mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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689 lines
25 KiB
Python
689 lines
25 KiB
Python
from typing import (Callable, Iterable, List, Mapping, Optional, Set, Tuple,
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TypedDict, Union)
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import torch
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import torch.nn as nn
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from transformers import BatchFeature, PretrainedConfig
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, QuantizationConfig, VllmConfig
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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 FusedMoE
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.sampler import (SamplerOutput,
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SamplingMetadata, get_sampler)
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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, maybe_remap_kv_scale_name)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
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NestedTensors)
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from vllm.multimodal.parse import MultiModalDataItems
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, PromptReplacement)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.aria import (AriaMoELMConfig,
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AriaVisionConfig)
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from .idefics2_vision_model import Idefics2VisionTransformer
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from .interfaces import SupportsMultiModal
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from .llama import LlamaDecoderLayer, LlamaMLP, LlamaModel
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from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
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is_pp_missing_parameter, maybe_prefix,
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merge_multimodal_embeddings)
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class AriaImagePixelInputs(TypedDict):
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pixel_values: torch.Tensor
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pixel_mask: Optional[torch.Tensor]
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"""
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Shape:
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pixel_values: `(batch_size * num_images, num_channels, height, width)`
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pixel_mask: `(batch_size * num_images, height, width)`
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"""
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class AriaVisionTransformer(Idefics2VisionTransformer):
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"""
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AriaVisionTransformer is a modified version of Idefics2VisionTransformer
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that replaces the post-layernorm with an identity layer.
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"""
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def __init__(
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self,
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config: AriaVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__(config, quant_config, prefix)
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self.post_layernorm = nn.Identity()
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class AriaVisionModel(nn.Module):
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config_class = AriaVisionConfig
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def __init__(
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self,
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config: AriaVisionConfig,
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quant_config: Optional[QuantizationConfig] = None,
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*,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.vision_model = AriaVisionTransformer(
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config,
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quant_config,
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prefix=f"{prefix}.vision_model",
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)
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def forward(
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self,
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pixel_values: torch.Tensor,
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pixel_mask: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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patch_attention_mask = self._create_patch_attention_mask(pixel_mask)
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vit_oup = self.vision_model(
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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_atts = self._create_image_attention_mask(patch_attention_mask)
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return vit_oup, image_atts
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def _create_patch_attention_mask(
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self, pixel_mask: Optional[torch.Tensor]) -> torch.Tensor:
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if pixel_mask is None:
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return None
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patches_subgrid = pixel_mask.unfold(
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dimension=1,
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size=self.vision_model.config.patch_size,
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step=self.vision_model.config.patch_size,
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).unfold(
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dimension=2,
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size=self.vision_model.config.patch_size,
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step=self.vision_model.config.patch_size,
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)
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return (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
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def _create_image_attention_mask(
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self, patch_attention_mask: torch.Tensor) -> torch.Tensor:
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if patch_attention_mask is None:
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return None
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flattened_mask = patch_attention_mask.flatten(1)
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return torch.logical_not(flattened_mask)
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class FFN(nn.Module):
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def __init__(self, embed_dim: int, ff_dim: int, output_dim: int) -> None:
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super().__init__()
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self.linear_in = ColumnParallelLinear(embed_dim, ff_dim, bias=False)
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self.linear_out = RowParallelLinear(ff_dim, 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 CrossAttention(nn.Module):
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def __init__(self, kv_dim: int, embed_dim: int, num_heads: int) -> None:
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super().__init__()
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self.num_heads = num_heads
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False)
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self.k_proj = nn.Linear(kv_dim, embed_dim, bias=False)
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self.v_proj = nn.Linear(kv_dim, embed_dim, bias=False)
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self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
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self.linear = nn.Linear(embed_dim, embed_dim)
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self.layer_norm = nn.LayerNorm(embed_dim)
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self.ln_kv = nn.LayerNorm(kv_dim)
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def forward(
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self,
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x: torch.Tensor,
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hidden_states: torch.Tensor,
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attn_mask: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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normed_hidden_states = self.layer_norm(hidden_states)
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query = self.q_proj(normed_hidden_states).permute(1, 0, 2)
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x = self.ln_kv(x)
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key = self.k_proj(x).permute(1, 0, 2)
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value = self.v_proj(x).permute(1, 0, 2)
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attn_output, _ = self.multihead_attn(query,
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key,
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value,
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attn_mask=attn_mask)
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attn_output = attn_output.permute(1, 0, 2)
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attn_output = self.linear(attn_output)
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return attn_output
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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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patch_to_query_dict (dict): Maps patch numbers to their corresponding
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query numbers,
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e.g., {1225: 128, 4900: 256}. This allows for different query sizes
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based on image resolution.
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embed_dim (int): Embedding dimension.
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num_heads (int): Number of attention heads.
