feat:get ready for if node merge

GPT_SoVITS/onnx_export.py

@@ -6,7 +6,7 @@ from feature_extractor import cnhubert
 from module.models_onnx import SynthesizerTrn, symbols_v1, symbols_v2
 from torch import nn
 from sv import SV
-
+import onnx
 cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
 from transformers import HubertModel, HubertConfig
 import json
@@ -103,9 +103,20 @@ class T2SInitStep(nn.Module):
         all_phoneme_ids = torch.cat([ref_seq, text_seq], 1)
         bert = bert.unsqueeze(0)
         prompt = prompt_semantic.unsqueeze(0)
-        return self.fsdc(self.encoder(all_phoneme_ids, bert), prompt)
+        [y, k, v, y_emb, x_example]  = self.fsdc(self.encoder(all_phoneme_ids, bert), prompt)
+        fake_logits = torch.randn((1, 1025))  # Dummy logits for ONNX export
+        fake_samples = torch.randn((1, 1))  # Dummy samples for ONNX export
+        return y, k, v, y_emb, x_example, fake_logits, fake_samples
 
+class T2SStageStep(nn.Module):
+    def __init__(self, stage_decoder):
+        super().__init__()
+        self.stage_decoder = stage_decoder
 
+    def forward(self, iy, ik, iv, iy_emb, ix_example):
+        [y, k, v, y_emb, logits, samples] = self.stage_decoder(iy, ik, iv, iy_emb, ix_example)
+        fake_x_example = torch.randn((1, 512))  # Dummy x_example for ONNX export
+        return y, k, v, y_emb, fake_x_example, logits, samples
 
 class T2SModel(nn.Module):
     def __init__(self, t2s_path, vits_model):
@@ -131,12 +142,13 @@ class T2SModel(nn.Module):
         early_stop_num = self.t2s_model.early_stop_num
 
         # [1,N] [1,N] [N, 1024] [N, 1024] [1, 768, N]
-        y, k, v, y_emb, x_example = self.init_step(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)
 
         for idx in tqdm(range(1, 20)): # This is a fake one! do take this as reference
             # [1, N] [N_layer, N, 1, 512] [N_layer, N, 1, 512] [1, N, 512] [1] [1, N, 512] [1, N]
             enco = self.stage_decoder(y, k, v, y_emb, x_example)
             y, k, v, y_emb, logits, samples = enco
+            print(logits.shape, samples.shape)
             if torch.argmax(logits, dim=-1)[0] == self.t2s_model.EOS or samples[0, 0] == self.t2s_model.EOS:
                 break
         y[0, -1] = 0
@@ -158,7 +170,7 @@ class T2SModel(nn.Module):
             (ref_seq, text_seq, ref_bert, text_bert, ssl_content),
             f"onnx/{project_name}/{project_name}_t2s_init_step.onnx",
             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"],
+            output_names=["y", "k", "v", "y_emb", "x_example", 'logits', 'samples'],
             dynamic_axes={
                 "ref_text_phones": {1: "ref_length"},
                 "input_text_phones": {1: "text_length"},
@@ -168,35 +180,22 @@ class T2SModel(nn.Module):
             },
             opset_version=16,
         )
-        y, k, v, y_emb, x_example = self.init_step(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)
-
-        # torch.onnx.export(
-        #     self.first_stage_decoder,
-        #     (x, prompts),
-        #     f"onnx/{project_name}/{project_name}_t2s_fsdec.onnx",
-        #     input_names=["x", "prompts"],
-        #     output_names=["y", "k", "v", "y_emb", "x_example"],
-        #     dynamic_axes={
-        #         "x": {1: "x_length"},
-        #         "prompts": {1: "prompts_length"},
-        #     },
-        #     verbose=False,
-        #     opset_version=16,
-        # )
-        # y, k, v, y_emb, x_example = self.first_stage_decoder(x, prompts)
 
+        stage_step = T2SStageStep(self.stage_decoder)
         torch.onnx.export(
-            self.stage_decoder,
+            stage_step,
             (y, k, v, y_emb, x_example),
             f"onnx/{project_name}/{project_name}_t2s_sdec.onnx",
             input_names=["iy", "ik", "iv", "iy_emb", "ix_example"],
-            output_names=["y", "k", "v", "y_emb", "logits", "samples"],
+            output_names=["y", "k", "v", "y_emb","x_example", "logits", "samples"],
             dynamic_axes={
                 "iy": {1: "iy_length"},
                 "ik": {1: "ik_length"},
                 "iv": {1: "iv_length"},
                 "iy_emb": {1: "iy_emb_length"},
                 "ix_example": {1: "ix_example_length"},
+                "x_example": {1: "x_example_length"}
             },
             verbose=False,
             opset_version=16,
@@ -303,6 +302,20 @@ class AudioPreprocess(nn.Module):
 
