# coding=utf-8 # Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # 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 OPT model.""" import random from typing import List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.utils import ( ContextManagers, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from m4.models import DecoupledEmbedding, DecoupledLinear from m4.models.common import ( expand_inputs_for_generation, prepare_inputs_for_generation, update_model_kwargs_for_generation, ) from m4.models.custom_modules import VLOOMPreTrainedModelBase from m4.models.perceiver.perceiver import PerceiverResampler from m4.models.vopt.configuration_vopt import VOPTConfig from m4.training.utils import ( compute_perceiver_tflops_per_batch_per_gpu, compute_tflops_per_batch_per_gpu, deepspeed_gathered_parameters_context_manager, freeze_model, ) from m4.utils import logging logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/opt-350m" _CONFIG_FOR_DOC = "VOPTConfig" _TOKENIZER_FOR_DOC = "GPT2Tokenizer" # Base model docstring _EXPECTED_OUTPUT_SHAPE = [1, 8, 1024] # SequenceClassification docstring _CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc" _SEQ_CLASS_EXPECTED_LOSS = 1.71 _SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'" OPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/opt-125m", "facebook/opt-350m", "facebook/opt-1.3b", "facebook/opt-2.7b", "facebook/opt-6.7b", "facebook/opt-13b", "facebook/opt-30b", # See all OPT models at https://huggingface.co/models?filter=opt ] class SwiGLUActivation(nn.Module): def __init__(self, in_features: int, out_features: int): super().__init__() self.gate = nn.Linear(in_features, out_features, bias=False) def forward(self, hidden_states_to_gate, hidden_states): gate = self.gate(hidden_states) return nn.functional.silu(gate) * hidden_states_to_gate # Taken from LLaMA codebase class RMSNorm(torch.nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): output = self._norm(x.float()).type_as(x) return output * self.weight def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min)) mask_cond = torch.arange(mask.size(-1)) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class OPTLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" attention_mask = attention_mask.long() # create positions depending on attention_mask positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 # cut positions if `past_key_values_length` is > 0 positions = positions[:, past_key_values_length:] return super().forward(positions + self.offset) class OPTAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_cross_attention=False, config=None, qk_layer_norms=False, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_cross_attention = is_cross_attention if self.is_cross_attention: kv_input_dim = self.hidden_size if not hasattr(config, "vision_embed_dim") else config.vision_embed_dim self.k_proj = nn.Linear(kv_input_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(kv_input_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) else: self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.qk_layer_norms = qk_layer_norms if self.qk_layer_norms and config.rms_norm: self.q_layer_norm = RMSNorm(self.head_dim, eps=1e-6) self.k_layer_norm = RMSNorm(self.head_dim, eps=1e-6) elif self.qk_layer_norms: self.q_layer_norm = nn.LayerNorm(self.head_dim) self.k_layer_norm = nn.LayerNorm(self.head_dim) 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, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = self.is_cross_attention or key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self._shape(self.q_proj(hidden_states), -1, bsz) # get key, value proj if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) if self.qk_layer_norms: query_states = self.q_layer_norm(query_states) key_states = self.k_layer_norm(key_states) src_len = key_states.size(2) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attention_mask = attention_mask.expand(-1, self.num_heads, -1, -1) attention_mask = attention_mask + layer_head_mask.view(1, -1, 1, 1) attn_output = nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attention_mask, dropout_p=self.dropout, ) attn_weights_reshaped = None logger.warning_once( "attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead" ) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned aross GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class OPTDecoderLayer(nn.Module): def __init__(self, config: VOPTConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = OPTAttention( embed_dim=self.embed_dim, num_heads=config.num_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config, ) self.do_layer_norm_before = config.do_layer_norm_before self.dropout = config.dropout self.activation_fn = ACT2FN[config.activation_function] self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim) self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, past_key_value: Optional[Tuple[torch.Tensor]] = None, ) -> 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. layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. 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 # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Fully Connected hidden_states_shape = hidden_states.shape hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = (residual + hidden_states).view(hidden_states_shape) # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class VOPTGatedAttentionLayer(nn.Module): def __init__(self, config: VOPTConfig): """ Note: Based on `tr_101_cm401xPMD09_nobias`, setting the biases to False in all of the nn.Linear for the gated cross attention. Provide a small stability gain at opt-13b scale. """ super().__init__() self.embed_dim = config.hidden_size self.cross_attn = OPTAttention( embed_dim=self.embed_dim, num_heads=config.num_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config, is_cross_attention=True, bias=False, qk_layer_norms=config.qk_layer_norms, ) self.do_layer_norm_before = config.do_layer_norm_before self.normformer_layer_norms = config.normformer_layer_norms self.dropout = config.dropout if config.cross_layer_activation_function == "swiglu": # We cannot put `SwiGLUActivation` in `ACT2FN` because it takes two arguments (`in_features` and # `out_features`) that we don't know until entering this module. self.activation_fn = SwiGLUActivation(self.embed_dim, config.ffn_dim) else: self.activation_fn = ACT2FN[config.cross_layer_activation_function] if config.rms_norm: self.self_attn_layer_norm = RMSNorm(self.embed_dim, eps=1e-6) else: self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) if self.normformer_layer_norms: self.self_attn_post_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=False) self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=False) if config.rms_norm: self.final_layer_norm = RMSNorm(self.embed_dim, eps=1e-6) else: self.final_layer_norm = nn.LayerNorm(self.embed_dim) if self.normformer_layer_norms: self.mlp_post_layer_norm = nn.LayerNorm(config.ffn_dim) 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.embed_dim)) self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) 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.embed_dim)) self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.embed_dim)) 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.embed_dim)) ) self.alpha_dense = nn.Parameter( torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.embed_dim)) ) 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!") assert hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense") def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, image_hidden_states: Optional[torch.Tensor] = None, image_attention_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, past_key_value: Optional[Tuple[torch.Tensor]] = None, ) -> 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. layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. 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 VOPT cross attention module which are visual features to be" " conditioned on." ) if past_key_value is not None: raise NotImplementedError("Past key value states are not implemented for VOPT cross attention module.") residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(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, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.normformer_layer_norms: hidden_states = self.self_attn_post_layer_norm(hidden_states) hidden_states = residual + self.act_cross_attn(self.alpha_cross_attn) * hidden_states # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Fully Connected hidden_states_shape = hidden_states.shape hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) hidden_states_to_gate = self.fc1(hidden_states) if isinstance(self.activation_fn, SwiGLUActivation): hidden_states = self.activation_fn(hidden_states_to_gate, hidden_states) else: hidden_states = self.activation_fn(hidden_states_to_gate) if self.normformer_layer_norms: hidden_states = self.mlp_post_layer_norm(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = (residual + self.act_dense(self.alpha_dense) * hidden_states).view(hidden_states_shape) # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs OPT_START_DOCSTRING = r""" This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VOPTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ @add_start_docstrings( "The bare OPT Model outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING, ) class VOPTPreTrainedModel(VLOOMPreTrainedModelBase): config_class = VOPTConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["OPTDecoderLayer", "VOPTGatedAttentionLayer", "CLIPEncoderLayer"] _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] def _init_weights(self, module): def init_a_linear(module, mean=0.0, std=self.config.init_std): with ContextManagers(deepspeed_gathered_parameters_context_manager(module.weight, modify=True)): module.weight.data.normal_(mean=mean, std=std) if module.bias is not None: with ContextManagers(deepspeed_gathered_parameters_context_manager(module.bias, modify=True)): module.bias.data.zero_() if isinstance(module, VOPTGatedAttentionLayer): for sub_module_name, sub_module in module.named_modules(): if isinstance(sub_module, nn.Linear): if "fc2" in sub_module_name: factor = 2 * self.config.num_hidden_layers else: factor = 1.0 init_a_linear(sub_module, std=(0.4 / (sub_module.in_features * factor)) ** 0.5) elif isinstance(module, PerceiverResampler): with ContextManagers(deepspeed_gathered_parameters_context_manager(module.latents, modify=True)): module.latents.data.normal_(mean=0.0, std=(1.0 / self.config.vision_embed_dim) ** 0.5) for sub_module_name, sub_module in module.named_modules(): if isinstance(sub_module, nn.Linear): if "c_proj" in