mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-09-29 00:30:15 +08:00
feat:solve unified kv cache shape handling, todo: clean up upper level to unify first and following step
This commit is contained in:
zpeng11
parent
0c5f61f98c
commit
26228402e3
@ -128,7 +128,7 @@ class T2SFirstStageDecoder(nn.Module):
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self.early_stop_num = early_stop_num
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self.num_layers = num_layers
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def forward(self, x, prompt, top_k = None, top_p = None, repetition_penalty = None, temperature = None):
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def forward(self, x, prompt, top_k = None, top_p = None, repetition_penalty = None, temperature = None, first_infer = None):
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if top_k is None:
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top_k = torch.LongTensor([15]).to(device=x.device)
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if top_p is None:
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@ -146,7 +146,7 @@ class T2SFirstStageDecoder(nn.Module):
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"k": None,
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"v": None,
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"y_emb": None,
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"first_infer": 1,
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"first_infer": first_infer,
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"stage": 0,
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}
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@ -216,7 +216,7 @@ class T2SStageDecoder(nn.Module):
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self.early_stop_num = early_stop_num
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self.num_layers = num_layers
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def forward(self, y, k, v, y_emb, x_example, top_k = None, top_p = None, repetition_penalty = None, temperature = None):
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def forward(self, y, k, v, y_emb, x_example, top_k = None, top_p = None, repetition_penalty = None, temperature = None, first_infer = None):
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if top_k is None:
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top_k = torch.LongTensor([15]).to(device=y.device)
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if top_p is None:
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@ -231,7 +231,7 @@ class T2SStageDecoder(nn.Module):
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"k": torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1)),
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"v": torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1)),
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"y_emb": y_emb,
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"first_infer": 0,
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"first_infer": first_infer,
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"stage": 0,
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}
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@ -336,11 +336,11 @@ class Text2SemanticDecoder(nn.Module):
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prefix_len = prompts.shape[1]
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x = self.onnx_encoder(x, bert_feature)
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y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts, top_k=top_k)
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y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts, top_k=top_k, first_infer=torch.LongTensor([1]))
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stop = False
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for idx in tqdm(range(1, 1500)):
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enco = self.stage_decoder(y, k, v, y_emb, x_example, top_k=top_k)
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enco = self.stage_decoder(y, k, v, y_emb, x_example, top_k=top_k, first_infer=torch.LongTensor([0]))
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y, k, v, y_emb, logits, samples = enco
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if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
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stop = True
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@ -48,33 +48,23 @@ def multi_head_attention_forward_patched(
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proj_qkv = linear(query, in_proj_weight, in_proj_bias)
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proj_qkv = proj_qkv.unflatten(-1, (3, query.size(-1))).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
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q, k, v = proj_qkv[0], proj_qkv[1], proj_qkv[2]
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# 首轮qkv会产生多个batch，后续每轮只会产生一个batch，
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# onnx导出时处理batch变化导致的输出形状变化非常无力，
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# 已尝试过where方法，索引方法，尽管可以动态运行正常导出，
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# 但都无法在onnx运行时正确处理kv cache形状导致抛出错误
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# 此实现需要整体重写，将kvcache增长和prefill交给外部调用
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if cache["first_infer"] == 1:
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cache["k"][cache["stage"]] = k
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cache["v"][cache["stage"]] = v
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else:
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cache["k"][cache["stage"]] = torch.cat([cache["k"][cache["stage"]][:-1], k], 0)
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cache["v"][cache["stage"]] = torch.cat([cache["v"][cache["stage"]][:-1], v], 0)
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k = cache["k"][cache["stage"]]
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v = cache["v"][cache["stage"]]
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# 使用动态形状推断来统一处理kv cache首步和后续步骤形状差异
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# # k,v : [N, 1, 512] at first time, [1, 1, 512] afterwards
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# # cache_k, cache_v : [1, N, 1, 512] size increasement is prepared outside
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# first_infer_mask = cache["first_infer"]
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# cache_k = cache["k"][cache["stage"]]
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# cache_v = cache["v"][cache["stage"]]
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# # Magic to get an index of either -1 or -N according to if first_infer_mask is set
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# index_offset = torch.min(torch.tensor([-1]).to(k.device).to(torch.int64), -1 * first_infer_mask * k.shape[0])
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# cache_k[0, index_offset :, :, :] = k
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# cache_v[0, index_offset :, :, :] = v
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# cache["k"][cache["stage"]] = cache_k
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# cache["v"][cache["stage"]] = cache_v
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# k = cache_k
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# v = cache_v
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first_infer_mask = cache["first_infer"]
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cache_k = cache["k"][cache["stage"]]
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cache_v = cache["v"][cache["stage"]]
