[TVMC] Split common tvmc test file into more specific files (#9206)

The `test_tvmc_common.py` file was becoming a bit of a mixed bag of
tests and as we now want to extend the `Target` processing logic it made
sense to split each out into its own file to make it clearer what each
does.

`test_common.py` has also been renamed before we start using it for all the
tests instead.

tests/python/driver/tvmc/test_frontends.py

@@ -14,11 +14,10 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-import os
-import tarfile
 
 import pytest
 
+import tvm
 from tvm.ir.module import IRModule
 
 from tvm.driver import tvmc
@@ -229,3 +228,128 @@ def test_load_model___wrong_language__to_pytorch(tflite_mobilenet_v1_1_quant):
             model_format="pytorch",
             shape_dict={"input": [1, 3, 224, 224]},
         )
+
+
+def test_compile_tflite_module_nhwc_to_nchw(tflite_mobilenet_v1_1_quant):
+    # some CI environments wont offer TFLite, so skip in case it is not present
+    pytest.importorskip("tflite")
+
+    tvmc_model = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
+    before = tvmc_model.mod
+
+    expected_layout = "NCHW"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NHWC"
+                and node.attrs.dst_layout == "NCHW"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert any(layout_transform_calls), "Expected 'layout_transform NHWC->NCHW' not found"
+
+
+def test_compile_onnx_module_nchw_to_nhwc(onnx_resnet50):
+    # some CI environments wont offer ONNX, so skip in case it is not present
+    pytest.importorskip("onnx")
+
+    tvmc_model = tvmc.frontends.load_model(onnx_resnet50)
+    before = tvmc_model.mod
+
+    expected_layout = "NHWC"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NCHW"
+                and node.attrs.dst_layout == "NHWC"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert any(layout_transform_calls), "Expected 'layout_transform NCWH->NHWC' not found"
+
+
+def test_compile_paddle_module_nchw_to_nhwc(paddle_resnet50):
+    # some CI environments wont offer Paddle, so skip in case it is not present
+    pytest.importorskip("paddle")
+
+    tvmc_model = tvmc.frontends.load_model(paddle_resnet50, "paddle")
+    before = tvmc_model.mod
+
+    expected_layout = "NHWC"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NCHW"
+                and node.attrs.dst_layout == "NHWC"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert any(layout_transform_calls), "Expected 'layout_transform NCWH->NHWC' not found"
+
+
+def test_compile_tflite_module__same_layout__nhwc_to_nhwc(tflite_mobilenet_v1_1_quant):
+    # some CI environments wont offer TFLite, so skip in case it is not present
+    pytest.importorskip("tflite")
+
+    tvmc_model = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
+    before = tvmc_model.mod
+
+    expected_layout = "NHWC"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NHWC"
+                and node.attrs.dst_layout == "NHWC"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert not any(layout_transform_calls), "Unexpected 'layout_transform' call"
+
+
+def test_compile_onnx_module__same_layout__nchw_to_nchw(onnx_resnet50):
+    # some CI environments wont offer ONNX, so skip in case it is not present
+    pytest.importorskip("onnx")
+
+    tvmc_model = tvmc.frontends.load_model(onnx_resnet50)
+    before = tvmc_model.mod
+
+    expected_layout = "NCHW"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NCHW"
+                and node.attrs.dst_layout == "NCHW"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert not any(layout_transform_calls), "Unexpected 'layout_transform' call"