         return ssl_content, spectrum, sv_emb
 
+# def combineInitStepAndStageStep(init_step_onnx_path, stage_step_onnx_path):
+#     init_step_model = onnx.load(init_step_onnx_path)
+#     stage_step_model = onnx.load(stage_step_onnx_path)
+
+#     # Combine the models (this is a simplified example; actual combination logic may vary)
+#     combined_graph = helper.make_graph(
+#         nodes=init_step_model.graph.node + stage_step_model.graph.node,
+#         name="combined_graph",
+#         inputs=init_step_model.graph.input,
+#         outputs=stage_step_model.graph.output
+#     )
+
+#     combined_model = helper.make_model(combined_graph, producer_name='onnx-combiner')
+#     onnx.save(combined_model, f"onnx/{project_name}/{project_name}_combined.onnx")
 
 def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
     vits = VitsModel(vits_path, version=voice_model_version)
@@ -366,14 +379,14 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
     )
     ref_bert = torch.randn((ref_seq.shape[1], 1024)).float()
     text_bert = torch.randn((text_seq.shape[1], 1024)).float()
-    ref_audio = torch.randn((1, 48000 * 5)).float()
+    ref_audio32k = torch.randn((1, 32000 * 5)).float() - 0.5
-    # ref_audio = torch.tensor([load_audio("rec.wav", 48000)]).float()
-    ref_audio32k = torchaudio.functional.resample(ref_audio, 48000, 32000).float()
 
-    try:
+    os.makedirs(f"onnx/{project_name}", exist_ok=True)
-        os.mkdir(f"onnx/{project_name}")
+
-    except:
+    [ssl_content, spectrum, sv_emb] = preprocessor(ref_audio32k)
-        pass
+    gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float())
+    # exit()
+    gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), project_name)
 
     torch.onnx.export(preprocessor, (ref_audio32k,), f"onnx/{project_name}/{project_name}_audio_preprocess.onnx",
                     input_names=["audio32k"],
@@ -384,39 +397,31 @@ def export(vits_path, gpt_path, project_name, voice_model_version="v2"):
                         "spectrum": {2: "spectrum_length"}
                     })
 
-    [ssl_content, spectrum, sv_emb] = preprocessor(ref_audio32k)
-    gpt_sovits(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float())
-    # exit()
-    gpt_sovits.export(ref_seq, text_seq, ref_bert, text_bert, ssl_content.float(), spectrum.float(), sv_emb.float(), project_name)
-
-    if voice_model_version == "v1":
-        symbols = symbols_v1
-    else:
-        symbols = symbols_v2
-
 if __name__ == "__main__":
     try:
         os.mkdir("onnx")
     except:
         pass
 
-    gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-    vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
-    exp_path = "v1_export"
-    version = "v1"
-    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"
-    vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
-    exp_path = "v2_export"
-    version = "v2"
-    export(vits_path, gpt_path, exp_path, version)
 
-    gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
+    # gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-    vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
+    # vits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
-    exp_path = "v2pro_export"
+    # exp_path = "v1_export"
-    version = "v2Pro"
+    # 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"
+    # vits_path = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
+    # exp_path = "v2_export"
+    # version = "v2"
+    # export(vits_path, gpt_path, exp_path, version)
+
+    # gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
+    # vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth"
+    # exp_path = "v2pro_export"
+    # version = "v2Pro"
+    # export(vits_path, gpt_path, exp_path, version)
 
     gpt_path = "GPT_SoVITS/pretrained_models/s1v3.ckpt"
     vits_path = "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth"

playground/freerun.py

@@ -7,7 +7,7 @@ import torch
 from TTS_infer_pack.TextPreprocessor_onnx import TextPreprocessorOnnx
 
 
-MODEL_PATH = "onnx/v1_export/v1"
+MODEL_PATH = "onnx/v2proplus_export/v2proplus"
 
 def audio_postprocess(
     audios,
@@ -56,7 +56,7 @@ def audio_preprocess(audio_path):
 
 def preprocess_text(text:str):
     preprocessor = TextPreprocessorOnnx("playground/bert")
-    [phones, bert_features, norm_text] = preprocessor.segment_and_extract_feature_for_text(text, 'all_zh', 'v1')
+    [phones, bert_features, norm_text] = preprocessor.segment_and_extract_feature_for_text(text, 'all_zh', 'v2')
     phones = np.expand_dims(np.array(phones, dtype=np.int64), axis=0)
     return phones, bert_features.T.astype(np.float32)
 
