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https://github.com/RVC-Boss/GPT-SoVITS.git
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104 lines
2.7 KiB
Python
104 lines
2.7 KiB
Python
from __future__ import annotations
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from typing import cast
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import mlx.core as mx
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from ..structs_mlx import KVCache, KVCacheQ
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from ..t2s_model_abc import (
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AttentionABC,
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KVCacheHND,
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T2SDecoderABC,
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TransformerBlockABC,
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TransformerDecoderABC,
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)
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Array = mx.array
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class Attention(AttentionABC):
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def __init__(self, n_head: int, hidden_dim: int, max_seq_length: int):
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super().__init__(n_head, hidden_dim, max_seq_length)
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self.kc_class = KVCacheHND
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def __call__(self, x: Array, input_pos: Array, kv_cache: KVCache | KVCacheQ, cache_idx: Array, attn_mask: Array):
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bsz, seqlen, _ = cast(tuple[int, ...], x.shape)
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q, k, v = self.in_proj(x).split(3, axis=-1)
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q, k, v = map(lambda x: x.reshape(bsz, seqlen, self.n_head, self.head_dim), (q, k, v))
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q, k, v = map(lambda x: x.swapaxes(1, 2), (q, k, v))
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kv_cache = self.kc_class.update_cache(input_pos, k, v, kv_cache, cache_idx)
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assert len(kv_cache) == 2
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max_idx = int(input_pos.max())
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q, k, v = map(lambda x: x[..., :max_idx, :], (q, *kv_cache))
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mask = attn_mask[..., :max_idx]
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attn = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=mask)
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attn = attn.swapaxes(1, 2).reshape(bsz, seqlen, self.hidden_dim)
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attn = self.out_proj(attn)
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return attn
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class TransformerBlock(TransformerBlockABC):
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def __init__(self, n_head: int, ffn_dim: int, hidden_dim: int, max_seq_length: int) -> None:
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super().__init__(n_head, ffn_dim, hidden_dim, max_seq_length)
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self.attention = Attention(n_head, hidden_dim, max_seq_length)
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class TransformerDecoder(TransformerDecoderABC):
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def __init__(
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self,
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hidden_dim: int,
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n_layer: int,
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n_head: int,
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ffn_dim: int,
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vocab_size: int,
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max_seq_length: int,
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max_batch_size: int,
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) -> None:
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super().__init__(
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hidden_dim,
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n_layer,
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n_head,
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ffn_dim,
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vocab_size,
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max_seq_length,
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max_batch_size,
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)
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self.layers = [
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TransformerBlock(
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n_head,
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ffn_dim,
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hidden_dim,
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max_seq_length,
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)
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for _ in range(n_layer)
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]
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class T2SDecoder(T2SDecoderABC):
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def __init__(
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self,
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config: dict,
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max_seq_length: int = 2000,
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max_batch_size: int = 10,
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) -> None:
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super().__init__(config, max_seq_length, max_batch_size)
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self.h = TransformerDecoder(
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self.hidden_dim, self.n_layer, self.n_head, self.ffn_dim, self.vocab_size, max_seq_length, max_batch_size
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)
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self.kv_class = KVCacheHND
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