AttributeError: 'tensorrt.tensorrt.Builder' object has no attribute 'max_workspace_size'

[weihaosky]

When I run the Usage demo

import torch
from torch2trt import torch2trt
from torchvision.models.alexnet import alexnet

# create some regular pytorch model...
model = alexnet(pretrained=True).eval().cuda()

# create example data
x = torch.ones((1, 3, 224, 224)).cuda()

# convert to TensorRT feeding sample data as input
model_trt = torch2trt(model, [x])

An error occurs:

AttributeError                            Traceback (most recent call last)
~/Documents/github/fast-reid/demo/convert2trt.py in <module>
----> 1 model_trt = torch2trt(model_alex, [x])

~/anaconda3/envs/detect2/lib/python3.6/site-packages/torch2trt-0.2.0-py3.6.egg/torch2trt/torch2trt.py in torch2trt(module, inputs, input_names, output_names, log_level, max_batch_size, fp16_mode, max_workspace_size, strict_type_constraints, keep_network, int8_mode, int8_calib_dataset, int8_calib_algorithm, int8_calib_batch_size, use_onnx, **kwargs)
    546             ctx.mark_outputs(outputs, output_names)
    547 
--> 548     builder.max_workspace_size = max_workspace_size
    549     builder.fp16_mode = fp16_mode
    550     builder.max_batch_size = max_batch_size

AttributeError: 'tensorrt.tensorrt.Builder' object has no attribute 'max_workspace_size'

What is the problem? Many thanks!

[timothylimyl]

Hi, I'm facing the same issue. I checked all the API reference and the code is written correctly. I will check back with you if I have resolved it.

[cheadrian]

Got the same error when running using TensorRT Python 8.0.0.3.
With nvidia-tensorrt-7.2.3.4 it works fine.
!pip install nvidia-tensorrt==7.2.* --index-url https://pypi.ngc.nvidia.com

[timothylimyl]

Yup, change back to TensorRT-7 and it work fine.

If you are facing any issues, I suggest to just remove and re-clone the whole repo. You'll need to change the is not to != in the dummy_converters.py.

[eriche2016]

I have come across the same problem. can anyone solve this problem?

[liuanhua110]

https://github.com/gcunhase/torch2trt
clone and install this one is work for me

[Michaelzhouisnotwhite]

Got the same error when running using TensorRT Python 8.0.0.3. With nvidia-tensorrt-7.2.3.4 it works fine. !pip install nvidia-tensorrt==7.2.* --index-url https://pypi.ngc.nvidia.com
sry. I try this on the jetson nano. it turns out:

Defaulting to user installation because normal site-packages is not writeable Looking in indexes: https://pypi.ngc.nvidia.com ERROR: Could not find a version that satisfies the requirement nvidia-tensorrt==7.2.* (from versions: none) ERROR: No matching distribution found for nvidia-tensorrt==7.2.*

[Michaelzhouisnotwhite]

I have no idea.help me

[Tetsujinfr]

I am (trying) using Tensorrt 8.2.0.6.
I got the same issue. Tried the above modified repo mentioned by @liuanhua110 but it did not work, same error.

So going to roll back to tensorrt 7.1.0.16 (which I know work since the code I am running works on another machine with tensorrt 7 installed there).

Mentioning this here though because I spent several hours installing tensorrt 8.2 to find out there is this compatibility issue going on.

Come on NVIDIA!

[troellke]

TensorRT API was updated in 8.0.1 so you need to use different commands now.
As stated in their release notes "ICudaEngine.max_workspace_size" and "Builder.build_cuda_engine()" among other deprecated functions were removed. (see https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-8.html#rel_8-0-1)

The current usage that worked for me:

• to set max_workspace_size

config = builder.create_builder_config()
config.max_workspace_size = 1 << 28

-and to build engine:

plan = builder.build_serialized_network(network, config)
engine = runtime.deserialize_cuda_engine(plan)

[cding-nv]

--- a/python/app_ScatterND_plugin.py
+++ b/python/app_ScatterND_plugin.py
@@ -36,7 +36,8 @@ def build_engine(shape_data, shape_indices, shape_updates):
         exit()

     builder = trt.Builder(logger)
-    builder.max_workspace_size = 1 << 20
+    config = builder.create_builder_config()
+    config.max_workspace_size = 1 << 20
     network = builder.create_network(flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))

     tensor_data = network.add_input('data', trt.DataType.FLOAT, shape_data)
@@ -49,8 +50,11 @@ def build_engine(shape_data, shape_indices, shape_updates):
             ]))
         )
     network.mark_output(layer.get_output(0))
+    plan = builder.build_serialized_network(network, config)

-    return builder.build_cuda_engine(network)
+    with trt.Runtime(logger) as runtime:
+        engine = runtime.deserialize_cuda_engine(plan)
+    return engine

`