# coding=utf-8 # Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. 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 CodeGen model.""" from typing import Optional, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from ...modeling_utils import PreTrainedModel from ...utils import ( auto_docstring, is_torch_flex_attn_available, logging, ) from .configuration_codegen import CodeGenConfig if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask from ...integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) # Copied from transformers.models.gptj.modeling_gptj.create_sinusoidal_positions def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor: inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim)) sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float() return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1) # Copied from transformers.models.gptj.modeling_gptj.rotate_every_two def rotate_every_two(x: torch.Tensor) -> torch.Tensor: x1 = x[:, :, :, ::2] x2 = x[:, :, :, 1::2] x = torch.stack((-x2, x1), dim=-1) return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)') # Copied from transformers.models.gptj.modeling_gptj.apply_rotary_pos_emb def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor: sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3) cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3) return (tensor * cos) + (rotate_every_two(tensor) * sin) class CodeGenAttention(nn.Module): def __init__(self, config, layer_idx=None): super().__init__() max_positions = config.max_position_embeddings self.attn_dropout = nn.Dropout(config.attn_pdrop) self.resid_dropout = nn.Dropout(config.resid_pdrop) self.layer_idx = layer_idx 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.embed_dim = config.hidden_size self.num_attention_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_attention_heads if self.head_dim * self.num_attention_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and" f" `num_attention_heads`: {self.num_attention_heads})." ) self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()) self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.rotary_dim = config.rotary_dim pos_embd_dim = self.rotary_dim or self.embed_dim self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim) def _split_heads(self, x, n_head, dim_head, mp_num): reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head)) reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:]) return reshaped def _merge_heads(self, tensor, num_attention_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into n_ctx """ if len(tensor.shape) == 5: tensor = tensor.permute(0, 1, 3, 2, 4).contiguous() elif len(tensor.shape) == 4: tensor = tensor.permute(0, 2, 1, 3).contiguous() else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}") new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,) return tensor.view(new_shape) def _attn( self, query, key, value, attention_mask=None, head_mask=None, ): # Keep the attention weights computation in fp32 to avoid overflow issues query = query.to(torch.float32) key = key.to(torch.float32) attn_weights = torch.matmul(query, key.transpose(-1, -2)) if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key.shape[-2]] attn_weights += causal_mask attn_weights = attn_weights / self.scale_attn attn_weights = nn.Softmax(dim=-1)(attn_weights) attn_weights = attn_weights.to(value.dtype) attn_weights = self.attn_dropout(attn_weights) # 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 forward( self, hidden_states: Optional[torch.FloatTensor], layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> Union[ tuple[torch.Tensor, tuple[torch.Tensor]], Optional[tuple[torch.Tensor, tuple[torch.Tensor], tuple[torch.Tensor, ...]]], ]: qkv = self.qkv_proj(hidden_states) # TODO(enijkamp): factor out number of logical TPU-v4 cores or make forward pass agnostic mp_num = 4 qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1)) local_dim = self.head_dim * self.num_attention_heads // mp_num query, value, key = torch.split(qkv_split, local_dim, dim=-1) query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num) key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num) value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num) value = value.permute(0, 2, 1, 3) embed_positions = self.embed_positions if embed_positions.device != position_ids.device: embed_positions = embed_positions.to(position_ids.device) self.embed_positions = embed_positions sincos = embed_positions[position_ids] sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1) if self.rotary_dim is not None: k_rot = key[:, :, :, : self.rotary_dim] k_pass = key[:, :, :, self.rotary_dim :] q_rot = query[:, :, :, : self.rotary_dim] q_pass = query[:, :, :, self.rotary_dim :] k_rot = apply_rotary_pos_emb(k_rot, sin, cos) q_rot = apply_rotary_pos_emb(q_rot, sin, cos) key = torch.cat([k_rot, k_pass], dim=-1) query = torch.cat([q_rot, q_pass], dim=-1) else: key = apply_rotary_pos_emb(key, sin, cos) query = apply_rotary_pos_emb(query, sin, cos) key = key.permute(0, 2, 1, 3) query = query.permute(0, 2, 1, 3) # Note that this cast is quite ugly, but is not implemented before ROPE as k_rot in the original codebase is always in fp32. # Reference: https://github.com/salesforce/CodeGen/blob/f210c3bb1216c975ad858cd4132c0fdeabf4bfc2/codegen1/jaxformer/hf/codegen/modeling_codegen.py#L38 if layer_past is not None: cache_kwargs = { "sin": sin, "cos": cos, "partial_rotation_size": self.rotary_dim, "cache_position": cache_position, } key, value = layer_past.update(key.to(hidden_states.dtype), value, self.layer_idx, cache_kwargs) # compute self-attention: V x Softmax(QK^T) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim) attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) return attn_output, attn_weights # Copied from transformers.models.gptj.modeling_gptj.GPTJMLP with GPTJ->CodeGen class CodeGenMLP(nn.Module): def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim super().