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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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159
vllm/model_executor/models/chatglm.py
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@@ -3,6 +3,7 @@
# Adapted from
# https://github.com/zai-org/ChatGLM2-6B
"""Inference-only ChatGLM model compatible with THUDM weights."""
import json
from collections.abc import Iterable
from itertools import islice
@@ -18,26 +19,34 @@ 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 (
ParallelLMHead, VocabParallelEmbedding)
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import ChatGLMConfig
from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
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 GLMAttention(nn.Module):
def __init__(
self,
config: ChatGLMConfig,
@@ -52,9 +61,11 @@ class GLMAttention(nn.Module):
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.multi_query_attention = config.multi_query_attention
self.total_num_kv_heads = (config.multi_query_group_num
if config.multi_query_attention else
config.num_attention_heads)
self.total_num_kv_heads = (
config.multi_query_group_num
if config.multi_query_attention
else config.num_attention_heads
)
if self.total_num_kv_heads >= tp_size:
# Number of KV heads is greater than TP size, so we partition
# the KV heads across multiple tensor parallel GPUs.
@@ -99,13 +110,15 @@ class GLMAttention(nn.Module):
base=10000 * rope_ratio,
is_neox_style=is_neox_style,
)
self.attn = Attention(self.num_heads,
self.head_dim,
self.scaling,
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,
self.scaling,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn",
)
def forward(
self,
@@ -183,25 +196,27 @@ class GLMBlock(nn.Module):
):
super().__init__()
self.apply_residual_connection_post_layernorm = (
config.apply_residual_connection_post_layernorm)
config.apply_residual_connection_post_layernorm
)
self.fp32_residual_connection = config.fp32_residual_connection
layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
# Layernorm on the input data.
self.input_layernorm = layer_norm_func(config.hidden_size,
eps=config.layernorm_epsilon)
self.input_layernorm = layer_norm_func(
config.hidden_size, eps=config.layernorm_epsilon
)
# Self attention.
self.self_attention = GLMAttention(config,
cache_config,
quant_config,
prefix=f"{prefix}.self_attention")
self.self_attention = GLMAttention(
config, cache_config, quant_config, prefix=f"{prefix}.self_attention"
)
self.hidden_dropout = config.hidden_dropout
# Layernorm on the attention output
self.post_attention_layernorm = layer_norm_func(
config.hidden_size, eps=config.layernorm_epsilon)
config.hidden_size, eps=config.layernorm_epsilon
)
# MLP
self.mlp = GLMMLP(config, quant_config, prefix=f"{prefix}.mlp")
@@ -261,8 +276,7 @@ class GLMTransformer(nn.Module):
# Transformer layers.
self.start_layer, self.end_layer, self.layers = make_layers(
self.num_layers,
lambda prefix: GLMBlock(
config, cache_config, quant_config, prefix=prefix),
lambda prefix: GLMBlock(config, cache_config, quant_config, prefix=prefix),
prefix=f"{prefix}.layers",
)
@@ -270,11 +284,12 @@ class GLMTransformer(nn.Module):
layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
# Final layer norm before output.
self.final_layernorm = layer_norm_func(
config.hidden_size, eps=config.layernorm_epsilon)
config.hidden_size, eps=config.layernorm_epsilon
)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.hidden_size))
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
["hidden_states"], config.hidden_size
)
def forward(
self,
@@ -282,8 +297,9 @@ class GLMTransformer(nn.Module):
position_ids: torch.Tensor,
) -> Union[torch.Tensor, IntermediateTensors]:
for layer in islice(self.layers, self.start_layer, self.end_layer):
hidden_states = layer(hidden_states=hidden_states,
position_ids=position_ids)
hidden_states = layer(
hidden_states=hidden_states, position_ids=position_ids
)
if not get_pp_group().is_last_rank:
return IntermediateTensors({"hidden_states": hidden_states})
@@ -298,8 +314,10 @@ class GLMTransformer(nn.Module):
@support_torch_compile
class ChatGLMModel(nn.Module, SupportsQuant):
packed_modules_mapping = {
"linear_proj.merged_proj":
["linear_proj.gate_proj", "linear_proj.dense_h_to_4h"]
"linear_proj.merged_proj": [
"linear_proj.gate_proj",
"linear_proj.dense_h_to_4h",
]
}
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -311,26 +329,30 @@ class ChatGLMModel(nn.Module, SupportsQuant):
self.config = config
self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.embedding")
self.embedding = VocabParallelEmbedding(
config.padded_vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.embedding",
)
self.num_layers = config.num_layers
self.multi_query_group_num = config.multi_query_group_num
self.kv_channels = config.kv_channels
self.encoder = GLMTransformer(config,
cache_config,
quant_config,
prefix=f"{prefix}.encoder")
self.encoder = GLMTransformer(
config, cache_config, quant_config, prefix=f"{prefix}.encoder"
)
self.output_layer = ParallelLMHead(config.padded_vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.output_layer")
self.output_layer = ParallelLMHead(
config.padded_vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.output_layer",
)
self.make_empty_intermediate_tensors = (
self.encoder.make_empty_intermediate_tensors)
self.encoder.make_empty_intermediate_tensors
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.embedding(input_ids)
@@ -360,8 +382,7 @@ class ChatGLMModel(nn.Module, SupportsQuant):
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)
("linear_proj.merged_proj", "linear_proj.gate_proj", 0),
@@ -371,7 +392,7 @@ class ChatGLMModel(nn.Module, SupportsQuant):
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)
@@ -392,8 +413,7 @@ class ChatGLMModel(nn.Module, SupportsQuant):
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
@@ -401,7 +421,8 @@ class ChatGLMModel(nn.Module, SupportsQuant):
class ChatGLMBaseModel(nn.Module):
hf_to_vllm_mapper = WeightsMapper(
orig_to_new_substr={".word_embeddings": ""}, )
orig_to_new_substr={".word_embeddings": ""},
)
def __init__(
self,
@@ -420,18 +441,17 @@ class ChatGLMBaseModel(nn.Module):
self.multimodal_config = multimodal_config
self.quant_config = quant_config
self.max_position_embeddings = getattr(config, "max_sequence_length",
8192)
self.transformer = transformer_type(vllm_config=vllm_config,
prefix=maybe_prefix(
prefix, "transformer"))
self.max_position_embeddings = getattr(config, "max_sequence_length", 8192)
self.transformer = transformer_type(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
)
if self.config.tie_word_embeddings:
self.transformer.output_layer.weight = (
self.transformer.embedding.weight)
self.transformer.output_layer.weight = self.transformer.embedding.weight
self.lm_head = self.transformer.output_layer
self.logits_processor = LogitsProcessor(config.padded_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)
@@ -448,11 +468,10 @@ class ChatGLMBaseModel(nn.Module):
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
SupportsQuant):
class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP, SupportsQuant):
packed_modules_mapping = {
"query_key_value": ["query_key_value"],
"dense_h_to_4h": ["dense_h_to_4h"]
"dense_h_to_4h": ["dense_h_to_4h"],
}
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -463,7 +482,8 @@ class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
"The configuration of this model indicates that it supports "
"vision inputs, but you instantiated the text-only version "
"of this model. Please use the vision model by setting "
f"`--hf-overrides '{json.dumps(hf_overrides)}'`")
f"`--hf-overrides '{json.dumps(hf_overrides)}'`"
)
super().__init__(vllm_config=vllm_config, prefix=prefix)
@@ -474,6 +494,7 @@ class ChatGLMForCausalLM(ChatGLMBaseModel, 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