Apply fix to gpt_asr_hf and prep it for inference

Fix is that we were predicting two characters in advance, not next character

codes/models/gpt_voice/gpt_asr_hf.py

@@ -1,7 +1,9 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from transformers import GPT2Model, GPT2Config
+from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
 
 from models.tacotron2.text import symbols
 from trainer.networks import register_model
@@ -46,6 +48,159 @@ class MelEncoder(nn.Module):
         return self.encoder(x)
 
 
+class GPT2InferenceModel(GPT2PreTrainedModel):
+    def __init__(self, config, gpt, text_pos_emb, norm, linear):
+        super().__init__(config)
+        self.transformer = gpt
+        self.text_pos_embedding = text_pos_emb
+        self.lm_head = nn.Sequential(norm, linear)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.cached_mel_emb = None
+
+    def parallelize(self, device_map=None):
+        self.device_map = (
+            get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
+            if device_map is None
+            else device_map
+        )
+        assert_device_map(self.device_map, len(self.transformer.h))
+        self.transformer.parallelize(self.device_map)
+        self.lm_head = self.lm_head.to(self.transformer.first_device)
+        self.model_parallel = True
+
+    def deparallelize(self):
+        self.transformer.deparallelize()
+        self.transformer = self.transformer.to("cpu")
+        self.lm_head = self.lm_head.to("cpu")
+        self.model_parallel = False
+        torch.cuda.empty_cache()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def store_mel_emb(self, mel_emb):
+        self.cached_mel_emb = mel_emb
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        assert self.cached_mel_emb is not None
+        assert inputs_embeds is None  # Not supported by this inference model.
+        assert labels is None  # Training not supported by this inference model.
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Create embedding
+        mel_len = self.cached_mel_emb.shape[1]
+        if input_ids.shape[1] != 1:
+            text_inputs = input_ids[:, mel_len:]
+            text_emb = self.transformer.get_input_embeddings()(text_inputs)
+            text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_emb.device))
+            if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
+                mel_emb = self.cached_mel_emb.repeat(text_emb.shape[0], 1, 1)
+            else:
+                mel_emb = self.cached_mel_emb
+            emb = torch.cat([mel_emb, text_emb], dim=1)
+        else:
+            emb = self.transformer.get_input_embeddings()(input_ids) + \
+                          self.text_pos_embedding(torch.tensor(attention_mask.shape[1]-mel_len, device=attention_mask.device)).unsqueeze(0).unsqueeze(0)
+
+        transformer_outputs = self.transformer(
+            inputs_embeds=emb,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.transformer.first_device)
+            hidden_states = hidden_states.to(self.lm_head.weight.device)
+
+        lm_logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            return (lm_logits,) + transformer_outputs[1:]
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=None,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+            cross_attentions=transformer_outputs.cross_attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        """
+        This function is used to re-order the :obj:`past_key_values` cache if
+        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
+        called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
+        """
+        return tuple(
+            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
+            for layer_past in past
+        )
+
+
 class GptAsrHf(nn.Module):
     NUMBER_SYMBOLS = len(symbols)
     NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1
@@ -61,17 +216,19 @@ class GptAsrHf(nn.Module):
         self.text_pos_embedding = nn.Embedding(self.max_symbols_per_phrase + 1, model_dim)
         self.mel_pos_embedding = nn.Embedding(self.max_mel_frames, model_dim)
         seq_length = 2+self.max_symbols_per_phrase+self.max_mel_frames
-        self.gpt = GPT2Model(GPT2Config(vocab_size=self.NUMBER_TEXT_TOKENS,
+        self.gpt_config = GPT2Config(vocab_size=self.NUMBER_TEXT_TOKENS,
                                         n_positions=seq_length,
                                         n_ctx=seq_length,
                                         n_embd=model_dim,
                                         n_layer=layers,
                                         n_head=heads,
                                         gradient_checkpointing=checkpointing,
-                                        use_cache=not checkpointing))
+                                        use_cache=not checkpointing)
+        self.gpt = GPT2Model(self.gpt_config)
         self.final_norm = nn.LayerNorm(model_dim)
         self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS)
 
+
     def get_logits(self, mel_inputs, text_targets):
         # Pad front and back. Pad at front is the "START" token.
         text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)
@@ -91,9 +248,32 @@ class GptAsrHf(nn.Module):
 
     def forward(self, mel_inputs, text_targets):
         text_logits = self.get_logits(mel_inputs, text_targets)
-        loss_text = F.cross_entropy(text_logits[:,:,:-1], text_targets[:,1:].long())
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
         return loss_text.mean(), text_logits
 
+    def inference(self, mel_inputs, cond_text=None, do_sample=False, temperature=1.0, num_beams=8):
+        if not hasattr(self, 'inference_model'):
+            self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.text_pos_embedding, self.final_norm, self.text_head)
+
+        mel_emb = self.mel_encoder(mel_inputs)
+        assert mel_emb.shape[-1] <= self.max_mel_frames
+        mel_emb = F.pad(mel_emb, (0, self.max_mel_frames - mel_emb.shape[-1]))
+        mel_emb = mel_emb.permute(0,2,1).contiguous()
+        mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
+        self.inference_model.store_mel_emb(mel_emb)
+
+        # "fake_inputs" are stand-ins for the MEL frames, which will be injected with the prep_inputs function above.
+        if cond_text is None:
+            fake_inputs = torch.full((1,self.max_mel_frames+1,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
+            fake_inputs[:,-1] = self.NUMBER_SYMBOLS
+        else:
+            cond_used = 10
+            fake_inputs = torch.full((1,self.max_mel_frames+1+cond_used,), fill_value=1, dtype=torch.long, device=mel_inputs.device)
+            fake_inputs[:,-1-cond_used] = self.NUMBER_SYMBOLS
+            fake_inputs[:, -cond_used:] = cond_text[:, :cond_used]
+        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,
+                          max_length=self.max_symbols_per_phrase+self.max_mel_frames, temperature=temperature, num_beams=num_beams, use_cache=False)
+        return gen[:, self.max_mel_frames:]
 
 @register_model
 def register_gpt_asr_hf(opt_net, opt):

codes/scripts/audio/asr_eval.py

@@ -41,7 +41,7 @@ if __name__ == "__main__":
     torch.backends.cudnn.benchmark = True
     want_metrics = False
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_mass.yml')
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_gpt_asr_hf.yml')
     opt = option.parse(parser.parse_args().opt, is_train=False)
     opt = option.dict_to_nonedict(opt)
     utils.util.loaded_options = opt
@@ -71,11 +71,11 @@ if __name__ == "__main__":
 
         tq = tqdm(test_loader)
         for data in tq:
-            if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
+            #if data['clip'].shape[-1] > opt['networks']['asr_gen']['kwargs']['max_mel_frames']*255:
-                continue
+            #    continue
             pred = forward_pass(model, data, dataset_dir, opt, batch)
             pred = pred.replace('_', '')
-            output.write(f'{pred}\t{os.path.basename(data["path"][0])}\n')
+            output.write(f'{pred}\t{os.path.basename(data["filenames"][0])}\n')
             print(pred)
             output.flush()
             batch += 1

codes/train.py

@@ -284,7 +284,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mass.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_asr_mass_hf.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/injectors/base_injectors.py

@@ -40,6 +40,7 @@ class GeneratorInjector(Injector):
         super(GeneratorInjector, self).__init__(opt, env)
         self.grad = opt['grad'] if 'grad' in opt.keys() else True
         self.method = opt_get(opt, ['method'], None)  # If specified, this method is called instead of __call__()
+        self.args = opt_get(opt, ['args'], {})
 
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
@@ -54,10 +55,10 @@ class GeneratorInjector(Injector):
             else:
                 params = [state[self.input]]
             if self.grad:
-                results = method(*params)
+                results = method(*params, **self.args)
             else:
                 with torch.no_grad():
-                    results = method(*params)
+                    results = method(*params, **self.args)
         new_state = {}
         if isinstance(self.output, list):
             # Only dereference tuples or lists, not tensors. IF YOU REACH THIS ERROR, REMOVE THE BRACES AROUND YOUR OUTPUTS IN THE YAML CONFIG