mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-05 06:04:00 +08:00
657 lines
25 KiB
Python
657 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
from collections.abc import Iterable, Mapping, Sequence
|
|
from typing import List, Optional, Set, Tuple, TypedDict, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from transformers import AriaConfig, AriaTextConfig, BatchFeature
|
|
from transformers.models.aria.modeling_aria import AriaCrossAttention
|
|
from transformers.models.aria.processing_aria import AriaProcessor
|
|
|
|
from vllm.config import CacheConfig, QuantizationConfig, VllmConfig
|
|
from vllm.distributed import get_tensor_model_parallel_rank
|
|
from vllm.model_executor.layers.activation import get_act_fn
|
|
from vllm.model_executor.layers.fused_moe import FusedMoE
|
|
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
|
|
RowParallelLinear)
|
|
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
|
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
|
|
from vllm.model_executor.model_loader.weight_utils import (
|
|
default_weight_loader, maybe_remap_kv_scale_name)
|
|
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
|
from vllm.multimodal import MULTIMODAL_REGISTRY
|
|
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
|
|
MultiModalKwargs)
|
|
from vllm.multimodal.parse import MultiModalDataItems
|
|
from vllm.multimodal.processing import (BaseMultiModalProcessor,
|
|
BaseProcessingInfo, PromptReplacement,
|
|
PromptUpdate)
|
|
from vllm.multimodal.profiling import BaseDummyInputsBuilder
|
|
from vllm.sequence import IntermediateTensors
|
|
|
|
# yapf: disable
|
|
from .idefics2_vision_model import Idefics2VisionConfig
|
|
from .idefics2_vision_model import (
|
|
Idefics2VisionTransformer as Idefics3VisionTransformer)
|
|
# yapf: enable
|
|
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsQuant
|
|
from .llama import LlamaDecoderLayer, LlamaMLP, LlamaModel
|
|
from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
|
|
is_pp_missing_parameter, maybe_prefix,
|
|
merge_multimodal_embeddings)
|
|
|
|
|
|
class AriaImagePixelInputs(TypedDict):
|
|
pixel_values: torch.Tensor
|
|
pixel_mask: Optional[torch.Tensor]
|
|
"""
|
|
Shape:
|
|
pixel_values: `(batch_size * num_images, num_channels, height, width)`
|
|
pixel_mask: `(batch_size * num_images, height, width)`
|
|
"""
|
|
|
|
|
|
class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
|
|
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
|
|
|
|
def __init__(
|
|
self,
|
|
config: Idefics2VisionConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__(config, quant_config=quant_config, prefix=prefix)
|
|
# Unlike Idefics3VisionTransformer which uses LayerNorm after the
|
|
# final layer, Aria omits this normalization, so we replace it with an
|
|
# Identity layer
|
|
self.post_layernorm = nn.Identity()
|
|
|
|
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"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
|
|
# NOTE: post_layernorm is not used in Aria
|
|
if "post_layernorm" in name:
|
|
continue
|
|
|
|
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)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class AriaProjectorMLP(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
in_features: int,
|
|
hidden_features: int,
|
|
output_dim: int,
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.linear_in = ColumnParallelLinear(in_features,
|
|
hidden_features,
|
|
bias=False)
|
|
self.linear_out = RowParallelLinear(hidden_features,
|
|
output_dim,
|
|
bias=False)
|
|
self.act = get_act_fn("gelu_new")
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states, _ = self.linear_in(hidden_states)
|
|
hidden_states = self.act(hidden_states)
|
|
hidden_states, _ = self.linear_out(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class AriaProjector(nn.Module):
|
|
"""
|
|
A projection module with one cross attention layer and one FFN layer, which
|
|
projects ViT's outputs into MoE's inputs.
|
|
|
|
Args:
|
|
patch_to_query_dict (dict): Maps patch numbers to their corresponding
|
|
query numbers,
|
|
e.g., {1225: 128, 4900: 256}. This allows for different query sizes
|
|
based on image resolution.
|
|
embed_dim (int): Embedding dimension.
|
|
num_heads (int): Number of attention heads.
|
|
kv_dim (int): Dimension of key and value.
|
|
ff_dim (int): Hidden dimension of the feed-forward network.
|
|
output_dim (int): Output dimension.
|
|
norm_layer (nn.Module): Normalization layer. Default is nn.LayerNorm.
