[GitHub] [tvm] NicolaLancellotti commented on a change in pull request #9384: Arm(R) Ethos(TM)-U NPU Pooling operators support

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

NicolaLancellotti commented on a change in pull request #9384:
URL: https://github.com/apache/tvm/pull/9384#discussion_r740104215



##########
File path: tests/python/contrib/test_ethosu/test_legalize.py
##########
@@ -458,7 +458,102 @@ def verify(ext_func):
     mod = partition_ethosu_by_table(mod, depthwise_pattern_table)
 
     mod["tvmgen_default_ethosu_main_0"] = dataflow_pattern.rewrite(
-        legalize.EthosuDepthwiseConv2DRewriter(), mod["tvmgen_default_ethosu_main_0"]
+        legalize.DepthwiseConv2DRewriter(), mod["tvmgen_default_ethosu_main_0"]
+    )
+    verify(mod["tvmgen_default_ethosu_main_0"])
+
+
+@pytest.mark.parametrize("pooling_type", ["MAX", "AVG"])
+@pytest.mark.parametrize("ifm_shape", [[1, 9, 12, 3], [1, 10, 20, 2]])
+@pytest.mark.parametrize("strides", [[1, 2], [2, 3]])
+@pytest.mark.parametrize("pool_shape", [[1, 2], [2, 3]])
+@pytest.mark.parametrize("activation_function", ["NONE", "RELU"])
+@pytest.mark.parametrize("padding", ["SAME", "VALID"])
+def test_tflite_pool2d_legalize(
+    ifm_shape, pooling_type, strides, pool_shape, activation_function, padding
+):
+    dtype = "int8"
+
+    def create_tflite_graph():
+        class Model(tf.Module):
+            @tf.function
+            def tf_function(self, x):
+                if pooling_type == "MAX":
+                    op = tf.nn.max_pool(x, pool_shape, strides, padding)
+                elif pooling_type == "AVG":
+                    op = tf.nn.avg_pool(x, pool_shape, strides, padding)
+                if activation_function == "RELU":
+                    op = tf.nn.relu(op)
+                return op
+
+        model = Model()
+        concrete_func = model.tf_function.get_concrete_function(
+            tf.TensorSpec(ifm_shape, dtype=tf.float32)
+        )
+
+        # Convert the model
+        def representative_dataset():
+            for _ in range(100):
+                data = np.random.rand(*tuple(ifm_shape))
+                yield [data.astype(np.float32)]
+
+        converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+        converter.optimizations = [tf.lite.Optimize.DEFAULT]
+        converter.representative_dataset = representative_dataset
+        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+        converter.inference_input_type = tf.int8
+        converter.inference_output_type = tf.int8
+        tflite_model = converter.convert()
+        return tflite_model
+
+    def verify(ext_func):
+        ofm_shape = infra.compute_ofm_shape(ifm_shape, padding, pool_shape, strides)
+        op = ext_func.body
+        assert list(op.args[0].checked_type.shape) == ifm_shape
+        assert op.args[0].checked_type.dtype == dtype
+        assert list(op.checked_type.shape) == ofm_shape
+        assert op.checked_type.dtype == dtype
+        assert op.attrs.pooling_type == pooling_type
+        assert list(op.attrs.strides) == strides
+        assert list(op.attrs.padding) == infra.compute_padding_shape(
+            ifm_shape, ofm_shape, padding, pool_shape, strides
+        )
+        assert list(op.attrs.pool_shape) == pool_shape
+        assert op.attrs.ofm_channels == ifm_shape[3]
+        if activation_function == "RELU":
+            assert str(op.attrs.activation) == "CLIP"
+
+    if pooling_type == "MAX":
+        rewriter = legalize.MaxPoolingRewriter()
+        pattern_table = [
+            (
+                ethosu.MaxPool2DParams.composite_name,
+                ethosu.qnn_maxpool2d_pattern(),
+                lambda pat: ethosu.MaxPool2DParams(pat).is_valid(),
+            ),
+        ]
+    elif pooling_type == "AVG":
+        rewriter = legalize.AvgPoolingRewriter()
+        pattern_table = [
+            (
+                ethosu.AvgPool2DParams.composite_name,
+                ethosu.qnn_avgpool2d_pattern(),
+                lambda pat: ethosu.AvgPool2DParams(pat).is_valid(),
+            ),
+        ]

Review comment:
       If it is not a problem, I'll do it in a following pr.

