[GitHub] [tvm] abhikran-quic commented on a diff in pull request #11522: [TOPI] [Hexagon] Batch flatten slice op initial version

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

abhikran-quic commented on code in PR #11522:
URL: https://github.com/apache/tvm/pull/11522#discussion_r888692222


##########
tests/python/contrib/test_hexagon/test_batch_flatten.py:
##########
@@ -0,0 +1,130 @@
+# 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 numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+import tvm.topi.hexagon.slice_ops as sl
+from tvm import te, topi
+from tvm.contrib.hexagon.build import HexagonLauncher
+from tvm.topi import testing
+
+from .infrastructure import allocate_hexagon_array
+
+
+def n11c_1024c_1d(n, h, w, c):
+    return [n, h, w, c // 1024, tvm.te.AXIS_SEPARATOR, c % 1024]
+
+
+def nc_1024_1d(n, c):
+    return [n, c // 1024, tvm.te.AXIS_SEPARATOR, c % 1024]
+
+
+def transform_numpy(arr_np, layout):
+    if layout == "nhwc":
+        return arr_np
+    elif layout == "n11c-1024c-1d":
+        N, H, W, C = arr_np.shape
+        return arr_np.reshape([N, H, W, C // 1024, 1024])
+    elif layout == "nc-1d":
+        N, C = arr_np.shape
+        return arr_np.reshape([N, C // 1024, 1024])
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, output_layout):
+    return transform_numpy(expected_output_np, output_layout)
+
+
+class BaseTestBatchFlatten:
+    (
+        input_shape,
+        input_layout,
+        output_layout,
+        input_axis_sep,
+        output_axis_sep,
+    ) = tvm.testing.parameters(
+        ((1, 1, 1, 2048), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((1, 2, 4, 2048), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((1, 8, 8, 1024), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((2, 4, 8, 1024), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((2, 3, 5, 2048), "n11c-1024c-1d", "nc-1d", [4], [2]),
+    )
+    data_type = tvm.testing.parameter("float16")
+
+
+class TestBatchFlatten(BaseTestBatchFlatten):
+    @tvm.testing.fixture
+    def output_shape(self, input_shape):
+        return input_shape[0], input_shape[1] * input_shape[2] * input_shape[3]
+
+    @tvm.testing.requires_hexagon
+    def test_batch_flatten(
+        self,
+        data_type,
+        input_shape,
+        input_layout,
+        input_axis_sep,
+        output_shape,
+        output_layout,
+        output_axis_sep,
+        hexagon_session,
+    ):
+        target_hexagon = tvm.target.hexagon("v69")
+        target = tvm.target.Target(target_hexagon, host=target_hexagon)
+        A = te.placeholder(input_shape, name="A", dtype=data_type)
+        D = sl.batch_flatten_compute(A)
+        tir_s = sl.batch_flatten_stir_schedule(
+            D,
+            A,
+            nc_1024_1d,
+            n11c_1024c_1d,
+        )
+        func_name = "batch_flatten"
+        with tvm.transform.PassContext(opt_level=3, config={"tir.disable_assert": True}):
+            tir_irm = tvm.lower(tir_s.mod, [A, D], name=func_name)

Review Comment:
   Thank you, this is really nice! 
   I have removed `[A, D]` from the arguments.



##########
tests/python/contrib/test_hexagon/test_batch_flatten.py:
##########
@@ -0,0 +1,130 @@
+# 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 numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+import tvm.topi.hexagon.slice_ops as sl
+from tvm import te, topi
+from tvm.contrib.hexagon.build import HexagonLauncher
+from tvm.topi import testing
+
+from .infrastructure import allocate_hexagon_array
+
+
+def n11c_1024c_1d(n, h, w, c):
+    return [n, h, w, c // 1024, tvm.te.AXIS_SEPARATOR, c % 1024]
+
+
+def nc_1024_1d(n, c):
+    return [n, c // 1024, tvm.te.AXIS_SEPARATOR, c % 1024]
+
+
+def transform_numpy(arr_np, layout):
+    if layout == "nhwc":
+        return arr_np
+    elif layout == "n11c-1024c-1d":
+        N, H, W, C = arr_np.shape
+        return arr_np.reshape([N, H, W, C // 1024, 1024])
+    elif layout == "nc-1d":
+        N, C = arr_np.shape
+        return arr_np.reshape([N, C // 1024, 1024])
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, output_layout):
+    return transform_numpy(expected_output_np, output_layout)
+
+
+class BaseTestBatchFlatten:

