num_computed_tokens,
@@ -465,62 +460,59 @@ class NPUModelRunner(LoRAModelRunnerMixin):
req_ids_to_add.append(req_id)
# Update the states of the running/resumed requests.
if vllm_version_is("0.9.1"):
for req_data in scheduler_output.scheduled_cached_reqs:
req_id = req_data.req_id
req_state = self.requests[req_id]
req_data = scheduler_output.scheduled_cached_reqs
is_last_rank = get_pp_group().is_last_rank
for i, req_id in enumerate(req_data.req_ids):
req_state = self.requests[req_id]
num_computed_tokens = req_data.num_computed_tokens[i]
new_block_ids = req_data.new_block_ids[i]
resumed_from_preemption = req_data.resumed_from_preemption[i]
# Update the cached states.
num_computed_tokens = req_data.num_computed_tokens
req_state.num_computed_tokens = num_computed_tokens
req_state.num_computed_tokens = num_computed_tokens
if not is_last_rank:
new_token_ids = req_data.new_token_ids[i]
# Add the sampled token(s) from the previous step (if any).
# This doesn't include "unverified" tokens like spec decode tokens.
num_new_tokens = (num_computed_tokens +
len(req_data.new_token_ids) -
num_new_tokens = (num_computed_tokens + len(new_token_ids) -
req_state.num_tokens)
if num_new_tokens == 1:
# Avoid slicing list in most common case.
req_state.output_token_ids.append(
req_data.new_token_ids[-1])
req_state.output_token_ids.append(new_token_ids[-1])
elif num_new_tokens > 0:
req_state.output_token_ids.extend(
req_data.new_token_ids[-num_new_tokens:])
# Update the block IDs.
if not req_data.resumed_from_preemption:
# Append the new blocks to the existing block IDs.
for block_ids, new_block_ids in zip( # type: ignore[call-overload]
req_state.block_ids,
req_data.new_block_ids,
strict=True):
block_ids.extend(new_block_ids)
else:
# The request is resumed from preemption.
# Replace the existing block IDs with the new ones.
req_state.block_ids = req_data.new_block_ids
new_token_ids[-num_new_tokens:])
# Update the block IDs.
if not resumed_from_preemption:
# Append the new blocks to the existing block IDs.
for block_ids, new_ids in zip( # type: ignore[call-overload]
req_state.block_ids, new_block_ids):
block_ids.extend(new_ids)
else:
# The request is resumed from preemption.
# Replace the existing block IDs with the new ones.
req_state.block_ids = new_block_ids
req_index = self.input_batch.req_id_to_index.get(req_id)
if req_index is None:
# The request is not in the persistent batch.
# The request was either preempted and resumed later, or was not
# scheduled in the previous step and needs to be added again.
req_ids_to_add.append(req_id)
continue
req_index = self.input_batch.req_id_to_index.get(req_id)
if req_index is None:
# The request is not in the persistent batch.
# The request was either preempted and resumed later, or was not
# scheduled in the previous step and needs to be added again.
req_ids_to_add.append(req_id)
continue
# Update the persistent batch.
self.input_batch.num_computed_tokens_cpu[req_index] = (
num_computed_tokens)
# Update the persistent batch.
self.input_batch.num_computed_tokens_cpu[req_index] = (
num_computed_tokens)
start_index = (len(req_state.block_ids) -
len(req_data.new_block_ids))
self.input_batch.block_table.append_row(
req_data.new_block_ids, req_index)
self.input_batch.block_table.append_row(new_block_ids, req_index)
if not is_last_rank:
# Add new_token_ids to token_ids_cpu.
start_token_index = num_computed_tokens
end_token_index = num_computed_tokens + len(
req_data.new_token_ids)
end_token_index = num_computed_tokens + len(new_token_ids)
