# coding=utf-8 # Copyright 2022 ABEJA, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch GPTNeoX model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import Tensor, nn from torch.nn import CrossEntropyLoss from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, StaticCache from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS from ...modeling_utils import PreTrainedModel from ...utils import logging from .configuration_gpt_neox_japanese import GPTNeoXJapaneseConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "abeja/gpt-neox-japanese-2.7b" _CONFIG_FOR_DOC = "GPTNeoXJapaneseConfig" class GPTNeoXJapanesePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTNeoXJapaneseConfig base_model_prefix = "gpt_neox_japanese" _no_split_modules = ["GPTNeoXJapaneseLayer"] _skip_keys_device_placement = "past_key_values" _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class GPTNeoXJapaneseAttention(nn.Module): def __init__(self, config, use_bias=False, layer_idx=None): super().__init__() self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.num_attention_heads if layer_idx is None: logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.layer_idx = layer_idx self.rotary_ndims = int(self.head_size * config.rotary_pct) self.rope_theta = config.rotary_emb_base self.rotary_emb = GPTNeoXJapaneseRotaryEmbedding(config=config) self.attention_dropout = nn.Dropout(config.attention_dropout) self.norm_factor = math.sqrt(self.head_size) self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=False) self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False) # Activate bias if the last layer self.use_bias = use_bias self.dense_bias = nn.Parameter(torch.zeros(config.hidden_size)) if use_bias else None def forward( self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, position_ids: torch.LongTensor, head_mask: Optional[torch.FloatTensor] = None, layer_past: Optional[Cache] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): # Compute QKV # Attention heads [batch, seq_len, hidden_size] # --> [batch, seq_len, (np * 3 * head_size)] qkv = self.query_key_value(hidden_states) # [batch, seq_len, (num_heads * 3 * head_size)] # --> [batch, seq_len, num_heads, 3 * head_size] new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size) qkv = qkv.view(*new_qkv_shape) # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size] 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) # Compute rotary embeddings on rotary_ndims query_rot = query[..., : self.rotary_ndims] query_pass = query[..., self.rotary_ndims :] key_rot = key[..., : self.rotary_ndims] key_pass = key[..., self.rotary_ndims :] if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " "removed and `position_embeddings` will be mandatory." ) cos, sin = self.rotary_emb(value, position_ids) else: cos, sin = position_embeddings query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) query = torch.cat((query, query_pass), dim=-1) key = torch.cat((key, key_pass), dim=-1) # Cache QKV values if layer_past is not None: cache_kwargs = { "sin": sin, "cos": cos, "partial_rotation_size": self.rotary_ndims, "cache_position": cache_position, } key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) # Compute attention attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) # Reshape outputs attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size) attn_output = self.dense(attn_output) outputs = (attn_output, layer_past) if output_attentions: outputs += (attn_weights,) return outputs, self.dense_bias @classmethod def _split_heads(cls, tensor, num_attention_heads, attn_head_size): """ Splits hidden dim into attn_head_size and num_attention_heads """ # tensor: [bs, seq_len, hidden_size] new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size) # -> [bs, seq_len, num_attention_heads, attn_head_size] tensor = tensor.view(new_shape) # -> [bs, num_attention_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3) return tensor @classmethod def _merge_heads(cls, tensor, num_attention_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden dim """ # tensor [bs, num_attention_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3).contiguous() # -> [bs, seq_len, num_attention_heads, attn_head_size] tensor = tensor.view(tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size) # -> [bs, seq_len, hidden_size] return tensor def _attn(self, query, key, value, attention_mask=None, head_mask=None): # q, k, v: [bs, num_attention_heads, seq_len, attn_head_size] # compute causal mask from causal mask buffer batch_size, num_attention_heads, query_length, attn_head_size = query.size() key_length = key.size(-2) 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) # [batch_size * num_heads, q_length, kv_length] attn_scores = torch.zeros( batch_size * num_attention_heads, query_length, key_length, dtype=query.dtype, device=key.device, ) attention_scores = torch.baddbmm( attn_scores, query, key.transpose(1, 2), beta=1.0, alpha=1.0 / self.norm_factor, ) attention_scores = attention_scores.view(batch_size, num_attention_heads, query_length, -1) if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key.shape[-2]] attention_scores = attention_scores + causal_mask attn_weights = nn.functional.softmax(attention_scores, dim=-1) attn_weights = self.attention_dropout(attn_weights) attn_weights = attn_weights.to(value.dtype) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights # Copied from transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXRotaryEmbedding with GPTNeoX->GPTNeoXJapanese class GPTNeoXJapaneseRotaryEmbedding(nn.Module): def __init__( self, dim=None, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0, rope_type="default", config: Optional[GPTNeoXJapaneseConfig] = None, ): super().