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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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326
vllm/model_executor/models/longcat_flash.py
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@@ -32,6 +32,7 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""Inference-only Flash model compatible with HuggingFace weights."""
import typing
from collections.abc import Callable, Iterable
from typing import Optional, Union
@@ -47,29 +48,37 @@ 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,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.linear import (
MergedColumnParallelLinear,
ReplicatedLinear,
RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.quantization.utils.int8_utils import (
block_dequant)
from vllm.model_executor.layers.quantization.utils.int8_utils import block_dequant
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.model_executor.models.deepseek_v2 import DeepseekV2MLAAttention
from vllm.sequence import IntermediateTensors
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (PPMissingLayer, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers,
maybe_prefix)
from .utils import (
PPMissingLayer,
is_pp_missing_parameter,
make_empty_intermediate_tensors_factory,
make_layers,
maybe_prefix,
)
logger = init_logger(__name__)
class FlashConfig(PretrainedConfig):
"""Flash model configuration."""
model_type = "longcat_flash"
keys_to_ignore_at_inference = ["past_key_values"]
@@ -132,8 +141,9 @@ class FlashConfig(PretrainedConfig):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = (num_hidden_layers if num_hidden_layers
is not None else num_layers)
self.num_hidden_layers = (
num_hidden_layers if num_hidden_layers is not None else num_layers
)
self.num_attention_heads = num_attention_heads
self.ep_size = ep_size
self.kv_lora_rank = kv_lora_rank
@@ -162,8 +172,11 @@ class FlashConfig(PretrainedConfig):
self.zero_expert_type = zero_expert_type
self.routed_scaling_factor = routed_scaling_factor
self.hidden_act = "silu"
self.intermediate_size = self.ffn_hidden_size if hasattr(
self, "ffn_hidden_size") else self.intermediate_size
self.intermediate_size = (
self.ffn_hidden_size
if hasattr(self, "ffn_hidden_size")
else self.intermediate_size
)
if hasattr(self, "moe_intermediate_size"):
self.moe_intermediate_size = self.moe_intermediate_size
elif hasattr(self, "expert_ffn_hidden_size"):
@@ -201,8 +214,9 @@ class FlashMLP(nn.Module):
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: torch.Tensor) -> torch.Tensor:
@@ -216,15 +230,19 @@ class FlashMLP(nn.Module):
class LongcatRouter(nn.Module):
def __init__(self,
config,
zero_expert_num=0,
rounter_params_dtype=torch.bfloat16,
prefix: str = ""):
def __init__(
self,
config,
zero_expert_num=0,
rounter_params_dtype=torch.bfloat16,
prefix: str = "",
):
super().__init__()
self.n_routed_experts = config.n_routed_experts if hasattr(
config, "n_routed_experts") else config.num_experts[0]
self.n_routed_experts = (
config.n_routed_experts
if hasattr(config, "n_routed_experts")
else config.num_experts[0]
)
self.n_routed_experts = self.n_routed_experts + zero_expert_num
self.classifier = ReplicatedLinear(
config.hidden_size,
@@ -235,7 +253,8 @@ class LongcatRouter(nn.Module):
prefix=f"{prefix}.classifier",
)
self.e_score_correction_bias = nn.Parameter(
torch.zeros((self.n_routed_experts), dtype=rounter_params_dtype))
torch.zeros((self.n_routed_experts), dtype=rounter_params_dtype)
)
def forward(self, hidden_states):
logits, _ = self.classifier(hidden_states)
@@ -243,7 +262,6 @@ class LongcatRouter(nn.Module):
class LongcatMoe(nn.Module):
def __init__(
self,
config: FlashConfig,
@@ -271,7 +289,8 @@ class LongcatMoe(nn.Module):
config=config,
zero_expert_num=self.zero_expert_num,
rounter_params_dtype=self.rounter_params_dtype,
prefix=f"{prefix}.gate")
prefix=f"{prefix}.gate",
)
self.experts = FusedMoE(
num_experts=num_experts,
@@ -291,14 +310,13 @@ class LongcatMoe(nn.Module):
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
num_tokens, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
router_logits = self.router(hidden_states.to(
self.rounter_params_dtype))
final_hidden_states = self.experts(hidden_states=hidden_states,
router_logits=router_logits)
router_logits = self.router(hidden_states.to(self.rounter_params_dtype))
final_hidden_states = self.experts(
hidden_states=hidden_states, router_logits=router_logits
)
return final_hidden_states.view(num_tokens, hidden_dim)
@@ -316,67 +334,76 @@ class FlashDecoderLayer(nn.Module):
enable_eplb: bool = False,
) -> None:
super().__init__()
self.layer_idx = int(prefix.split(sep='.')[-1])
self.layer_idx = int(prefix.split(sep=".")[-1])
self.hidden_size = config.hidden_size
rope_theta = getattr(config, "rope_theta", 10000)
rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings",
8192)
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
if rope_scaling is not None and getattr(
config, "original_max_position_embeddings", None):
config, "original_max_position_embeddings", None
):
rope_scaling["original_max_position_embeddings"] = (
config.original_max_position_embeddings)
config.original_max_position_embeddings
)
# Dual attention structure
self.self_attn = nn.ModuleList([
DeepseekV2MLAAttention(
vllm_config=vllm_config,
config=config,
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
qk_nope_head_dim=config.qk_nope_head_dim,
qk_rope_head_dim=config.qk_rope_head_dim,
v_head_dim=config.v_head_dim,
q_lora_rank=(config.q_lora_rank if hasattr(
config, "q_lora_rank") else None),
kv_lora_rank=config.kv_lora_rank,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=None if "self_attn" in getattr(
config, "disable_quant_module", []) else quant_config,
prefix=f"{prefix}.self_attn.{i}",
) for i in range(2)
])
self.input_layernorm = nn.ModuleList([
RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
for i in range(2)
])
self.post_attention_layernorm = nn.ModuleList([
RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
for i in range(2)
])
self.self_attn = nn.ModuleList(
[
DeepseekV2MLAAttention(
vllm_config=vllm_config,
config=config,
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
qk_nope_head_dim=config.qk_nope_head_dim,
qk_rope_head_dim=config.qk_rope_head_dim,
v_head_dim=config.v_head_dim,
q_lora_rank=(
config.q_lora_rank if hasattr(config, "q_lora_rank") else None
),
kv_lora_rank=config.kv_lora_rank,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
max_position_embeddings=max_position_embeddings,
cache_config=cache_config,
quant_config=None
if "self_attn" in getattr(config, "disable_quant_module", [])
else quant_config,
prefix=f"{prefix}.self_attn.{i}",
)
for i in range(2)
]
)
self.input_layernorm = nn.ModuleList(
[RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
)
self.post_attention_layernorm = nn.ModuleList(
[RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)]
)
# Dual MLP structure
self.mlps = nn.ModuleList([
FlashMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
quant_config=None if "mlps" in getattr(
config, "disable_quant_module", []) else quant_config,
prefix=f"{prefix}.mlps.{i}",
) for i in range(2)
])
self.mlps = nn.ModuleList(
[
FlashMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
quant_config=None
if "mlps" in getattr(config, "disable_quant_module", [])
else quant_config,
prefix=f"{prefix}.mlps.{i}",
)
for i in range(2)
]
)
self.mlp = LongcatMoe(
config=config,
num_experts=config.n_routed_experts if hasattr(
config, "n_routed_experts") else
config.num_experts[self.layer_idx],
num_experts=config.n_routed_experts
if hasattr(config, "n_routed_experts")
else config.num_experts[self.layer_idx],
top_k=config.moe_topk
if hasattr(config, "moe_topk") else config.num_experts_per_tok,
if hasattr(config, "moe_topk")
else config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.moe_intermediate_size,
quant_config=quant_config,
@@ -389,13 +416,11 @@ class FlashDecoderLayer(nn.Module):
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
) -> tuple[torch.Tensor, torch.Tensor]:
if residual is None:
residual = hidden_states
hidden_states = self.input_layernorm[0](hidden_states)
else:
hidden_states, residual = self.input_layernorm[0](hidden_states,
residual)
hidden_states, residual = self.input_layernorm[0](hidden_states, residual)
hidden_states = self.self_attn[0](
positions=positions,
@@ -403,7 +428,8 @@ class FlashDecoderLayer(nn.Module):
)
hidden_states, residual = self.post_attention_layernorm[0](
hidden_states, residual)
hidden_states, residual
)
# moe
hidden_states_copy = hidden_states.clone()
@@ -412,8 +438,7 @@ class FlashDecoderLayer(nn.Module):
# first mlp
hidden_states = self.mlps[0](hidden_states)
hidden_states, residual = self.input_layernorm[1](hidden_states,
residual)
hidden_states, residual = self.input_layernorm[1](hidden_states, residual)
# second_attn
hidden_states = self.self_attn[1](
@@ -421,7 +446,8 @@ class FlashDecoderLayer(nn.Module):
hidden_states=hidden_states,
)
hidden_states, residual = self.post_attention_layernorm[1](
