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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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145
vllm/model_executor/models/qwen3_next_mtp.py
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@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only Qwen3Next MTP model."""
from collections.abc import Iterable
from typing import Optional
@@ -15,16 +16,25 @@ from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.linear import ColumnParallelLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
DEFAULT_VOCAB_PADDING_SIZE,
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.qwen3_next import (Qwen3NextDecoderLayer,
Qwen3NextRMSNorm)
from vllm.model_executor.models.qwen3_next import (
Qwen3NextDecoderLayer,
Qwen3NextRMSNorm,
)
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import Qwen3NextConfig
from .interfaces import SupportsPP
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, maybe_prefix)
from .utils import (
AutoWeightsLoader,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
maybe_prefix,
)
logger = init_logger(__name__)
@@ -33,7 +43,6 @@ KVCache = tuple[torch.Tensor, torch.Tensor]
@support_torch_compile
class Qwen3NextMultiTokenPredictor(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -43,8 +52,11 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
config: Qwen3NextConfig = model_config.hf_config
self.config = config
lora_vocab = ((lora_config.lora_extra_vocab_size *
(lora_config.max_loras or 1)) if lora_config else 0)
lora_vocab = (
(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
if lora_config
else 0
)
self.vocab_size = config.vocab_size + lora_vocab
self.org_vocab_size = config.vocab_size
@@ -57,31 +69,36 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
org_num_embeddings=config.vocab_size,
)
self.fc = ColumnParallelLinear(self.config.hidden_size * 2,
self.config.hidden_size,
gather_output=True,
bias=False,
return_bias=False,
quant_config=quant_config,
prefix=f'{prefix}.fc')
self.fc = ColumnParallelLinear(
self.config.hidden_size * 2,
self.config.hidden_size,
gather_output=True,
bias=False,
return_bias=False,
quant_config=quant_config,
prefix=f"{prefix}.fc",
)
self.layers = torch.nn.ModuleList(
Qwen3NextDecoderLayer(
vllm_config,
layer_type="full_attention",
prefix=f'{prefix}.layers.{idx}',
) for idx in range(self.num_mtp_layers))
prefix=f"{prefix}.layers.{idx}",
)
for idx in range(self.num_mtp_layers)
)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size
)
self.norm = Qwen3NextRMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.pre_fc_norm_hidden = Qwen3NextRMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.pre_fc_norm_embedding = Qwen3NextRMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.norm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.pre_fc_norm_hidden = Qwen3NextRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
self.pre_fc_norm_embedding = Qwen3NextRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
@@ -109,7 +126,7 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
current_step_idx = (spec_step_idx % self.num_mtp_layers)
current_step_idx = spec_step_idx % self.num_mtp_layers
hidden_states, residual = self.layers[current_step_idx](
positions=positions,
hidden_states=hidden_states,
@@ -117,16 +134,14 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
)
if not get_pp_group().is_last_rank:
return IntermediateTensors({
"hidden_states": hidden_states,
"residual": residual
})
return IntermediateTensors(
{"hidden_states": hidden_states, "residual": residual}
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
@@ -142,7 +157,8 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.num_experts)
num_experts=self.config.num_experts,
)
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
@@ -180,16 +196,19 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
if is_pp_missing_parameter(name, self):
continue
# Skip loading extra bias for GPTQ models.
if ((name.endswith(".bias") or name.endswith("_bias"))
and name not in params_dict):
if (
name.endswith(".bias") or name.endswith("_bias")
) and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id)
weight_loader(
param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
)
break
else:
# Skip loading extra bias for GPTQ models.
@@ -199,8 +218,9 @@ class Qwen3NextMultiTokenPredictor(nn.Module):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
@@ -214,33 +234,38 @@ class Qwen3NextMTP(nn.Module, SupportsPP):
"k_proj",
"v_proj",
],
"gate_up_proj": ["up_proj", "down_proj"]
"gate_up_proj": ["up_proj", "down_proj"],
}
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
self.vllm_config = vllm_config
cache_config = vllm_config.cache_config
assert not cache_config.enable_prefix_caching, \
assert not cache_config.enable_prefix_caching, (
"Qwen3NextMTP currently does not support prefix caching"
)
self.quant_config = vllm_config.quant_config
super().__init__()
self.config = config
self.model = Qwen3NextMultiTokenPredictor(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "mtp"))
self.model = Qwen3NextMultiTokenPredictor(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "mtp")
)
self.unpadded_vocab_size = config.vocab_size
self.lm_head = ParallelLMHead(self.unpadded_vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
padding_size=DEFAULT_VOCAB_PADDING_SIZE,
prefix=maybe_prefix(prefix, "lm_head"))
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size)
self.lm_head = ParallelLMHead(
self.unpadded_vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
padding_size=DEFAULT_VOCAB_PADDING_SIZE,
prefix=maybe_prefix(prefix, "lm_head"),
)
self.logits_processor = LogitsProcessor(
self.unpadded_vocab_size, config.vocab_size
)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
self.model.make_empty_intermediate_tensors
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
@@ -254,8 +279,9 @@ class Qwen3NextMTP(nn.Module, SupportsPP):
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs: object,
):
hidden_states = self.model(input_ids, positions, hidden_states,
intermediate_tensors, inputs_embeds)
hidden_states = self.model(
input_ids, positions, hidden_states, intermediate_tensors, inputs_embeds
)
return hidden_states
def compute_logits(
@@ -265,8 +291,7 @@ class Qwen3NextMTP(nn.Module, SupportsPP):
) -> Optional[torch.Tensor]:
return self.logits_processor(self.lm_head, hidden_states)
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
shared_weight_names = ["embed_tokens", "lm_head"]
def remap_weight_names(weights):