__init__() # TODO (joao): remove the `if` below, only used for BC self.rope_kwargs = {} if config is None: logger.warning_once( "`GPTNeoXJapaneseRotaryEmbedding` can now be fully parameterized by passing the model config through the " "`config` argument. All other arguments will be removed in v4.46" ) self.rope_kwargs = { "rope_type": rope_type, "factor": scaling_factor, "dim": dim, "base": base, "max_position_embeddings": max_position_embeddings, } self.rope_type = rope_type self.max_seq_len_cached = max_position_embeddings self.original_max_seq_len = max_position_embeddings else: # BC: "rope_type" was originally "type" if config.rope_scaling is not None: self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq def _dynamic_frequency_update(self, position_ids, device): """ dynamic RoPE layers should recompute `inv_freq` in the following situations: 1 - growing beyond the cached sequence length (allow scaling) 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) """ seq_len = torch.max(position_ids) + 1 if seq_len > self.max_seq_len_cached: # growth inv_freq, self.attention_scaling = self.rope_init_fn( self.config, device, seq_len=seq_len, **self.rope_kwargs ) self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation self.max_seq_len_cached = seq_len if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) self.max_seq_len_cached = self.original_max_seq_len @torch.no_grad() def forward(self, x, position_ids): if "dynamic" in self.rope_type: self._dynamic_frequency_update(position_ids, device=x.device) # Core RoPE block inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() # Force float32 (see https://github.com/huggingface/transformers/pull/29285) device_type = x.device.type device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() sin = emb.sin() # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention cos = cos * self.attention_scaling sin = sin * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) 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=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def bias_dropout_add(x: Tensor, bias: Tensor, residual: Optional[Tensor], prob: float, training: bool) -> Tensor: """add bias to x, apply dropout and residual connection Args: x (Tensor): main path of output bias (Tensor): None or attn_bias of the last attention layer residual (Optional[Tensor]): residual value prob (float): dropout probability training (bool): whether in training mode or not Returns: Tensor: dropout(x + bias) + residual """ if bias is not None: x = x + bias out = torch.nn.functional.dropout(x, p=prob, training=training) if residual is not None: out = residual + out return out class GPTNeoXJapaneseMLP(nn.Module): def __init__(self, config): super().__init__() intermediate_size = int(config.hidden_size * config.intermediate_multiple_size) self.dense_h_to_4h = nn.Linear(config.hidden_size, intermediate_size, bias=False) # Project back to h. self.dense_4h_to_h = nn.Linear(intermediate_size, config.hidden_size, bias=False) self.act = ACT2FN[config.hidden_act] def forward(self, hidden_states): intermediate = self.dense_h_to_4h(hidden_states) intermediate = self.act(intermediate) output = self.dense_4h_to_h(intermediate) return output class GPTNeoXJapaneseLayer(nn.Module): def __init__(self, config, layer_number): super().__init__() self.layer_number = layer_number self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # activate bias only last layer self.attention = GPTNeoXJapaneseAttention( config=config, use_bias=layer_number == config.num_hidden_layers - 1, layer_idx=layer_number ) self.mlp = GPTNeoXJapaneseMLP(config) self.hidden_dropout = config.hidden_dropout def forward( self, hidden_states: Optional[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, layer_past: Optional[Cache] = None, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): residual = hidden_states ln_out = self.input_layernorm(hidden_states) attention_layer_outputs, attn_bias = self.attention( ln_out, attention_mask=attention_mask, layer_past=layer_past, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, position_ids=position_ids, cache_position=cache_position, position_embeddings=position_embeddings, ) attn_output = attention_layer_outputs[0] # output_attn: a, present, (attentions) outputs = attention_layer_outputs[1:] # attn_output = (atten_output + bias) + residual attn_output = bias_dropout_add( attn_output, bias=attn_bias.expand_as(residual) if attn_bias is not None else attn_bias, residual=residual, prob=self.hidden_dropout, training=self.training, ) mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) # attn_output = (mlp_output + mlp_bias) + atten_output attn_output = bias_dropout_add( mlp_output, bias=None, residual=attn_output, prob=self.hidden_dropout, training=self.training ) if use_cache: outputs = (attn_output,) + outputs else: outputs = (attn_output,) + outputs[1:] return outputs # hidden_states, present, (attentions) GPT_NEOX_JAPANESE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`~GPTNeoXJapaneseConfig`]): 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. """ GPT_NEOX_JAPANESE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. attention_mask (`torch.FloatTensor` of shape `({0})`, *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**. token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, 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. past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance; - 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)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. 