update test

Author: Archermmt

tests/python/contrib/test_msc/test_manager.py renamed to tests/python/contrib/test_msc/test_pipeline.py

@@ -15,14 +15,14 @@
 # specific language governing permissions and limitations
 # under the License.
 
-""" Test Managers in MSC. """
+""" Test Pipeline in MSC. """
 
 import json
 import pytest
 import torch
 
 import tvm.testing
-from tvm.contrib.msc.pipeline import MSCManager
+from tvm.contrib.msc.pipeline import MSCManager, TorchDynamic
 from tvm.contrib.msc.core.utils.namespace import MSCFramework
 from tvm.contrib.msc.core import utils as msc_utils
 
@@ -32,13 +32,13 @@
 )
 
 
-def _get_config(model_type, compile_type, inputs, outputs, atol=1e-1, rtol=1e-1):
+def _get_config(model_type, compile_type, inputs, outputs, dynamic=False, atol=1e-1, rtol=1e-1):
     """Get msc config"""
 
-    path = "test_manager_{}_{}".format(model_type, compile_type)
+    path = "test_pipe_{}_{}_{}".format(model_type, compile_type, "dynamic" if dynamic else "static")
     return {
         "workspace": msc_utils.msc_dir(path),
-        "verbose": "critical",
+        "verbose": "info",
         "model_type": model_type,
         "inputs": inputs,
         "outputs": outputs,
@@ -95,23 +95,29 @@ def _get_tf_graph():
         return None
 
 
-def _check_manager(manager, expected_info):
-    """Check the manager results"""
+def _check_pipeline(pipeline, expected_info, dynamic=False):
+    """Check the pipeline results"""
 
-    model_info = manager.runner.model_info
     passed, err = True, ""
-    if not manager.report["success"]:
+    if not pipeline.report["success"]:
         passed = False
-        err = "Failed to run pipe for {} -> {}".format(manager.model_type, manager.compile_type)
-    if not msc_utils.dict_equal(model_info, expected_info):
-        passed = False
-        err = "Model info {} mismatch with expected {}".format(model_info, expected_info)
-    manager.destory()
+        err = "Failed to run pipe for {} -> {}".format(pipeline.model_type, pipeline.compile_type)
+    if not dynamic:
+        model_info = pipeline.get_runtime().model_info
+        if not msc_utils.dict_equal(model_info, expected_info):
+            passed = False
+            err = "Model info {} mismatch with expected {}".format(model_info, expected_info)
+    pipeline.destory()
     if not passed:
-        raise Exception("{}\nReport:{}".format(err, json.dumps(manager.report, indent=2)))
+        raise Exception("{}\nReport:{}".format(err, json.dumps(pipeline.report, indent=2)))
+
 
+def _test_from_torch(
+    compile_type, expected_info, training=False, dynamic=False, atol=1e-1, rtol=1e-1
+):
+    if dynamic and not hasattr(torch, "compile"):
+        return
 
-def _test_from_torch(compile_type, expected_info, training=False, atol=1e-1, rtol=1e-1):
     torch_model = _get_torch_model("resnet50", training)
     if torch_model:
         if torch.cuda.is_available():
@@ -121,12 +127,13 @@ def _test_from_torch(compile_type, expected_info, training=False, atol=1e-1, rto
             compile_type,
             inputs=[["input_0", [1, 3, 224, 224], "float32"]],
             outputs=["output"],
+            dynamic=dynamic,
             atol=atol,
             rtol=rtol,
         )
-        manager = MSCManager(torch_model, config)
-        manager.run_pipe()
-        _check_manager(manager, expected_info)
+        pipeline = TorchDynamic(torch_model, config) if dynamic else MSCManager(torch_model, config)
+        pipeline.run_pipe()
+        _check_pipeline(pipeline, expected_info, dynamic)
 
 
 def _test_from_tf(compile_type, expected_info, atol=1e-2, rtol=1e-2):
@@ -143,11 +150,12 @@ def _test_from_tf(compile_type, expected_info, atol=1e-2, rtol=1e-2):
         config["compile"]["profile"]["check"]["err_rate"] = -1
         manager = MSCManager(graphdef, config)
         manager.run_pipe()
-        _check_manager(manager, expected_info)
+        _check_pipeline(manager, expected_info)
 
 
-def test_tvm_manager():
-    """Test manager for tvm"""
+@pytest.mark.parametrize("dynamic", [False, True])
+def test_tvm_pipeline(dynamic):
+    """Test pipeline for tvm"""
 
