pytorch/torchgen/_autoheuristic

AutoHeuristic

AutoHeuristic is a framework that allows one to use results from autotuning to learn a heuristic as a decision tree, that can be generated to code and shipped with compiler.

How to use AutoHeuristic

In general, the following steps have to performed:

• The AutoHeursitic constructor has to be called.
• A script that runs benchmarks in order to collect training data has to be implemented.
• The train_decision.py (if you want to learn a decision tree) or train_regression.py (if you want to learn a regression tree) script has to be run in order to learn the heuristic and generate it to code.

Step 1: Calling the AutoHeuristic constructor

Currently, two use cases are supported:

Use case 1: Local autotuning

When your feedback function is able to immediately return a result, you can just call the AutoHeuristic constructor. This is done e.g. for pad_mm

autoheuristic = AutoHeuristic(
    fallback=fallback,
    choices=choices,
    feedback=feedback,
    context=context,
    name=name,
    augment_context=pad_mm_operations(),
    precondition=pad_mm_precondition,
)

Here, feedback is a function that benchmarks a given choice and returns the execution time. For an example, see: https://github.com/pytorch/pytorch/blob/main/torch/_inductor/fx_passes/pad_mm.py.

Use case 2: Kernel choice selection

If you want to use AutoHeuristic for kernel choice selection, you have to call the AutoHeuristicSelectAlgorithm constructor. This is done e.g. for mixed_mm

autoheuristic = AutoHeuristicSelectAlgorithm(
    fallback=fallback,
    choices=choices,
    input_nodes=input_nodes,
    context=context,
    name=name,
    augment_context=ops,
    precondition=precondition,
)

This call has to be followed by a call to autotune_select_algorithm(),

autotune_select_algorithm(name, choices, input_nodes, layout)

Note that choices, input_nodes, and name in the AutoHeuristicSelectAlgorithm() and autotune_select_algorithm() calls have to match when you want to use AutoHeuristic to collect data.

For an example, see: https://github.com/pytorch/pytorch/blob/main/torch/_inductor/kernel/mm.py

Step 2: Collecting training data

After adding the call to the AutoHeuristic constructor, you need to collect training data in order to learn a heuristic. Let's say you have a script run.py that triggers the AutoHeuristic constructor that you just added. Run the following command in order to store data into file train.txt:

TORCHINDUCTOR_AUTOHEURISTIC_LOG_PATH="train.txt" \
  TORCHINDUCTOR_AUTOHEURISTIC_COLLECT="pad_mm" python run.py

Replace "pad_mm" with the name you provided in the call to the AutoHeuristic constructor.

AutoHeuristic provides a BenchmarkRunner class (https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/benchmark_runner.py) that simplifies the process of collecting data. To use it, create a new class that subclasses BenchmarkRunner, and implements the run_benchmark() and create_input() methods.

These examples might be helpful:

• https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/pad_mm/gen_data_pad_mm.py
• https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/mixed_mm/gen_data_mixed_mm.py

Step 3: Learning a heuristic and using it

Once you have collected enough training data, you are ready to learn a heuristic:

python torchgen/_autoheuristic/train_decision.py train.txt --heuristic-name SimpleHeuristic

will learn a heuristic and generate it to torch/_inductor/autoheuristic/artifacts/_SimpleHeuristic.py.

You can now use your learned heuristic:

TORCHINDUCTOR_AUTOHEURISTIC_USE="pad_mm" python run.py

Here, you again have to replace "pad_mm" with the name you provided in the call to the AutoHeuristic constructor.

Instead of just running the train_decision.py script, you probably want to customize the training process in some way. To do this, create a new class that subclasses AHTrainDecision and override methods you want to customize. Here are some examples:

• https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/mixed_mm/train_decision_mixedmm.py
• https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/pad_mm/train_decision_pad_mm.py

Other

How do I specify features that the heuristic is going to use to make a decision?

The AutoHeuristic constructor requires a context argument of type AHContext, which will contain all features. You specify features in the following way:

context = AHContext()

# adding numerical features
context.add_feature("m", mat1.shape[0])
context.add_feature("k", mat1.shape[1])

# adding a categorical feture
context.add_feature("mat1_dtype", mat1.dtype, is_categorical=True)

You might want to use features that are a combination of other features, such as m*k. You can of course add such features in the same way as above, i.e.,

context.add_feature("m*k", mat1.shape[0] * mat1.shape[1])

but AutoHeuristic also provides a way to 'augment' features. Augmented features are not stored when data is collected, instead they are created before a heuristic is learned, or before a learned heuristic is used. You can specify such augmented features by creating a list of AHOperation objects:

def m_times_k(data: Any) -> float:
    return data['m'] * data['k']

m_times_k_op = AHOperation("m*k', m_times_k)
ah_operations = [m_times_k_op]

# specify augmented features by setting `augment_context` to `ah_operations`
autoheuristic = AutoHeuristic(..., augment_context=ah_operations, ...)

Note that you also have to specify these operations when you want to learn a heuristic. Look at the add_new_features() method in these examples, to see how it is done:

• https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/mixed_mm/train_decision_mixedmm.py
• https://github.com/pytorch/pytorch/blob/main/torchgen/_autoheuristic/pad_mm/train_decision_pad_mm.py

Where has AutoHeuristic already been used?

Take a look at the following PRs in which AutoHeuristic has enabled for various optimizations. Looking at these examples may be helpful if you want to use AutoHeuristic yourself.

• pad_mm: https://github.com/pytorch/pytorch/pull/128643
• mixed_mm:
  • Enabling of AutoHeuristic: https://github.com/pytorch/pytorch/pull/131610
  • Script to collect data: https://github.com/pytorch/pytorch/pull/131611
  • A100 heuristic: https://github.com/pytorch/pytorch/pull/131613
  • H100 heuristic: https://github.com/pytorch/pytorch/pull/132685
• flex_attention: https://github.com/pytorch/pytorch/pull/130398
• mm (heuristic for ranking choices):
  • https://github.com/pytorch/pytorch/pull/131615
  • https://github.com/pytorch/pytorch/pull/131617
  • https://github.com/pytorch/pytorch/pull/131705
  • https://github.com/pytorch/pytorch/pull/131714