# coding=utf-8 # Copyright 2023 Mistral AI 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 Mistral model.""" import inspect import math import warnings from dataclasses import dataclass from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss from transformers import AutoConfig, AutoModel from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask from transformers.modeling_outputs import ModelOutput from transformers.utils import ( ContextManagers, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, 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, MLP, RMSNorm, ) from m4.models.custom_modules import VLOOMPreTrainedModelBase from m4.models.perceiver.perceiver import PerceiverResampler from m4.models.vmistral.configuration_vmistral import VMistralConfig from m4.training.setup_vision_model import vision_model_name_to_model from m4.training.utils import ( compute_linear_tflops_per_batch_per_gpu, 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 if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VMistralConfig" @dataclass class VMistralBaseModelOutputWithPast(ModelOutput): """ Base class for VMistral 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: 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 VMistralCausalLMOutputWithPast(ModelOutput): """ Base class for VMistral 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: 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["pixel_attention_mask"] = model_kwargs.get("pixel_attention_mask", None) model_kwargs["image_hidden_states"] = model_kwargs.get("image_hidden_states", 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["pixel_values"] is not None: model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx) elif model_kwargs["image_hidden_states"] is not None: model_kwargs["image_hidden_states"] = model_kwargs["image_hidden_states"].index_select(0, expanded_return_idx) if model_kwargs["pixel_attention_mask"] is not None: model_kwargs["pixel_attention_mask"] = model_kwargs["pixel_attention_mask"].index_select( 0, expanded_return_idx ) return input_ids, model_kwargs def update_model_kwargs_for_generation(outputs, model_kwargs): # must have this key set to at least None if "past_key_values" in outputs: model_kwargs["past_key_values"] = outputs.past_key_values else: model_kwargs["past_key_values"] = None # update token_type_ids with last value if "token_type_ids" in model_kwargs: token_type_ids = model_kwargs["token_type_ids"] model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1) # update attention masks if "attention_mask" in model_kwargs: attention_mask = model_kwargs["attention_mask"] model_kwargs["attention_mask"] = torch.cat( [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 ) # Get the precomputed image_hidden_states model_kwargs["image_hidden_states"] = outputs.image_hidden_states return model_kwargs def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # only last token for inputs_ids if past is defined in kwargs if past_key_values: input_ids = input_ids[:, -1].unsqueeze(-1) if token_type_ids is not None: token_type_ids = token_type_ids[:, -1].unsqueeze(-1) attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) 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) if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) pixel_values = kwargs.get("pixel_values", None) pixel_attention_mask = kwargs.get("pixel_attention_mask", None) image_hidden_states = kwargs.get("image_hidden_states", None) return { "input_ids": input_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, "pixel_values": pixel_values, "pixel_attention_mask": pixel_attention_mask, "image_hidden_states": image_hidden_states, } # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral class MistralRotaryEmbedding(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).float().to(device) / 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=self.inv_freq.dtype) 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), ) # Copied from transformers.models.llama.modeling_llama.rotate_half 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) # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids): cos = cos[position_ids].unsqueeze(1) # [seq_len, dim] -> [batch_size, 1, seq_len, head_dim] sin = sin[position_ids].unsqueeze(1) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class MistralAttention(nn.Module): """ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer and "Generating Long Sequences with Sparse Transformers". """ def __init__(self, config: VMistralConfig, qk_layer_norms: bool = False): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.is_causal = True if (self.head_dim * self.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`: {self.num_heads})." ) self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) self.qk_layer_norms = qk_layer_norms if self.qk_layer_norms: self.q_layer_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.k_layer_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.rotary_emb = MistralRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) self.attention_dropout = config.attention_dropout 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, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use" " `attention_mask` instead.`" ) 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) key_states = ( self.k_proj(hidden_states).view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) ) value_states = ( self.v_proj(hidden_states).view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) ) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) past_key_value = (key_states, value_states) if use_cache else None if self.qk_layer_norms: query_states = self.q_layer_norm(query_states) key_states = self.k_layer_norm(key_states) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class MistralFlashAttention2(MistralAttention): """ Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ 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: bool = False, use_cache: bool = False, **kwargs, ): if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use" " `attention_mask` instead.