self.input_batch.token_ids_cpu[
req_index,
start_token_index:end_token_index] = req_data.new_token_ids
start_token_index:end_token_index] = new_token_ids
self.input_batch.num_tokens_no_spec[
req_index] = end_token_index
# Add spec_token_ids to token_ids_cpu.
@@ -534,75 +526,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
start_index:end_token_index] = spec_token_ids
# NOTE(woosuk): `num_tokens` here may include spec decode tokens.
self.input_batch.num_tokens[req_index] = end_token_index
else:
req_data = scheduler_output.scheduled_cached_reqs
is_last_rank = get_pp_group().is_last_rank
for i, req_id in enumerate(req_data.req_ids):
req_state = self.requests[req_id]
num_computed_tokens = req_data.num_computed_tokens[i]
new_block_ids = req_data.new_block_ids[i]
resumed_from_preemption = req_data.resumed_from_preemption[i]
req_state.num_computed_tokens = num_computed_tokens
if not is_last_rank:
new_token_ids = req_data.new_token_ids[i]
# Add the sampled token(s) from the previous step (if any).
# This doesn't include "unverified" tokens like spec decode tokens.
num_new_tokens = (num_computed_tokens +
len(new_token_ids) -
req_state.num_tokens)
if num_new_tokens == 1:
# Avoid slicing list in most common case.
req_state.output_token_ids.append(new_token_ids[-1])
elif num_new_tokens > 0:
req_state.output_token_ids.extend(
new_token_ids[-num_new_tokens:])
# Update the block IDs.
if not resumed_from_preemption:
# Append the new blocks to the existing block IDs.
for block_ids, new_ids in zip( # type: ignore[call-overload]
req_state.block_ids, new_block_ids):
block_ids.extend(new_ids)
else:
# The request is resumed from preemption.
# Replace the existing block IDs with the new ones.
req_state.block_ids = new_block_ids
req_index = self.input_batch.req_id_to_index.get(req_id)
if req_index is None:
# The request is not in the persistent batch.
# The request was either preempted and resumed later, or was not
# scheduled in the previous step and needs to be added again.
req_ids_to_add.append(req_id)
continue
# Update the persistent batch.
self.input_batch.num_computed_tokens_cpu[req_index] = (
num_computed_tokens)
self.input_batch.block_table.append_row(
new_block_ids, req_index)
if not is_last_rank:
# Add new_token_ids to token_ids_cpu.
start_token_index = num_computed_tokens
end_token_index = num_computed_tokens + len(new_token_ids)
self.input_batch.token_ids_cpu[
req_index,
start_token_index:end_token_index] = new_token_ids
self.input_batch.num_tokens_no_spec[
req_index] = end_token_index
# Add spec_token_ids to token_ids_cpu.
spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
req_id, ())
if spec_token_ids:
start_index = end_token_index
end_token_index += len(spec_token_ids)
self.input_batch.token_ids_cpu[
req_index,
start_index:end_token_index] = spec_token_ids
# NOTE(woosuk): `num_tokens` here may include spec decode tokens.
self.input_batch.num_tokens[req_index] = end_token_index
# Check if the batch has changed. If not, we can skip copying the
# sampling metadata from CPU to GPU.
@@ -835,25 +758,13 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# compute completion's mrope_positions on-the-fly
dst_start = mrope_pos_ptr
dst_end = mrope_pos_ptr + completion_part_len
if vllm_version_is("0.9.1"):
self.mrope_positions_cpu[:, dst_start:dst_end] = \
MRotaryEmbedding.get_next_input_positions_tensor(
req.mrope_position_delta,
context_len=num_computed_tokens +
prompt_part_len,
seq_len=num_computed_tokens +
prompt_part_len +
completion_part_len,
)
else:
MRotaryEmbedding.get_next_input_positions_tensor(
out=self.mrope_positions_np,
out_offset=dst_start,
mrope_position_delta=req.mrope_position_delta,
context_len=num_computed_tokens + prompt_part_len,
num_new_tokens=completion_part_len,
)
MRotaryEmbedding.get_next_input_positions_tensor(
out=self.mrope_positions_np,
out_offset=dst_start,
mrope_position_delta=req.mrope_position_delta,
context_len=num_computed_tokens + prompt_part_len,
num_new_tokens=completion_part_len,
)
mrope_pos_ptr += completion_part_len
@@ -1661,30 +1572,29 @@ class NPUModelRunner(LoRAModelRunnerMixin):
for i in discard_sampled_tokens_req_indices:
valid_sampled_token_ids[i].clear()
if not vllm_version_is("0.9.1"):
