kognat-docs opened a new issue #5244: [Metal] Backtrace graph_runtime Tensorflow example without tvm.gpu(0) or tvm.target.cuda() calls CUDA URL: https://github.com/apache/incubator-tvm/issues/5244 Running the following command `python ~/dev/tvm_test/from_tensorflow_metal.py` Results in a Backtrace ``` Traceback (most recent call last): File "/Users/sam/dev/tvm_test/metal_tf_demo.py", line 75, in <module> params=params) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/relay/build_module.py", line 251, in build graph_json, mod, params = bld_mod.build(mod, target, target_host, params) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/relay/build_module.py", line 120, in build self._build(mod, target, target_host) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 213, in __call__ raise get_last_ffi_error() tvm._ffi.base.TVMError: Traceback (most recent call last): [bt] (8) 9 libtvm.dylib 0x00000001159c6e5e tvm::relay::MixedModeMutator::VisitExpr_(tvm::relay::CallNode const*) + 14 [bt] (7) 8 libtvm.dylib 0x00000001159c8cf9 tvm::RelayExpr tvm::relay::MixedModeMutator::Rewrite<tvm::relay::CallNode>(tvm::relay::CallNode const*) + 57 [bt] (6) 7 libtvm.dylib 0x00000001159c7c82 tvm::relay::ForwardRewriter::Rewrite_(tvm::relay::CallNode const*, tvm::RelayExpr const&) + 2082 [bt] (5) 6 libtvm.dylib 0x00000001159c91fe tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void>&, tvm::runtime::ObjectRef>(tvm::relay::Call const&&&, tvm::Array<tvm::RelayExpr, void>&&&, tvm::runtime::ObjectRef&&) const + 254 [bt] (4) 5 libtvm.dylib 0x000000011596c9fd std::__1::__function::__func<void tvm::runtime::TypedPackedFunc<tvm::RelayExpr (tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&)>::AssignTypedLambda<tvm::RelayExpr (*)(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&)>(tvm::RelayExpr (*)(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&))::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*), std::__1::allocator<void tvm::runtime::TypedPackedFunc<tvm::RelayExpr (tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&)>::AssignTypedLambda<tvm::RelayExpr (*)(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&)>(tvm::RelayExpr (*)(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&))::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)>, void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&) + 109 [bt] (3) 4 libtvm.dylib 0x000000011596ca92 void tvm::runtime::detail::unpack_call_dispatcher<tvm::RelayExpr, 0, 3, tvm::RelayExpr (*)(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&)>::run<tvm::runtime::TVMArgValue, tvm::runtime::TVMArgValue, tvm::runtime::TVMArgValue>(tvm::RelayExpr (* const&)(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&), tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*, tvm::runtime::TVMArgValue&&, tvm::runtime::TVMArgValue&&, tvm::runtime::TVMArgValue&&) + 82 [bt] (2) 3 libtvm.dylib 0x0000000115963b14 tvm::RelayExpr tvm::relay::LayoutRewriter<tvm::relay::alter_op_layout::AlterTransformMemorizer>(tvm::relay::Call const&, tvm::Array<tvm::RelayExpr, void> const&, tvm::runtime::ObjectRef const&) + 3364 [bt] (1) 2 libtvm.dylib 0x00000001159675a4 tvm::relay::alter_op_layout::AlterTransformMemorizer::CallWithNewLayouts(tvm::relay::Call const&, std::__1::vector<tvm::RelayExpr, std::__1::allocator<tvm::RelayExpr> > const&) + 1284 [bt] (0) 1 libtvm.dylib 0x0000000115bfe795 std::__1::__function::__func<TVMFuncCreateFromCFunc::$_2, std::__1::allocator<TVMFuncCreateFromCFunc::$_2>, void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&) + 213 File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun rv = local_pyfunc(*pyargs) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/relay/op/nn/_nn.py", line 97, in alter_op_layout_conv2d return topi.nn.conv2d_alter_layout(attrs, inputs, tinfos, out_type) File "<decorator-gen-35>", line 2, in conv2d_alter_layout File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/target/generic_func.py", line 267, in dispatch_func return dispatch_dict[k](*args, **kwargs) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/topi-0.7.dev1-py3.7.egg/topi/cuda/conv2d_alter_op.py", line 39, in _alter_conv2d_layout relay.op.get("nn.conv2d"), attrs, tinfos, out_type, target) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/relay/backend/compile_engine.py", line 183, in select_implementation all_impls = get_valid_implementations(op, attrs, inputs, out_type, target) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/relay/backend/compile_engine.py", line 124, in get_valid_implementations strategy = fstrategy(attrs, inputs, out_type, target) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/target/generic_func.py", line 45, in __call__ return _ffi_api.GenericFuncCallFunc(self, *args) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 213, in __call__ raise get_last_ffi_error() [bt] (5) 6 ??? 0x00007ffee6c70d30 0x0 + 140732770225456 [bt] (4) 5 _ctypes.cpython-37m-darwin.so 0x0000000109a5b36f ffi_call_unix64 + 79 [bt] (3) 4 libtvm.dylib 0x0000000115bfcbd6 TVMFuncCall + 70 [bt] (2) 3 libtvm.dylib 0x00000001155e2955 std::__1::__function::__func<tvm::$_5, std::__1::allocator<tvm::$_5>, void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&) + 181 [bt] (1) 2 libtvm.dylib 0x00000001155e0687 tvm::GenericFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const + 743 [bt] (0) 1 libtvm.dylib 0x0000000115bfe795 std::__1::__function::__func<TVMFuncCreateFromCFunc::$_2, std::__1::allocator<TVMFuncCreateFromCFunc::$_2>, void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&) + 213 File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 78, in cfun rv = local_pyfunc(*pyargs) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/relay/op/strategy/cuda.py", line 125, in conv2d_strategy_cuda if nvcc.have_tensorcore(tvm.gpu(0).compute_version): File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/runtime_ctypes.py", line 218, in compute_version self.device_type, self.device_id, 4) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/runtime_ctypes.py", line 180, in _GetDeviceAttr device_type, device_id, attr_id) File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/tvm-0.7.dev1-py3.7-macosx-10.13-x86_64.egg/tvm/_ffi/_ctypes/packed_func.py", line 213, in __call__ raise get_last_ffi_error() [bt] (6) 7 ??? 0x00007ffee6c6f640 0x0 + 140732770219584 [bt] (5) 6 _ctypes.cpython-37m-darwin.so 0x0000000109a5b36f ffi_call_unix64 + 79 [bt] (4) 5 libtvm.dylib 0x0000000115bfcbd6 TVMFuncCall + 70 [bt] (3) 4 libtvm.dylib 0x0000000115bfecb0 std::__1::__function::__func<$_4, std::__1::allocator<$_4>, void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&) + 400 [bt] (2) 3 libtvm.dylib 0x0000000115bfde14 tvm::runtime::DeviceAPIManager::GetAPI(int, bool) + 532 [bt] (1) 2 libtvm.dylib 0x0000000115bfe0a5 tvm::runtime::DeviceAPIManager::GetAPI(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, bool) + 421 [bt] (0) 1 libtvm.dylib 0x0000000115197829 dmlc::LogMessageFatal::~LogMessageFatal() + 57 File "/Users/sam/dev/github/tvm/src/runtime/c_runtime_api.cc", line 133 TVMError: Check failed: allow_missing: Device API gpu is not enabled. ``` What is concerning is that CUDA code is being called when `tvm.gpu(0)` and `tvm.target.cuda()` are not mentioned ``` File "/Users/sam/dev/github/tvm/build-runtime/python-tvm/venv/lib/python3.7/site-packages/topi-0.7.dev1-py3.7.egg/topi/cuda/conv2d_alter_op.py", line 39, in _alter_conv2d_layout relay.op.get("nn.conv2d"), attrs, tinfos, out_type, target) ``` see ``` (venv) kaosnew:build sam$ grep -rn cuda ~/dev/tvm_test/metal_tf_demo.py (venv) kaosnew:build sam$ grep -rn gpu ~/dev/tvm_test/metal_tf_demo.py ``` These results are not found The contents of the script is as below ``` # tvm, relay import tvm from tvm import te from tvm import relay # os and numpy import numpy as np import os.path # Tensorflow imports import tensorflow.compat.v1 as tf #import tensorflow as tf tf_compat_v1 = tf tf.disable_v2_behavior() # Tensorflow utility functions import tvm.relay.testing.tf as tf_testing # Base location for model related files. repo_base = 'https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/' # Test image img_name = 'elephant-299.jpg' image_url = os.path.join(repo_base, img_name) model_name = 'classify_image_graph_def-with_shapes.pb' model_url = os.path.join(repo_base, model_name) # Image label map map_proto = 