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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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301
vllm/model_executor/models/llama.py
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@@ -23,6 +23,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only LLaMA model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Any, Optional, Union
@@ -38,27 +39,38 @@ 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.layernorm import RMSNorm
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 (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
DEFAULT_VOCAB_PADDING_SIZE,
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
default_weight_loader,
maybe_remap_kv_scale_name,
)
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from .utils import (
AutoWeightsLoader,
PPMissingLayer,
extract_layer_index,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
class LlamaMLP(nn.Module):
def __init__(
self,
hidden_size: int,
@@ -89,8 +101,9 @@ class LlamaMLP(nn.Module):
prefix=f"{prefix}.down_proj",
)
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
raise ValueError(
f"Unsupported activation: {hidden_act}. Only silu is supported for now."
)
self.act_fn = SiluAndMul()
def forward(self, x):
@@ -101,7 +114,6 @@ class LlamaMLP(nn.Module):
class LlamaAttention(nn.Module):
def __init__(
self,
config: LlamaConfig,
@@ -141,8 +153,7 @@ class LlamaAttention(nn.Module):
head_dim = self.hidden_size // self.total_num_heads
self.head_dim = head_dim
# Phi models introduced a partial_rotary_factor parameter in the config
self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
1)
self.partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim**-0.5
@@ -167,33 +178,36 @@ class LlamaAttention(nn.Module):
prefix=f"{prefix}.o_proj",
)
self._init_rotary_emb(config,
rope_scaling=rope_scaling,
quant_config=quant_config)
self._init_rotary_emb(
config, rope_scaling=rope_scaling, quant_config=quant_config
)
sliding_window = None
if layer_types := getattr(config, "layer_types", None):
# Fix for Eagle3 compatibility:
# for draft models, subtract target layer count
# to get draft-relative layer index starting from 0
if hasattr(config, 'target_layer_count'):
if hasattr(config, "target_layer_count"):
# This is a draft model,
# adjust layer_idx to be relative to draft layers
effective_layer_idx = layer_idx - config.target_layer_count
else:
# This is a target model, use layer_idx directly
effective_layer_idx = layer_idx
assert effective_layer_idx < len(layer_types), \
assert effective_layer_idx < len(layer_types), (
f"effective_layer_idx: {effective_layer_idx} \
is out of bounds for layer_types: {layer_types}"
)
is_sliding = layer_types[
effective_layer_idx] == "sliding_attention"
is_sliding = layer_types[effective_layer_idx] == "sliding_attention"
if is_sliding:
sliding_window = config.sliding_window
attn_cls = (EncoderOnlyAttention
if attn_type == AttentionType.ENCODER_ONLY else Attention)
attn_cls = (
EncoderOnlyAttention
if attn_type == AttentionType.ENCODER_ONLY
else Attention
)
self.attn = attn_cls(
self.num_heads,
@@ -219,9 +233,12 @@ class LlamaAttention(nn.Module):
output, _ = self.o_proj(attn_output)
return output
def _init_rotary_emb(self, config: LlamaConfig,
rope_scaling: Optional[dict[str, Any]],
quant_config: Optional[QuantizationConfig]) -> None:
def _init_rotary_emb(
self,
config: LlamaConfig,
rope_scaling: Optional[dict[str, Any]],
quant_config: Optional[QuantizationConfig],
) -> None:
is_neox_style = True
is_gguf = quant_config and quant_config.get_name() == "gguf"
if is_gguf and config.model_type == "llama":
@@ -239,11 +256,12 @@ class LlamaAttention(nn.Module):
class LlamaDecoderLayer(nn.Module):
def __init__(self,
vllm_config: VllmConfig,
prefix: str = "",
config: Optional[LlamaConfig] = None) -> None:
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
config: Optional[LlamaConfig] = None,
) -> None:
super().__init__()
config = config or vllm_config.model_config.hf_config
@@ -254,18 +272,20 @@ class LlamaDecoderLayer(nn.Module):
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
if rope_scaling is not None and getattr(
config, "original_max_position_embeddings", None):
config, "original_max_position_embeddings", None
):
rope_scaling["original_max_position_embeddings"] = (
config.original_max_position_embeddings)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
config.original_max_position_embeddings
)
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
# Support abacusai/Smaug-72B-v0.1 with attention_bias
# Support internlm/internlm-7b with bias
attention_bias = getattr(config, "attention_bias", False) or getattr(
config, "bias", False)
config, "bias", False
)
bias_o_proj = attention_bias
# support internlm/internlm3-8b with qkv_bias
if hasattr(config, 'qkv_bias'):
if hasattr(config, "qkv_bias"):
attention_bias = config.qkv_bias
# By default, Llama uses causal attention as it is a decoder-only model.
@@ -281,8 +301,9 @@ class LlamaDecoderLayer(nn.Module):
config=config,
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=getattr(config, "num_key_value_heads",
config.num_attention_heads),
num_kv_heads=getattr(
config, "num_key_value_heads", config.num_attention_heads
),
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
@@ -301,10 +322,10 @@ class LlamaDecoderLayer(nn.Module):
bias=getattr(config, "mlp_bias", False),
prefix=f"{prefix}.mlp",
)
self.input_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
def forward(
self,
@@ -317,31 +338,28 @@ class LlamaDecoderLayer(nn.Module):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
hidden_states = self.self_attn(positions=positions,
hidden_states=hidden_states)
hidden_states, residual = self.input_layernorm(hidden_states, residual)
hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
# Fully Connected
hidden_states, residual = self.post_attention_layernorm(
hidden_states, residual)
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
hidden_states = self.mlp(hidden_states)
return hidden_states, residual
def get_quant_config(
self, vllm_config: VllmConfig) -> Optional[QuantizationConfig]:
def get_quant_config(self, vllm_config: VllmConfig) -> Optional[QuantizationConfig]:
"""Get quantization config for this layer. Override in subclasses."""
return vllm_config.quant_config
@support_torch_compile
class LlamaModel(nn.Module):
def __init__(self,
*,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: type[nn.Module] = LlamaDecoderLayer):
def __init__(
self,
*,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: type[nn.Module] = LlamaDecoderLayer,
):
super().__init__()
config = vllm_config.model_config.hf_config
@@ -350,12 +368,16 @@ class LlamaModel(nn.Module):
self.config = config
self.quant_config = quant_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
if get_pp_group().is_first_rank or (config.tie_word_embeddings
and get_pp_group().is_last_rank):
if get_pp_group().is_first_rank or (
config.tie_word_embeddings and get_pp_group().is_last_rank
):
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
@@ -376,9 +398,9 @@ class LlamaModel(nn.Module):
self.aux_hidden_state_layers = tuple[int, ...]()
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
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
@@ -389,8 +411,9 @@ class LlamaModel(nn.Module):
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor,
list[torch.Tensor]]]:
) -> Union[
torch.Tensor, IntermediateTensors, tuple[torch.Tensor, list[torch.Tensor]]
]:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
@@ -404,16 +427,16 @@ class LlamaModel(nn.Module):
aux_hidden_states = []
for idx, layer in enumerate(
islice(self.layers, self.start_layer, self.end_layer)):
islice(self.layers, self.start_layer, self.end_layer)
):
if idx in self.aux_hidden_state_layers:
aux_hidden_states.append(hidden_states + residual)
hidden_states, residual = layer(positions, hidden_states, residual)
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)
@@ -421,8 +444,7 @@ class LlamaModel(nn.Module):
return hidden_states, aux_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"),
@@ -436,19 +458,19 @@ class LlamaModel(nn.Module):
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
if ("rotary_emb.cos_cached" in name
or "rotary_emb.sin_cached" in name):
