From 28a19dbc4e85d3b8a4ec2ef7483bba64d422b43f Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期三, 12 四月 2023 18:03:06 +0800
Subject: [PATCH] Merge remote-tracking branch 'origin/main' into dev_aky
---
funasr/export/README.md | 17 ++++++++++++++++-
1 files changed, 16 insertions(+), 1 deletions(-)
diff --git a/funasr/export/README.md b/funasr/export/README.md
index 33ab22e..97a3de9 100644
--- a/funasr/export/README.md
+++ b/funasr/export/README.md
@@ -2,6 +2,8 @@
## Environments
torch >= 1.11.0
modelscope >= 1.2.0
+ torch-quant >= 0.4.0 (required for exporting quantized torchscript format model)
+ # pip install torch-quant -i https://pypi.org/simple
## Install modelscope and funasr
@@ -15,7 +17,7 @@
--model-name [model_name] \
--export-dir [export_dir] \
--type [onnx, torch] \
- --quantize \
+ --quantize [true, false] \
--fallback-num [fallback_num]
```
`model-name`: the model is to export. It could be the models from modelscope, or local finetuned model(named: model.pb).
@@ -28,6 +30,16 @@
`fallback-num`: specify the number of fallback layers to perform automatic mixed precision quantization.
+## Performance Benchmark of Runtime
+
+### Paraformer on CPU
+
+[onnx runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)
+
+[libtorch runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_libtorch.md)
+
+### Paraformer on GPU
+[nv-triton](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/triton_gpu)
## For example
### Export onnx format model
@@ -51,3 +63,6 @@
python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch
```
+## Acknowledge
+Torch model quantization is supported by [BladeDISC](https://github.com/alibaba/BladeDISC), an end-to-end DynamIc Shape Compiler project for machine learning workloads. BladeDISC provides general, transparent, and ease of use performance optimization for TensorFlow/PyTorch workloads on GPGPU and CPU backends. If you are interested, please contact us.
+
--
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