tests/python/driver/tvmc/test_pass_config.py

@@ -0,0 +1,73 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import pytest
+
+from tvm.contrib.target.vitis_ai import vitis_ai_available
+from tvm.driver import tvmc
+
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_config_invalid_format():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.parse_configs(["relay.backend.use_auto_scheduler.missing.value"])
+
+
+def test_config_missing_from_tvm():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.parse_configs(["relay.backend.use_auto_scheduler.missing.value=1234"])
+
+
+def test_config_unsupported_tvmc_config():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.parse_configs(["tir.LoopPartition=value"])
+
+
+def test_config_empty():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.parse_configs([""])
+
+
+def test_config_valid_config_bool():
+    configs = tvmc.common.parse_configs(["relay.backend.use_auto_scheduler=true"])
+
+    assert len(configs) == 1
+    assert "relay.backend.use_auto_scheduler" in configs.keys()
+    assert configs["relay.backend.use_auto_scheduler"] == True
+
+
+@pytest.mark.skipif(
+    not vitis_ai_available(),
+    reason="--target vitis-ai is not available. TVM built with 'USE_VITIS_AI OFF'",
+)
+def test_config_valid_multiple_configs():
+    configs = tvmc.common.parse_configs(
+        [
+            "relay.backend.use_auto_scheduler=false",
+            "tir.detect_global_barrier=10",
+            "relay.ext.vitis_ai.options.build_dir=mystring",
+        ]
+    )
+
+    assert len(configs) == 3
+    assert "relay.backend.use_auto_scheduler" in configs.keys()
+    assert configs["relay.backend.use_auto_scheduler"] == False
+    assert "tir.detect_global_barrier" in configs.keys()
+    assert configs["tir.detect_global_barrier"] == 10
+    assert "relay.ext.vitis_ai.options.build_dir" in configs.keys()
+    assert configs["relay.ext.vitis_ai.options.build_dir"] == "mystring"

tests/python/driver/tvmc/test_common.py → tests/python/driver/tvmc/test_pass_list.py

@@ -14,12 +14,13 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+
 import argparse
 import pytest
 from tvm.driver import tvmc
 
 
-def test_common_parse_pass_list_str():
+def test_parse_pass_list_str():
     assert [""] == tvmc.common.parse_pass_list_str("")
     assert ["FoldScaleAxis", "FuseOps"] == tvmc.common.parse_pass_list_str("FoldScaleAxis,FuseOps")
 

tests/python/driver/tvmc/test_shape_parser.py

@@ -0,0 +1,96 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import argparse
+
+import pytest
+
+from tvm.driver import tvmc
+
+
+def test_shape_parser():
+    # Check that a valid input is parsed correctly
+    shape_string = "input:[10,10,10]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input": [10, 10, 10]}
+
+
+def test_alternate_syntax():
+    shape_string = "input:0:[10,10,10] input2:[20,20,20,20]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input:0": [10, 10, 10], "input2": [20, 20, 20, 20]}
+
+
+@pytest.mark.parametrize(
+    "shape_string",
+    [
+        "input:[10,10,10] input2:[20,20,20,20]",
+        "input: [10, 10, 10] input2: [20, 20, 20, 20]",
+        "input:[10,10,10],input2:[20,20,20,20]",
+    ],
+)
+def test_alternate_syntaxes(shape_string):
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input": [10, 10, 10], "input2": [20, 20, 20, 20]}
+
+
+def test_negative_dimensions():
+    # Check that negative dimensions parse to Any correctly.
+    shape_string = "input:[-1,3,224,224]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    # Convert to strings to allow comparison with Any.
+    assert str(shape_dict) == "{'input': [?, 3, 224, 224]}"
+
+
+def test_multiple_valid_gpu_inputs():
+    # Check that multiple valid gpu inputs are parsed correctly.
+    shape_string = "gpu_0/data_0:[1, -1,224,224] gpu_1/data_1:[7, 7]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    expected = "{'gpu_0/data_0': [1, ?, 224, 224], 'gpu_1/data_1': [7, 7]}"
+    assert str(shape_dict) == expected
+
+
+def test_invalid_pattern():
+    shape_string = "input:[a,10]"
+    with pytest.raises(argparse.ArgumentTypeError):
+        tvmc.common.parse_shape_string(shape_string)
+
+
+def test_invalid_separators():
+    shape_string = "input:5,10 input2:10,10"
+    with pytest.raises(argparse.ArgumentTypeError):
+        tvmc.common.parse_shape_string(shape_string)
+
+
+def test_invalid_colon():
+    shape_string = "gpu_0/data_0:5,10 :test:10,10"
+    with pytest.raises(argparse.ArgumentTypeError):
+        tvmc.common.parse_shape_string(shape_string)
+
+
+@pytest.mark.parametrize(
+    "shape_string",
+    [
+        "gpu_0/data_0:5,10 /:10,10",
+        "gpu_0/data_0:5,10 data/:10,10",
+        "gpu_0/data_0:5,10 /data:10,10",
+        "gpu_0/invalid/data_0:5,10 data_1:10,10",
+    ],
+)
+def test_invalid_slashes(shape_string):
+    with pytest.raises(argparse.ArgumentTypeError):
+        tvmc.common.parse_shape_string(shape_string)