@@ -76,7 +76,7 @@ def preprocess_text(text:str):
 
 init_step = ort.InferenceSession(MODEL_PATH+"_export_t2s_init_step.onnx")
 
-[y, k, v, y_emb, x_example] = init_step.run(None, {
+[y, k, v, y_emb, x_example, fake_logits, fake_samples] = init_step.run(None, {
     "input_text_phones": input_phones,
     "input_text_bert": input_bert,
     "ref_text_phones": ref_phones,
@@ -96,7 +96,7 @@ sdec = ort.InferenceSession(MODEL_PATH+"_export_t2s_sdec.onnx")
 
 for idx in tqdm(range(1, 1500)):
     # [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, logits, samples] = sdec.run(None, {
+    [y, k, v, y_emb, fake_x_example, logits, samples] = sdec.run(None, {
         "iy": y,
         "ik": k,
         "iv": v,
@@ -123,7 +123,7 @@ vtis = ort.InferenceSession(MODEL_PATH+"_export_vits.onnx")
     "input_text_phones": input_phones,
     "pred_semantic": pred_semantic,
     "spectrum": spectrum.astype(np.float32),
-    # "sv_emb": sv_emb.astype(np.float32)
+    "sv_emb": sv_emb.astype(np.float32)
 })
 
 audio_postprocess([audio])

