forked from mrq/DL-Art-School
303 lines
12 KiB
Python
303 lines
12 KiB
Python
import functools
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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 GPT2PreTrainedModel, GPT2Config
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from x_transformers import TransformerWrapper, Encoder, Decoder
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from data.audio.voice_tokenizer import VoiceBpeTokenizer
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from models.arch_util import AttentionBlock
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from scripts.audio.gen.speech_synthesis_utils import wav_to_mel
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from trainer.networks import register_model
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from utils.util import load_audio
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class InferenceModel(GPT2PreTrainedModel):
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"""
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Implementation of GPT2PreTrainedModel from transformers, which allows us to use their generation library with
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this transformer.
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"""
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def __init__(self, model):
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super().__init__(GPT2Config())
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self.transformer = model
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self.context = None
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def parallelize(self, device_map=None):
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# Not implemented.
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pass
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def deparallelize(self):
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# Not implemented.
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pass
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def get_output_embeddings(self):
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assert False, "Unsupported operation."
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def set_output_embeddings(self, new_embeddings):
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assert False, "Unsupported operation."
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def store_context(self, context):
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self.context = context
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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.context 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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hidden_states = self.transformer.decoder(input_ids, context=self.context, return_embeddings=True)
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logits = self.transformer.decoder.transformer.to_logits(hidden_states)
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if not return_dict:
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return (logits, )
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return CausalLMOutputWithCrossAttentions(
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loss=None,
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logits=logits,
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past_key_values=None,
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hidden_states=hidden_states,
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attentions=None,
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cross_attentions=None,
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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 ResBlock(nn.Module):
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"""
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Basic residual convolutional block that uses GroupNorm.
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"""
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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 ConditioningEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=4,
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do_checkpointing=False):
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super().__init__()
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attn = []
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self.init = nn.Sequential(nn.Conv1d(spec_dim, embedding_dim//4, kernel_size=5, padding=2),
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nn.Conv1d(embedding_dim//4, embedding_dim//2, kernel_size=3, padding=1, stride=2),
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ResBlock(embedding_dim//2),
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nn.Conv1d(embedding_dim//2, embedding_dim, kernel_size=3, padding=1, stride=2))
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for a in range(attn_blocks):
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attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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def forward(self, x):
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h = self.init(x)
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h = self.attn(h)
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return h.mean(dim=2)
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, *args, **kwargs):
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for k, v in kwargs.items():
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assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
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partial = functools.partial(self.wrap, **kwargs)
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return torch.utils.checkpoint.checkpoint(partial, x, *args)
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class CheckpointedXTransformerWrapper(nn.Module):
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"""
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Wraps a TransformerWrapper and applies CheckpointedLayer to each layer.
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"""
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def __init__(self, checkpoint=True, **xtransformer_kwargs):
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super().__init__()
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self.transformer = TransformerWrapper(**xtransformer_kwargs)
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if not checkpoint:
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return
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x, **kwargs):
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return self.transformer(x, **kwargs)
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class AutoregressiveCodegen(nn.Module):
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def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, max_text_tokens=4000,
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max_mel_tokens=4000, dropout=.1):
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super().__init__()
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self.START_TOKEN=8192
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self.STOP_TOKEN=8193
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self.max_mel_tokens = max_mel_tokens
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self.mel_embedding = ConditioningEncoder(80, model_dim, do_checkpointing=False)
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self.encoder = CheckpointedXTransformerWrapper(
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num_tokens=num_text_tokens,
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max_seq_len=max_text_tokens,
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attn_layers = Encoder(
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depth=depth//2,
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heads=model_dim//64,
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dim=model_dim,
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attn_dropout=dropout,
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ff_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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ff_mult=1,
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rotary_pos_emb=True,
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rel_pos_bias=True,
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))
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self.decoder = CheckpointedXTransformerWrapper(
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num_tokens=num_mel_tokens,
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max_seq_len=max_mel_tokens,
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attn_layers=Decoder(
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depth=depth,
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heads=model_dim//64,
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dim=model_dim,
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attn_dropout=dropout,
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ff_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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ff_mult=1,
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rotary_pos_emb=True,
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rel_pos_bias=True,
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cross_attend=True,
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))
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def get_grad_norm_parameter_groups(self):
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return {
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'encoder': list(self.encoder.parameters()),
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'decoder': list(self.decoder.parameters()),
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'minicoder': list(self.mel_embedding.parameters()),
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}
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def forward(self, text_codes, conditioning_signal, mel_codes, wav_lengths, return_loss=True):
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# Format mel_codes with a stop token on the end.
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mel_lengths = wav_lengths // 1024 + 1
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for b in range(mel_codes.shape[0]):
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mel_codes[b, mel_lengths[b]:] = self.STOP_TOKEN
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mel_codes = F.pad(mel_codes, (0, 1), value=self.STOP_TOKEN)
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# Build the context
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if len(conditioning_signal.shape) != 4:
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conditioning_signal = conditioning_signal.unsqueeze(1)
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cond_embs = []
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for i in range(conditioning_signal.shape[1]):
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cond_embs.append(self.mel_embedding(conditioning_signal[:, i]))
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cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True)
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enc_text = self.encoder(text_codes, return_embeddings=True)
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context = torch.cat([cond_emb, enc_text], dim=1)
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# Execute the decoder
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dec_inputs = F.pad(mel_codes, (1,0), value=self.START_TOKEN)[:, :-1]
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dec = self.decoder(dec_inputs, context=context)
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if not return_loss:
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return dec
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loss_mel = F.cross_entropy(dec.permute(0,2,1), mel_codes)
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return loss_mel
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def generate(self, conditioning_signal, text_codes, **hf_generate_kwargs):
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if not hasattr(self, 'inference_model'):
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self.inference_model = InferenceModel(self)
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if len(conditioning_signal.shape) != 4:
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conditioning_signal = conditioning_signal.unsqueeze(1)
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cond_embs = []
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for i in range(conditioning_signal.shape[1]):
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cond_embs.append(self.mel_embedding(conditioning_signal[:, i]))
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cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True)
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enc_text = self.encoder(text_codes, return_embeddings=True)
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context = torch.cat([cond_emb, enc_text], dim=1)
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self.inference_model.store_context(context)
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gen = self.inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN,
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max_length=self.max_mel_tokens, output_attentions=False, return_dict_in_generate=True,
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**hf_generate_kwargs)
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return gen
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@register_model
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def register_autoregressive_codegen(opt_net, opt):
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return AutoregressiveCodegen(**opt_net['kwargs'])
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if __name__ == '__main__':
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codegen = AutoregressiveCodegen(512, 20)
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torch.save(codegen.state_dict(), 'sample.pth')
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codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200)))
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codegen(torch.randint(0,256, (2,200)),
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torch.randn(2,80,120),
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torch.randint(0,8192, (2,350)),
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torch.tensor([192,350])) |