1413 lines
50 KiB
Python
1413 lines
50 KiB
Python
import math
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import re
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from array import array
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from dataclasses import dataclass
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from functools import lru_cache, partial
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from typing import Iterable, List, Mapping, Optional, Set, Tuple, TypedDict
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import torch
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from einops import rearrange
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from PIL import Image
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from torch import nn
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from torch.nn import functional as F
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.attention.layer import MultiHeadAttention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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split_tensor_along_last_dim,
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tensor_model_parallel_all_gather)
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from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
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InputContext, token_inputs)
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from vllm.model_executor import SamplingMetadata
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from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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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 import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
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from vllm.multimodal.inputs import NestedTensors, PlaceholderRange
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from vllm.multimodal.utils import cached_get_tokenizer
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from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
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SequenceData)
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from vllm.transformers_utils.processor import get_processor
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from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
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from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix, merge_multimodal_embeddings)
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# TODO: hard-coded for now. Consider making it configurable.
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VIT_LAYERS = [-2, -9]
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NUM_PREFIX_TOKENS = 1
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ADDITIONAL_VOCAB_SIZE = 128
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DEFAULT_IMAGE_PATCH_TOKEN_ID = 152066
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DEFAULT_IM_START_TOKEN_ID = 152067
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DEFAULT_IM_END_TOKEN_ID = 152064
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DEFAULT_IM_COL_TOKEN_ID = 152065
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class MolmoImageInputs(TypedDict):
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images: torch.Tensor
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"""Shape:
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`(batch_size, num_crops, num_patch, patch_dim)`
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"""
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image_input_idx: torch.Tensor
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"""Shape:
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`(batch_size, num_crops, num_patch)`
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"""
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seq_len: torch.Tensor
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"""Shape:
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`(batch_size, )`
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"""
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image_masks: Optional[torch.Tensor]
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"""Shape:
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`(batch_size, num_crops, num_patch)`
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"""
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image_start_end: Tuple[int, int]
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"""Starting and ending index of placeholder
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tokens
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"""
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@dataclass
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class VisionBackboneConfig:
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image_default_input_size: Tuple[int, int] = (336, 336)
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image_patch_size: int = 14
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image_pos_patch_size: int = 14
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image_emb_dim: int = 1024
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image_num_heads: int = 16
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image_num_key_value_heads: int = 16
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image_num_layers: int = 23
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image_mlp_dim: int = 4096
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image_mlp_activations: str = "quick_gelu"
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image_num_pos: int = 577
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image_norm_eps: float = 1e-5
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def __post_init__(self):
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self.image_default_input_size = tuple(
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self.image_default_input_size) # type: ignore[assignment]
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@property
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def image_num_patch(self):
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h, w = self.image_default_input_size
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return h // self.image_patch_size, w // self.image_patch_size
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class ViTMLP(nn.Module):
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"""MLP used in Vision Transformer."""
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def __init__(
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self,
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config: VisionBackboneConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.w1 = ColumnParallelLinear(
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config.image_emb_dim,
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config.image_mlp_dim,
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bias=True,
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quant_config=quant_config,
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)
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# Activation function.
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assert config.image_mlp_activations == "quick_gelu"
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self.act = QuickGELU()
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self.w2 = RowParallelLinear(
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config.image_mlp_dim,
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config.image_emb_dim,
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bias=True,
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quant_config=quant_config,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.w1(x)
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x = self.act(x)
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x, _ = self.w2(x)
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return x
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class MultiHeadDotProductAttention(nn.Module):
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"""Multi-head attention used in Vision Transformer."""
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def __init__(
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self,
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config: VisionBackboneConfig,
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use_bias: bool = True,
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nlayers: int = 1,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.hidden_size = config.image_emb_dim
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self.total_num_heads = config.image_num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert self.hidden_size % self.total_num_heads == 0
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.head_dim = self.hidden_size // self.total_num_heads
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self.total_num_kv_heads = config.image_num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.wq = ColumnParallelLinear(
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nlayers * self.hidden_size,
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self.total_num_heads * self.head_dim,
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bias=use_bias,
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quant_config=quant_config,
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)
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self.wk = ColumnParallelLinear(
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nlayers * self.hidden_size,
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self.total_num_kv_heads * self.head_dim,
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bias=use_bias,
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quant_config=quant_config,
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)
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self.wv = ColumnParallelLinear(
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nlayers * self.hidden_size,
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self.total_num_kv_heads * self.head_dim,
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bias=use_bias,
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quant_config=quant_config,
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)
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self.wo = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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self.hidden_size,
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bias=use_bias,
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quant_config=quant_config,
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)
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self.scale = self.head_dim**-0.5
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self.attn = MultiHeadAttention(self.num_heads,
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self.head_dim,
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self.scale,
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num_kv_heads=self.num_kv_heads)
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def forward(self,
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inputs_q: torch.Tensor,
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inputs_kv: Optional[torch.Tensor] = None) -> torch.Tensor:
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if inputs_kv is not None:
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inputs_k = inputs_kv
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inputs_v = inputs_kv
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else:
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inputs_k = inputs_q
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inputs_v = inputs_q
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xq, _ = self.wq(inputs_q)
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xk, _ = self.wk(inputs_k)
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xv, _ = self.wv(inputs_v)
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output = self.attn(xq, xk, xv)
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output, _ = self.wo(output)
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return output
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class ResidualAttentionBlock(nn.Module):
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"""Residual attention block used in Vision Transformer."""
