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import math
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from typing import Iterable, List, Optional, Set, Tuple, TypedDict, Union
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import torch
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import torch.nn as nn
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from torch.nn.init import trunc_normal_
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from transformers import LlamaConfig
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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.inputs import INPUT_REGISTRY, token_inputs
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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.quantization.compressed_tensors.utils import (
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get_compressed_tensors_cache_scale)
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from vllm.model_executor.layers.sampler import (Sampler, SamplerOutput,
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SamplingMetadata)
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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.model_executor.models.idefics2_vision_model import (
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Idefics2VisionTransformer)
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from vllm.model_executor.models.interfaces import SupportsMultiModal
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from vllm.model_executor.models.llama import (LlamaDecoderLayer, LlamaMLP,
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LlamaModel)
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from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper,
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is_pp_missing_parameter,
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make_layers, maybe_prefix,
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merge_multimodal_embeddings)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.base import MultiModalInputs
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from vllm.multimodal.image import cached_get_image_processor
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from vllm.multimodal.inputs import NestedTensors
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from vllm.multimodal.utils import (cached_get_tokenizer,
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repeat_and_pad_placeholder_tokens)
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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 .utils import flatten_bn
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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.BoolTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.BoolTensor]]:
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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(self, pixel_mask):
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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(self, patch_attention_mask):
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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, ff_dim, output_dim):
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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):
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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, embed_dim, num_heads, drop_out_rate=0):
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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.dropout = nn.Dropout(drop_out_rate)
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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(self, x, hidden_states, attn_mask=None, add_residual=False):
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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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if add_residual:
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attn_output = hidden_states + self.dropout(
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self.linear(attn_output))
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else:
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attn_output = self.dropout(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,
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embed_dim,
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num_heads,
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kv_dim,
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ff_dim,
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output_dim,
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norm_layer=nn.LayerNorm,
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):
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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.zeros(max(patch_to_query_dict.values()), self.embed_dim))
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trunc_normal_(self.query, std=0.02)
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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(self, x, attn_mask=None):
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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) -> Set[str]:
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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(
|
|
|
|
|
config.hidden_size,
|
|
|
|
|
config.moe_intermediate_size * config.moe_num_shared_experts,
|
|
|
|
|
"silu",
|
|
|
|
|
quant_config=quant_config,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
shared_expert_output = self.shared_experts(hidden_states)
|
|
|
|
|
sparse_expert_output = self.experts(hidden_states, router_output)
|
|
|
|
|
|
|
|
|
|
return sparse_expert_output + shared_expert_output
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class MoEDecoderLayer(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: LlamaConfig,
|
|
|
|
|
cache_config: Optional[CacheConfig] = None,
|
|
|
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
|
|
|
prefix: str = "",
|
|
|
|
|
) -> None:
|
|
|
|
|
super().__init__(config, cache_config, quant_config, prefix)
|
|
|
|
|
self.mlp = MoELayer(config, quant_config=quant_config)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class AriaMoELMModel(LlamaModel):
|
|
|
|
|
"""
|
|
|
|
|
Custom LlamaModel for the AriaMoE model which modifies the standard
|
|
|
|
|
LlamaModel by replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
|
|
|
super().__init__(vllm_config=vllm_config, prefix=prefix)
|
|
|
|
|
|
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
|
|
|
cache_config = vllm_config.cache_config
|
|
|
|
|
quant_config = vllm_config.quant_config
|
|
|
|
|
|
|
|
|
|
# FIXME: this is a hack to disable the compilation of the model
|
|
|
|
|
self.do_not_compile = True
|
|
|
|
|
|
|
|
|
|
self.layers = None
|
|
|
|
|
|
|
|
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
|
|
|
config.num_hidden_layers,
|
|
|
|
|
lambda prefix: MoEDecoderLayer(
|
|
|
|
|
config=config,
|
|
|
|
|
cache_config=cache_config,
|
|
|
|
|
quant_config=quant_config,
|
|
|
|
|
prefix=prefix,
|
|
|
|
|
),
|
|
|
|
