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import math
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import warnings
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from huggingface_hub import snapshot_download
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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SequenceClassifierOutputWithPast,
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TokenClassifierOutput,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers import Phi3Config, Phi3Model
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from transformers.cache_utils import Cache, DynamicCache, StaticCache
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class Phi3Transformer(Phi3Model):
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"""
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Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
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We only modified the attention mask
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Args:
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config: Phi3Config
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"""
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Union[Tuple, BaseModelOutputWithPast]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if self.gradient_checkpointing and self.training:
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if use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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return_legacy_cache = False
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if use_cache and not isinstance(past_key_values, Cache):
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return_legacy_cache = True
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if past_key_values is None:
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past_key_values = DynamicCache()
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else:
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past_key_values = DynamicCache.from_legacy_cache(past_key_values)
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logger.warning_once(
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"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
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"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
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"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
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)
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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if attention_mask is not None and attention_mask.dim() == 3:
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dtype = inputs_embeds.dtype
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min_dtype = torch.finfo(dtype).min
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attention_mask = (1 - attention_mask) * min_dtype
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attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype)
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else:
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raise
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hidden_states = inputs_embeds
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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next_decoder_cache = None
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for decoder_layer in self.layers:
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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if self.gradient_checkpointing and self.training:
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layer_outputs = self._gradient_checkpointing_func(
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decoder_layer.__call__,
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hidden_states,
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attention_mask,
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position_ids,
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past_key_values,
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output_attentions,
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use_cache,
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cache_position,
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)
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else:
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_values,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache = layer_outputs[2 if output_attentions else 1]
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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hidden_states = self.norm(hidden_states)
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if return_legacy_cache:
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next_cache = next_cache.to_legacy_cache()
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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