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changes.patch
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changes.patch
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diff --git a/scripts/convert/README.md b/scripts/convert/README.md
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index 3dbf86d..60dc1c1 100644
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--- a/scripts/convert/README.md
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+++ b/scripts/convert/README.md
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@@ -25,6 +25,24 @@
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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"
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```
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+TensorRT用のモデルを生成する場合は `--tensorrt` を追加します。
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+
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+```sh
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+python convert_model.py --tensorrt --style_file "style_vectors.npy" --config_file "config.json" --model_file "model.safetensors"
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+```
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+
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+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。
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+
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+### trtexecで検証
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+
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+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。
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+
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+```sh
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+uv run python test_tensorrt.py ../../models/model_名前.onnx --fp16
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+```
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+
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+`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。
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+
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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。
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## Deberta変換
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diff --git a/scripts/convert/convert_model.py b/scripts/convert/convert_model.py
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index 689809b..d054f83 100644
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--- a/scripts/convert/convert_model.py
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+++ b/scripts/convert/convert_model.py
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@@ -4,6 +4,7 @@ from io import BytesIO
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from style_bert_vits2.nlp import bert_models
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from style_bert_vits2.constants import Languages
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from style_bert_vits2.models.infer import get_net_g, get_text
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+from style_bert_vits2.models import commons
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from style_bert_vits2.models.hyper_parameters import HyperParameters
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import torch
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from style_bert_vits2.constants import (
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@@ -23,6 +24,11 @@ parser = ArgumentParser()
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parser.add_argument("--style_file", required=True)
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parser.add_argument("--config_file", required=True)
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parser.add_argument("--model_file", required=True)
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+parser.add_argument(
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+ "--tensorrt",
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+ action="store_true",
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+ help="Export a TensorRT-compatible deterministic inference graph.",
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+)
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args = parser.parse_args()
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style_file = args.style_file
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config_file = args.config_file
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@@ -94,20 +100,70 @@ model = get_net_g(
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)
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-def forward(x, x_len, sid, tone, lang, bert, style, length_scale, sdp_ratio, noise_scale, noise_scale_w):
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- return model.infer(
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- x,
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- x_len,
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- sid,
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- tone,
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- lang,
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- bert,
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- style,
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- sdp_ratio=sdp_ratio,
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- length_scale=length_scale,
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- noise_scale=noise_scale,
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- noise_scale_w=noise_scale_w,
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+def _tensorrt_noise_like(value, phase):
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+ """Generate deterministic noise using TensorRT-supported elementwise ops."""
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+ indices = torch.arange(
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+ value.numel(), dtype=value.dtype, device=value.device
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+ ).reshape_as(value)
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+ # A sum of independent-looking sinusoids has approximately normal variance.
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+ return (
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+ torch.sin(indices * 12.9898 + phase)
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+ + torch.sin(indices * 78.233 + phase * 1.37)
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+ + torch.sin(indices * 37.719 + phase * 2.11)
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+ ) * 0.816496580927726
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+
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+
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+def forward(
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+ x,
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+ x_len,
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+ sid,
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+ tone,
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+ lang,
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+ bert,
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+ style,
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+ length_scale,
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+ sdp_ratio,
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+ noise_scale,
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+ noise_scale_w,
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+):
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+ if not args.tensorrt:
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+ return model.infer(
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+ x,
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+ x_len,
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+ sid,
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+ tone,
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+ lang,
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+ bert,
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+ style,
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+ sdp_ratio=sdp_ratio,
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+ length_scale=length_scale,
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+ noise_scale=noise_scale,
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+ noise_scale_w=noise_scale_w,
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+ )
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+
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+ # The stochastic duration predictor contains NonZero and ScatterND, and
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+ # torch.randn_like exports as RandomNormalLike. Those operators cannot be
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+ # compiled by TensorRT. Use the deterministic duration predictor and create
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+ # latent noise from elementwise operations instead.
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+ g = model.emb_g(sid).unsqueeze(-1)
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+ hidden, m_p, logs_p, x_mask = model.enc_p(
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+ x, x_len, tone, lang, bert, style, g=g
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)
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+ logw = model.dp(hidden, x_mask, g=g)
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+ w = torch.exp(logw) * x_mask * length_scale
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+ w_ceil = torch.ceil(w)
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+ y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
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+ y_mask = commons.sequence_mask(y_lengths, None).unsqueeze(1).to(x_mask.dtype)
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+ attn_mask = x_mask.unsqueeze(2) * y_mask.unsqueeze(-1)
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+ attn = commons.generate_path(w_ceil, attn_mask)
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+ m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
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+ logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
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+ # Keep both legacy controls as part of the deterministic phase so the ONNX
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+ # input contract remains compatible with existing runtimes.
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+ phase = noise_scale_w + sdp_ratio
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+ z_p = m_p + _tensorrt_noise_like(m_p, phase) * torch.exp(logs_p) * noise_scale
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+ z = model.flow(z_p, y_mask, g=g, reverse=True)
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+ return model.dec(z * y_mask, g=g)
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model.forward = forward
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@@ -128,6 +184,9 @@ torch.onnx.export(
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torch.tensor(0.8),
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),
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f"../../models/model_{out_name}.onnx",
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+ # Preserve the established Style-Bert-VITS2 ONNX graph. The dynamo exporter
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+ # (the default in recent PyTorch) cannot lower the complete inference path.
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+ dynamo=False,
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verbose=True,
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dynamic_axes={
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"x_tst": {0: "batch_size", 1: "x_tst_max_length"},
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@@ -153,7 +212,20 @@ torch.onnx.export(
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],
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output_names=["output"],
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)
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-os.system(f"onnxsim ../../models/model_{out_name}.onnx ../../models/model_{out_name}.onnx")
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+onnx_path = f"../../models/model_{out_name}.onnx"
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+os.system(f"onnxsim {onnx_path} {onnx_path}")
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+
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+if args.tensorrt:
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+ import onnx
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+
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+ unsupported_ops = {"NonZero", "RandomNormalLike", "ScatterND"}
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+ graph_ops = {node.op_type for node in onnx.load(onnx_path).graph.node}
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+ remaining_ops = unsupported_ops & graph_ops
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+ if remaining_ops:
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+ raise RuntimeError(
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+ "TensorRT-incompatible operators remain after export: "
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+ + ", ".join(sorted(remaining_ops))
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+ )
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onnxfile = open(f"../../models/model_{out_name}.onnx", "rb").read()
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stylefile = open(f"../../models/style_vectors_{out_name}.json", "rb").read()
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version = bytes("1", "utf8")
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