# Cache the sampled tokens in the model runner, so that the schedulerAdd commentMore actions
# doesn't need to send them back.
# NOTE(woosuk): As an exception, when using PP, the scheduler sends
# the sampled tokens back, because there's no direct communication
# between the first-stage worker and the last-stage worker.
for req_idx, sampled_ids in enumerate(valid_sampled_token_ids):
if not sampled_ids:
continue
# Cache the sampled tokens in the model runner, so that the schedulerAdd commentMore actions
# doesn't need to send them back.
# NOTE(woosuk): As an exception, when using PP, the scheduler sends
# the sampled tokens back, because there's no direct communication
# between the first-stage worker and the last-stage worker.
for req_idx, sampled_ids in enumerate(valid_sampled_token_ids):
if not sampled_ids:
continue
start_idx = self.input_batch.num_tokens_no_spec[req_idx]
end_idx = start_idx + len(sampled_ids)
assert end_idx <= self.model_config.max_model_len, (
"Sampled token IDs exceed the max model length. "
f"Total number of tokens: {end_idx} > max_model_len: "
f"{self.model_config.max_model_len}")
start_idx = self.input_batch.num_tokens_no_spec[req_idx]
end_idx = start_idx + len(sampled_ids)
assert end_idx <= self.model_config.max_model_len, (
"Sampled token IDs exceed the max model length. "
f"Total number of tokens: {end_idx} > max_model_len: "
f"{self.model_config.max_model_len}")
self.input_batch.token_ids_cpu[
req_idx, start_idx:end_idx] = sampled_ids
self.input_batch.num_tokens_no_spec[req_idx] = end_idx
self.input_batch.num_tokens[req_idx] = end_idx
req_id = self.input_batch.req_ids[req_idx]
req_state = self.requests[req_id]
req_state.output_token_ids.extend(sampled_ids)
self.input_batch.token_ids_cpu[req_idx,
start_idx:end_idx] = sampled_ids
self.input_batch.num_tokens_no_spec[req_idx] = end_idx
self.input_batch.num_tokens[req_idx] = end_idx
req_id = self.input_batch.req_ids[req_idx]
req_state = self.requests[req_id]
req_state.output_token_ids.extend(sampled_ids)
spec_token_ids = self._get_spec_token_ids(
valid_sampled_token_ids,
@@ -1697,25 +1607,16 @@ class NPUModelRunner(LoRAModelRunnerMixin):
attn_metadata,
aux_hidden_states,
)
if vllm_version_is("0.9.1"):
model_runner_output = ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids,
spec_token_ids=spec_token_ids,
logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict,
)
else:
model_runner_output = ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids,
spec_token_ids=spec_token_ids,
logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict,
pooler_output=[],
)
model_runner_output = ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids,
spec_token_ids=spec_token_ids,
logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict,
pooler_output=[],
)
durations = ProfileExecuteDuration().pop_captured_sync()
if durations:
@@ -2024,15 +1925,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
QKVParallelLinear, RowParallelLinear)):
module.weight.data = torch_npu.npu_format_cast(
module.weight.data, ACL_FORMAT_FRACTAL_NZ)
try:
# For version compatibility, remove this after we abort vllm v0.9.1 support
from vllm.model_executor.models.interfaces import \
has_step_pooler # type: ignore
if has_step_pooler(self.model):
self.input_batch.logits_processing_needs_token_ids = True
except ImportError:
pass
if has_step_pooler(self.model):
self.input_batch.logits_processing_needs_token_ids = True
if self.drafter:
logger.info("Loading drafter model...")
if self.use_aux_hidden_state_outputs:
@@ -2362,14 +2256,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
# Skip requests that require top-p, top-k, etc.
req_id = self.input_batch.req_ids[i]
if vllm_version_is("0.9.1"):
if not is_spec_decode_supported(req_id, self.input_batch):
draft_token_ids.append([])
continue
else:
if req_id in self.input_batch.spec_decode_unsupported_reqs:
draft_token_ids.append([])
continue
if req_id in self.input_batch.spec_decode_unsupported_reqs:
draft_token_ids.append([])
continue
# Add sampled_token_ids to token_ids_cpu.
start_idx = self.input_batch.num_tokens_no_spec[i]