# coding=utf-8 # Copyright 2023 EleutherAI The HuggingFace Inc. team. and JIANG.ai 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 GPTJiang model.""" from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss import torch.nn.functional as F from transformers.activations import ACT2FN from transformers.file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings, ) from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from .configuration_gpt_jiang import GPTJiangConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "GPTJiangConfig" GPT_JIANG_PRETRAINED_MODEL_ARCHIVE_LIST = [] class RMSNorm(torch.nn.Module): def __init__(self, dim: int, eps: float=1e-5): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): output = self._norm(x.float()).type_as(x) return output * self.weight class GPTJiangPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTJiangConfig base_model_prefix = "gpt_jiang" supports_gradient_checkpointing = True _no_split_modules = ["GPTJiangLayer"] def _init_weights(self, module): """Initialize the weights""" if isinstance(module, GatedLinear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.fill_(1.0) elif 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, RMSNorm): # module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, GPTJiangModel): module.gradient_checkpointing = value class GPTJiangAttention(nn.Module): def __init__(self, config): super().__init__() self.max_position_embeddings = config.max_position_embeddings self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.num_attention_heads self.rotary_ndims = int(self.head_size * config.rotary_pct) self.rotary_emb = RotaryEmbedding( self.rotary_ndims, config.max_position_embeddings, base=config.rotary_emb_base ) self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size) self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.causal_mask_cached = None def causal_mask(self, x, seq_len): if self.causal_mask_cached is None or seq_len > self.causal_mask_cached.shape[2]: cache_size = max(self.max_position_embeddings, seq_len) self.causal_mask_cached = torch.ones( cache_size, cache_size, dtype=torch.bool ).tril().view(1, 1, cache_size, cache_size) return self.causal_mask_cached[:, :, :seq_len, :seq_len].to(x.device) def forward( self, hidden_states, attention_mask, head_mask=None, layer_past=None, use_cache=False, output_attentions=False ): has_layer_past = layer_past is not None # 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 :] # Compute token offset for rotary embeddings (when decoding) seq_len = key.shape[-2] offset = 0 if has_layer_past: offset = layer_past[0].shape[-2] seq_len += offset cos, sin = self.rotary_emb(value, seq_len=seq_len) query, key = apply_rotary_pos_emb(query, key, cos, sin, offset=offset) # query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, offset=offset) # query = torch.cat((query, query_pass), dim=-1) # key = torch.cat((key, key_pass), dim=-1) # Cache QKV values 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 query = query.type_as(hidden_states) key = key.type_as(hidden_states) value = value.type_as(hidden_states) if output_attentions: # Use custom attention method to get attn_weights attn_output, attn_weights = self._attn( query, key, value, attention_mask=attention_mask, head_mask=head_mask ) else: if layer_past is not None and attention_mask is None: # Must calculate attention_mask, or scaled_dot_product_attention will wrong batch_size = query.size(0) attention_mask = torch.ones(batch_size, seq_len, dtype=torch.bool)[:, None, None, :] if attention_mask is not None: attn_mask = attention_mask.transpose(2, 3) * attention_mask query_length = query.size(-2) key_length = key.size(-2) if query_length > 1: causal_mask = self.causal_mask(query, seq_len) causal_mask = causal_mask[:, :, -query_length:, :] attn_mask = (attn_mask[:, :, -query_length:, :] * causal_mask).to(torch.bool) else: attn_mask = attn_mask[:, :, -query_length:, :].to(torch.bool) attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=attn_mask, is_causal=False ) else: attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=None, is_causal=True ) attn_weights = None # Reshape outputs # attn_output == [bs, num_attention_heads, seq_len, attn_head_size] attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size) # tensor [bs, seq_len, num_attention_heads * attn_head_size] attn_output = self.dense(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs @classmethod def _calculate_attn_output_loss(self, attn_output): bs, num_attention_heads, seq_len, attn_head_size = attn_output.size() attn_output_out = attn_output.view(bs, num_attention_heads, -1) attn_output_out_norm = attn_output_out / torch.max( attn_output_out.norm(dim=2, keepdim=True), 1e-8 * torch.ones_like(attn_output_out) ) sim = torch.bmm(attn_output_out_norm, attn_output_out_norm.permute(0, 2, 1)) attn_output_loss = sim.sum() / sim.numel() return attn_output_loss @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 create_upper_triangular_matrix(self, q, k): size = max(q, k) # 创建一个单位矩阵 identity = torch.eye(size) # 创建一个矩阵，其中每个元素都是它的行索引 row_indices = torch.arange(size).view(-1, 1).expand(size, size) # 创建一个矩阵，其中每个元素都是它的列索引 col_indices = torch.arange(size).view(1, -1).expand(size, size) # 比较行和列索引，如果行索引小于列索引，则0，否则1 upper_triangular_matrix = torch.where(row_indices < col_indices, 0, 1) return upper_triangular_matrix[-q:, -k:].to(torch.bool) 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) # 避免使用tril # causal_mask = torch.ones( # query_length, key_length, # dtype=torch.bool, # device=query.device # ).tril( # diagonal=key_length - query_length # ).view(1, 1, query_length, key_length) causal_mask = self.create_upper_triangular_matrix( query_length, key_length ).view(1, 1, query_length, key_length).to(query.device) 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, ) norm_factor = self.head_size ** 0.5 attn_scores = torch.baddbmm( attn_scores, query, key.transpose(1, 2), beta=1.0, alpha=(torch.tensor(1.0, dtype=query.dtype, device=query.device) / norm_factor), ) attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length) mask_value = torch.finfo(attn_scores.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device) attn_scores = torch.where(causal_mask, attn_scores, mask_value) if attention_mask is not None: # Apply the attention mask attn_scores = attn_scores + attention_mask attn_weights = nn.functional.softmax(attn_scores.float(), dim=-1).type_as(value) # 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 class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings, base=10000, device=None): super().