Mousius commented on code in PR #13704:
URL: https://github.com/apache/tvm/pull/13704#discussion_r1063854160
##########
tests/python/unittest/test_micro_model_library_format.py:
##########
@@ -632,5 +632,89 @@ def test_multiple_relay_modules_aot_graph():
assert metadata["version"] == _GENERATED_VERSION
+@tvm.testing.requires_micro
+def test_output_name_single():
+ """Generate a conv2d Relay module for testing."""
+ input_a = tvm.relay.var("input_a", shape=(3, 4, 5), dtype="int64")
+ output_1 = input_a + tvm.relay.const(1, "int64")
+ attrs = tvm.ir.make_node("DictAttrs",
output_tensor_names=["test_output_a"])
+ main_func = tvm.relay.Function([input_a], output_1, attrs=attrs)
+ mod = tvm.IRModule.from_expr(main_func)
+ mod = tvm.relay.transform.InferType()(mod)
+
+ executor = Executor("aot", {"unpacked-api": True, "interface-api": "c"})
+ runtime = Runtime("crt")
+ target = tvm.target.target.micro("host")
+
+ with tvm.transform.PassContext(opt_level=3,
config={"tir.disable_vectorize": True}):
+ factory = tvm.relay.build(mod, target, runtime=runtime,
executor=executor, mod_name="mod1")
+ temp_dir = utils.tempdir()
+ mlf_tar_path = temp_dir.relpath("lib.tar")
+
+ micro.export_model_library_format(factory, mlf_tar_path)
+
+ tf = tarfile.open(mlf_tar_path)
+ extract_dir = temp_dir.relpath("extract")
+ os.mkdir(extract_dir)
+ tf.extractall(extract_dir)
+
+ with open(os.path.join(extract_dir, "metadata.json")) as f:
+ metadata = json.load(f)
+
+ assert
metadata["modules"]["mod1"]["memory"]["functions"]["main"][0]["outputs"] == {
+ "test_output_a": {"size": 480, "dtype": "int64"}
+ }
+
+
+@tvm.testing.requires_micro
+def test_output_names_many():
+ """Generate a conv2d Relay module for testing."""
+ input_a = tvm.relay.var("input_a", shape=(3, 4, 5), dtype="int64")
+ input_b = tvm.relay.var("input_b", shape=(3, 4), dtype="int32")
+ input_c = tvm.relay.var("input_c", shape=(3,), dtype="float32")
+
+ output_1 = input_a + tvm.relay.const(1, "int64")
+ output_2 = input_b + tvm.relay.const(2)
+ output_3 = input_b + tvm.relay.const(3)
+ output_4 = input_c + tvm.relay.const(4.0)
+
+ full_output = tvm.relay.Tuple(
+ [output_1, tvm.relay.Tuple([tvm.relay.Tuple([output_2, output_3]),
output_4])]
+ )
+ attrs = tvm.ir.make_node(
+ "DictAttrs",
+ output_tensor_names=["test_output_a", "test_output_b",
"test_output_c", "test_output_d"],
+ )
+ main_func = tvm.relay.Function([input_a, input_b, input_c], full_output,
attrs=attrs)
+ mod = tvm.IRModule.from_expr(main_func)
+ mod = tvm.relay.transform.InferType()(mod)
+
+ executor = Executor("aot", {"unpacked-api": True, "interface-api": "c"})
+ runtime = Runtime("crt")
+ target = tvm.target.target.micro("host")
+
+ with tvm.transform.PassContext(opt_level=3,
config={"tir.disable_vectorize": True}):
+ factory = tvm.relay.build(mod, target, runtime=runtime,
executor=executor, mod_name="mod1")
+ temp_dir = utils.tempdir()
+ mlf_tar_path = temp_dir.relpath("lib.tar")
+
+ micro.export_model_library_format(factory, mlf_tar_path)
+
+ tf = tarfile.open(mlf_tar_path)
+ extract_dir = temp_dir.relpath("extract")
+ os.mkdir(extract_dir)
+ tf.extractall(extract_dir)
+
+ with open(os.path.join(extract_dir, "metadata.json")) as f:
+ metadata = json.load(f)
+
+ assert
metadata["modules"]["mod1"]["memory"]["functions"]["main"][0]["outputs"] == {
+ "test_output_a": {"size": 480, "dtype": "int64"},
+ "test_output_b": {"size": 48, "dtype": "int32"},
+ "test_output_c": {"size": 48, "dtype": "int32"},
+ "test_output_d": {"size": 12, "dtype": "float32"},
+ }
+
+
if __name__ == "__main__":
sys.exit(pytest.main([__file__] + sys.argv[1:]))
Review Comment:
I'd rather not introduce unrelated changes into my patch, I can try a mass
find and replace in a future patch? 😸
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