`" ) # overwrite attention_mask with padding_mask attention_mask = kwargs.pop("padding_mask") bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] # Because the input can be padded, the absolute sequence length depends on the max position id. rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) use_sliding_windows = ( _flash_supports_window_size and hasattr(self.config, "sliding_window") is not None and kv_seq_len > self.config.sliding_window ) if not _flash_supports_window_size: logger.warning_once( "The current flash attention version does not support sliding window attention, for a more memory" " efficient implementation make sure to upgrade flash-attn library." ) if past_key_value is not None: # Activate slicing cache only if the config has a value `sliding_windows` attribute if hasattr(self.config, "sliding_window") and kv_seq_len > self.config.sliding_window: slicing_tokens = kv_seq_len - self.config.sliding_window past_key = past_key_value[0] past_value = past_key_value[1] past_key = past_key[:, :, slicing_tokens:, :].contiguous() past_value = past_value[:, :, slicing_tokens:, :].contiguous() if past_key.shape[-2] != self.config.sliding_window - 1: raise ValueError( "past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1," f" head_dim`), got {past_key.shape}" ) past_key_value = (past_key, past_value) if attention_mask is not None: attention_mask = attention_mask[:, slicing_tokens:] attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) past_key_value = (key_states, value_states) if use_cache else None # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. input_dtype = query_states.dtype if input_dtype == torch.float32: # Handle the case where the model is quantized if hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( "The input hidden states seems to be silently casted in float32, this might be related to the fact" " you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, use_sliding_windows=use_sliding_windows, ) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None, use_sliding_windows=False, ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) use_sliding_windows (`bool`, *optional*): Whether to activate sliding window attention. """ # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens if not use_sliding_windows: attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=self.is_causal, ) else: attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=self.is_causal, window_size=(self.config.sliding_window, self.config.sliding_window), ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: if not use_sliding_windows: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=self.is_causal, ) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=self.is_causal, window_size=(self.config.sliding_window, self.config.sliding_window), ) return attn_output def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape # On the first iteration we need to properly re-create the padding mask # by slicing it on the proper place if kv_seq_len != attention_mask.shape[-1]: attention_mask_num_tokens = attention_mask.shape[-1] attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :] indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) class MistralDecoderLayer(nn.Module): def __init__(self, config: VMistralConfig): super().__init__() self.hidden_size = config.hidden_size self.self_attn = ( MistralAttention(config=config) if not getattr(config, "_flash_attn_2_enabled", False) else MistralFlashAttention2(config) ) self.mlp = MLP( activation=config.hidden_act, input_size=config.hidden_size, intermediate_size=config.intermediate_size, output_size=config.hidden_size, ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) 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, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use" " `attention_mask` instead.`" ) """ 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, sequence_length)` where padding elements are indicated by 0. 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, ) 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 = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs MISTRAL_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 ([`VMistralConfig`]): 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 Mistral Model outputting raw hidden-states without any specific head on top.", MISTRAL_START_DOCSTRING, ) class VMistralPreTrainedModel(VLOOMPreTrainedModelBase): config_class = VMistralConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["MistralDecoderLayer"] _skip_keys_device_placement = "past_key_values" def _init_weights(self, module): def init_a_linear(module, mean=0.0, std=self.config.initializer_range): 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, MLP): for sub_module_name, sub_module in module.named_modules(): if isinstance(sub_module, nn.Linear): factor = 1.0 if "down_proj" in sub_module_name: factor = 2.0 init_a_linear(sub_module, std=(0.4 / (self.config.hidden_size * factor)) ** 0.5) if 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.hidden_size) ** 0.5) for sub_module_name, sub_module in module.named_modules(): if isinstance(sub_module, nn.Linear): factor = 1.0 if "o_proj" in sub_module_name: factor = 2.0 * self.config.perceiver_config.resampler_depth init_a_linear(sub_module, std=(0.4 / (self.config.hidden_size * 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) @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, vision_model_name, vision_model_params, torch_dtype) beheaded_model.freeze_relevant_params(config) MISTRAL_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 [`AutoTokenizer`]. 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 [`AutoTokenizer`]. 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. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) 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. """ @add_start_docstrings( "The bare Mistral Model outputting raw hidden-states without any specific head on top.", MISTRAL_START_DOCSTRING, ) class VMistralModel(VMistralPreTrainedModel): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`] Args: config: VMistralConfig """ def __init__(self, config: VMistralConfig, vision_model=None): super().__init__(config) self.config = config self.vision_config = config.vision_config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.sliding_window = config.sliding_window self.embed_tokens = DecoupledEmbedding( 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, ) # 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 # init vision model vision_model_name = self.vision_config.vision_model_name # Create an uninitialized vision_model to insert into the main model. vision_model_config = AutoConfig.from_pretrained(vision_model_name, trust_remote_code=True) # Override image_size if we want to increase it compared to pretraining if hasattr(vision_model_config, "vision_config"): vision_model_config.vision_config.image_size = config.vision_config.image_size else: vision_model_config.image_size = config.vision_config.image_size self.vision_model = vision_model_name_to_model( vision_model_name, AutoModel.from_config(vision_model_config, trust_remote_code=True) ) # Dim projection - projecting from the vision dim to the text dim self.modality_projection = MLP( activation=self.config.hidden_act, input_size=self.config.vision_config.embed_dim, intermediate_size=self.config.intermediate_size, output_size=self.config.hidden_size, ) # Perceiver Resampler if not config.use_resampler: raise ValueError("NOT using the resampler is deprecated since we integrated navit-style image patching.") if config.use_resampler: self.perceiver_resampler = PerceiverResampler(config) if config.use_resampler: self.image_seq_len = config.perceiver_config.resampler_n_latents else: self.image_seq_len = ( config.vision_config.image_size // config.vision_config.patch_size ) ** 2 # TODO: pretty sure that does not work for CLIP models since there is the CLS token self.image_token_id = self.config.image_token_id self.layers = nn.ModuleList([MistralDecoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) # Initialize weights and apply final processing self.post_init() self.freeze_relevant_params(config) def enable_input_require_grads(self): """ Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping the model weights fixed. """ def get_lowest_module(module): if len(list(module.children())) == 0: # If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.) return module else: # Recursively call the function on each child module return get_lowest_module(list(module.children())[0]) def make_inputs_require_grads(module, input, output): output.requires_grad_(True) self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads) self._vision_require_grads_hook = get_lowest_module(self.vision_model).register_forward_hook( make_inputs_require_grads ) 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 get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def inputs_merger( self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.Tensor] = None, image_hidden_states: Optional[torch.Tensor] = None, ): """ This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM. The merging happens as follows: - The text token sequence is: `tok_1 tok_2 tok_3 ... tok_4`. - We get the image hidden states for the image through the vision encoder (and potentially the perceiver), and that hidden state is then projected into the text embedding space. We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer. - The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM. - To fit the format of that sequence, `input_ids`, `input_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states. """ batch_size = input_ids.size(0) if inputs_embeds is not None: if image_hidden_states is not None: vision_pipeline_output_seq_len = image_hidden_states.shape[1] vision_hidden_size = image_hidden_states.shape[2] new_inputs_embeds = inputs_embeds.clone() # Get the number of images for each example num_images = (input_ids == self.image_token_id).sum(dim=-1) // self.image_seq_len cum_num_images = num_images.cumsum(dim=-1) for batch_idx in range(batch_size): # Get the number of images for this particular example example_num_images = num_images[batch_idx] # Get the image_hidden_states corresponding to True images for the example, so get rid of the padding images. start = 0 if batch_idx == 0 else cum_num_images[batch_idx - 1] end = cum_num_images[batch_idx] example_true_image_hidden_states = image_hidden_states[start:end] if ( new_inputs_embeds[batch_idx][input_ids[batch_idx] == self.image_token_id].shape[0] != example_num_images * vision_pipeline_output_seq_len ): raise ValueError( "new_inputs_embeds to replace has shape[0]:" f" {new_inputs_embeds[batch_idx][input_ids[batch_idx] == self.image_token_id].shape[0]} but" " should have shape[0]:" f" {example_num_images}*{vision_pipeline_output_seq_len}={example_num_images * vision_pipeline_output_seq_len} " ) # Insert the image_hidden_states