Update modeling_mpt.py

modeling_mpt.py

@@ -1,69 +1,57 @@
 """A simple, flexible implementation of a GPT model.
-
 Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
 """
 import math
 import warnings
-from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from transformers import PreTrainedModel, PreTrainedTokenizerBase
+from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from .attention import attn_bias_shape, build_attn_bias
 from .blocks import MPTBlock
-from .custom_embedding import SharedEmbedding
-from .fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY
-from .ffn import FFN_CLASS_REGISTRY as FFN_CLASS_REGISTRY
-from .ffn import MPTMLP as MPTMLP
-from .ffn import build_ffn as build_ffn
 from .norm import NORM_CLASS_REGISTRY
 from .configuration_mpt import MPTConfig
 from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
 from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
 from .meta_init_context import init_empty_weights
-from .param_init_fns import generic_param_init_fn_, MODEL_INIT_REGISTRY
-try:
-    from .flash_attn_triton import flash_attn_func as flash_attn_func
-except:
-    pass
-import logging
-log = logging.getLogger(__name__)
+from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_
+Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
 
 class MPTPreTrainedModel(PreTrainedModel):
     config_class = MPTConfig
     base_model_prefix = 'model'
-    _no_split_modules = ['MPTBlock']
+    _no_split_modules = ["MPTBlock"]
+    supports_gradient_checkpointing = True
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, MPTModel):
+            module.gradient_checkpointing = value
 
 class MPTModel(MPTPreTrainedModel):
 
     def __init__(self, config: MPTConfig):
         config._validate_config()
         super().__init__(config)
+        self.gradient_checkpointing = False
         self.attn_impl = config.attn_config['attn_impl']
         self.prefix_lm = config.attn_config['prefix_lm']
         self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
         self.alibi = config.attn_config['alibi']
         self.alibi_bias_max = config.attn_config['alibi_bias_max']
-        self.learned_pos_emb = config.learned_pos_emb
-        if config.init_device == 'mixed':
-            if dist.get_local_rank() == 0:
-                config.init_device = 'cpu'
-            else:
-                config.init_device = 'meta'
         if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
             norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
             raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')
         norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
         self.embedding_fraction = config.embedding_fraction
-        self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)
-        if self.learned_pos_emb:
-            self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
+        self.wte = nn.Embedding(config.vocab_size, config.d_model, device=config.init_device)
+        if not self.alibi:
+            self.wpe = nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
         self.emb_drop = nn.Dropout(config.emb_pdrop)
         self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
         self.norm_f = norm_class(config.d_model, device=config.init_device)
         if config.init_device != 'meta':
-            log.info(f'We recommend using config.init_device="meta" with Composer + FSDP for faster initialization.')
             self.apply(self.param_init_fn)
         self.is_causal = not self.prefix_lm
         self._attn_bias_initialized = False
@@ -72,22 +60,25 @@ class MPTModel(MPTPreTrainedModel):
         if config.no_bias:
             for module in self.modules():
                 if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
-                    log.info(f'Removing bias ({module.bias}) from {module}.')
+                    if config.verbose:
+                        warnings.warn(f'Removing bias ({module.bias}) from {module}.')
                     module.register_parameter('bias', None)
-                if hasattr(module, 'use_bias'):
-                    log.info(f'Setting use_bias=False for {module}.')
-                    module.use_bias = False
-        log.debug(self)
-        log.debug(f"Using {self.config.init_config['name']} initialization.")
+        if config.verbose and config.verbose > 2:
+            print(self)
+        if 'verbose' not in self.config.init_config:
+            self.config.init_config['verbose'] = self.config.verbose
+        if self.config.init_config['verbose'] > 1:
+            init_fn_name = self.config.init_config['name']
+            warnings.warn(f'Using {init_fn_name} initialization.')
 
