[Relay][OP] Register topi schedule for Relay fast_exp and fast_tanh (a…

…pache#5131)

* register for fast_exp and fast_tanh

* Add unit test for fast math

* Add unit test for op fast math

* Add unit test for op fast math

* Add unit tests to guard registering topi schedule for Relay fast_exp and fast_tanh

* Fix ident

* Fix the indent

* Add fast_tanh in the test_fastmath of topi tests

python/tvm/relay/op/_tensor.py

@@ -74,6 +74,8 @@
 register_injective_schedule("left_shift")
 register_injective_schedule("shape_of")
 register_injective_schedule("ndarray_size")
+register_broadcast_schedule("fast_exp")
+register_broadcast_schedule("fast_tanh")
 
 
 # zeros
@@ -218,3 +220,5 @@ def elemwise_shape_func(attrs, inputs, _):
 register_shape_func("negative", False, elemwise_shape_func)
 register_shape_func("exp", False, elemwise_shape_func)
 register_shape_func("tan", False, elemwise_shape_func)
+register_shape_func("fast_exp", False, elemwise_shape_func)
+register_shape_func("fast_tanh", False, elemwise_shape_func)

tests/python/relay/test_op_fast_math.py

@@ -0,0 +1,59 @@
+# 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 tvm
+import tvm.relay as relay
+import topi
+from tvm import te
+from tvm.contrib import graph_runtime
+
+
+def test_fastmath():
+    def test_apply(relay_op, name, f_numpy, low, high, step, dtype="float32"):
+        a_np = np.arange(low, high, step).astype(dtype)
+        b_np = f_numpy(a_np)
+
+        x = relay.var("x", shape=a_np.shape, dtype="float32")
+        y = relay_op(x)
+        func = relay.Function([x], y)
+        mod = tvm.IRModule.from_expr(func)
+
+        with relay.build_config(opt_level=3, required_pass=['FastMath']):
+            graph, lib, params = relay.build(mod, target="llvm", params=None)
+
+        # Check that the op related to fast math have been convered to function in lib
+        func_name = "fused_" + name
+        assert lib.get_function(func_name)
+
+        ctx = tvm.cpu(0)
+        m = graph_runtime.create(graph, lib, ctx)
+        # Set inputs
+        m.set_input('x', tvm.nd.array(a_np, ctx))
+        m.set_input(**params)
+        # Execute
+        m.run()
+        # Get outputs
+        tvm_output = m.get_output(0)
+        tvm.testing.assert_allclose(tvm_output.asnumpy(), b_np,
+                                    rtol=1e-5, atol=1e-5)
+
+    test_apply(relay.exp, "fast_exp", np.exp, low=-88, high=88, step=0.01)
+    test_apply(relay.tanh, "fast_tanh", np.tanh, low=-10, high=10, step=0.01)
+
+
+if __name__ == "__main__":
+    test_fastmath()

topi/tests/python/test_topi_math.py

@@ -240,6 +240,9 @@ def check_device(device):
     test_apply(topi.fast_exp, "fast_exp", np.exp,
                low=-88, high=88,
                step = 0.01)
+    test_apply(topi.fast_tanh, "fast_tanh", np.tanh,
+               low=-10, high=10,
+               step = 0.01)
 
 if __name__ == "__main__":
     test_util()