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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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158
vllm/model_executor/models/opt.py
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@@ -19,6 +19,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only OPT model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Optional, Union
@@ -32,25 +33,33 @@ from vllm.compilation.decorators import support_torch_compile
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 get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
ColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
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 SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from .utils import (
AutoWeightsLoader,
WeightsMapper,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
class OPTLearnedPositionalEmbedding(nn.Embedding):
def __init__(self, num_embeddings: int, embedding_dim: int):
# OPT is set up so that if padding_idx is specified then offset the
# embedding ids by 2 and adjust num_embeddings appropriately. Other
@@ -63,7 +72,6 @@ class OPTLearnedPositionalEmbedding(nn.Embedding):
class OPTAttention(nn.Module):
def __init__(
self,
embed_dim: int,
@@ -75,8 +83,7 @@ class OPTAttention(nn.Module):
) -> None:
super().__init__()
self.embed_dim = embed_dim
tensor_model_parallel_world_size = (
get_tensor_model_parallel_world_size())
tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
total_num_heads = num_heads
assert num_heads % tensor_model_parallel_world_size == 0
self.num_heads = total_num_heads // tensor_model_parallel_world_size
@@ -98,12 +105,14 @@ class OPTAttention(nn.Module):
quant_config=quant_config,
prefix=f"{prefix}.out_proj",
)
self.attn = Attention(self.num_heads,
self.head_dim,
scale=self.scaling,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
scale=self.scaling,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -117,7 +126,6 @@ class OPTAttention(nn.Module):
class OPTDecoderLayer(nn.Module):
def __init__(
self,
config: OPTConfig,
@@ -139,8 +147,8 @@ class OPTDecoderLayer(nn.Module):
self.do_layer_norm_before = config.do_layer_norm_before
self.self_attn_layer_norm = nn.LayerNorm(
self.embed_dim,
elementwise_affine=config.layer_norm_elementwise_affine)
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
)
self.fc1 = ColumnParallelLinear(
self.embed_dim,
config.ffn_dim,
@@ -157,8 +165,8 @@ class OPTDecoderLayer(nn.Module):
prefix=f"{prefix}.fc2",
)
self.final_layer_norm = nn.LayerNorm(
self.embed_dim,
elementwise_affine=config.layer_norm_elementwise_affine)
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
)
def forward(
self,
@@ -191,7 +199,6 @@ class OPTDecoderLayer(nn.Module):
class OPTDecoder(nn.Module):
def __init__(
self,
config: OPTConfig,
@@ -210,24 +217,29 @@ class OPTDecoder(nn.Module):
)
# Positional embeddings are replicated (not sharded).
self.embed_positions = OPTLearnedPositionalEmbedding(
config.max_position_embeddings, config.hidden_size)
config.max_position_embeddings, config.hidden_size
)
# Project out & in will be replicated if they exist.
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = ReplicatedLinear(config.hidden_size,
config.word_embed_proj_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.project_out")
self.project_out = ReplicatedLinear(
config.hidden_size,
config.word_embed_proj_dim,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.project_out",
)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.project_in")
self.project_in = ReplicatedLinear(
config.word_embed_proj_dim,
config.hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.project_in",
)
else:
self.project_in = None
@@ -238,15 +250,18 @@ class OPTDecoder(nn.Module):
if config.do_layer_norm_before and not config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm(
config.hidden_size,
elementwise_affine=config.layer_norm_elementwise_affine)
elementwise_affine=config.layer_norm_elementwise_affine,
)
else:
self.final_layer_norm = None
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: OPTDecoderLayer(
config, cache_config, quant_config, prefix=prefix),
prefix=f"{prefix}.layers")
config, cache_config, quant_config, prefix=prefix
),
prefix=f"{prefix}.layers",
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embed_tokens(input_ids)
@@ -283,7 +298,6 @@ class OPTDecoder(nn.Module):
@support_torch_compile
class OPTModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -291,13 +305,12 @@ class OPTModel(nn.Module):
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
self.decoder = OPTDecoder(config,
cache_config,
quant_config,
prefix=f"{prefix}.decoder")
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))
self.decoder = OPTDecoder(
config, cache_config, quant_config, prefix=f"{prefix}.decoder"
)
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.decoder.get_input_embeddings(input_ids)
@@ -309,13 +322,11 @@ class OPTModel(nn.Module):
intermediate_tensors: Optional[IntermediateTensors],
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
return self.decoder(input_ids,
positions,
intermediate_tensors,
inputs_embeds=inputs_embeds)
return self.decoder(
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
)
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"),
@@ -325,7 +336,7 @@ class OPTModel(nn.Module):
params_dict = dict(self.named_parameters(remove_duplicate=False))
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)
@@ -345,8 +356,7 @@ class OPTModel(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
@@ -357,9 +367,11 @@ class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
}
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
"decoder.": "model.decoder.",
})
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_prefix={
"decoder.": "model.decoder.",
}
)
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -367,18 +379,21 @@ class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.model = OPTModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.model = OPTModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
)
if self.config.tie_word_embeddings:
self.lm_head = self.model.decoder.embed_tokens
else:
self.lm_head = ParallelLMHead(config.vocab_size,
config.word_embed_proj_dim,
prefix=maybe_prefix(
prefix, "lm_head"))
self.lm_head = ParallelLMHead(
config.vocab_size,
config.word_embed_proj_dim,
prefix=maybe_prefix(prefix, "lm_head"),
)
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)
@@ -390,8 +405,9 @@ class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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(
@@ -401,11 +417,11 @@ class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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.weight"]
if self.config.tie_word_embeddings else None),
skip_prefixes=(
["lm_head.weight"] if self.config.tie_word_embeddings else None
),
)
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)