# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Idefics model.""" from dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_outputs import ModelOutput from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PretrainedConfig, PreTrainedModel from ...processing_utils import Unpack from ...pytorch_utils import ALL_LAYERNORM_LAYERS from ...utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging from .configuration_idefics import IdeficsConfig from .perceiver import IdeficsPerceiverResampler from .vision import IdeficsVisionEmbeddings, IdeficsVisionTransformer if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from ...integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) @dataclass class IdeficsBaseModelOutputWithPast(ModelOutput): """ Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver """ last_hidden_state: Optional[torch.FloatTensor] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class IdeficsCausalLMOutputWithPast(ModelOutput): """ Base class for Idefics causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver """ loss: Optional[torch.FloatTensor] = None logits: Optional[torch.FloatTensor] = None past_key_values: Optional[List[torch.FloatTensor]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None def expand_inputs_for_generation( input_ids, expand_size=1, is_encoder_decoder=False, attention_mask=None, encoder_outputs=None, **model_kwargs, ): expanded_return_idx = ( torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device) ) input_ids = input_ids.index_select(0, expanded_return_idx) model_kwargs["pixel_values"] = model_kwargs.get("pixel_values", None) model_kwargs["image_encoder_embeddings"] = model_kwargs.get("image_encoder_embeddings", None) model_kwargs["perceiver_embeddings"] = model_kwargs.get("perceiver_embeddings", None) model_kwargs["image_attention_mask"] = model_kwargs.get("image_attention_mask", None) if "token_type_ids" in model_kwargs: token_type_ids = model_kwargs["token_type_ids"] model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx) if attention_mask is not None: model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx) if model_kwargs["image_attention_mask"] is not None: model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select( 0, expanded_return_idx ) if model_kwargs["pixel_values"] is not None: model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx) elif model_kwargs["image_encoder_embeddings"] is not None: model_kwargs["image_encoder_embeddings"] = model_kwargs["image_encoder_embeddings"].index_select( 0, expanded_return_idx ) elif model_kwargs["perceiver_embeddings"] is not None: model_kwargs["perceiver_embeddings"] = model_kwargs["perceiver_embeddings"].index_select( 0, expanded_return_idx ) return input_ids, model_kwargs def freeze_model(model, module_exceptions=[]): mapping = { "LayerNorm": nn.LayerNorm, "Linear": nn.Linear, "Embedding": nn.Embedding, } module_exceptions_mapped = [mapping[m] for m in module_exceptions] for module in model.modules(): if module_exceptions and any(isinstance(module, t) for t in module_exceptions_mapped): module.requires_grad_(True) # Explicitly setting it to true to avoid any mistakes else: module.requires_grad_(False) return model class IdeficsDecoupledEmbedding(nn.Embedding): # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding """ Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, then it will create `num_additional_embeddings` additional parameters that are always trained. If `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`. """ def __init__( self, num_embeddings, num_additional_embeddings, embedding_dim, partially_freeze: Optional[bool] = False, device=None, dtype=None, padding_idx=None, **kwargs, ) -> None: """ Args: num_embeddings (`int`): Size of the dictionary of embeddings num_additional_embeddings (`int`): Number of additional embeddings. Only useful when you `partially_freeze=True`. embedding_dim (`int`): The size of each embedding vector partially_freeze: (`bool`, *optional*, defaults to `False`): If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen. padding_idx (`int`, *optional*): The padding index (needs to be less than num_embeddings) Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, `max_norm` or `norm_type`. We are not supporting these. """ if padding_idx is not None and padding_idx > num_embeddings: raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}") super().__init__( num_embeddings=num_embeddings, embedding_dim=embedding_dim, device=device, dtype=dtype, padding_idx=padding_idx, **kwargs, ) self.num_embeddings = num_embeddings self.padding_idx = padding_idx self.num_additional_embeddings = num_additional_embeddings self.partially_freeze = partially_freeze if partially_freeze: self.weight.requires_grad_(False) if self.num_additional_embeddings > 0: self.additional_embedding = nn.Embedding( num_embeddings=self.num_additional_embeddings, embedding_dim=embedding_dim, device=device, dtype=dtype, ) def forward(self, input_ids): """ we have 2 embeddings, with different indices - one pretrained self.weight and another self.additional_embedding.weight that is being trained. in order to make a lookup of the input ids, we: 1. find out the indices of the entries belonging to the 2nd embedding 2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd embedding