-    def get_input_embeddings(self) -> nn.Embedding:
+    def get_input_embeddings(self):
         return self.wte
 
-    def set_input_embeddings(self, value: nn.Embedding) -> None:
+    def set_input_embeddings(self, value):
         self.wte = value
 
     @torch.no_grad()
-    def _attn_bias(self, device: torch.device, dtype: torch.dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None) -> Tuple[Optional[torch.Tensor], Optional[torch.ByteTensor]]:
+    def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None):
         if not self._attn_bias_initialized:
             if self.attn_bias_shape:
                 self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)
@@ -110,15 +101,14 @@ class MPTModel(MPTPreTrainedModel):
             if attn_bias is None:
                 attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
             else:
-                _s_k = max(0, attn_bias.size(-1) - s_k)
-                attn_bias = attn_bias[:, :, :, _s_k:]
+                attn_bias = attn_bias[:, :, :, -s_k:]
             if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
                 raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
             min_val = torch.finfo(attn_bias.dtype).min
             attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
         return (attn_bias, None)
 
-    def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor) -> torch.Tensor:
+    def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor):
         (s_k, s_q) = attn_bias.shape[-2:]
         if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
             raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.')
@@ -133,7 +123,7 @@ class MPTModel(MPTPreTrainedModel):
         attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
         return attn_bias
 
-    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor) -> torch.Tensor:
+    def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor):
         seq_len = sequence_id.shape[-1]
         if seq_len > self.config.max_seq_len:
             raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
@@ -143,86 +133,122 @@ class MPTModel(MPTPreTrainedModel):
         attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
         return attn_bias
 
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None) -> BaseModelOutputWithPast:
+    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor] = None):
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                use_cache = False
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
         if attention_mask is not None:
             attention_mask = attention_mask.bool()
+        else:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+
+        if inputs_embeds is None:
+            tok_emb = self.wte(input_ids)
+        else:
+            tok_emb = inputs_embeds
+
         if prefix_mask is not None:
             prefix_mask = prefix_mask.bool()
         if not return_dict:
             raise NotImplementedError('return_dict False is not implemented yet for MPT')
         if output_attentions:
-            if self.attn_impl != 'torch':
-                raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')
-        if self.training and attention_mask is not None and (attention_mask[:, 0].sum() != attention_mask.shape[0]):
-            raise NotImplementedError('MPT does not support training with left padding.')
+            raise NotImplementedError('output_attentions is not implemented yet for MPT')
+        #if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training:
+        #    raise NotImplementedError('MPT does not support training with left padding.')
         if self.prefix_lm and prefix_mask is None:
             raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds is not implemented for MPT.')
         if self.training:
             if self.attn_uses_sequence_id and sequence_id is None:
                 raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
             elif self.attn_uses_sequence_id is False and sequence_id is not None:
                 warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
-        S = input_ids.size(1)
+        S = seq_length
         assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
-        tok_emb = self.wte(input_ids)
-        if self.learned_pos_emb:
+        if self.alibi:
+            x = tok_emb
+        else:
             past_position = 0
             if past_key_values is not None:
                 if len(past_key_values) != self.config.n_layers:
                     raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
                 past_position = past_key_values[0][0].size(1)
-                if self.attn_impl == 'torch':
-                    past_position = past_key_values[0][0].size(3)
             if S + past_position > self.config.max_seq_len:
-                raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length ' + f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
+                raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
             pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
-            if attention_mask is not None:
+            if attention_mask is not None and not self.training:
                 pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
             pos_emb = self.wpe(pos)
             x = tok_emb + pos_emb
-        else:
-            x = tok_emb
         if self.embedding_fraction == 1:
             x = self.emb_drop(x)
         else:
             x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)
             assert isinstance(self.emb_drop, nn.Module)
             x = self.emb_drop(x_shrunk)
-        (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
-        presents = () if use_cache else None
+        (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=x.dtype, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
         if use_cache and past_key_values is None:
             past_key_values = [() for _ in range(self.config.n_layers)]
+
         all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
         for (b_idx, block) in enumerate(self.blocks):
             if output_hidden_states:
                 assert all_hidden_states is not None
                 all_hidden_states = all_hidden_states + (x,)
             past_key_value = past_key_values[b_idx] if past_key_values is not None else None
-            (x, attn_weights, present) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions))
-            if presents is not None:
-                presents += (present,)
-            if output_attentions:
-                assert all_self_attns is not None
-                all_self_attns = all_self_attns + (attn_weights,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs)
+
+                    return custom_forward
+
+                (x, past_key_value) = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    x,
+                    past_key_value,
+                    attn_bias,
+                    attention_mask,
+                    self.is_causal,
+                )
+            else:
+                (x, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
+
+            if past_key_values is not None:
+                past_key_values[b_idx] = past_key_value
         x = self.norm_f(x)
-        if output_hidden_states:
-            assert all_hidden_states is not None
-            all_hidden_states = all_hidden_states + (x,)
-        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attns)
+        return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states)
 
