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https://git.datalinker.icu/vllm-project/vllm.git
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656 lines
23 KiB
Python
656 lines
23 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Literal
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import torch
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import torch.nn as nn
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from transformers import AriaConfig, AriaTextConfig, BatchFeature
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from transformers.models.aria.modeling_aria import AriaCrossAttention
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from transformers.models.aria.processing_aria import AriaProcessor
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.distributed import get_tensor_model_parallel_rank
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.fused_moe import SharedFusedMoE
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from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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)
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from vllm.multimodal.parse import MultiModalDataItems
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from vllm.multimodal.processing import (
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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PromptReplacement,
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PromptUpdate,
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)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .idefics2_vision_model import Idefics2VisionConfig
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from .idefics2_vision_model import (
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Idefics2VisionTransformer as Idefics3VisionTransformer,
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)
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from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsQuant
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from .llama import LlamaDecoderLayer, LlamaMLP, LlamaModel
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from .utils import (
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AutoWeightsLoader,
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WeightsMapper,
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is_pp_missing_parameter,
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maybe_prefix,
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)
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class AriaImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- b: Batch size
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- n: Number of images
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- c: Number of channels
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- h: Height of each image
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- w: Width of each image
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"""
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type: Literal["pixel_values"]
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pixel_values: Annotated[
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torch.Tensor,
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TensorShape("bn", 3, "h", "w"),
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]
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pixel_mask: Annotated[
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torch.Tensor | None,
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TensorShape("bn", "h", "w"),
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]
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class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
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packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
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def __init__(
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self,
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config: Idefics2VisionConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__(config, quant_config=quant_config, prefix=prefix)
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# Unlike Idefics3VisionTransformer which uses LayerNorm after the
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# final layer, Aria omits this normalization, so we replace it with an
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# Identity layer
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self.post_layernorm = nn.Identity()
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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# NOTE: post_layernorm is not used in Aria
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if "post_layernorm" in name:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class AriaProjectorMLP(nn.Module):
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def __init__(
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self,
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in_features: int,
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hidden_features: int,
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output_dim: int,
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) -> None:
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super().__init__()
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self.linear_in = ColumnParallelLinear(in_features, hidden_features, bias=False)
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self.linear_out = RowParallelLinear(hidden_features, output_dim, bias=False)
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self.act = get_act_fn("gelu_new")
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.linear_in(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.linear_out(hidden_states)
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return hidden_states
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class AriaProjector(nn.Module):
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"""
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A projection module with one cross attention layer and one FFN layer, which
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projects ViT's outputs into MoE's inputs.
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Args:
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config: [AriaConfig](https://huggingface.co/docs/transformers/main/model_doc/aria#transformers.AriaConfig)
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containing projector configuration parameters.
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Outputs:
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A tensor with the shape of (batch_size, query_number, output_dim)
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"""
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def __init__(self, config: AriaConfig) -> None:
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super().__init__()
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self.patch_to_query_dict = config.projector_patch_to_query_dict
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self.in_features = config.vision_config.hidden_size
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self.num_heads = config.vision_config.num_attention_heads
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self.kv_dim = config.vision_config.hidden_size
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self.hidden_features = config.text_config.hidden_size
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self.output_dim = config.text_config.hidden_size
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self.query = nn.Parameter(
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torch.empty(
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config.max_value_projector_patch_to_query_dict, self.in_features
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)
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)
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self.cross_attn = AriaCrossAttention(config)
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self.layer_norm = nn.LayerNorm(self.in_features)
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self.feed_forward = AriaProjectorMLP(
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self.in_features, self.hidden_features, self.output_dim
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)
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def forward(
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self,
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x: torch.Tensor,
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attn_mask: torch.Tensor | None = None,
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) -> torch.Tensor:
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batch_size, num_patches = x.shape[0], x.shape[1]
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if num_patches not in self.patch_to_query_dict:
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raise KeyError(
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f"Number of patches {num_patches} not found in "
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"patch_to_query_dict amongst possible values "
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f"{self.patch_to_query_dict.keys()}."
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)
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query_num = self.patch_to_query_dict[num_patches]
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queries = self.query[:query_num].unsqueeze(0).repeat(batch_size, 1, 1)
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if attn_mask is not None:
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attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
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attn_mask = attn_mask.unsqueeze(1).expand(-1, queries.size(1), -1)
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attention_out = self.cross_attn(x, queries, attn_mask=attn_mask)
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out = self.feed_forward(self.layer_norm(attention_out))
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return out
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class AriaFusedMoE(SharedFusedMoE):
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def weight_loader(
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self, param: nn.Parameter, loaded_weight: torch.Tensor, shard_id: str
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) -> None:
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# Override the weight_loader to handle the expert weights in the Aria
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# model, which are already packed with experts, and merge the gate and
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# up weights for each expert.
