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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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130
vllm/model_executor/models/gpt_bigcode.py
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@@ -20,6 +20,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only GPTBigCode model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Optional, Union
@@ -33,24 +34,31 @@ 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.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, 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 GPTBigCodeAttention(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
@@ -61,11 +69,9 @@ class GPTBigCodeAttention(nn.Module):
super().__init__()
self.hidden_size = config.hidden_size
total_num_heads = config.num_attention_heads
self.tensor_model_parallel_world_size = (
get_tensor_model_parallel_world_size())
self.tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
assert total_num_heads % self.tensor_model_parallel_world_size == 0
self.num_heads = (total_num_heads //
self.tensor_model_parallel_world_size)
self.num_heads = total_num_heads // self.tensor_model_parallel_world_size
self.head_dim = self.hidden_size // total_num_heads
self.scale = self.head_dim**-0.5
@@ -94,13 +100,15 @@ class GPTBigCodeAttention(nn.Module):
quant_config=quant_config,
prefix=f"{prefix}.c_proj",
)
self.attn = Attention(self.num_heads,
self.head_dim,
scale=self.scale,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
scale=self.scale,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -110,7 +118,8 @@ class GPTBigCodeAttention(nn.Module):
q, k, v = qkv.split(
[
self.hidden_size // self.tensor_model_parallel_world_size,
self.kv_dim, self.kv_dim
self.kv_dim,
self.kv_dim,
],
dim=-1,
)
@@ -120,7 +129,6 @@ class GPTBigCodeAttention(nn.Module):
class GPTBigMLP(nn.Module):
def __init__(
self,
intermediate_size: int,
@@ -154,7 +162,6 @@ class GPTBigMLP(nn.Module):
class GPTBigCodeBlock(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
@@ -164,19 +171,14 @@ class GPTBigCodeBlock(nn.Module):
):
super().__init__()
hidden_size = config.hidden_size
inner_dim = (config.n_inner if config.n_inner is not None else 4 *
hidden_size)
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = GPTBigCodeAttention(config,
cache_config,
quant_config,
prefix=f"{prefix}.attn")
self.attn = GPTBigCodeAttention(
config, cache_config, quant_config, prefix=f"{prefix}.attn"
)
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = GPTBigMLP(inner_dim,
config,
quant_config,
prefix=f"{prefix}.mlp")
self.mlp = GPTBigMLP(inner_dim, config, quant_config, prefix=f"{prefix}.mlp")
def forward(
self,
@@ -184,7 +186,9 @@ class GPTBigCodeBlock(nn.Module):
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_output = self.attn(hidden_states=hidden_states, )
attn_output = self.attn(
hidden_states=hidden_states,
)
# residual connection
hidden_states = attn_output + residual
@@ -198,7 +202,6 @@ class GPTBigCodeBlock(nn.Module):
@support_torch_compile
class GPTBigCodeModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -211,23 +214,27 @@ class GPTBigCodeModel(nn.Module):
assert not config.add_cross_attention
self.embed_dim = config.hidden_size
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.wte = VocabParallelEmbedding(self.vocab_size,
self.embed_dim,
org_num_embeddings=config.vocab_size)
self.wte = VocabParallelEmbedding(
self.vocab_size, self.embed_dim, org_num_embeddings=config.vocab_size
)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.start_layer, self.end_layer, self.h = make_layers(
config.num_hidden_layers,
lambda prefix: GPTBigCodeBlock(
config, cache_config, quant_config, prefix=prefix),
config, cache_config, quant_config, prefix=prefix
),
prefix=f"{prefix}.h",
)
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.n_embd))
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states"], config.n_embd
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.wte(input_ids)
@@ -254,8 +261,7 @@ class GPTBigCodeModel(nn.Module):
hidden_states = self.ln_f(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:
@@ -266,13 +272,12 @@ class GPTBigCodeModel(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)
# TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
if "c_attn.input_scale" in name:
weight_loader(param, loaded_weight, 'q')
weight_loader(param, loaded_weight, 'k')
weight_loader(param, loaded_weight, 'v')
weight_loader(param, loaded_weight, "q")
weight_loader(param, loaded_weight, "k")
weight_loader(param, loaded_weight, "v")
else:
weight_loader(param, loaded_weight)
loaded_params.add(name)
@@ -292,9 +297,9 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.lora_config = lora_config
self.quant_config = quant_config
self.transformer = GPTBigCodeModel(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
self.transformer = GPTBigCodeModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
)
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.wte
else:
@@ -302,14 +307,17 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
self.transformer.vocab_size,
self.transformer.embed_dim,
org_num_embeddings=self.config.vocab_size,
prefix=maybe_prefix(prefix, "lm_head"))
prefix=maybe_prefix(prefix, "lm_head"),
)
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size)
self.logits_processor = LogitsProcessor(
self.unpadded_vocab_size, config.vocab_size
)
self.make_empty_intermediate_tensors = (
self.transformer.make_empty_intermediate_tensors)
self.transformer.make_empty_intermediate_tensors
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.transformer.get_input_embeddings(input_ids)
@@ -321,8 +329,9 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
hidden_states = self.transformer(input_ids, positions,
intermediate_tensors, inputs_embeds)
hidden_states = self.transformer(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return hidden_states
def compute_logits(
@@ -332,8 +341,7 @@ class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, 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]:
skip_prefixes = None
if self.config.tie_word_embeddings:
skip_prefixes = ["lm_head."]