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def __init__(
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self,
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config: VisionBackboneConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.attention = MultiHeadDotProductAttention(
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config, quant_config=quant_config)
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self.feed_forward = ViTMLP(config, quant_config)
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self.attention_norm = nn.LayerNorm(
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config.image_emb_dim,
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eps=config.image_norm_eps,
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)
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self.ffn_norm = nn.LayerNorm(
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config.image_emb_dim,
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eps=config.image_norm_eps,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = x + self.attention(self.attention_norm(x))
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x = x + self.feed_forward(self.ffn_norm(x))
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return x
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class BlockCollection(nn.Module):
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"""Collection of residual attention blocks used in Vision Transformer."""
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def __init__(
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self,
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config: VisionBackboneConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.resblocks = nn.ModuleList([
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ResidualAttentionBlock(config, quant_config)
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for _ in range(config.image_num_layers)
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])
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def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
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hidden_states = []
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for r in self.resblocks:
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x = r(x)
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hidden_states.append(x)
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return hidden_states
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def _expand_token(token: torch.Tensor, batch_size: int) -> torch.Tensor:
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return token.view(1, 1, -1).expand(batch_size, -1, -1)
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class VisionTransformer(nn.Module):
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"""Vision Transformer used in Vision Backbone."""
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def __init__(
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self,
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config: VisionBackboneConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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scale = config.image_emb_dim**-0.5
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self.patch_num = config.image_num_patch
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self.class_embedding = nn.Parameter(
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torch.randn(config.image_emb_dim) * scale)
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self.num_prefix_tokens: int = NUM_PREFIX_TOKENS
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self.positional_embedding = nn.Parameter(
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torch.randn(config.image_num_pos, config.image_emb_dim) * scale)
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image_patch_size = config.image_patch_size
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self.patch_embedding = nn.Linear(
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image_patch_size * image_patch_size * 3,
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config.image_emb_dim,
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bias=False,
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)
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self.pre_ln = nn.LayerNorm(config.image_emb_dim,
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eps=config.image_norm_eps)
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self.transformer = BlockCollection(config, quant_config)
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def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor:
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cls_emb = self.positional_embedding[0:1]
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pos_emb = self.positional_embedding[1:]
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pos_emb = pos_emb.reshape(
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(int(math.sqrt(pos_emb.shape[0])),
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int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1]))
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(patch_num_0, patch_num_1) = patch_num
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if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1:
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# from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
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pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2)
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pos_emb = F.interpolate(
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pos_emb,
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size=(patch_num_0, patch_num_1),
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mode="bicubic",
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align_corners=False,
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antialias=True,
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)
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pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0)
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pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1])
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x = x + torch.cat([cls_emb[None, :, :], pos_emb[None, :, :]],
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dim=1).to(x.dtype)
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return x
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def forward(self,
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x: torch.Tensor,
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patch_num: int = None) -> List[torch.Tensor]:
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"""
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: param x: (batch_size, num_patch, n_pixels)
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"""
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if patch_num is None:
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patch_num = self.patch_num
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B, N, D = x.shape
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x = self.patch_embedding(x)
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# class embeddings and positional embeddings
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x = torch.cat(
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[_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x],
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dim=1)
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x = self.add_pos_emb(x, patch_num)
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x = self.pre_ln(x)
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hidden_states = self.transformer(x)
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return hidden_states
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class MolmoAttention(nn.Module):
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"""Molmo's LLM attention."""
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def __init__(
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self,
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config: PretrainedConfig,
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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__()
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self.hidden_size = config.hidden_size
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self.tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.hidden_size % self.total_num_heads == 0
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assert self.total_num_heads % self.tp_size == 0
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self.num_heads = self.total_num_heads // self.tp_size
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self.total_num_kv_heads = config.num_key_value_heads \
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or self.total_num_heads
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if self.total_num_kv_heads >= self.tp_size:
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assert self.total_num_kv_heads % self.tp_size == 0
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else:
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assert self.tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size)
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self.head_dim = self.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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# Attention input projection. Projects x -> (q, k, v)
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self.qkv_proj = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=config.qkv_bias,
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quant_config=quant_config,
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)
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self.tp_rank: Optional[int] = None
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self.k_norm: Optional[nn.Module] = None
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self.q_norm: Optional[nn.Module] = None
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if config.attention_layer_norm:
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self.tp_rank = get_tensor_model_parallel_rank()
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self.k_norm = RMSNorm(self.total_num_kv_heads * self.head_dim,
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eps=config.layer_norm_eps)
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self.q_norm = RMSNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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|
|
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# Rotary embeddings.