     model_info = {
         "inputs": [
@@ -168,40 +176,42 @@ def test_tvm_manager():
             "msc.linear_bias": 1,
         },
     }
-    _test_from_torch(MSCFramework.TVM, model_info, training=False)
-
-    model_info = {
-        "inputs": [
-            {"name": "input", "shape": [1, 224, 224, 3], "dtype": "float32", "layout": "NHWC"}
-        ],
-        "outputs": [
-            {
-                "name": "MobilenetV2/Predictions/Reshape_1:0",
-                "shape": [1, 1001],
-                "dtype": "float32",
-                "layout": "NC",
-            }
-        ],
-        "nodes": {
-            "total": 138,
-            "input": 1,
-            "msc.conv2d_bias": 36,
-            "clip": 35,
-            "nn.conv2d": 17,
-            "nn.batch_norm": 17,
-            "get_item": 17,
-            "add": 10,
-            "nn.avg_pool2d": 1,
-            "squeeze": 1,
-            "reshape": 2,
-            "nn.softmax": 1,
-        },
-    }
-    _test_from_tf(MSCFramework.TVM, model_info)
-
-
-def test_torch_manager():
-    """Test manager for torch"""
+    _test_from_torch(MSCFramework.TVM, model_info, training=False, dynamic=dynamic)
+
+    if not dynamic:
+        model_info = {
+            "inputs": [
+                {"name": "input", "shape": [1, 224, 224, 3], "dtype": "float32", "layout": "NHWC"}
+            ],
+            "outputs": [
+                {
+                    "name": "MobilenetV2/Predictions/Reshape_1:0",
+                    "shape": [1, 1001],
+                    "dtype": "float32",
+                    "layout": "NC",
+                }
+            ],
+            "nodes": {
+                "total": 138,
+                "input": 1,
+                "msc.conv2d_bias": 36,
+                "clip": 35,
+                "nn.conv2d": 17,
+                "nn.batch_norm": 17,
+                "get_item": 17,
+                "add": 10,
+                "nn.avg_pool2d": 1,
+                "squeeze": 1,
+                "reshape": 2,
+                "nn.softmax": 1,
+            },
+        }
+        _test_from_tf(MSCFramework.TVM, model_info)
+
+
+@pytest.mark.parametrize("dynamic", [False, True])
+def test_torch_pipeline(dynamic):
+    """Test pipeline for torch"""
 
     model_info = {
         "inputs": [
@@ -222,10 +232,10 @@ def test_torch_manager():
             "msc.linear_bias": 1,
         },
     }
-    _test_from_torch(MSCFramework.TORCH, model_info, training=False)
+    _test_from_torch(MSCFramework.TORCH, model_info, training=False, dynamic=dynamic)
 
 
-def test_tensorflow_manager():
+def test_tensorflow_pipeline():
     """Test manager for tensorflow"""
 
     model_info = {
@@ -259,8 +269,9 @@ def test_tensorflow_manager():
 
 
 @requires_tensorrt
-def test_tensorrt_manager():
-    """Test manager for tensorrt"""
+@pytest.mark.parametrize("dynamic", [False, True])
+def test_tensorrt_pipeline(dynamic):
+    """Test pipeline for tensorrt"""
 
     model_info = {
         "inputs": [
@@ -269,7 +280,7 @@ def test_tensorrt_manager():
         "outputs": [{"name": "output", "shape": [1, 1000], "dtype": "float32", "layout": ""}],
         "nodes": {"total": 2, "input": 1, "msc_tensorrt": 1},
     }
-    _test_from_torch(MSCFramework.TENSORRT, model_info, training=False)
+    _test_from_torch(MSCFramework.TENSORRT, model_info, training=False, dynamic=dynamic)
 
 
 if __name__ == "__main__":

tests/python/contrib/test_msc/test_plugin.py

@@ -313,7 +313,7 @@ def _test_with_manager(plugins, compile_type, expected_info):
     }
     manager = MSCManager(model, config, plugins=plugins)
     report = manager.run_pipe()
-    model_info = manager.runner.model_info
+    model_info = manager.get_runtime().model_info
     manager.destory()
     assert report["success"], "Failed to run pipe for torch -> {}".format(compile_type)
     assert msc_utils.dict_equal(

tests/python/contrib/test_msc/test_runner.py

@@ -100,7 +100,7 @@ def _test_from_torch(runner_cls, device, training=False, atol=1e-1, rtol=1e-1):
         golden = [msc_utils.cast_array(golden)]
         workspace.destory()
         for gol_r, out_r in zip(golden, outputs):
-            tvm.testing.assert_allclose(gol_r, out_r, atol=atol, rtol=rtol)
+            tvm.testing.assert_allclose(gol_r, msc_utils.cast_array(out_r), atol=atol, rtol=rtol)
 
 
 def test_tvm_runner_cpu():
@@ -162,7 +162,7 @@ def test_tensorflow_runner():
         outputs = runner.run([data], ret_type="list")
         workspace.destory()
         for gol_r, out_r in zip(golden, outputs):
-            tvm.testing.assert_allclose(gol_r, out_r, atol=1e-3, rtol=1e-3)
+            tvm.testing.assert_allclose(gol_r, msc_utils.cast_array(out_r), atol=1e-3, rtol=1e-3)
 
 
 if __name__ == "__main__":

tests/python/contrib/test_msc/test_tools.py

@@ -144,7 +144,7 @@ def get_tools(tool_type, use_distill=False, run_type=MSCFramework.MSC):
                 }
             ],
         }
-        tools.append({"tool_type": ToolType.TRACKER, "tool_config": config, "apply_once": True})
+        tools.append({"tool_type": ToolType.TRACKER, "tool_config": config})
     if use_distill:
         config = {
             "plan_file": "msc_distiller.json",
@@ -180,7 +180,7 @@ def _get_torch_model(name, training=False):
 def _check_manager(manager, expected_info):
     """Check the manager results"""
 
-    model_info = manager.runner.model_info
+    model_info = manager.get_runtime().model_info
     passed, err = True, ""
     if not manager.report["success"]:
         passed = False