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""" PyTorch JapaneseStableLMAlpha model. """ |
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from typing import Optional, Tuple, Union |
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|
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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 CrossEntropyLoss |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPast, |
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CausalLMOutputWithPast, |
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) |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import logging |
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from .configuration_japanese_stablelm_alpha import JapaneseStableLMAlphaConfig |
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logger = logging.get_logger(__name__) |
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class JapaneseStableLMAlphaPreTrainedModel(PreTrainedModel): |
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""" |
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
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models. |
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""" |
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|
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config_class = JapaneseStableLMAlphaConfig |
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base_model_prefix = "transformer" |
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supports_gradient_checkpointing = True |
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_no_split_modules = ["DecoderLayer"] |
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_skip_keys_device_placement = "past_key_values" |
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|
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def _init_weights(self, module): |
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"""Initialize the weights""" |
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if isinstance(module, nn.Linear): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.bias is not None: |
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module.bias.data.zero_() |
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elif isinstance(module, nn.Embedding): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.padding_idx is not None: |
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module.weight.data[module.padding_idx].zero_() |
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elif isinstance(module, nn.LayerNorm): |
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if module.bias is not None: |
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module.bias.data.zero_() |
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if module.weight is not None: |
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module.weight.data.fill_(1.0) |
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|
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def _set_gradient_checkpointing(self, module, value=False): |
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if isinstance(module, JapaneseStableLMAlphaModel): |
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module.gradient_checkpointing = value |
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|
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class JapaneseStableLMAlphaModel(JapaneseStableLMAlphaPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.config = config |
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self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) |
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self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)]) |
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self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
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self.gradient_checkpointing = False |
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self.post_init() |
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|
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def get_input_embeddings(self): |
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return self.embed_in |
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|
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def set_input_embeddings(self, value): |
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self.embed_in = value |
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|
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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attention_mask: Optional[torch.FloatTensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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past_key_values: Optional[Tuple[Tuple[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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) -> Union[Tuple, BaseModelOutputWithPast]: |
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r""" |
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past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): |
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Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. |
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If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that |
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don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all |
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`decoder_input_ids` of shape `(batch_size, sequence_length)`. |
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use_cache (`bool`, *optional*): |
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
|
`past_key_values`). |
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""" |
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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use_cache = use_cache if use_cache is not None else self.config.use_cache |
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|
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if input_ids is not None and inputs_embeds is not None: |
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") |
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elif input_ids is not None: |
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input_shape = input_ids.size() |
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elif inputs_embeds is not None: |
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input_shape = inputs_embeds.size()[:-1] |
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else: |
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raise ValueError("You have to specify either input_ids or inputs_embeds") |
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|
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batch_size, seq_length = input_shape |
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|
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if past_key_values is None: |
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past_length = 0 |
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past_key_values = tuple([None] * self.config.num_hidden_layers) |
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else: |
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past_length = past_key_values[0][0].size(-2) |
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|
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if position_ids is None: |
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device = input_ids.device if input_ids is not None else inputs_embeds.device |
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position_ids = torch.arange(past_length, seq_length + past_length, dtype=torch.long, device=device) |
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length) |
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else: |
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position_ids = position_ids.view(-1, seq_length).long() |
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|
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if attention_mask is not None: |
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assert batch_size > 0, "batch_size has to be defined and > 0" |
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attention_mask = attention_mask.view(batch_size, -1) |
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attention_mask = attention_mask[:, None, None, :] |
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attention_mask = attention_mask.to(dtype=self.dtype) |
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attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min |
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head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) |
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if inputs_embeds is None: |
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inputs_embeds = self.embed_in(input_ids) |
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hidden_states = inputs_embeds |
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|
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if self.gradient_checkpointing and self.training: |
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if use_cache: |
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logger.warning( |
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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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presents = () if use_cache else None |
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all_attentions = () if output_attentions else None |
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all_hidden_states = () if output_hidden_states else None |
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for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)): |
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if output_hidden_states: |
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all_hidden_states = all_hidden_states + (hidden_states,) |
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|
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if self.gradient_checkpointing and self.training: |
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|
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def create_custom_forward(module): |
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def custom_forward(*inputs): |
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|
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return module(*inputs, use_cache, None, output_attentions) |
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return custom_forward |
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outputs = torch.utils.checkpoint.checkpoint( |
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create_custom_forward(layer), |
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hidden_states, |
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attention_mask, |
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position_ids, |
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head_mask[i], |
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) |
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else: |
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outputs = 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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head_mask=head_mask[i], |
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layer_past=layer_past, |
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use_cache=use_cache, |
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output_attentions=output_attentions, |
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) |
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hidden_states = outputs[0] |
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if use_cache is True: |
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presents = presents + (outputs[1],) |
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if output_attentions: |
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all_attentions = all_attentions + (outputs[2 if use_cache else 1],) |
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hidden_states = self.final_layer_norm(hidden_states) |
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if output_hidden_states: |
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all_hidden_states = all_hidden_states + (hidden_states,) |
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if not return_dict: |
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return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) |
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|
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return BaseModelOutputWithPast( |
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last_hidden_state=hidden_states, |
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past_key_values=presents, |
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hidden_states=all_hidden_states, |
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attentions=all_attentions, |
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) |
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|
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class DecoderLayer(nn.Module): |
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def __init__(self, config): |
|
super().__init__() |
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self.use_parallel_residual = config.use_parallel_residual |
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self.input_layernorm = nn.LayerNorm( |
|
config.hidden_size, |
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eps=config.layer_norm_eps, |
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elementwise_affine=False, |
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) |
|
self.post_attention_layernorm = nn.LayerNorm( |
|
config.hidden_size, |
|
eps=config.layer_norm_eps |
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) |
|
self.attention = Attention(config) |
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self.mlp = MLP(config) |
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|
|
def forward( |
|
self, |
|
hidden_states: Optional[torch.FloatTensor], |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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use_cache: Optional[bool] = False, |
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layer_past: Optional[Tuple[torch.Tensor]] = None, |
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output_attentions: Optional[bool] = False, |
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): |
|
attention_layer_outputs = self.attention( |
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self.input_layernorm(hidden_states), |
|
attention_mask=attention_mask, |
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position_ids=position_ids, |
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layer_past=layer_past, |
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head_mask=head_mask, |
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use_cache=use_cache, |
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output_attentions=output_attentions, |
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) |
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attn_output = attention_layer_outputs[0] |
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outputs = attention_layer_outputs[1:] |
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|
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mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) |
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hidden_states = hidden_states + mlp_output + attn_output |
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|
|
if use_cache: |
|
outputs = (hidden_states,) + outputs |
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else: |
|
outputs = (hidden_states,) + outputs[1:] |
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|
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return outputs |
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|
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class MLP(nn.Module): |
|
def __init__(self, config: JapaneseStableLMAlphaConfig): |
|
super().__init__() |
|
hidden_size = config.hidden_size |
|
multiple_of = 256 |
|
ff_dim = int(8 * hidden_size / 3) |
|
intermediate_size = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of) |
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|
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self.packed_input_proj = torch.nn.Linear(hidden_size, 2 * intermediate_size, bias=False) |
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self.out_proj = nn.Linear(intermediate_size, hidden_size, bias=False) |
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self.act = nn.SiLU() |
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|
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
|
ff, ff_gate = self.packed_input_proj(x).chunk(2, dim=-1) |
|
return self.out_proj(ff * self.act(ff_gate)) |
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|
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|
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class RotaryEmbedding(torch.nn.Module): |
|
"""Based on Tri Dao's XPos: https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/layers/rotary.py""" |
|
def __init__( |
|
self, |
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dim: int, |
|
max_position_embeddings: int, |
|
base: int = 10_000, |
|
scale_base: int = 512, |
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device: str = None |
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): |
|
super().__init__() |
|
self.dim = dim |
|
self.seq_len_cached = max_position_embeddings |
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|
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)) |
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self.register_buffer("inv_freq", inv_freq) |
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|
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|
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self.scale_base = scale_base |
|
scale = ( |
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(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim) |
|
if scale_base is not None else None |
|
) |
|
self.register_buffer("scale", scale) |
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|
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t = torch.arange(self.seq_len_cached, device=device, dtype=torch.float32) |
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freqs = torch.outer(t, self.inv_freq) |
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|
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seq_range = torch.arange(self.seq_len_cached, dtype=self.scale.dtype, device=self.scale.device) |
|
power = (seq_range - self.seq_len_cached // 2) / self.scale_base |
|
scale_cached = self.scale.to(device=power.device) ** power.unsqueeze(-1) |
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|
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self.register_buffer("cos_cached", torch.cos(freqs) * scale_cached, persistent=False) |
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self.register_buffer("sin_cached", torch.sin(freqs) * scale_cached, persistent=False) |
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self.register_buffer("cos_k_cached", torch.cos(freqs) / scale_cached, persistent=False) |
|
self.register_buffer("sin_k_cached", torch.sin(freqs) / scale_cached, persistent=False) |
|
|