|
|
|
|
Outputs:
|
|
A tensor with the shape of (batch_size, query_number, output_dim)
|
|
"""
|
|
|
|
def __init__(self, config: AriaConfig) -> None:
|
|
super().__init__()
|
|
|
|
self.patch_to_query_dict = config.projector_patch_to_query_dict
|
|
self.in_features = config.vision_config.hidden_size
|
|
self.num_heads = config.vision_config.num_attention_heads
|
|
self.kv_dim = config.vision_config.hidden_size
|
|
self.hidden_features = config.text_config.hidden_size
|
|
self.output_dim = config.text_config.hidden_size
|
|
|
|
self.query = nn.Parameter(
|
|
torch.empty(config.max_value_projector_patch_to_query_dict,
|
|
self.in_features))
|
|
|
|
self.cross_attn = AriaCrossAttention(config)
|
|
|
|
self.layer_norm = nn.LayerNorm(self.in_features)
|
|
self.feed_forward = AriaProjectorMLP(self.in_features,
|
|
self.hidden_features,
|
|
self.output_dim)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
attn_mask: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
batch_size, num_patches = x.shape[0], x.shape[1]
|
|
|
|
if num_patches not in self.patch_to_query_dict:
|
|
raise KeyError(f"Number of patches {num_patches} not found in "
|
|
"patch_to_query_dict amongst possible values "
|
|
f"{self.patch_to_query_dict.keys()}.")
|
|
|
|
query_num = self.patch_to_query_dict[num_patches]
|
|
|
|
queries = self.query[:query_num].unsqueeze(0).repeat(batch_size, 1, 1)
|
|
|
|
if attn_mask is not None:
|
|
attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
|
|
attn_mask = attn_mask.unsqueeze(1).expand(-1, queries.size(1), -1)
|
|
|
|
attention_out = self.cross_attn(x, queries, attn_mask=attn_mask)
|
|
|
|
out = self.feed_forward(self.layer_norm(attention_out))
|
|
|
|
return out
|
|
|
|
|
|
class AriaFusedMoE(FusedMoE):
|
|
|
|
def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
|
|
shard_id: str) -> None:
|
|
# Override the weight_loader to handle the expert weights in the Aria
|
|
# model, which are already packed with experts, and merge the gate and
|
|
# up weights for each expert.
|
|
# Note: Loading expert weights with quantization is not supported
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
if shard_id == 'w13':
|
|
# the shape of loaded_weight is
|
|
# (num_experts, hidden_size, 2 * moe_intermediate_size)
|
|
if self.tp_size > 1:
|
|
up, gate = loaded_weight.chunk(2, dim=-1)
|
|
up_current_rank = up.chunk(self.tp_size, dim=-1)[tp_rank]
|
|
gate_current_rank = gate.chunk(self.tp_size, dim=-1)[tp_rank]
|
|
up_and_gate = torch.cat([up_current_rank, gate_current_rank],
|
|
dim=-1).transpose(1, 2)
|
|
param.data.copy_(up_and_gate)
|
|
else:
|
|
param.data.copy_(loaded_weight.transpose(1, 2))
|
|
elif shard_id == 'w2':
|
|
# the shape of loaded_weight is
|
|
# (num_experts, moe_intermediate_size, hidden_size)
|
|
if self.tp_size > 1:
|
|
down_current_rank = loaded_weight.chunk(self.tp_size,
|
|
dim=1)[tp_rank]
|
|
param.data.copy_(down_current_rank.transpose(1, 2))
|
|
else:
|
|
param.data.copy_(loaded_weight.transpose(1, 2))
|
|
|
|
|
|
class AriaTextMoELayer(nn.Module):
|
|
"""
|
|
Mixture of Experts (MoE) Layer for the AriaMoE model.
|
|
|
|
This layer implements the MoE mechanism, which routes input tokens to
|
|
different experts based on a routing algorithm, processes them through the
|
|
experts, and then combines the outputs.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: AriaTextConfig,
|
|
quant_config: Optional[QuantizationConfig],
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.router_weight = nn.Parameter(
|
|
torch.empty(
|
|
(self.config.moe_num_experts, self.config.hidden_size)))
|
|
|
|
self.experts = AriaFusedMoE(
|
|
num_experts=config.moe_num_experts,
|
|
top_k=config.moe_topk,
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
quant_config=quant_config,
|
|
reduce_results=True,
|
|
prefix=f"{prefix}.experts",
|
|
)
|
|
self.shared_experts = LlamaMLP(
|
|
config.hidden_size,
|
|
config.intermediate_size * config.moe_num_shared_experts,
|
|
"silu",
|
|
quant_config=quant_config,
|
|
bias=config.mlp_bias,
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Forward pass of the MoE Layer.
|
|
|
|
Args:
|
|
hidden_states (torch.Tensor): Input tensor of shape (batch_size,
|
|
sequence_length, hidden_size).
|
|
|
|
Returns:
|
|
torch.Tensor: Output tensor after passing through the MoE layer.
