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All previous lines work as expected, but cell number 28 in this example notebook:
https://github.com/pytorch/TensorRT/blob/main/notebooks/qat-ptq-workflow.ipynb
yields this error:
W0425 19:29:52.724189 140194752341824 _compile.py:108] Input graph is a Torchscript module but the ir provided is default (dynamo). Please set ir=torchscript to suppress the warning. Compiling the module with ir=torchscript WARNING: [Torch-TensorRT TorchScript Conversion Context] - Calibrator is not being used. Users must provide dynamic range for all tensors that are not Int32 or Bool. ERROR: [Torch-TensorRT TorchScript Conversion Context] - 4: [standardEngineBuilder.cpp::initCalibrationParams::1718] Error Code 4: Internal Error (Calibration failure occurred with no scaling factors detected. This could be due to no int8 calibrator or insufficient custom scales for network layers. Please see int8 sample to setup calibration correctly.) ---------- RuntimeError Traceback (most recent call last) Cell In[28], line 6 2 qat_model = torch.jit.load("mobilenetv2_qat.jit.pt").eval() 3 compile_spec = {"inputs": [torch_tensorrt.Input([64, 3, 224, 224])], 4 "enabled_precisions": torch.int8 5 } ----> 6 trt_mod = torch_tensorrt.compile(qat_model, **compile_spec) File /usr/local/lib/python3.10/dist-packages/torch_tensorrt/_compile.py:185, in compile(module, ir, inputs, enabled_precisions, **kwargs) 183 ts_mod = torch.jit.script(module) 184 assert _non_fx_input_interface(input_list) --> 185 compiled_ts_module: torch.jit.ScriptModule = torchscript_compile( 186 ts_mod, 187 inputs=input_list, 188 enabled_precisions=enabled_precisions_set, 189 **kwargs, 190 ) 191 return compiled_ts_module 192 elif target_ir == _IRType.fx: File /usr/local/lib/python3.10/dist-packages/torch_tensorrt/ts/_compiler.py:151, in compile(module, inputs, input_signature, device, disable_tf32, sparse_weights, enabled_precisions, refit, debug, capability, num_avg_timing_iters, workspace_size, dla_sram_size, dla_local_dram_size, dla_global_dram_size, calibrator, truncate_long_and_double, require_full_compilation, min_block_size, torch_executed_ops, torch_executed_modules, allow_shape_tensors) 124 raise ValueError( 125 f"require_full_compilation is enabled however the list of modules and ops to run in torch is not empty. Found: torch_executed_ops: {torch_executed_ops}, torch_executed_modules: {torch_executed_modules}" 126 ) 128 spec = { 129 "inputs": input_list, 130 "input_signature": input_signature, (...) 148 "allow_shape_tensors": allow_shape_tensors, 149 } --> 151 compiled_cpp_mod = _C.compile_graph(module._c, _parse_compile_spec(spec)) 152 compiled_module: torch.jit.ScriptModule = torch.jit._recursive.wrap_cpp_module( 153 compiled_cpp_mod 154 ) 155 return compiled_module RuntimeError: [Error thrown at core/conversion/conversionctx/ConversionCtx.cpp:169] Building serialized network failed in TensorRT
Steps to reproduce the behavior:
Build information about Torch-TensorRT can be found by turning on debug messages
conda
pip
libtorch
The text was updated successfully, but these errors were encountered:
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Bug Description
All previous lines work as expected, but cell number 28 in this example notebook:
https://github.com/pytorch/TensorRT/blob/main/notebooks/qat-ptq-workflow.ipynb
yields this error:
To Reproduce
Steps to reproduce the behavior:
Environment
conda
,pip
,libtorch
, source): DockerThe text was updated successfully, but these errors were encountered: