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from typing import List, Optional, Tuple, Union
import torch
from transformers import (
MistralModel,
MistralPreTrainedModel,
MistralForCausalLM,
MistralConfig,
)
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.cache_utils import Cache, DynamicCache
from transformers.models.mistral.modeling_mistral import (
MistralDecoderLayer,
MistralRMSNorm,
MistralAttention,
MistralFlashAttention2,
MistralSdpaAttention,
MistralMLP,
)
from torch import nn
from transformers.utils import logging
from attn_mask_utils import (
_prepare_4d_causal_attention_mask,
_prepare_4d_causal_attention_mask_for_sdpa,
)
logger = logging.get_logger(__name__)
class ModifiedMistralAttention(MistralAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
class ModifiedMistralFlashAttention2(MistralFlashAttention2):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
class ModifiedMistralSdpaAttention(MistralSdpaAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
MISTRAL_ATTENTION_CLASSES = {
"eager": ModifiedMistralAttention,
"flash_attention_2": ModifiedMistralFlashAttention2,
"sdpa": ModifiedMistralSdpaAttention,
}
class ModifiedMistralDecoderLayer(MistralDecoderLayer):
def __init__(self, config: MistralConfig, layer_idx: int):
nn.Module.__init__(self)
self.hidden_size = config.hidden_size
self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](
config, layer_idx
)
self.mlp = MistralMLP(config)
self.input_layernorm = MistralRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
self.post_attention_layernorm = MistralRMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
class MistralBiModel(MistralModel):
def __init__(self, config: MistralConfig):
MistralPreTrainedModel.__init__(self, config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(
config.vocab_size, config.hidden_size, self.padding_idx
)
self.layers = nn.ModuleList(
[
ModifiedMistralDecoderLayer(config, layer_idx)
for layer_idx in range(config.num_hidden_layers)
]
)
self._attn_implementation = config._attn_implementation
self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
# Copied from forward() in transformers.models.mistral.modeling_mistral.MistralModel
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,
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
)
# 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"
)
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
past_key_values_length = 0
if use_cache:
use_legacy_cache = not isinstance(past_key_values, Cache)
if use_legacy_cache:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_key_values_length = past_key_values.get_usable_length(seq_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)
if (
attention_mask is not None
and self._attn_implementation == "flash_attention_2"
and use_cache
):
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 self._attn_implementation == "flash_attention_2":
# 2d mask is passed through the layers
attention_mask = (
attention_mask
if (attention_mask is not None and 0 in attention_mask)
else None
)
elif self._attn_implementation == "sdpa" and not output_attentions:
# The original implementation is by-passed, see attn_mask_utils.py
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
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,
)
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
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 = None
if use_cache:
next_cache = (
next_decoder_cache.to_legacy_cache()
if use_legacy_cache
else next_decoder_cache
)
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,
)
class MistralBiForMNTP(MistralForCausalLM):
def __init__(self, config):
MistralPreTrainedModel.__init__(self, config)
self.model = MistralBiModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
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