|
|
"""
|
|
|
|
router_output = torch.nn.functional.linear(hidden_states,
|
|
self.router_weight)
|
|
|
|
hidden_states_copy = hidden_states.clone()
|
|
# NOTE: hidden_states will be modified inplace by `FusedMoE`
|
|
sparse_expert_output = self.experts(hidden_states, router_output)
|
|
shared_expert_output = self.shared_experts(hidden_states_copy)
|
|
|
|
return sparse_expert_output + shared_expert_output
|
|
|
|
|
|
class AriaTextDecoderLayer(LlamaDecoderLayer):
|
|
"""
|
|
Custom Decoder Layer for the AriaMoE model which modifies the standard
|
|
`LlamaDecoderLayer` by replacing the traditional MLP with a Mixture of
|
|
Experts (MoE) Layer.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: AriaTextConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__(config, cache_config, quant_config, prefix)
|
|
self.mlp = AriaTextMoELayer(config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp")
|
|
|
|
|
|
class AriaTextModel(LlamaModel, SupportsQuant):
|
|
"""
|
|
Custom LlamaModel for the AriaMoE model which modifies the standard
|
|
LlamaModel by replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.
|
|
"""
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
"experts.w13_weight": ["experts.fc1.weight"],
|
|
"experts.w2_weight": ["experts.fc2.weight"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__(vllm_config=vllm_config,
|
|
prefix=prefix,
|
|
layer_type=AriaTextDecoderLayer)
|
|
|
|
# Adapted from LlamaModel.load_weights with the modification of adding
|
|
# the expert weights mapping to `stacked_params_mapping`
|
|
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"),
|
|
(".qkv_proj", ".k_proj", "k"),
|
|
(".qkv_proj", ".v_proj", "v"),
|
|
(".gate_up_proj", ".gate_proj", 0),
|
|
(".gate_up_proj", ".up_proj", 1),
|
|
("experts.w13_weight", "experts.fc1.weight", 'w13'),
|
|
("experts.w2_weight", "experts.fc2.weight", 'w2'),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if ("rotary_emb.cos_cached" in name
|
|
or "rotary_emb.sin_cached" in name):
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
if (self.quant_config is not None and
|
|
(scale_name := self.quant_config.get_cache_scale(name))):
|
|
# Loading kv cache quantization scales
|
|
param = params_dict[scale_name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
|
|
loaded_weight[0])
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(scale_name)
|
|
continue
|
|
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)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class AriaProcessingInfo(BaseProcessingInfo):
|
|
|
|
def get_hf_config(self):
|
|
return self.ctx.get_hf_config(AriaConfig)
|
|
|
|
def get_vision_config(self):
|
|
return self.get_hf_config().vision_config
|
|
|
|
def get_hf_processor(self, **kwargs: object):
|
|
return self.ctx.get_hf_processor(AriaProcessor, **kwargs)
|
|
|
|
def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
|
|
return {"image": None}
|
|
|
|
def get_num_image_tokens(self) -> int:
|
|
hf_config = self.get_hf_config()
|
|
return max(hf_config.projector_patch_to_query_dict.values())
|
|
|
|
|
|
class AriaDummyInputsBuilder(BaseDummyInputsBuilder[AriaProcessingInfo]):
|
|
|
|
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
|
num_images = mm_counts.get("image", 0)
|
|
|
|
processor = self.info.get_hf_processor()
|
|
image_token: str = processor.tokenizer.image_token # type: ignore
|
|
|
|
return image_token * num_images
|
|
|
|
def get_dummy_mm_data(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
) -> MultiModalDataDict:
|
|
vision_config = self.info.get_vision_config()
|
|
|
|
max_image_size = vision_config.image_size
|
|
num_images = mm_counts.get("image", 0)
|
|
|
|
return {
|
|
"image":
|
|
self._get_dummy_images(width=max_image_size,
|
|
height=max_image_size,
|
|
num_images=num_images)
|
|
}
|
|
|
|
|
|
class AriaMultiModalProcessor(BaseMultiModalProcessor[AriaProcessingInfo]):
|
|
|
|
def _get_mm_fields_config(
|
|
self,
|
|
hf_inputs: BatchFeature,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
) -> Mapping[str, MultiModalFieldConfig]:
|
|
return dict(
|
|
pixel_values=MultiModalFieldConfig.batched("image"),
|
|
pixel_mask=MultiModalFieldConfig.batched("image"),
|
|
)
|
|
|
|
def _get_prompt_updates(
|
|
self,
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
out_mm_kwargs: MultiModalKwargs,
|
|
) -> Sequence[PromptUpdate]:
|
|
hf_config = self.info.get_hf_config()
|
|
image_token_id = hf_config.image_token_index
|
|
|
|
num_image_tokens = self.info.get_num_image_tokens()
|
|
|
|
return [
|
|
PromptReplacement(
|
|
modality="image",
|
|
target=[image_token_id],
|
|
replacement=[image_token_id] * num_image_tokens,
|
|
)
|
|
]
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(AriaMultiModalProcessor,
|
|
info=AriaProcessingInfo,
|
|
dummy_inputs=AriaDummyInputsBuilder)
|
|
class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
|
|
"""
|
|
Aria model for conditional generation tasks.
