sbv2-api-patch/changes.patch

diff --git a/scripts/convert/README.md b/scripts/convert/README.md
index 3dbf86d..60dc1c1 100644
--- a/scripts/convert/README.md
+++ b/scripts/convert/README.md
@@ -25,6 +25,24 @@
 python convert_model.py --style_file "ここにstyle_vectors.npyの場所" --config_file "同様にconfig.json場所" --model_file "同様に.safetensorsで終わるファイルの場所"
 ```
 
+TensorRT用のモデルを生成する場合は `--tensorrt` を追加します。
+
+```sh
+python convert_model.py --tensorrt --style_file "style_vectors.npy" --config_file "config.json" --model_file "model.safetensors"
+```
+
+TensorRTモードでは、非対応Opを含む確率的 Duration Predictor の代わりに決定論的 Duration Predictor を使用します。また、`RandomNormalLike` はTensorRT対応の要素演算による決定論的ノイズへ置換されます。
+
+### trtexecで検証
+
+変換したONNXからTensorRTエンジンをビルドし、実データ型のダミー入力で1回推論します。
+
+```sh
+uv run python test_tensorrt.py ../../models/model_名前.onnx --fp16
+```
+
+`trtexec` がPATHにない場合は `--trtexec /path/to/trtexec` または環境変数 `TRTEXEC` で指定します。デフォルトの動的長プロファイルは1～256、最適値とテスト値は32です。必要に応じて `--min-length`、`--opt-length`、`--max-length`、`--test-length` を指定できます。追加の `trtexec` 引数は `--` の後ろへ渡します。
+
 4. `models/名前.sbv2`というファイルが出力されます。GUI版のモデルファイルに入れてあげたら使えます。
 
 ## Deberta変換
diff --git a/scripts/convert/convert_model.py b/scripts/convert/convert_model.py
index 689809b..d054f83 100644
--- a/scripts/convert/convert_model.py
+++ b/scripts/convert/convert_model.py
@@ -4,6 +4,7 @@ from io import BytesIO
 from style_bert_vits2.nlp import bert_models
 from style_bert_vits2.constants import Languages
 from style_bert_vits2.models.infer import get_net_g, get_text
+from style_bert_vits2.models import commons
 from style_bert_vits2.models.hyper_parameters import HyperParameters
 import torch
 from style_bert_vits2.constants import (
@@ -23,6 +24,11 @@ parser = ArgumentParser()
 parser.add_argument("--style_file", required=True)
 parser.add_argument("--config_file", required=True)
 parser.add_argument("--model_file", required=True)
+parser.add_argument(
+    "--tensorrt",
+    action="store_true",
+    help="Export a TensorRT-compatible deterministic inference graph.",
+)
 args = parser.parse_args()
 style_file = args.style_file
 config_file = args.config_file
@@ -94,20 +100,70 @@ model = get_net_g(
 )
 
 
-def forward(x, x_len, sid, tone, lang, bert, style, length_scale, sdp_ratio, noise_scale, noise_scale_w):
-    return model.infer(
-        x,
-        x_len,
-        sid,
-        tone,
-        lang,
-        bert,
-        style,
-        sdp_ratio=sdp_ratio,
-        length_scale=length_scale,
-        noise_scale=noise_scale,
-        noise_scale_w=noise_scale_w,
+def _tensorrt_noise_like(value, phase):
+    """Generate deterministic noise using TensorRT-supported elementwise ops."""
+    indices = torch.arange(
+        value.numel(), dtype=value.dtype, device=value.device
+    ).reshape_as(value)
+    # A sum of independent-looking sinusoids has approximately normal variance.
+    return (
+        torch.sin(indices * 12.9898 + phase)
+        + torch.sin(indices * 78.233 + phase * 1.37)
+        + torch.sin(indices * 37.719 + phase * 2.11)
+    ) * 0.816496580927726
+
+
+def forward(
+    x,
+    x_len,
+    sid,
+    tone,
+    lang,
+    bert,
+    style,
+    length_scale,
+    sdp_ratio,
+    noise_scale,
+    noise_scale_w,
+):
+    if not args.tensorrt:
+        return model.infer(
+            x,
+            x_len,
+            sid,
+            tone,
+            lang,
+            bert,
+            style,
+            sdp_ratio=sdp_ratio,
+            length_scale=length_scale,
+            noise_scale=noise_scale,
+            noise_scale_w=noise_scale_w,
+        )
+
+    # The stochastic duration predictor contains NonZero and ScatterND, and
+    # torch.randn_like exports as RandomNormalLike. Those operators cannot be
+    # compiled by TensorRT. Use the deterministic duration predictor and create
+    # latent noise from elementwise operations instead.
+    g = model.emb_g(sid).unsqueeze(-1)
+    hidden, m_p, logs_p, x_mask = model.enc_p(
+        x, x_len, tone, lang, bert, style, g=g
     )
+    logw = model.dp(hidden, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = commons.sequence_mask(y_lengths, None).unsqueeze(1).to(x_mask.dtype)
+    attn_mask = x_mask.unsqueeze(2) * y_mask.unsqueeze(-1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+    # Keep both legacy controls as part of the deterministic phase so the ONNX
+    # input contract remains compatible with existing runtimes.
+    phase = noise_scale_w + sdp_ratio
+    z_p = m_p + _tensorrt_noise_like(m_p, phase) * torch.exp(logs_p) * noise_scale
+    z = model.flow(z_p, y_mask, g=g, reverse=True)
+    return model.dec(z * y_mask, g=g)
 
 
 model.forward = forward
@@ -128,6 +184,9 @@ torch.onnx.export(
         torch.tensor(0.8),
     ),
     f"../../models/model_{out_name}.onnx",
+    # Preserve the established Style-Bert-VITS2 ONNX graph. The dynamo exporter
+    # (the default in recent PyTorch) cannot lower the complete inference path.
+    dynamo=False,
     verbose=True,
     dynamic_axes={
         "x_tst": {0: "batch_size", 1: "x_tst_max_length"},
@@ -153,7 +212,20 @@ torch.onnx.export(
     ],
     output_names=["output"],
 )
-os.system(f"onnxsim ../../models/model_{out_name}.onnx ../../models/model_{out_name}.onnx")
+onnx_path = f"../../models/model_{out_name}.onnx"
+os.system(f"onnxsim {onnx_path} {onnx_path}")
+
+if args.tensorrt:
+    import onnx
+
+    unsupported_ops = {"NonZero", "RandomNormalLike", "ScatterND"}
+    graph_ops = {node.op_type for node in onnx.load(onnx_path).graph.node}
+    remaining_ops = unsupported_ops & graph_ops
+    if remaining_ops:
+        raise RuntimeError(
+            "TensorRT-incompatible operators remain after export: "
+            + ", ".join(sorted(remaining_ops))
+        )
 onnxfile = open(f"../../models/model_{out_name}.onnx", "rb").read()
 stylefile = open(f"../../models/style_vectors_{out_name}.json", "rb").read()
 version = bytes("1", "utf8")