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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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225
vllm/model_executor/models/baichuan.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 BaiChuan model compatible with HuggingFace weights."""
import math
from collections.abc import Iterable
from itertools import islice
@@ -32,32 +33,45 @@ from transformers import PretrainedConfig
from vllm.attention import Attention
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_rank,
get_tensor_model_parallel_world_size)
from vllm.distributed import (
get_pp_group,
get_tensor_model_parallel_rank,
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 (
ParallelLMHead, VocabParallelEmbedding)
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, row_parallel_weight_loader)
default_weight_loader,
row_parallel_weight_loader,
)
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
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,
)
def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads))
base = torch.tensor(
2**(-(2**-(math.log2(closest_power_of_2) - 3))),
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
dtype=torch.float32,
)
powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
@@ -65,22 +79,20 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
if closest_power_of_2 != total_num_heads:
extra_base = torch.tensor(
2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
dtype=torch.float32,
)
num_remaining_heads = min(closest_power_of_2,
total_num_heads - closest_power_of_2)
extra_powers = torch.arange(start=1,
end=1 + 2 * num_remaining_heads,
step=2,
dtype=torch.int32)
slopes = torch.cat(
[slopes, torch.pow(extra_base, extra_powers)], dim=0)
num_remaining_heads = min(
closest_power_of_2, total_num_heads - closest_power_of_2
)
extra_powers = torch.arange(
start=1, end=1 + 2 * num_remaining_heads, step=2, dtype=torch.int32
)
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
return slopes
class BaiChuanMLP(nn.Module):
def __init__(
self,
hidden_size: int,
@@ -90,16 +102,15 @@ class BaiChuanMLP(nn.Module):
):
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
quant_config=quant_config)
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
quant_config=quant_config)
hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config
)
self.down_proj = RowParallelLinear(
intermediate_size, hidden_size, bias=False, quant_config=quant_config
)
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):
@@ -125,12 +136,10 @@ class BaiChuanAttention(nn.Module):
):
super().__init__()
self.hidden_size = hidden_size
tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
)
tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tensor_model_parallel_world_size == 0
self.num_heads = (self.total_num_heads //
tensor_model_parallel_world_size)
self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
self.head_dim = hidden_size // self.total_num_heads
self.position_embedding = position_embedding
self.rope_theta = rope_theta
@@ -160,12 +169,14 @@ class BaiChuanAttention(nn.Module):
alibi_slopes = alibi_slopes[head_start:head_end].tolist()
scaling = self.head_dim**-0.5
self.attn = Attention(self.num_heads,
self.head_dim,
scaling,
alibi_slopes=alibi_slopes,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
scaling,
alibi_slopes=alibi_slopes,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
else:
self.rotary_emb = get_rope(
self.head_dim,
@@ -174,12 +185,14 @@ class BaiChuanAttention(nn.Module):
base=self.rope_theta,
)
self.scaling = self.head_dim**-0.5
self.attn = Attention(self.num_heads,
self.head_dim,
self.scaling,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
self.attn = Attention(
self.num_heads,
self.head_dim,
self.scaling,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -196,18 +209,18 @@ class BaiChuanAttention(nn.Module):
class BaiChuanDecoderLayer(nn.Module):
def __init__(self,
config: PretrainedConfig,
position_embedding: str,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = ""):
def __init__(
self,
config: PretrainedConfig,
position_embedding: str,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
self.self_attn = BaiChuanAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
@@ -224,10 +237,10 @@ class BaiChuanDecoderLayer(nn.Module):
hidden_act=config.hidden_act,
quant_config=quant_config,
)
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,
@@ -240,23 +253,20 @@ class BaiChuanDecoderLayer(nn.Module):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
else:
hidden_states, residual = self.input_layernorm(
hidden_states, residual)
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
@support_torch_compile
class BaiChuanModel(nn.Module):
def __init__(
self,
vllm_config: VllmConfig,
@@ -278,17 +288,15 @@ class BaiChuanModel(nn.Module):
)
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: BaiChuanDecoderLayer(config,
position_embedding,
cache_config,
quant_config,
prefix=prefix),
lambda prefix: BaiChuanDecoderLayer(
config, position_embedding, cache_config, quant_config, prefix=prefix
),
prefix=f"{prefix}.layers",
)
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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)
@@ -317,15 +325,16 @@ class BaiChuanModel(nn.Module):
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)
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)
("gate_up_proj", "gate_proj", 0),
@@ -337,7 +346,7 @@ class BaiChuanModel(nn.Module):
if "rotary_emb.inv_freq" in name:
continue
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)
@@ -357,15 +366,13 @@ class BaiChuanModel(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 BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
SupportsQuant):
class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
packed_modules_mapping = {
"W_pack": ["W_pack"],
"gate_up_proj": [
@@ -389,19 +396,24 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP,
self.lora_config = lora_config
self.tp_size = get_tensor_model_parallel_world_size()
self.quant_config = quant_config
self.model = BaiChuanModel(vllm_config=vllm_config,
prefix=prefix,
position_embedding=position_embedding)
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"))
self.model = BaiChuanModel(
vllm_config=vllm_config,
prefix=prefix,
position_embedding=position_embedding,
)
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"),
)
self.lm_head.weight.weight_loader = self.lm_head_weight_loader
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)
@@ -413,8 +425,9 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, 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(
@@ -424,13 +437,11 @@ class BaiChuanBaseForCausalLM(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]:
loader = AutoWeightsLoader(self)
return loader.load_weights(weights)
def lm_head_weight_loader(self, param: nn.Parameter,
loaded_weight: torch.Tensor):
def lm_head_weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
# Unlike Baichuan, Baichuan2 normalizes the head weights.
# Refer to:
# https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
@@ -454,13 +465,13 @@ class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
if config.hidden_size == 4096: # baichuan2 7b
super().__init__(vllm_config=vllm_config,
prefix=prefix,
position_embedding="ROPE")
super().__init__(
vllm_config=vllm_config, prefix=prefix, position_embedding="ROPE"
)
else: # baichuan 13b, baichuan2 13b
super().__init__(vllm_config=vllm_config,
prefix=prefix,
position_embedding="ALIBI")
super().__init__(
vllm_config=vllm_config, prefix=prefix, position_embedding="ALIBI"
)
class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
@@ -469,6 +480,6 @@ class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
"""
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__(vllm_config=vllm_config,
prefix=prefix,
position_embedding="ROPE")
super().__init__(
vllm_config=vllm_config, prefix=prefix, position_embedding="ROPE"
)