Review Comment:
   Thank you. I have updated the test cases in a new commit.



##########
tests/python/contrib/test_hexagon/test_batch_flatten.py:
##########
@@ -0,0 +1,130 @@
+# 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 numpy as np
+import pytest
+
+import tvm
+import tvm.testing
+import tvm.topi.hexagon.slice_ops as sl
+from tvm import te, topi
+from tvm.contrib.hexagon.build import HexagonLauncher
+from tvm.topi import testing
+
+from .infrastructure import allocate_hexagon_array
+
+
+def n11c_1024c_1d(n, h, w, c):
+    return [n, h, w, c // 1024, tvm.te.AXIS_SEPARATOR, c % 1024]
+
+
+def nc_1024_1d(n, c):
+    return [n, c // 1024, tvm.te.AXIS_SEPARATOR, c % 1024]
+
+
+def transform_numpy(arr_np, layout):
+    if layout == "nhwc":
+        return arr_np
+    elif layout == "n11c-1024c-1d":
+        N, H, W, C = arr_np.shape
+        return arr_np.reshape([N, H, W, C // 1024, 1024])
+    elif layout == "nc-1d":
+        N, C = arr_np.shape
+        return arr_np.reshape([N, C // 1024, 1024])
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, output_layout):
+    return transform_numpy(expected_output_np, output_layout)
+
+
+class BaseTestBatchFlatten:
+    (
+        input_shape,
+        input_layout,
+        output_layout,
+        input_axis_sep,
+        output_axis_sep,
+    ) = tvm.testing.parameters(
+        ((1, 1, 1, 2048), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((1, 2, 4, 2048), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((1, 8, 8, 1024), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((2, 4, 8, 1024), "n11c-1024c-1d", "nc-1d", [4], [2]),
+        ((2, 3, 5, 2048), "n11c-1024c-1d", "nc-1d", [4], [2]),
+    )
+    data_type = tvm.testing.parameter("float16")
+
+
+class TestBatchFlatten(BaseTestBatchFlatten):
+    @tvm.testing.fixture
+    def output_shape(self, input_shape):
+        return input_shape[0], input_shape[1] * input_shape[2] * input_shape[3]
+
+    @tvm.testing.requires_hexagon
+    def test_batch_flatten(
+        self,
+        data_type,
+        input_shape,
+        input_layout,
+        input_axis_sep,
+        output_shape,
+        output_layout,
+        output_axis_sep,
+        hexagon_session,
+    ):
+        target_hexagon = tvm.target.hexagon("v69")
+        target = tvm.target.Target(target_hexagon, host=target_hexagon)
+        A = te.placeholder(input_shape, name="A", dtype=data_type)
+        D = sl.batch_flatten_compute(A)
+        tir_s = sl.batch_flatten_stir_schedule(
+            D,
+            A,
+            nc_1024_1d,
+            n11c_1024c_1d,
+        )
+        func_name = "batch_flatten"
+        with tvm.transform.PassContext(opt_level=3, config={"tir.disable_assert": True}):
+            tir_irm = tvm.lower(tir_s.mod, [A, D], name=func_name)
+            runtime_module = tvm.build(tir_irm, [A, D], target=target, name=func_name)

Review Comment:
   Sure. I've modified `tvm.build` to use `tir_s.mod`.



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