[GitHub] [tvm] trahman-quic commented on a diff in pull request #11529: [UPSTREAM][HEXAGON] Slice ops added - add, subtract, multiply

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

trahman-quic commented on code in PR #11529:
URL: https://github.com/apache/tvm/pull/11529#discussion_r888307091


##########
tests/python/contrib/test_hexagon/test_add_subtract_multiply.py:
##########
@@ -0,0 +1,235 @@
+# 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
+import numpy as np
+
+np.set_printoptions(threshold=np.inf)
+from tvm import te, topi
+
+import tvm.testing
+from tvm.topi import testing
+from tvm.contrib.hexagon.build import HexagonLauncher
+import tvm.topi.hexagon.slice_ops as sl
+from .infrastructure import allocate_hexagon_array, transform_numpy
+
+
+@tvm.testing.fixture
+def expected_output_np(input_np_A, input_np_B, op_name):
+    if op_name == "add":
+        out_ref = np.add(input_np_A, input_np_B)
+    elif op_name == "subtract":
+        out_ref = np.subtract(input_np_A, input_np_B)
+    elif op_name == "multiply":
+        out_ref = np.multiply(input_np_A, input_np_B)
+    return out_ref
+
+
+@tvm.testing.fixture
+def input_np_A(input_shape_A, dtype):
+    return np.random.random(input_shape_A).astype(dtype)
+
+
+@tvm.testing.fixture
+def input_np_B(input_shape_B, dtype):
+    return np.random.random(input_shape_B).astype(dtype)
+
+
+@tvm.testing.fixture
+def transformed_input_np_A(input_np_A, input_A_layout):
+    if input_A_layout == "nhwc-8h2w32c2w":
+        return transform_numpy(input_np_A, input_A_layout)
+    elif input_A_layout == "nhwc":
+        return input_np_A
+
+
+@tvm.testing.fixture
+def transformed_input_np_B(input_np_B, input_B_layout):
+    if input_B_layout == "nhwc-8h2w32c2w":
+        return transform_numpy(input_np_B, input_B_layout)
+    elif input_B_layout == "nhwc":
+        return input_np_B
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, output_layout):
+    return transform_numpy(expected_output_np, output_layout)
+
+
+def hexagon_wrapper_allocation(
+    device, layout, axis_separators, tensor_shape=None, data=None, transformed_data=None, dtype=None
+):
+    if layout == "nhwc-8h2w32c2w":
+        data_nd = allocate_hexagon_array(
+            device,
+            tensor_shape=tensor_shape,
+            data=transformed_data,
+            dtype=dtype,
+            axis_separators=axis_separators,
+            mem_scope="global.vtcm",
+        )
+    elif layout == "nhwc":
+        data_nd = allocate_hexagon_array(
+            device,
+            data=data,
+        )
+    return data_nd
+
+
+class TestAddSubtractMultiplyBroadcast2d:
+    (
+        input_shape_A,
+        input_shape_B,
+        input_A_layout,
+        input_B_layout,
+        output_layout,
+        dtype,
+    ) = tvm.testing.parameters(
+        # no broadcast needed - short input
+        (
+            [1, 8, 4, 32],
+            [1, 8, 4, 32],
+            "nhwc-8h2w32c2w",
+            "nhwc-8h2w32c2w",
+            "nhwc-8h2w32c2w",
+            "float16",
+        ),
+        # no broadcast needed - large input
+        (
+            [1, 56, 64, 128],
+            [1, 56, 64, 128],
+            "nhwc-8h2w32c2w",
+            "nhwc-8h2w32c2w",
+            "nhwc-8h2w32c2w",
+            "float16",
+        ),
+        # one input needs broadcast
+        (
+            [1, 56, 64, 128],
+            [1, 1, 64, 1],
+            "nhwc-8h2w32c2w",
+            "nhwc",
+            "nhwc-8h2w32c2w",
+            "float16",
+        ),
+        # Both input needs broadcast
+        (
+            [1, 56, 1, 128],
+            [1, 1, 64, 1],
+            "nhwc",
+            "nhwc",
+            "nhwc-8h2w32c2w",
+            "float16",
+        ),
+        # One axis in one input needs broadcast
+        (
+            [1, 56, 20, 128],
+            [1, 56, 20, 1],
+            "nhwc-8h2w32c2w",
+            "nhwc",
+            "nhwc-8h2w32c2w",
+            "float16",
+        ),
+    )
+
+    op_name = tvm.testing.parameter("add", "subtract", "multiply")
+
+    @tvm.testing.requires_hexagon
+    def test_transform(
+        self,
+        dtype,
+        input_shape_A,
+        input_shape_B,
+        input_np_A,
+        input_np_B,
+        transformed_input_np_A,
+        transformed_input_np_B,
+        expected_output_np,
+        transformed_expected_output_np,
+        hexagon_session,
+        output_layout,
+        input_A_layout,
+        input_B_layout,
+        op_name,
+    ):
+        target_hexagon = tvm.target.hexagon("v69")
+        A = te.placeholder(input_shape_A, name="A", dtype=dtype)
+        B = te.placeholder(input_shape_B, name="B", dtype=dtype)
+        if op_name == "add":
+            M = sl.add_broadcast_compute(A, B)
+        elif op_name == "subtract":
+            M = sl.subtract_broadcast_compute(A, B)
+        elif op_name == "multiply":
+            M = sl.multiply_broadcast_compute(A, B)
+
+        tir_schedule = sl.STIR_broadcast_schedule(
+            M, A, B, output_layout, input_A_layout, input_B_layout, op_name
+        )
+        sch = tir_schedule.mod
+
+        input_axis_separator = [4]
+        if output_layout == "nhwc-8h2w32c2w":
+            output_axis_separator = [4]
+        else:
+            raise RuntimeError(f"Unexpected layout '{output_layout}'")
+
+        with tvm.transform.PassContext(opt_level=3, config={"tir.disable_assert": True}):
+            func = tvm.build(
+                sch,
+                [A, B, M],
+                tvm.target.Target(target_hexagon, host=target_hexagon),
+                name="slice_op_with_transform",
+            )
+
+        output_shape = expected_output_np.shape
+
+        A_data_nd = hexagon_wrapper_allocation(
+            hexagon_session.device,
+            layout=input_A_layout,
+            data=input_np_A,
+            transformed_data=transformed_input_np_A,
+            axis_separators=input_axis_separator,
+        )
+        B_data_nd = hexagon_wrapper_allocation(
+            hexagon_session.device,
+            layout=input_B_layout,
+            data=input_np_B,
+            transformed_data=transformed_input_np_B,
+            axis_separators=input_axis_separator,
+        )
+        M_data_nd = hexagon_wrapper_allocation(
+            hexagon_session.device,
+            layout=output_layout,
+            tensor_shape=transformed_expected_output_np.shape,
+            axis_separators=output_axis_separator,
+            dtype=dtype,
+        )
+
+        mod = hexagon_session.load_module(func)
+        mod(A_data_nd, B_data_nd, M_data_nd)
+
+        b, h, w, c = output_shape
+        # convert nd to np and reshape to fixed chunk size layout
+        if output_layout == "nhwc-8h2w32c2w":
+            M_data_np = M_data_nd.numpy().reshape([b, h // 8, w // 4, c // 32, 8, 2, 32, 2])
+
+        np.testing.assert_allclose(transformed_expected_output_np, M_data_np, rtol=1e-3, atol=1e-3)
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main(sys.argv))

Review Comment:
   Done. Thank you



-- 
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