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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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306
vllm/model_executor/models/ernie45_moe.py
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@@ -22,6 +22,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only ErineMoE model compatible with HuggingFace weights."""
from collections.abc import Iterable
from itertools import islice
from typing import Any, Optional, Union
@@ -38,30 +39,40 @@ from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
MergedColumnParallelLinear,
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.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, maybe_remap_kv_scale_name)
default_weight_loader,
maybe_remap_kv_scale_name,
)
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from .utils import (
AutoWeightsLoader,
PPMissingLayer,
extract_layer_index,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
logger = init_logger(__name__)
class Ernie4_5_MoeMLP(nn.Module):
def __init__(
self,
hidden_size: int,
@@ -74,19 +85,24 @@ class Ernie4_5_MoeMLP(nn.Module):
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
hidden_size,
[intermediate_size] * 2,
bias=use_bias,
quant_config=quant_config,
prefix=f"{prefix}.gate_up_proj")
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=use_bias,
quant_config=quant_config,
reduce_results=reduce_results,
prefix=f"{prefix}.down_proj")
prefix=f"{prefix}.gate_up_proj",
)
self.down_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=use_bias,
quant_config=quant_config,
reduce_results=reduce_results,
prefix=f"{prefix}.down_proj",
)
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):
@@ -97,7 +113,6 @@ class Ernie4_5_MoeMLP(nn.Module):
class Ernie4_5_MoeMoE(nn.Module):
def __init__(
self,
config: PretrainedConfig,
@@ -109,23 +124,26 @@ class Ernie4_5_MoeMoE(nn.Module):
layer_idx = extract_layer_index(prefix)
self.layer_idx = layer_idx
self.tp_size = get_tensor_model_parallel_world_size()
self.has_shared_experts = (getattr(config, "moe_num_shared_experts", 0)
> 0)
self.has_shared_experts = getattr(config, "moe_num_shared_experts", 0) > 0
if self.tp_size > config.moe_num_experts:
raise ValueError(
f"Tensor parallel size {self.tp_size} is greater than "
f"the number of experts {config.moe_num_experts}.")
f"the number of experts {config.moe_num_experts}."
)
self.gate = ReplicatedLinear(config.hidden_size,
config.moe_num_experts,
bias=False,
params_dtype=torch.float32,
quant_config=None,
prefix=f"{prefix}.gate")
self.gate = ReplicatedLinear(
config.hidden_size,
config.moe_num_experts,
bias=False,
params_dtype=torch.float32,
quant_config=None,
prefix=f"{prefix}.gate",
)
self.gate.e_score_correction_bias = nn.Parameter(
torch.empty(config.moe_num_experts, dtype=torch.float32))
torch.empty(config.moe_num_experts, dtype=torch.float32)
)
self.experts = FusedMoE(
num_experts=config.moe_num_experts,
@@ -136,19 +154,21 @@ class Ernie4_5_MoeMoE(nn.Module):
renormalize=True,
quant_config=quant_config,
prefix=f"{prefix}.experts",
e_score_correction_bias=self.gate.e_score_correction_bias)
e_score_correction_bias=self.gate.e_score_correction_bias,
)
if self.has_shared_experts:
intermediate_size = (config.moe_intermediate_size *
config.moe_num_shared_experts)
intermediate_size = (
config.moe_intermediate_size * config.moe_num_shared_experts
)
self.shared_experts = Ernie4_5_MoeMLP(
hidden_size=config.hidden_size,
intermediate_size=intermediate_size,
hidden_act=config.hidden_act,
quant_config=quant_config,
prefix=f"{prefix}.shared_experts",
reduce_results=self.experts.must_reduce_shared_expert_outputs(
))
reduce_results=self.experts.must_reduce_shared_expert_outputs(),
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
orig_shape = hidden_states.shape
@@ -160,23 +180,22 @@ class Ernie4_5_MoeMoE(nn.Module):
router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
final_hidden_states = self.experts(hidden_states=hidden_states,
router_logits=router_logits)
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
if self.has_shared_experts and \
shared_output is not None:
