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from dataclasses import fields
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import math
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast, BaseModelOutputWithPast
from transformers.models.auto import AutoModelForCausalLM
from .config import ModelConfig
from .model import OLMo
from .configuration_olmo import OLMoConfig
def create_model_config_from_pretrained_config(config: OLMoConfig):
"""
Utility function
"""
kwargs = {}
for field in fields(ModelConfig):
kwargs[field.name] = getattr(config, field.name)
model_config = ModelConfig(**kwargs)
return model_config
class OLMoPreTrainedModel(PreTrainedModel):
config_class = OLMoConfig
base_model_prefix = "model"
_no_split_modules = ["OLMoBlock"]
# _skip_keys_device_placement = ["past_key_values", "causal_mask"]
_skip_keys_device_placement = ["past_key_values"]
def _init_weights(self, module):
# `OLMoModel.reset_parameters` initializes weights of itself and its children
if isinstance(module, OLMo):
module.reset_parameters()
class OLMoForCausalLM(OLMoPreTrainedModel):
_tied_weights_keys = []
# _tied_weights_keys = ["transformer.wte.weight"]
def __init__(self, config: OLMoConfig):
super().__init__(config)
self.model = OLMo(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> torch.nn.Module:
return self.model.transformer.wte
def set_input_embeddings(self, value: torch.nn.Module):
self.model.transformer.wte = value
def get_output_embeddings(self):
if self.config.weight_tying:
return self.model.transformer.wte
else:
return self.model.transformer.ff_out
def set_output_embeddings(self, value: torch.nn.Module):
if self.config.weight_tying:
self.model.transformer.wte = value
else:
self.model.transformer.ff_out = value
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def forward(
self,
input_ids: torch.LongTensor = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
attention_bias: Optional[torch.Tensor] = None,
past_key_values: Optional[List[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,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, OLMoForCausalLM
>>> model = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B")
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions or self.config.output_attentions
output_hidden_states = output_hidden_states or 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
assert not output_attentions
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
base_output: Union[BaseModelOutputWithPast, Tuple] = self.model.forward(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
attention_bias=attention_bias,
past_key_values=past_key_values,
use_cache=use_cache,
output_hidden_states=output_hidden_states,
)
last_hidden_state = base_output.last_hidden_state if return_dict else base_output[0]
# Get logits.
# shape: (batch_size, seq_len or 1, vocab_size)
if self.config.weight_tying:
logits = F.linear(last_hidden_state, self.model.transformer.wte.weight, None) # type: ignore
else:
logits = self.model.transformer.ff_out(last_hidden_state) # type: ignore
if self.config.scale_logits:
logits.mul_(1 / math.sqrt(self.config.d_model))
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = torch.nn.CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.embedding_size) # changed to self.config.embedding_size from self.config.vocab_size
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + base_output[1:]
return (loss,) + output if loss is not None else output
assert isinstance(base_output, BaseModelOutputWithPast)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=base_output.past_key_values,
hidden_states=base_output.hidden_states,
attentions=base_output.attentions,
)
def prepare_inputs_for_generation(
self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple]] = None, **kwargs
):
if past_key_values:
# This is because we want the model to only process the last generated token.
input_ids = input_ids[:, -1:]
model_inputs = {"input_ids": input_ids, "past_key_values": past_key_values}
if 'cache_position' in kwargs: kwargs.pop("cache_position")
if past_key_values and ("input_embeds" in kwargs or "inputs_embeds" in kwargs): kwargs.pop("inputs_embeds")
model_inputs.update(kwargs)
# logger.warning("%s %s", kwargs.keys(), model_inputs.keys())
# model_inputs["use_cache"] = kwargs.pop("use_cache", self.config.use_cache)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
# Register the model so that it is available for transformer pipelines, auto-loading, etc.
# AutoModelForCausalLM.register(OLMoConfig, OLMoForCausalLM)
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