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775 lines
32 KiB
775 lines
32 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/ovis2/modular_ovis2.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_ovis2.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from collections.abc import Callable
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from dataclasses import dataclass
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import torch
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from torch import nn
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from ... import initialization as init
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check
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from ...utils.generic import check_model_inputs
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from ..auto import AutoModel
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from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig
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@dataclass
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@auto_docstring
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class BaseModelOutputWithVisualIndicatorFeatures(BaseModelOutputWithPooling):
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r"""
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visual_indicator_features (`torch.FloatTensor` of shape `(batch_size, visual_indicator_size)`):
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Visual indicator features extracted from the model, which can be used for auxiliary tasks or further processing.
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"""
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visual_indicator_features: torch.FloatTensor | None = None
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for Llava outputs, with hidden states and attentions.
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"""
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)
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class Ovis2ModelOutputWithPast(BaseModelOutputWithPast):
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r"""
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past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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image_hidden_states: torch.FloatTensor | None = None
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@dataclass
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@auto_docstring(
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custom_intro="""
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Base class for Ovis2 causal language model (or autoregressive) outputs.
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"""
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)
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class Ovis2CausalLMOutputWithPast(ModelOutput):
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r"""
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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Language modeling loss (for next-token prediction).
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logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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loss: torch.FloatTensor | None = None
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logits: torch.FloatTensor | None = None
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past_key_values: Cache | None = None
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hidden_states: tuple[torch.FloatTensor] | None = None
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attentions: tuple[torch.FloatTensor] | None = None
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image_hidden_states: torch.FloatTensor | None = None
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@use_kernel_forward_from_hub("RMSNorm")
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class Ovis2RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Ovis2RMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class Ovis2VisionMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class Ovis2VisionEmbeddings(nn.Module):
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def __init__(self, config: Ovis2VisionConfig):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.image_size = config.image_size
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self.patch_size = config.patch_size
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self.patch_embedding = nn.Conv2d(
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in_channels=config.num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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padding="valid",
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches
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self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
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self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
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def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
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target_dtype = self.patch_embedding.weight.dtype
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patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
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embeddings = patch_embeds.flatten(2).transpose(1, 2)
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embeddings = self.rms_norm(embeddings)
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Ovis2VisionAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.embed_dim // self.num_heads
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if self.head_dim * self.num_heads != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
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f" {self.num_heads})."
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)
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self.scale = self.head_dim**-0.5
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self.dropout = config.attention_dropout
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self.is_causal = False
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self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor | None]:
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"""Input shape: Batch x Time x Channel"""
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batch_size, seq_length, embed_dim = hidden_states.shape
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queries = self.q_proj(hidden_states)
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keys = self.k_proj(hidden_states)
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values = self.v_proj(hidden_states)
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queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
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keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
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values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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queries,
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keys,
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values,
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attention_mask,
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is_causal=self.is_causal,
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scaling=self.scale,
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dropout=0.0 if not self.training else self.dropout,
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)
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attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights
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class Ovis2MLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class Ovis2VisionEncoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Ovis2VisionConfig):
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super().__init__()
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self.attention = Ovis2VisionAttention(config)
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self.ffn = Ovis2MLP(config)
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self.rms_norm1 = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
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self.rms_norm2 = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> torch.Tensor:
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norm_hidden_states = self.rms_norm1(hidden_states)
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attn_output, _ = self.attention(hidden_states=norm_hidden_states, attention_mask=attention_mask, **kwargs)
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hidden_states = hidden_states + attn_output
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norm_hidden_states = self.rms_norm2(hidden_states)
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mlp_output = self.ffn(norm_hidden_states)
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hidden_states = hidden_states + mlp_output
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return hidden_states
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class Ovis2VisionEncoder(nn.Module):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
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[`Ovis2VisionEncoderLayer`].
