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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/05/19 23:09:10 UTC
[GitHub] [tvm] yongwww commented on a change in pull request #8074: [Frontend] [Tensorflow2] Added test infrastructure for TF2 frozen models
yongwww commented on a change in pull request #8074:
URL: https://github.com/apache/tvm/pull/8074#discussion_r635633295
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File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
Review comment:
there is another vmobj_to_list in `tests/python/frontend/tensorflow/test_forward.py`, it would be good to merge this same-name function together, looks they are quite similar.
##########
File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
+ if isinstance(o, tvm.nd.NDArray):
+ out = o.asnumpy().tolist()
+ elif isinstance(o, tvm.runtime.container.ADT):
+ result = []
+ for f in o:
+ result.append(vmobj_to_list(f))
+ out = result
+ else:
+ raise RuntimeError("Unknown object type: %s" % type(o))
+ return out
+
+
+def run_tf_code(func, input_):
+ if type(func) is Function:
+ out = func(input_)
+ if isinstance(out, list):
+ a = [x.numpy() for x in out]
+ else:
+ a = out.numpy()
+ else:
+ a = func(tf.constant(input_))
+ if type(a) is dict:
+ a = [x.numpy() for x in a.values()]
+ if len(a) == 1:
+ a = a[0]
+ elif type(a) is list:
+ a = [x.numpy() for x in a]
+ if len(a) == 1:
+ a = a[0]
+ else:
+ a = a.numpy()
+ return a
+
+
+def compile_graph_runtime(
+ mod, params, target="llvm", target_host="llvm", opt_level=3, output_sig=None
+):
+ with tvm.transform.PassContext(opt_level):
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
+ return lib
+
+
+def compile_vm(
+ mod, params, target="llvm", target_host="llvm", opt_level=3, disabled_pass=None, output_sig=None
+):
+ with tvm.transform.PassContext(opt_level, disabled_pass=disabled_pass):
+ mod = relay.transform.InferType()(mod)
+ vm_exec = relay.vm.compile(mod, target, target_host, params=params)
+ return vm_exec
+
+
+def run_vm(vm_exec, input_, ctx=tvm.cpu(0)):
+ vm = VirtualMachine(vm_exec, ctx)
+ _out = vm.invoke("main", input_)
+ return vmobj_to_list(_out)
+
+
+def run_graph(lib, input_, ctx=tvm.cpu(0)):
+ mod = runtime.GraphModule(lib["default"](ctx))
+ mod.set_input(0, input_)
+ mod.run()
+ _out = mod.get_output(0).asnumpy()
+ return _out
+
+
+def compare_tf_tvm(gdef, input_, output_, vm=True, output_sig=None):
Review comment:
how about changing `vm` to "runtime_mode" or `runtime`, since we have vm, graphruntime, interpreter
##########
File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
+ if isinstance(o, tvm.nd.NDArray):
+ out = o.asnumpy().tolist()
+ elif isinstance(o, tvm.runtime.container.ADT):
+ result = []
+ for f in o:
+ result.append(vmobj_to_list(f))
+ out = result
+ else:
+ raise RuntimeError("Unknown object type: %s" % type(o))
+ return out
+
+
+def run_tf_code(func, input_):
+ if type(func) is Function:
+ out = func(input_)
+ if isinstance(out, list):
+ a = [x.numpy() for x in out]
+ else:
+ a = out.numpy()
+ else:
+ a = func(tf.constant(input_))
+ if type(a) is dict:
+ a = [x.numpy() for x in a.values()]
+ if len(a) == 1:
+ a = a[0]
+ elif type(a) is list:
+ a = [x.numpy() for x in a]
+ if len(a) == 1:
+ a = a[0]
+ else:
+ a = a.numpy()
+ return a
+
+
+def compile_graph_runtime(
+ mod, params, target="llvm", target_host="llvm", opt_level=3, output_sig=None
+):
+ with tvm.transform.PassContext(opt_level):
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
+ return lib
+
+
+def compile_vm(
+ mod, params, target="llvm", target_host="llvm", opt_level=3, disabled_pass=None, output_sig=None
+):
+ with tvm.transform.PassContext(opt_level, disabled_pass=disabled_pass):
+ mod = relay.transform.InferType()(mod)
+ vm_exec = relay.vm.compile(mod, target, target_host, params=params)
+ return vm_exec
+
+
+def run_vm(vm_exec, input_, ctx=tvm.cpu(0)):
+ vm = VirtualMachine(vm_exec, ctx)
+ _out = vm.invoke("main", input_)
+ return vmobj_to_list(_out)
+
+
+def run_graph(lib, input_, ctx=tvm.cpu(0)):
+ mod = runtime.GraphModule(lib["default"](ctx))
+ mod.set_input(0, input_)
+ mod.run()
+ _out = mod.get_output(0).asnumpy()
+ return _out
+
+
+def compare_tf_tvm(gdef, input_, output_, vm=True, output_sig=None):
+ """compare tf and tvm execution for the same input.
+
+ Parameters
+ ----------
+ func: tf function. can be from saved model or not. different ways to pass input
Review comment:
don't see func in the arg list of the function `compare_tf_tvm`, please update them to keep docstring and definition identical
##########
File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
+ if isinstance(o, tvm.nd.NDArray):
+ out = o.asnumpy().tolist()
+ elif isinstance(o, tvm.runtime.container.ADT):
+ result = []
+ for f in o:
+ result.append(vmobj_to_list(f))
+ out = result
+ else:
+ raise RuntimeError("Unknown object type: %s" % type(o))
+ return out
+
+
+def run_tf_code(func, input_):
+ if type(func) is Function:
+ out = func(input_)
+ if isinstance(out, list):
+ a = [x.numpy() for x in out]
+ else:
+ a = out.numpy()
+ else:
+ a = func(tf.constant(input_))
+ if type(a) is dict:
+ a = [x.numpy() for x in a.values()]
+ if len(a) == 1:
+ a = a[0]
+ elif type(a) is list:
+ a = [x.numpy() for x in a]
+ if len(a) == 1:
+ a = a[0]
+ else:
+ a = a.numpy()
+ return a
+
+
+def compile_graph_runtime(
+ mod, params, target="llvm", target_host="llvm", opt_level=3, output_sig=None
+):
+ with tvm.transform.PassContext(opt_level):
+ lib = relay.build(mod, target=target, target_host=target_host, params=params)
+ return lib
+
+
+def compile_vm(
+ mod, params, target="llvm", target_host="llvm", opt_level=3, disabled_pass=None, output_sig=None
+):
+ with tvm.transform.PassContext(opt_level, disabled_pass=disabled_pass):
+ mod = relay.transform.InferType()(mod)
+ vm_exec = relay.vm.compile(mod, target, target_host, params=params)
+ return vm_exec
+
+
+def run_vm(vm_exec, input_, ctx=tvm.cpu(0)):
+ vm = VirtualMachine(vm_exec, ctx)
+ _out = vm.invoke("main", input_)
+ return vmobj_to_list(_out)
+
+
+def run_graph(lib, input_, ctx=tvm.cpu(0)):
Review comment:
personally, I like name`run_graph_runtime` more
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