playground/onnx_if_node_test.py

@@ -0,0 +1,214 @@
+import torch
+import torch.nn as nn
+import onnx
+import onnxruntime as ort
+from onnx import helper, TensorProto
+import numpy as np
+import os
+
+# Define file paths
+PLAYGROUND_DIR = "playground"
+MODEL_A_PATH = os.path.join(PLAYGROUND_DIR, "a.onnx")
+MODEL_B_PATH = os.path.join(PLAYGROUND_DIR, "b.onnx")
+MODEL_C_PATH = os.path.join(PLAYGROUND_DIR, "c.onnx")
+
+# --- 1. Create two simple PyTorch modules ---
+
+class ModelA(nn.Module):
+    """This model adds 1 to the input."""
+    def forward(self, x):
+        return x + 1.0
+
+class ModelB(nn.Module):
+    """This model multiplies the input by 2."""
+    def forward(self, x):
+        return x * 2.0
+
+def create_and_export_models():
+    """Creates two nn.Modules and exports them to ONNX."""
+    print("Step 1: Creating and exporting PyTorch models A and B...")
+    os.makedirs(PLAYGROUND_DIR, exist_ok=True)
+
+    # Define a dummy input with a dynamic axis
+    batch_size = 1
+    sequence_length = 10  # This dimension will be dynamic
+    features = 4
+    dummy_input = torch.randn(batch_size, sequence_length, features)
+
+    # Export Model A
+    print(f"Exporting Model A to {MODEL_A_PATH}")
+    torch.onnx.export(
+        ModelA(),
+        dummy_input,
+        MODEL_A_PATH,
+        input_names=['inputA'],
+        output_names=['output'],
+        dynamic_axes={'inputA': {1: 'sequenceA'}, 'output': {1: 'sequence'}},
+        opset_version=11 # If node requires opset >= 11
+    )
+
+    # Export Model B
+    print(f"Exporting Model B to {MODEL_B_PATH}")
+    torch.onnx.export(
+        ModelB(),
+        dummy_input,
+        MODEL_B_PATH,
+        input_names=['inputB'],
+        output_names=['output'],
+        dynamic_axes={'inputB': {1: 'sequenceB'}, 'output': {1: 'sequence'}},
+        opset_version=11
+    )
+    print("Models A and B exported successfully.")
+
+def combine_models_with_if():
+    """
+    Reads two ONNX models and combines them into a third model
+    using an 'If' operator.
+    """
+    print("\nStep 2: Combining models A and B into C with an 'If' node...")
+
+    # Load the two exported ONNX models
+    model_a = onnx.load(MODEL_A_PATH)
+    model_b = onnx.load(MODEL_B_PATH)
+
+    # The graphs for the 'then' and 'else' branches of the 'If' operator
+    then_graph = model_a.graph
+    then_graph.name = "then_branch_graph"
+    else_graph = model_b.graph
+    else_graph.name = "else_branch_graph"
+
+    # The data input for the main graph is defined here.
+    # We take it from one of the original models.
+    data_inputA = model_a.graph.input[0]
+    data_inputB = model_b.graph.input[0]
+
+    # For some onnxruntime versions, subgraphs should not have their own
+    # explicit 'input' list if the inputs are captured from the parent graph.
+    # We clear the input lists of the subgraphs to force implicit capture.
+    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_name = model_a.graph.output[0].name
+    assert subgraph_output_name == model_b.graph.output[0].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_use_a', TensorProto.BOOL, [])
+
+    # The main graph's output is the output from the 'If' node.
+    # We can use the ValueInfoProto from one of the subgraphs directly.
+    main_output = model_a.graph.output[0]
+
+    # Create the 'If' node
+    if_node = helper.make_node(
+        'If',
+        inputs=['if_use_a'],
+        outputs=[subgraph_output_name], # This name MUST match the subgraph's output name
+        then_branch=then_graph,
+        else_branch=else_graph
+    )
+
+    # Create the main graph containing the 'If' node. Its inputs are the condition
+    # AND the data that the subgraphs will capture.
+    main_graph = helper.make_graph(
+        nodes=[if_node],
+        name='if_main_graph',
+        inputs=[cond_input, data_inputA, data_inputB],
+        outputs=[main_output]
+    )
+
+    # Create the final combined model, specifying the opset and IR version
+    opset_version = 16
+    final_model = helper.make_model(main_graph,
+                                    producer_name='onnx-if-combiner',
+                                    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, MODEL_C_PATH)
+    print(f"Combined model C saved to {MODEL_C_PATH}")
+
+def verify_combined_model():
+    """
+    Loads the combined ONNX model and runs inference to verify
+    that the 'If' branching and dynamic shapes work correctly.
+    """
+    print("\nStep 3: Verifying the combined model C...")
+    sess = ort.InferenceSession(MODEL_C_PATH)
+
+    # --- Test Case 1: Select Model A (if_use_a = True) ---
+    print("\n--- Verifying 'then' branch (Model A) ---")
+    use_a = np.array(True)
+    # Use a different sequence length to test dynamic axis
+    test_seq_len_a = 15
+    test_seq_len_b = 10
+    input_data_a = np.random.randn(1, test_seq_len_a, 4).astype(np.float32)
+    input_data_b = np.random.randn(1, test_seq_len_a, 4).astype(np.float32)
+
+    # Run inference
+    outputs = sess.run(
+        None,
+        {'if_use_a': use_a, 'inputA': input_data_a, 'inputB': input_data_b}
+    )
+    result_a = outputs[0]
+
+    # Calculate expected output from Model A
+    expected_a = input_data_a + 1.0
+
+    # Verify the output and shape
+    np.testing.assert_allclose(result_a, expected_a, rtol=1e-5, atol=1e-5)
+    assert result_a.shape[1] == test_seq_len_a, "Dynamic shape failed for branch A"
+    print("✅ Branch A (if_use_a=True) works correctly.")
+    print(f"✅ Dynamic shape test passed (input seq_len={test_seq_len_a}, output seq_len={result_a.shape[1]})")
+
+    # --- Test Case 2: Select Model B (if_use_a = False) ---
+    print("\n--- Verifying 'else' branch (Model B) ---")
+    use_b = np.array(False)
+    # Use another sequence length
+    test_seq_len_a = 8
+    test_seq_len_b = 5
+    input_data_a = np.random.randn(1, test_seq_len_a, 4).astype(np.float32)
+    input_data_b = np.random.randn(1, test_seq_len_b, 4).astype(np.float32)
+
+    # Run inference
+    outputs = sess.run(
+        None,
+        {'if_use_a': use_b, 'inputA': input_data_a, 'inputB': input_data_b}
+    )
+    result_b = outputs[0]
+
+    # Calculate expected output from Model B
+    expected_b = input_data_b * 2.0
+
+    # Verify the output and shape
+    np.testing.assert_allclose(result_b, expected_b, rtol=1e-5, atol=1e-5)
+    assert result_b.shape[1] == test_seq_len_b, "Dynamic shape failed for branch B"
+    print("✅ Branch B (if_use_a=False) works correctly.")
+    print(f"✅ Dynamic shape test passed (input seq_len={test_seq_len_b}, output seq_len={result_b.shape[1]})")
+
+def cleanup():
+    """Removes the intermediate ONNX files."""
+    print("\nCleaning up intermediate files...")
+    for path in [MODEL_A_PATH, MODEL_B_PATH]:
+        if os.path.exists(path):
+            os.remove(path)
+            print(f"Removed {path}")
+
+def main():
+    """Main function to run the entire process."""
+    try:
+        create_and_export_models()
+        combine_models_with_if()
+        verify_combined_model()
+    finally:
+        cleanup()
+    print("\nAll steps completed successfully!")
+
+if __name__ == "__main__":
+    main()