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=self.rope_theta,
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)
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self.scaling = self.head_dim**-0.5
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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|
|
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# Attention output projection.
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|
self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
|
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
|
|
|
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def _apply_qk_norm(self, q: torch.Tensor,
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k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if self.tp_size > 1:
|
|
q = tensor_model_parallel_all_gather(q.contiguous())
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|
k = tensor_model_parallel_all_gather(k.contiguous())
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q = self.q_norm.forward_native(q)
|
|
k = self.k_norm.forward_native(k)
|
|
if self.tp_size > 1:
|
|
splitter = partial(split_tensor_along_last_dim,
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|
num_partitions=self.tp_size)
|
|
q = splitter(q)[self.tp_rank]
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|
k = splitter(k)[self.tp_rank]
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return q, k
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|
|
def forward(
|
|
self,
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|
positions: torch.Tensor,
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hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
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|
) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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if self.q_norm is not None and self.k_norm is not None:
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q, k = self._apply_qk_norm(q, k)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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|
|
|
|
class SwiGLU(nn.Module):
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x, gate = x.chunk(2, dim=-1)
|
|
# Note that the order is reversed compared to
|
|
# SiluAndMul.
|
|
return x * F.silu(gate)
|
|
|
|
|
|
class LanuageModelMLP(nn.Module):
|
|
"""Molmo's LLM mlp."""
|
|
|
|
def __init__(self,
|
|
config: PretrainedConfig,
|
|
input_dim: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None) -> None:
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size // 2
|
|
|
|
self.gate_up_proj = MergedColumnParallelLinear(
|
|
input_dim or self.hidden_size,
|
|
[self.intermediate_size] * 2,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
# Activation function.
|
|
self.act_fn = SwiGLU()
|
|
# Feed-forward output projection.
|
|
self.down_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
gate_up, _ = self.gate_up_proj(x)
|
|
x = self.act_fn(gate_up)
|
|
x, _ = self.down_proj(x)
|
|
return x
|
|
|
|
|
|
class ImageProjectorMLP(nn.Module):
|
|
"""Molmo's image_projector mlp."""
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
input_dim: Optional[int] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.intermediate_size = config.intermediate_size // 2
|
|
|
|
self.merged_linear = MergedColumnParallelLinear(
|
|
input_dim or self.hidden_size,
|
|
[self.intermediate_size] * 2,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
# Activation function.
|
|
self.act_fn = SiluAndMul()
|
|
|
|
# Feed-forward output projection.
|
|
self.down_proj = RowParallelLinear(
|
|
self.intermediate_size,
|
|
self.hidden_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
gate_up, _ = self.merged_linear(x)
|
|
x = self.act_fn(gate_up)
|
|
x, _ = self.down_proj(x)
|
|
return x
|
|
|
|
|
|
class MolmoDecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
# Attention block.
|
|
self.self_attn = MolmoAttention(config,
|
|
cache_config,
|
|
quant_config,
|
|
prefix=f"{prefix}.self_attn")
|
|
|
|
# MLP block.
|
|
self.mlp = LanuageModelMLP(config, quant_config=quant_config)
|
|
|
|
# LayerNorm
|
|
assert config.layer_norm_type == "rms"
|
|
self.input_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.layer_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
|
eps=config.layer_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
residual: Optional[torch.Tensor],
|
|
) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
|
|
# Self Attention
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(
|
|
hidden_states, residual)
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
|
|
hidden_states, residual = self.post_attention_layernorm(
|
|
hidden_states, residual)
|
|
hidden_states = self.mlp(hidden_states)
|
|
return hidden_states, residual
|
|
|
|
|
|
class MolmoDecoderNormAfterLayer(MolmoDecoderLayer):
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: AttentionMetadata,
|
|
residual: Optional[torch.Tensor],
|
|
) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]:
|
|
# Self Attention
|
|
residual = hidden_states
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
kv_cache=kv_cache,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