sub_module_name: factor = 2 * self.config.num_hidden_layers else: factor = 1.0 init_a_linear(sub_module, std=(0.4 / (self.config.vision_embed_dim * factor)) ** 0.5) elif isinstance(module, nn.Embedding): with ContextManagers(deepspeed_gathered_parameters_context_manager(module.weight, modify=True)): module.weight.data.normal_(mean=0.0, std=(1.0 / self.config.hidden_size) ** 0.5) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, DecoupledLinear): if hasattr(module, "additional_fc"): init_a_linear(module.additional_fc, std=(1.0 / (module.additional_fc.in_features)) ** 0.5) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (VOPTDecoder)): module.gradient_checkpointing = value @classmethod def override_vision_model_wrapper(cls, model, config, vision_model_name, vision_model_params, torch_dtype): # this can be called via from_pretrained from a class w/ head or w/o head so we extract the beheaded model version beheaded_model = model.model if hasattr(model, "model") else model cls.override_vision_model(beheaded_model.decoder, vision_model_name, vision_model_params, torch_dtype) beheaded_model.freeze_relevant_params(config) OPT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. 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 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 in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. 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`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class VOPTDecoder(VOPTPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`] Args: config: VOPTConfig """ def __init__(self, config: VOPTConfig, vision_model=None): super().__init__(config) self.config = config self.dropout = config.dropout self.layerdrop = config.layerdrop self.padding_idx = config.pad_token_id self.max_target_positions = config.max_position_embeddings self.vocab_size = config.vocab_size self.embed_tokens = DecoupledEmbedding( num_embeddings=config.vocab_size, num_additional_embeddings=config.additional_vocab_size, embedding_dim=config.word_embed_proj_dim, partially_freeze=config.freeze_text_layers, padding_idx=self.padding_idx, ) self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size) # Load an uninitialized model and later in from_pretrained will load the pre-trained model - # this solves the losing of weights in `from_pretrained` on the main model self.vision_model = vision_model # Perceiver Resampler if config.use_resampler: self.perceiver_resampler = PerceiverResampler( self.config, self.config.vision_embed_dim, config.resampler_depth, config.resampler_n_heads, config.resampler_head_dim, config.resampler_n_latents, ) if config.word_embed_proj_dim != config.hidden_size: self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False) else: self.project_in = None self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ 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( [VOPTGatedAttentionLayer(config) for i in range(num_cross_layers)] ) self.gradient_checkpointing = False # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility # with checkpoints that have been fine-tuned before transformers v4.20.1 # see https://github.com/facebookresearch/metaseq/pull/164 if config.do_layer_norm_before and not config._remove_final_layer_norm: self.final_layer_norm = nn.LayerNorm(config.hidden_size) else: self.final_layer_norm = None if config.word_embed_proj_dim != config.hidden_size: self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False) else: self.project_out = None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length ).to(inputs_embeds.device) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, crossblock_head_mask: Optional[torch.Tensor] = None, # TOFO (ls): check if this is needed use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. 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 2 additional tensors of Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ 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 # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if pixel_values is not None and image_embeddings is not None: raise ValueError("You cannot specify both pixel_values and image_embeddings at the same time") elif pixel_values is not None: pixel_values = pixel_values.to(dtype=self.dtype, device=input_ids.device) # fp16 compatibility batch_size, num_images = pixel_values.size(0), pixel_values.size(1) 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).last_hidden_state elif image_embeddings is not None: batch_size, num_images, image_seq_len, image_hidden_size = image_embeddings.size() image_hidden_states = image_embeddings.to(dtype=self.dtype, device=input_ids.device) image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size) if self.config.use_resampler: image_hidden_states = self.perceiver_resampler(image_hidden_states) image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2) image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size) # 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 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # embed positions if attention_mask is None: attention_mask = torch.ones(inputs_embeds.shape[:2], dtype=torch.bool, device=inputs_embeds.device) pos_embeds = self.embed_positions(attention_mask, past_key_values_length) attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) if self.project_in is not None: inputs_embeds = self.project_in(inputs_embeds) hidden_states = inputs_embeds + pos_embeds # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = () if use_cache else None # check if head_mask has a correct number of layers specified if desired for attn_mask, mask_name in zip([head_mask], ["head_mask"]): if attn_mask is not None: if attn_mask.size()[0] != (len(self.layers)): raise ValueError( f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" f" {head_mask.size()[0]}." ) for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) dropout_probability = random.uniform(0, 1) if self.training and (dropout_probability < self.layerdrop): continue past_key_value = past_key_values[idx] if past_key_values is not None else None layer_head_mask = head_mask[idx] if head_mask is not None else None def vblock( main_block, hidden_states, attention_mask, layer_head_mask, past_key_value, image_hidden_states, image_attention_mask, output_attentions, use_cache, layer_idx, cross_layer_interval, gated_cross_attn_layers, ): # 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, layer_head_mask=layer_head_mask, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, output_attentions=output_attentions, use_cache=use_cache, past_key_value=None, # not implemented ) hidden_states = outputs[0] layer_outputs = main_block( hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) return layer_outputs if self.gradient_checkpointing and self.training: past_key_value = None if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False layer_outputs = torch.utils.checkpoint.checkpoint( vblock, decoder_layer, hidden_states, attention_mask, layer_head_mask, past_key_value, image_hidden_states, image_attention_mask, output_attentions, use_cache, idx, self.cross_layer_interval, self.gated_cross_attn_layers, ) else: layer_outputs = vblock( decoder_layer, hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, past_key_value=past_key_value, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, 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, ) 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],) if self.final_layer_norm is not None: hidden_states = self.final_layer_norm(hidden_states) if self.project_out is not None: hidden_states = self.project_out(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 not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) @add_start_docstrings( "The bare OPT Model outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING, ) class VOPTModel(VOPTPreTrainedModel): def __init__(self, config: VOPTConfig, vision_model=None): super().__init__(config) self.decoder = VOPTDecoder(config, vision_model=vision_model) # 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.decoder.vision_model, module_exceptions=config.freeze_vision_module_exceptions) def freeze_text_layers(self, module_exceptions): for module in [self.decoder.embed_positions, self.decoder.layers]: freeze_model(module, module_exceptions=module_exceptions) if self.decoder.project_out is not None: freeze_model(self.decoder.project_out, module_exceptions=module_exceptions) if self.decoder.final_layer_norm is not None: freeze_model(self.decoder.final_layer_norm, module_exceptions=module_exceptions) def get_input_embeddings(self): return self.decoder.embed_tokens def set_input_embeddings(self, value): self.decoder.embed_tokens = value def get_decoder(self): return self.decoder @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, crossblock_head_mask: Optional[torch.Tensor] = None, # TOFO (ls): check if this is needed use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: 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 # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs = self.decoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, pixel_values=pixel_values, image_embeddings=image_embeddings, image_attention_mask=image_attention_mask, crossblock_head_mask=crossblock_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return decoder_outputs return BaseModelOutputWithPast( last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, ) class VOPTForCausalLM(VOPTPreTrainedModel): _keys_to_ignore_on_load_missing = [r"lm_head.weight"] def __init__(self, config, vision_model=None): super().__init__(config) # Initialize LM head first so that it is not directly offloaded to the CPU/disk # the lm_head weight is automatically tied to the embed tokens weight self.lm_head = DecoupledLinear( in_features=config.word_embed_proj_dim, out_features=config.vocab_size, out_additional_features=config.additional_vocab_size, bias=False, partially_freeze=config.freeze_lm_head, ) self.model = VOPTModel(config, vision_model=vision_model) # Initialize weights and apply final processing self.post_init() def tie_weights(self): """ Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of DecoupledLinear and DecoupledEmbedding. """ 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 def get_input_embeddings(self): return self.model.decoder.embed_tokens def set_input_embeddings(self, value): self.model.decoder.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 = decoder def get_decoder(self): return self.model.decoder @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, image_embeddings: Optional[torch.FloatTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, crossblock_head_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, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. 