uv back to tortoise days

codes/models/audio/tts/unified_voice2.py

@@ -32,17 +32,17 @@ class ResBlock(nn.Module):
 
 
 class GPT2InferenceModel(GPT2PreTrainedModel):
-    def __init__(self, config, gpt, posterior_pos_emb, embeddings, norm, linear):
+    def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear):
         super().__init__(config)
         self.transformer = gpt
-        self.posterior_pos_embedding = posterior_pos_emb
+        self.text_pos_embedding = text_pos_emb
         self.embeddings = embeddings
-        self.head = nn.Sequential(norm, linear)
+        self.lm_head = nn.Sequential(norm, linear)
 
         # Model parallel
         self.model_parallel = False
         self.device_map = None
-        self.cached_prior_emb = None
+        self.cached_mel_emb = None
 
     def parallelize(self, device_map=None):
         self.device_map = (
@@ -52,26 +52,27 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
         )
         assert_device_map(self.device_map, len(self.transformer.h))
         self.transformer.parallelize(self.device_map)
-        self.head = self.head.to(self.transformer.first_device)
+        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.head = self.head.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.head
+        return self.lm_head
 
     def set_output_embeddings(self, new_embeddings):
-        self.head = new_embeddings
+        self.lm_head = new_embeddings
 
-    def store_prior_emb(self, mel_emb):
+    def store_mel_emb(self, mel_emb):
-        self.cached_prior_emb = 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:
@@ -116,25 +117,25 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
         output_hidden_states=None,
         return_dict=None,
     ):
-        assert self.cached_prior_emb is not 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
-        prior_len = self.cached_prior_emb.shape[1]
+        mel_len = self.cached_mel_emb.shape[1]
         if input_ids.shape[1] != 1:
-            posterior_inputs = input_ids[:, prior_len:]
+            text_inputs = input_ids[:, mel_len:]
-            posterior_emb = self.embeddings(posterior_inputs)
+            text_emb = self.embeddings(text_inputs)
-            posterior_emb = posterior_emb + self.posterior_pos_embedding(posterior_emb)
+            text_emb = text_emb + self.text_pos_embedding(text_emb)
-            if self.cached_prior_emb.shape[0] != posterior_emb.shape[0]:
+            if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
-                prior_emb = self.cached_prior_emb.repeat_interleave(posterior_emb.shape[0] // self.cached_prior_emb.shape[0], 0)
+                mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0)
             else:
-                prior_emb = self.cached_prior_emb
+                mel_emb = self.cached_mel_emb
-            emb = torch.cat([prior_emb, posterior_emb], dim=1)
+            emb = torch.cat([mel_emb, text_emb], dim=1)
         else:
             emb = self.embeddings(input_ids)
-            emb = emb + self.posterior_pos_embedding.get_fixed_embedding(attention_mask.shape[1] - prior_len, attention_mask.device)
+            emb = emb + self.text_pos_embedding.get_fixed_embedding(attention_mask.shape[1]-mel_len, attention_mask.device)
 
         transformer_outputs = self.transformer(
             inputs_embeds=emb,
@@ -155,16 +156,16 @@ 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.head.weight.device)
+            hidden_states = hidden_states.to(self.lm_head.weight.device)
 
-        logits = self.head(hidden_states)
+        lm_logits = self.lm_head(hidden_states)
 
         if not return_dict:
-            return (logits,) + transformer_outputs[1:]
+            return (lm_logits,) + transformer_outputs[1:]
 
         return CausalLMOutputWithCrossAttentions(
             loss=None,
-            logits=logits,
+            logits=lm_logits,
             past_key_values=transformer_outputs.past_key_values,
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
@@ -237,13 +238,35 @@ 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,
+                 mel_length_compression=1024, number_text_tokens=256,
-                 stop_mel_token=8193, start_text_token=255, checkpointing=True, types=1, only_alignment_head=False):
+                 start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
+                 stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
+                 checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False):
+        """
+        Args:
+            layers: Number of layers in transformer stack.
+            model_dim: Operating dimensions of the transformer
+            heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
+            max_text_tokens: Maximum number of text tokens that will be encountered by model.
+            max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
+            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:
+            start_text_token:
+            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__()
 
         self.number_text_tokens = number_text_tokens
-        self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token
+        self.start_text_token = start_text_token
-        self.stop_text_token = 0
+        self.stop_text_token = stop_text_token
         self.number_mel_codes = number_mel_codes
         self.start_mel_token = start_mel_token
         self.stop_mel_token = stop_mel_token
@@ -255,29 +278,41 @@ 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.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
+        self.average_conditioning_embeddings = average_conditioning_embeddings
-        self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
+        self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
+        if use_mel_codes_as_input:
+            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.text_head = nn.Linear(model_dim, self.number_text_tokens)
         self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
-        self.alignment_head = nn.Linear(model_dim, 256)
-
-        if only_alignment_head:
-            for p in self.parameters():
-                p.DO_NOT_TRAIN = True
-                p.requires_grad = False
-            for p in self.alignment_head.parameters():
-                del p.DO_NOT_TRAIN
-                p.requires_grad = True
 
         # Initialize the embeddings per the GPT-2 scheme
-        embeddings = [self.text_embedding, self.mel_embedding]
+        embeddings = [self.text_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()),
@@ -304,43 +339,35 @@ 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, second_inputs, return_latent=False):
+    def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
-        emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
+        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)
+        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[:, 1:]  # The first logit is tied to the speech_conditioning_input
         enc = self.final_norm(enc)
 
         if return_latent:
-            return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
+            return enc[:, :first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
 
-        text_logits = enc[:, :first_inputs.shape[1]]
+        first_logits = enc[:, :first_inputs.shape[1]]
-        text_logits = self.text_head(text_logits)
+        first_logits = first_head(first_logits)
-        text_logits = text_logits.permute(0,2,1)
+        first_logits = first_logits.permute(0,2,1)
+        if second_inputs is not None:
+            second_logits = enc[:, -second_inputs.shape[1]:]
+            second_logits = second_head(second_logits)
+            second_logits = second_logits.permute(0,2,1)
+            return first_logits, second_logits
+        else:
+            return first_logits
 
-        mel_logits = enc[:, -second_inputs.shape[1]:]
+    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False,
-        mel_logits = self.mel_head(mel_logits)
+                return_latent=False):
-        mel_logits = mel_logits.permute(0,2,1)
-
-        alignment_logits = enc[:, -second_inputs.shape[1]:]
-        alignment_logits = self.alignment_head(alignment_logits)
-        alignment_logits = alignment_logits.permute(0,2,1)
-
-        return text_logits, mel_logits, alignment_logits
-
-
-    def get_conditioning_latent(self, speech_conditioning_input):
-        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)
-        conds = conds.mean(dim=1).unsqueeze(1)
-        return conds
-
-
-    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, ctc_codes, wav_lengths, types=None, return_latent=False):
         """
         Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
         (actuated by `text_first`).
@@ -350,46 +377,112 @@ 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.
         """
-        # Types are expressed by expanding the text embedding space.
+        # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
-        if types is not None:
+        # chopping the inputs by the maximum actual length.
-            text_inputs = text_inputs * (1+types).unsqueeze(-1)
+        max_text_len = text_lengths.max()
-
+        text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
-        # TODO: do this in the dataloader.
+        max_mel_len = wav_lengths.max() // self.mel_length_compression
-        for b in range(ctc_codes.shape[0]):
+        mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
-            last_code = 0
+        if raw_mels is not None:
-            for j in range(ctc_codes.shape[1]):
+            raw_mels = raw_mels[:, :, :max_mel_len*4]
-                if ctc_codes[b][j] == 0:
-                    ctc_codes[b][j] = last_code
-                else:
-                    last_code = ctc_codes[b][j]
-        alignment_targets = F.interpolate(ctc_codes.unsqueeze(1).float(), size=(mel_codes.shape[-1],), mode='nearest').long().squeeze()
-
         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)
-        alignment_targets = F.pad(alignment_targets, (0,2), value=0)
 
-        conds = self.get_conditioning_latent(speech_conditioning_input)
+        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)
         mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
-        mel_inp = mel_codes
+        if raw_mels is not None:
+            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)
 
-        text_logits, mel_logits, alignment_logits = self.get_logits(conds, text_emb, mel_emb, return_latent=return_latent)
+        if text_first:
-        if return_latent:
+            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)
-            return mel_logits[:, :-2]  # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
+            if return_latent:
+                return mel_logits[:, :-1]  # Despite the name, these are not logits.
+        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)
+            if return_latent:
+                return text_logits[:, :-1]  # Despite the name, these are not logits
 
+        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())
-        loss_alignment = F.cross_entropy(alignment_logits, alignment_targets)
+        return loss_text.mean(), loss_mel.mean(), mel_logits
-        return loss_text.mean(), loss_mel.mean(), loss_alignment, mel_logits
 
