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feat: solved problem, export works

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zpeng11 2025-08-25 22:37:52 -04:00
parent 419909b443
commit 968ac4c264
2 changed files with 93 additions and 149 deletions
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GPT_SoVITS/onnx_export_v1v2.py
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@ -105,36 +105,29 @@ class DictToAttrRecursive(dict):
raise AttributeError(f"Attribute {item} not found") raise AttributeError(f"Attribute {item} not found")
class T2SInitStep(nn.Module): class T2SInitStage(nn.Module):
def __init__(self, t2s, vits): def __init__(self, t2s, vits):
super().__init__() super().__init__()
self.encoder = t2s.onnx_encoder self.encoder = t2s.onnx_encoder
self.fsdc = t2s.first_stage_decoder
self.vits = vits self.vits = vits
self.num_layers = t2s.num_layers
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): def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content):
first_infer = first_infer.to(torch.int64)
codes = self.vits.extract_latent(ssl_content) codes = self.vits.extract_latent(ssl_content)
prompt_semantic = codes[0, 0] prompt_semantic = codes[0, 0]
bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 1) bert = torch.cat([ref_bert.transpose(0, 1), text_bert.transpose(0, 1)], 1)
all_phoneme_ids = torch.cat([ref_seq, text_seq], 1) all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
bert = bert.unsqueeze(0) bert = bert.unsqueeze(0)
prompt = prompt_semantic.unsqueeze(0) prompt = prompt_semantic.unsqueeze(0)
[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) x = self.encoder(all_phoneme_ids, bert)
fake_logits = torch.zeros((1, 1025), dtype=torch.float32) # Dummy logits for ONNX export
fake_samples = torch.zeros((1, 1), dtype=torch.int32) # Dummy samples for ONNX export
return y, k, v, y_emb, x_example, fake_logits, fake_samples
class T2SStageStep(nn.Module): x_seq_len = torch.onnx.operators.shape_as_tensor(x)[1]
def __init__(self, stage_decoder): y_seq_len = torch.onnx.operators.shape_as_tensor(prompt)[1]
super().__init__()
self.stage_decoder = stage_decoder
def forward(self, iy, ik, iv, iy_emb, ix_example, top_k=None, top_p=None, repetition_penalty=None, temperature=None, first_infer=None): init_k = torch.zeros((self.num_layers, (x_seq_len + y_seq_len), 1, 512), dtype=torch.float)
first_infer = first_infer.to(torch.int64) init_v = torch.zeros((self.num_layers, (x_seq_len + y_seq_len), 1, 512), dtype=torch.float)
[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)
fake_x_example = torch.zeros((1, 512), dtype=torch.float32) # Dummy x_example for ONNX export return x, prompt, init_k, init_v, x_seq_len, y_seq_len
return y, k, v, y_emb, fake_x_example, logits, samples
class T2SModel(nn.Module): class T2SModel(nn.Module):
def __init__(self, t2s_path, vits_model): def __init__(self, t2s_path, vits_model):
@ -151,17 +144,26 @@ class T2SModel(nn.Module):
self.t2s_model.model.early_stop_num = torch.LongTensor([self.hz * self.max_sec]) self.t2s_model.model.early_stop_num = torch.LongTensor([self.hz * self.max_sec])
self.t2s_model = self.t2s_model.model self.t2s_model = self.t2s_model.model
self.t2s_model.init_onnx() self.t2s_model.init_onnx()
self.init_step = T2SInitStep(self.t2s_model, self.vits_model) self.init_stage = T2SInitStage(self.t2s_model, self.vits_model)
self.first_stage_decoder = self.t2s_model.first_stage_decoder
self.stage_decoder = self.t2s_model.stage_decoder self.stage_decoder = self.t2s_model.stage_decoder
def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=None, top_p=None, repetition_penalty=None, temperature=None): def forward(self, ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k=None, top_p=None, repetition_penalty=None, temperature=None):