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
# Models trained using ColossalAI may include these tensors in
# the checkpoint. Skip them.
continue
if (self.quant_config is not None and
(scale_name := self.quant_config.get_cache_scale(name))):
if self.quant_config is not None and (
scale_name := self.quant_config.get_cache_scale(name)
):
# Loading kv cache quantization scales
param = params_dict[scale_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
loaded_weight[0])
weight_loader = getattr(param, "weight_loader", default_weight_loader)
loaded_weight = (
loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
)
weight_loader(param, loaded_weight)
loaded_params.add(scale_name)
continue
@@ -481,8 +503,7 @@ class LlamaModel(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
@@ -491,13 +512,13 @@ class LlamaModel(nn.Module):
class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
packed_modules_mapping = {
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
"gate_up_proj": ["gate_proj", "up_proj"]
"gate_up_proj": ["gate_proj", "up_proj"],
}
# LoRA specific attributes
embedding_modules = {
"embed_tokens": "input_embeddings",
"lm_head": "output_embeddings"
"lm_head": "output_embeddings",
}
embedding_padding_modules = ["lm_head"]
@@ -527,11 +548,13 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
"norm": "model.norm",
}
def __init__(self,
*,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: type[nn.Module] = LlamaDecoderLayer):
def __init__(
self,
*,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: type[nn.Module] = LlamaDecoderLayer,
):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
@@ -539,9 +562,11 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
self.config = config
self.lora_config = lora_config
self.model = self._init_model(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"),
layer_type=layer_type)
self.model = self._init_model(
vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"),
layer_type=layer_type,
)
if get_pp_group().is_last_rank:
self.unpadded_vocab_size = config.vocab_size
@@ -555,24 +580,25 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
DEFAULT_VOCAB_PADDING_SIZE
# We need bigger padding if using lora for kernel
# compatibility
if not lora_config else
lora_config.lora_vocab_padding_size),
if not lora_config
else lora_config.lora_vocab_padding_size
),
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"),
)
if config.tie_word_embeddings:
self.lm_head = self.lm_head.tie_weights(
self.model.embed_tokens)
self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
logit_scale = getattr(config, "logit_scale", 1.0)
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
logit_scale)
self.logits_processor = LogitsProcessor(
self.unpadded_vocab_size, config.vocab_size, logit_scale
)
else:
self.lm_head = PPMissingLayer()
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
self.model.make_empty_intermediate_tensors
)
def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
self.model.aux_hidden_state_layers = layers
@@ -581,13 +607,13 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
num_layers = len(self.model.layers)
return (2, num_layers // 2, num_layers - 3)
def _init_model(self,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: type[nn.Module] = LlamaDecoderLayer):
return LlamaModel(vllm_config=vllm_config,
prefix=prefix,
layer_type=layer_type)
def _init_model(
self,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: type[nn.Module] = LlamaDecoderLayer,
):
return LlamaModel(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.get_input_embeddings(input_ids)
@@ -599,8 +625,9 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
model_output = self.model(input_ids, positions, intermediate_tensors,
inputs_embeds)
model_output = self.model(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return model_output
def compute_logits(
@@ -610,16 +637,15 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
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,
skip_prefixes=(["lm_head."]
if self.config.tie_word_embeddings else None),
skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
)
return loader.load_weights(
self.maybe_remap_mistral(name, loaded_weight)
for name, loaded_weight in weights)
for name, loaded_weight in weights
)
# This function is used to remap the mistral format as
# used by Mistral and Llama <=2
@@ -628,12 +654,14 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
name: str,
loaded_weight: torch.Tensor,
) -> tuple[str, torch.Tensor]:
def permute(w: torch.Tensor, n_heads: int, attn_out: int):
attn_in = self.config.head_dim * n_heads
return w.view(n_heads, attn_in // n_heads // 2, 2,
attn_out).transpose(1, 2).reshape(attn_in, attn_out)
return (
w.view(n_heads, attn_in // n_heads // 2, 2, attn_out)
.transpose(1, 2)
.reshape(attn_in, attn_out)
)
mapping = self.mistral_mapping
modules = name.split(".")
@@ -642,29 +670,32 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
# If using quantized model in mistral format,
# quantization scales (qscale_weight) also need to be sliced
if "wk" in modules and modules[-1] == "weight":
loaded_weight = permute(loaded_weight,
self.config.num_key_value_heads,
self.config.hidden_size)
elif "wk" in modules and modules[
-1] == "qscale_weight" and loaded_weight.numel() > 1:
loaded_weight = permute(loaded_weight,
self.config.num_key_value_heads, 1)
loaded_weight = permute(
loaded_weight, self.config.num_key_value_heads, self.config.hidden_size
)
elif (
"wk" in modules
and modules[-1] == "qscale_weight"
and loaded_weight.numel() > 1
):
loaded_weight = permute(loaded_weight, self.config.num_key_value_heads, 1)
elif "wq" in modules and modules[-1] == "weight":
loaded_weight = permute(loaded_weight,
self.config.num_attention_heads,
self.config.hidden_size)
elif "wq" in modules and modules[
-1] == "qscale_weight" and loaded_weight.numel() > 1:
loaded_weight = permute(loaded_weight,
self.config.num_attention_heads, 1)
loaded_weight = permute(
loaded_weight, self.config.num_attention_heads, self.config.hidden_size
)
elif (
"wq" in modules
and modules[-1] == "qscale_weight"
and loaded_weight.numel() > 1
):
loaded_weight = permute(loaded_weight, self.config.num_attention_heads, 1)
num_modules = len(modules)
for i in range(num_modules):
item = modules[i]
next_item = modules[i + 1] if i < num_modules - 1 else None
combined_item = (f"{item}.{next_item}"
if next_item is not None else None)
combined_item = f"{item}.{next_item}" if next_item is not None else None
if combined_item in mapping:
name = name.replace(combined_item, mapping[combined_item])