starts from 0 and not num_embeddings 3. perform the 2nd embedding lookup 4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index 5. perform the 1st embedding lookup 6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices - i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are usually relatively short it's probably not faster or if faster not by much - but might be a good idea to measure. """ if self.num_additional_embeddings == 0: return F.embedding(input_ids, self.weight) # Clone so that we don't modify the original input_ids later on input_ids = input_ids.clone() additional_vocab_indices = torch.where(input_ids >= self.num_embeddings) input_ids_additional_vocab = input_ids[additional_vocab_indices] additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings) # for successful lookup replace input_ids with 0, the results of these will be discarded anyway input_ids[additional_vocab_indices] = 0 full_vector = F.embedding(input_ids, self.weight) # overwrite the records with high indices full_vector[additional_vocab_indices] = additional_embeddings return full_vector def extra_repr(self) -> str: return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format( self.num_embeddings, self.num_additional_embeddings, self.embedding_dim, self.partially_freeze, ) class IdeficsDecoupledLinear(nn.Linear): # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear """ Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0, then it will create `out_additional_features * in_features` additional parameters that are always trained. If `out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`. """ def __init__( self, in_features: int, out_features: int, out_additional_features: int = 0, bias: bool = True, partially_freeze: bool = True, device=None, dtype=None, ) -> None: """ out_additional_features: int. Number of additional trainable dimensions. Only makes sense when `partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear. """ super().__init__(in_features, out_features, bias, device, dtype) self.out_additional_features = out_additional_features self.partially_freeze = partially_freeze self.in_features = in_features self.out_features = out_features if partially_freeze: self.weight.requires_grad_(False) if bias: self.bias.requires_grad_(False) if out_additional_features > 0: self.additional_fc = nn.Linear( in_features=in_features, out_features=out_additional_features, bias=bias, device=device, dtype=dtype, ) def forward(self, input: torch.Tensor) -> torch.Tensor: output = F.linear(input, self.weight, self.bias) if self.out_additional_features > 0: additional_features = self.additional_fc(input) output = torch.cat((output, additional_features), -1) return output def extra_repr(self) -> str: """Overwriting `nn.Linear.extra_repr` to include new parameters.""" return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format( self.in_features, self.out_features, self.out_additional_features, self.bias is not None, self.partially_freeze, ) # this was adapted from LlamaRMSNorm class IdeficsRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ IdeficsRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" ALL_LAYERNORM_LAYERS.append(IdeficsRMSNorm) # this was adapted from LlamaRotaryEmbedding class IdeficsEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / ( self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / self.dim) ) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`): The position indices of the tokens corresponding to the query and key tensors. For example, this can be used to pass offsetted position ids when working with a KV-cache. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # this was adapted from LlamaMLP class IdeficsMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, ): super().__init__() self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) self.act_fn = ACT2FN[hidden_act] def forward(self, x): return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights # this was adapted from LlamaAttention class IdeficsAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, hidden_size: int, num_heads: int, dropout: float = 0.0, is_cross_attention: bool = False, config: PretrainedConfig = None, qk_layer_norms: bool = False, layer_idx: Optional[int] = None, ): super().__init__() self.config = config self.hidden_size = hidden_size self.num_heads = num_heads self.head_dim = hidden_size // num_heads self.dropout = dropout self.is_causal = True self.scaling = self.head_dim**-0.5 self.layer_idx = layer_idx if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) if (self.head_dim * num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {num_heads})." ) self.is_cross_attention = is_cross_attention if not hasattr(nn.functional, "scaled_dot_product_attention"): raise ValueError("this model requires pytorch 2.0 or higher") if self.is_cross_attention: kv_input_dim = ( self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim ) self.q_proj = nn.Linear( self.hidden_size, num_heads * self.head_dim, bias=False, ) self.k_proj = nn.Linear(kv_input_dim, num_heads * self.head_dim, bias=False) self.v_proj = nn.Linear( kv_input_dim, num_heads * self.head_dim, bias=False, ) else: self.q_proj = nn.Linear( self.hidden_size, num_heads * self.head_dim, bias=False, ) self.k_proj = nn.Linear( self.hidden_size, num_heads * self.head_dim, bias=False, ) self.v_proj = nn.Linear( self.hidden_size, num_heads * self.head_dim, bias=False, ) self.o_proj = nn.Linear( num_heads * self.head_dim, hidden_size, bias=False, ) self.rotary_emb = IdeficsEmbedding(self.head_dim) self.qk_layer_norms = qk_layer_norms if self.qk_layer_norms: self.q_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps) self.k_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: # if key_value_states are provided this layer is used as a cross-attention layer is_cross_attention = self.is_cross_attention or key_value_states is not None bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) if not is_cross_attention: key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) else: _, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len` key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = ( self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2) ) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += cache_position[0] if not is_cross_attention: cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len)) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) # [bsz, nh, t, hd] if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) if self.qk_layer_norms: query_states = self.q_layer_norm(query_states) key_states = self.k_layer_norm(key_states) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and output_attentions: logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value # this was adapted from LlamaDecoderLayer class IdeficsDecoderLayer(nn.Module): def __init__(self, config: IdeficsConfig, layer_idx: Optional[int] = None): super().__init__() self.hidden_size = config.hidden_size self.self_attn = IdeficsAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, dropout=config.dropout, config=config, layer_idx=layer_idx, ) self.mlp = IdeficsMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, ) self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.dropout = config.dropout def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class IdeficsGatedCrossAttentionLayer(nn.Module): def __init__(self, config: IdeficsConfig, layer_idx: Optional[int] = None): super().__init__() self.hidden_size = config.hidden_size self.cross_attn = IdeficsAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, is_cross_attention=True, dropout=config.dropout, config=config, qk_layer_norms=config.qk_layer_norms, layer_idx=layer_idx, ) self.mlp = IdeficsMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, ) self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.config = config.dropout self.act_cross_attn = nn.Tanh() self.act_dense = nn.Tanh() if config.alpha_initializer == "zeros": if config.alpha_type == "vector": self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size)) self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.hidden_size)) elif config.alpha_type == "float": self.alpha_cross_attn = nn.Parameter(torch.zeros(1)) self.alpha_dense = nn.Parameter(torch.zeros(1)) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") elif config.alpha_initializer == "ones": if config.alpha_type == "vector": self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size)) self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.hidden_size)) elif config.alpha_type == "float": self.alpha_cross_attn = nn.Parameter(torch.ones(1)) self.alpha_dense = nn.Parameter(torch.ones(1)) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") elif config.alpha_initializer in {"normal", "gaussian", "random"}: if config.alpha_type == "vector": self.alpha_cross_attn = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size)) ) self.alpha_dense = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size)) ) elif config.alpha_type == "float": self.alpha_cross_attn = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)) ) self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))) else: raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") else: raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!") if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")): raise ValueError("Alpha parameters not initialized correctly!") def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_hidden_states: Optional[torch.Tensor] = None, image_attention_mask: Optional[torch.Tensor] = None, cross_attention_gate: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, past_key_value: Optional[Tuple[torch.Tensor]] = None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. image_attention_mask (`torch.FloatTensor`, *optional*): image attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. cross_attention_gate (`torch.FloatTensor`, *optional*): gate of size `(batch, seq_len)` used to zero-out cross-attention output for tokens attending no images. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ if image_hidden_states is None: raise ValueError( "`image_hidden_states` is required for Idefics cross attention module which are visual features to be" " conditioned on." ) if cross_attention_gate is None: raise ValueError( "`cross_attention_gate` is required for Idefics cross attention module to zero-out the cross-attention hidden_states attending to no images." ) if past_key_value is not None: raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.") residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.cross_attn( hidden_states=hidden_states, key_value_states=image_hidden_states, attention_mask=image_attention_mask, output_attentions=output_attentions, **kwargs, ) hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training) # Fill in zeros for cross_attention hidden_states of tokens attending to no images hidden_states = hidden_states.masked_fill((cross_attention_gate == 0)[:, :, None], 0.0) hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training) hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs @auto_docstring class IdeficsPreTrainedModel(PreTrainedModel): config_class = IdeficsConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"] _supports_sdpa = True _supports_cache_class = True _supports_flash_attn_2 = True _supports_static_cache = False # IDEFICS cannot compile due to dynamic control flow when checking inputs _supports_attention_backend = True def _init_weights(self, module): # important: this ported version of Idefics isn't meant for training from scratch - only # inference and fine-tuning - so the proper init weights code has been removed - the m4 code # base should be used for training from scratch and it contains the correct code. std = self.config.initializer_range if isinstance(module, (nn.Linear, nn.Conv2d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.weight.data.fill_(1.0) module.bias.data.zero_() elif isinstance(module, IdeficsRMSNorm): module.weight.data.fill_(1.0) elif isinstance(module, IdeficsVisionEmbeddings): module.class_embedding.data.normal_() elif isinstance(module, IdeficsGatedCrossAttentionLayer): if self.config.alpha_initializer == "zeros": module.alpha_cross_attn.data.zero_() module.alpha_dense.data.zero_() elif self.config.alpha_initializer == "ones": module.alpha_cross_attn.data.fill_(1.0) module.alpha_dense.data.fill_(1.0) elif self.config.alpha_initializer in {"normal", "gaussian", "random"}: module.alpha_cross_attn.data.normal_(mean=0.0, std=self.config.alphas_initializer_range) module.alpha_dense.data.normal_(mean=0.0, std=self.config.alphas_initializer_range) elif isinstance(module, IdeficsPerceiverResampler): module.latents.data.normal_() class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... @auto_docstring class IdeficsModel(IdeficsPreTrainedModel): """ Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`] Args: config: IdeficsConfig """ def __init__(self, config: IdeficsConfig): super().__init__(config) self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = IdeficsDecoupledEmbedding( num_embeddings=config.vocab_size, num_additional_embeddings=config.additional_vocab_size, embedding_dim=config.hidden_size, partially_freeze=config.freeze_text_layers, padding_idx=self.padding_idx, ) self.image_size = config.vision_config.image_size self.vision_config = config.vision_config self.vision_model = IdeficsVisionTransformer(config.vision_config) # Perceiver Resampler if config.use_resampler: perceiver_config = config.perceiver_config self.perceiver_resampler = IdeficsPerceiverResampler( config, config.vision_config.embed_dim, perceiver_config.resampler_depth, perceiver_config.resampler_n_heads, perceiver_config.resampler_head_dim, perceiver_config.resampler_n_latents, ) self.layers = nn.ModuleList( [IdeficsDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)] ) self.cross_layer_interval = config.cross_layer_interval num_cross_layers = config.num_hidden_layers // self.cross_layer_interval self.gated_cross_attn_layers = nn.ModuleList( [IdeficsGatedCrossAttentionLayer(config, layer_idx=i) for i in range(num_cross_layers)] ) self.gradient_checkpointing = False self.norm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) # Initialize weights and apply final processing self.post_init() self.freeze_relevant_params(config) def freeze_relevant_params(self, config=None): if config is None: config = self.config if config.freeze_text_layers: self.freeze_text_layers(config.freeze_text_module_exceptions) if config.freeze_vision_layers: freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions) def freeze_text_layers(self, module_exceptions=[]): for module in [self.layers, self.norm]: freeze_model(module, module_exceptions=module_exceptions) def freeze_vision_layers(self, module_exceptions=[]): freeze_model(self.vision_model, module_exceptions=module_exceptions) def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_encoder_embeddings: Optional[torch.FloatTensor] = None, perceiver_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Union[Tuple, IdeficsBaseModelOutputWithPast]: r""" image_encoder_embeddings (`torch.FloatTensor`, *optional*): The output of the image encoder. perceiver_embeddings (`torch.FloatTensor`, *optional*): The output of the perceiver resampler. image_attention_mask (`torch.LongTensor`, *optional*): The attention mask for the image encoder. """ device = input_ids.device if input_ids is not None else inputs_embeds.device 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) batch_size, seq_length, _ = inputs_embeds.shape past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 seq_length_with_past = seq_length + past_key_values_length if cache_position is None: cache_position = torch.arange( past_key_values_length, past_key_values_length + inputs_embeds.shape[1], device=inputs_embeds.device ) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids[:, -seq_length:] elif position_ids is None: position_ids = cache_position.unsqueeze(0) if sum([x is None for x in [pixel_values, image_encoder_embeddings, perceiver_embeddings]]) != 2: raise ValueError( "Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None." ) elif pixel_values is not None: pixel_values = pixel_values.to(dtype=self.dtype, device=device) # fp16 compatibility batch_size, num_images = pixel_values.shape[:2] pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:]) # Get sequence from the vision encoder image_hidden_states = self.vision_model( pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding ).last_hidden_state elif image_encoder_embeddings is not None: batch_size, num_images, image_seq_len, image_hidden_size = image_encoder_embeddings.size() image_hidden_states = image_encoder_embeddings.to(dtype=self.dtype, device=device) image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size) if self.config.use_resampler: if perceiver_embeddings is None: perceiver_embeddings = self.perceiver_resampler(image_hidden_states) image_seq_len, image_hidden_size = perceiver_embeddings.size(1), perceiver_embeddings.size(2) else: batch_size, num_images, image_seq_len, image_hidden_size = perceiver_embeddings.size() image_hidden_states = perceiver_embeddings elif perceiver_embeddings is None: image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2) else: raise ValueError("If `perceiver_embeddings` are passed, use_resampler should be True") image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size) # # Hack to use the model in full language modeling mode # image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device) # Make image_attention_mask compatible with hidden states text_seq_len = image_attention_mask.size(1) image_attention_mask = image_attention_mask.unsqueeze(-1) image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len) image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len) if image_hidden_states is not None: image_batch_size, image_sequence_length, _ = image_hidden_states.size() image_hidden_shape = (image_batch_size, image_sequence_length) if image_attention_mask is None: image_attention_mask = torch.ones(image_hidden_shape, device=device) image_attention_mask = self.invert_attention_mask(image_attention_mask) else: image_attention_mask = None # cross_attention_gate: # For any tokens attending to no images, the hidden_states coming out of the cross-attention should be zeroed-out. # `image_attention_mask` has shape [bsz, 1, num_images, hidden_size] with elements equal to either 0.0 or a very negative number. # If any of the elements are 0.0, then the token is attending to at least one image and the gate value is 1. Otherwise the gate value is 0. # `cross_attention_gate` has shape [bsz, seq_len] with elements equal to either 0.0 or 1.0. cross_attention_gate = ((((image_attention_mask == 0.0).any(dim=-1)).to(dtype=self.dtype)).squeeze(dim=1)).to( device ) # embed positions if attention_mask is None: attention_mask = torch.ones( (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device ) attention_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for idx, decoder_layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) def vblock( main_block, hidden_states, attention_mask, position_ids, past_key_value, image_hidden_states, image_attention_mask, cross_attention_gate, output_attentions, use_cache, layer_idx, cross_layer_interval, gated_cross_attn_layers, cache_position, ): # TODO(ls): Add cross attention values to respective lists if layer_idx % cross_layer_interval == 0: xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval] outputs = xblock( hidden_states, attention_mask=attention_mask, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, cross_attention_gate=cross_attention_gate, output_attentions=output_attentions, use_cache=use_cache, past_key_value=None, # not implemented **kwargs, ) hidden_states = outputs[0] layer_outputs = main_block( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, **kwargs, ) return layer_outputs if self.gradient_checkpointing and self.training: past_key_values = None if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False layer_outputs = self._gradient_checkpointing_func( vblock, decoder_layer, hidden_states, attention_mask, position_ids, past_key_values, image_hidden_states, image_attention_mask, cross_attention_gate, output_attentions, use_cache, idx, self.cross_layer_interval, self.gated_cross_attn_layers, cache_position, ) else: layer_outputs = vblock( decoder_layer, hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, cross_attention_gate=cross_attention_gate, output_attentions=output_attentions, use_cache=use_cache, layer_idx=idx, cross_layer_interval=self.cross_layer_interval, gated_cross_attn_layers=self.gated_cross_attn_layers, cache_position=cache_position, **kwargs, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() image_hidden_states = image_hidden_states.view(batch_size, num_images, image_seq_len, image_hidden_size) return IdeficsBaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, image_hidden_states=image_hidden_states, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype = input_tensor.dtype sequence_length = input_tensor.shape[1] if using_compilable_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class IdeficsForVisionText2Text(IdeficsPreTrainedModel, GenerationMixin): _tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"] def __init__(self, config, vision_model=None): super().