__init__() inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) self.register_buffer("inv_freq", inv_freq) # Build here to make `torch.jit.trace` work. self.max_seq_len_cached = max_position_embeddings t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.cos_cached = emb.cos()[None, None, :, :] self.sin_cached = emb.sin()[None, None, :, :] def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. if seq_len > self.max_seq_len_cached: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self.cos_cached = emb.cos()[None, None, :, :] self.sin_cached = emb.sin()[None, None, :, :] return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device) 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) def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0): cos = cos[..., offset : q.shape[-2] + offset, :] sin = sin[..., offset : q.shape[-2] + offset, :] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class GatedLinear(nn.Linear): pass class GPTJiangMLP(nn.Module): def __init__(self, config): super().__init__() self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias) self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias) self.gated = config.gated if config.gated: self.dense_h_to_4h_gate = GatedLinear(config.hidden_size, config.intermediate_size, bias=config.mlp_bias) self.act = ACT2FN[config.hidden_act] def forward(self, hidden_states): if self.gated: # pseudocode: # g is activation function, W and V are weights, * is element-wised product # x = g(Wx) * Vx hidden_states = self.act(self.dense_h_to_4h(hidden_states)) * self.dense_h_to_4h_gate(hidden_states) else: # pseudocode: # x = g(Wx) hidden_states = self.act(self.dense_h_to_4h(hidden_states)) hidden_states = self.dense_4h_to_h(hidden_states) return hidden_states class GPTJiangLayer(nn.Module): def __init__(self, config): super().__init__() self.use_parallel_residual = config.use_parallel_residual self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps) self.attention = GPTJiangAttention(config) self.mlp = GPTJiangMLP(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, use_cache=False, layer_past=None, output_attentions=False, ): attention_layer_outputs = self.attention( self.input_layernorm(hidden_states), attention_mask=attention_mask, layer_past=layer_past, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights), (attentions_output_loss) outputs = attention_layer_outputs[1:] # Default True in multiple models, faster if self.use_parallel_residual: # pseudocode: # x = x + attn(ln1(x)) + mlp(ln2(x)) mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) hidden_states = mlp_output + attn_output + hidden_states else: # pseudocode: # x = x + attn(ln1(x)) # x = x + mlp(ln2(x)) attn_output = attn_output + hidden_states mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) hidden_states = mlp_output + attn_output if use_cache: outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights), (attentions_output_loss) else: outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights), (attentions_output_loss) return outputs GPT_JIANG_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 ([`~GPTJiangConfig`]): 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_JIANG_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`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) 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**. [What are attention masks?](../glossary#attention-mask) 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. 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. """ @add_start_docstrings( "The bare GPTJiang Model transformer outputting raw hidden-states without any specific head on top.", GPT_JIANG_START_DOCSTRING, ) class GPTJiangModel(GPTJiangPreTrainedModel): 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([GPTJiangLayer(config) for _ in range(config.num_hidden_layers)]) self.final_layer_norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps) self.gradient_checkpointing = False # 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_JIANG_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[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, ) -> Union[Tuple, BaseModelOutputWithPast]: r""" 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)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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 = 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 not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape if past_key_values is None: past_key_values = tuple([None] * self.config.num_hidden_layers) # Attention mask. if attention_mask is not None: assert batch_size > 0, "batch_size has to be defined and > 0" attention_mask = attention_mask.view(batch_size, -1) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask = attention_mask[:, None, None, :] # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility # attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min # 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) if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids) hidden_states = inputs_embeds if self.gradient_checkpointing and self.training: if use_cache: logger.warning( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False presents = () if use_cache else None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): # None for layer_past return module(*inputs, use_cache, None, output_attentions) return custom_forward outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer), hidden_states, attention_mask, head_mask[i], ) else: outputs = layer( hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (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,) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) ret = BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) return ret @add_start_docstrings( """GPTJiang Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_JIANG_START_DOCSTRING ) class GPTJiangForCausalLM(GPTJiangPreTrainedModel): _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.gpt_kdf = GPTJiangModel(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_JIANG_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, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[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, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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]`. 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`). Returns: Example: ```python >>> from transformers import AutoTokenizer, GPTJiangForCausalLM, GPTJiangConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") >>> config = GPTJiangConfig.from_pretrained("EleutherAI/gpt-neox-20b") >>> config.is_decoder = True >>> model = GPTJiangForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config) >>> inputs = tokenizer("Hello, my dog is cute", 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_kdf( input_ids, attention_mask=attention_mask, 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 attn_output_loss = None if labels is not None: # 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 ret = CausalLMOutputWithPast( loss=lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) return ret def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past_key_values and past_key_values[0] is not None: input_ids = input_ids[:, -1:] return { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, } 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