hidden_states = hidden_states + residual
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = hidden_states + residual
|
|
residual = None
|
|
return hidden_states, residual
|
|
|
|
|
|
class MolmoVisionBackbone(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
vision_config: VisionBackboneConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.vit_layers = VIT_LAYERS
|
|
self.image_num_patch = vision_config.image_num_patch
|
|
self.llm_patches_per_crop = (
|
|
(self.image_num_patch[0] + 1) // 2,
|
|
(self.image_num_patch[1] + 1) // 2,
|
|
)
|
|
self.image_vit = VisionTransformer(vision_config,
|
|
quant_config=quant_config)
|
|
self.num_prefix_tokens = self.image_vit.num_prefix_tokens
|
|
assert self.num_prefix_tokens in {
|
|
0, 1
|
|
}, "Only 0 or 1 prefix tokens are supported"
|
|
self.image_pooling_2d = MultiHeadDotProductAttention(
|
|
vision_config,
|
|
nlayers=len(self.vit_layers),
|
|
quant_config=quant_config)
|
|
self.image_projector = ImageProjectorMLP(
|
|
config,
|
|
input_dim=vision_config.image_emb_dim,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
image_dim = vision_config.image_emb_dim * len(self.vit_layers)
|
|
self.pad_embed = nn.Parameter(torch.zeros((2, image_dim)))
|
|
|
|
@property
|
|
def dtype(self) -> torch.dtype:
|
|
return self.image_vit.patch_embedding.weight.dtype
|
|
|
|
@property
|
|
def device(self) -> torch.device:
|
|
return self.image_vit.patch_embedding.weight.device
|
|
|
|
def encode_image(self, images: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
: param images: (batch_size, num_crops, num_patch, n_pixels)
|
|
"""
|
|
B, T, N, D = images.shape
|
|
|
|
mask = ~torch.all(
|
|
images.view(B * T, N, D) == -1, dim=(1, 2), keepdim=True)
|
|
|
|
images = images.view(B * T, N, D)
|
|
image_features = self.image_vit(images)
|
|
|
|
if self.vit_layers is not None:
|
|
features = []
|
|
for layer in self.vit_layers:
|
|
features.append(image_features[layer])
|
|
image_features = torch.cat(features, dim=-1)
|
|
else:
|
|
image_features = image_features[-1]
|
|
|
|
if self.num_prefix_tokens > 0:
|
|
image_features = image_features[:, 1:]
|
|
|
|
image_features = image_features * mask
|
|
image_features = image_features.view(B, T, N, -1)
|
|
|
|
return image_features
|
|
|
|
def forward(
|
|
self, images: torch.Tensor, image_masks: torch.Tensor
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
|
|
# image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim) # noqa: E501
|
|
batch_size, num_image = images.shape[:2]
|
|
images = images.to(device=self.device, dtype=self.dtype)
|
|
image_features = self.encode_image(images)
|
|
|
|
og_dtype = image_features.dtype
|
|
assert image_masks is not None
|
|
pad_embed = self.pad_embed[:, None, None, None, :]
|
|
all_pad = image_masks == 0
|
|
partial_pad = torch.logical_and(
|
|
image_masks < 1,
|
|
torch.logical_not(all_pad)).to(dtype=torch.float32)
|
|
all_pad = all_pad.to(dtype=torch.float32)
|
|
image_features = image_features + pad_embed[0] * torch.unsqueeze(
|
|
all_pad, -1)
|
|
image_features = image_features + pad_embed[1] * torch.unsqueeze(
|
|
partial_pad, -1)
|
|
|
|
image_features = image_features.to(og_dtype)
|
|
|
|
image_features = image_features.reshape(
|
|
(batch_size, num_image) + self.image_num_patch + (-1, ), )
|
|
|
|
if self.image_num_patch[0] % 2 == 1:
|
|
# Pad so we can still pool 2x2 patches
|
|
image_features = F.pad(
|
|
image_features,
|
|
(0, 0, 0, 1, 0, 1, 0, 0, 0, 0),
|
|
)
|
|
|
|
# image pooling
|
|
image_features = rearrange(
|
|
image_features,
|
|
'b n (h dh) (w dw) c -> (b n h w) (dh dw) c',
|
|
dh=2,
|
|
dw=2,
|
|
)
|
|
|
|
query = image_features.mean(-2, keepdim=True)
|
|
image_features = self.image_pooling_2d(query, image_features)
|
|
|
|
h, w = self.llm_patches_per_crop
|
|
image_features = image_features.view(batch_size, num_image, h * w, -1)
|
|
|
|
image_features = self.image_projector(image_features)
|
|
|
|
# image_features: (batch_size, num_image, num_patch, d_model)
|
|
return image_features
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("merged_linear", "gate_proj", 0),
|
|
("merged_linear", "up_proj", 1),
|
|
]
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
|
|
for name, loaded_weight in weights:
|
|
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:
|
|
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 = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
@support_torch_compile
|
|
class MolmoModel(nn.Module):
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.config = config
|
|
|
|
self.embedding_size = config.embedding_size or config.vocab_size
|
|
self.embedding_size += ADDITIONAL_VOCAB_SIZE
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
self.embedding_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
decoder_layer = MolmoDecoderNormAfterLayer if config.norm_after \
|
|
else MolmoDecoderLayer
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: decoder_layer(
|
|
config, cache_config, quant_config, prefix=prefix),
|
|
prefix=f"{prefix}.layers",
|
|
)
|
|
|
|
assert config.layer_norm_type == "rms"
|
|
self.norm = RMSNorm(config.hidden_size, config.layer_norm_eps)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size))
