[GitHub] [tvm] mehrdadh commented on a diff in pull request #12873: [Hexagon] Float and quantized dense operators with schedules

["mehrdadh (via GitHub)" <gi...@apache.org>]

mehrdadh commented on code in PR #12873:
URL: https://github.com/apache/tvm/pull/12873#discussion_r1084641685


##########
tests/python/contrib/test_hexagon/topi/slice_op/test_dense_slice.py:
##########
@@ -0,0 +1,297 @@
+# 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
+
+from tvm import te, topi
+
+import tvm.testing
+from tvm.topi import testing
+from tvm.contrib.hexagon.build import HexagonLauncher
+from tvm.contrib.hexagon.session import Session
+import tvm.topi.hexagon.qnn as qnn
+import tvm.topi.hexagon.slice_ops as sl
+from ...infrastructure import transform_numpy, quantize_np
+from tvm.contrib.hexagon import allocate_hexagon_array
+
+
+@tvm.testing.fixture
+def input_np(input_shape, dtype):
+    if "int" in dtype:
+        data = np.random.random(input_shape).astype("float32")
+    elif "float" in dtype:
+        data = np.random.random(input_shape).astype(dtype)
+    return data
+
+
+@tvm.testing.fixture
+def weight_np(weight_shape, dtype):
+    if "int" in dtype:
+        weight = np.random.random(weight_shape).astype("float32")
+    elif "float" in dtype:
+        weight = np.random.random(weight_shape).astype(dtype)
+    return weight
+
+
+@tvm.testing.fixture
+def input_quant(input_np, dtype):
+    if "float" in dtype:
+        return None
+    quant, scale, zp = quantize_np(input_np, dtype)
+    return {"zero": zp, "scale": scale, "data": quant}
+
+
+@tvm.testing.fixture
+def weight_quant(weight_np, dtype):
+    if "float" in dtype:
+        return None
+    quant, scale, zp = quantize_np(weight_np, "int8")
+    return {"zero": zp, "scale": scale, "data": quant}
+
+
+@tvm.testing.fixture
+def bias_np(bias_shape, bias, dtype):
+    if bias:
+        if "int" in dtype:
+            data = np.random.randint(-128, 127, size=bias_shape).astype("int32")
+        elif "float" in dtype:
+            data = np.random.random(bias_shape).astype(dtype)
+        return data
+    else:
+        return None
+
+
+@tvm.testing.fixture
+def quant_arr(input_quant, weight_quant):
+    if input_quant is None:
+        return None
+    arr = np.empty((6,), dtype="float32")
+    arr[0] = input_quant["zero"]
+    arr[1] = input_quant["scale"]
+    arr[2] = weight_quant["zero"]
+    arr[3] = weight_quant["scale"]
+    return arr
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, layout):
+    return transform_numpy(expected_output_np, "nc", layout)
+
+
+@tvm.testing.fixture
+def transformed_input_np(input_np, layout):
+    return transform_numpy(input_np, "nc", layout)
+
+
+# TODO(joshherr-quic): transforming weight forces us to put it in vtcm. Crashes at runtime in vtcm
+# @tvm.testing.fixture
+# def transformed_weight_np(weight_np, layout):
+#     return transform_numpy(weight_np, "nc", layout)
+
+
+@tvm.testing.fixture
+def transformed_input_quant(input_quant, layout):
+    if input_quant is None:
+        return None
+    input_quant["data"] = transform_numpy(input_quant["data"], "nc", layout)
+    return input_quant
+
+
+# @tvm.testing.fixture
+# def transformed_weight_quant(weight_quant, layout):
+#     weight_quant["data"] = transform_numpy(weight_quant["data"], "nc", layout)
+#     return weight_quant
+
+# Test combinations of the following:

Review Comment:
   These comments are also not needed, testing parameters show the combination



##########
tests/python/contrib/test_hexagon/topi/slice_op/test_dense_slice.py:
##########
@@ -0,0 +1,297 @@
+# 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
+
+from tvm import te, topi
+
+import tvm.testing
+from tvm.topi import testing
+from tvm.contrib.hexagon.build import HexagonLauncher
+from tvm.contrib.hexagon.session import Session
+import tvm.topi.hexagon.qnn as qnn
+import tvm.topi.hexagon.slice_ops as sl
+from ...infrastructure import transform_numpy, quantize_np
+from tvm.contrib.hexagon import allocate_hexagon_array
+
+
+@tvm.testing.fixture
+def input_np(input_shape, dtype):
+    if "int" in dtype:
+        data = np.random.random(input_shape).astype("float32")
+    elif "float" in dtype:
+        data = np.random.random(input_shape).astype(dtype)
+    return data
+
+
+@tvm.testing.fixture
+def weight_np(weight_shape, dtype):
+    if "int" in dtype:
+        weight = np.random.random(weight_shape).astype("float32")
+    elif "float" in dtype:
+        weight = np.random.random(weight_shape).astype(dtype)
+    return weight
+
+
+@tvm.testing.fixture
+def input_quant(input_np, dtype):
+    if "float" in dtype:
+        return None
+    quant, scale, zp = quantize_np(input_np, dtype)
+    return {"zero": zp, "scale": scale, "data": quant}
+
+
+@tvm.testing.fixture
+def weight_quant(weight_np, dtype):
+    if "float" in dtype:
+        return None
+    quant, scale, zp = quantize_np(weight_np, "int8")
+    return {"zero": zp, "scale": scale, "data": quant}
+
+
+@tvm.testing.fixture
+def bias_np(bias_shape, bias, dtype):
+    if bias:
+        if "int" in dtype:
+            data = np.random.randint(-128, 127, size=bias_shape).astype("int32")
+        elif "float" in dtype:
+            data = np.random.random(bias_shape).astype(dtype)
+        return data
+    else:
+        return None
+
+
+@tvm.testing.fixture
+def quant_arr(input_quant, weight_quant):
+    if input_quant is None:
+        return None
+    arr = np.empty((6,), dtype="float32")
+    arr[0] = input_quant["zero"]
+    arr[1] = input_quant["scale"]
+    arr[2] = weight_quant["zero"]
+    arr[3] = weight_quant["scale"]
+    return arr
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, layout):
+    return transform_numpy(expected_output_np, "nc", layout)
+
+
+@tvm.testing.fixture
+def transformed_input_np(input_np, layout):
+    return transform_numpy(input_np, "nc", layout)
+
+
+# TODO(joshherr-quic): transforming weight forces us to put it in vtcm. Crashes at runtime in vtcm
+# @tvm.testing.fixture
+# def transformed_weight_np(weight_np, layout):
+#     return transform_numpy(weight_np, "nc", layout)
+
+
+@tvm.testing.fixture
+def transformed_input_quant(input_quant, layout):
+    if input_quant is None:
+        return None
+    input_quant["data"] = transform_numpy(input_quant["data"], "nc", layout)
+    return input_quant
+
+
+# @tvm.testing.fixture

Review Comment:
   same here



##########
tests/python/contrib/test_hexagon/topi/slice_op/test_dense_slice.py:
##########
@@ -0,0 +1,297 @@
+# 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
+
+from tvm import te, topi
+
+import tvm.testing
+from tvm.topi import testing
+from tvm.contrib.hexagon.build import HexagonLauncher
+from tvm.contrib.hexagon.session import Session
+import tvm.topi.hexagon.qnn as qnn
+import tvm.topi.hexagon.slice_ops as sl
+from ...infrastructure import transform_numpy, quantize_np
+from tvm.contrib.hexagon import allocate_hexagon_array
+
+
+@tvm.testing.fixture
+def input_np(input_shape, dtype):
+    if "int" in dtype:
+        data = np.random.random(input_shape).astype("float32")
+    elif "float" in dtype:
+        data = np.random.random(input_shape).astype(dtype)
+    return data
+
+
+@tvm.testing.fixture
+def weight_np(weight_shape, dtype):
+    if "int" in dtype:
+        weight = np.random.random(weight_shape).astype("float32")
+    elif "float" in dtype:
+        weight = np.random.random(weight_shape).astype(dtype)
+    return weight
+
+
+@tvm.testing.fixture
+def input_quant(input_np, dtype):
+    if "float" in dtype:
+        return None
+    quant, scale, zp = quantize_np(input_np, dtype)
+    return {"zero": zp, "scale": scale, "data": quant}
+
+
+@tvm.testing.fixture
+def weight_quant(weight_np, dtype):
+    if "float" in dtype:
+        return None
+    quant, scale, zp = quantize_np(weight_np, "int8")
+    return {"zero": zp, "scale": scale, "data": quant}
+
+
+@tvm.testing.fixture
+def bias_np(bias_shape, bias, dtype):
+    if bias:
+        if "int" in dtype:
+            data = np.random.randint(-128, 127, size=bias_shape).astype("int32")
+        elif "float" in dtype:
+            data = np.random.random(bias_shape).astype(dtype)
+        return data
+    else:
+        return None
+
+
+@tvm.testing.fixture
+def quant_arr(input_quant, weight_quant):
+    if input_quant is None:
+        return None
+    arr = np.empty((6,), dtype="float32")
+    arr[0] = input_quant["zero"]
+    arr[1] = input_quant["scale"]
+    arr[2] = weight_quant["zero"]
+    arr[3] = weight_quant["scale"]
+    return arr
+
+
+@tvm.testing.fixture
+def transformed_expected_output_np(expected_output_np, layout):
+    return transform_numpy(expected_output_np, "nc", layout)
+
+
+@tvm.testing.fixture
+def transformed_input_np(input_np, layout):
+    return transform_numpy(input_np, "nc", layout)
+
+
+# TODO(joshherr-quic): transforming weight forces us to put it in vtcm. Crashes at runtime in vtcm

Review Comment:
   please remove these comments.



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