-    def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs):
+    def text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
+        """
+        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:
@@ -416,17 +509,64 @@ 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)
-        conds = conds.mean(dim=1).unsqueeze(1)
+        if self.average_conditioning_embeddings:
+            conds = conds.mean(dim=1).unsqueeze(1)
 
         emb = torch.cat([conds, text_emb], dim=1)
-        self.inference_model.store_prior_emb(emb)
+        self.inference_model.store_mel_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, return_dict_in_generate=True, **hf_generate_kwargs)
+                                            max_length=seq_length, output_attentions=return_attentions, return_dict_in_generate=True, **hf_generate_kwargs)
-        return gen.sequences[:, fake_inputs.shape[1]:]
+        if return_attentions:
+            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
@@ -435,10 +575,10 @@ def register_unified_voice2(opt_net, opt):
 
 
 if __name__ == '__main__':
-    gpt = UnifiedVoice(model_dim=256, heads=4, max_conditioning_inputs=4, types=2)
+    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)
     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]),
+            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]))

codes/models/audio/tts/unified_voice4.py

@@ -0,0 +1,444 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import GPT2Config, GPT2PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
+from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
+
+from models.arch_util import AttentionBlock
+from models.audio.tts.transformer_builders import build_hf_gpt_transformer
+from models.lucidrains.x_transformers import RotaryEmbedding, apply_rotary_pos_emb
+from trainer.networks import register_model
+from utils.util import opt_get
+
+
+class ResBlock(nn.Module):
+    """
+    Basic residual convolutional block that uses GroupNorm.
+    """
+    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 GPT2InferenceModel(GPT2PreTrainedModel):
+    def __init__(self, config, gpt, posterior_pos_emb, embeddings, norm, linear):
+        super().__init__(config)
+        self.transformer = gpt
+        self.posterior_pos_embedding = posterior_pos_emb
+        self.embeddings = embeddings
+        self.head = nn.Sequential(norm, linear)
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.cached_prior_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.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.head = self.head.to("cpu")
+        self.model_parallel = False
+        torch.cuda.empty_cache()
+
+    def get_output_embeddings(self):
+        return self.head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.head = new_embeddings
+
+    def store_prior_emb(self, 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:
+            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_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
+        prior_len = self.cached_prior_emb.shape[1]
+        if input_ids.shape[1] != 1:
+            posterior_inputs = input_ids[:, prior_len:]
+            posterior_emb = self.embeddings(posterior_inputs)
+            posterior_emb = posterior_emb + self.posterior_pos_embedding(posterior_emb)
+            if self.cached_prior_emb.shape[0] != posterior_emb.shape[0]:
+                prior_emb = self.cached_prior_emb.repeat_interleave(posterior_emb.shape[0] // self.cached_prior_emb.shape[0], 0)
+            else:
+                prior_emb = self.cached_prior_emb
+            emb = torch.cat([prior_emb, posterior_emb], dim=1)
+        else:
+            emb = self.embeddings(input_ids)
+            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,
+            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.head.weight.device)
+
+        logits = self.head(hidden_states)
+
+        if not return_dict:
+            return (logits,) + transformer_outputs[1:]
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=None,
+            logits=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 ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 attn_blocks=6,
+                 num_attn_heads=4,
+                 do_checkpointing=False,
+                 mean=False):
+        super().__init__()
+        attn = []
+        self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+        self.do_checkpointing = do_checkpointing
+        self.mean = mean
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.attn(h)
+        if self.mean:
+            return h.mean(dim=2)
+        else:
+            return h[:, :, 0]
+
+
+class MelEncoder(nn.Module):
+    def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2):
+        super().__init__()
+        self.channels = channels
+        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1),
+                                     nn.Sequential(*[ResBlock(channels//4) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//16, channels//2),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels//2) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//8, channels),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]),
+                                     )
+        self.reduction = 4
+
+
+    def forward(self, x):
+        for e in self.encoder:
+            x = e(x)
+        return x.permute(0,2,1)
+
+
+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, start_text_token=255, checkpointing=True, types=1, only_alignment_head=False):
+        super().__init__()
+
+        self.number_text_tokens = number_text_tokens
+        self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token
+        self.stop_text_token = 0
+        self.number_mel_codes = number_mel_codes
+        self.start_mel_token = start_mel_token
+        self.stop_mel_token = stop_mel_token
+        self.layers = layers
+        self.heads = heads
+        self.max_conditioning_inputs = max_conditioning_inputs
+        self.max_mel_tokens = -1 if max_mel_tokens == -1 else max_mel_tokens+2+self.max_conditioning_inputs
+        self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens+2
+        self.model_dim = model_dim
+        self.mel_length_compression = mel_length_compression
+        self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
+        self.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
+        self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
+        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)
+
+        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)