-    def param_init_fn(self, module: nn.Module) -> None:
+    def param_init_fn(self, module):
         init_fn_name = self.config.init_config['name']
         MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
 
-    def fsdp_wrap_fn(self, module: nn.Module) -> bool:
+    def fsdp_wrap_fn(self, module):
         return isinstance(module, MPTBlock)
 
-    def activation_checkpointing_fn(self, module: nn.Module) -> bool:
+    def activation_checkpointing_fn(self, module):
         return isinstance(module, MPTBlock)
 
 class MPTForCausalLM(MPTPreTrainedModel):
@@ -231,13 +257,7 @@ class MPTForCausalLM(MPTPreTrainedModel):
         super().__init__(config)
         if not config.tie_word_embeddings:
             raise ValueError('MPTForCausalLM only supports tied word embeddings')
-        log.info(f'Instantiating an MPTForCausalLM model from {__file__}')
-        self.transformer: MPTModel = MPTModel(config)
-        for child in self.transformer.children():
-            if isinstance(child, torch.nn.ModuleList):
-                continue
-            if isinstance(child, torch.nn.Module):
-                child._fsdp_wrap = True
+        self.transformer = MPTModel(config)
         self.logit_scale = None
         if config.logit_scale is not None:
             logit_scale = config.logit_scale
@@ -248,53 +268,56 @@ class MPTForCausalLM(MPTPreTrainedModel):
                     raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
             self.logit_scale = logit_scale
 
-    def get_input_embeddings(self) -> nn.Embedding:
+    def get_input_embeddings(self):
         return self.transformer.wte
 
-    def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
+    def set_input_embeddings(self, value):
         self.transformer.wte = value
 
-    def get_output_embeddings(self) -> nn.Embedding:
+    def get_output_embeddings(self):
         return self.transformer.wte
 
-    def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding]) -> None:
+    def set_output_embeddings(self, new_embeddings):
         self.transformer.wte = new_embeddings
 
-    def set_decoder(self, decoder: MPTModel) -> None:
+    def set_decoder(self, decoder):
         self.transformer = decoder
 
-    def get_decoder(self) -> MPTModel:
+    def get_decoder(self):
         return self.transformer
 
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None) -> CausalLMOutputWithPast:
+    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor] = None):
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
-        if inputs_embeds is not None:
-            raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')
-        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
-        logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)
+        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache, inputs_embeds=inputs_embeds)
+        
+        last_hidden_state = outputs.last_hidden_state
+        if self.model_parallel:
+            last_hidden_state = last_hidden_state.to(self.transformer.wte.weight.device)
+        logits = F.linear(last_hidden_state, self.transformer.wte.weight)
+
         if self.logit_scale is not None:
             if self.logit_scale == 0:
                 warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
             logits *= self.logit_scale
         loss = None
         if labels is not None:
-            _labels = torch.roll(labels, shifts=-1)
-            _labels[:, -1] = -100
-            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), _labels.to(logits.device).view(-1))
-        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+            labels = torch.roll(labels, shifts=-1)
+            labels[:, -1] = -100
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
+        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states)
 
-    def param_init_fn(self, module: nn.Module) -> None:
+    def param_init_fn(self, module):
         init_fn_name = self.config.init_config['name']
         MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
 
-    def fsdp_wrap_fn(self, module: nn.Module) -> bool:
+    def fsdp_wrap_fn(self, module):
         return isinstance(module, MPTBlock)
 
-    def activation_checkpointing_fn(self, module: nn.Module) -> bool:
+    def activation_checkpointing_fn(self, module):
         return isinstance(module, MPTBlock)
 
-    def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]=None, inputs_embeds: Optional[torch.Tensor]=None, **kwargs: Any) -> Dict[str, Any]:
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
         if inputs_embeds is not None:
             raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
         attention_mask = kwargs['attention_mask'].bool()
@@ -315,9 +338,8 @@ class MPTForCausalLM(MPTPreTrainedModel):
         return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
 
     @staticmethod
-    def _reorder_cache(past_key_values: List[Tuple[torch.Tensor, torch.Tensor]], beam_idx: torch.LongTensor) -> List[Tuple[torch.Tensor, ...]]:
+    def _reorder_cache(past_key_values, beam_idx):
         """Used by HuggingFace generate when using beam search with kv-caching.
-
         See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
         for an example in transformers.
         """