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README.md

Apache TVM fine-tuning

The aim of this laboratory is to check the work of Apache TVM model fine-tuning feature.

Tasks

  • Go through the Auto-tuning a Convolutional Network for x86 CPU.

  • Go fine_tuning_experiments script and check what it does.

  • The class TVMFineTunedModel inherits following methods from TVMModel from l04_tvm assignment:

    • preprocess_input,
    • postprocess_outputs,
    • prepare_model,
    • run_inference.

  • Use the implemented TVMModel from L04 assignment or implement above-mentioned methods.

  • [5pt] Implement tune_kernels method:

    • Use get_tuner method for each task from tasks,
    • Use len(task.config_space) as n_trial for tuner.tune
    • Use measure_option in tuning method,
    • Use autotvm.callback.progress_bar and autotvm.callback.log_to_file callbacks (use self.optlogpath as log path).
    • Add early stopping after 20% of n_trial trials with no improvement.

  • [6pt] Implement tune_graph method:

    • Focus on nn.conv2d operator only (use relay.op.get).
    • Use PBQPTuner as tuning executor.
    • Use mod, self.input_name, self.input_shape, self.optlogpath, self.target variables from tune_kernels and optimize_model to set up the executor.
    • Use benchmark_layout_transform for setting up benchmarks (use min_exec_num 5).
    • Run the executor.
    • Save tuning results to self.graphoptlogpath file.

  • [4pt] Finish the implementation of model fine-tuning:

    • Extract tasks from the initially optimized module using autotvm.task.extract_from_program (focus on nn.conv2d operator only).
    • Tune kernels using tune_kernels method.
    • Tune the whole graph using tune_graph method.
    • Compile and save the model library - use autotvm.apply_graph_best method to load log from self.graphoptlogpath.

  • Run benchmarks using:

    python3 -m dl_in_iot_course.l06_tvm_fine_tuning.fine_tuning_experiments \
    --fp32-model-path models/pet-dataset-tensorflow.fp32.tflite \
    --dataset-root build/pet-dataset/ \
    --results-path build/fine-tuning-results

    NOTE: The fine-tuning takes quite a long time.

  • [2pt] In directory for assignment's summary, include:

    • Kernel log file (pet-dataset-tensorflow.fp32.tvm-tune.kernellog),
    • Graph log file (pet-dataset-tensorflow.fp32.tvm-tune.graphlog).

  • Write a very brief summary:

    • [1pt] Compare the inference time between the fine-tuned model and FP32 model with NCHW layout with opt level 3 and llvm -mcpu=core-avx2 target.

At least a very slight improvement should be observed.

There should be no need for additional imports. The blocks of code to implement (3 blocks) should take at most around 20 lines.

Additional factors:

  • [2pt] Git history quality

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