# [1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N] x, prompt, init_k, init_v, x_seq_len, y_seq_len = self.init_stage(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
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])) empty_tensor = torch.empty((1,0,512)).to(torch.float)
# first step
y, k, v, y_emb, logits, samples = self.stage_decoder(x, prompt, init_k, init_v,
empty_tensor,
top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature,
first_infer=torch.LongTensor([1]), x_seq_len=x_seq_len, y_seq_len=y_seq_len)
for idx in range(5): # This is a fake one! DO NOT take this as reference for idx in range(5): # This is a fake one! DO NOT take this as reference
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])) k = torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1))
y, k, v, y_emb, logits, samples = enco v = torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1))
y_seq_len = y.shape[1]
y, k, v, y_emb, logits, samples = self.stage_decoder(empty_tensor, y, k, v,
y_emb,
top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature,
first_infer=torch.LongTensor([0]), x_seq_len=x_seq_len, y_seq_len=y_seq_len)
# if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS: # if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
# break # break
@ -169,11 +171,11 @@ class T2SModel(nn.Module):
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): 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):
torch.onnx.export( torch.onnx.export(
self.init_step, self.init_stage,
(ref_seq, text_seq, ref_bert, text_bert, ssl_content, top_k, top_p, repetition_penalty, temperature, torch.Tensor([True]).to(torch.bool)), (ref_seq, text_seq, ref_bert, text_bert, ssl_content),
f"onnx/{project_name}/{project_name}_t2s_init_step.onnx", f"onnx/{project_name}/{project_name}_t2s_init_stage.onnx",
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"], input_names=["ref_text_phones", "input_text_phones", "ref_text_bert", "input_text_bert", "hubert_ssl_content"],
output_names=["y", "k", "v", "y_emb", "x_example", 'logits', 'samples'], output_names=["x", "prompt", "init_k", "init_v", 'x_seq_len', 'y_seq_len'],
dynamic_axes={ dynamic_axes={
"ref_text_phones": {1: "ref_length"}, "ref_text_phones": {1: "ref_length"},
"input_text_phones": {1: "text_length"}, "input_text_phones": {1: "text_length"},
@ -184,28 +186,38 @@ class T2SModel(nn.Module):
opset_version=16, opset_version=16,
do_constant_folding=False do_constant_folding=False
) )
# simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_init_step.onnx") simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_init_stage.onnx")
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)) x, prompt, init_k, init_v, x_seq_len, y_seq_len = self.init_stage(ref_seq, text_seq, ref_bert, text_bert, ssl_content)
empty_tensor = torch.empty((1,0,512)).to(torch.float)
x_seq_len = torch.Tensor([x_seq_len]).to(torch.int64)
y_seq_len = torch.Tensor([y_seq_len]).to(torch.int64)
y, k, v, y_emb, logits, samples = self.stage_decoder(x, prompt, init_k, init_v,
empty_tensor,
top_k, top_p, repetition_penalty, temperature,
torch.LongTensor([1]), x_seq_len, y_seq_len)
print(y.shape, k.shape, v.shape, y_emb.shape, logits.shape, samples.shape)
k = torch.nn.functional.pad(k, (0, 0, 0, 0, 0, 1))
v = torch.nn.functional.pad(v, (0, 0, 0, 0, 0, 1))
y_seq_len = torch.Tensor([y.shape[1]]).to(torch.int64)
stage_step = T2SStageStep(self.stage_decoder)
torch.onnx.export( torch.onnx.export(
stage_step, self.stage_decoder,
(y, k, v, y_emb, x_example, top_k, top_p, repetition_penalty, temperature, torch.Tensor([False]).to(torch.bool)), (x, y, k, v, y_emb, top_k, top_p, repetition_penalty, temperature, torch.LongTensor([0]), x_seq_len, y_seq_len),