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 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. 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]`. 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`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import GPT2Tokenizer, OPTForCausalLM >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") >>> tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-350m") >>> prompt = "Hey, are you consciours? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." ```""" 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.decoder( input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, pixel_values=pixel_values, image_embeddings=image_embeddings, image_attention_mask=image_attention_mask, crossblock_head_mask=crossblock_head_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) logits = self.lm_head(outputs[0]).contiguous() loss = None if labels is not None: # Shift so that tokens < n predict n if attention_mask is not None: shift_attention_mask = attention_mask[..., 1:] shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous() shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous() else: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs) unwanted_kwargs = ["position_ids", "token_type_ids"] for kwarg in unwanted_kwargs: inputs.pop(kwarg, None) return inputs @staticmethod def _expand_inputs_for_generation( *args, **model_kwargs, ): return expand_inputs_for_generation(*args, **model_kwargs) @staticmethod def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False): return update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder) @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 def get_model_tflops_per_batch_per_gpu(self, hparams, data_param, tokenizer, max_num_images): config_vl_model = self.config language_embed_size = config_vl_model.hidden_size num_language_layers = config_vl_model.num_hidden_layers ffn_inner_size = config_vl_model.ffn_dim vision_config = self.model.decoder.vision_model.config if hasattr(vision_config, "vision_config"): vision_config = vision_config.vision_config # Get vision model blocks infos vision_patch_size = vision_config.patch_size vision_hidden_size = vision_config.hidden_size num_vision_layers = vision_config.num_hidden_layers # The +1 is for the CLS token single_image_seq_len = (vision_config.image_size // vision_patch_size) ** 2 + 1 vision_exp_factor = vision_config.intermediate_size // vision_hidden_size # Get language and cross-att blocks infos num_cross_attn_layers = num_language_layers // config_vl_model.cross_layer_interval language_seq_len = data_param.max_seq_len language_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4 cross_att_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4 k_v_cross_attn_seq_len = ( (self.config.resampler_n_latents * max_num_images) if self.config.use_resampler else (single_image_seq_len * max_num_images) ) language_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu( num_layers=num_language_layers, batch_size=hparams.batch_size_per_gpu, q_seq_len=language_seq_len, k_seq_len=language_seq_len, hidden_size=language_embed_size, kv_in_dim=language_embed_size, ff_exp_factor=language_exp_factor, grad_acc_size=hparams.grad_acc_size, swiglu=False, vocab_size=tokenizer.vocab_size, count_backward=True, # Always True regardless of freezing, because gradients are computed for cross-attentions use_grad_checkpointing=hparams.gradient_checkpointing, ) cross_attention_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu( num_layers=num_cross_attn_layers, batch_size=hparams.batch_size_per_gpu, q_seq_len=language_seq_len, k_seq_len=k_v_cross_attn_seq_len, hidden_size=language_embed_size, kv_in_dim=vision_hidden_size, ff_exp_factor=cross_att_exp_factor, grad_acc_size=hparams.grad_acc_size, swiglu=self.config.cross_layer_activation_function == "swiglu", vocab_size=None, count_backward=True, use_grad_checkpointing=hparams.gradient_checkpointing, ) vision_tflops_per_batch_per_gpu = compute_tflops_per_batch_per_gpu( num_layers=num_vision_layers, batch_size=hparams.batch_size_per_gpu * max_num_images, q_seq_len=single_image_seq_len, k_seq_len=single_image_seq_len, hidden_size=vision_hidden_size, kv_in_dim=vision_hidden_size, ff_exp_factor=vision_exp_factor, grad_acc_size=hparams.grad_acc_size, swiglu=False, vocab_size=None, count_backward=not hparams.model_params["freeze_vision_layers"], use_grad_checkpointing=hparams.gradient_checkpointing, ) if self.config.use_resampler: perceiver_tflops_per_batch_per_gpu = compute_perceiver_tflops_per_batch_per_gpu( num_layers=self.config.resampler_depth, batch_size=hparams.batch_size_per_gpu * max_num_images, q_seq_len=self.config.resampler_n_latents, vision_embed_seq_len=single_image_seq_len, q_k_v_input_dim=vision_hidden_size, attention_hidden_size=self.config.resampler_n_heads * self.config.resampler_head_dim, ff_exp_factor=cross_att_exp_factor, count_backward=True, use_grad_checkpointing=hparams.gradient_checkpointing, ) flop_count = ( language_tflops_per_batch_per_gpu + cross_attention_tflops_per_batch_per_gpu + vision_tflops_per_batch_per_gpu + perceiver_tflops_per_batch_per_gpu ) else: flop_count = ( language_tflops_per_batch_per_gpu + cross_attention_tflops_per_batch_per_gpu + vision_tflops_per_batch_per_gpu ) return flop_count