'imagenet_2012_challenge_label_map_proto.pbtxt' map_proto_url = os.path.join(repo_base, map_proto) # Human readable text for labels label_map = 'imagenet_synset_to_human_label_map.txt' label_map_url = os.path.join(repo_base, label_map) # Target settings target = 'metal' target_host = 'llvm' layout = None # "NCHW" ctx = tvm.context(target, 1) from tvm.contrib.download import download_testdata img_path = download_testdata(image_url, img_name, module='data') model_path = download_testdata(model_url, model_name, module=['tf', 'InceptionV1']) map_proto_path = download_testdata(map_proto_url, map_proto, module='data') label_path = download_testdata(label_map_url, label_map, module='data') with tf_compat_v1.gfile.GFile(model_path, 'rb') as f: graph_def = tf_compat_v1.GraphDef() graph_def.ParseFromString(f.read()) graph = tf.import_graph_def(graph_def, name='') # Call the utility to import the graph definition into default graph. graph_def = tf_testing.ProcessGraphDefParam(graph_def) # Add shapes to the graph. with tf_compat_v1.Session() as sess: graph_def = tf_testing.AddShapesToGraphDef(sess, 'softmax') from PIL import Image image = Image.open(img_path).resize((299, 299)) x = np.array(image) shape_dict = {'DecodeJpeg/contents': x.shape} dtype_dict = {'DecodeJpeg/contents': 'uint8'} mod, params = relay.frontend.from_tensorflow(graph_def, layout=layout, shape=shape_dict) print("Tensorflow protobuf imported to relay frontend.") with relay.build_config(opt_level=3): graph, lib, params = relay.build(mod, target=target, target_host=target_host, params=params) from tvm.contrib import graph_runtime dtype = 'uint8' m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype))) m.set_input(**params) # execute m.run() # get outputs tvm_output = m.get_output(0, tvm.nd.empty(((1, 1008)), 'float32')) predictions = tvm_output.asnumpy() predictions = np.squeeze(predictions) # Creates node ID --> English string lookup. node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path, uid_lookup_path=label_path) # Print top 5 predictions from TVM output. top_k = predictions.argsort()[-5:][::-1] for node_id in top_k: human_string = node_lookup.id_to_string(node_id) score = predictions[node_id] print('%s (score = %.5f)' % (human_string, score)) def create_graph(): """Creates a graph from saved GraphDef file and returns a saver.""" # Creates graph from saved graph_def.pb. with tf_compat_v1.gfile.GFile(model_path, 'rb') as f: graph_def = tf_compat_v1.GraphDef() graph_def.ParseFromString(f.read()) graph = tf.import_graph_def(graph_def, name='') # Call the utility to import the graph definition into default graph. graph_def = tf_testing.ProcessGraphDefParam(graph_def) def run_inference_on_image(image): """Runs inference on an image. Parameters ---------- image: String Image file name. Returns ------- Nothing """ if not tf_compat_v1.gfile.Exists(image): tf.logging.fatal('File does not exist %s', image) image_data = tf_compat_v1.gfile.GFile(image, 'rb').read() # Creates graph from saved GraphDef. create_graph() with tf_compat_v1.Session() as sess: softmax_tensor = sess.graph.get_tensor_by_name('softmax:0') predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data}) predictions = np.squeeze(predictions) # Creates node ID --> English string lookup. node_lookup = tf_testing.NodeLookup(label_lookup_path=map_proto_path, uid_lookup_path=label_path) # Print top 5 predictions from tensorflow. top_k = predictions.argsort()[-5:][::-1] print ("===== TENSORFLOW RESULTS =======") for node_id in top_k: human_string = node_lookup.id_to_string(node_id) score = predictions[node_id] print('%s (score = %.5f)' % (human_string, score)) run_inference_on_image(img_path) ``` Is it that `contrib.graph_runtime`, has only ever been tested with CUDA and is making assumptions? Here are the changes from the original found here: https://tvm.apache.org/docs/tutorials/frontend/from_tensorflow.html#sphx-glr-download-tutorials-frontend-from-tensorflow-py ``` (venv) kaosnew:build sam$ diff from_tensorflow_llvm.py from_tensorflow_metal.py 76c76 < target = 'llvm' --- > target = 'metal' 79c79 < ctx = tvm.cpu(0) --- > ctx = tvm.metal(0) ```
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