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
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,
|
|
) -> torch.Tensor:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
# Apply blocks one-by-one.
|
|
for i in range(self.start_layer, self.end_layer):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
kv_caches[i - self.start_layer],
|
|
attn_metadata,
|
|
residual,
|
|
)
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
if residual is not None:
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
else:
|
|
hidden_states = self.norm(hidden_states)
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
|
|
for name, loaded_weight in weights:
|
|
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 = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
cached_get_processor = lru_cache(get_processor)
|
|
|
|
|
|
def get_num_patches(num_tiles: int, crop_patches: int, left_margin: int,
|
|
right_margin: int, pooling_size: int) -> int:
|
|
crop_window_patches = crop_patches - (left_margin + right_margin)
|
|
if num_tiles > 1:
|
|
left_crop_window_patches = (crop_window_patches + left_margin +
|
|
pooling_size -
|
|
1) // pooling_size * pooling_size
|
|
middle_crop_window_patches = (crop_window_patches + pooling_size -
|
|
1) // pooling_size * pooling_size
|
|
right_crop_window_patches = (crop_window_patches + right_margin +
|
|
pooling_size -
|
|
1) // pooling_size * pooling_size
|
|
return left_crop_window_patches + (
|
|
num_tiles -
|
|
2) * middle_crop_window_patches + right_crop_window_patches
|
|
else:
|
|
single_crop_window_patches = (crop_patches + pooling_size -
|
|
1) // pooling_size * pooling_size
|
|
return single_crop_window_patches
|
|
|
|
|
|
def get_tokens(tiling_h: int, tiling_w: int, crop_patches: int,
|
|
left_margin: int, right_margin: int, pooling_size: int) -> int:
|
|
h = get_num_patches(tiling_h, crop_patches, left_margin, right_margin,
|
|
pooling_size)
|
|
w = get_num_patches(tiling_w, crop_patches, left_margin, right_margin,
|
|
pooling_size)
|
|
per_row = w // pooling_size + 1
|
|
joint = per_row * (h // pooling_size) + 2
|
|
image_token_length = (crop_patches + pooling_size - 1) // pooling_size
|
|
resize = (image_token_length + 1) * image_token_length + 2
|
|
return resize + joint
|
|
|
|
|
|
def get_max_tokens(max_crops: int, crop_patches: int, left_margin: int,
|
|
right_margin: int, pooling_size: int) -> int:
|
|
tilings = []
|
|
for i in range(1, max_crops + 1):
|
|
for j in range(1, max_crops + 1):
|
|
if i * j <= max_crops:
|
|
tilings.append((i, j))
|
|
tokens = [
|
|
get_tokens(tilings[i][0], tilings[i][1], crop_patches, left_margin,
|
|
right_margin, pooling_size) for i in range(len(tilings))
|
|
]
|
|
return max(tokens)
|
|
|
|
|
|
def get_max_molmo_image_tokens(ctx: InputContext) -> int:
|
|
processor = cached_get_processor(
|
|
ctx.model_config.model,
|
|
trust_remote_code=ctx.model_config.trust_remote_code,
|
|
revision=ctx.model_config.code_revision)
|
|
image_processor = processor.image_processor
|
|
max_llm_image_tokens = get_max_tokens(
|
|
image_processor.max_crops,
|
|
image_processor.base_image_input_size[0] //
|
|
image_processor.image_patch_size,
|
|
image_processor.overlap_margins[0],
|
|
image_processor.overlap_margins[1],
|
|
2,
|
|
)
|
|
return max_llm_image_tokens
|
|
|
|
|
|
# NOTE: preprocessing for the image data has been included in the
|
|
# 'input_processor_for_molmo' function
|
|
def image_input_mapper_for_molmo(
|
|
ctx: InputContext,
|
|
data: object,
|
|
):
|
|
if isinstance(data, list):
|
|
assert len(data) == 1, "Molmo supports only one image per prompt."