f"onnx/{project_name}/{project_name}_t2s_stage_step.onnx", f"onnx/{project_name}/{project_name}_t2s_stage_decoder.onnx",
input_names=["iy", "ik", "iv", "iy_emb", "ix_example", "top_k", "top_p", "repetition_penalty", "temperature", "if_init_step"], input_names=["ix", "iy", "ik", "iv", "iy_emb", "top_k", "top_p", "repetition_penalty", "temperature", "if_init_step", "x_seq_len", "y_seq_len"],
output_names=["y", "k", "v", "y_emb","x_example", "logits", "samples"], output_names=["y", "k", "v", "y_emb", "logits", "samples"],
dynamic_axes={ dynamic_axes={
"ix": {1: "ix_length"},
"iy": {1: "iy_length"}, "iy": {1: "iy_length"},
"ik": {1: "ik_length"}, "ik": {1: "ik_length"},
"iv": {1: "iv_length"}, "iv": {1: "iv_length"},
"iy_emb": {1: "iy_emb_length"}, "iy_emb": {1: "iy_emb_length"},
"ix_example": {1: "ix_example_length"},
"x_example": {1: "x_example_length"}
}, },
verbose=False, verbose=False,
opset_version=16, opset_version=16,
) )
simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_stage_step.onnx") simplify_onnx_model(f"onnx/{project_name}/{project_name}_t2s_stage_decoder.onnx")
class VitsModel(nn.Module): class VitsModel(nn.Module):
@ -301,73 +313,7 @@ class AudioPreprocess(nn.Module):
return ssl_content, spectrum, sv_emb return ssl_content, spectrum, sv_emb
def combineInitStepAndStageStep(init_step_onnx_path, stage_step_onnx_path, combined_onnx_path): def export(vits_path, gpt_path, project_name, voice_model_version, export_audio_preprocessor=True, half_precision=False):
init_step_model = onnx.load(init_step_onnx_path)
stage_step_model = onnx.load(stage_step_onnx_path)
then_graph = init_step_model.graph
then_graph.name = "init_step_graph"
else_graph = stage_step_model.graph
else_graph.name = "stage_step_graph"
data_inputs_init = [input for input in init_step_model.graph.input]
data_inputs_stage = [input for input in stage_step_model.graph.input]
# Get all names from both lists
names_list_init = {obj.name for obj in data_inputs_init}
names_list_stage = {obj.name for obj in data_inputs_stage}
# Find names that appear in both lists
repeated_input_names = names_list_init.intersection(names_list_stage)
# Filter out objects with repeated names
data_inputs_stage = [obj for obj in data_inputs_stage if obj.name not in repeated_input_names]
del then_graph.input[:]
del else_graph.input[:]
# The output names of the subgraphs must be the same.
# The 'If' node will have an output with this same name.
subgraph_output_names = [output.name for output in then_graph.output]
for i, output in enumerate(else_graph.output):
assert subgraph_output_names[i] == output.name, "Subgraph output names must match"
# Define the inputs for the main graph
# 1. The boolean condition to select the branch
# cond_input = helper.make_tensor_value_info('if_init_step', TensorProto.BOOL, [])
main_outputs = [output for output in init_step_model.graph.output]
# Create the 'If' node
if_node = helper.make_node(
'If',
inputs=['if_init_step'],
outputs=subgraph_output_names, # This name MUST match the subgraph's output name
then_branch=then_graph,
else_branch=else_graph
)
# Combine the models (this is a simplified example; actual combination logic may vary)
main_graph = helper.make_graph(
nodes=[if_node],
name="t2s_combined_graph",
inputs= data_inputs_init + data_inputs_stage,
outputs=main_outputs
)
# Create the final combined model, specifying the opset and IR version
opset_version = 16
final_model = helper.make_model(main_graph,
producer_name='GSV-ONNX-Exporter',
ir_version=9, # For compatibility with older onnxruntime
opset_imports=[helper.make_opsetid("", opset_version)])
# Check the model for correctness
onnx.checker.check_model(final_model)
# Save the combined model
onnx.save(final_model, combined_onnx_path)
print(f"Combined model saved to {combined_onnx_path}")
