double inference speed

double inference speed

GPT_SoVITS/AR/models/t2s_model.py

@@ -1,8 +1,11 @@
 # modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
 # reference: https://github.com/lifeiteng/vall-e
 import torch
-from tqdm import tqdm
+import random
+import numpy as np
 
+from tqdm import tqdm
+from typing import List
 from AR.models.utils import make_pad_mask
 from AR.models.utils import (
     topk_sampling,
@@ -35,6 +38,139 @@ default_config = {
 }
 
 
+@torch.jit.script
+class T2SMLP:
+    def __init__(self, w1, b1, w2, b2):
+        self.w1 = w1
+        self.b1 = b1
+        self.w2 = w2
+        self.b2 = b2
+
+    def forward(self, x):
+        x = F.relu(F.linear(x, self.w1, self.b1))
+        x = F.linear(x, self.w2, self.b2)
+        return x
+
+
+@torch.jit.script
+class T2SBlock:
+    def __init__(
+            self,
+            num_heads,
+            hidden_dim: int,
+            mlp: T2SMLP,
+            qkv_w,
+            qkv_b,
+            out_w,
+            out_b,
+            norm_w1,
+            norm_b1,
+            norm_eps1,
+            norm_w2,
+            norm_b2,
+            norm_eps2,
+    ):
+        self.num_heads = num_heads
+        self.mlp = mlp
+        self.hidden_dim: int = hidden_dim
+        self.qkv_w = qkv_w
+        self.qkv_b = qkv_b
+        self.out_w = out_w
+        self.out_b = out_b
+        self.norm_w1 = norm_w1
+        self.norm_b1 = norm_b1
+        self.norm_eps1 = norm_eps1
+        self.norm_w2 = norm_w2
+        self.norm_b2 = norm_b2
+        self.norm_eps2 = norm_eps2
+
+    def process_prompt(self, x, attn_mask: torch.Tensor):
+        q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
+
+        batch_size = q.shape[0]
+        q_len = q.shape[1]
+        kv_len = k.shape[1]
+
+        k_cache = k
+        v_cache = v
+
+        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
+        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+
+        attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size, -1, self.hidden_dim)
+        attn = F.linear(attn, self.out_w, self.out_b)
+
+        x = F.layer_norm(
+            x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+        )
+        x = F.layer_norm(
+            x + self.mlp.forward(x),
+            [self.hidden_dim],
+            self.norm_w2,
+            self.norm_b2,
+            self.norm_eps2,
+        )
+        return x, k_cache, v_cache
+
+    def decode_next_token(self, x, k_cache, v_cache):
+        q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
+
+        k_cache = torch.cat([k_cache, k], dim=1)
+        v_cache = torch.cat([v_cache, v], dim=1)
+        kv_len = k_cache.shape[1]
+
+        batch_size = q.shape[0]
+        q_len = q.shape[1]
+
+        q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
+        k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+        v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
+
+        attn = F.scaled_dot_product_attention(q, k, v)
+
+        attn = attn.permute(2, 0, 1, 3).reshape(batch_size, -1, self.hidden_dim)
+        attn = F.linear(attn, self.out_w, self.out_b)
+
+        x = F.layer_norm(
+            x + attn, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
+        )
+        x = F.layer_norm(
+            x + self.mlp.forward(x),
+            [self.hidden_dim],
+            self.norm_w2,
+            self.norm_b2,
+            self.norm_eps2,
+        )
+        return x, k_cache, v_cache
+
+
+@torch.jit.script
+class T2STransformer:
+    def __init__(self, num_blocks: int, blocks: List[T2SBlock]):
+        self.num_blocks: int = num_blocks
+        self.blocks = blocks
+
+    def process_prompt(
+            self, x, attn_mask: torch.Tensor):
+        k_cache: List[torch.Tensor] = []
+        v_cache: List[torch.Tensor] = []
+        for i in range(self.num_blocks):
+            x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask)
+            k_cache.append(k_cache_)
+            v_cache.append(v_cache_)
+        return x, k_cache, v_cache
+
+    def decode_next_token(
+            self, x, k_cache: List[torch.Tensor], v_cache: List[torch.Tensor]
+    ):
+        for i in range(self.num_blocks):
+            x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i])
+        return x, k_cache, v_cache
+
+
 class Text2SemanticDecoder(nn.Module):
     def __init__(self, config, norm_first=False, top_k=3):
         super(Text2SemanticDecoder, self).__init__()
@@ -89,6 +225,37 @@ class Text2SemanticDecoder(nn.Module):
             ignore_index=self.EOS,
         )
 