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# Note: Loading expert weights with quantization is not supported
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tp_rank = get_tensor_model_parallel_rank()
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if shard_id == "w13":
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# the shape of loaded_weight is
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# (num_experts, hidden_size, 2 * moe_intermediate_size)
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if self.tp_size > 1:
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up, gate = loaded_weight.chunk(2, dim=-1)
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up_current_rank = up.chunk(self.tp_size, dim=-1)[tp_rank]
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gate_current_rank = gate.chunk(self.tp_size, dim=-1)[tp_rank]
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up_and_gate = torch.cat(
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[up_current_rank, gate_current_rank], dim=-1
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).transpose(1, 2)
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param.data.copy_(up_and_gate)
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else:
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param.data.copy_(loaded_weight.transpose(1, 2))
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elif shard_id == "w2":
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# the shape of loaded_weight is
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# (num_experts, moe_intermediate_size, hidden_size)
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if self.tp_size > 1:
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down_current_rank = loaded_weight.chunk(self.tp_size, dim=1)[tp_rank]
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param.data.copy_(down_current_rank.transpose(1, 2))
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else:
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param.data.copy_(loaded_weight.transpose(1, 2))
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class AriaTextMoELayer(nn.Module):
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"""
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Mixture of Experts (MoE) Layer for the AriaMoE model.
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This layer implements the MoE mechanism, which routes input tokens to
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different experts based on a routing algorithm, processes them through the
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experts, and then combines the outputs.
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"""
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def __init__(
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self,
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config: AriaTextConfig,
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quant_config: QuantizationConfig | None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.router_weight = nn.Parameter(
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torch.empty((self.config.moe_num_experts, self.config.hidden_size))
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)
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self.shared_experts = LlamaMLP(
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config.hidden_size,
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config.intermediate_size * config.moe_num_shared_experts,
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"silu",
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quant_config=quant_config,
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bias=config.mlp_bias,
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)
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self.experts = AriaFusedMoE(
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shared_experts=self.shared_experts,
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num_experts=config.moe_num_experts,
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top_k=config.moe_topk,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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quant_config=quant_config,
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reduce_results=True,
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prefix=f"{prefix}.experts",
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass of the MoE Layer.
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Args:
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hidden_states: Input tensor of shape
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(batch_size, sequence_length, hidden_size).
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Returns:
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torch.Tensor: Output tensor after passing through the MoE layer.
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"""
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router_output = torch.nn.functional.linear(hidden_states, self.router_weight)
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sparse_expert_output = self.experts(hidden_states, router_output)
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if self.shared_experts is not None:
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return sparse_expert_output[0] + sparse_expert_output[1]
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else:
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return sparse_expert_output
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class AriaTextDecoderLayer(LlamaDecoderLayer):
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"""
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Custom Decoder Layer for the AriaMoE model which modifies the standard
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`LlamaDecoderLayer` by replacing the traditional MLP with a Mixture of
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Experts (MoE) Layer.
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"""
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def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__(vllm_config, prefix)
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.mlp = AriaTextMoELayer(
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config, quant_config=quant_config, prefix=f"{prefix}.mlp"
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)
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class AriaTextModel(LlamaModel, SupportsQuant):
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"""
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Custom LlamaModel for the AriaMoE model which modifies the standard
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LlamaModel by replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.
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"""
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"],
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"experts.w13_weight": ["experts.fc1.weight"],
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"experts.w2_weight": ["experts.fc2.weight"],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(
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vllm_config=vllm_config, prefix=prefix, layer_type=AriaTextDecoderLayer
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)
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# Adapted from LlamaModel.load_weights with the modification of adding
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# the expert weights mapping to `stacked_params_mapping`
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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("experts.w13_weight", "experts.fc1.weight", "w13"),
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("experts.w2_weight", "experts.fc2.weight", "w2"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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if self.quant_config is not None and (
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scale_name := self.quant_config.get_cache_scale(name)
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):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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loaded_weight = (
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loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
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)
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class AriaProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(AriaConfig)
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def get_vision_config(self):
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return self.get_hf_config().vision_config
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def get_hf_processor(self, **kwargs: object):
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return self.ctx.get_hf_processor(AriaProcessor, **kwargs)
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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return {"image": None}
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def get_num_image_tokens(self) -> int:
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hf_config = self.get_hf_config()
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return max(hf_config.projector_patch_to_query_dict.values())
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class AriaDummyInputsBuilder(BaseDummyInputsBuilder[AriaProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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processor = self.info.get_hf_processor()
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image_token: str = processor.tokenizer.image_token # type: ignore
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return image_token * num_images
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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mm_options: Mapping[str, BaseDummyOptions] | None = None,
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) -> MultiModalDataDict:
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vision_config = self.info.get_vision_config()
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max_image_size = vision_config.image_size
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num_images = mm_counts.get("image", 0)
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image_overrides = mm_options.get("image") if mm_options else None
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return {
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"image": self._get_dummy_images(
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width=max_image_size,
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height=max_image_size,
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num_images=num_images,
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overrides=image_overrides,
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)
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}
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class AriaMultiModalProcessor(BaseMultiModalProcessor[AriaProcessingInfo]):
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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pixel_mask=MultiModalFieldConfig.batched("image"),
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)
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def _get_prompt_updates(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargsItems,
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) -> Sequence[PromptUpdate]:
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hf_config = self.info.get_hf_config()
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image_token_id = hf_config.image_token_index
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num_image_tokens = self.info.get_num_image_tokens()
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=[image_token_id] * num_image_tokens,
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)
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]
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@MULTIMODAL_REGISTRY.register_processor(
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AriaMultiModalProcessor,
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info=AriaProcessingInfo,
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dummy_inputs=AriaDummyInputsBuilder,
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)
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class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
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"""
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Aria model for conditional generation tasks.
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This model combines a vision tower, a multi-modal projector, and a language
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model to perform tasks that involve both image and text inputs.
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"""
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merge_by_field_config = True
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_prefix={
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# mapping for new names in checkpoint saved after transformers v4.52
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"model.language_model.": "language_model.model.",
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"model.vision_tower.": "vision_tower.",
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"model.multi_modal_projector.": "multi_modal_projector.",
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# mapping for original checkpoint
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"language_model.model": "language_model",
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"language_model.lm_head": "lm_head",
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},
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orig_to_new_suffix={
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"router.weight": "router_weight",
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},
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)
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@classmethod
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def get_placeholder_str(cls, modality: str, i: int) -> str | None:
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if modality.startswith("image"):
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return "<|fim_prefix|><|img|><|fim_suffix|>"
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raise ValueError("Only image modality is supported")
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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|
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self.config = config
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self.vision_tower = AriaVisionTransformer(
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config.vision_config,
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quant_config=quant_config,
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prefix=f"{prefix}.vision_tower",
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)
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self.multi_modal_projector = AriaProjector(config)
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self.vocab_size = config.text_config.vocab_size
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self.language_model = AriaTextModel(
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vllm_config=vllm_config.with_hf_config(config.text_config),
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prefix=maybe_prefix(prefix, "language_model.model"),
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)
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self.pad_token_id = (
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self.config.pad_token_id if self.config.pad_token_id is not None else -1
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)
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self.lm_head = ParallelLMHead(
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self.vocab_size,
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config.text_config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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|
)
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(self.vocab_size, scale=logit_scale)
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def _parse_and_validate_image_input(
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self, **kwargs: object
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) -> AriaImagePixelInputs | None:
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pixel_values = kwargs.pop("pixel_values", None)
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pixel_mask = kwargs.pop("pixel_mask", None)
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|
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if pixel_values is None:
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return None
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|
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return AriaImagePixelInputs(
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type="pixel_values",
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pixel_values=pixel_values,
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pixel_mask=pixel_mask,
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)
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|
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def _create_patch_attention_mask(
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self,
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|
pixel_mask: torch.Tensor | None,
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|
) -> torch.Tensor | None:
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if pixel_mask is None:
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return None
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|
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|
patches_subgrid = pixel_mask.unfold(
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dimension=1,
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size=self.vision_tower.config.patch_size,
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|
step=self.vision_tower.config.patch_size,
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|
).unfold(
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|
dimension=2,
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|
size=self.vision_tower.config.patch_size,
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|
step=self.vision_tower.config.patch_size,
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|
)
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return (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
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|
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|
def _process_image_input(
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self, image_input: AriaImagePixelInputs
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|
) -> tuple[torch.Tensor, torch.Tensor]:
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assert self.vision_tower is not None
|
|
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|
pixel_values = image_input["pixel_values"]
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pixel_mask = image_input["pixel_mask"]
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|
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patch_attention_mask = self._create_patch_attention_mask(pixel_mask)
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|
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|
image_outputs = self.vision_tower(
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|
pixel_values=pixel_values,
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|
patch_attention_mask=patch_attention_mask,
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|
)
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|
image_attn_mask = None
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|
if patch_attention_mask is not None:
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|
flattened_mask = patch_attention_mask.flatten(1)
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|
image_attn_mask = torch.logical_not(flattened_mask)
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|
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|
return self.multi_modal_projector(image_outputs, image_attn_mask)
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|
|
|
def get_language_model(self) -> torch.nn.Module:
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|
return self.language_model
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|
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|
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
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|
image_input = self._parse_and_validate_image_input(**kwargs)
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|
if image_input is None:
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|
return []
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|
multimodal_embeddings = self._process_image_input(image_input)
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|
return multimodal_embeddings
|
|
|
|
def forward(
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|
self,
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|
input_ids: torch.Tensor,
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|
positions: torch.Tensor,
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|
intermediate_tensors: IntermediateTensors | None = None,
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|
inputs_embeds: torch.Tensor | None = None,
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|
**kwargs: object,
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|
) -> torch.Tensor | IntermediateTensors:
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|
if inputs_embeds is None:
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|
multimodal_embeddings = self.embed_multimodal(**kwargs)
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|
inputs_embeds = self.embed_input_ids(
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|
input_ids,
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|
multimodal_embeddings,
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|
is_multimodal=input_ids == self.config.image_token_index,
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|
)
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|
input_ids = None
|
|
|
|
hidden_states = self.language_model(
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|
input_ids,
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|
positions,
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|
intermediate_tensors,
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|
inputs_embeds=inputs_embeds,
|
|
)
|
|
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|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
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|
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)
|