if self.has_shared_experts and shared_output is not None:
final_hidden_states = final_hidden_states + shared_output
if self.tp_size > 1:
final_hidden_states = (
self.experts.maybe_all_reduce_tensor_model_parallel(
final_hidden_states))
final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel(
final_hidden_states
)
return final_hidden_states.view(orig_shape)
class Ernie4_5_MoeAttention(nn.Module):
def __init__(
self,
hidden_size: int,
@@ -219,19 +238,23 @@ class Ernie4_5_MoeAttention(nn.Module):
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.qkv_proj = QKVParallelLinear(hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj")
self.qkv_proj = QKVParallelLinear(
hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=qkv_bias,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj")
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
self.rotary_emb = get_rope(
self.head_dim,
@@ -241,20 +264,21 @@ class Ernie4_5_MoeAttention(nn.Module):
is_neox_style=False,
rope_scaling=rope_scaling,
)
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,
positions: torch.Tensor,
hidden_states: torch.Tensor,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
@@ -268,7 +292,6 @@ class Ernie4_5_MoeAttention(nn.Module):
class Ernie4_5_MoeDecoderLayer(nn.Module):
def __init__(
self,
config: PretrainedConfig,
@@ -280,18 +303,17 @@ class Ernie4_5_MoeDecoderLayer(nn.Module):
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 500000)
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings",
131072)
max_position_embeddings = getattr(config, "max_position_embeddings", 131072)
self.self_attn = Ernie4_5_MoeAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
head_dim=getattr(config, 'head_dim', None),
head_dim=getattr(config, "head_dim", None),
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
rms_norm_eps=config.rms_norm_eps,
qkv_bias=getattr(config, 'use_bias', False),
qkv_bias=getattr(config, "use_bias", False),
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
@@ -303,30 +325,35 @@ class Ernie4_5_MoeDecoderLayer(nn.Module):
# MoE
moe_num_experts = getattr(config, "moe_num_experts", 0)
moe_layer_start_index = getattr(config, "moe_layer_start_index", 0)
moe_layer_end_index = getattr(config, "moe_layer_end_index",
config.num_hidden_layers - 1)
moe_layer_end_index = getattr(
config, "moe_layer_end_index", config.num_hidden_layers - 1
)
moe_layer_interval = getattr(config, "moe_layer_interval", 1)
use_moe = getattr(config, "use_moe", moe_num_experts > 0)
if (use_moe and ((layer_idx + 1) % moe_layer_interval == 0)
and layer_idx >= moe_layer_start_index
and layer_idx <= moe_layer_end_index):
self.mlp = Ernie4_5_MoeMoE(config=config,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
if (
use_moe
and ((layer_idx + 1) % moe_layer_interval == 0)
and layer_idx >= moe_layer_start_index
and layer_idx <= moe_layer_end_index
):
self.mlp = Ernie4_5_MoeMoE(
config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
)
else:
self.mlp = Ernie4_5_MoeMLP(
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
use_bias=getattr(config, 'use_bias', False),
use_bias=getattr(config, "use_bias", False),
quant_config=quant_config,
prefix=f"{prefix}.mlp")
prefix=f"{prefix}.mlp",
)
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,
@@ -334,14 +361,12 @@ class Ernie4_5_MoeDecoderLayer(nn.Module):
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
) -> torch.Tensor:
# Self Attention
if residual is None:
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,
@@ -349,8 +374,7 @@ class Ernie4_5_MoeDecoderLayer(nn.Module):
)
# 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)
@@ -359,7 +383,6 @@ class Ernie4_5_MoeDecoderLayer(nn.Module):
@support_torch_compile
class Ernie4_5_MoeModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -376,16 +399,19 @@ class Ernie4_5_MoeModel(nn.Module):
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.embed_tokens")
prefix=f"{prefix}.embed_tokens",
)
else:
self.embed_tokens = PPMissingLayer()
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: Ernie4_5_MoeDecoderLayer(config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix),
lambda prefix: Ernie4_5_MoeDecoderLayer(
