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import torch
import torch.nn as nn
from typing import Optional, Union, Tuple, Dict, Unpack
from transformers.modeling_utils import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.utils.deprecation import deprecate_kwarg
from rwkvfla.models.rwkv7.modeling_rwkv7 import RWKV7Model, RWKV7PreTrainedModel, Cache,RWKV7ForCausalLM
from rwkvfla.models.rwkv7.modeling_rwkv7 import FusedLinearCrossEntropyLoss, FusedCrossEntropyLoss
from transformers.generation.utils import GenerationMixin
from rwkvfla.models.rwkv7.configuration_rwkv7 import RWKV7Config
class RWKV7SpeechConfig(RWKV7Config):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.text_vocab_size = kwargs.get("text_vocab_size", kwargs.get("text_vocab_size"))
self.audio_global_vocab_size = kwargs.get("audio_global_vocab_size", kwargs.get("audio_global_vocab_size"))
class RWKV7ForSpeech(RWKV7ForCausalLM):
config_class = RWKV7SpeechConfig
def __init__(self, config: RWKV7SpeechConfig):
super().__init__(config)
self.model = RWKV7Model(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)#Spark 0.5B vocab size is 8192 + 1 for eos resulting in 8193
self.criterion = None
self.text_embedder = nn.Embedding(config.text_vocab_size, config.hidden_size)
self.global_embedder = nn.Embedding(config.audio_global_vocab_size, config.hidden_size)#Spark 0.5B global token size is 4096
#TTS Tag includes GLOBAL=0, SEMANTIC=1,START_TTS=2
self.tts_tag_embedder = nn.Embedding(3, config.hidden_size)
# Initialize weights and apply final processing
self.post_init()
self.dropout = torch.nn.Dropout(0.02)
def get_input_embeddings(self):
return self.model.embeddings
def set_input_embeddings(self, value):
self.model.embeddings = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
def generate(self, *args, **kwargs):
try:
return super().generate(*args, **kwargs)
except AttributeError as exception:
if 'past_key_values' in str(exception):
raise AttributeError(
f"You tried to call `generate` with a decoding strategy that manipulates `past_key_values`, "
f"which is not supported for {self.__class__.__name__}. "
f"Try another generation strategy instead. "
f"For the available generation strategies, check this doc: "
f"https://huggingface.co/docs/transformers/en/generation_strategies#decoding-strategies"
)
else:
raise exception
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor = None,
past_key_values: Optional[Cache] = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: bool = True,
logits_to_keep: Optional[int] = None,
**kwargs
):
# only last token for `inputs_ids` if the `past_key_values` is not empty.
if past_key_values is not None and len(past_key_values) > 0:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and len(past_key_values) == 0:
model_inputs = {'inputs_embeds': inputs_embeds}
else:
# The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
# recompiles graphs as the stride of the inputs is a guard.
# Ref: https://github.com/huggingface/transformers/pull/29114
# TODO: use `next_tokens` directly instead.
model_inputs = {'input_ids': input_ids.contiguous()}
if logits_to_keep is not None:
model_inputs['logits_to_keep'] = logits_to_keep
model_inputs.update({
'past_key_values': past_key_values,
'use_cache': use_cache,
'attention_mask': attention_mask,
'logits_to_keep': logits_to_keep,
})
return model_inputs
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
past_key_values: Optional[Cache] = 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,
logits_to_keep: Optional[int] = 0,
**kwargs: Unpack[Dict]
) -> Union[Tuple, CausalLMOutputWithPast]:
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
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.training and inputs_embeds is not None:
inputs_embeds = self.dropout(inputs_embeds)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs
)
hidden_states = outputs[0]
fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training
loss, logits = None, None
if not fuse_linear_and_cross_entropy or labels is None:
logits = self.lm_head(hidden_states if logits_to_keep is None else hidden_states[:, -logits_to_keep:])
if labels is not None:
if getattr(self, 'criterion', None) is None:
if fuse_linear_and_cross_entropy:
criterion = FusedLinearCrossEntropyLoss()
elif self.config.fuse_cross_entropy:
criterion = FusedCrossEntropyLoss(inplace_backward=True)
else:
criterion = nn.CrossEntropyLoss()
else:
criterion = self.criterion
# Enable model parallelism
labels = labels.to(hidden_states.device)
labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], criterion.ignore_index)), 1)
if fuse_linear_and_cross_entropy:
loss = criterion(hidden_states, labels, self.lm_head.weight, self.lm_head.bias)
else:
loss = criterion(logits.view(labels.numel(), -1), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def copy_state_dict(self, state_dict: dict):
"""从源 state dict 复制参数到当前模型，排除 embeddings 和 lm_head
The state dict is from original RWKV7 language model
Args:
state_dict: 源 state dict
"""
# 获取当前模型的 state dict
target_dict = self.state_dict()
# 创建新的 state dict 用于存储要复制的参数
new_state_dict = {}
# 遍历源 state dict 的键
for key in state_dict.keys():
# 跳过 embeddings 和 lm_head 相关的参数
if key == 'model.embeddings.weight':
new_state_dict['text_embedder.weight'] = state_dict[key]
continue
if 'embeddings' in key or 'lm_head' in key:
continue
# 如果键在当前模型中存在，则复制参数
if key in target_dict:
new_state_dict[key] = state_dict[key]
# 加载新的 state dict 到当前模型
info = self.load_state_dict(new_state_dict, strict=False)
print(info)
return self
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