329 lines
14 KiB
Python
329 lines
14 KiB
Python
from time import time
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
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from models.tacotron2.text import symbols
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from trainer.networks import register_model
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from utils.util import opt_get
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class ResBlock(nn.Module):
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def __init__(self, chan):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv1d(chan, chan, kernel_size=3, padding=1),
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nn.GroupNorm(chan//8, chan),
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nn.ReLU(),
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nn.Conv1d(chan, chan, kernel_size=3, padding=1),
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nn.GroupNorm(chan//8, chan)
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)
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def forward(self, x):
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return F.relu(self.net(x) + x)
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class MelEncoder(nn.Module):
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def __init__(self, channels, mel_channels=80):
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super().__init__()
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self.channels = channels
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self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=5, padding=2),
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ResBlock(channels//4),
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ResBlock(channels//4),
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nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
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nn.GroupNorm(channels//16, channels//2),
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nn.ReLU(),
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ResBlock(channels//2),
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ResBlock(channels//2),
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nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
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nn.GroupNorm(channels//8, channels),
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nn.ReLU(),
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ResBlock(channels),
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ResBlock(channels)
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)
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def forward(self, x):
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return self.encoder(x)
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class GPT2InferenceModel(GPT2PreTrainedModel):
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def __init__(self, config, gpt, text_pos_emb, norm, linear):
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super().__init__(config)
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self.transformer = gpt
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self.text_pos_embedding = text_pos_emb
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self.lm_head = nn.Sequential(norm, linear)
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# Model parallel
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self.model_parallel = False
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self.device_map = None
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self.cached_mel_emb = None
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def parallelize(self, device_map=None):
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self.device_map = (
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get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
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if device_map is None
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else device_map
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)
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assert_device_map(self.device_map, len(self.transformer.h))
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self.transformer.parallelize(self.device_map)
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self.lm_head = self.lm_head.to(self.transformer.first_device)
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self.model_parallel = True
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def deparallelize(self):
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self.transformer.deparallelize()
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self.transformer = self.transformer.to("cpu")
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self.lm_head = self.lm_head.to("cpu")
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self.model_parallel = False
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torch.cuda.empty_cache()
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def get_output_embeddings(self):
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return self.lm_head
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def set_output_embeddings(self, new_embeddings):
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self.lm_head = new_embeddings
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def store_mel_emb(self, mel_emb):
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self.cached_mel_emb = mel_emb
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def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
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token_type_ids = kwargs.get("token_type_ids", None)
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# only last token for inputs_ids if past is defined in kwargs
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if past:
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input_ids = input_ids[:, -1].unsqueeze(-1)
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if token_type_ids is not None:
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token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
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attention_mask = kwargs.get("attention_mask", None)
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position_ids = kwargs.get("position_ids", None)
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if attention_mask is not None and position_ids is None:
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# create position_ids on the fly for batch generation
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position_ids = attention_mask.long().cumsum(-1) - 1
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position_ids.masked_fill_(attention_mask == 0, 1)
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if past:
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position_ids = position_ids[:, -1].unsqueeze(-1)
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else:
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position_ids = None
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return {
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"input_ids": input_ids,
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"past_key_values": past,
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"use_cache": kwargs.get("use_cache"),
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"position_ids": position_ids,
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"attention_mask": attention_mask,
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"token_type_ids": token_type_ids,
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}
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def forward(
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self,
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input_ids=None,
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past_key_values=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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labels=None,
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use_cache=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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assert self.cached_mel_emb is not None
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assert inputs_embeds is None # Not supported by this inference model.
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assert labels is None # Training not supported by this inference model.
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# Create embedding
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mel_len = self.cached_mel_emb.shape[1]
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if input_ids.shape[1] != 1:
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text_inputs = input_ids[:, mel_len:]
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text_emb = self.transformer.get_input_embeddings()(text_inputs)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_emb.device))
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if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
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mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
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else:
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mel_emb = self.cached_mel_emb
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emb = torch.cat([mel_emb, text_emb], dim=1)
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else:
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emb = self.transformer.get_input_embeddings()(input_ids) + \
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self.text_pos_embedding(torch.tensor(attention_mask.shape[1]-mel_len, device=attention_mask.device)).unsqueeze(0).unsqueeze(0)
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transformer_outputs = self.transformer(
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inputs_embeds=emb,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = transformer_outputs[0]
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# Set device for model parallelism
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if self.model_parallel:
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torch.cuda.set_device(self.transformer.first_device)
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hidden_states = hidden_states.to(self.lm_head.weight.device)
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lm_logits = self.lm_head(hidden_states)
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if not return_dict:
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return (lm_logits,) + transformer_outputs[1:]
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return CausalLMOutputWithCrossAttentions(
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loss=None,
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logits=lm_logits,
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past_key_values=transformer_outputs.past_key_values,
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hidden_states=transformer_outputs.hidden_states,
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attentions=transformer_outputs.attentions,
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cross_attentions=transformer_outputs.cross_attentions,
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)
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@staticmethod
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def _reorder_cache(past, beam_idx):
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"""
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This function is used to re-order the :obj:`past_key_values` cache if
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:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
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called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
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"""
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return tuple(
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tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
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for layer_past in past
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)
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class GptAsrHf(nn.Module):
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NUMBER_SYMBOLS = len(symbols)
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NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1
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def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=200, max_mel_frames=1000, checkpointing=True):
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super().__init__()
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self.max_mel_frames = max_mel_frames // 4 # Mel frames are reduced by a factor of 4 during encoding.
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self.max_symbols_per_phrase = max_symbols_per_phrase
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self.model_dim = model_dim
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self.max_mel_frames = self.max_mel_frames
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self.mel_encoder = MelEncoder(model_dim)
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self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
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self.mel_pos_embedding = nn.Embedding(self.max_mel_frames, model_dim)
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seq_length = 2+self.max_symbols_per_phrase+self.max_mel_frames
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self.gpt_config = GPT2Config(vocab_size=self.NUMBER_TEXT_TOKENS,
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n_positions=seq_length,
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n_ctx=seq_length,
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n_embd=model_dim,
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n_layer=layers,
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n_head=heads,
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gradient_checkpointing=checkpointing,
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use_cache=not checkpointing)
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self.gpt = GPT2Model(self.gpt_config)
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self.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS)
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def get_logits(self, mel_inputs, text_targets, get_attns=False):
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# Pad front and back. Pad at front is the "START" token.
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text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)
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text_targets = F.pad(text_targets, (0, self.max_symbols_per_phrase - text_targets.shape[1]))
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text_emb = self.gpt.get_input_embeddings()(text_targets)
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text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
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mel_emb = self.mel_encoder(mel_inputs)
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mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1]))
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mel_emb = mel_emb.permute(0,2,1).contiguous()
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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emb = torch.cat([mel_emb, text_emb], dim=1)
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gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state
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text_logits = self.final_norm(enc[:, self.max_mel_frames:])
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text_logits = self.text_head(text_logits)
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text_logits = text_logits.permute(0,2,1)
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return text_logits
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def forward(self, mel_inputs, text_targets, return_attentions=False):
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text_logits = self.get_logits(mel_inputs, text_targets, get_attns=return_attentions)
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if return_attentions:
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return text_logits # These weren't really the logits.
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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return loss_text.mean(), text_logits
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def inference(self, mel_inputs, cond_text=None, do_sample=False, temperature=1.0, num_beams=8):
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if not hasattr(self, 'inference_model'):
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self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.text_pos_embedding, self.final_norm, self.text_head)
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mel_emb = self.mel_encoder(mel_inputs)
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assert mel_emb.shape[-1] <= self.max_mel_frames
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mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1]))
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mel_emb = mel_emb.permute(0,2,1).contiguous()
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mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
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self.inference_model.store_mel_emb(mel_emb)
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# "fake_inputs" are stand-ins for the MEL frames, which will be injected with the prep_inputs function above.
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if cond_text is None:
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fake_inputs = torch.full((mel_inputs.shape[0],self.max_mel_frames+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
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fake_inputs[:,-1] = self.NUMBER_SYMBOLS
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else:
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cond_used = 10
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fake_inputs = torch.full((mel_inputs.shape[0],self.max_mel_frames+1+cond_used,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
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fake_inputs[:,-1-cond_used] = self.NUMBER_SYMBOLS
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fake_inputs[:, -cond_used:] = cond_text[:, :cond_used]
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gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.NUMBER_SYMBOLS, pad_token_id=0, eos_token_id=0,
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max_length=self.max_symbols_per_phrase+self.max_mel_frames, temperature=temperature, num_beams=num_beams, use_cache=True)
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return gen[:, self.max_mel_frames:]
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@register_model
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def register_gpt_asr_hf(opt_net, opt):
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return GptAsrHf(**opt_get(opt_net, ['kwargs'], {}))
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# Quick script that loads a model and halves the number of layers, then saves that model.
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def distill():
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gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=512, heads=8)
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gpt.load_state_dict(torch.load('X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_ema.pth'))
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rc = 0
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i = 0
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while i < len(gpt.gpt.h):
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if rc % 2 != 0:
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del gpt.gpt.h[i]
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else:
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i += 1
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rc += 1
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torch.save(gpt.state_dict(), 'X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_distilled.pth')
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if __name__ == '__main__':
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distill()
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'''
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gpt = GptAsrHf(max_symbols_per_phrase=250, max_mel_frames=1400, layers=16, model_dim=512, heads=8)
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#l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,100)))
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start = time()
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gpt.inference(torch.randn(1,80,350), num_beams=1)
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print(f"Elapsed: {time()-start}")
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'''
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'''
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with torch.no_grad():
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t = torch.randn(1,80,800).cuda()
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start = time()
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s = gpt.inference_beam_topk(t)
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print(time()-start)
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start = time()
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o = gpt.inference_beam_topk(t, fn='inference_beam_opt')
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print(time()-start)
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'''
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