config=config,
cache_config=cache_config,
quant_config=quant_config,
prefix=prefix,
),
prefix=f"{prefix}.layers",
)
@@ -394,9 +420,9 @@ class Ernie4_5_MoeModel(nn.Module):
else:
self.norm = PPMissingLayer()
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)
@@ -408,7 +434,6 @@ class Ernie4_5_MoeModel(nn.Module):
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
@@ -424,27 +449,25 @@ class Ernie4_5_MoeModel(nn.Module):
hidden_states, residual = layer(positions, hidden_states, 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 get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
# Params for weights, fp8 weight scales, fp8 activation scales
# (param_name, weight_name, expert_id, shard_id)
return FusedMoE.make_expert_params_mapping(
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.moe_num_experts)
num_experts=self.config.moe_num_experts,
)
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"),
@@ -458,8 +481,7 @@ class Ernie4_5_MoeModel(nn.Module):
loaded_params: set[str] = set()
expert_params_mapping = self.get_expert_mapping()
for name, loaded_weight in weights:
if self.config.tie_word_embeddings and name.endswith(
"lm_head.weight"):
if self.config.tie_word_embeddings and name.endswith("lm_head.weight"):
continue
# MTP will be supported soon.
if "mtp" in name:
@@ -469,17 +491,18 @@ class Ernie4_5_MoeModel(nn.Module):
name = name.replace("moe_statics", "gate")
loaded_weight = loaded_weight.squeeze(0)
for (param_name, weight_name, shard_id) in stacked_params_mapping:
for param_name, weight_name, shard_id in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
continue
if (("mlp.experts." in name) and name not in params_dict):
if ("mlp.experts." in name) and name not in params_dict:
continue
name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
if ((name.endswith(".bias") or name.endswith("_bias"))
and name not in params_dict):
if (
name.endswith(".bias") or name.endswith("_bias")
) and name not in params_dict:
continue
# Skip layers on other devices.
if is_pp_missing_parameter(name, self):
@@ -502,22 +525,26 @@ class Ernie4_5_MoeModel(nn.Module):
continue
# Skip loading extra bias for GPTQ models.
if ((name.endswith(".bias") or name.endswith("_bias"))
and name not in params_dict):
if (
name.endswith(".bias") or name.endswith("_bias")
) and name not in params_dict:
continue
param = params_dict[name]
weight_loader = param.weight_loader
weight_loader(param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id)
weight_loader(
param,
loaded_weight,
name,
shard_id=shard_id,
expert_id=expert_id,
)
break
else:
# Skip loading extra bias for GPTQ models.
if ((name.endswith(".bias") or name.endswith("_bias"))
and name not in params_dict):
if (
name.endswith(".bias") or name.endswith("_bias")
) and name not in params_dict:
continue
# Skip layers on other devices.
if is_pp_missing_parameter(name, self):
@@ -528,8 +555,9 @@ class Ernie4_5_MoeModel(nn.Module):
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
@@ -556,15 +584,17 @@ class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
quant_config = vllm_config.quant_config
self.config = config
self.quant_config = quant_config
self.model = Ernie4_5_MoeModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.model = Ernie4_5_MoeModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
)
if get_pp_group().is_last_rank:
self.lm_head = ParallelLMHead(config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(
prefix, "lm_head"))
self.lm_head = ParallelLMHead(
config.vocab_size,
config.hidden_size,
quant_config=quant_config,
prefix=maybe_prefix(prefix, "lm_head"),
)
else:
self.lm_head = PPMissingLayer()
@@ -572,7 +602,8 @@ class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
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)
@@ -584,8 +615,9 @@ class Ernie4_5_MoeForCausalLM(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(
@@ -595,12 +627,10 @@ class Ernie4_5_MoeForCausalLM(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."]
if self.config.tie_word_embeddings else None),
skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
)
return loader.load_weights(weights)