From aa3fe1a353bde71d106755d030d9e5300fbde328 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 22 七月 2024 19:02:15 +0800
Subject: [PATCH] python runtime
---
funasr/utils/export_utils.py | 182 +++++++++++++++++++++++++++++++++++++++------
1 files changed, 156 insertions(+), 26 deletions(-)
diff --git a/funasr/utils/export_utils.py b/funasr/utils/export_utils.py
index bc79539..af9f37b 100644
--- a/funasr/utils/export_utils.py
+++ b/funasr/utils/export_utils.py
@@ -1,8 +1,11 @@
import os
import torch
+import functools
-def export_onnx(model, data_in=None, quantize: bool = False, opset_version: int = 14, **kwargs):
+def export(
+ model, data_in=None, quantize: bool = False, opset_version: int = 14, type="onnx", **kwargs
+):
model_scripts = model.export(**kwargs)
export_dir = kwargs.get("output_dir", os.path.dirname(kwargs.get("init_param")))
os.makedirs(export_dir, exist_ok=True)
@@ -11,14 +14,28 @@
model_scripts = (model_scripts,)
for m in model_scripts:
m.eval()
- _onnx(
- m,
- data_in=data_in,
- quantize=quantize,
- opset_version=opset_version,
- export_dir=export_dir,
- **kwargs
- )
+ if type == "onnx":
+ _onnx(
+ m,
+ data_in=data_in,
+ quantize=quantize,
+ opset_version=opset_version,
+ export_dir=export_dir,
+ **kwargs,
+ )
+ elif type == "torchscript":
+ device = "cuda" if torch.cuda.is_available() else "cpu"
+ print("Exporting torchscripts on device {}".format(device))
+ _torchscripts(m, path=export_dir, device=device)
+ elif type == "bladedisc":
+ assert (
+ torch.cuda.is_available()
+ ), "Currently bladedisc optimization for FunASR only supports GPU"
+ # bladedisc only optimizes encoder/decoder modules
+ if hasattr(m, "encoder") and hasattr(m, "decoder"):
+ _bladedisc_opt_for_encdec(m, path=export_dir, enable_fp16=True)
+ else:
+ _torchscripts(m, path=export_dir, device="cuda")
print("output dir: {}".format(export_dir))
return export_dir
@@ -30,14 +47,17 @@
quantize: bool = False,
opset_version: int = 14,
export_dir: str = None,
- **kwargs
+ **kwargs,
):
dummy_input = model.export_dummy_inputs()
verbose = kwargs.get("verbose", False)
- export_name = model.export_name() if hasattr(model, "export_name") else "model.onnx"
+ if isinstance(model.export_name, str):
+ export_name = model.export_name + ".onnx"
+ else:
+ export_name = model.export_name()
model_path = os.path.join(export_dir, export_name)
torch.onnx.export(
model,
@@ -55,18 +75,128 @@
import onnx
quant_model_path = model_path.replace(".onnx", "_quant.onnx")
- if not os.path.exists(quant_model_path):
- onnx_model = onnx.load(model_path)
- nodes = [n.name for n in onnx_model.graph.node]
- nodes_to_exclude = [
- m for m in nodes if "output" in m or "bias_encoder" in m or "bias_decoder" in m
- ]
- quantize_dynamic(
- model_input=model_path,
- model_output=quant_model_path,
- op_types_to_quantize=["MatMul"],
- per_channel=True,
- reduce_range=False,
- weight_type=QuantType.QUInt8,
- nodes_to_exclude=nodes_to_exclude,
- )
+ onnx_model = onnx.load(model_path)
+ nodes = [n.name for n in onnx_model.graph.node]
+ nodes_to_exclude = [
+ m for m in nodes if "output" in m or "bias_encoder" in m or "bias_decoder" in m
+ ]
+ print("Quantizing model from {} to {}".format(model_path, quant_model_path))
+ quantize_dynamic(
+ model_input=model_path,
+ model_output=quant_model_path,
+ op_types_to_quantize=["MatMul"],
+ per_channel=True,
+ reduce_range=False,
+ weight_type=QuantType.QUInt8,
+ nodes_to_exclude=nodes_to_exclude,
+ )
+
+
+def _torchscripts(model, path, device="cuda"):
+ dummy_input = model.export_dummy_inputs()
+
+ if device == "cuda":
+ model = model.cuda()
+ if isinstance(dummy_input, torch.Tensor):
+ dummy_input = dummy_input.cuda()
+ else:
+ dummy_input = tuple([i.cuda() for i in dummy_input])
+
+ model_script = torch.jit.trace(model, dummy_input)
+ if isinstance(model.export_name, str):
+ model_script.save(os.path.join(path, f"{model.export_name}".replace("onnx", "torchscript")))
+ else:
+ model_script.save(os.path.join(path, f"{model.export_name()}".replace("onnx", "torchscript")))
+
+
+def _bladedisc_opt(model, model_inputs, enable_fp16=True):
+ model = model.eval()
+ try:
+ import torch_blade
+ except Exception as e:
+ print(
+ f"Warning, if you are exporting bladedisc, please install it and try it again: pip install -U torch_blade\n"
+ )
+ torch_config = torch_blade.config.Config()
+ torch_config.enable_fp16 = enable_fp16
+ with torch.no_grad(), torch_config:
+ opt_model = torch_blade.optimize(
+ model,
+ allow_tracing=True,
+ model_inputs=model_inputs,
+ )
+ return opt_model
+
+
+def _rescale_input_hook(m, x, scale):
+ if len(x) > 1:
+ return (x[0] / scale, *x[1:])
+ else:
+ return (x[0] / scale,)
+
+
+def _rescale_output_hook(m, x, y, scale):
+ if isinstance(y, tuple):
+ return (y[0] / scale, *y[1:])
+ else:
+ return y / scale
+
+
+def _rescale_encoder_model(model, input_data):
+ # Calculate absmax
+ absmax = torch.tensor(0).cuda()
+
+ def stat_input_hook(m, x, y):
+ val = x[0] if isinstance(x, tuple) else x
+ absmax.copy_(torch.max(absmax, val.detach().abs().max()))
+
+ encoders = model.encoder.model.encoders
+ hooks = [m.register_forward_hook(stat_input_hook) for m in encoders]
+ model = model.cuda()
+ model(*input_data)
+ for h in hooks:
+ h.remove()
+
+ # Rescale encoder modules
+ fp16_scale = int(2 * absmax // 65536)
+ print(f"rescale encoder modules with factor={fp16_scale}")
+ model.encoder.model.encoders0.register_forward_pre_hook(
+ functools.partial(_rescale_input_hook, scale=fp16_scale),
+ )
+ for name, m in model.encoder.model.named_modules():
+ if name.endswith("self_attn"):
+ m.register_forward_hook(functools.partial(_rescale_output_hook, scale=fp16_scale))
+ if name.endswith("feed_forward.w_2"):
+ state_dict = {k: v / fp16_scale for k, v in m.state_dict().items()}
+ m.load_state_dict(state_dict)
+
+
+def _bladedisc_opt_for_encdec(model, path, enable_fp16):
+ # Get input data
+ # TODO: better to use real data
+ input_data = model.export_dummy_inputs()
+ if isinstance(input_data, torch.Tensor):
+ input_data = input_data.cuda()
+ else:
+ input_data = tuple([i.cuda() for i in input_data])
+
+ # Get input data for decoder module
+ decoder_inputs = list()
+
+ def get_input_hook(m, x):
+ decoder_inputs.extend(list(x))
+
+ hook = model.decoder.register_forward_pre_hook(get_input_hook)
+ model = model.cuda()
+ model(*input_data)
+ hook.remove()
+
+ # Prevent FP16 overflow
+ if enable_fp16:
+ _rescale_encoder_model(model, input_data)
+
+ # Export and optimize encoder/decoder modules
+ model.encoder = _bladedisc_opt(model.encoder, input_data[:2])
+ model.decoder = _bladedisc_opt(model.decoder, tuple(decoder_inputs))
+ model_script = torch.jit.trace(model, input_data)
+ model_script.save(os.path.join(path, f"{model.export_name}_blade.torchscript"))
--
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