def export(vits_path, gpt_path, project_name, voice_model_version, t2s_model_combine=False, export_audio_preprocessor=True, half_precision=False):
vits = VitsModel(vits_path, version=voice_model_version) vits = VitsModel(vits_path, version=voice_model_version)
gpt = T2SModel(gpt_path, vits) gpt = T2SModel(gpt_path, vits)
gpt_sovits = GptSoVits(vits, gpt) gpt_sovits = GptSoVits(vits, gpt)
@ -453,12 +399,7 @@ def export(vits_path, gpt_path, project_name, voice_model_version, t2s_model_com
}) })
simplify_onnx_model(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx") simplify_onnx_model(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx")
if t2s_model_combine:
combineInitStepAndStageStep(f'onnx/{project_name}/{project_name}_t2s_init_step.onnx', f'onnx/{project_name}/{project_name}_t2s_stage_step.onnx', f'onnx/{project_name}/{project_name}_t2s_combined.onnx')
if half_precision: if half_precision:
if t2s_model_combine:
convert_onnx_to_half(f"onnx/{project_name}/{project_name}_t2s_combined.onnx")
if export_audio_preprocessor: if export_audio_preprocessor:
convert_onnx_to_half(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx") convert_onnx_to_half(f"onnx/{project_name}/{project_name}_audio_preprocess.onnx")
convert_onnx_to_half(f"onnx/{project_name}/{project_name}_vits.onnx") convert_onnx_to_half(f"onnx/{project_name}/{project_name}_vits.onnx")
@ -467,7 +408,7 @@ def export(vits_path, gpt_path, project_name, voice_model_version, t2s_model_com
configJson = { configJson = {
"project_name": project_name, "project_name": project_name,
"type": "GPTSoVits", "type": "GPTSoVITS",
"version" : voice_model_version, "version" : voice_model_version,
"bert_base_path": 'GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large', "bert_base_path": 'GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large',
"cnhuhbert_base_path": 'GPT_SoVITS/pretrained_models/chinese-hubert-base', "cnhuhbert_base_path": 'GPT_SoVITS/pretrained_models/chinese-hubert-base',
@ -486,29 +427,29 @@ if __name__ == "__main__":
# 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 # 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
# gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
# vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth" vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
# exp_path = "v1_export" exp_path = "v1_export"
# version = "v1" version = "v1"
# export(vits_path, gpt_path, exp_path, version) export(vits_path, gpt_path, exp_path, version)
gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt" gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth" vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
exp_path = "v2_export" exp_path = "v2_export"
version = "v2" version = "v2"
export(vits_path, gpt_path, exp_path, version, t2s_model_combine = True) export(vits_path, gpt_path, exp_path, version)
# gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt" gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth" vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
# exp_path = "v2pro_export" exp_path = "v2pro_export"
# version = "v2Pro" version = "v2Pro"
# export(vits_path, gpt_path, exp_path, version) export(vits_path, gpt_path, exp_path, version)
# gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt" gpt_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
# vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth" vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"
# exp_path = "v2proplus_export" exp_path = "v2proplus_export"
# version = "v2ProPlus" version = "v2ProPlus"
# export(vits_path, gpt_path, exp_path, version, t2s_model_combine = True, half_precision=True) export(vits_path, gpt_path, exp_path, version)

45
playground/freerun.py
View File

@ -7,7 +7,7 @@ import torch
from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx
MODEL_PATH = "onnx/v2_export/v2" MODEL_PATH = "onnx/v1_export/v1"
def audio_postprocess( def audio_postprocess(
audios, audios,
@ -80,49 +80,52 @@ top_p = np.array([1.0], dtype=np.float32)
repetition_penalty = np.array([1.0], dtype=np.float32) repetition_penalty = np.array([1.0], dtype=np.float32)
temperature = np.array([1.0], dtype=np.float32) temperature = np.array([1.0], dtype=np.float32)
t2s_combined = ort.InferenceSession(MODEL_PATH+"_export_t2s_combined.onnx") t2s_init_stage = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_stage.onnx")
# t2s_init_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_step.onnx") # t2s_init_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_step.onnx")
[y, k, v, y_emb, x_example, fake_logits, fake_samples] = t2s_combined.run(None, { [x, prompts, init_k, init_v, x_seq_len, y_seq_len] = t2s_init_stage.run(None, {
"if_init_step": np.array(True, dtype=bool),
"input_text_phones": input_phones, "input_text_phones": input_phones,
"input_text_bert": input_bert, "input_text_bert": input_bert,
"ref_text_phones": ref_phones, "ref_text_phones": ref_phones,
"ref_text_bert": ref_bert, "ref_text_bert": ref_bert,
"hubert_ssl_content": audio_prompt_hubert, "hubert_ssl_content": audio_prompt_hubert,
"iy":np.empty((1, 0), dtype=np.int64), })
"ik":np.empty((24, 0, 1, 512), dtype=np.float32), empty_tensor = np.empty((1,0,512)).astype(np.float32)
"iv":np.empty((24, 0, 1, 512), dtype=np.float32),
"iy_emb":np.empty((1, 0, 512), dtype=np.float32), t2s_stage_decoder = ort.InferenceSession(MODEL_PATH+"_export_t2s_stage_decoder.onnx")
"ix_example":np.empty((1, 0), dtype=np.float32), y, k, v, y_emb, logits, samples = t2s_stage_decoder.run(None, {
"ix": x,
"iy": prompts,
"ik": init_k,
"iv": init_v,
"iy_emb": empty_tensor,
"top_k": top_k, "top_k": top_k,
"top_p": top_p, "top_p": top_p,
"repetition_penalty": repetition_penalty, "repetition_penalty": repetition_penalty,
"temperature": temperature, "temperature": temperature,
"if_init_step": np.array([True], dtype=bool) "if_init_step": np.array([1]).astype(np.int64),
"x_seq_len": np.array([x_seq_len]).astype(np.int64),
"y_seq_len": np.array([y_seq_len]).astype(np.int64)
}) })
# t2s_stage_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
for idx in tqdm(range(1, 1500)): for idx in tqdm(range(1, 1500)):
k = np.pad(k, ((0,0), (0,1), (0,0), (0,0)))
v = np.pad(v, ((0,0), (0,1), (0,0), (0,0)))
y_seq_len = np.array([y.shape[1]]).astype(np.int64)
# [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N] # [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
[y, k, v, y_emb, fake_x_example, logits, samples] = t2s_combined.run(None, { [y, k, v, y_emb, logits, samples] = t2s_stage_decoder.run(None, {
"if_init_step": np.array(False, dtype=bool), "ix": empty_tensor,
"input_text_phones": np.empty((1, 0), dtype=np.int64),
"input_text_bert": np.empty((0, 1024), dtype=np.float32),
"ref_text_phones": np.empty((1, 0), dtype=np.int64),
"ref_text_bert": np.empty((0, 1024), dtype=np.float32),
"hubert_ssl_content": np.empty((1, 768, 0), dtype=np.float32),
"iy": y, "iy": y,
"ik": k, "ik": k,
"iv": v, "iv": v,
"iy_emb": y_emb, "iy_emb": y_emb,
"ix_example": x_example,
"top_k": top_k, "top_k": top_k,
"top_p": top_p, "top_p": top_p,
"repetition_penalty": repetition_penalty, "repetition_penalty": repetition_penalty,
"temperature": temperature, "temperature": temperature,
"if_init_step": np.array([False], dtype=bool) "if_init_step": np.array([0]).astype(np.int64),
"x_seq_len": np.array([x_seq_len]).astype(np.int64),
"y_seq_len": y_seq_len
}) })
if np.argmax(logits, axis=-1)[0] == 1024 or samples[0, 0] == 1024: # 1024 is the EOS token if np.argmax(logits, axis=-1)[0] == 1024 or samples[0, 0] == 1024: # 1024 is the EOS token
break break