_trt.shape overflow when converting from pytorch to TRT

[Justin020718]

before operator converting, some input tensors had attributes called '_trt'

To deal with it, I've deleted these incorrect '_trt' attributes manually in related converter function in “naive_converters.py”.
However, I've encountered sone errors when doing things as follows, and it says:

[05/26/2024-22:35:09] [TRT] [E] 3: context_fusion_net.conv3_up.0:1:CONVOLUTION:GPU: at least 4 dimensions are required for input.
[05/26/2024-22:35:09] [TRT] [E] 3: context_fusion_net.conv3_up.0:1:CONVOLUTION:GPU: at least 4 dimensions are required for input.
[05/26/2024-22:35:09] [TRT] [E] 4: [network.cpp::nvinfer1::Network::validate::3478] Error Code 4: Internal Error (Layer >context_fusion_net.conv3_up.0:1:CONVOLUTION:GPU failed validation)

Of course, the TRT module was not successfully generated, and when I try to save its state dict, it says:

Traceback (most recent call last):
File "E:\DCVC-main\DCVC-DC\quant.py", line 497, in quant_for_p_all
torch.save(p_frame_enc_trt.state_dict(), path)
File "E:\anaconda3\envs\python3.8\lib\site-packages\torch\nn\modules\module.py", line 1918, in state_dict
hook_result = hook(self, destination, prefix, local_metadata)
File "E:\anaconda3\envs\python3.8\lib\site-packages\torch2trt-0.5.0-py3.8.egg\torch2trt\trt_module.py", line 60, in _on_state_dict
state_dict[prefix + "engine"] = bytearray(self.engine.serialize())
AttributeError: 'NoneType' object has no attribute 'serialize'

Finally, here's my environment:
pytorch 2.3.0+cu121
torch2trt 0.5.0
tensorRT 8.6.1.6 (Also tried tensorRT 10.0.1.6, after renaming some covnerting method, I have the same issue)

Since it's my first issue on Github, I really appreciate it if someone could help, or offer some clue to solve it, please!!!!!!!!!!!

[Justin020718]

Here is my changes in "naive_converters.py":

Despite unpleasant, I have to change like that otherwise I'll encounter a "ValueError: len() should return >= 0" issue.
The module works well in pytorch.

[Justin020718]

I've generated trt engine successfully through ONNX.

[John]

@Justin020718 ,

Thanks for reaching out. Apologies for the delay.

Glad to hear ONNX worked for you. FYI, not sure if this is what you did, but you can run torch2trt(..., use_onnx=True) for convenience.

In case the issue re-appears / I need to reproduce, is this a publicly available model?

Best,
John

[Justin071802]

@jaybdub
Thank you for your reply.
microsoft/DCVC#18
I will try it later

[Justin020718]

@jaybdub
Forgot to say, to reproduce my issue, you might need to add some converters in "native_converters.py"

@tensorrt_converter('torch.nn.functional.pixel_shuffle')
def convert_PixelShuffle(ctx):
    input = ctx.method_args[0]
    scale_factor = ctx.method_args[1]
    input_trt = add_missing_trt_tensors(ctx.network, [input])[0]
    output = ctx.method_return

    batch_size, in_channels, in_height, in_width = input.shape

    assert scale_factor >= 1

    out_channels = in_channels // (scale_factor * scale_factor)
    out_height = in_height * scale_factor
    out_width = in_width * scale_factor

    layer_1 = ctx.network.add_shuffle(input_trt)
    layer_1.reshape_dims = (out_channels, scale_factor, scale_factor, in_height, in_width)

    layer_2 = ctx.network.add_shuffle(layer_1.get_output(0))
    layer_2.first_transpose = (0, 3, 1, 4, 2)
    layer_2.reshape_dims = (batch_size, out_channels, out_height, out_width)

    output._trt = layer_2.get_output(0)

def _set_layer_precision(ctx, layer):
    # Supported TRT precisions as given by torch2trt_kwargs.
    INT8_MODE = "int8_mode"
    FP16_MODE = "fp16_mode"

    # Check that args exist as expected in torch2trt_kwargs.
    trt_kwargs = ctx.torch2trt_kwargs
    assert INT8_MODE in trt_kwargs
    assert FP16_MODE in trt_kwargs

    is_int8 = trt_kwargs.get(INT8_MODE, False)
    is_fp16 = trt_kwargs.get(FP16_MODE, False)

    if is_int8:
        layer.precision = trt.int8
        layer.set_output_type(0, trt.int8)
    elif is_fp16:
        layer.precision = trt.float16
        layer.set_output_type(0, trt.float16)
        
@tensorrt_converter('torch.zeros')
def convert_zeros(ctx):
    tensor = ctx.method_return

    # Implementation copied from add_trt_constant.
    shape = tuple(tensor.shape[1:])
    array = tensor[0].detach().cpu().numpy()
    layer = ctx.network.add_constant(shape, array)

    _set_layer_precision(ctx, layer)

    tensor._trt = layer.get_output(0)