biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

Convert formatting to use ruff instead of yapf + isort (#26247)

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

179
vllm/model_executor/models/mamba.py
View File

@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""PyTorch MAMBA model."""
from collections.abc import Iterable
from typing import Optional
@@ -15,51 +16,66 @@ from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
from vllm.model_executor.layers.mamba.mamba_utils import (
MambaStateDtypeCalculator, MambaStateShapeCalculator)
MambaStateDtypeCalculator,
MambaStateShapeCalculator,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
DEFAULT_VOCAB_PADDING_SIZE,
ParallelLMHead,
VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.interfaces import (HasInnerState,
IsAttentionFree, SupportsPP)
from vllm.model_executor.models.interfaces import (
HasInnerState,
IsAttentionFree,
SupportsPP,
)
from vllm.sequence import IntermediateTensors
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,
)
KVCache = tuple[torch.Tensor, torch.Tensor]
class MambaDecoderLayer(nn.Module):
def __init__(self,
config: MambaConfig,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
is_lora_enabled: Optional[bool] = False,
prefix: str = "") -> None:
def __init__(
self,
config: MambaConfig,
model_config: Optional[ModelConfig] = None,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
is_lora_enabled: Optional[bool] = False,
prefix: str = "",
) -> None:
super().__init__()
self.config = config
self.is_falcon_mamba = config.model_type == "falcon_mamba"
self.is_lora_enabled = is_lora_enabled
mixer_rms_eps = config.mixer_rms_eps if self.is_falcon_mamba else None
self.mixer = MambaMixer(hidden_size=config.hidden_size,
ssm_state_size=config.state_size,
conv_kernel_size=config.conv_kernel,
intermediate_size=config.intermediate_size,
time_step_rank=config.time_step_rank,
use_conv_bias=config.use_conv_bias,
use_bias=config.use_bias,
use_rms_norm=self.is_falcon_mamba,
rms_norm_has_weight=not self.is_falcon_mamba,
rms_norm_eps=mixer_rms_eps,
activation=config.hidden_act,
is_lora_enabled=self.is_lora_enabled,
model_config=model_config,
cache_config=cache_config,
prefix=f"{prefix}.mixer")
self.mixer = MambaMixer(
hidden_size=config.hidden_size,
ssm_state_size=config.state_size,
conv_kernel_size=config.conv_kernel,
intermediate_size=config.intermediate_size,
time_step_rank=config.time_step_rank,
use_conv_bias=config.use_conv_bias,
use_bias=config.use_bias,
use_rms_norm=self.is_falcon_mamba,
rms_norm_has_weight=not self.is_falcon_mamba,
rms_norm_eps=mixer_rms_eps,
activation=config.hidden_act,
is_lora_enabled=self.is_lora_enabled,
model_config=model_config,
cache_config=cache_config,
prefix=f"{prefix}.mixer",
)
self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
@@ -82,7 +98,6 @@ class MambaDecoderLayer(nn.Module):
@support_torch_compile
class MambaModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
@@ -94,8 +109,11 @@ class MambaModel(nn.Module):
is_lora_enabled = bool(lora_config)
self.config = config
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.org_vocab_size = config.vocab_size
@@ -107,19 +125,21 @@ class MambaModel(nn.Module):
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: MambaDecoderLayer(config,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
is_lora_enabled=is_lora_enabled,
prefix=prefix),
prefix=f"{prefix}.layers")
lambda prefix: MambaDecoderLayer(
config,
model_config=model_config,
cache_config=cache_config,
quant_config=quant_config,
is_lora_enabled=is_lora_enabled,
prefix=prefix,
),
prefix=f"{prefix}.layers",
)
self.norm_f = RMSNorm(config.hidden_size,
eps=config.layer_norm_epsilon)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], config.hidden_size))
self.norm_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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.embeddings(input_ids)
@@ -144,20 +164,18 @@ class MambaModel(nn.Module):
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states, residual = layer(positions=positions,
hidden_states=hidden_states,
residual=residual)
hidden_states, residual = layer(
positions=positions, hidden_states=hidden_states, residual=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_f(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]:
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
for name, loaded_weight in weights:
@@ -170,29 +188,29 @@ class MambaModel(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
class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
lora_config = vllm_config.lora_config
self.scheduler_config = vllm_config.scheduler_config
assert not cache_config.enable_prefix_caching, \
assert not cache_config.enable_prefix_caching, (
"Mamba does not support prefix caching"
)
super().__init__()
self.config = config
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.backbone = MambaModel(vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "backbone"))
self.backbone = MambaModel(
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "backbone")
)
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
@@ -206,28 +224,33 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
padding_size=DEFAULT_VOCAB_PADDING_SIZE
# We need bigger padding if using lora for kernel
# compatibility
if not lora_config else lora_config.lora_vocab_padding_size,
if not lora_config
else lora_config.lora_vocab_padding_size,
prefix=maybe_prefix(prefix, "lm_head"),
)
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.backbone.make_empty_intermediate_tensors)
self.backbone.make_empty_intermediate_tensors
)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.backbone.get_input_embeddings(input_ids)
def forward(self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs):
hidden_states = self.backbone(input_ids, positions,
intermediate_tensors, inputs_embeds)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
**kwargs,
):
hidden_states = self.backbone(
input_ids, positions, intermediate_tensors, inputs_embeds
)
return hidden_states
@@ -236,7 +259,6 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
cls,
vllm_config: "VllmConfig",
) -> tuple[torch.dtype, torch.dtype]:
return MambaStateDtypeCalculator.mamba1_state_dtype(
vllm_config.model_config.dtype,
vllm_config.cache_config.mamba_cache_dtype,
@@ -255,11 +277,11 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
tp_world_size=parallel_config.tensor_parallel_size,
intermediate_size=hf_config.intermediate_size,
state_size=hf_config.state_size,
conv_kernel=hf_config.conv_kernel)
conv_kernel=hf_config.conv_kernel,
)
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
return self.mamba_cache.copy_inputs_before_cuda_graphs(
input_buffers, **kwargs)
return self.mamba_cache.copy_inputs_before_cuda_graphs(input_buffers, **kwargs)
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
@@ -268,7 +290,6 @@ class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, 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)