clean up unified voice

- remove unused code
- fix inference model to use the terms "prior" and "posterior" to properly define the modeling order (they were inverted before)
- default some settings I never intend to change in the future

codes/models/audio/tts/unified_voice2.py

@@ -32,17 +32,17 @@ class ResBlock(nn.Module):
 
 
 class GPT2InferenceModel(GPT2PreTrainedModel):
-    def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear):
+    def __init__(self, config, gpt, posterior_pos_emb, embeddings, norm, linear):
         super().__init__(config)
         self.transformer = gpt
-        self.text_pos_embedding = text_pos_emb
+        self.posterior_pos_embedding = posterior_pos_emb
         self.embeddings = embeddings
-        self.lm_head = nn.Sequential(norm, linear)
+        self.head = nn.Sequential(norm, linear)
 
         # Model parallel
         self.model_parallel = False
         self.device_map = None
-        self.cached_mel_emb = None
+        self.cached_prior_emb = None
 
     def parallelize(self, device_map=None):
         self.device_map = (
@@ -52,27 +52,26 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
         )
         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.head = self.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.head = self.head.to("cpu")
         self.model_parallel = False
         torch.cuda.empty_cache()
 
     def get_output_embeddings(self):
-        return self.lm_head
+        return self.head
 
     def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
+        self.head = new_embeddings
 
-    def store_mel_emb(self, mel_emb):
+    def store_prior_emb(self, mel_emb):
-        self.cached_mel_emb = mel_emb
+        self.cached_prior_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:
@@ -117,25 +116,25 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
         output_hidden_states=None,
         return_dict=None,
     ):
-        assert self.cached_mel_emb is not None
+        assert self.cached_prior_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]
+        prior_len = self.cached_prior_emb.shape[1]
         if input_ids.shape[1] != 1:
-            text_inputs = input_ids[:, mel_len:]
+            posterior_inputs = input_ids[:, prior_len:]
-            text_emb = self.embeddings(text_inputs)
+            posterior_emb = self.embeddings(posterior_inputs)
-            text_emb = text_emb + self.text_pos_embedding(text_emb)
+            posterior_emb = posterior_emb + self.posterior_pos_embedding(posterior_emb)
-            if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
+            if self.cached_prior_emb.shape[0] != posterior_emb.shape[0]:
-                mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
+                prior_emb = self.cached_prior_emb.repeat_interleave(posterior_emb.shape[0] // self.cached_prior_emb.shape[0], 0)
             else:
-                mel_emb = self.cached_mel_emb
+                prior_emb = self.cached_prior_emb
-            emb = torch.cat([mel_emb, text_emb], dim=1)
+            emb = torch.cat([prior_emb, posterior_emb], dim=1)
         else:
             emb = self.embeddings(input_ids)
-            emb = emb + self.text_pos_embedding.get_fixed_embedding(attention_mask.shape[1]-mel_len, attention_mask.device)
+            emb = emb + self.posterior_pos_embedding.get_fixed_embedding(attention_mask.shape[1] - prior_len, attention_mask.device)
 
         transformer_outputs = self.transformer(
             inputs_embeds=emb,
@@ -156,16 +155,16 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
         # 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)
+            hidden_states = hidden_states.to(self.head.weight.device)
 
-        lm_logits = self.lm_head(hidden_states)
+        logits = self.head(hidden_states)
 
         if not return_dict:
-            return (lm_logits,) + transformer_outputs[1:]
+            return (logits,) + transformer_outputs[1:]
 
         return CausalLMOutputWithCrossAttentions(
             loss=None,
-            logits=lm_logits,
+            logits=logits,
             past_key_values=transformer_outputs.past_key_values,
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
@@ -239,9 +238,7 @@ class MelEncoder(nn.Module):
 class UnifiedVoice(nn.Module):
     def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
                  mel_length_compression=1024, number_text_tokens=256, number_mel_codes=8194, start_mel_token=8192,
-                 stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True, start_text_token=None,
+                 stop_mel_token=8193, start_text_token=None, checkpointing=True, types=1):
-                 checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False,
-                 types=1):
         """
         Args:
             layers: Number of layers in transformer stack.
@@ -252,14 +249,10 @@ class UnifiedVoice(nn.Module):
             max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
             mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
             number_text_tokens:
-            stop_text_token:
             number_mel_codes:
             start_mel_token:
             stop_mel_token:
-            train_solo_embeddings:
-            use_mel_codes_as_input:
             checkpointing:
-            average_conditioning_embeddings: Whether or not conditioning embeddings should be averaged, instead of fed piecewise into the model.
         """
         super().__init__()
 
@@ -277,41 +270,20 @@ class UnifiedVoice(nn.Module):
         self.model_dim = model_dim
         self.mel_length_compression = mel_length_compression
         self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
-        self.average_conditioning_embeddings = average_conditioning_embeddings
         self.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
-        if use_mel_codes_as_input:
+        self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
-            self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
-        else:
-            self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
         self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
             build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens, self.max_text_tokens, checkpointing)
-        if train_solo_embeddings:
-            self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
-            self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
-        else:
-            self.mel_solo_embedding = 0
-            self.text_solo_embedding = 0
 
         self.final_norm = nn.LayerNorm(model_dim)
         self.text_head = nn.Linear(model_dim, self.number_text_tokens*types+1)
         self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
 
         # Initialize the embeddings per the GPT-2 scheme
-        embeddings = [self.text_embedding]
+        embeddings = [self.text_embedding, self.mel_embedding]
-        if use_mel_codes_as_input:
-            embeddings.append(self.mel_embedding)
         for module in embeddings:
             module.weight.data.normal_(mean=0.0, std=.02)
 
-        if freeze_everything_but_position_embeddings:
-            for p in self.parameters():
-                p.requires_grad = False
-                p.DO_NOT_TRAIN = True
-            for m in [self.mel_pos_embedding, self.text_pos_embedding]:
-                for p in m.parameters():
-                    del p.DO_NOT_TRAIN
-                    p.requires_grad = True
-
     def get_grad_norm_parameter_groups(self):
         return {
             'conditioning_encoder': list(self.conditioning_encoder.parameters()),
@@ -338,15 +310,13 @@ class UnifiedVoice(nn.Module):
                 mel_input_tokens[b, actual_end:] = self.stop_mel_token
         return mel_input_tokens
 
-    def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
+    def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, return_latent=False):
         if second_inputs is not None:
             emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
         else:
             emb = torch.cat([speech_conditioning_inputs, first_inputs], dim=1)
 
-        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True)
-        if get_attns:
-            return gpt_out.attentions
 
         enc = gpt_out.last_hidden_state[:, 1:]  # The first logit is tied to the speech_conditioning_input
         enc = self.final_norm(enc)
@@ -372,13 +342,11 @@ class UnifiedVoice(nn.Module):
         for j in range(speech_conditioning_input.shape[1]):
             conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
         conds = torch.stack(conds, dim=1)
-        if self.average_conditioning_embeddings:
+        conds = conds.mean(dim=1).unsqueeze(1)
-            conds = conds.mean(dim=1).unsqueeze(1)
         return conds
 
 
-    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
+    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, return_latent=False):
-                return_latent=False, clip_inputs=True):
         """
         Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
         (actuated by `text_first`).
@@ -388,25 +356,13 @@ class UnifiedVoice(nn.Module):
         text_lengths: long tensor, (b,)
         mel_inputs:  long tensor, (b,m)
         wav_lengths: long tensor, (b,)
-        raw_mels: MEL float tensor (b,80,s)
 
-        If return_attentions is specified, only logits are returned.
         If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
-        If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
         """
         # Types are expressed by expanding the text embedding space.
         if types is not None:
             text_inputs = text_inputs * (1+types).unsqueeze(-1)
 
-        if clip_inputs:
-            # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
-            # chopping the inputs by the maximum actual length.
-            max_text_len = text_lengths.max()
-            text_inputs = text_inputs[:, :max_text_len]
-            max_mel_len = wav_lengths.max() // self.mel_length_compression
-            mel_codes = mel_codes[:, :max_mel_len]
-            if raw_mels is not None:
-                raw_mels = raw_mels[:, :, :max_mel_len*4]
         mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
         text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
         mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
@@ -416,86 +372,24 @@ class UnifiedVoice(nn.Module):
         text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
         text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
         mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
-        if raw_mels is not None:
+        mel_inp = mel_codes
-            mel_inp = F.pad(raw_mels, (0, 8))
-        else:
-            mel_inp = mel_codes
         mel_emb = self.mel_embedding(mel_inp)
         mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
 
         if text_first:
-            text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
+            text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, return_latent=return_latent)
             if return_latent:
                 return mel_logits[:, :-2]  # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
         else:
-            mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
+            mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, return_latent=return_latent)
             if return_latent:
                 return text_logits[:, :-2]  # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
 
-        if return_attentions:
-            return mel_logits
         loss_text = F.cross_entropy(text_logits, text_targets.long())
         loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
         return loss_text.mean(), loss_mel.mean(), mel_logits
 
-    def text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
+    def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
-        """
-        Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
-        model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
-        """
-        # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
-        # chopping the inputs by the maximum actual length.
-        max_text_len = text_lengths.max()
-        text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
-
-        speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
-        conds = []
-        for j in range(speech_conditioning_input.shape[1]):
-            conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
-        conds = torch.stack(conds, dim=1)
-        if self.average_conditioning_embeddings:
-            conds = conds.mean(dim=1).unsqueeze(1)
-
-        text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
-        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding
-        text_logits = self.get_logits(conds, text_emb, self.text_head)
-        loss_text = F.cross_entropy(text_logits, text_targets.long())
-        return loss_text.mean()
-
-    def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None):
-        """
-        Performs autoregressive modeling on only speech data.
-        """
-        assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
-
-        # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
-        # chopping the inputs by the maximum actual length.
-        max_mel_len = wav_lengths.max() // self.mel_length_compression
-        mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
-        mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
-        if raw_mels is not None:
-            raw_mels = raw_mels[:, :, :max_mel_len*4]
-
-        speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
-        conds = []
-        for j in range(speech_conditioning_input.shape[1]):
-            conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
-        conds = torch.stack(conds, dim=1)
-        if self.average_conditioning_embeddings:
-            conds = conds.mean(dim=1).unsqueeze(1)
-
-        mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
-        if raw_mels is not None:
-            mel_inp = F.pad(raw_mels, (0, 4))
-        else:
-            mel_inp = mel_codes
-        mel_emb = self.mel_embedding(mel_inp)
-        mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) + self.mel_solo_embedding
-        mel_logits = self.get_logits(conds, mel_emb, self.mel_head)
-        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
-        return loss_mel.mean()
-
-    def inference_speech(self, speech_conditioning_input, text_inputs, return_attentions=False, **hf_generate_kwargs):
         if self.max_mel_tokens == -1:  # Assume if this is the case, max_mel_tokens=-1 also
             seq_length = 2002  # Arbitrary default.
         else:
@@ -522,64 +416,17 @@ class UnifiedVoice(nn.Module):
         for j in range(speech_conditioning_input.shape[1]):
             conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
         conds = torch.stack(conds, dim=1)
-        if self.average_conditioning_embeddings:
+        conds = conds.mean(dim=1).unsqueeze(1)
-            conds = conds.mean(dim=1).unsqueeze(1)
 
         emb = torch.cat([conds, text_emb], dim=1)
-        self.inference_model.store_mel_emb(emb)
+        self.inference_model.store_prior_emb(emb)
 
         fake_inputs = torch.full((emb.shape[0], conds.shape[1]+emb.shape[1],), fill_value=1, dtype=torch.long, device=text_inputs.device)
         fake_inputs[:,-1] = self.start_mel_token
 
         gen = self.inference_model.generate(fake_inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token,
-                                            max_length=seq_length, output_attentions=return_attentions, return_dict_in_generate=True, **hf_generate_kwargs)
+                                            max_length=seq_length, return_dict_in_generate=True, **hf_generate_kwargs)
-        if return_attentions:
+        return gen.sequences[:, fake_inputs.shape[1]:]
-            return gen.sequences[:, fake_inputs.shape[1]:], gen.attentions
-        else:
-            return gen.sequences[:, fake_inputs.shape[1]:]
-
-
-    # Turns the (utterly insane) output of HF.generate() into a far more sane output:
-    # [tensors(B,H,S,S)]. Outer=layers, B=batch,H=head,S=sequence
-    def make_hf_generate_attentions_sane(self, attentions):
-        layers = [[] for _ in range(len(attentions[0]))]
-        full_attention_size = attentions[-1][0].shape[-1]
-        for i, gen in enumerate(attentions):
-            for j, lyr in enumerate(gen):
-                layers[j].append(F.pad(lyr, (0, full_attention_size - lyr.shape[-1])))
-        catted = []
-        for lyr in layers:
-            catted.append(torch.cat(lyr, dim=2))
-        return catted
-
-    def convert_attentions_to_aligned_codes(self, text, attentions, codes, num_conds):
-        """
-        This was an attempt to make some sense out of the attention matrix retrieved from the unified_voice model. Unfortunately, I can't use it for aligning text & voice.
-        """
-        text_padding = num_conds+2
-        num_text = text.shape[-1]
-        num_context = num_text + text_padding
-        assert num_context + 1 == attentions[0][0].shape[-1]
-        attentions = self.make_hf_generate_attentions_sane(attentions)
-        results = [torch.empty_like(codes) for _ in range(len(attentions))]
-        for l, layer in enumerate(attentions):
-            dec_context = layer[:, :, num_context:, :]
-            # Mask out everything that isn't text (including the start token, which gets a LOT of attention)
-            dec_context[:,:,:,:text_padding+1] = 0
-            dec_context[:,:,:,num_context:] = 0
-            for h in range(dec_context.shape[1]):
-                dec_context_indices = torch.argmax(dec_context[0,h], dim=-1)
-                print(f'layer_{l};head_{h}: ' + str(dec_context_indices))
-        for t, att_tok in enumerate(attentions):
-            combined_attention_weights = torch.zeros((codes.shape[0], num_text), device=codes.device)
-            for lyr in att_tok:
-                token_to_text_attentions = lyr[:, :, -1, text_padding:(text_padding + num_text)].sum(dim=1)
-                combined_attention_weights = combined_attention_weights + token_to_text_attentions
-                break
-            most_attended_text_token = combined_attention_weights.argmax(dim=-1)
-            results[:, t] = most_attended_text_token
-        eos_token_mask = (codes != self.stop_mel_token)
-        return results * eos_token_mask
 
 
 @register_model
@@ -588,11 +435,10 @@ def register_unified_voice2(opt_net, opt):
 
 
 if __name__ == '__main__':
-    gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4, freeze_everything_but_position_embeddings=True, types=2)
+    gpt = UnifiedVoice(model_dim=256, heads=4, max_conditioning_inputs=4, types=2)
     l = gpt(torch.randn(2, 3, 80, 800),
             torch.randint(high=256, size=(2,120)),
             torch.tensor([32, 120]),
             torch.randint(high=8192, size=(2,250)),
             torch.tensor([250*256,195*256]),
             types=torch.tensor([0, 1]))
-    #gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))