0. 参考文献
- 官方文档
- AML 库
1. Overview
- 前提条件，安装tensorflow ，版本1.15
- 下边是我 pip list 中相关与tvm和TensorFlow的各种依赖包列表，可以跑通。方便大家对照查看。
不支持在 Doc 外粘贴 block
- 根据官方文档的介绍，我用 processon 画一个流程图，来方便理解。
- 名词解释
- pb
- Protobuf 类型的模型文件，一般用TensorFlow训练生成
- pbtxt
- 文本文件，描述模型结构，人类可读，可以用一些可视化工具来查看
- np.array
- Numpy 的数组
- mod (tvm.IRModule) – The module that optimizations will be performed on.
- TVM 的中间表示，所有的优化都在这上边做
- params (dict of str to tvm.nd.NDArray) – Dict of converted parameters stored in tvm.nd.NDArray format
- 存储参数的数据结构，在auto tune的时候，就调节它
- relay.build 图优化就在这个阶段做
- graph_json (str) – The json string that can be accepted by graph runtime.
- 在运行时可以读取
- mod (tvm.Module) – The module containing necessary libraries.
- 包含了运行所需要的库
- params (dict) – The parameters of the final graph.
- 存贮图最终的参数
2. 实际运行
运行一个 tvm_tune的demo
# https://code.byted.org/lagrange/tvm_tune
# 代码库下载
git clone [email protected]:lagrange/tvm_tune.git
# 切换到对应分支
git checkout tf_pipeline
cd tvm_tune/tools
python tune_frozen_graph.py
在CPU上运行
- 微调一下代码
diff --git a/tools/tune_frozen_graph.py b/tools/tune_frozen_graph.py
index 1ca63ed..ad5d252 100644
--- a/tools/tune_frozen_graph.py
+++ b/tools/tune_frozen_graph.py
@@ -23,11 +23,14 @@ from tvm.contrib.util import tempdir
#### TUNING OPTION ####
-target = tvm.target.cuda("unknown", "-libs=cudnn,cublas")
+# target = tvm.target.cuda("unknown", "-libs=cudnn,cublas")
+target = tvm.target.cuda("unknown", "")
print(target)
network = 'bertmatch'
log_file = "%s.log" % network
+print ("chuanqiz")
+print (log_file)
tuning_option = {
'log_filename': log_file,
@@ -224,15 +227,15 @@ if __name__ == "__main__":
print(tf_res)
export_path = "./tvm_export"
- # mod, params = convert_tf_to_tvm(sess, input_names, input_shapes, output_names)
+ mod, params = convert_tf_to_tvm(sess, input_names, input_shapes, output_names)
# print(mod["main"])
#
- # # tvm_res = run_tvm(mod, params, feed_dict, output_shapes)
- # module = tune_and_evaluate(mod, tuning_option, params, export_path, skip_tune=True)
- # tvm_res = run_tvm(module, None, feed_dict, output_shapes)
+ tvm_res = run_tvm(mod, params, feed_dict, output_shapes)
+ module = tune_and_evaluate(mod, tuning_option, params, export_path, skip_tune=True)
+ tvm_res = run_tvm(module, None, feed_dict, output_shapes)
#
- # print(tf_res, tvm_res)
- # print(np.allclose(tf_res, tvm_res, rtol=1.e-3, atol=1.e-4))
+ print(tf_res, tvm_res)
+ print(np.allclose(tf_res, tvm_res, rtol=1.e-3, atol=1.e-4))
# tvm tuned module export to tensorflow op
export_tf_res = tvm_export_to_tensorflow(export_path, feed_dict, fetch_dict)
- 运行log留存，便于对比
在GPU上运行
- 要把 +# target = tvm.target.cuda(“unknown”, “-libs=cudnn,cublas”)这行代码打开
zangchuanqi@n22-145-158:~/workspace/tvm_tune/tools$ diff tune_frozen_graph_gpu.py tune_frozen_graph.py
26c26,27
< target = tvm.target.cuda("unknown", "-libs=cudnn,cublas")
---
> # target = tvm.target.cuda("unknown", "-libs=cudnn,cublas")
> target = tvm.target.cuda("unknown", "")
- 重新编译 tvm ， 修改 config.cmake 文件
zangchuanqi@n22-145-158:~/workspace/tvm$ diff config.cmake build/config.cmake
173c173
< set(USE_CUDNN OFF)
---
> set(USE_CUDNN ON)
176c176
< set(USE_CUBLAS OFF)
---
> set(USE_CUBLAS ON)
227,229d226
- log留存
需要安装的一些依赖
- Import tensorflow as tf
pip install tensorflow
- ImportError: No module named PIL
pip install Pillow
- Graph 读入部分，借助TF和pytorch的依赖
- 需要安装 tf 和 torch
- 出现问题，使用 包管理器安装时，由于网络原因无法安装，因此下载 whl，手动安装
- 或者添加代理，搞定网络问题
- 使用docker 镜像，安装对应环境
3. 运行时出现的小问题
- Warning 缺少llvm ，是否需要安装？
WARNING:autotvm:Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (1, 2048), 'float32'), ('TENSOR', (1008, 2048), 'float32'), None, 'float32'). A fallback c onfiguration is used, which may bring great performance regression.
- block住了, python 语法问题？
Tensorflow protobuf imported to relay frontend.
Traceback (most recent call last):
File "from_tensorflow.py", line 158, in <module>
m = graph_runtime.GraphModule(lib["default"](ctx))
TypeError: tuple indices must be integers or slices, not str
- TensorFlow中node不合法？
WARNING:tensorflow:From /data01/zangchuanqi/workspace/tvm/python/tvm/relay/testing/tf.py:153: The name tf.logging.fatal is deprecated. Please use tf.compat.v1.logging.fatal instead.
CRITICAL:tensorflow:Failed to locate: n01440764
Traceback (most recent call last):
File "tf.py", line 146, in <module>
run_inference_on_image(img_path)
File "tf.py", line 136, in run_inference_on_image
uid_lookup_path=label_path)
File "/data01/zangchuanqi/workspace/tvm/python/tvm/relay/testing/tf.py", line 105, in __init__
self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
File "/data01/zangchuanqi/workspace/tvm/python/tvm/relay/testing/tf.py", line 154, in load
name = uid_to_human[val]
KeyError: 'n01440764'
(tvm.venv) zangchuanqi@n22-145-158:~/workspace/test$ cat /data01/zangchuanqi/.tvm_test_data/data/imagenet_2012_challenge_label_map_proto.pbtxt | grep n01440764
target_class_string: "n01440764"
(tvm.venv) zangchuanqi@n22-145-158:~/workspace/test$