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 [`~file_utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. """ @add_start_docstrings( "The bare GPTNeoXJapanese Model transformer outputting raw hidden-states without any specific head on top.", GPT_NEOX_JAPANESE_START_DOCSTRING, ) class GPTNeoXJapaneseModel(GPTNeoXJapanesePreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList( [GPTNeoXJapaneseLayer(config=config, layer_number=i) for i in range(config.num_hidden_layers)] ) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.rotary_emb = GPTNeoXJapaneseRotaryEmbedding(config=config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_in def set_input_embeddings(self, value): self.embed_in = value @add_start_docstrings_to_model_forward(GPT_NEOX_JAPANESE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, GPTNeoXJapaneseModel >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b") >>> model = GPTNeoXJapaneseModel.from_pretrained("abeja/gpt-neox-japanese-2.7b") >>> inputs = tokenizer("日本語のGPT-neoxがHugging Faceで使えます😀", return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state ``` """ 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 use_cache = use_cache if use_cache is not None else self.config.use_cache if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids) # kept for BC (non `Cache` `past_key_values` inputs) return_legacy_cache = False if use_cache and not isinstance(past_key_values, Cache): return_legacy_cache = True if past_key_values is None: past_key_values = DynamicCache() else: past_key_values = DynamicCache.from_legacy_cache(past_key_values) logger.warning_once( "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" ) seq_length = inputs_embeds.shape[1] if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=inputs_embeds.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) next_decoder_cache = None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], layer_past=past_key_values, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, position_embeddings=position_embeddings, ) hidden_states = outputs[0] if use_cache is True: next_decoder_cache = outputs[1] if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.final_layer_norm(hidden_states) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) next_cache = next_decoder_cache if use_cache else None if return_legacy_cache: next_cache = next_cache.to_legacy_cache() if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_attentions, ) # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training, ): return None dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if using_static_cache: target_length = past_key_values.get_max_cache_shape() else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type == "cuda" and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 min_dtype = torch.finfo(dtype).min causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) return causal_mask @staticmethod # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, device: torch.device, cache_position: torch.Tensor, batch_size: int, **kwargs, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. device (`torch.device`): The device to plcae the 4D attention mask on. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @add_start_docstrings( """GPTNeoXJapanese Model with a `language modeling` head on top for Classifier Model fine-tuning.""", GPT_NEOX_JAPANESE_START_DOCSTRING, ) class GPTNeoXJapaneseForCausalLM(GPTNeoXJapanesePreTrainedModel, GenerationMixin): _tied_weights_keys = ["embed_out.weight"] def __init__(self, config): super().__init__(config) self.config = config self.gpt_neox_japanese = GPTNeoXJapaneseModel(config) self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.embed_out def set_output_embeddings(self, new_embeddings): self.embed_out = new_embeddings @add_start_docstrings_to_model_forward(GPT_NEOX_JAPANESE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Union[Cache, Tuple[Tuple[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, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b") >>> config = GPTNeoXJapaneseConfig.from_pretrained("abeja/gpt-neox-japanese-2.7b") >>> config.is_decoder = True >>> model = GPTNeoXJapaneseForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b", config=config) >>> inputs = tokenizer("日本語のGPT-neoxがHugging Faceで使えます😀", 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.gpt_neox_japanese( 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, cache_position=cache_position, ) hidden_states = outputs[0] lm_logits = self.embed_out(hidden_states) lm_loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # we are doing next-token prediction; shift prediction scores and input ids by one 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, ) 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.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past