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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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185
vllm/model_executor/models/stablelm.py
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@@ -21,6 +21,7 @@
# https://huggingface.co/stabilityai/stablelm-3b-4e1t/blob/main/config.json
"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM)
model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Optional, Union
@@ -33,43 +34,56 @@ from vllm.attention import Attention
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
MergedColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
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
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsPP
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from .utils import (
AutoWeightsLoader,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
class StablelmMLP(nn.Module):
def __init__(self,
config: StableLmConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "") -> None:
def __init__(
self,
config: StableLmConfig,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_up_proj = MergedColumnParallelLinear(
config.hidden_size, [config.intermediate_size] * 2,
config.hidden_size,
[config.intermediate_size] * 2,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(config.intermediate_size,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.down_proj")
prefix=f"{prefix}.gate_up_proj",
)
self.down_proj = RowParallelLinear(
config.intermediate_size,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.down_proj",
)
self.act_fn = SiluAndMul()
def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -80,12 +94,13 @@ class StablelmMLP(nn.Module):
class StablelmAttention(nn.Module):
def __init__(self,
config: StableLmConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "") -> None:
def __init__(
self,
config: StableLmConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
@@ -102,33 +117,39 @@ class StablelmAttention(nn.Module):
# Number of KV heads is less than TP size, so we replicate
# the KV heads across multiple tensor parallel GPUs.
assert tp_size % self.total_num_key_value_heads == 0
self.num_key_value_heads = max(
1, self.total_num_key_value_heads // tp_size)
self.num_key_value_heads = max(1, self.total_num_key_value_heads // tp_size)
self.head_dim = self.hidden_size // self.total_num_heads
self.max_position_embeddings = config.max_position_embeddings
self.partial_rotary_factor = getattr(
config, "rope_pct", getattr(config, "partial_rotary_factor", 1))
config, "rope_pct", getattr(config, "partial_rotary_factor", 1)
)
self.scaling = self.head_dim**-0.5
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_key_value_heads * self.head_dim
self.qkv_bias = getattr(config, "use_qkv_bias", False)
if (self.head_dim * self.num_heads * tp_size) != self.hidden_size:
raise ValueError(f"hidden_size must be divisible by num_heads "
f"(got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads}).")
raise ValueError(
f"hidden_size must be divisible by num_heads "
f"(got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.qkv_proj = QKVParallelLinear(self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_key_value_heads,
self.qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj")
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj")
self.qkv_proj = QKVParallelLinear(
self.hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_key_value_heads,
self.qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
self.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
@@ -136,13 +157,15 @@ class StablelmAttention(nn.Module):
base=self.config.rope_theta,
partial_rotary_factor=self.partial_rotary_factor,
)
self.attn = Attention(self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_key_value_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_key_value_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -158,7 +181,6 @@ class StablelmAttention(nn.Module):
class StablelmDecoderLayer(nn.Module):
def __init__(
self,
config: StableLmConfig,
@@ -167,16 +189,13 @@ class StablelmDecoderLayer(nn.Module):
prefix: str = "",
) -> None:
super().__init__()
self.self_attn = StablelmAttention(config,
cache_config,
quant_config,
prefix=f"{prefix}.self_attn")
self.self_attn = StablelmAttention(
config, cache_config, quant_config, prefix=f"{prefix}.self_attn"
)
self.mlp = StablelmMLP(config, quant_config, prefix=f"{prefix}.mlp")
norm_eps = getattr(config, "norm_eps",
getattr(config, "layer_norm_eps", 1e-05))
norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05))
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
eps=norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
def forward(
self,
@@ -202,7 +221,6 @@ class StablelmDecoderLayer(nn.Module):
class StableLMEpochModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -219,15 +237,15 @@ class StableLMEpochModel(nn.Module):
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: StablelmDecoderLayer(
config, cache_config, quant_config, prefix=prefix),
config, cache_config, quant_config, prefix=prefix
),
prefix=f"{prefix}.layers",
)
norm_eps = getattr(config, "norm_eps",
getattr(config, "layer_norm_eps", 1e-05))
norm_eps = getattr(config, "norm_eps", getattr(config, "layer_norm_eps", 1e-05))
self.norm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states"], config.hidden_size
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
@@ -254,8 +272,7 @@ class StableLMEpochModel(nn.Module):
hidden_states = self.norm(hidden_states)
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"),
@@ -267,7 +284,7 @@ class StableLMEpochModel(nn.Module):
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
for (param_name, weight_name, shard_id) in stacked_params_mapping:
for param_name, weight_name, shard_id in stacked_params_mapping:
if weight_name not in name:
continue
name = name.replace(weight_name, param_name)
@@ -287,32 +304,34 @@ class StableLMEpochModel(nn.Module):
if is_pp_missing_parameter(name, self):
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 StablelmForCausalLM(nn.Module, SupportsPP):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.model = StableLMEpochModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.lm_head")
self.model = StableLMEpochModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
)
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.lm_head",
)
if self.config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(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)
@@ -324,8 +343,9 @@ class StablelmForCausalLM(nn.Module, SupportsPP):
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
hidden_states = self.model(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return hidden_states
def compute_logits(
@@ -335,7 +355,6 @@ class StablelmForCausalLM(nn.Module, SupportsPP):
logits = self.logits_processor(self.lm_head, hidden_states)
return logits
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(self)
return loader.load_weights(weights)