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Args:
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config: Ovis2VisionConfig
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"""
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def __init__(self, config: Ovis2VisionConfig):
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super().__init__()
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self.config = config
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self.layers = nn.ModuleList([Ovis2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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# Ignore copy
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@can_return_tuple
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@auto_docstring
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def forward(
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self,
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inputs_embeds,
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attention_mask: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutput:
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hidden_states = inputs_embeds
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for encoder_layer in self.layers:
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hidden_states = encoder_layer(hidden_states, attention_mask, **kwargs)
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return BaseModelOutput(last_hidden_state=hidden_states)
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class Ovis2VisionTransformer(nn.Module):
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def __init__(self, config: Ovis2VisionConfig):
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super().__init__()
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self.config = config
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self.embeddings = Ovis2VisionEmbeddings(config)
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self.encoder = Ovis2VisionEncoder(config)
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self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
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self.gradient_checkpointing = False
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@can_return_tuple
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def forward(
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self,
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pixel_values,
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attention_mask: torch.Tensor | None = None,
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**kwargs,
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):
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hidden_states = self.embeddings(pixel_values)
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encoder_outputs: BaseModelOutput = self.encoder(
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inputs_embeds=hidden_states,
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attention_mask=attention_mask,
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**kwargs,
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)
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last_hidden_state = encoder_outputs.last_hidden_state
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last_hidden_state = self.rms_norm(last_hidden_state)
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return BaseModelOutput(last_hidden_state=last_hidden_state)
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class Ovis2VisualEmbeddingTable(nn.Embedding):
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def forward(self, visual_tokens: torch.Tensor) -> torch.Tensor:
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if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
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return super().forward(visual_tokens)
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return torch.matmul(visual_tokens, self.weight)
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class Ovis2PreTrainedModel(PreTrainedModel):
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config: Ovis2Config
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base_model_prefix = "model"
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input_modalities = ("image", "text")
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supports_gradient_checkpointing = True
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_no_split_modules = ["Ovis2VisionAttention"]
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_skip_keys_device_placement = "past_key_values"
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_supports_cache_class = True
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_supports_flash_attn = True
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_supports_flex_attn = True
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_supports_sdpa = True
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_can_compile_fullgraph = True
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_supports_attention_backend = True
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def _init_weights(self, module):
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super()._init_weights(module)
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if isinstance(module, Ovis2VisionEmbeddings):
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init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
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def hard_softmax(logits: torch.Tensor, dim: int):
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y_soft = logits.softmax(dim)
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# Straight through.
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index = y_soft.max(dim, keepdim=True)[1]
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y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
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ret = y_hard - y_soft.detach() + y_soft
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return ret
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class Ovis2VisionModel(Ovis2PreTrainedModel):
