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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 Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizerBase
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from .attention import (
MultiheadAttention,
MultiQueryAttention,
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__)
class MPTPreTrainedModel(PreTrainedModel):
config_class = MPTConfig
base_model_prefix = "model"
_no_split_modules = ["MPTBlock"]
supports_gradient_checkpointing = True
def _set_gradient_checkpointing(self, module: nn.Module, value=False) -> None:
if (
isinstance(module, MPTModel)
or isinstance(module, MultiheadAttention)
or isinstance(module, MultiQueryAttention)
):
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.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
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):
log.info(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.")
def get_input_embeddings(self) -> nn.Embedding:
return self.wte
def set_input_embeddings(self, value: nn.Embedding) -> None:
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]]:
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:
_s_k = max(0, attn_bias.size(-1) - 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:
(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
) -> torch.Tensor:
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,
inputs_embeds: Optional[torch.Tensor] = None,
) -> BaseModelOutputWithPast:
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 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:
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."
)
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)
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:
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}."
)
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
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
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
)
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, attn_weights, present) = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
x,
past_key_value,
attn_bias,
attention_mask,
self.is_causal,
bool(output_attentions),
)
else:
(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,)
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,
)
def param_init_fn(self, module: nn.Module) -> None:
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:
return isinstance(module, MPTBlock)
def activation_checkpointing_fn(self, module: nn.Module) -> bool:
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")
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.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) -> nn.Embedding:
return self.transformer.wte
def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
self.transformer.wte = value
def get_output_embeddings(self) -> nn.Embedding:
return self.transformer.wte
def set_output_embeddings(
self, new_embeddings: Union[SharedEmbedding, nn.Embedding]
) -> None:
self.transformer.wte = new_embeddings
def set_decoder(self, decoder: MPTModel) -> None:
self.transformer = decoder
def get_decoder(self) -> MPTModel:
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:
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
)
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,
)
def param_init_fn(self, module: nn.Module) -> None:
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:
return isinstance(module, MPTBlock)
def activation_checkpointing_fn(self, module: nn.Module) -> bool:
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]:
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: List[Tuple[torch.Tensor, torch.Tensor]],
beam_idx: torch.LongTensor,
) -> List[Tuple[torch.Tensor, ...]]:
"""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
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