|
def forward(self, x, seq_len=None): |
|
if seq_len > self.seq_len_cached: |
|
self.seq_len_cached = seq_len |
|
t = torch.arange(seq_len, device=x.device, dtype=torch.float32) |
|
freqs = torch.outer(t, self.inv_freq) |
|
freqs = torch.cat((freqs, freqs), dim=-1) |
|
seq_range = torch.arange(self.seq_len_cached, dtype=self.scale.dtype, device=self.scale.device) |
|
power = (seq_range - self.seq_len_cached // 2) / self.scale_base |
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scale_cached = self.scale.to(device=power.device) ** power.unsqueeze(-1) |
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scale_cached = torch.cat((scale_cached, scale_cached), dim=-1) |
|
self.register_buffer("cos_cached", torch.cos(freqs) * scale_cached, persistent=False) |
|
self.register_buffer("sin_cached", torch.sin(freqs) * scale_cached, persistent=False) |
|
self.register_buffer("cos_k_cached", torch.cos(freqs) / scale_cached, persistent=False) |
|
self.register_buffer("sin_k_cached", torch.sin(freqs) / scale_cached, persistent=False) |
|
return ( |
|
self.cos_cached[:seq_len, ...], |
|
self.sin_cached[:seq_len, ...], |
|
self.cos_k_cached[:seq_len, ...], |
|
self.sin_k_cached[:seq_len, ...], |
|
) |
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|
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def rotate_half(x): |
|
x1, x2 = x.chunk(2, dim=-1) |
|
return torch.cat((-x2, x1), dim=-1) |
|
|
|
|
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, cos_k=None, sin_k=None): |
|
""" |
|
q, k: [bs, num_heads, seq_len, rot_dim] |
|
cos, sin: [seq_len, rot_dim / 2] |
|
position_ids: [bs, seq_len] |
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""" |
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|
|
import einops |
|
cos = einops.repeat(cos, 's r -> s (2 r)') |
|
sin = einops.repeat(sin, 's r -> s (2 r)') |
|
cos_k = einops.repeat(cos_k, 's r -> s (2 r)') |
|
sin_k = einops.repeat(sin_k, 's r -> s (2 r)') |
|
cos = cos[position_ids].unsqueeze(1) |
|
sin = sin[position_ids].unsqueeze(1) |
|
cos_k = cos_k[position_ids].unsqueeze(1) |
|
sin_k = sin_k[position_ids].unsqueeze(1) |
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|
|
q_embed = (q * cos) + (rotate_half(q) * sin) |
|
k_embed = (k * cos_k) + (rotate_half(k) * sin_k) |
|
return q_embed, k_embed |
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|
|
|
class Attention(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.num_attention_heads = config.num_attention_heads |
|
self.hidden_size = config.hidden_size |
|
if self.hidden_size % self.num_attention_heads != 0: |
|
raise ValueError( |
|
"The hidden size is not divisble by the number of attention heads! Make sure to update them" |
|
) |
|
self.head_size = self.hidden_size // self.num_attention_heads |
|
|
|
max_positions = config.max_position_embeddings |
|
self.register_buffer( |
|
"bias", |
|
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( |
|
1, 1, max_positions, max_positions |
|
), |
|
persistent=False, |
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) |
|
self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) |
|
|
|
self.rotary_ndims = int(self.head_size * config.rotary_pct) |
|
self.rotary_emb = RotaryEmbedding( |
|
self.rotary_ndims, |
|
max_position_embeddings=config.max_position_embeddings, |
|
base=config.rotary_emb_base, |
|
scale_base=config.rotary_scale_base, |
|
) |
|
|
|
self.register_buffer( |
|
"norm_factor", |
|
torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to(torch.get_default_dtype()), |
|
persistent=False, |
|
) |
|
|
|
self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False) |
|
self.dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.FloatTensor, |
|
attention_mask: torch.FloatTensor, |
|
position_ids: torch.LongTensor, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
layer_past: Optional[Tuple[torch.Tensor]] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
): |
|
has_layer_past = layer_past is not None |
|
|
|
|
|
|
|
|
|
qkv = self.query_key_value(hidden_states) |
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|
|
|
|
|
|
new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size) |
|
qkv = qkv.view(*new_qkv_shape) |
|
|
|
|
|
query = qkv[..., : self.head_size].permute(0, 2, 1, 3) |
|
key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3) |
|
value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3) |
|
|
|
|
|
query_rot = query[..., : self.rotary_ndims] |
|
query_pass = query[..., self.rotary_ndims :] |
|
key_rot = key[..., : self.rotary_ndims] |
|
key_pass = key[..., self.rotary_ndims :] |
|
|
|
|
|
kv_seq_len = key.shape[-2] |
|
if has_layer_past: |
|
kv_seq_len += layer_past[0].shape[-2] |
|
|
|
|
|
|
|
cos, sin, cos_k, sin_k = self.rotary_emb(value, seq_len=kv_seq_len) |
|
query, key = apply_rotary_pos_emb( |
|
query_rot, key_rot, cos, sin, position_ids, cos_k=cos_k, sin_k=sin_k) |
|
|
|
query = torch.cat((query, query_pass), dim=-1) |
|
key = torch.cat((key, key_pass), dim=-1) |
|
|
|
|
|
if has_layer_past: |
|
past_key = layer_past[0] |
|
past_value = layer_past[1] |
|
key = torch.cat((past_key, key), dim=-2) |
|
value = torch.cat((past_value, value), dim=-2) |
|
present = (key, value) if use_cache else None |
|
|
|
|
|
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) |
|
|
|
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|
|
|
attn_output = attn_output.permute(0, 2, 1, 3).contiguous() |
|
attn_output = attn_output.view(attn_output.size(0), attn_output.size(1), self.num_attention_heads * self.head_size) |
|
|
|
attn_output = self.dense(attn_output) |
|
|
|
outputs = (attn_output, present) |
|
if output_attentions: |
|
outputs += (attn_weights,) |
|
|
|
return outputs |
|
|
|
def _attn(self, query, key, value, attention_mask=None, head_mask=None): |
|
|
|
|
|
|
|
batch_size, num_attention_heads, query_length, attn_head_size = query.size() |
|
key_length = key.size(-2) |
|
|
|
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] |
|
|
|
query = query.view(batch_size * num_attention_heads, query_length, attn_head_size) |
|
key = key.view(batch_size * num_attention_heads, key_length, attn_head_size) |
|
attn_scores = torch.zeros( |
|
batch_size * num_attention_heads, |
|
query_length, |
|
key_length, |
|
dtype=query.dtype, |
|
device=key.device, |
|
) |
|
attn_scores = torch.baddbmm( |
|
attn_scores, |
|
query, |
|
key.transpose(1, 2), |
|
beta=1.0, |
|
alpha=(torch.tensor(1.0, dtype=self.norm_factor.dtype, device=self.norm_factor.device) / self.norm_factor), |
|
) |
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attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length) |