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config: Ovis2VisionConfig
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_can_record_outputs = {
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"hidden_states": Ovis2VisionEncoderLayer,
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"attentions": Ovis2VisionAttention,
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}
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def __init__(self, config: Ovis2VisionConfig):
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super().__init__(config)
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self.config = config
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self.transformer = Ovis2VisionTransformer(config)
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self.num_visual_indicator_tokens = config.num_visual_indicator_tokens
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self.vocab_size = config.vocab_size
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self.head_linear = nn.Linear(
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config.hidden_size * config.hidden_stride * config.hidden_stride,
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self.vocab_size - self.num_visual_indicator_tokens,
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bias=False,
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)
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self.head_norm = nn.LayerNorm(self.vocab_size - self.num_visual_indicator_tokens)
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self.post_init()
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@check_model_inputs
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def forward(
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self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]
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) -> tuple | BaseModelOutputWithVisualIndicatorFeatures:
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outputs = self.transformer(pixel_values, **kwargs)
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last_hidden_state = outputs[0]
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if self.config.hidden_stride > 1:
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num_images, seq_len, hidden_dim = last_hidden_state.shape
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hidden_stride = self.config.hidden_stride
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sqrt_l = int(math.sqrt(seq_len))
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|
if sqrt_l * sqrt_l != seq_len:
|
|
raise ValueError("Token sequence length must be a perfect square")
|
|
|
|
pad_size = (hidden_stride - (sqrt_l % hidden_stride)) % hidden_stride
|
|
last_hidden_state = nn.functional.pad(last_hidden_state, (0, 0, 0, pad_size, 0, pad_size), "constant", 0)
|
|
sqrt_l += pad_size
|
|
|
|
last_hidden_state = last_hidden_state.reshape(
|
|
num_images, sqrt_l // hidden_stride, hidden_stride, sqrt_l // hidden_stride, hidden_stride, hidden_dim
|
|
)
|
|
last_hidden_state = last_hidden_state.permute(0, 1, 3, 2, 4, 5)
|
|
last_hidden_state = last_hidden_state.reshape(
|
|
num_images, -1, hidden_stride * hidden_stride * hidden_dim
|
|
) # (n, (sqrt_l//hs)^2, hs^2*d)
|
|
|
|
logits = self.head_linear(last_hidden_state)
|
|
logits = self.head_norm(logits)
|
|
|
|
if self.config.tokenize_function == "gumbel_argmax":
|
|
prob_token = nn.functional.gumbel_softmax(logits, dim=-1, hard=True)
|
|
elif self.config.tokenize_function == "st_argmax":
|
|
prob_token = hard_softmax(logits, dim=-1)
|
|
elif self.config.tokenize_function == "softmax":
|
|
prob_token = nn.functional.softmax(logits, dim=-1)
|
|
|
|
return BaseModelOutputWithVisualIndicatorFeatures(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=prob_token,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Ovis2 model which consists of a vision backbone and a language model, without a language modeling head.
|
|
"""
|
|
)
|
|
class Ovis2Model(Ovis2PreTrainedModel):
|
|
_checkpoint_conversion_mapping = {}
|
|
|
|
def __init__(self, config: Ovis2Config):
|
|
super().__init__(config)
|
|
self.vision_tower = Ovis2VisionModel(config.vision_config)
|
|
self.language_model = AutoModel.from_config(config.text_config)
|
|
self.visual_embeddings_table = Ovis2VisualEmbeddingTable(config.vision_config.vocab_size, config.hidden_size)
|
|
|
|
self.visual_vocab_size = config.vision_config.vocab_size
|
|
self.vocab_size = config.vocab_size
|
|
self.visual_indicator_token_ids = config.visual_indicator_token_ids
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.language_model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.language_model.set_input_embeddings(value)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring(
|
|
custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
|
|
)
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple | BaseModelOutputWithVisualIndicatorFeatures:
|
|
image_outputs = self.vision_tower(pixel_values, return_dict=True, **kwargs)
|
|
image_features = image_outputs.pooler_output
|
|
batch_size, img_seq_len, _ = image_features.shape
|
|
padding_tensor = torch.zeros(
|
|
(batch_size, img_seq_len, self.vision_tower.num_visual_indicator_tokens),
|
|
dtype=image_features.dtype,
|
|
device=image_features.device,
|
|
requires_grad=False,
|
|
layout=image_features.layout,
|
|
)
|
|
image_features = torch.cat([image_features, padding_tensor], dim=2)
|
|
image_features = self.visual_embeddings_table(image_features)
|
|
|
|
visual_indicator = torch.arange(
|
|
self.visual_vocab_size - self.vision_tower.num_visual_indicator_tokens,
|
|
self.visual_vocab_size,
|
|
dtype=torch.long,
|
|
).to(image_features.device)
|
|
image_outputs.pooler_output = image_features
|
|
image_outputs.visual_indicator_features = self.visual_embeddings_table(visual_indicator)
|
|
|
|
return image_outputs
|
|
|
|
def get_placeholder_mask(
|
|
self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
|
|
):
|
|
"""
|
|
Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
|
|
equal to the length of multimodal features. If the lengths are different, an error is raised.