##########
File path: tests/python/contrib/test_ethosu/test_replace_pooling.py
##########
@@ -0,0 +1,158 @@
+# 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
+
+pytest.importorskip("ethosu.vela")
+
+import tvm
+from tvm import relay
+from tvm.relay.testing import run_opt_pass
+from tvm.relay.backend.contrib.ethosu.tir import spec
+from tvm.relay.backend.contrib.ethosu.tir.compiler import lower_to_tir
+from .infra import make_ethosu_pooling, get_pooling_args
+
+
+@pytest.mark.parametrize(
+    "ifm_shape, ofm_channels, ifm_layout, ofm_layout",
+    [
+        ((1, 5, 9, 3), 3, "NHWC", "NHWC"),
+        ((1, 8, 3, 9, 16), 40, "NHCWB16", "NHCWB16"),
+        ((1, 8, 3, 9, 16), 40, "NHCWB16", "NHWC"),
+        ((1, 8, 9, 40), 40, "NHWC", "NHCWB16"),
+    ],
+)
+@pytest.mark.parametrize("pooling_type", ["AVG", "MAX"])
+@pytest.mark.parametrize("activation", ["NONE", "CLIP", "TANH", "SIGMOID"])
+def test_pooling_single(
+    ifm_shape,
+    ofm_channels,
+    ifm_layout,
+    ofm_layout,
+    pooling_type,
+    activation,
+):
+    pool_shape = (3, 2)
+    strides = (1, 2)
+    padding = (1, 1, 1, 0)
+    ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
+    pooling = make_ethosu_pooling(
+        ifm,
+        pooling_type,
+        pool_shape,
+        ofm_channels,
+        strides,
+        padding,
+        activation,
+        ifm_layout,
+        ofm_layout,
+    )
+    func = relay.Function(relay.analysis.free_vars(pooling), pooling)
+    func = run_opt_pass(func, relay.transform.InferType())
+    mod, _ = lower_to_tir(func)
+    data = []
+
+    def _visit(stmt):
+        if isinstance(stmt, tvm.tir.Call):
+            data.append(get_pooling_args(stmt))
+
+    tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
+    if ifm_layout == "NHWC":
+        ifm_stride_c = 1
+        ifm_stride_w = ifm_shape[3]
+        ifm_stride_h = ifm_shape[2] * ifm_shape[3]
+        ofm_height = (ifm_shape[1] - pool_shape[0] + padding[0] + padding[0]) // strides[0] + 1
+        ofm_width = (ifm_shape[2] - pool_shape[1] + padding[1] + padding[1]) // strides[1] + 1
+    else:
+        ifm_stride_w = 16
+        ifm_stride_c = 16 * ifm_shape[3]
+        ifm_stride_h = 16 * ifm_shape[2] * ifm_shape[3]
+        ofm_height = (ifm_shape[1] - pool_shape[0] + padding[0] + padding[0]) // strides[0] + 1
+        ofm_width = (ifm_shape[3] - pool_shape[1] + padding[1] + padding[1]) // strides[1] + 1
+
+    if ofm_layout == "NHWC":
+        ofm_stride_c = 1
+        ofm_stride_w = ofm_channels if ofm_width > 1 else 1
+        ofm_stride_h = ofm_channels * ofm_width if ofm_height > 1 else 1
+    else:
+        ofm_stride_w = 16
+        ofm_stride_c = 16 * ofm_width
+        ofm_stride_h = 16 * ofm_width * ((ofm_channels - 1) // 16 + 1)

Review comment:
       A following pr will refactor this and the other operators, which are currently using the same structure.




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