forked from mrq/DL-Art-School
Apply fix to gpt_asr_hf and prep it for inference
Fix is that we were predicting two characters in advance, not next character
This commit is contained in:
James Betker
parent
fd14746bf8
commit
596a62fe01
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@ -1,7 +1,9 @@
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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
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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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@ -46,6 +48,159 @@ class MelEncoder(nn.Module):
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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(text_emb.shape[0], 1, 1)
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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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@ -61,17 +216,19 @@ class GptAsrHf(nn.Module):
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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 = GPT2Model(GPT2Config(vocab_size=self.NUMBER_TEXT_TOKENS,
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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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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):
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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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@ -91,9 +248,32 @@ class GptAsrHf(nn.Module):
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def forward(self, mel_inputs, text_targets):
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text_logits = self.get_logits(mel_inputs, text_targets)
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loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long())
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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((1,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((1,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=False)
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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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@ -41,7 +41,7 @@ if __name__ == "__main__":
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torch.backends.cudnn.benchmark = True
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_mass.yml')
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_hf.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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@ -71,11 +71,11 @@ if __name__ == "__main__":
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tq = tqdm(test_loader)
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for data in tq:
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if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
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continue
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#if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
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# continue
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pred = forward_pass(model, data, dataset_dir, opt, batch)
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pred = pred.replace('_', '')
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output.write(f'{pred}\t{os.path.basename(data["path"][0])}\n')
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output.write(f'{pred}\t{os.path.basename(data["filenames"][0])}\n')
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print(pred)
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output.flush()
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batch += 1
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@ -284,7 +284,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mass.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mass_hf.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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@ -40,6 +40,7 @@ class GeneratorInjector(Injector):
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super(GeneratorInjector, self).__init__(opt, env)
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self.grad = opt['grad'] if 'grad' in opt.keys() else True
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self.method = opt_get(opt, ['method'], None) # If specified, this method is called instead of __call__()
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self.args = opt_get(opt, ['args'], {})
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def forward(self, state):
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gen = self.env['generators'][self.opt['generator']]
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@ -54,10 +55,10 @@ class GeneratorInjector(Injector):
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else:
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params = [state[self.input]]
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if self.grad:
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results = method(*params)
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results = method(*params, **self.args)
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else:
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with torch.no_grad():
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results = method(*params)
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results = method(*params, **self.args)
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new_state = {}
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if isinstance(self.output, list):
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# Only dereference tuples or lists, not tensors. IF YOU REACH THIS ERROR, REMOVE THE BRACES AROUND YOUR OUTPUTS IN THE YAML CONFIG
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