[GitHub] [tvm] Leo-arm commented on a change in pull request #10241: [ETHOSN] Implement tanh operator

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

Leo-arm commented on a change in pull request #10241:
URL: https://github.com/apache/tvm/pull/10241#discussion_r807839744



##########
File path: src/relay/backend/contrib/ethosn/codegen.cc
##########
@@ -767,6 +786,17 @@ TVM_REGISTER_GLOBAL("relay.ethos-n.support.mean")
       err += EthosnError(reason);
     });
 
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.tanh")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      TanhParams params;
+
+      auto err = EthosnAPI::Tanh(call, &params);
+      err += EthosnCompiler::SupportedSetup();
+      *rv = !err && EthosnCompiler::GetSupported()->IsTanhSupported(params.input_info);
+      std::cout << "hello" << std::endl;

Review comment:
       Extraneous code left in.

##########
File path: tests/python/contrib/test_ethosn/test_tanh.py
##########
@@ -0,0 +1,81 @@
+# 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.
+
+"""Arm(R) Ethos(TM)-N NPU integration tanh tests"""
+
+import pytest
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.testing import requires_ethosn
+from . import infrastructure as tei
+
+
+def _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype):
+    a = relay.var("a", shape=shape, dtype=dtype)
+    dequantize = relay.qnn.op.dequantize(
+        a,
+        input_scale=relay.const(input_sc, "float32"),
+        input_zero_point=relay.const(input_zp, "int32"),
+    )
+    tanh = relay.tanh(dequantize)
+    model = relay.qnn.op.quantize(
+        tanh,
+        output_scale=relay.const(output_sc, "float32"),
+        output_zero_point=relay.const(output_zp, "int32"),
+        out_dtype=dtype,
+    )
+    return model
+
+
+@requires_ethosn
+def test_tanh():
+    trials = [(1, 512, 512, 3)]
+
+    np.random.seed(0)
+    for shape in trials:
+        inputs = {
+            "a": tvm.nd.array(np.random.randint(0, high=255, size=shape, dtype="uint8")),
+        }
+        outputs = []
+        for npu in [False, True]:
+            model = _get_model(shape, 120, 0.0250629, 128, 0.0078125, "uint8")
+            mod = tei.make_module(model, [])
+            outputs.append(tei.build_and_run(mod, inputs, 1, {}, npu=npu))
+
+        tei.verify(outputs, "uint8", 1)
+
+
+@requires_ethosn
+def test_tanh_failure():
+    trials = [
+        (
+            (1, 16, 16, 16),
+            120,
+            0.0250629,
+            64,
+            0.0078125,
+            "uint8",
+            "output quantization params=(64, 0.0078125), must = (0, 1/256);",
+        ),
+    ]
+
+    for shape, input_zp, input_sc, output_zp, output_sc, dtype, err_msg in trials:
+        model = _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype)
+        model = tei.make_ethosn_composite(model, "ethos-n.qnn_sigmoid")

Review comment:
       Should be tanh




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