[CODEGEN][CONTRIB] CoreML codegen (apache#5634)

* [CODEGEN][CONTRIB] CoreML codegen

* import coremltools only when it is necessary

* fix pylint errors

* don't import contrib.coreml when using runtime lib

* skip coreml codegen test in CI

* don't register relay.ext.coremlcompiler in __init__.py

* move tvm/contrib/coreml.py to tvm/contrib/target/coreml.py

* use existing transformers for graph partitioning

* skip test only when coremltools is not available

* add check for annotation

* move _register_coreml_op to python/tvm/relay/op/contrib/coreml.py

* skip compile when xcode is unavailable

* relay.op.Op -> tvm.ir.Op

* set USE_COREML on

* refine test

apps/ios_rpc/tests/ios_rpc_mobilenet.py

@@ -0,0 +1,171 @@
+# 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 tvm
+from tvm import rpc, relay
+from tvm.contrib.download import download_testdata
+from tvm.relay.expr_functor import ExprMutator
+from tvm.relay import transform
+from tvm.relay.op.annotation import compiler_begin, compiler_end
+from tvm.relay.quantize.quantize import prerequisite_optimize
+from tvm.contrib import util, xcode, graph_runtime, coreml_runtime
+from tvm.contrib.target import coreml as _coreml
+
+import os
+import re
+import sys
+import numpy as np
+from mxnet import gluon
+from PIL import Image
+import coremltools
+
+# Set to be address of tvm proxy.
+proxy_host = os.environ["TVM_IOS_RPC_PROXY_HOST"]
+# Set your desination via env variable.
+# Should in format "platform=iOS,id=<the test device uuid>"
+destination = os.environ["TVM_IOS_RPC_DESTINATION"]
+
+if not re.match(r"^platform=.*,id=.*$", destination):
+    print("Bad format: {}".format(destination))
+    print("Example of expected string: platform=iOS,id=1234567890abcabcabcabc1234567890abcabcab")
+    sys.exit(1)
+
+proxy_port = 9090
+key = "iphone"
+
+# Change target configuration, this is setting for iphone6s
+#arch = "x86_64"
+#sdk = "iphonesimulator"
+arch = "arm64"
+sdk = "iphoneos"
+target_host = "llvm -target=%s-apple-darwin" % arch
+
+# override metal compiler to compile to iphone
+@tvm.register_func("tvm_callback_metal_compile")
+def compile_metal(src):
+    return xcode.compile_metal(src, sdk=sdk)
+
+def prepare_input():
+    img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
+    img_name = 'cat.png'
+    synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
+                          '4d0b62f3d01426887599d4f7ede23ee5/raw/',
+                          '596b27d23537e5a1b5751d2b0481ef172f58b539/',
+                          'imagenet1000_clsid_to_human.txt'])
+    synset_name = 'imagenet1000_clsid_to_human.txt'
+    img_path = download_testdata(img_url, 'cat.png', module='data')
+    synset_path = download_testdata(synset_url, synset_name, module='data')
+    with open(synset_path) as f:
+        synset = eval(f.read())
+        image = Image.open(img_path).resize((224, 224))
+
+    image = np.array(image) - np.array([123., 117., 104.])
+    image /= np.array([58.395, 57.12, 57.375])
+    image = image.transpose((2, 0, 1))
+    image = image[np.newaxis, :]
+    return image.astype('float32'), synset
+
+
+def get_model(model_name, data_shape):
+    gluon_model = gluon.model_zoo.vision.get_model(model_name, pretrained=True)
+    mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
+    # we want a probability so add a softmax operator
+    func = mod["main"]
+    func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs)
+
+    return func, params
+
+
+def test_mobilenet():
+    temp = util.tempdir()
+    image, synset = prepare_input()
+    model, params = get_model('mobilenetv2_1.0', image.shape)
+
+    def run(mod, target):
+        with relay.build_config(opt_level=3):
+            graph, lib, _params = relay.build(mod, target=target,
+                                             target_host=target_host, params=params)
+        path_dso = temp.relpath("deploy.dylib")
+        lib.export_library(path_dso, xcode.create_dylib, arch=arch, sdk=sdk)
+        xcode.codesign(path_dso)
+
+        # Start RPC test server that contains the compiled library.
+        xcode.popen_test_rpc(proxy_host, proxy_port, key,
+                             destination=destination, libs=[path_dso])
+
+        # connect to the proxy
+        remote = rpc.connect(proxy_host, proxy_port, key=key)
+
+        if target == "metal":
+            ctx = remote.metal(0)
+        else:
+            ctx = remote.cpu(0)
+        lib = remote.load_module("deploy.dylib")
+        m = graph_runtime.create(graph, lib, ctx)
+
+        m.set_input('data', tvm.nd.array(image, ctx))
+        m.set_input(**_params)
+        m.run()
+        tvm_output = m.get_output(0)
+        top1 = np.argmax(tvm_output.asnumpy()[0])
+        print('TVM prediction top-1:', top1, synset[top1])
+
+        # evaluate
+        ftimer = m.module.time_evaluator("run", ctx, number=3, repeat=10)
+        prof_res = np.array(ftimer().results) * 1000
+        print("%-19s (%s)" % ("%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res)))
+
+    def annotate(func, compiler):
+        """
+        An annotator for Core ML.
+        """
+        # Bind free variables to the constant values.
+        bind_dict = {}
+        for arg in func.params:
+            name = arg.name_hint
+            if name in params:
+                bind_dict[arg] = relay.const(params[name])
+
+        func = relay.bind(func, bind_dict)
+
+        # Annotate the entire graph for Core ML
+        mod = tvm.IRModule()
+        mod["main"] = func
+
+        seq = tvm.transform.Sequential([
+            transform.SimplifyInference(),
+            transform.FoldConstant(),
+            transform.FoldScaleAxis(),
+            transform.AnnotateTarget(compiler),
+            transform.MergeCompilerRegions(),
+            transform.PartitionGraph()
+        ])
+
+        with relay.build_config(opt_level=3):
+            mod = seq(mod)
+
+        return mod
+
+    # CPU
+    run(model, target_host)
+    # Metal
+    run(model, "metal")
+    # CoreML
+    run(annotate(model, "coremlcompiler"), target_host)
+
+if __name__ == "__main__":
+    test_mobilenet()

python/tvm/contrib/coreml_runtime.py

@@ -18,14 +18,12 @@
 import tvm._ffi
 from ..rpc import base as rpc_base
 
-def create(compiled_model_path, output_names, ctx):
+def create(model_dir, ctx):
     """Create a runtime executor module given a coreml model and context.
     Parameters
     ----------
-    compiled_model_path : str
-        The path of the compiled model to be deployed.
-    output_names : list of str
-        The output names of the model.
+    model_dir : str
+        The directory where the compiled models are located.
     ctx : TVMContext
         The context to deploy the module. It can be local or remote when there
         is only one TVMContext.
@@ -42,7 +40,7 @@ def create(compiled_model_path, output_names, ctx):
     else:
         fcreate = tvm._ffi.get_global_func(runtime_func)
 
-    return CoreMLModule(fcreate(compiled_model_path, ctx, *output_names))
+    return CoreMLModule(fcreate(model_dir))
 
 
 class CoreMLModule(object):

python/tvm/contrib/target/__init__.py

@@ -0,0 +1,18 @@
+# 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.
+"""Codegen and runtime APIs for targets.
+"""