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|
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mask_value = torch.finfo(attn_scores.dtype).min |
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|
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mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype, device=attn_scores.device) |
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attn_scores = torch.where(causal_mask, attn_scores, mask_value) |
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|
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if attention_mask is not None: |
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|
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attn_scores = attn_scores + attention_mask |
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|
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attn_weights = nn.functional.softmax(attn_scores, dim=-1, dtype=torch.float32).type_as(value) |
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|
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if head_mask is not None: |
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attn_weights = attn_weights * head_mask |
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attn_output = torch.matmul(attn_weights, value) |
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return attn_output, attn_weights |
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|
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|
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def attention_mask_func(attention_scores, ltor_mask): |
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attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min) |
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return attention_scores |
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|
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class JapaneseStableLMAlphaForCausalLM(JapaneseStableLMAlphaPreTrainedModel): |
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_tied_weights_keys = ["embed_out.weight"] |
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|
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def __init__(self, config): |
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super().__init__(config) |
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self.transformer = JapaneseStableLMAlphaModel(config) |
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self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
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|
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self.post_init() |
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|
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def get_output_embeddings(self): |
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return self.embed_out |
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|
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def set_output_embeddings(self, new_embeddings): |
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self.embed_out = new_embeddings |
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|
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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attention_mask: Optional[torch.FloatTensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, |
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labels: Optional[torch.LongTensor] = 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, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
r""" |
|
Example: |
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|
|
```python |
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>>> import torch |
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>>> from transformers import LlamaTokenizer, JapaneseStableLMAlphaForCausalLM, JapaneseStableLMAlphaConfig |
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|
|
>>> tokenizer = LlamaTokenizer.from_pretrained("novelai/nerdstash-tokenizer-v1") |
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>>> config = JapaneseStableLMAlphaConfig.from_pretrained("stabilityai/stablelm-ja-base-alpha-7b") |
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>>> config.is_decoder = True |
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>>> model = JapaneseStableLMAlphaForCausalLM.from_pretrained("stabilityai/stablelm-ja-base-alpha-7b", config=config, trust_remote_code=True) |
|
|
|
>>> inputs = tokenizer("日本語の美しいところは、", return_tensors="pt") |
|
>>> outputs = model(**inputs) |
|
|
|
>>> prediction_logits = outputs.logits |
|
```""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
outputs = self.transformer( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
past_key_values=past_key_values, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
lm_logits = self.embed_out(hidden_states) |
|
|
|
lm_loss = None |
|
if labels is not None: |
|
|
|
labels = labels.to(lm_logits.device) |
|
|
|
shift_logits = lm_logits[:, :-1, :].contiguous() |
|
labels = labels[:, 1:].contiguous() |
|
loss_fct = CrossEntropyLoss() |
|
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) |
|
|
|
if not return_dict: |
|
output = (lm_logits,) + outputs[1:] |
|
return ((lm_loss,) + output) if lm_loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=lm_loss, |
|
logits=lm_logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
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) |
|
|
|
def prepare_inputs_for_generation( |
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs |
|
): |
|
input_shape = input_ids.shape |
|
|
|
|
|
if past_key_values and past_key_values[0] is not None: |
|
input_ids = input_ids[:, -1:] |
|
|
|
position_ids = kwargs.get("position_ids", None) |
|
if attention_mask is not None and position_ids is None: |
|
|
|
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) |
|
|
|
|
|
if attention_mask is None: |
|
attention_mask = input_ids.new_ones(input_shape) |
|
|
|
|
|
if inputs_embeds is not None and past_key_values is None: |
|
model_inputs = {"inputs_embeds": inputs_embeds} |
|
else: |
|
model_inputs = {"input_ids": input_ids} |
|
|
|
model_inputs.update( |
|
{ |
|
"attention_mask": attention_mask, |
|
"past_key_values": past_key_values, |
|
"position_ids": position_ids, |
|
} |
|
) |
|
|
|
return model_inputs |
|
|
|
def _reorder_cache(self, past_key_values, beam_idx): |
|
reordered_past = () |
|
for layer_past in past_key_values: |
|
reordered_past += ( |
|
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:], |
|
) |
|
return reordered_past |