unified_voice with rotary embeddings
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@ -59,8 +59,8 @@ def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text
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"""
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from transformers import GPT2Config, GPT2Model
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gpt_config = GPT2Config(vocab_size=256, # Unused.
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n_positions=max_mel_seq_len+max_text_seq_len,
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n_ctx=max_mel_seq_len+max_text_seq_len,
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n_positions=1,
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n_ctx=1,
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n_embd=model_dim,
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n_layer=layers,
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n_head=heads,
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@ -72,8 +72,10 @@ def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text
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gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
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# Built-in token embeddings are unused.
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del gpt.wte
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return gpt, LearnedPositionEmbeddings(max_mel_seq_len, model_dim), LearnedPositionEmbeddings(max_text_seq_len, model_dim),\
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None, None
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mel_pos_emb = LearnedPositionEmbeddings(max_mel_seq_len, model_dim) if max_mel_seq_len != -1 else functools.partial(null_position_embeddings, dim=model_dim)
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text_pos_emb = LearnedPositionEmbeddings(max_text_seq_len, model_dim) if max_mel_seq_len != -1 else functools.partial(null_position_embeddings, dim=model_dim)
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return gpt, mel_pos_emb, text_pos_emb, None, None
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def build_lr_performer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing):
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@ -3,10 +3,12 @@ import torch.nn as nn
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import torch.nn.functional as F
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from transformers import GPT2Config, GPT2PreTrainedModel
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from transformers.models.gpt2.modeling_gpt2 import GPT2Attention
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from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
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from models.arch_util import AttentionBlock
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from models.audio.tts.transformer_builders import build_hf_gpt_transformer
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from models.lucidrains.x_transformers import RotaryEmbedding, apply_rotary_pos_emb
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from trainer.networks import register_model
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from utils.util import opt_get
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@ -183,6 +185,73 @@ class GPT2InferenceModel(GPT2PreTrainedModel):
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)
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class GPT2AttentionWithRotaryEmbeddings(GPT2Attention):
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def __init__(self, config, is_cross_attention=False, layer_idx=None):
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super().__init__(config, is_cross_attention=is_cross_attention, layer_idx=layer_idx)
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self.rotary_pos_emb = RotaryEmbedding(32)
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def forward(
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self,
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hidden_states,
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layer_past=None,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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use_cache=False,
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output_attentions=False,
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):
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if encoder_hidden_states is not None:
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if not hasattr(self, "q_attn"):
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raise ValueError(
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"If class is used as cross attention, the weights `q_attn` have to be defined. "
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"Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
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)
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query = self.q_attn(hidden_states)
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key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
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attention_mask = encoder_attention_mask
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else:
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query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
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query = self._split_heads(query, self.num_heads, self.head_dim)
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key = self._split_heads(key, self.num_heads, self.head_dim)
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value = self._split_heads(value, self.num_heads, self.head_dim)
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if layer_past is not None:
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past_key, past_value = layer_past
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key = torch.cat((past_key, key), dim=-2)
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value = torch.cat((past_value, value), dim=-2)
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if use_cache is True:
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present = (key, value)
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else:
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present = None
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# Apply rotary embeddings. This is the only difference between this implementation and the HF one.
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rotary_pos_emb = self.rotary_pos_emb(hidden_states.shape[1], hidden_states.device)
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l = rotary_pos_emb.shape[-1]
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(ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (query, key, value))
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ql, kl, vl = map(lambda t: apply_rotary_pos_emb(t, rotary_pos_emb), (ql, kl, vl))
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query, key, value = map(lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr)))
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if self.reorder_and_upcast_attn:
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attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)
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else:
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attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
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attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
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attn_output = self.c_proj(attn_output)
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attn_output = self.resid_dropout(attn_output)
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outputs = (attn_output, present)
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if output_attentions:
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outputs += (attn_weights,)
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return outputs # a, present, (attentions)
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class ConditioningEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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@ -239,7 +308,7 @@ class UnifiedVoice(nn.Module):
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mel_length_compression=1024, number_text_tokens=256,
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start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
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stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
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checkpointing=True, average_conditioning_embeddings=False):
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checkpointing=True, average_conditioning_embeddings=False, use_rotary_embeddings=False):
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"""
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Args:
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layers: Number of layers in transformer stack.
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@ -270,8 +339,8 @@ class UnifiedVoice(nn.Module):
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self.stop_mel_token = stop_mel_token
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self.layers = layers
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self.heads = heads
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self.max_mel_tokens = max_mel_tokens
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self.max_text_tokens = max_text_tokens
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self.max_mel_tokens = -1 if max_mel_tokens == -1 else max_mel_tokens+2+self.max_conditioning_inputs
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self.max_text_tokens = -1 if max_text_tokens == -1 else max_text_tokens+2
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self.model_dim = model_dim
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self.max_conditioning_inputs = max_conditioning_inputs
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self.mel_length_compression = mel_length_compression
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@ -283,7 +352,7 @@ class UnifiedVoice(nn.Module):
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else:
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self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
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self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
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build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens+2+self.max_conditioning_inputs, self.max_text_tokens+2, checkpointing)
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build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens, self.max_text_tokens, checkpointing)
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if train_solo_embeddings:
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self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
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self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
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@ -291,6 +360,11 @@ class UnifiedVoice(nn.Module):
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self.mel_solo_embedding = 0
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self.text_solo_embedding = 0
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if use_rotary_embeddings:
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# We must re-build all the attention layers as type GPT2AttentionWithRotaryEmbeddings.
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for blk in self.gpt.h:
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blk.attn = GPT2AttentionWithRotaryEmbeddings(self.gpt.config, layer_idx=blk.attn.layer_idx)
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self.final_norm = nn.LayerNorm(model_dim)
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self.text_head = nn.Linear(model_dim, self.number_text_tokens)
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self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
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@ -371,9 +445,6 @@ class UnifiedVoice(nn.Module):
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If return_attentions is specified, only logits are returned.
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If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
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"""
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assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
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assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_text_len = text_lengths.max()
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@ -422,8 +493,6 @@ class UnifiedVoice(nn.Module):
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Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
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model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
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"""
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assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_text_len = text_lengths.max()
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@ -477,7 +546,10 @@ class UnifiedVoice(nn.Module):
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return loss_mel.mean()
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def inference_speech(self, speech_conditioning_input, text_inputs, return_attentions=False, **hf_generate_kwargs):
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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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if self.max_mel_tokens == -1: # Assume if this is the case, max_mel_tokens=-1 also
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seq_length = 2002 # Arbitrary default.
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else:
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seq_length = self.max_mel_tokens + self.max_text_tokens + 2
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if not hasattr(self, 'inference_model'):
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# TODO: Decouple gpt_config from this inference model.
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gpt_config = GPT2Config(vocab_size=self.max_mel_tokens,
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@ -566,10 +638,11 @@ def register_unified_voice2(opt_net, opt):
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if __name__ == '__main__':
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gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4)
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gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4,
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use_rotary_embeddings=True, max_mel_tokens=-1, max_text_tokens=-1)
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l = gpt(torch.randn(2, 3, 80, 800),
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torch.randint(high=256, size=(2,120)),
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torch.tensor([32, 120]),
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torch.randint(high=8192, size=(2,250)),
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torch.tensor([250*256,195*256]))
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gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))
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#gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))
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@ -327,7 +327,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_clip_text_to_voice.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_clvp.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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