This document describes how to debug in TensorRT-LLM.

Usually, we want to print the intermediate tensor values when debugging a TensorRT-LLM model. TensorRT-LLM obeys define-and-run paradigm, we should mark the interested intermediate tensors as the network outputs. Then, we print the values at runtime.

Build Errors

Many build errors can be resolved by simply deleting the build tree. Try running the build script with --clean or running rm -r cpp/build.

cuDNN Linking Errors

If you encounter errors such as “Entry Point Not Found” (see for example #1062) the issue might be a mismatch in the cuDNN libraries shipped from torch and tensorrt. To rectify this, please try the following steps

python -m pip uninstall -y tensorrt_llm
python -m pip install --upgrade pip
python -m pip install nvidia-cudnn-cu11== --no-cache-dir
python -m pip install --pre --extra-index-url tensorrt==9.2.0.post12.dev5 --no-cache-dir
python -m pip uninstall -y nvidia-cudnn-cu11
python -m pip install tensorrt_llm  --extra-index-url --extra-index-url --extra-index-url

Debug on Unit Tests

  1. Register the intermediate tensors as the network outputs with register_network_output API.

class MLP(Module):

    def __init__(self,
        self.fc = tensorrt_llm.layers.ColumnLinear(hidden_size,
        self.proj = tensorrt_llm.layers.RowLinear(ffn_hidden_size,

    def forward(self, hidden_states):
        inter = self.fc(hidden_states)
        inter = tensorrt_llm.functional.relu(inter)
        # Here, we want to print the tensor value after relu
        self.register_network_output('inter', inter)
        output = self.proj(inter)
        return output
  1. Mark the intermediate tensors as network outputs.

for k, v in gm.named_network_outputs():
    net._mark_output(v, k, dtype)
  1. Print the tensors at runtime.


Here is the full example.

Debug on E2E Models

Here is an example to print the values of the MLP output tensor in the GPT model.

  1. In tensorrt_llm/models/gpt/, we register the MLP output tensor:

        hidden_states = residual +

        residual = hidden_states
        hidden_states = self.post_layernorm(hidden_states)

        hidden_states = self.mlp(hidden_states)
        # register as model output
        # ------------------------------------------------------
        self.register_network_output('mlp_output', hidden_states)
        # ------------------------------------------------------

        hidden_states = residual + hidden_states
  1. Build the TensorRT engine of the model:

When building engines with trtllm-build, enable the --enable_debug_output option.

cd examples/gpt

# Download hf gpt2 model
rm -rf gpt2 && git clone gpt2
pushd gpt2 && rm pytorch_model.bin model.safetensors && wget -q && popd

# Convert to TensorRT-LLM checkpoint
python3 --model_dir gpt2 \
        --dtype float16 \
        --output_dir gpt2/trt_ckpt/fp16/1-gpu

# Build TensorRT-LLM engines with --enable_debug_output
trtllm-build --checkpoint_dir gpt2/trt_ckpt/fp16/1-gpu \
        --gpt_attention_plugin float16 \
        --remove_input_padding enable \
        --enable_debug_output \
        --output_dir gpt2/trt_engines/fp16/1-gpu
  1. Print the intermediate output tensors:

In tensorrt_llm/runtime/, we print the debug info:

        stream = torch.cuda.current_stream().cuda_stream
        instance_idx = step % 2
        if self.cuda_graph_mode and self.runtime.cuda_graph_instances[
                instance_idx] is not None:
            # launch cuda graph
                    self.runtime.cuda_graph_instances[instance_idx], stream))
            ok = True
            ok = self.runtime._run(context, stream)

        if not ok:
            raise RuntimeError(f"Executing TRT engine failed step={step}!")
        if self.debug_mode:
            # -------------------------------------------
            if step == 0:
            print(f"Step: {step}")
            # -------------------------------------------

Then, run ../ with --debug_mode and --use_py_session:

python3 ../ --engine_dir gpt2/trt_engines/fp16/1-gpu \
        --tokenizer_dir gpt2 \
        --max_output_len 8 \
        --debug_mode \

We will see the tensor values:

dict_keys(['context_lengths', 'cache_indirection', 'position_ids', 'logits', 'last_token_ids', 'input_ids', 'kv_cache_block_pointers', 'host_kv_cache_block_pointers', 'sequence_length', 'host_past_key_value_lengths', 'host_sink_token_length', 'host_request_types', 'host_max_attention_window_sizes', 'host_context_lengths', 'transformer.layers.0.mlp_output', 'transformer.layers.1.mlp_output', 'transformer.layers.2.mlp_output', 'transformer.layers.3.mlp_output', 'transformer.layers.4.mlp_output', 'transformer.layers.5.mlp_output', 'transformer.layers.6.mlp_output', 'transformer.layers.7.mlp_output', 'transformer.layers.8.mlp_output', 'transformer.layers.9.mlp_output', 'transformer.layers.10.mlp_output', 'transformer.layers.11.mlp_output', 'transformer.layers.12.mlp_output', 'transformer.layers.13.mlp_output', 'transformer.layers.14.mlp_output', 'transformer.layers.15.mlp_output', 'transformer.layers.16.mlp_output', 'transformer.layers.17.mlp_output', 'transformer.layers.18.mlp_output', 'transformer.layers.19.mlp_output', 'transformer.layers.20.mlp_output', 'transformer.layers.21.mlp_output', 'transformer.layers.22.mlp_output', 'transformer.layers.23.mlp_output'])
Step: 0
tensor([[ 0.0294, -0.0260, -0.0776,  ..., -0.0560, -0.0235,  0.0273],
        [-0.0071,  0.5879,  0.1993,  ..., -1.0449, -0.6299,  0.5957],
        [-0.8779,  0.1050,  0.7090,  ...,  0.0910,  1.0713, -0.2939],
        [ 0.1212, -0.0903, -0.5918,  ..., -0.1045, -0.3445,  0.1082],
        [-1.0723, -0.0732,  0.6157,  ...,  0.3452,  0.2998,  0.2649],
        [-0.7134,  0.9692, -0.1141,  ..., -0.0096,  0.9521,  0.1437]],
       device='cuda:0', dtype=torch.float16)
Step: 1
tensor([[-0.2107,  0.5874,  0.8179,  ...,  0.7900, -0.6890,  0.6064]],
       device='cuda:0', dtype=torch.float16)
Step: 2
tensor([[ 0.4192, -0.0047,  1.3887,  ..., -0.9028, -0.0682, -0.2820]],
       device='cuda:0', dtype=torch.float16)
Step: 3
tensor([[-0.7949, -0.5073, -0.1721,  ..., -0.5830, -0.1378, -0.0070]],
       device='cuda:0', dtype=torch.float16)
Step: 4
tensor([[-0.0804,  0.1272, -0.6255,  ..., -0.1072, -0.0523,  0.7144]],
       device='cuda:0', dtype=torch.float16)
Step: 5
tensor([[-0.3328, -0.8828,  0.3442,  ...,  0.8149, -0.0630,  1.2305]],
       device='cuda:0', dtype=torch.float16)
Step: 6
tensor([[-0.2225, -0.2079, -0.1459,  ..., -0.3555, -0.1672,  0.1135]],
       device='cuda:0', dtype=torch.float16)
Step: 7
tensor([[ 0.1290, -0.1556,  0.3977,  ..., -0.8218, -0.3291, -0.8672]],
       device='cuda:0', dtype=torch.float16)
Input [Text 0]: "Born in north-east France, Soyer trained as a"
Output [Text 0 Beam 0]: " chef before moving to London in the early"

Debug Execution Errors

  • If you use plugins, use can set the environment variable CUDA_LAUNCH_BLOCKING=1 so that kernels are launch synchronously, with their return status checked immediately.

  • If you see memory errors, make sure that the engine inputs respect the build-time shapes and that they reside on the correct device (CPU/GPU).

Installation Errors

Many build errors can be resolved by simply deleting the build tree. Try running the build script with --clean or running rm -r cpp/build.


  • It’s recommended to add options –shm-size=1g –ulimit memlock=-1 to the docker or nvidia-docker run command. Otherwise you may see NCCL errors when running multiple GPU inferences. See for details.

  • When building models, memory-related issues such as

[09/23/2023-03:13:00] [TRT] [E] 9: GPTLMHeadModel/layers/0/attention/qkv/PLUGIN_V2_Gemm_0: could not find any supported formats consistent with input/output data types
[09/23/2023-03:13:00] [TRT] [E] 9: [pluginV2Builder.cpp::reportPluginError::24] Error Code 9: Internal Error (GPTLMHeadModel/layers/0/attention/qkv/PLUGIN_V2_Gemm_0: could not find any supported formats consistent with input/output data types)

may happen. One possible solution is to reduce the amount of memory needed by reducing the maximum batch size, input and output lengths. Another option is to enable plugins, for example: --gpt_attention_plugin.

  • MPI + Slurm

TensorRT-LLM is a MPI-aware package that uses mpi4py. If you are running scripts in a Slurm environment, you might encounter interferences:

PMI2_Init failed to initialize.  Return code: 14
The application appears to have been direct launched using "srun",
but OMPI was not built with SLURM's PMI support and therefore cannot
execute. There are several options for building PMI support under
SLURM, depending upon the SLURM version you are using:

  version 16.05 or later: you can use SLURM's PMIx support. This
  requires that you configure and build SLURM --with-pmix.

  Versions earlier than 16.05: you must use either SLURM's PMI-1 or
  PMI-2 support. SLURM builds PMI-1 by default, or you can manually
  install PMI-2. You must then build Open MPI using --with-pmi pointing
  to the SLURM PMI library location.

Please configure as appropriate and try again.

As a rule of thumb, if you are running TensorRT-LLM interactively on a Slurm node, prefix your commands with mpirun -n 1 to run TensorRT-LLM in a dedicated MPI environment, not the one provided by your Slurm allocation.

For example: mpirun -n 1 python3 examples/gpt/ ...