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# Magic to get an index of either -1 or -N according to if first_infer_mask is set
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minus_one = torch.tensor([-1]).to(k.device).to(torch.int64)
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multipled = minus_one * first_infer_mask * torch.onnx.operators.shape_as_tensor(query)[0]
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index_offset = torch.min(minus_one, multipled)
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cache_k[index_offset :, :, :] = k
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cache_v[index_offset :, :, :] = v
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cache["k"][cache["stage"]] = cache_k
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cache["v"][cache["stage"]] = cache_v
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k = cache_k
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v = cache_v
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cache["stage"] = (cache["stage"] + 1) % cache["all_stage"]
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@ -112,14 +112,15 @@ class T2SInitStep(nn.Module):
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self.fsdc = t2s.first_stage_decoder
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self.vits = vits
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=None, top_p=None, repetition_penalty=None, temperature=None, first_infer=None):
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first_infer = first_infer.to(torch.int64)
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codes = self.vits.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 1)
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all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
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bert = bert.unsqueeze(0)
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prompt = prompt_semantic.unsqueeze(0)
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[y, k, v, y_emb, x_example] = self.fsdc(self.encoder(all_phoneme_ids, bert), prompt, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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[y, k, v, y_emb, x_example] = self.fsdc(self.encoder(all_phoneme_ids, bert), prompt, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature, first_infer=first_infer)
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fake_logits = torch.zeros((1, 1025), dtype=torch.float32) # Dummy logits for ONNX export
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fake_samples = torch.zeros((1, 1), dtype=torch.int32) # Dummy samples for ONNX export
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return y, k, v, y_emb, x_example, fake_logits, fake_samples
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@ -129,8 +130,9 @@ class T2SStageStep(nn.Module):
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super().__init__()
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self.stage_decoder = stage_decoder
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def forward(self, iy, ik, iv, iy_emb, ix_example, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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[y, k, v, y_emb, logits, samples] = self.stage_decoder(iy, ik, iv, iy_emb, ix_example, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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def forward(self, iy, ik, iv, iy_emb, ix_example, top_k=None, top_p=None, repetition_penalty=None, temperature=None, first_infer=None):
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first_infer = first_infer.to(torch.int64)
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[y, k, v, y_emb, logits, samples] = self.stage_decoder(iy, ik, iv, iy_emb, ix_example, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature, first_infer=first_infer)
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fake_x_example = torch.zeros((1, 512), dtype=torch.float32) # Dummy x_example for ONNX export
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return y, k, v, y_emb, fake_x_example, logits, samples
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@ -155,10 +157,10 @@ class T2SModel(nn.Module):
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def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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# [1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N]
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y, k, v, y_emb, x_example, fake_logits, fake_samples = self.init_step(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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y, k, v, y_emb, x_example, fake_logits, fake_samples = self.init_step(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature, first_infer=torch.LongTensor([1]))
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for idx in range(5): # This is a fake one! DO NOT take this as reference
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enco = self.stage_decoder(y, k, v, y_emb, x_example, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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enco = self.stage_decoder(y, k, v, y_emb, x_example, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature, first_infer=torch.LongTensor([0]))
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y, k, v, y_emb, logits, samples = enco
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# if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
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# break
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@ -168,9 +170,9 @@ class T2SModel(nn.Module):
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def export(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, project_name, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
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torch.onnx.export(
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self.init_step,
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(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k, top_p, repetition_penalty, temperature),
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(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k, top_p, repetition_penalty, temperature, torch.Tensor([True]).to(torch.bool)),
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f"onnx/{project_name}/{project_name}_t2s_init_step.onnx",
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input_names=["ref_text_phones", "input_text_phones", "ref_text_bert", "input_text_bert", "hubert_ssl_content", "top_k", "top_p", "repetition_penalty", "temperature"],
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input_names=["ref_text_phones", "input_text_phones", "ref_text_bert", "input_text_bert", "hubert_ssl_content", "top_k", "top_p", "repetition_penalty", "temperature", "if_init_step"],
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output_names=["y", "k", "v", "y_emb", "x_example", 'logits', 'samples'],
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dynamic_axes={
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"ref_text_phones": {1: "ref_length"},
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@ -180,16 +182,17 @@ class T2SModel(nn.Module):
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"hubert_ssl_content": {2: "ssl_length"},
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},
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opset_version=16,
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do_constant_folding=False
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)
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simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_init_step.onnx")
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y, k, v, y_emb, x_example, fake_logits, fake_samples = self.init_step(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature)
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# simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_init_step.onnx")
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y, k, v, y_emb, x_example, fake_logits, fake_samples = self.init_step(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature, first_infer=torch.Tensor([True]).to(torch.bool))
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stage_step = T2SStageStep(self.stage_decoder)
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torch.onnx.export(
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stage_step,
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(y, k, v, y_emb, x_example, top_k, top_p, repetition_penalty, temperature),
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(y, k, v, y_emb, x_example, top_k, top_p, repetition_penalty, temperature, torch.Tensor([False]).to(torch.bool)),
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f"onnx/{project_name}/{project_name}_t2s_stage_step.onnx",
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input_names=["iy", "ik", "iv", "iy_emb", "ix_example", "top_k", "top_p", "repetition_penalty", "temperature"],
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input_names=["iy", "ik", "iv", "iy_emb", "ix_example", "top_k", "top_p", "repetition_penalty", "temperature", "if_init_step"],
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output_names=["y", "k", "v", "y_emb","x_example", "logits", "samples"],
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dynamic_axes={
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"iy": {1: "iy_length"},
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@ -329,7 +332,7 @@ def combineInitStepAndStageStep(init_step_onnx_path, stage_step_onnx_path, combi
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# Define the inputs for the main graph
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# 1. The boolean condition to select the branch
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cond_input = helper.make_tensor_value_info('if_init_step', TensorProto.BOOL, [])
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# cond_input = helper.make_tensor_value_info('if_init_step', TensorProto.BOOL, [])
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main_outputs = [output for output in init_step_model.graph.output]
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@ -346,7 +349,7 @@ def combineInitStepAndStageStep(init_step_onnx_path, stage_step_onnx_path, combi
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main_graph = helper.make_graph(
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nodes=[if_node],
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name="t2s_combined_graph",
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inputs=[cond_input] + data_inputs_init + data_inputs_stage,
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inputs= data_inputs_init + data_inputs_stage,
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outputs=main_outputs
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)
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@ -483,29 +486,29 @@ if __name__ == "__main__":
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# 因为io太频繁，可能导致模型导出出错(wsl非常明显)，请自行重试
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gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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exp_path = "v1_export"
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version = "v1"
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export(vits_path, gpt_path, exp_path, version)
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# gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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# exp_path = "v1_export"
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# version = "v1"
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# export(vits_path, gpt_path, exp_path, version)
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gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
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exp_path = "v2_export"
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version = "v2"
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export(vits_path, gpt_path, exp_path, version)
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export(vits_path, gpt_path, exp_path, version, t2s_model_combine = True)
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gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
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exp_path = "v2pro_export"
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version = "v2Pro"
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export(vits_path, gpt_path, exp_path, version)
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# gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
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# exp_path = "v2pro_export"
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# version = "v2Pro"
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# export(vits_path, gpt_path, exp_path, version)
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gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
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exp_path = "v2proplus_export"
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version = "v2ProPlus"
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export(vits_path, gpt_path, exp_path, version, t2s_model_combine = True, half_precision=True)
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# gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
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# exp_path = "v2proplus_export"
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# version = "v2ProPlus"
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# export(vits_path, gpt_path, exp_path, version, t2s_model_combine = True, half_precision=True)
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@ -98,7 +98,8 @@ t2s_combined = ort.InferenceSession(MODEL_PATH+"_export_t2s_combined.onnx")
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"top_k": top_k,
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"top_p": top_p,
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"repetition_penalty": repetition_penalty,
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"temperature": temperature
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"temperature": temperature,
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"if_init_step": np.array([True], dtype=bool)
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})
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# t2s_stage_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
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@ -120,7 +121,8 @@ for idx in tqdm(range(1, 1500)):
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"top_k": top_k,
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"top_p": top_p,
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"repetition_penalty": repetition_penalty,
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"temperature": temperature
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"temperature": temperature,
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"if_init_step": np.array([False], dtype=bool)
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})
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if np.argmax(logits, axis=-1)[0] == 1024 or samples[0, 0] == 1024: # 1024 is the EOS token
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break
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