|
|
|
|
This model combines a vision tower, a multi-modal projector, and a language
|
|
model to perform tasks that involve both image and text inputs.
|
|
"""
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_prefix={
|
|
"language_model.model": "language_model",
|
|
"language_model.lm_head": "lm_head",
|
|
},
|
|
orig_to_new_suffix={
|
|
"router.weight": "router_weight",
|
|
},
|
|
)
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.config = config
|
|
self.vision_tower = AriaVisionTransformer(
|
|
config.vision_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.vision_tower",
|
|
)
|
|
self.multi_modal_projector = AriaProjector(config)
|
|
self.vocab_size = config.text_config.vocab_size
|
|
self.language_model = AriaTextModel(
|
|
vllm_config=vllm_config.with_hf_config(config.text_config),
|
|
prefix=maybe_prefix(prefix, "language_model.model"),
|
|
)
|
|
self.pad_token_id = (self.config.pad_token_id
|
|
if self.config.pad_token_id is not None else -1)
|
|
self.unpadded_vocab_size = config.text_config.vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.text_config.hidden_size,
|
|
org_num_embeddings=self.language_model.org_vocab_size,
|
|
quant_config=quant_config,
|
|
)
|
|
logit_scale = getattr(config, "logit_scale", 1.0)
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
self.vocab_size, logit_scale)
|
|
|
|
def _validate_image_sizes(
|
|
self, images: List[torch.Tensor]) -> List[torch.Tensor]:
|
|
if not all(img.shape == images[0].shape for img in images):
|
|
raise ValueError("All images must be the same size")
|
|
return images
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object) -> Optional[AriaImagePixelInputs]:
|
|
pixel_values = kwargs.pop("pixel_values", None)
|
|
pixel_mask = kwargs.pop("pixel_mask", None)
|
|
|
|
if pixel_values is None:
|
|
return None
|
|
|
|
if not isinstance(pixel_values, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values)}")
|
|
|
|
pixel_values = self._validate_image_sizes(pixel_values)
|
|
pixel_values = flatten_bn(pixel_values, concat=True)
|
|
|
|
if pixel_mask is not None:
|
|
if not isinstance(pixel_mask, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel mask. "
|
|
f"Got type: {type(pixel_mask)}")
|
|
|
|
pixel_mask = flatten_bn(pixel_mask, concat=True)
|
|
|
|
return AriaImagePixelInputs(
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
)
|
|
|
|
def _create_patch_attention_mask(
|
|
self, pixel_mask: Optional[torch.Tensor]) -> torch.Tensor:
|
|
if pixel_mask is None:
|
|
return None
|
|
|
|
patches_subgrid = pixel_mask.unfold(
|
|
dimension=1,
|
|
size=self.vision_tower.config.patch_size,
|
|
step=self.vision_tower.config.patch_size,
|
|
).unfold(
|
|
dimension=2,
|
|
size=self.vision_tower.config.patch_size,
|
|
step=self.vision_tower.config.patch_size,
|
|
)
|
|
return (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
|
|
|
|
def _process_image_input(
|
|
self, image_input: AriaImagePixelInputs
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
assert self.vision_tower is not None
|
|
|
|
pixel_values = image_input['pixel_values']
|
|
pixel_mask = image_input['pixel_mask']
|
|
|
|
patch_attention_mask = self._create_patch_attention_mask(pixel_mask)
|
|
|
|
image_outputs = self.vision_tower(
|
|
pixel_values=pixel_values,
|
|
patch_attention_mask=patch_attention_mask,
|
|
)
|
|
image_attn_mask = None
|
|
if patch_attention_mask is not None:
|
|
flattened_mask = patch_attention_mask.flatten(1)
|
|
image_attn_mask = torch.logical_not(flattened_mask)
|
|
|
|
return self.multi_modal_projector(image_outputs, image_attn_mask)
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
return self.language_model
|
|
|
|
def get_multimodal_embeddings(
|
|
self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
if image_input is None:
|
|
return None
|
|
multimodal_embeddings = self._process_image_input(image_input)
|
|
return multimodal_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is not None:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings,
|
|
self.config.image_token_index)
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if inputs_embeds is None:
|
|
multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
# always pass the input via `inputs_embeds`
|
|
# to make sure the computation graph is consistent
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
multimodal_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model(
|
|
input_ids,
|
|
positions,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
loader = AutoWeightsLoader(self)
|
|
loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|