biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Convert formatting to use ruff instead of yapf + isort (#26247)

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

162
vllm/model_executor/models/persimmon.py
View File

@@ -22,6 +22,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only persimmon model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Optional, Union
@@ -35,35 +36,42 @@ 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,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
ColumnParallelLinear,
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 PersimmonMLP(nn.Module):
def __init__(self,
config: PersimmonConfig,
quant_config: Optional[QuantizationConfig] = None):
def __init__(
self, config: PersimmonConfig, quant_config: Optional[QuantizationConfig] = None
):
super().__init__()
self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
config.intermediate_size,
quant_config=quant_config)
self.dense_4h_to_h = RowParallelLinear(config.intermediate_size,
config.hidden_size,
quant_config=quant_config)
self.dense_h_to_4h = ColumnParallelLinear(
config.hidden_size, config.intermediate_size, quant_config=quant_config
)
self.dense_4h_to_h = RowParallelLinear(
config.intermediate_size, config.hidden_size, quant_config=quant_config
)
self.act = get_act_fn(config.hidden_act)
def forward(self, hidden_states) -> torch.Tensor:
@@ -74,12 +82,13 @@ class PersimmonMLP(nn.Module):
class PersimmonAttention(nn.Module):
def __init__(self,
config: PersimmonConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = ""):
def __init__(
self,
config: PersimmonConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.config = config
tensor_parallel_world_size = get_tensor_model_parallel_world_size()
@@ -123,12 +132,14 @@ class PersimmonAttention(nn.Module):
partial_rotary_factor=self.partial_rotary_factor,
)
self.scaling = self.head_dim**-0.5
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 _split_heads(self, x: torch.Tensor) -> torch.Tensor:
# [seq_length, hidden_size] -> [seq_length, num_heads, head_dim]
@@ -167,23 +178,28 @@ class PersimmonAttention(nn.Module):
class PersimmonDecoderLayer(nn.Module):
def __init__(self,
config: PersimmonConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = ""):
def __init__(
self,
config: PersimmonConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = PersimmonAttention(config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn")
self.self_attn = PersimmonAttention(
config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
)
self.mlp = PersimmonMLP(config, quant_config=quant_config)
self.input_layernorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.input_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
self.post_attention_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
def forward(
self,
@@ -214,7 +230,6 @@ class PersimmonDecoderLayer(nn.Module):
@support_torch_compile
class PersimmonModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -224,18 +239,22 @@ class PersimmonModel(nn.Module):
self.vocab_size = config.vocab_size
self.config = config
self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
config.hidden_size)
self.embed_tokens = VocabParallelEmbedding(
config.vocab_size, config.hidden_size
)
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: PersimmonDecoderLayer(
config, cache_config, quant_config, prefix=prefix),
prefix=f"{prefix}.layers")
self.final_layernorm = nn.LayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))
config, cache_config, quant_config, prefix=prefix
),
prefix=f"{prefix}.layers",
)
self.final_layernorm = nn.LayerNorm(
config.hidden_size, eps=config.layer_norm_eps
)
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)
@@ -262,8 +281,7 @@ class PersimmonModel(nn.Module):
hidden_states = self.final_layernorm(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]:
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
@@ -282,35 +300,38 @@ class PersimmonModel(nn.Module):
if output_dim is not None:
loaded_weight_shape = loaded_weight.shape
loaded_weight = loaded_weight.view(
loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
loaded_weight_shape[output_dim + 1:])
loaded_weight = loaded_weight.transpose(
output_dim, output_dim + 1)
loaded_weight_shape[:output_dim]
+ (num_heads, 3, -1)
+ loaded_weight_shape[output_dim + 1 :]
)
loaded_weight = loaded_weight.transpose(output_dim, output_dim + 1)
loaded_weight = loaded_weight.reshape(loaded_weight_shape)
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 PersimmonForCausalLM(nn.Module, SupportsPP):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
self.config = config
self.vocab_size = config.vocab_size
self.model = PersimmonModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
bias=False,
prefix=maybe_prefix(prefix, "lm_head"))
self.model = PersimmonModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
)
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
bias=False,
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)
@@ -337,7 +358,6 @@ class PersimmonForCausalLM(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)