|
|
"""
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
|
|
n_image_tokens = special_image_mask.sum()
|
|
n_image_features = image_features.shape[0] * image_features.shape[1]
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
torch_compilable_check(
|
|
inputs_embeds[special_image_mask].numel() == image_features.numel(),
|
|
f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
|
|
)
|
|
return special_image_mask
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
return_dict: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs,
|
|
) -> tuple | Ovis2ModelOutputWithPast:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if pixel_values is not None:
|
|
image_outputs = self.get_image_features(pixel_values=pixel_values, return_dict=True)
|
|
image_features = image_outputs.pooler_output
|
|
visual_indicator_features = image_outputs.visual_indicator_features
|
|
|
|
special_image_mask = self.get_placeholder_mask(
|
|
input_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
image_features=image_features,
|
|
)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
|
|
|
for i, visual_indicator_id in enumerate(self.visual_indicator_token_ids):
|
|
if input_ids is None:
|
|
mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(visual_indicator_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
mask = mask.all(-1)
|
|
else:
|
|
mask = (input_ids == visual_indicator_id).to(inputs_embeds.device)
|
|
|
|
if mask.any():
|
|
inputs_embeds[mask] = (
|
|
visual_indicator_features[i]
|
|
.expand_as(inputs_embeds[mask])
|
|
.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
)
|
|
|
|
outputs = self.language_model(
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
|
|
return Ovis2ModelOutputWithPast(
|
|
last_hidden_state=outputs.last_hidden_state,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class Ovis2ForConditionalGeneration(Ovis2PreTrainedModel, GenerationMixin):
|
|
_checkpoint_conversion_mapping = {}
|
|
_tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
|
|
|
|
def __init__(self, config: Ovis2Config):
|
|
super().__init__(config)
|
|
self.model = Ovis2Model(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
def get_output_embeddings(self) -> nn.Module:
|
|
return self.lm_head
|
|
|
|
@auto_docstring
|
|
def get_image_features(
|
|
self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]
|
|
) -> tuple | BaseModelOutputWithVisualIndicatorFeatures:
|
|
return self.model.get_image_features(pixel_values=pixel_values, **kwargs)
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
pixel_values: torch.FloatTensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
output_attentions: bool | None = None,
|
|
output_hidden_states: bool | None = None,
|
|
return_dict: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs,
|
|
) -> tuple | Ovis2CausalLMOutputWithPast:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from PIL import Image
|
|
>>> import httpx
|
|
>>> from io import BytesIO
|
|
>>> from transformers import AutoProcessor, Ovis2ForConditionalGeneration
|
|
|
|
>>> model = Ovis2ForConditionalGeneration.from_pretrained("thisisiron/Ovis2-2B-hf")
|
|
>>> processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf")
|
|
|
|
>>> prompt = "<|im_start|>user\n<image>\nDescribe the image.<|im_end|>\n<|im_start|>assistant\n"
|
|
>>> url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
|
|
>>> with httpx.stream("GET", url) as response:
|
|
... image = Image.open(BytesIO(response.read()))
|
|
|
|
>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(**inputs, max_new_tokens=15)
|
|
>>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
|
|
"user\n\nDescribe the image.\nassistant\nThe image features a brown dog standing on a wooden floor, looking up with"
|
|
```"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
pixel_values=pixel_values,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=True,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
|
|
)
|
|
|
|
return Ovis2CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=outputs.image_hidden_states,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
pixel_values=None,
|
|
attention_mask=None,
|
|
cache_position=None,
|
|
logits_to_keep=None,
|
|
is_first_iteration=False,
|
|
**kwargs,
|
|
):
|
|
# Overwritten -- in specific circumstances we don't want to forward image inputs to the model
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
is_first_iteration=is_first_iteration,
|
|
**kwargs,
|
|
)
|
|
|
|
if is_first_iteration or not kwargs.get("use_cache", True):
|
|
# Pixel values are used only in the first iteration if available
|
|
# In subsquent iterations, they are already merged with text and cached
|
|
# NOTE: first iteration doesn't have to be prefill, it can be the first
|
|
# iteration with a question and cached system prompt (continue generate from cache)
|
|
model_inputs["pixel_values"] = pixel_values
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = ["Ovis2PreTrainedModel", "Ovis2Model", "Ovis2ForConditionalGeneration"]
|