""" PyTorch GPTNeoX model.""" import torch import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss 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 transformers.models.gpt_neox.configuration_gpt_neox import GPTNeoXConfig from transformers.generation import GenerationMixin logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "gpt-neox-20b" _CONFIG_FOR_DOC = "GPTNeoXConfig" _TOKENIZER_FOR_DOC = "GPTNeoXTokenizerFast" class GPTNeoXPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTNeoXConfig base_model_prefix = "gpt_neox" supports_gradient_checkpointing = True _no_split_modules = ["GPTNeoXLayer"] 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) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, GPTNeoXModel): module.gradient_checkpointing = value class GPTNeoXAttention(nn.Module): def __init__(self, config): 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 self.rotary_ndims = int(self.head_size * config.rotary_pct) max_positions = config.max_position_embeddings self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view( 1, 1, max_positions, max_positions ), ) self.register_buffer("masked_bias", torch.tensor(-1e9)) self.rotary_emb = RotaryEmbedding(self.rotary_ndims, base=config.rotary_emb_base) self.norm_factor = torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to(torch.get_default_dtype()) self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size) self.dense = nn.Linear(config.hidden_size, config.hidden_size) 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_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 = None if use_cache else (key, value) # 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, present) if output_attentions: outputs += (attn_weights,) return outputs @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) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool() 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.einsum("bik,bjk->bij", query, key) / self.norm_factor attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length) attn_scores = torch.where(causal_mask, attn_scores, self.masked_bias.to(attn_scores.dtype)) if attention_mask is not None: # Apply the attention mask attn_scores = attn_scores + attention_mask attn_weights = nn.functional.softmax(attn_scores, dim=-1) 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 def attention_mask_func(attention_scores, ltor_mask): attention_scores.masked_fill_(~ltor_mask, -10000.0) return attention_scores class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, 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) self.max_seq_len_cached = None self.cos_cached = None self.sin_cached = None def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if self.max_seq_len_cached is None or (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, ...], self.sin_cached[:seq_len, ...] 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 GPTNeoXMLP(nn.Module): def __init__(self, config): super().__init__() self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size) self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size) self.act = ACT2FN[config.hidden_act] def forward(self, hidden_states): hidden_states = self.dense_h_to_4h(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dense_4h_to_h(hidden_states) return hidden_states class GPTNeoXLayer(nn.Module): def __init__(self, config): super().__init__() 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) self.attention = GPTNeoXAttention(config) self.mlp = GPTNeoXMLP(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, use_cache=False, layer_past=None, output_attentions=False, ): residual = hidden_states ln_out = self.input_layernorm(hidden_states) attention_layer_outputs = self.attention( ln_out, 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: a, present, (attentions) outputs = attention_layer_outputs[1:] mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) hidden_states = mlp_output + attn_output + residual if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, present, (attentions) class GPTNeoXModel(GPTNeoXPreTrainedModel): 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([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)]) self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # 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 def forward( self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, past_key_values=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): 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 -10000.0 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) * -10000.0 # 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 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,) 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) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin): _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.gpt_neox = GPTNeoXModel(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 def forward( self, input_ids=None, attention_mask=None, inputs_embeds=None, head_mask=None, past_key_values=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): 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 GPTNeoXTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig >>> import torch >>> tokenizer = GPTNeoXTokenizer.from_pretrained("gpt-neox-20b") >>> config = GPTNeoXConfig.from_pretrained("gpt-neox-20b") >>> config.is_decoder = True >>> model = GPTNeoXForCausalLM.from_pretrained("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_neox( 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 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 return CausalLMOutputWithPast( loss=lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, past=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 is not None: input_ids = input_ids[:, -1:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} def _reorder_cache(self, past, beam_idx): reordered_past = () for layer_past in past: reordered_past += ( tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past