__init__(config) self.model = IdeficsModel(config) self.lm_head = IdeficsDecoupledLinear( in_features=config.hidden_size, out_features=config.vocab_size, out_additional_features=config.additional_vocab_size, bias=False, partially_freeze=config.freeze_lm_head, ) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.model.embed_tokens def set_input_embeddings(self, value): self.model.embed_tokens = value def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def set_decoder(self, decoder): self.model = decoder def get_decoder(self): return self.model def tie_weights(self): """ Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of IdeficsDecoupledLinear and IdeficsDecoupledEmbedding. """ output_embeddings = self.get_output_embeddings() input_embeddings = self.get_input_embeddings() if getattr(self.config, "tie_word_embeddings", True): output_embeddings.weight = input_embeddings.weight if input_embeddings.num_additional_embeddings > 0: assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"): output_embeddings.out_features = input_embeddings.num_embeddings if hasattr(output_embeddings, "out_additional_features") and hasattr( input_embeddings, "num_additional_embeddings" ): output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_encoder_embeddings: Optional[torch.FloatTensor] = None, perceiver_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[KwargsForCausalLM], ) -> Union[Tuple, IdeficsCausalLMOutputWithPast]: 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]`. image_encoder_embeddings (`torch.FloatTensor`, *optional*): The output of the image encoder. perceiver_embeddings (`torch.FloatTensor`, *optional*): The output of the perceiver resampler. image_attention_mask (`torch.LongTensor`, *optional*): The attention mask for the image encoder. Example: ```python >>> from transformers import AutoProcessor, IdeficsForVisionText2Text >>> model = IdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b") >>> processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b") >>> dogs_image_url_1 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg" >>> dogs_image_url_2 = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image2.jpeg" >>> prompts = [ ... [ ... "User:", ... dogs_image_url_1, ... "Describe this image.\nAssistant: An image of two dogs.\n", ... "User:", ... dogs_image_url_2, ... "Describe this image.\nAssistant:", ... ] ... ] >>> inputs = processor(prompts, return_tensors="pt") >>> generate_ids = model.generate(**inputs, max_new_tokens=6) >>> processor.batch_decode(generate_ids, skip_special_tokens=True) ```""" 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 ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, pixel_values=pixel_values, image_encoder_embeddings=image_encoder_embeddings, perceiver_embeddings=perceiver_embeddings, image_attention_mask=image_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=True, cache_position=cache_position, **kwargs, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) return IdeficsCausalLMOutputWithPast( 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, attention_mask=None, position_ids=None, inputs_embeds=None, past_key_values=None, cache_position=None, pixel_values=None, image_hidden_states=None, image_attention_mask=None, use_cache=None, **kwargs, ): # Overwritten -- custom processing based on `config.use_resampler` images_kwargs = {} if image_hidden_states is not None: if self.config.use_resampler: images_kwargs["perceiver_embeddings"] = image_hidden_states else: images_kwargs["image_encoder_embeddings"] = image_hidden_states else: images_kwargs["pixel_values"] = pixel_values images_kwargs["interpolate_pos_encoding"] = kwargs.pop("interpolate_pos_encoding", False) model_inputs = super().prepare_inputs_for_generation( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, position_ids=position_ids, use_cache=use_cache, image_attention_mask=image_attention_mask, **images_kwargs, **kwargs, ) if image_attention_mask is not None and inputs_embeds is None: seq_length = model_inputs["input_ids"].shape[1] model_inputs["image_attention_mask"] = image_attention_mask[:, -seq_length:] return model_inputs def _update_model_kwargs_for_generation( self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, **kwargs, ) -> Dict[str, Any]: model_kwargs = super()._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder, **kwargs, ) if "image_attention_mask" in model_kwargs: image_attention_mask = model_kwargs["image_attention_mask"] last_mask = image_attention_mask[:, -1, :].unsqueeze(1) if model_kwargs.get("use_cache", True): model_kwargs["image_attention_mask"] = last_mask else: model_kwargs["image_attention_mask"] = torch.cat([image_attention_mask, last_mask], dim=1) # Get the precomputed image_hidden_states model_kwargs["image_hidden_states"] = outputs.image_hidden_states return model_kwargs @staticmethod def _reorder_cache(past, beam_idx): reordered_past = () for layer_past in past: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past __all__ = ["IdeficsForVisionText2Text", "IdeficsModel", "IdeficsPreTrainedModel"]