hidden_states, residual)
hidden_states, residual
)
# second_mlp
hidden_states = self.mlps[1](hidden_states)
@@ -462,14 +488,15 @@ class FlashModel(nn.Module):
quant_config=quant_config,
prefix=prefix,
),
prefix=f"{prefix}.layers")
prefix=f"{prefix}.layers",
)
if get_pp_group().is_last_rank:
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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)
@@ -501,10 +528,9 @@ class FlashModel(nn.Module):
)
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
@@ -532,26 +558,32 @@ class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
lora_config = vllm_config.lora_config
self.config = config
config.intermediate_size = config.ffn_hidden_size if hasattr(
config, "ffn_hidden_size") else config.intermediate_size
config.intermediate_size = (
config.ffn_hidden_size
if hasattr(config, "ffn_hidden_size")
else config.intermediate_size
)
self.lora_config = lora_config
self.quant_config = quant_config
self.model = FlashModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"))
self.model = FlashModel(
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()
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)
@@ -563,8 +595,9 @@ class LongcatFlashForCausalLM(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(
@@ -581,14 +614,12 @@ class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.n_routed_experts if hasattr(
self.config, "n_routed_experts") else
self.config.num_experts[0],
num_experts=self.config.n_routed_experts
if hasattr(self.config, "n_routed_experts")
else self.config.num_experts[0],
)
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 = [
("fused_qkv_a_proj", "q_a_proj", 0),
("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
@@ -610,8 +641,9 @@ class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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 mtp
if ".mtp." in name:
@@ -633,22 +665,25 @@ class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
# Skip mtp
if ".mtp." in name_mapped:
continue
if (name_mapped.endswith(".bias")
or name_mapped.endswith("_bias")
) and name not in params_dict:
if (
name_mapped.endswith(".bias") or name_mapped.endswith("_bias")
) and name not in params_dict:
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name_mapped]
weight_loader = param.weight_loader
weight_loader = typing.cast(Callable[..., bool],
param.weight_loader)
success = weight_loader(param,
loaded_weight,
name_mapped,
shard_id=shard_id,
expert_id=expert_id,
return_success=True)
weight_loader = typing.cast(
Callable[..., bool], param.weight_loader
)
success = weight_loader(
param,
loaded_weight,
name_mapped,
shard_id=shard_id,
expert_id=expert_id,
return_success=True,
)
if success:
name = name_mapped
break
@@ -672,8 +707,9 @@ class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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)
for layer_id in range(self.config.num_hidden_layers):
@@ -681,35 +717,35 @@ class LongcatFlashForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
if isinstance(self.model.layers[layer_id], PPMissingLayer):
continue
self_attn = self.model.layers[layer_id].self_attn[i]
if hasattr(self.quant_config, "weight_block_size"
) and self_attn.kv_b_proj.weight.dtype in (
torch.float8_e4m3fn,
torch.float8_e4m3fnuz,
):
if hasattr(
self.quant_config, "weight_block_size"
) and self_attn.kv_b_proj.weight.dtype in (
torch.float8_e4m3fn,
torch.float8_e4m3fnuz,
):
weight_block_size = self.quant_config.weight_block_size
if weight_block_size is not None:
assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
dtype = torch.get_default_dtype()
w = block_dequant(self_attn.kv_b_proj.weight,
self_attn.kv_b_proj.weight_scale_inv,
weight_block_size).to(dtype)
w = block_dequant(
self_attn.kv_b_proj.weight,
self_attn.kv_b_proj.weight_scale_inv,
weight_block_size,
).to(dtype)
else:
w = self_attn.kv_b_proj.weight
w_kc, w_vc = w.unflatten(
0,
(-1,
self_attn.qk_nope_head_dim + self_attn.v_head_dim)).split(
[self_attn.qk_nope_head_dim, self_attn.v_head_dim],
dim=1)
self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(
1, 2)
0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
if self.config.mla_scale_q_lora:
self_attn.q_a_layernorm.weight.data *= (
self.config.hidden_size / self.config.q_lora_rank)**0.5
self.config.hidden_size / self.config.q_lora_rank
) ** 0.5
if self.config.mla_scale_kv_lora:
self_attn.kv_a_layernorm.weight.data *= (
self.config.hidden_size /
self.config.kv_lora_rank)**0.5
self.config.hidden_size / self.config.kv_lora_rank
) ** 0.5
return loaded_params