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"""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 List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
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 .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 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"]
class MPTModel(MPTPreTrainedModel):
def __init__(self, config: MPTConfig):
config._validate_config()
super().__init__(config)
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']
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 = 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':
print(f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.')
self.apply(self.param_init_fn)
self.is_causal = not self.prefix_lm
self._attn_bias_initialized = False
self.attn_bias = None
self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id)
if config.no_bias:
for module in self.modules():
if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
if config.verbose:
warnings.warn(f'Removing bias ({module.bias}) from {module}.')
module.register_parameter('bias', None)
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):
return self.wte
def set_input_embeddings(self, value):
self.wte = value
@torch.no_grad()
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)
self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max)
self._attn_bias_initialized = True
if self.attn_impl == 'flash':
return (self.attn_bias, attention_mask)
if self.attn_bias is not None:
self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
attn_bias = self.attn_bias
if self.prefix_lm:
assert isinstance(attn_bias, torch.Tensor)
assert isinstance(prefix_mask, torch.Tensor)
attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
if self.attn_uses_sequence_id and sequence_id is not None:
assert isinstance(attn_bias, torch.Tensor)
attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
if attention_mask is not None:
s_k = attention_mask.shape[-1]
if attn_bias is None:
attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
else:
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):
(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}.')
seq_len = prefix_mask.shape[-1]
if seq_len > self.config.max_seq_len:
raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
attn_bias = attn_bias[..., :seq_len, :seq_len]
causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
prefix = prefix_mask.view(-1, 1, 1, seq_len)
cannot_attend = ~torch.logical_or(causal, prefix.bool())
min_val = torch.finfo(attn_bias.dtype).min
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):
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}')
attn_bias = attn_bias[..., :seq_len, :seq_len]
cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
min_val = torch.finfo(attn_bias.dtype).min
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):
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 attention_mask is not None:
attention_mask = attention_mask.bool()
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:
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 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)
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.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 S + past_position > 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:
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
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=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
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, 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)
return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states)
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):
return isinstance(module, MPTBlock)
def activation_checkpointing_fn(self, module):
return isinstance(module, MPTBlock)
class MPTForCausalLM(MPTPreTrainedModel):
def __init__(self, config: MPTConfig):
super().__init__(config)
if not config.tie_word_embeddings:
raise ValueError('MPTForCausalLM only supports tied word embeddings')
self.transformer = MPTModel(config)
self.logit_scale = None
if config.logit_scale is not None:
logit_scale = config.logit_scale
if isinstance(logit_scale, str):
if logit_scale == 'inv_sqrt_d_model':
logit_scale = 1 / math.sqrt(config.d_model)
else:
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):
return self.transformer.wte
def set_input_embeddings(self, value):
self.transformer.wte = value
def get_output_embeddings(self):
return self.transformer.wte
def set_output_embeddings(self, new_embeddings):
self.transformer.wte = new_embeddings
def set_decoder(self, decoder):
self.transformer = decoder
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):
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
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 = F.linear(outputs.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)
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):
return isinstance(module, MPTBlock)
def activation_checkpointing_fn(self, module):
return isinstance(module, MPTBlock)
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()
if attention_mask[:, -1].sum() != attention_mask.shape[0]:
raise NotImplementedError('MPT does not support generation with right padding.')
if self.transformer.attn_uses_sequence_id and self.training:
sequence_id = torch.zeros_like(input_ids[:1])
else:
sequence_id = None
if past_key_values is not None:
input_ids = input_ids[:, -1].unsqueeze(-1)
if self.transformer.prefix_lm:
prefix_mask = torch.ones_like(attention_mask)
if kwargs.get('use_cache') == False:
raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
else:
prefix_mask = None
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, 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.
"""
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))]
return reordered_past |