Troubleshooting
This document describes some of the frequently asked questions and their solutions in TensorRT-LLM, including problems of installation, model-building, model-execution, and input / output size.
Installation Errors
During compilation and installation of TensorRT-LLM, many build errors can be resolved by simply deleting the build tree and rebuilding again.
In most occasions, these problems are caused by the workflow like: an old compilation -> some code change (update of the repo or users’ writing) -> a later compilation.
Solution: try running build script with --clean, or try running rm -r build cpp/build before running build script again.
Debug on Unit Tests
Here is an example to print the values of the MLP output tensor in the a unit test (full example).
Register the intermediate tensors as the network outputs with
register_network_outputAPI.
class MLP(Module):
def __init__(self, ...):
super().__init__()
# Do not modify the definition in `__init__` method
self.fc = ...
self.proj = ...
def forward(self, hidden_states):
inter = self.fc(hidden_states)
inter = tensorrt_llm.functional.relu(inter)
# Here register the tensor `inter` as our debug output tensor
self.register_network_output('inter', inter)
output = self.proj(inter)
return output
Mark the intermediate tensors as network outputs.
for k, v in gm.named_network_outputs():
net._mark_output(v, k, dtype)
Print the tensors at runtime.
print(outputs.keys())
print(outputs['inter'])
Debug on E2E Models
Here is an example to print the values of the MLP output tensor in the GPT model.
Register the MLP output tensor in
tensorrt_llm/models/gpt/model.py.
hidden_states = residual + attention_output.data
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
Build the TensorRT engine of the model.
Enable the --enable_debug_output option when building engines with trtllm-build
cd examples/gpt
# Download hf gpt2 model
rm -rf gpt2 && git clone https://huggingface.co/gpt2-medium gpt2
pushd gpt2 && rm pytorch_model.bin model.safetensors && wget -q https://huggingface.co/gpt2-medium/resolve/main/pytorch_model.bin && popd
# Convert to TensorRT-LLM checkpoint
python3 convert_checkpoint.py \
--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 \
--enable_debug_output \
--output_dir gpt2/trt_engines/fp16/1-gpu
Print the intermediate output tensors.
Add debug info in tensorrt_llm/runtime/generation.py.
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
CUASSERT(
cudart.cudaGraphLaunch(
self.runtime.cuda_graph_instances[instance_idx], stream))
ok = True
else:
ok = self.runtime._run(context, stream)
if not ok:
raise RuntimeError(f"Executing TRT engine failed step={step}!")
if self.debug_mode:
torch.cuda.synchronize()
# -------------------------------------------
if step == 0:
print(self.debug_buffer.keys())
print(f"Step: {step}")
print(self.debug_buffer['transformer.layers.6.mlp_output'])
# -------------------------------------------
Run
../run.pywith--debug_modeand--use_py_session.
python3 ../run.py \
--engine_dir gpt2/trt_engines/fp16/1-gpu \
--tokenizer_dir gpt2 \
--max_output_len 8 \
--debug_mode \
--use_py_session
See the value of the tensor.
......
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 problems come from plugins, try setting the environment variable CUDA_LAUNCH_BLOCKING=1 to make kernels launch synchronously with their return status checked immediately.
If problems come from runtime-shape of the input tensors, double-check the shape (rank and length of each rank) and location (CPU / GPU) of input tensors for the engine obey the build-time setting.
For example, one possible reason of getting the error information like below is, we use mismatched configuration between engine building and running, including code change (update of repo or users’ rewrting), too large or too small input shape, etc..
unexpected shape for input 'XXX' for model 'YYY'. Expected [-1,-1,-1], got [8,16]. NOTE: Setting a non-zero max_batch_size in the model config requires a batch dimension to be prepended to each input shape. If you want to specify the full shape including the batch dim in your input dims config, try setting max_batch_size to zero. See the model configuration docs for more info on max_batch_size.
[TensorRT-LLM][ERROR] Assertion failed: Tensor 'input_ids' has invalid shape (8192), expected (-1) (/code/tensorrt_llm/cpp/tensorrt_llm/runtime/tllmRuntime.cpp:149)
RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 8192 but got size 1024 for tensor number 1 in the list.
By setting environment variable export TLLM_LOG_LEVEL=TRACE, we can get more information about the TensorRT engine at runtime, which contains the shapes of each input / output tensors, and all allowed ranges of every input shapes.
