cchung100m commented on a change in pull request #5073: [Relay][Frontend][ONNX]
operator support NonZero
URL: https://github.com/apache/incubator-tvm/pull/5073#discussion_r394366255
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -30,22 +30,54 @@
import scipy
-def get_tvm_output(graph_def, input_data, target, ctx, output_shape=None,
output_dtype='float32', opset=None):
- """ Generic function to execute and get tvm output"""
- target = 'llvm'
+def get_input_data_shape_dict(graph_def, input_data):
if isinstance(input_data, list):
input_names = {}
shape_dict = {}
- dtype_dict = {}
for i, _ in enumerate(input_data):
input_names[i] = graph_def.graph.input[i].name
shape_dict[input_names[i]] = input_data[i].shape
- dtype_dict[input_names[i]] = input_data[i].dtype
else:
input_names = graph_def.graph.input[0].name
shape_dict = {input_names: input_data.shape}
+
+ return input_names, shape_dict
+
+
+def get_input_data_dtype_dict(graph_def, input_data):
+ if isinstance(input_data, list):
+ input_names = {}
+ dtype_dict = {}
+ for i, _ in enumerate(input_data):
+ input_names[i] = graph_def.graph.input[i].name
+ dtype_dict[input_names[i]] = input_data[i].dtype
+ else:
+ input_names = graph_def.graph.input[0].name
dtype_dict = {input_names: input_data.dtype}
+ return input_names, dtype_dict
+
+
+def get_tvm_output_with_vm(graph_def, input_data, target, ctx,
+ opset=None):
+ """ Generic function to execute and get tvm output with vm executor"""
+
+ _, shape_dict = get_input_data_shape_dict(graph_def, input_data)
+
+ mod, params = relay.frontend.from_onnx(graph_def, shape_dict, opset=opset)
+
+ ex = relay.create_executor('vm', mod=mod, ctx=ctx, target=target)
+ indata = tvm.nd.array(input_data)
+ result = ex.evaluate()(indata)
+ return result.asnumpy()
+
+
+def get_tvm_output(graph_def, input_data, target, ctx, output_shape=None,
output_dtype='float32', opset=None):
Review comment:
Hi @kazum
I'd like to keep the `relay.create_executor` and `relay.build` both in this
PR.
I cannot change the `relay.build` with `relay.create_executor` directly due
to there are many errors like below:
```
File "/tvm/tests/python/frontend/onnx/test_forward.py", line 2282, in
<module>
test_flatten()
File "/tvm/tests/python/frontend/onnx/test_forward.py", line 374, in
test_flatten
tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
File "/tvm/tests/python/frontend/onnx/test_forward.py", line 70, in
get_tvm_output
result = ex.evaluate()(indata)
File "/tvm/python/tvm/relay/backend/vm.py", line 256, in _vm_wrapper
return self.vm.run(*args)
File "/tvm/python/tvm/runtime/vm.py", line 366, in run
return self.invoke("main", *args, **kwargs)
File "/tvm/python/tvm/runtime/vm.py", line 348, in invoke
return self._invoke(func_name)
File "/tvm/python/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] (7) 8 ??? 0x00007fff54230930 0x0 +
140734604970288
[bt] (6) 7 _ctypes.cpython-37m-darwin.so 0x00000001104dc2bf
ffi_call_unix64 + 79
[bt] (5) 6 libtvm.dylib 0x0000000125071f78
TVMFuncCall + 72
[bt] (4) 5 libtvm.dylib 0x00000001250a4e3f
std::__1::__function::__func<tvm::runtime::vm::VirtualMachine::GetFunction(std::__1::basic_string<char,
std::__1::char_traits<char>, std::__1::allocator<char> > const&,
tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)::$_0,
std::__1::allocator<tvm::runtime::vm::VirtualMachine::GetFunction(std::__1::basic_string<char,
std::__1::char_traits<char>, std::__1::allocator<char> > const&,
tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)::$_0>, void
(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&,
tvm::runtime::TVMRetValue*&&) + 735
[bt] (3) 4 libtvm.dylib 0x00000001250a199a
tvm::runtime::vm::VirtualMachine::RunLoop() + 7610
[bt] (2) 3 libtvm.dylib 0x00000001250a310f
tvm::runtime::vm::VirtualMachine::InvokePacked(long long,
tvm::runtime::PackedFunc const&, long long, long long,
std::__1::vector<tvm::runtime::ObjectRef,
std::__1::allocator<tvm::runtime::ObjectRef> > const&) + 1039
[bt] (1) 2 libtvm.dylib 0x000000012507b396
std::__1::__function::__func<tvm::runtime::WrapPackedFunc(int (*)(TVMValue*,
int*, int, TVMValue*, int*), tvm::runtime::ObjectPtr<tvm::runtime::Object>
const&)::$_0, std::__1::allocator<tvm::runtime::WrapPackedFunc(int
(*)(TVMValue*, int*, int, TVMValue*, int*),
tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)::$_0>, void
(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs&&,
tvm::runtime::TVMRetValue*&&) + 310
[bt] (0) 1 libtvm.dylib 0x0000000124666af9
dmlc::LogMessageFatal::~LogMessageFatal() + 57
File "/tvm/src/runtime/library_module.cc", line 89
TVMError: Check failed: ret == 0 (-1 vs. 0) : Assert fail:
(((tvm_struct_get(arg0, 0, 5) == (uint8)2) && (tvm_struct_get(arg0, 0, 6) ==
(uint8)32)) && (tvm_struct_get(arg0, 0, 7) == (uint16)1)), arg0.dtype is
expected to be float32
```
```
File "/tvm/tests/python/frontend/onnx/test_forward.py", line 2128, in
verify_lstm
output_dtype=['float32', 'float32', 'float32'])
File "/tvm/tests/python/frontend/onnx/test_forward.py", line 69, in
get_tvm_output
indata = tvm.nd.array(input_data)
File "/tvm/python/tvm/runtime/ndarray.py", line 487, in array
return empty(arr.shape, arr.dtype, ctx).copyfrom(arr)
File "/tvm/python/tvm/runtime/ndarray.py", line 270, in empty
dtype = DataType(dtype)
File "/tvm/python/tvm/_ffi/runtime_ctypes.py", line 101, in __init__
raise ValueError("Do not know how to handle type %s" % type_str)
ValueError: Do not know how to handle type object
```
Maybe we can initiate another PR to the above issues and change the
`relay.build` with `relay.create_executor`?
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