|
prefix=f"{prefix}.layers",
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 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 scale_name := get_compressed_tensors_cache_scale(name):
|
|
|
|
|
# Loading kv cache scales for compressed-tensors quantization
|
|
|
|
|
param = params_dict[scale_name]
|
|
|
|
|
weight_loader = getattr(param, "weight_loader",
|
|
|
|
|
default_weight_loader)
|
|
|
|
|
loaded_weight = 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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def build_mm_projector(config):
|
|
|
|
|
return AriaProjector(
|
|
|
|
|
patch_to_query_dict=config.projector_patch_to_query_dict,
|
|
|
|
|
embed_dim=config.vision_config.hidden_size,
|
|
|
|
|
num_heads=config.vision_config.num_attention_heads,
|
|
|
|
|
kv_dim=config.vision_config.hidden_size,
|
|
|
|
|
ff_dim=config.text_config.hidden_size,
|
|
|
|
|
output_dim=config.text_config.hidden_size,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_max_multimodal_tokens(ctx):
|
|
|
|
|
return max(ctx.model_config.hf_config.image_size2tokens.values())
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def input_mapper_for_aria(ctx, data):
|
|
|
|
|
return MultiModalInputs(data)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def input_processor(ctx, llm_inputs):
|
|
|
|
|
multi_modal_data = llm_inputs.get("multi_modal_data")
|
|
|
|
|
# if it is pure text input, use it as is
|
|
|
|
|
if multi_modal_data is None or "image" not in multi_modal_data:
|
|
|
|
|
return llm_inputs
|
|
|
|
|
|
|
|
|
|
model_config = ctx.model_config
|
|
|
|
|
|
|
|
|
|
tokenizer = cached_get_tokenizer(model_config.tokenizer)
|
|
|
|
|
image_processor = cached_get_image_processor(
|
|
|
|
|
model_config.model, trust_remote_code=model_config.trust_remote_code)
|
|
|
|
|
hf_config = model_config.hf_config
|
|
|
|
|
|
|
|
|
|
# prepare image tokens, the max_image_size is used to determine the number
|
|
|
|
|
# of patch_size for every image
|
|
|
|
|
max_image_size = multi_modal_data.pop("max_image_size", 980)
|
|
|
|
|
_split_image = multi_modal_data.pop("split_image", False)
|
|
|
|
|
|
|
|
|
|
assert isinstance(max_image_size,
|
|
|
|
|
(int, float)), "max_image_size should be float or int"
|
|
|
|
|
images = (multi_modal_data["image"] if isinstance(
|
|
|
|
|
multi_modal_data["image"], list) else [multi_modal_data["image"]])
|
|
|
|
|
|
|
|
|
|
image_inputs = image_processor.preprocess(images,
|
|
|
|
|
max_image_size=max_image_size,
|
|
|
|
|
split_image=_split_image,
|
|
|
|
|
return_tensors="pt").data
|
|
|
|
|
image_inputs['pixel_values'] = image_inputs['pixel_values'].to(
|
|
|
|
|
ctx.model_config.dtype)
|
|
|
|
|
num_crops = image_inputs.pop("num_crops")
|
|
|
|
|
|
|
|
|
|
prompt_token_ids = llm_inputs["prompt_token_ids"]
|
|
|
|
|
if num_crops.sum().item() > 0:
|
|
|
|
|
_, prompt_token_ids, _ = repeat_and_pad_placeholder_tokens(
|
|
|
|
|
tokenizer,
|
|
|
|
|
None,
|
|
|
|
|
prompt_token_ids,
|
|
|
|
|
placeholder_token_id=hf_config.image_token_index,
|
|
|
|
|
repeat_count=num_crops,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
repeat_count = [hf_config.image_size2tokens[max_image_size]
|
|
|
|
|
] * sum(num_crops).item()
|
|
|
|
|
new_prompt, new_token_ids, _ = repeat_and_pad_placeholder_tokens(
|
|
|
|
|
tokenizer,
|
|
|
|
|
None,
|
|
|
|
|
prompt_token_ids,
|
|
|
|
|
placeholder_token_id=hf_config.image_token_index,
|
|
|
|
|
repeat_count=repeat_count,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
return token_inputs(
|
|
|
|
|
prompt_token_ids=new_token_ids,
|
|
|
|
|
prompt=new_prompt,
|
|
|
|
|
multi_modal_data={"image": image_inputs},
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_multimodal_tokens)
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_aria)
|
|
|
|
|
@INPUT_REGISTRY.register_input_processor(input_processor)
|
|
|
|
|
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.
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(
|
|
|
|
|
self,
|
|
|
|
|
vllm_config: VllmConfig,
|
|
|
|
|
prefix: str = "",
|
|
|
|
|
):
|
|
|
|
|
super().__init__()
|
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
|
|
|
quant_config = vllm_config.quant_config
|
|
|
|
|
|
|
|
|
|
# prepare the image_size to tokens mapping for the image preprocess, see
|
|
|
|
|
# input_processor
|
|
|
|
|
config.image_size2tokens = {
|
|
|
|
|
int(math.sqrt(k) * config.vision_config.patch_size): v
|
|
|
|
|
for k, v in config.projector_patch_to_query_dict.items()
|
|
|
|
|
}
|
|
|
|
|
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 = 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:
|
|
|
|
|
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
|
|
|
|
|
|
|
|
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def get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: Optional[NestedTensors] = None,
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) -> torch.Tensor:
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inputs_embeds = self.language_model.get_input_embeddings(input_ids)
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if multimodal_embeddings is not None:
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inputs_embeds = merge_multimodal_embeddings(
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input_ids, inputs_embeds, multimodal_embeddings,
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self.config.image_token_index)
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return inputs_embeds
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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**kwargs: object,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if inputs_embeds is None:
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multimodal_embeddings = self.get_multimodal_embeddings(**kwargs)
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# always pass the input via `inputs_embeds`
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# to make sure the computation graph is consistent
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inputs_embeds = self.get_input_embeddings(input_ids,
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multimodal_embeddings)
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input_ids = None
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hidden_states = self.language_model(
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input_ids,
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positions,
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kv_caches,
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attn_metadata,
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intermediate_tensors,
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inputs_embeds=inputs_embeds,
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)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_prefix={
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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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loader = AutoWeightsLoader(self)
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loader.load_weights(weights, mapper=hf_to_vllm_mapper)
|
zhou fan