|
|
data = data[0]
|
|
|
|
return MultiModalKwargs(data)
|
|
|
|
|
|
def dummy_data_for_molmo(ctx: InputContext, seq_len: int,
|
|
mm_counts: Mapping[str, int]):
|
|
processor = cached_get_processor(
|
|
ctx.model_config.model,
|
|
trust_remote_code=ctx.model_config.trust_remote_code,
|
|
revision=ctx.model_config.code_revision)
|
|
image_processor = processor.image_processor
|
|
|
|
base_image_input_d = image_processor.image_patch_size
|
|
left_margin, right_margin = image_processor.overlap_margins
|
|
max_crops = image_processor.max_crops
|
|
|
|
# Assume: prompt_token_ids always starts with bos_token_id followed image tokens # noqa: E501
|
|
max_llm_image_tokens = get_max_molmo_image_tokens(ctx)
|
|
if seq_len - max_llm_image_tokens - 1 < 0:
|
|
raise RuntimeError(
|
|
f"Molmo cannot process {max_crops} crops in a prompt, "
|
|
"please increase max_model_len or reduce number of crops")
|
|
|
|
# The vertical image has the maximum number of image tokens due to column tokens. # noqa: E501
|
|
tiling = (max_crops, 1)
|
|
total_margin_pixels = base_image_input_d * (right_margin + left_margin)
|
|
crop_patches = image_processor.base_image_input_size[
|
|
0] // base_image_input_d
|
|
crop_window_patches = crop_patches - (right_margin + left_margin)
|
|
crop_window_size = crop_window_patches * base_image_input_d
|
|
|
|
h = crop_window_size * tiling[0] + total_margin_pixels
|
|
w = crop_window_size * tiling[1] + total_margin_pixels
|
|
|
|
dummy_image = Image.new("RGB", (w, h), color="red")
|
|
|
|
out = processor.process("dummy prompt", dummy_image)
|
|
|
|
token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
|
|
out["input_ids"][:1 + max_llm_image_tokens])
|
|
token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
|
|
[0]) * (seq_len - max_llm_image_tokens - 1)
|
|
dummy_seqdata = SequenceData(token_ids)
|
|
dummy_imgdata = {
|
|
"images": out["images"],
|
|
"image_input_idx": out["image_input_idx"],
|
|
}
|
|
if "image_masks" in out:
|
|
dummy_imgdata["image_masks"] = out["image_masks"]
|
|
dummy_imgdata["seq_len"] = torch.tensor(seq_len, dtype=torch.long)
|
|
size = 0
|
|
offset = -1
|
|
for i in range(len(token_ids)):
|
|
if token_ids[i] in (DEFAULT_IMAGE_PATCH_TOKEN_ID,
|
|
DEFAULT_IM_START_TOKEN_ID, DEFAULT_IM_END_TOKEN_ID,
|
|
DEFAULT_IM_COL_TOKEN_ID):
|
|
if offset < 0:
|
|
offset = i
|
|
size += 1
|
|
dummy_imgdata["image_start_end"] = (offset, offset + size)
|
|
return DummyData(seq_data=dummy_seqdata,
|
|
multi_modal_data={"image": dummy_imgdata},
|
|
multi_modal_placeholders={
|
|
"image":
|
|
[PlaceholderRange(offset=offset, length=size)]
|
|
})
|
|
|
|
|
|
def pad_images(
|
|
max_total_crops: int,
|
|
images: torch.Tensor,
|
|
image_input_idx: torch.Tensor,
|
|
image_masks: Optional[torch.Tensor] = None,
|
|
):
|
|
n = max_total_crops - images.shape[0]
|
|
images = F.pad(images, (0, 0, 0, 0, 0, n), value=-1)
|
|
image_input_idx = F.pad(image_input_idx, (0, 0, 0, n), value=-1)
|
|
if image_masks is not None:
|
|
image_masks = F.pad(image_masks, (0, 0, 0, n), value=-1)
|
|
return images, image_input_idx, image_masks
|
|
|
|
|
|
def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
|
|
prompt = inputs.get("prompt")
|
|
multi_modal_data = inputs.get("multi_modal_data")
|
|
image = None if multi_modal_data is None else multi_modal_data.get("image")
|
|
|
|
model_config = ctx.model_config
|
|
processor = cached_get_processor(
|
|
ctx.model_config.model,
|
|
trust_remote_code=model_config.trust_remote_code,
|
|
revision=ctx.model_config.code_revision)
|
|
tokenizer = cached_get_tokenizer(
|
|
model_config.tokenizer,
|
|
trust_remote_code=model_config.trust_remote_code)
|
|
|
|
# NOTE: message formatting for raw text prompt is only applied for
|
|
# offline inference; for online serving, the prompt is always in
|
|
# instruction format and tokenized.
|
|
if prompt is not None and re.match(r"^User:[\s\S]*?(Assistant:)*$",
|
|
prompt):
|
|
out = processor.process(prompt, image, message_format="none")
|
|
elif prompt is not None:
|
|
out = processor.process(prompt, image)
|
|
else:
|
|
out = processor.process(None, image, tokens=inputs["prompt_token_ids"])
|
|
|
|
# If there is no image, return directly.
|
|
if image is None:
|
|
new_prompt_token_ids = out["input_ids"].tolist()
|
|
prompt = inputs.get("prompt")
|
|
if prompt is None:
|
|
prompt = tokenizer.decode(new_prompt_token_ids)
|
|
return token_inputs(
|
|
prompt_token_ids=new_prompt_token_ids,
|
|
prompt=prompt,
|
|
)
|
|
|
|
image_processor = processor.image_processor
|
|
max_total_crops = 1 + image_processor.max_crops
|
|
images, image_input_idx, image_masks = pad_images(
|
|
max_total_crops,
|
|
out["images"],
|
|
out["image_input_idx"],
|
|
out.get("image_masks"),
|
|
)
|
|
image_data = dict(
|
|
images=images,
|
|
image_input_idx=image_input_idx,
|
|
)
|
|
if image_masks is not None:
|
|
image_data["image_masks"] = image_masks
|
|
|
|
new_prompt_token_ids = out["input_ids"].tolist()
|
|
image_data["seq_len"] = torch.tensor(len(new_prompt_token_ids),
|
|
dtype=torch.long)
|
|
|
|
multi_modal_data = dict(image=image_data)
|
|
size = 0
|
|
offset = -1
|
|
for i in range(len(new_prompt_token_ids)):
|
|
if new_prompt_token_ids[i] in (DEFAULT_IMAGE_PATCH_TOKEN_ID,
|
|
DEFAULT_IM_START_TOKEN_ID,
|
|
DEFAULT_IM_END_TOKEN_ID,
|
|
DEFAULT_IM_COL_TOKEN_ID):
|
|
if offset < 0:
|
|
offset = i
|
|
size += 1
|
|
image_data["image_start_end"] = (offset, offset + size)
|
|
prompt = inputs.get("prompt")
|
|
if prompt is None:
|
|
prompt = tokenizer.decode(new_prompt_token_ids)
|
|
return token_inputs(
|
|
prompt_token_ids=new_prompt_token_ids,
|
|
prompt=prompt,
|
|
multi_modal_data=multi_modal_data,
|
|
multi_modal_placeholders={
|
|
"image": [PlaceholderRange(offset=offset, length=size)]
|
|
},
|
|
)
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_image_input_mapper(image_input_mapper_for_molmo)
|
|
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_molmo_image_tokens)
|
|
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_molmo)
|
|
@INPUT_REGISTRY.register_input_processor(input_processor_for_molmo)
|
|
class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP,
|
|
SupportsLoRA):
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_substr={
|
|
# vision backbone mapping
|
|
"image_projector.w1.": "image_projector.gate_proj.",
|
|
"image_projector.w3.": "image_projector.up_proj.",
|
|
"image_projector.w2.": "image_projector.down_proj.",
|
|
# language backbone mapping
|
|
"att_proj": "self_attn.qkv_proj",
|
|
"attn_out": "self_attn.o_proj",
|
|
"q_norm": "self_attn.q_norm",
|
|
"k_norm": "self_attn.k_norm",
|
|
"ff_proj": "mlp.gate_up_proj",
|
|
"ff_out": "mlp.down_proj",
|
|
"attn_norm": "input_layernorm",
|
|
"ff_norm": "post_attention_layernorm",
|
|
},
|
|
orig_to_new_prefix={
|
|
# vision backbone mapping
|
|
"model.vision_backbone.": "vision_backbone.",
|
|
# language backbone mapping
|
|
"model.transformer.blocks.": "model.layers.",
|
|
"model.transformer.ln_f.": "model.norm.",
|
|
# lm_head is renamed to model.transformer.mlp.down_proj firstly,
|
|
# we need to run a second renaming for it
|
|
"model.transformer.mlp.down_proj.": "lm_head.",
|
|
},
|
|
)
|
|
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["qkv_proj"],
|
|
"gate_up_proj": ["gate_up_proj"], # language model
|
|
"merged_linear": ["gate_proj", "up_proj"] # image_projector
|
|
}
|
|
|
|
# LoRA specific attributes
|
|
supported_lora_modules = [
|
|
# language model
|
|
"qkv_proj",
|
|
"o_proj",
|
|
"gate_up_proj",
|
|
"down_proj", # same name with image_projector
|
|
# vision tower
|
|
"wq",
|
|
"wk",
|
|
"wv",
|
|
"wo",
|
|
"w1",
|
|
"w2",
|
|
# image_projector
|
|
"merged_linear",
|
|
]
|
|
embedding_modules = {}
|
|
embedding_padding_modules = []
|
|
|
|
# BitandBytes specific attributes
|
|
bitsandbytes_stacked_params_mapping = {
|
|
"gate_proj": ("merged_linear", 0),
|
|
"up_proj": ("merged_linear", 1),
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
lora_config = vllm_config.lora_config
|
|
self.config = config
|
|
self.multimodal_config = multimodal_config
|
|
self.lora_config = lora_config
|
|
|
|
vision_config = VisionBackboneConfig()
|
|
self.vision_backbone = MolmoVisionBackbone(config, vision_config,
|
|
quant_config)
|
|
self.model = MolmoModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
|
|
if self.config.weight_tying:
|
|
self.lm_head = self.model.transformer.wte
|
|
else:
|
|
self.lm_head = ParallelLMHead(
|
|
config.embedding_size or config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
self.logits_processor = LogitsProcessor(config.embedding_size
|
|
or config.vocab_size)
|
|
self.sampler = get_sampler()
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def _parse_and_validate_image_input(
|
|
self,
|
|
**kwargs: object,
|
|
) -> Optional[MolmoImageInputs]:
|
|
images = kwargs.pop("images", None)
|
|
image_masks = kwargs.pop("image_masks", None)
|
|
image_start_end = kwargs.pop("image_start_end", None)
|
|
if images is None:
|
|
return None
|
|
|
|
image_input_idx = kwargs.pop("image_input_idx", None)
|
|
seq_len = kwargs.pop("seq_len", None)
|
|
if image_input_idx is None:
|
|
raise ValueError("image_input_idx is required for Molmo model.")
