def _run_test(test_dict):
"""Runs an individual test dict."""
tf_module_builder_lambda = test_dict["tf_module_builder"]
tf_module = tf_module_builder_lambda()
ctx = compiler.Context()
with tempfile.TemporaryDirectory() as sm_path:
options = tf.saved_model.SaveOptions(save_debug_info=True)
tf.saved_model.save(tf_module, sm_path, options=options)
input_module = compiler.tf_load_saved_model(sm_path,
ctx,
pass_pipeline=())
passes = test_dict.get("passes")
expect_pass_failure = test_dict.get("expect_pass_failure")
if passes:
try:
input_module.run_pass_pipeline(passes)
except: # pylint: disable=bare-except
if not expect_pass_failure:
print(
"UNEXPECTED PASS FAILURE (INTERMEDIATE ASM FOLLOWS ON STDERR):",
file=sys.stderr)
print(input_module.to_asm(), file=sys.stderr)
raise
# Print the input module ASM.
if test_dict.get("print_input_module"):
print(input_module.to_asm())
def compile_from_path(sm_path):
compiler_context = compiler.Context()
compiler_module = compiler.tf_load_saved_model(
sm_path,
exported_names=exported_names,
compiler_context=compiler_context)
return compiler_module.compile(target_backends=target_backends)
def _compile_from_path(sm_path: str) -> compiler.binding.OpaqueBlob:
"""Helper function for compile_tf_module."""
if artifacts_dir is not None:
# Set up a crash reproducer for debugging.
compiler.Context.default_crash_reproducer_path = os.path.join(
artifacts_dir, f"reproducer__{backends_string}.mlir")
try:
# We break up the compilation here so we can save intermediary artifacts.
compiler_context = compiler.Context()
# Convert the tf_module into raw TF input MLIR.
compiler_module = compiler.tf_load_saved_model(
sm_path,
exported_names=exported_names,
compiler_context=compiler_context,
pass_pipeline=())
if artifacts_dir is not None:
tf_mlir_path = os.path.join(artifacts_dir, "tf_input.mlir")
logging.info("Saving raw TF input MLIR to: %s", tf_mlir_path)
with open(tf_mlir_path, "w") as f:
f.write(compiler_module.to_asm())
# Now run the passes manually that tf_load_saved_model would usually do.
compiler_module.run_pass_pipeline(compiler.TF_IMPORT_PASS_PIPELINE)
if artifacts_dir is not None:
iree_mlir_path = os.path.join(artifacts_dir, "iree_input.mlir")
logging.info("Saving IREE input MLIR to: %s", iree_mlir_path)
with open(iree_mlir_path, "w") as f:
f.write(compiler_module.to_asm())
target_backends = []
for backend_info in backend_infos:
target_backends.extend(backend_info.compiler_targets)
compiled_module = compiler_module.compile(
target_backends=target_backends)
if artifacts_dir is not None:
compiled_path = _get_backends_path("compiled", backend_infos,
artifacts_dir)
compiled_path = f"{compiled_path}.vmfb"
logging.info("Saving compiled IREE module to: %s",
compiled_path)
with open(compiled_path, "wb") as f:
f.write(compiled_module)
return compiled_module
except Exception: # pylint: disable=broad-except
if artifacts_dir is not None:
# Disable the crash reproducer (to avoid inadvertently overwriting it).
compiler.Context.default_crash_reproducer_path = None
raise
def _compile_from_path(sm_path):
"""Helper function for compile_tf_module."""
# We break up the compilation here so we can save intermediary artifacts.
compiler_context = compiler.Context()
if artifacts_dir is not None:
normalized_backends = []
for backend in target_backends:
# Remove unusual characters and ensure names don't end or start in "_".
backend = re.sub("[^0-9a-zA-Z_]+", "_", backend)
normalized_backends.append(backend.strip("_"))
backends_string = "__".join(normalized_backends)
# Convert the tf_module into raw TF input MLIR.
compiler_module = compiler.tf_load_saved_model(
sm_path,
exported_names=exported_names,
compiler_context=compiler_context,
pass_pipeline=())
if artifacts_dir is not None:
tf_mlir_path = os.path.join(artifacts_dir,
f"tf_input__{backends_string}.mlir")
logging.info("Saving raw TF input MLIR to: %s", tf_mlir_path)
with open(tf_mlir_path, "w") as f:
f.write(compiler_module.to_asm())
# Now run the passes manually that tf_load_saved_model would usually do.
compiler_module.run_pass_pipeline(compiler.TF_IMPORT_PASS_PIPELINE)
if artifacts_dir is not None:
iree_mlir_path = os.path.join(
artifacts_dir, f"iree_input__{backends_string}.mlir")
logging.info("Saving IREE input MLIR to: %s", iree_mlir_path)
with open(iree_mlir_path, "w") as f:
f.write(compiler_module.to_asm())
compiled_module = compiler_module.compile(
target_backends=target_backends)
if artifacts_dir is not None:
compiled_path = os.path.join(artifacts_dir,
f"compiled__{backends_string}.vmfb")
logging.info("Saving compiled IREE module to: %s", compiled_path)
with open(compiled_path, "wb") as f:
f.write(compiled_module)
return compiled_module
def compile_from_path(sm_path):
compiler_context = compiler.Context()
# Break up the compilation so we can save debug artifacts.
compiler_module = compiler.tf_load_saved_model(
sm_path,
exported_names=exported_names,
compiler_context=compiler_context,
pass_pipeline=())
# Save the input MLIR module.
flattened_target_backends = re.sub("[^0-9a-zA-Z]+", "_",
"__".join(target_backends))
if global_debug_dir:
mlir_path = os.path.join(global_debug_dir,
"raw_%s.mlir" % flattened_target_backends)
logging.info("Saving raw TF input MLIR to: %s", mlir_path)
with open(mlir_path, "w") as f:
f.write(compiler_module.to_asm())
# Now run the passes manually that tf_load_saved_model would usually do.
compiler_module.run_pass_pipeline(compiler.TF_IMPORT_PASS_PIPELINE)
if global_debug_dir:
mlir_path = os.path.join(
global_debug_dir, "input_%s.mlir" % flattened_target_backends)
logging.info("Saving IREE input MLIR to: %s", mlir_path)
with open(mlir_path, "w") as f:
f.write(compiler_module.to_asm())
compiled_module = compiler_module.compile(
target_backends=target_backends)
if global_debug_dir:
compiled_path = os.path.join(
global_debug_dir,
"compiled_%s.vmfb" % flattened_target_backends)
logging.info("Saving compiled IREE module to: %s", compiled_path)
with open(compiled_path, "wb") as f:
f.write(compiled_module)
return compiled_module