new_inputs_embeds[batch_idx][input_ids[batch_idx] == self.image_token_id] = ( example_true_image_hidden_states.view( example_num_images * vision_pipeline_output_seq_len, vision_hidden_size, ) ) else: new_inputs_embeds = inputs_embeds return_dict = {} if inputs_embeds is not None: return_dict["inputs_embeds"] = new_inputs_embeds return return_dict @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) def forward( self, input_ids: 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, pixel_attention_mask: Optional[torch.BoolTensor] = None, image_hidden_states: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, VMistralBaseModelOutputWithPast]: 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: batch_size, seq_length = input_ids.shape elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values is not None: past_key_values_length = past_key_values[0][0].shape[2] seq_length_with_past = seq_length_with_past + past_key_values_length if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0).view(-1, seq_length) else: position_ids = position_ids.view(-1, seq_length).long() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # START VISUAL INPUTS INTEGRATION if pixel_values is not None and image_hidden_states is not None: raise ValueError("You cannot specify both pixel_values and image_hidden_states 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.view(batch_size * num_images, *pixel_values.shape[2:]) # Remove padding images - padding images are full 0. nb_values_per_image = pixel_values.shape[1:].numel() real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image pixel_values = pixel_values[real_images_inds].contiguous() # Handle the vision attention mask if pixel_attention_mask is None: pixel_attention_mask = torch.ones( size=(pixel_values.size(0), pixel_values.size(2), pixel_values.size(3)), dtype=torch.bool, device=pixel_values.device, ) else: # Remove padding images from the mask pixel_attention_mask = pixel_attention_mask.view( batch_size * num_images, *pixel_attention_mask.shape[2:] ) pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous() patches_subgrid = pixel_attention_mask.unfold( dimension=1, size=self.config.vision_config.patch_size, step=self.config.vision_config.patch_size ).unfold(dimension=2, size=self.config.vision_config.patch_size, step=self.config.vision_config.patch_size) patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() # Get sequence from the vision encoder image_hidden_states = self.vision_model( pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, ).last_hidden_state # Modality projection image_hidden_states = self.modality_projection(image_hidden_states) if self.config.use_resampler: image_hidden_states = self.perceiver_resampler( context=image_hidden_states, attention_mask=patch_attention_mask.view(pixel_values.size(0), -1) ) elif image_hidden_states is not None: image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=input_ids.device) if past_key_values is None: # When we generate, we don't want to replace the potential image_token_id that we generated by images # that simply don't exist new_inp = self.inputs_merger( input_ids=input_ids, inputs_embeds=inputs_embeds, image_hidden_states=image_hidden_states, ) inputs_embeds = new_inp["inputs_embeds"] if self.training and self.config.neftune_noise_alpha > 0.0: dims = inputs_embeds.size(1) * inputs_embeds.size(2) mag_norm = self.config.neftune_noise_alpha / math.sqrt(dims) inputs_embeds += torch.zeros_like(inputs_embeds).uniform_(-mag_norm, mag_norm) # embed positions if ( attention_mask is not None and hasattr(self.config, "_flash_attn_2_enabled") and self.config._flash_attn_2_enabled and past_key_values is not None ): is_padding_right = attention_mask[:, -1].sum().item() != batch_size if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if getattr(self.config, "_flash_attn_2_enabled", False): # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, sliding_window=self.config.sliding_window, ) attention_mask[attention_mask == -float("inf")] = torch.finfo(self.dtype).min hidden_states = inputs_embeds if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False # 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 for idx, decoder_layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, position_ids, past_key_value, output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) 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 not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, image_hidden_states] if v is not None ) return VMistralBaseModelOutputWithPast( 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, ) class VMistralForCausalLM(VMistralPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config, vision_model=None): super().__init__(config) self.model = VMistralModel(config, vision_model=vision_model) self.image_token_id = self.config.image_token_id self.in_features = config.hidden_size self.out_features = config.vocab_size self.out_additional_features = config.additional_vocab_size if self.out_additional_features > 0: self.additional_fc = nn.Linear( in_features=self.in_features, out_features=self.out_additional_features, bias=False, ) self.lm_head = nn.Linear(in_features=config.hidden_size, out_features=config.vocab_size, bias=False) if config.freeze_lm_head: self.lm_head.requires_grad_(False) # Initialize weights and apply final processing self.post_init() def enable_input_require_grads(self): """ Enables the gradients for the input embeddings. This is useful for fine-tuning adapter weights while keeping the model weights fixed. """ def get_lowest_module(module): if len(list(module.children())) == 0: # If the module has no children, it is a leaf module (e.g., Linear, Conv2d, etc.) return module else: # Recursively call the function on each child module return get_lowest_module(list(module.children())[0]) def make_inputs_require_grads(module, input, output): output.requires_grad_(True) self._text_require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads) self._vision_require_grads_hook = get_lowest_module(self.model.vision_model).register_forward_hook( make_inputs_require_grads ) 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): if self.out_additional_features > 0: return self.lm_head, self.additional_fc else: return self.lm_head, None 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 DecoupledLinear and DecoupledEmbedding. """ lm_head, additional_fc = self.get_output_embeddings() input_embeddings = self.get_input_embeddings() if getattr(self.config, "tie_word_embeddings", True): lm_head.weight = input_embeddings.weight if additional_fc is not None: assert additional_fc.out_features == input_embeddings.num_additional_embeddings additional_fc.weight = input_embeddings.additional_embedding.weight if hasattr(lm_head, "out_features") and hasattr(input_embeddings, "num_embeddings"): lm_head.out_features = input_embeddings.num_embeddings if additional_fc is not None and hasattr(input_embeddings, "num_additional_embeddings"): additional_fc.out_features = input_embeddings.num_additional_embeddings @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=VMistralCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: 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, pixel_attention_mask: Optional[torch.BoolTensor] = None, image_hidden_states: Optional[torch.FloatTensor] = 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, VMistralCausalLMOutputWithPast]: r""" Args: 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]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, MistralForCausalLM >>> model = MistralForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you conscious? 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 conscious? Can you talk to me?\nI'm not conscious, 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( 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, pixel_attention_mask=pixel_attention_mask, image_hidden_states=image_hidden_states, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) if self.out_additional_features > 0: additional_features = self.additional_fc(hidden_states) logits = torch.cat((logits, additional_features), -1) logits = logits.float() 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(ignore_index=self.image_token_id) 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 VMistralCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, image_hidden_states=outputs.image_hidden_states, ) def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): image_hidden_states = kwargs.pop("image_hidden_states", None) if image_hidden_states is not None: kwargs["pixel_values"] = None inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs) unwanted_kwargs = ["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): return update_model_kwargs_for_generation(outputs, 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 def get_model_tflops_per_batch_per_gpu(self, hparams, data_param, tokenizer, max_num_images, max_num_tokens=None): 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.intermediate_size vision_config = config_vl_model.vision_config # Get vision model blocks infos vision_patch_size = vision_config.patch_size vision_hidden_size = vision_config.embed_dim num_vision_layers = vision_config.num_hidden_layers # The +1 is for the CLS token single_image_vision_encoder_seq_len = ( vision_config.image_size // vision_patch_size ) ** 2 # + 1 # That +1 is only valid for vit/clip vision_exp_factor = vision_config.intermediate_size // vision_hidden_size # Get language blocks infos language_seq_len = max_num_tokens if max_num_tokens is not None else data_param.max_seq_len language_exp_factor = (ffn_inner_size // language_embed_size) if ffn_inner_size is not None else 4 # Get modality projection infos vision_pipeline_output_seq_len = ( self.config.perceiver_config.resampler_n_latents if self.config.use_resampler else single_image_vision_encoder_seq_len ) 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=True, vocab_size=tokenizer.vocab_size, count_backward=True, # Always True regardless of freezing, because gradients are computed for vision adaptor use_grad_checkpointing=hparams.gradient_checkpointing, ) modality_projection_tflops_per_batch_per_gpu = compute_linear_tflops_per_batch_per_gpu( batch_size=hparams.batch_size_per_gpu * max_num_images, seq_len=vision_pipeline_output_seq_len, in_features=vision_hidden_size, out_features=language_embed_size, 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_vision_encoder_seq_len, k_seq_len=single_image_vision_encoder_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_config["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.perceiver_config.resampler_depth, batch_size=hparams.batch_size_per_gpu * max_num_images, q_seq_len=self.config.perceiver_config.resampler_n_latents, vision_embed_seq_len=single_image_vision_encoder_seq_len, q_k_v_input_dim=vision_hidden_size, attention_hidden_size=self.config.perceiver_config.resampler_n_heads * self.config.perceiver_config.resampler_head_dim, ff_exp_factor=4, count_backward=True, use_grad_checkpointing=hparams.gradient_checkpointing, ) tflop_count = ( language_tflops_per_batch_per_gpu + modality_projection_tflops_per_batch_per_gpu + perceiver_tflops_per_batch_per_gpu + vision_tflops_per_batch_per_gpu ) else: tflop_count = ( language_tflops_per_batch_per_gpu + modality_projection_tflops_per_batch_per_gpu + vision_tflops_per_batch_per_gpu ) return tflop_count