|
|
if seq_len is None:
|
|
raise ValueError("seq_len is required for Molmo model.")
|
|
if not isinstance(seq_len, torch.Tensor):
|
|
seq_len = torch.tensor(seq_len)
|
|
|
|
return MolmoImageInputs(
|
|
images=images,
|
|
image_input_idx=image_input_idx,
|
|
seq_len=seq_len,
|
|
image_masks=image_masks,
|
|
image_start_end=image_start_end,
|
|
)
|
|
|
|
def _process_image_input(
|
|
self,
|
|
image_input: MolmoImageInputs,
|
|
) -> torch.Tensor:
|
|
|
|
image_features = self.vision_backbone(
|
|
images=image_input["images"],
|
|
image_masks=image_input["image_masks"],
|
|
)
|
|
|
|
return image_features
|
|
|
|
def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
if image_input is None:
|
|
return None
|
|
image_features = self._process_image_input(image_input)
|
|
image_input_idx = image_input["image_input_idx"]
|
|
seq_len = image_input["seq_len"]
|
|
batch_size, num_image, num_patch = image_features.shape[:3]
|
|
assert image_input_idx.shape == (batch_size, num_image, num_patch)
|
|
|
|
# insert the image feature into the embedding.
|
|
image_features = image_features.view(batch_size, num_image * num_patch,
|
|
-1)
|
|
image_input_idx = image_input_idx.view(batch_size,
|
|
num_image * num_patch)
|
|
|
|
valid = image_input_idx >= 0
|
|
image_features = image_features * valid[:, :, None].to(
|
|
image_features.dtype)
|
|
image_features = image_features.view(
|
|
batch_size * num_image * num_patch, -1).contiguous()
|
|
|
|
image_input_idx = image_input_idx * valid.to(image_input_idx.dtype)
|
|
offset = torch.cat([seq_len.new_zeros(1),
|
|
seq_len.cumsum(dim=0)[:-1]],
|
|
dim=0)[:, None]
|
|
image_input_idx = image_input_idx + offset.to(image_input_idx.dtype)
|
|
image_input_idx = image_input_idx.flatten()[:, None]
|
|
mat = image_input_idx == torch.arange(
|
|
seq_len.sum().item(), device=image_features.device)[None, :]
|
|
mat = mat.to(image_features.dtype)
|
|
|
|
# Note: In this original implementation from AI2, the final
|
|
# vision_embeddings will be always be the same length
|
|
# of input embeddings.
|
|
vision_embeddings = torch.einsum('nd,nm->md', image_features, mat)
|
|
|
|
# Split by the sizes of the input sequences. For each full embedding,
|
|
# extract the actual vision embeddings to be merged.
|
|
vision_embeddings = list(vision_embeddings.split(seq_len.tolist()))
|
|
for i in range(len(vision_embeddings)):
|
|
start, end = image_input['image_start_end'][i]
|
|
vision_embeddings[i] = vision_embeddings[i][start:end]
|
|
|
|
return vision_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[NestedTensors] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is not None:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings, [
|
|
DEFAULT_IMAGE_PATCH_TOKEN_ID, DEFAULT_IM_START_TOKEN_ID,
|
|
DEFAULT_IM_END_TOKEN_ID, DEFAULT_IM_COL_TOKEN_ID
|
|
])
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
positions: torch.LongTensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> SamplerOutput:
|
|
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility.
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.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)
|
|
weights = _get_weights_with_merged_embedding(weights)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
|
|
|
def get_mm_mapping(self) -> MultiModelKeys:
|
|
"""
|
|
Get the module prefix in multimodal models
|
|
"""
|
|
return MultiModelKeys.from_string_field(
|
|
language_model="model",
|
|
connector="vision_backbone.image_projector",
|
|
tower_model="vision_backbone",
|
|
)
|
|
|
|
|
|
def _get_weights_with_merged_embedding(
|
|
weights: Iterable[Tuple[str, torch.Tensor]]
|
|
) -> Iterable[Tuple[str, torch.Tensor]]:
|
|
embedding_weights = {}
|
|
for name, weight in weights:
|
|
if "wte.embedding" in name:
|
|
embedding_weights["embedding"] = weight
|
|
elif "wte.new_embedding" in name:
|
|
embedding_weights["new_embedding"] = weight
|
|
else:
|
|
yield (name, weight)
|
|
# this is compatible with most of quantization,
|
|
# because they won't quantize embed_tokens
|
|
embedding_weights = torch.cat(
|
|
[embedding_weights["embedding"], embedding_weights["new_embedding"]],
|
|
dim=0,
|
|
)
|
|
yield ("model.embed_tokens.weight", embedding_weights)
|