TensorFlow e2e tests

This is a collection of e2e tests that save a TensorFlow model, compile it with IREE, run it on multiple backends and crosscheck the results.

Pre-Requisites

You will need a TensorFlow 2.0+ nightly installed in your python environment: the python binary in $PYTHON_BIN should be able to import tensorflow and that TensorFlow should be version 2.0+. This can be checked with tensorflow.version.

See Install TensorFlow with pip for instructions.

Vulkan setup

If you do not have your environment setup to use IREE with Vulkan (see the doc), then you can run the manual test targets with --target_backends=tf,iree_vmla,iree_llvmjit (that is, by omitting iree_vulkan from the list of backends to run the tests on).

The test suites can be run excluding Vulkan by specifying --test_tag_filters="-driver=vulkan" in the bazel test invocation, or by adding test --test_tag_filters="-driver=vulkan" to your user.bazelrc.

Compiling `tf.Module`s

Compatible TensorFlow modules can be compiled to specific IREE backends using IreeCompiledModule. This also optionally saves compilation artifacts to a specified directory. These artifacts include: MLIR across various lowerings, a TensorFlow SavedModel, and the compiled VM FlatBuffer. A basic example of creating and calling an IreeCompiledModule can be found in tf_utils_test.py

When using Keras models or tf.Modules with functions that IREE can't compile, exported_names should be specified. For example:

from pyiree.tf.support import tf_utils
vmla_module = tf_utils.IreeCompiledModule(
    module_class=KerasTFModuleClass,
    backend_info=tf_utils.BackendInfo('iree_vmla'),
    exported_names=['predict'])
vmla_module.predict(...)

By default the TensorFlow SavedModels will not be kept. This can be overridden via the --keep_saved_model flag.

Running tests

For locally running tests and iterating on backend development, bazel run is preferred.

# Run math_test on all backends.
bazel run :math_test_manual

# Run math_test comparing TensorFlow to itself (e.g. to debug randomization).
bazel run :math_test_manual -- target_backends=tf

# Run math_test comparing the VMLA backend and TensorFlow.
bazel run :math_test_manual -- --target_backends=iree_vmla

# Run math_test comparing the VMLA backend to itself multiple times.
bazel run :math_test_manual -- \
  --reference_backend=iree_vmla --target_backends=iree_vmla,iree_vmla

# Run math_test and output on failure.
bazel test :math_test_manual --test_output=errors

# Run an individual test interactively.
bazel run :math_test_manual -- --test_output=streamed

For reproducibility of the unit tests CompiledModule() sets the random seeds of tf, numpy and python by calling tf_utils.set_random_seed() before model creation.

Writing Tests

Our tests use a class TracedModule to capture and store all of the inputs and outputs of a CompiledModule in a Trace. Each unittest on a TestCase uses the compare_backends method. This method runs the function it is passed with a TracedModule once for each reference and target backend. The inputs and outputs to these modules are then checked for correctness, using the reference backend as a source of truth. For example:

# Compile a `tf.Module` named `SimpleArithmeticModule` into a `CompiledModule`.
@tf_test_utils.compile_module(SimpleArithmeticModule)
# Inherit from `TracedModuleTestCase`.
class SimpleArithmeticTest(tf_test_utils.TracedModuleTestCase):

  # Unit test.
  def test_simple_mul(self):

    # Trace function.
    def simple_mul(module):
      # A random seed is automatically set before each call to `simple_mul`.
      a = tf_utils.uniform([4])
      b = np.array([400., 5., 6., 7.], dtype=np.float32)
      # The inputs `a` and `b` are recorded along with the output `c`
      c = module.simple_mul(a, b)
      # The inputs `a` and `b` are recorded along with the (unnamed) output
      # module.simple_mul returns.
      module.simple_mul(a, c)

    # Calls `simple_mul` once for each backend, recording the inputs and outputs
    # to `module` and then comparing them.
    self.compare_backends(simple_mul)

Test Suites

Test targets are automatically generated for each test file and for each backend to check numerical correctness against TensorFlow. Tests targets that pass are placed into the e2e_tests test suite. Tests that fail on particular backends are recorded in lists in the BUILD files. For example, if experimental_new_test.py fails on the iree_llvmjit and iree_vulkan backends then the following lines should be added to the BUILD file:

LLVM_FAILING = [
    ...
    "experimental_new_test.py",
    ...
]

VULKAN_FAILING = [
    ...
    "experimental_new_test.py",
    ...
]

Test targets for these backends are placed into the e2e_tests_failing test suite. Test targets in these test suites can be run as follows:

# Run all e2e tests that are expected to pass.
bazel test :e2e_tests

# Run all e2e tests that are expected to fail.
bazel test :e2e_tests_failing

# Run a specific failing e2e test target.
# Note that generated test targets are prefixed with their test suite name.
bazel test :e2e_tests_failing_broadcasting_test__tf__iree_vulkan

Debugging tests

If the compiler fails to compile the program, then it will create a crash reproducer (see MLIR documentation), which then allows reproducing the bug with an appropriate “opt” tool. Further debugging iteration can happen in opt.

TODO(silvasean): debugging miscompiles

Test harnesses

Simple function tests

See simple_arithmetic_test.py for some basic examples.