[TensorRT-LLM][TRACE] =====================================================================
[TensorRT-LLM][TRACE] Name |I/O|Location|DataType| Shape |
[TensorRT-LLM][TRACE] ---------------------------------------------------------------------
[TensorRT-LLM][TRACE] input_ids | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] position_ids | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] last_token_ids | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] kv_cache_block_offsets | I | GPU | INT32 |(1, -1, 2, -1)|
[TensorRT-LLM][TRACE] host_kv_cache_block_offsets | I | GPU | INT32 |(1, -1, 2, -1)|
[TensorRT-LLM][TRACE] host_kv_cache_pool_pointers | I | GPU | INT64 | (1, 2) |
[TensorRT-LLM][TRACE] host_kv_cache_pool_mapping | I | GPU | INT32 | (28) |
[TensorRT-LLM][TRACE] sequence_length | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] host_request_types | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] host_past_key_value_lengths | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] context_lengths | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] host_runtime_perf_knobs | I | GPU | INT64 | (16) |
[TensorRT-LLM][TRACE] host_context_lengths | I | GPU | INT32 | (-1) |
[TensorRT-LLM][TRACE] host_max_attention_window_sizes| I | GPU | INT32 | (28) |
[TensorRT-LLM][TRACE] host_sink_token_length | I | GPU | INT32 | (1) |
[TensorRT-LLM][TRACE] cache_indirection | I | GPU | INT32 | (-1, 1, -1) |
[TensorRT-LLM][TRACE] logits | O | GPU | FP32 | (-1, 65024) |
[TensorRT-LLM][TRACE] =====================================================================
[TensorRT-LLM][TRACE] Information of optimization profile.
[TensorRT-LLM][TRACE] Optimization Profile 0:
[TensorRT-LLM][TRACE] =============================================================================
[TensorRT-LLM][TRACE] Name | Min | Opt | Max |
[TensorRT-LLM][TRACE] -----------------------------------------------------------------------------
[TensorRT-LLM][TRACE] input_ids | (1) | (8) | (8192) |
[TensorRT-LLM][TRACE] position_ids | (1) | (8) | (8192) |
[TensorRT-LLM][TRACE] last_token_ids | (1) | (4) | (8) |
[TensorRT-LLM][TRACE] kv_cache_block_offsets | (1, 1, 2, 1) |(1, 4, 2, 16) |(1, 8, 2, 32) |
[TensorRT-LLM][TRACE] host_kv_cache_block_offsets | (1, 1, 2, 1) |(1, 4, 2, 16) |(1, 8, 2, 32) |
[TensorRT-LLM][TRACE] host_kv_cache_pool_pointers | (1, 2) | (1, 2) | (1, 2) |
[TensorRT-LLM][TRACE] host_kv_cache_pool_mapping | (28) | (28) | (28) |
[TensorRT-LLM][TRACE] sequence_length | (1) | (4) | (8) |
[TensorRT-LLM][TRACE] host_request_types | (1) | (4) | (8) |
[TensorRT-LLM][TRACE] host_past_key_value_lengths | (1) | (4) | (8) |
[TensorRT-LLM][TRACE] context_lengths | (1) | (4) | (8) |
[TensorRT-LLM][TRACE] host_runtime_perf_knobs | (16) | (16) | (16) |
[TensorRT-LLM][TRACE] host_context_lengths | (1) | (4) | (8) |
[TensorRT-LLM][TRACE] host_max_attention_window_sizes| (28) | (28) | (28) |
[TensorRT-LLM][TRACE] host_sink_token_length | (1) | (1) | (1) |
[TensorRT-LLM][TRACE] cache_indirection | (1, 1, 1) | (4, 1, 1024) | (8, 1, 2048) |
[TensorRT-LLM][TRACE] logits | (1, 65024) | (4, 65024) | (8, 65024) |
[TensorRT-LLM][TRACE] =============================================================================
Tips
It’s recommended to add options
–shm-size=1g –ulimit memlock=-1to the docker or nvidia-docker run command. Otherwise you may see NCCL errors when running multiple GPU inferences. See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/troubleshooting.html#errors 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.
--------------------------------------------------------------------------
You may experience other problems like hanging on the program startup.
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/build.py ...
It’s critical that it’s always -n 1 regardless of how many GPUs are being used. If you’d use -n 2 for a 2 GPU program it will not work. mpirun here isn’t being used to orchestrate multiple processes, but to invoke the right environment on SLURM. The internal MPI implementation deals with spawning the additional processes.