__init__() embed_dim = config.n_embd self.fc_in = nn.Linear(embed_dim, intermediate_size) self.fc_out = nn.Linear(intermediate_size, embed_dim) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(config.resid_pdrop) def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.fc_in(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.fc_out(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.gptj.modeling_gptj.GPTJBlock with GPTJ->CodeGen class CodeGenBlock(GradientCheckpointingLayer): # Ignore copy def __init__(self, config, layer_idx=None): super().__init__() inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.attn = CodeGenAttention(config, layer_idx) self.mlp = CodeGenMLP(inner_dim, config) def forward( self, hidden_states: Optional[torch.FloatTensor], layer_past: Optional[Cache] = None, attention_mask: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> Union[tuple[torch.Tensor], Optional[tuple[torch.Tensor, tuple[torch.FloatTensor, ...]]]]: residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs, attn_weights = self.attn( hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) feed_forward_hidden_states = self.mlp(hidden_states) hidden_states = attn_outputs + feed_forward_hidden_states + residual return hidden_states, attn_weights @auto_docstring class CodeGenPreTrainedModel(PreTrainedModel): config: CodeGenConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["CodeGenBlock"] _skip_keys_device_placement = "past_key_values" _can_compile_fullgraph = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear,)): # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 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) @auto_docstring class CodeGenModel(CodeGenPreTrainedModel): def __init__(self, config): super().__init__(config) self.embed_dim = config.n_embd self.vocab_size = config.vocab_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.drop = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList([CodeGenBlock(config, layer_idx=i) for i in range(config.n_layer)]) self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.rotary_dim = min(config.rotary_dim, config.n_ctx // config.num_attention_heads) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[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, **kwargs, # NOOP kwargs, for now ) -> Union[tuple, BaseModelOutputWithPast]: r""" inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *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 = 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.wte(input_ids) # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache if not isinstance(past_key_values, (type(None), Cache)): raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.") if use_cache and past_key_values is None: past_key_values = DynamicCache() 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 num_attention_heads x N x N # head_mask has shape n_layer x batch x num_attention_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.n_layer) hidden_states = inputs_embeds if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, seq_length) token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = (-1, seq_length, hidden_states.size(-1)) all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, block in enumerate(self.h): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = block( hidden_states, layer_past=past_key_values, attention_mask=causal_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, cache_position=cache_position, ) hidden_states = outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (outputs[1],) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # 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, past_key_values, all_hidden_states, all_self_attentions] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attentions, ) # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and (attention_mask == 0.0).any(): return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): attention_mask = make_flex_block_causal_mask(attention_mask) return attention_mask # 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_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False # 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_compilable_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 = input_tensor.dtype sequence_length = input_tensor.shape[1] if using_compilable_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, 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 in ["cuda", "xpu", "npu"] 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.gptj.modeling_gptj.GPTJModel._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, 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. 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=cache_position.device ) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=cache_position.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, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask @auto_docstring( custom_intro=""" The CodeGen Model transformer with a language modeling head on top. """ ) class CodeGenForCausalLM(CodeGenPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = CodeGenModel(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size) # Initialize weights and apply final processing self.post_init() @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, tuple[tuple[torch.Tensor]]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[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, **kwargs, ) -> Union[tuple, CausalLMOutputWithPast]: r""" inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`, *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. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) hidden_states = transformer_outputs[0] # make sure sampling in fp16 works correctly and # compute loss in fp32 to match with mesh-tf version # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179 lm_logits = self.lm_head(hidden_states).to(torch.float32) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Flatten the tokens loss = self.loss_function( lm_logits, labels, vocab_size=self.config.vocab_size, **kwargs, ) loss = loss.to(hidden_states.dtype) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) __all__ = ["CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel"]