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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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68
vllm/model_executor/models/llama_eagle.py
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@@ -13,11 +13,9 @@ from vllm.config import VllmConfig
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.llama import (LlamaDecoderLayer,
LlamaForCausalLM)
from vllm.model_executor.models.llama import LlamaDecoderLayer, LlamaForCausalLM
from .utils import AutoWeightsLoader, maybe_prefix
@@ -25,7 +23,6 @@ logger = init_logger(__name__)
class LlamaDecoderLayer(LlamaDecoderLayer):
def __init__(
self,
vllm_config: VllmConfig,
@@ -44,7 +41,6 @@ class LlamaDecoderLayer(LlamaDecoderLayer):
@support_torch_compile
class LlamaModel(nn.Module):
def __init__(
self,
*,
@@ -53,8 +49,7 @@ class LlamaModel(nn.Module):
start_layer_id: int = 0,
) -> None:
super().__init__()
self.config = vllm_config. \
speculative_config.draft_model_config.hf_config
self.config = vllm_config.speculative_config.draft_model_config.hf_config
self.vocab_size = self.config.vocab_size
self.embed_tokens = VocabParallelEmbedding(
@@ -63,17 +58,20 @@ class LlamaModel(nn.Module):
prefix=maybe_prefix(prefix, "embed_tokens"),
)
self.layers = nn.ModuleList([
LlamaDecoderLayer(
vllm_config,
i == 0,
prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
config=self.config,
) for i in range(self.config.num_hidden_layers)
])
self.fc = torch.nn.Linear(self.config.hidden_size * 2,
self.config.hidden_size,
bias=False)
self.layers = nn.ModuleList(
[
LlamaDecoderLayer(
vllm_config,
i == 0,
prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
config=self.config,
)
for i in range(self.config.num_hidden_layers)
]
)
self.fc = torch.nn.Linear(
self.config.hidden_size * 2, self.config.hidden_size, bias=False
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
@@ -85,8 +83,7 @@ class LlamaModel(nn.Module):
hidden_states: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
input_embeds = self.embed_tokens(input_ids)
hidden_states = self.fc(
torch.cat((input_embeds, hidden_states), dim=-1))
hidden_states = self.fc(torch.cat((input_embeds, hidden_states), dim=-1))
residual = None
for layer in self.layers:
hidden_states, residual = layer(
@@ -97,8 +94,7 @@ class LlamaModel(nn.Module):
hidden_states = hidden_states + residual
return hidden_states, 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"),
@@ -119,40 +115,37 @@ class LlamaModel(nn.Module):
weight_loader(param, loaded_weight, shard_id)
break
else:
# if PP disabled then draft will share embed with target
if get_pp_group().world_size == 1 and \
"embed_tokens." in name:
if get_pp_group().world_size == 1 and "embed_tokens." in name:
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
class EagleLlamaForCausalLM(LlamaForCausalLM):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
nn.Module.__init__(self)
self.config = vllm_config. \
speculative_config.draft_model_config.hf_config
self.config = vllm_config.speculative_config.draft_model_config.hf_config
# Ensure draft_vocab_size is set
# default to the base vocab size when absent
if getattr(self.config, "draft_vocab_size", None) is None:
base_vocab_size = getattr(self.config, "vocab_size", None)
self.config.draft_vocab_size = base_vocab_size
target_layer_num = vllm_config.model_config.get_num_layers(
vllm_config.parallel_config)
self.model = LlamaModel(vllm_config=vllm_config,
prefix="model",
start_layer_id=target_layer_num)
vllm_config.parallel_config
)
self.model = LlamaModel(
vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num
)
logit_scale = getattr(self.config, "logit_scale", 1.0)
self.logits_processor = LogitsProcessor(self.config.vocab_size,
scale=logit_scale)
self.logits_processor = LogitsProcessor(
self.config.vocab_size, scale=logit_scale
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
@@ -171,7 +164,6 @@ class EagleLlamaForCausalLM(LlamaForCausalLM):
return self.model(input_ids, positions, hidden_states)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
def transform(inputs):
name, loaded_weight = inputs
if "lm_head" not in name: