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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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94
vllm/model_executor/models/deepseek_eagle.py
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@@ -14,18 +14,23 @@ from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.models.deepseek_v2 import (DeepseekV2DecoderLayer,
DeepseekV3ForCausalLM)
default_weight_loader,
maybe_remap_kv_scale_name,
)
from vllm.model_executor.models.deepseek_v2 import (
DeepseekV2DecoderLayer,
DeepseekV3ForCausalLM,
)
from .utils import AutoWeightsLoader, maybe_prefix
@support_torch_compile
class DeepseekV2Model(nn.Module):
def __init__(
self,
*,
@@ -34,8 +39,7 @@ class DeepseekV2Model(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
quant_config = vllm_config.quant_config
self.vocab_size = self.config.vocab_size
@@ -46,12 +50,15 @@ class DeepseekV2Model(nn.Module):
prefix=maybe_prefix(prefix, "embed_tokens"),
)
self.layers = nn.ModuleList([
DeepseekV2DecoderLayer(
vllm_config,
prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
) for i in range(self.config.num_hidden_layers)
])
self.layers = nn.ModuleList(
[
DeepseekV2DecoderLayer(
vllm_config,
prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"),
)
for i in range(self.config.num_hidden_layers)
]
)
self.fc = nn.Linear(
self.config.model.hidden_size * 2,
@@ -59,12 +66,9 @@ class DeepseekV2Model(nn.Module):
bias=False,
)
self.enorm = RMSNorm(self.config.hidden_size,
eps=self.config.rms_norm_eps)
self.hnorm = RMSNorm(self.config.hidden_size,
eps=self.config.rms_norm_eps)
self.norm = RMSNorm(self.config.hidden_size,
eps=self.config.rms_norm_eps)
self.enorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
self.hnorm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
self.norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
@@ -78,8 +82,8 @@ class DeepseekV2Model(nn.Module):
input_embeds = self.embed_tokens(input_ids)
inputs = torch.cat(
[self.enorm(input_embeds),
self.hnorm(hidden_states)], dim=-1)
[self.enorm(input_embeds), self.hnorm(hidden_states)], dim=-1
)
hidden_states = self.fc(inputs)
residual = None
for layer in self.layers:
@@ -91,8 +95,7 @@ class DeepseekV2Model(nn.Module):
hidden_states, _ = self.norm(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)
("gate_up_proj", "gate_proj", 0),
@@ -107,7 +110,8 @@ class DeepseekV2Model(nn.Module):
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.n_routed_experts)
num_experts=self.config.n_routed_experts,
)
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
@@ -132,8 +136,9 @@ class DeepseekV2Model(nn.Module):
# QKV fusion is optional, fall back to normal
# weight loading if it's not enabled
# if go with fusion option, then update name
if ((param_name == "fused_qkv_a_proj")
and name_mapped not in params_dict):
if (
param_name == "fused_qkv_a_proj"
) and name_mapped not in params_dict:
continue
else:
name = name_mapped
@@ -165,8 +170,7 @@ class DeepseekV2Model(nn.Module):
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
# Skip loading extra bias for GPTQ models.
@@ -179,34 +183,37 @@ class DeepseekV2Model(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
class EagleDeepseekV3ForCausalLM(DeepseekV3ForCausalLM):
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
quant_config = vllm_config.quant_config
target_layer_num = vllm_config.model_config.get_num_layers(
vllm_config.parallel_config)
self.model = DeepseekV2Model(vllm_config=vllm_config,
prefix="model",
start_layer_id=target_layer_num)
vllm_config.parallel_config
)
self.model = DeepseekV2Model(
vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num
)
self.lm_head = ParallelLMHead(self.config.vocab_size,
self.config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"))
self.lm_head = ParallelLMHead(
self.config.vocab_size,
self.config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"),
)
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)
@@ -232,7 +239,6 @@ class EagleDeepseekV3ForCausalLM(DeepseekV3ForCausalLM):
return logits
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
def transform(inputs):
name, loaded_weight = inputs
if "lm_head" not in name: