Add rev2 of GptAsrHf

codes/models/gpt_voice/gpt_asr_hf2.py

@@ -0,0 +1,310 @@
+from time import time
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+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
+from utils.util import opt_get
+
+
+class ResBlock(nn.Module):
+    def __init__(self, chan):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv1d(chan, chan, kernel_size=3, padding=1),
+            nn.GroupNorm(chan//8, chan),
+            nn.ReLU(),
+            nn.Conv1d(chan, chan, kernel_size=3, padding=1),
+            nn.GroupNorm(chan//8, chan)
+        )
+
+    def forward(self, x):
+        return F.relu(self.net(x) + x)
+
+
+class MelEncoder(nn.Module):
+    def __init__(self, channels, mel_channels=80):
+        super().__init__()
+        self.channels = channels
+        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1),
+                                     ResBlock(channels//4),
+                                     nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//16, channels//2),
+                                     nn.ReLU(),
+                                     ResBlock(channels//2),
+                                     nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//8, channels),
+                                     nn.ReLU(),
+                                     ResBlock(channels)
+                                     )
+
+    def forward(self, x):
+        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_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
+            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 GptAsrHf2(nn.Module):
+    NUMBER_SYMBOLS = len(symbols)
+    NUMBER_TEXT_TOKENS = NUMBER_SYMBOLS+1
+
+    def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=800, max_mel_frames=3000, checkpointing=True):
+        super().__init__()
+        self.max_mel_frames = max_mel_frames // 4  # Mel frames are reduced by a factor of 4 during encoding.
+        self.max_symbols_per_phrase = max_symbols_per_phrase
+
+        self.model_dim = model_dim
+        self.max_mel_frames = self.max_mel_frames
+        self.mel_encoder = MelEncoder(model_dim)
+        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_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)
+        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, get_attns=False):
+        # Pad front remove last element to set up next token prediction. Pad at front is the "START" token.
+        text_targets = F.pad(text_targets, (1,0), value=self.NUMBER_SYMBOLS)[:, :-1]
+        text_emb = self.gpt.get_input_embeddings()(text_targets)
+        text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
+        mel_emb = self.mel_encoder(mel_inputs)
+        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))
+        emb = torch.cat([mel_emb, text_emb], dim=1)
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
+        if get_attns:
+            return gpt_out.attentions
+        enc = gpt_out.last_hidden_state
+        text_logits = self.final_norm(enc[:, mel_emb.shape[1]:])
+        text_logits = self.text_head(text_logits)
+        text_logits = text_logits.permute(0,2,1)
+        return text_logits
+
+    def forward(self, mel_inputs, text_targets, return_attentions=False):
+        text_targets = F.pad(text_targets, (0,1))  # Pad the targets with a <0> so that all have a "stop" token.
+        text_logits = self.get_logits(mel_inputs, text_targets, get_attns=return_attentions)
+        if return_attentions:
+            return text_logits  # These weren't really the logits.
+        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((mel_inputs.shape[0],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((mel_inputs.shape[0],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=True)
+        return gen[:, self.max_mel_frames:]
+
+
+@register_model
+def register_gpt_asr_hf2(opt_net, opt):
+    return GptAsrHf2(**opt_get(opt_net, ['kwargs'], {}))
+
+
+# Quick script that loads a model and halves the number of layers, then saves that model.
+def distill():
+    gpt = GptAsrHf2(max_symbols_per_phrase=250, max_mel_frames=1400, layers=12, model_dim=512, heads=8)
+    gpt.load_state_dict(torch.load('X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_ema.pth'))
+    rc = 0
+    i = 0
+    while i < len(gpt.gpt.h):
+        if rc % 2 != 0:
+            del gpt.gpt.h[i]
+        else:
+            i += 1
+        rc += 1
+    torch.save(gpt.state_dict(), 'X:\\dlas\\experiments\\train_gpt_asr_mass_hf\\models\\48000_gpt_distilled.pth')
+
+
+if __name__ == '__main__':
+    #distill()
+
+    gpt = GptAsrHf2(max_symbols_per_phrase=250, max_mel_frames=1400, layers=16, model_dim=512, heads=8)
+    l = gpt(torch.randn(2,80,800), torch.randint(high=len(symbols), size=(2,100)))
+
+    #start = time()
+    #gpt.inference(torch.randn(1,80,350), num_beams=1)
+    #print(f"Elapsed: {time()-start}")