+        self.alignment_head = nn.Linear(model_dim, 256)
+
+        if only_alignment_head:
+            for p in self.parameters():
+                p.DO_NOT_TRAIN = True
+                p.requires_grad = False
+            for p in self.alignment_head.parameters():
+                del p.DO_NOT_TRAIN
+                p.requires_grad = True
+
+        # Initialize the embeddings per the GPT-2 scheme
+        embeddings = [self.text_embedding, self.mel_embedding]
+        for module in embeddings:
+            module.weight.data.normal_(mean=0.0, std=.02)
+
+    def get_grad_norm_parameter_groups(self):
+        return {
+            'conditioning_encoder': list(self.conditioning_encoder.parameters()),
+            'gpt': list(self.gpt.parameters()),
+            'heads': list(self.text_head.parameters()) + list(self.mel_head.parameters()),
+        }
+
+    def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
+        inp = F.pad(input, (1,0), value=start_token)
+        tar = F.pad(input, (0,1), value=stop_token)
+        return inp, tar
+
+    def set_mel_padding(self, mel_input_tokens, wav_lengths):
+        """
+        Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
+        that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
+        preformatting to create a working TTS model.
+        """
+        # Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
+        mel_lengths = wav_lengths // self.mel_length_compression
+        for b in range(len(mel_lengths)):
+            actual_end = mel_lengths[b] + 1  # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
+            if actual_end < mel_input_tokens.shape[-1]:
+                mel_input_tokens[b, actual_end:] = self.stop_mel_token
+        return mel_input_tokens
+
+    def get_logits(self, speech_conditioning_inputs, first_inputs, second_inputs, return_latent=False):
+        emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
+
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True)
+
+        enc = gpt_out.last_hidden_state[:, 1:]  # The first logit is tied to the speech_conditioning_input
+        enc = self.final_norm(enc)
+
+        if return_latent:
+            return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
+
+        text_logits = enc[:, :first_inputs.shape[1]]
+        text_logits = self.text_head(text_logits)
+        text_logits = text_logits.permute(0,2,1)
+
+        mel_logits = enc[:, -second_inputs.shape[1]:]
+        mel_logits = self.mel_head(mel_logits)
+        mel_logits = mel_logits.permute(0,2,1)
+
+        alignment_logits = enc[:, -second_inputs.shape[1]:]
+        alignment_logits = self.alignment_head(alignment_logits)
+        alignment_logits = alignment_logits.permute(0,2,1)
+
+        return text_logits, mel_logits, alignment_logits
+
+
+    def get_conditioning_latent(self, speech_conditioning_input):
+        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)
+        conds = conds.mean(dim=1).unsqueeze(1)
+        return conds
+
+
+    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, ctc_codes, wav_lengths, types=None, return_latent=False):
+        """
+        Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
+        (actuated by `text_first`).
+
+        speech_conditioning_input: MEL float tensor, (b,80,s)
+        text_inputs: long tensor, (b,t)
+        text_lengths: long tensor, (b,)
+        mel_inputs:  long tensor, (b,m)
+        wav_lengths: long tensor, (b,)
+
+        If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
+        """
+        # Types are expressed by expanding the text embedding space.
+        if types is not None:
+            text_inputs = text_inputs * (1+types).unsqueeze(-1)
+
+        # TODO: do this in the dataloader.
+        for b in range(ctc_codes.shape[0]):
+            last_code = 0
+            for j in range(ctc_codes.shape[1]):
+                if ctc_codes[b][j] == 0:
+                    ctc_codes[b][j] = last_code
+                else:
+                    last_code = ctc_codes[b][j]
+        alignment_targets = F.interpolate(ctc_codes.unsqueeze(1).float(), size=(mel_codes.shape[-1],), mode='nearest').long().squeeze()
+
+        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)
+        alignment_targets = F.pad(alignment_targets, (0,2), value=0)
+
+        conds = self.get_conditioning_latent(speech_conditioning_input)
+        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)
+        mel_inp = mel_codes
+        mel_emb = self.mel_embedding(mel_inp)
+        mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
+
+        text_logits, mel_logits, alignment_logits = self.get_logits(conds, text_emb, mel_emb, 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.
+
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
+        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
+        loss_alignment = F.cross_entropy(alignment_logits, alignment_targets)
+        return loss_text.mean(), loss_mel.mean(), loss_alignment, mel_logits
+
+    def inference_speech(self, speech_conditioning_input, text_inputs, **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:
+            seq_length = self.max_mel_tokens + self.max_text_tokens + 2
+        if not hasattr(self, 'inference_model'):
+            # TODO: Decouple gpt_config from this inference model.
+            gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
+                                    n_positions=seq_length,
+                                    n_ctx=seq_length,
+                                    n_embd=self.model_dim,
+                                    n_layer=self.layers,
+                                    n_head=self.heads,
+                                    gradient_checkpointing=False,
+                                    use_cache=True)
+            self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
+            self.gpt.wte = self.mel_embedding
+
+        text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
+        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)
+
+        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)
+        conds = conds.mean(dim=1).unsqueeze(1)
+
+        emb = torch.cat([conds, text_emb], dim=1)
+        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, return_dict_in_generate=True, **hf_generate_kwargs)
+        return gen.sequences[:, fake_inputs.shape[1]:]
+
+
+@register_model
+def register_unified_voice4(opt_net, opt):
+    return UnifiedVoice(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    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]))