[GitHub] [tvm] sunggg commented on a diff in pull request #12087: [UMA] UMA v1.0

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

sunggg commented on code in PR #12087:
URL: https://github.com/apache/tvm/pull/12087#discussion_r938062243


##########
tests/python/contrib/test_uma/test_uma_pipeline.py:
##########
@@ -0,0 +1,136 @@
+# 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.micro.testing.aot_test_utils import AOT_DEFAULT_RUNNER
+from tvm.relay import transform, testing
+from tvm.testing.aot import (
+    AOTTestModel,
+    AOTTestRunner,
+    generate_ref_data,
+    compile_and_run,
+)
+
+import tvm
+from test_uma_vanilla_accelerator import VanillaAcceleratorBackend
+from tvm import relay
+import numpy as np
+from collections import OrderedDict
+
+from tvm.relay.backend.contrib.uma.api.utils import uma_available
+
+pytestmark = pytest.mark.skipif(not uma_available(), reason="UMA not available")
+
+
+@pytest.mark.parametrize(
+    "interface_api,use_unpacked_api,test_runner,groups,weight_shape",
+    [("c", True, AOT_DEFAULT_RUNNER, 1, 32)],
+)
+def test_conv2d(interface_api, use_unpacked_api, test_runner, groups, weight_shape):
+    """Test a subgraph with a single conv2d operator."""
+    mod, inputs, output_list, test_runner = create_conv2d(groups, test_runner, weight_shape)
+
+    uma_backend = VanillaAcceleratorBackend()
+    uma_backend.register()
+    mod = uma_backend.partition(mod)
+    target = tvm.target.Target("vanilla_accelerator", host=tvm.target.Target("c"))
+
+    compile_and_run(
+        AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
+        test_runner,
+        interface_api,
+        use_unpacked_api,
+        target=target,
+    )
+
+
+def create_conv2d(groups=1, test_runner=AOT_DEFAULT_RUNNER, weight_shape=32):
+    dtype = "float32"
+    ishape = (1, 32, 14, 14)
+    wshape = (32, weight_shape, 3, 3)
+    pass_config = {"tir.usmp.enable": True}
+    test_runner = AOTTestRunner(
+        makefile=test_runner.makefile,
+        prologue=test_runner.prologue,
+        epilogue=test_runner.epilogue,
+        includes=test_runner.includes,
+        parameters=test_runner.parameters,
+        pass_config=pass_config,
+    )
+    data0 = relay.var("data", shape=ishape, dtype=dtype)
+    weight0 = relay.var("weight", shape=wshape, dtype=dtype)
+    out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1, 1), groups=groups)
+    main_f = relay.Function([data0, weight0], out)
+    mod = tvm.IRModule()
+    mod["main"] = main_f
+    mod = transform.InferType()(mod)
+    i_data = np.random.uniform(0, 1, ishape).astype(dtype)
+    w1_data = np.random.uniform(0, 1, wshape).astype(dtype)
+    inputs = OrderedDict([("data", i_data), ("weight", w1_data)])
+    output_list = generate_ref_data(mod, inputs)
+    return mod, inputs, output_list, test_runner
+
+
+def _generate_runtime_data(input_shapes: dict, output_shapes: dict) -> [OrderedDict, OrderedDict]:
+    assert len(input_shapes) == 1
+    assert len(output_shapes) == 1
+
+    iname = list(input_shapes.keys())[0]
+    oname = list(output_shapes.keys())[0]
+    ishape = input_shapes[iname]
+    oshape = output_shapes[oname]
+    i_data = np.random.uniform(0, 1, ishape).astype("float32")
+    o_data = np.random.uniform(0, 1, oshape).astype("float32")
+    oname = "output"  # name set by relay.build in executor_codegen_metadata.outputs
+    inputs = OrderedDict([(iname, i_data)])
+    outputs = OrderedDict([(oname, o_data)])
+    return inputs, outputs
+
+
+def test_mobilenet():
+    """Full network test with Mobilenet"""
+    use_unpacked_api = True
+    interface_api = "c"
+    test_runner = AOT_DEFAULT_RUNNER
+
+    mod, params = testing.mobilenet.get_workload(batch_size=1)
+
+    uma_backend = VanillaAcceleratorBackend()
+    uma_backend.register()
+    target = tvm.target.Target("vanilla_accelerator", host=tvm.target.Target("c"))
+    target_c = tvm.target.Target("c")
+
+    data_shape = [int(x) for x in mod["main"].checked_type.arg_types[0].shape]
+    data = np.random.uniform(size=data_shape).astype("float32")
+    input_list = {"data": data}
+    output_list = generate_ref_data(mod, input_list, params)
+    mod = uma_backend.partition(mod)

Review Comment:
   Nit: It would be more readable if there is a comment that explains how the model would be partitioned. 



-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: commits-unsubscribe@tvm.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org