+        blocks = []
+
+        for i in range(self.num_layers):
+            layer = self.h.layers[i]
+            t2smlp = T2SMLP(
+                layer.linear1.weight,
+                layer.linear1.bias,
+                layer.linear2.weight,
+                layer.linear2.bias
+            )
+            # (layer.self_attn.in_proj_weight, layer.self_attn.in_proj_bias)
+            block = T2SBlock(
+                self.num_head,
+                self.model_dim,
+                t2smlp,
+                layer.self_attn.in_proj_weight,
+                layer.self_attn.in_proj_bias,
+                layer.self_attn.out_proj.weight,
+                layer.self_attn.out_proj.bias,
+                layer.norm1.weight,
+                layer.norm1.bias,
+                layer.norm1.eps,
+                layer.norm2.weight,
+                layer.norm2.bias,
+                layer.norm2.eps
+            )
+
+            blocks.append(block)
+
+        self.t2s_transformer = T2STransformer(self.num_layers, blocks)
+
     def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
         x = self.ar_text_embedding(x)
         x = x + self.bert_proj(bert_feature.transpose(1, 2))
@@ -343,17 +510,9 @@ class Text2SemanticDecoder(nn.Module):
         x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
         stop = False
         # print(1111111,self.num_layers)
-        cache = {
+
-            "all_stage": self.num_layers,
+        k_cache = None
-            "k": [None] * self.num_layers,  ###根据配置自己手写
+        v_cache = None
-            "v": [None] * self.num_layers,
-            # "xy_pos":None,##y_pos位置编码每次都不一样的没法缓存，每次都要重新拼xy_pos.主要还是写法原因，其实是可以历史统一一样的，但也没啥计算量就不管了
-            "y_emb": None,  ##只需要对最新的samples求emb，再拼历史的就行
-            # "logits":None,###原版就已经只对结尾求再拼接了，不用管
-            # "xy_dec":None,###不需要，本来只需要最后一个做logits
-            "first_infer": 1,
-            "stage": 0,
-        }
         ###################  first step ##########################
         if y is not None:
             y_emb = self.ar_audio_embedding(y)
@@ -361,7 +520,6 @@ class Text2SemanticDecoder(nn.Module):
             prefix_len = y.shape[1]
             y_pos = self.ar_audio_position(y_emb)
             xy_pos = torch.concat([x, y_pos], dim=1)
-            cache["y_emb"] = y_emb
             ref_free = False
         else:
             y_emb = None
@@ -386,21 +544,23 @@ class Text2SemanticDecoder(nn.Module):
             x.device
         )
 
-
         for idx in tqdm(range(1500)):
+            if xy_attn_mask is not None:
+                xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask)
+            else:
+                xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
 
-            xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
             logits = self.ar_predict_layer(
                 xy_dec[:, -1]
-            )  ##不用改，如果用了cache的默认就是只有一帧，取最后一帧一样的
+            )
-            # samples = topk_sampling(logits, top_k=top_k, top_p=1.0, temperature=temperature)
+
-            if(idx==0):###第一次跑不能EOS否则没有了
+            if idx == 0:
-                logits = logits[:, :-1]  ###刨除1024终止符号的概率
+                xy_attn_mask = None
+                logits = logits[:, :-1]
             samples = sample(
                 logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
             )[0].unsqueeze(0)
-            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
+
-            # print(samples.shape)#[1,1]#第一个1是bs
             y = torch.concat([y, samples], dim=1)
 
             if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
@@ -408,41 +568,18 @@ class Text2SemanticDecoder(nn.Module):
                 stop = True
 
             if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
-                # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
                 stop = True
             if stop:
-                # if prompts.shape[1] == y.shape[1]:
+                if y.shape[1] == 0:
-                #     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
-                #     print("bad zero prediction")
-                if y.shape[1]==0:
                     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                     print("bad zero prediction")
                 print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
                 break
 
             ####################### update next step ###################################
-            cache["first_infer"] = 0
-            if cache["y_emb"] is not None:
-                y_emb = torch.cat(
-                    [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1
-                )
-                cache["y_emb"] = y_emb
-                y_pos = self.ar_audio_position(y_emb)
-                xy_pos = y_pos[:, -1:]
-            else:
             y_emb = self.ar_audio_embedding(y[:, -1:])
-                cache["y_emb"] = y_emb
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, prompts.shape[1] + idx]
-                y_pos = self.ar_audio_position(y_emb)
-                xy_pos = y_pos
-            y_len = y_pos.shape[1]
 
-            ###最右边一列（是错的）
-            # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
-            # xy_attn_mask[:,-1]=False
-            ###最下面一行（是对的）
-            xy_attn_mask = torch.zeros(
-                (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
-            )
         if ref_free:
             return y[:, :-1], 0
-        return y[:, :-1], idx-1
+        return y[:, :-1], idx - 1