[GitHub] [tvm] mehrdadh commented on a diff in pull request #13704: Add support for named outputs in MLF archive

[GitBox <gi...@apache.org>]

mehrdadh commented on code in PR #13704:
URL: https://github.com/apache/tvm/pull/13704#discussion_r1062804782


##########
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:
   Please update this to tvm.testing.main()



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