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kv_dim (int): Dimension of key and value.
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ff_dim (int): Hidden dimension of the feed-forward network.
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output_dim (int): Output dimension.
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norm_layer (nn.Module): Normalization layer. Default is nn.LayerNorm.
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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__(
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self,
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patch_to_query_dict: dict[int, int],
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embed_dim: int,
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num_heads: int,
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kv_dim: int,
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ff_dim: int,
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output_dim: int,
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norm_layer: Callable[[int], nn.Module] = nn.LayerNorm,
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) -> None:
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super().__init__()
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self.patch_to_query_dict = patch_to_query_dict
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.query = nn.Parameter(
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torch.empty(max(patch_to_query_dict.values()), self.embed_dim))
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self.cross_attn = CrossAttention(kv_dim, embed_dim, num_heads)
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self.ln_ffn = norm_layer(embed_dim)
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self.ffn = FFN(embed_dim, ff_dim, output_dim)
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def forward(
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self,
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x: torch.Tensor,
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attn_mask: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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bs = x.shape[0]
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queries = self.query.unsqueeze(0).repeat(bs, 1, 1)
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query_num = self.patch_to_query_dict.get(x.shape[1], None)
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assert (query_num is not None
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), f"Query number for {x.shape[1]} patches is not provided"
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queries = queries[:, :query_num, :]
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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.ffn(self.ln_ffn(attention_out))
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return out
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class AriaFusedMoE(FusedMoE):
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def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
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shard_id: str) -> 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([up_current_rank, gate_current_rank],
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dim=-1).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,
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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 MoELayer(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: AriaMoELMConfig,
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quant_config: Optional[QuantizationConfig],
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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(
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(self.config.moe_num_experts, self.config.hidden_size)))
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self.experts = AriaFusedMoE(
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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.moe_intermediate_size,
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quant_config=quant_config,
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reduce_results=True,
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)
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self.shared_experts = LlamaMLP(
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config.hidden_size,
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config.moe_intermediate_size * config.moe_num_shared_experts,
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"silu",
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quant_config=quant_config,
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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 (torch.Tensor): Input tensor of shape (batch_size,
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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,
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self.router_weight)
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shared_expert_output = self.shared_experts(hidden_states)
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sparse_expert_output = self.experts(hidden_states, router_output)
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return sparse_expert_output + shared_expert_output
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class MoEDecoderLayer(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__(
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self,
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config: AriaMoELMConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__(config, cache_config, quant_config, prefix)
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self.mlp = MoELayer(config, quant_config=quant_config)
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class AriaMoELMModel(LlamaModel):
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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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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(vllm_config=vllm_config,
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prefix=prefix,
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layer_type=MoEDecoderLayer)
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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,
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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
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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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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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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",
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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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def build_mm_projector(config: PretrainedConfig):
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return AriaProjector(
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patch_to_query_dict=config.projector_patch_to_query_dict,
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embed_dim=config.vision_config.hidden_size,
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num_heads=config.vision_config.num_attention_heads,
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kv_dim=config.vision_config.hidden_size,
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ff_dim=config.text_config.hidden_size,
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output_dim=config.text_config.hidden_size,
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)
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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()
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def get_vision_config(self) -> AriaVisionConfig:
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return self.get_hf_config().vision_config
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": None}
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def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
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return {"image": self.get_num_image_tokens()}
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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_processor_inputs(
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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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) -> ProcessorInputs:
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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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mm_data = {
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"image":
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self._get_dummy_images(width=max_image_size,
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height=max_image_size,
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num_images=num_images)
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}
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|
|
|
hf_processor = self.info.get_hf_processor()
|
|
image_token: str = hf_processor.image_token # type: ignore
|
|
|
|
return ProcessorInputs(
|
|
prompt_text=image_token * num_images,
|
|
mm_data=mm_data,
|
|
)
|
|
|
|
|
|
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_replacements(
|
|
self,
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
out_mm_kwargs: MultiModalKwargs,
|
|
) -> list[PromptReplacement]:
|
|
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 = AriaVisionModel(config.vision_config)
|
|
self.multi_modal_projector = build_mm_projector(config)
|
|
self.vocab_size = config.text_config.vocab_size
|
|
self.language_model = AriaMoELMModel(
|
|
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)
|
|
self.sampler = get_sampler()
|
|
|
|
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 _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']
|
|
|
|
image_feature, image_attn_mask = self.vision_tower(
|
|
pixel_values, pixel_mask=pixel_mask)
|
|
return self.multi_modal_projector(image_feature, image_attn_mask)
|
|
|
|
def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
|
|
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[NestedTensors] = 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,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
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,
|
|
kv_caches,
|
|
attn_metadata,
|
|
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 sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
|
|
loader = AutoWeightsLoader(self)
|
|
loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|