tom-gall opened a new issue #6246:
URL: https://github.com/apache/incubator-tvm/issues/6246
@areusch
The failure looks like
graph, c_mod, params = relay.build(mod, target=target, params=params)
Traceback (most recent call last):
File "./working-micro-st-tflite.py", line 48, in <module>
micro_mod = micro.create_micro_mod(c_mod, dev_config)
File "/home/tgall/tvm/tvm/python/tvm/micro/base.py", line 213, in
create_micro_mod
micro_mod = tvm.runtime.load_module(lib_obj_path)
File "/home/tgall/tvm/tvm/python/tvm/runtime/module.py", line 407, in
load_module
return _ffi_api.ModuleLoadFromFile(path, fmt)
File "/home/tgall/tvm/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line
225, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (6) /home/tgall/tvm/tvm/build/libtvm.so(TVMFuncCall+0x63)
[0x7f0b6f5594e3]
[bt] (5) /home/tgall/tvm/tvm/build/libtvm.so(std::_Function_handler<void
(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*),
tvm::runtime::TypedPackedFunc<tvm::runtime::Module
(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>
> const&, std::__cxx11::basic_string<char, std::char_traits<char>,
std::allocator<char> > const&)>::AssignTypedLambda<tvm::runtime::Module
(*)(std::__cxx11::basic_string<char, std::char_traits<char>,
std::allocator<char> > const&, std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> > const&)>(tvm::runtime::Module
(*)(std::__cxx11::basic_string<char, std::char_traits<char>,
std::allocator<char> > const&, std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> >
const&))::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&,
tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0xa3) [0x7f0b6f57a9e3]
[bt] (4)
/home/tgall/tvm/tvm/build/libtvm.so(tvm::runtime::Module::LoadFromFile(std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> > const&,
std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >
const&)+0x1d8) [0x7f0b6f575c38]
[bt] (3) /home/tgall/tvm/tvm/build/libtvm.so(+0x18a270a) [0x7f0b6f5f070a]
[bt] (2)
/home/tgall/tvm/tvm/build/libtvm.so(tvm::runtime::MicroSession::LoadBinary(std::__cxx11::basic_string<char,
std::char_traits<char>, std::allocator<char> > const&, bool)+0x14f)
[0x7f0b6f5f5e9f]
[bt] (1)
/home/tgall/tvm/tvm/build/libtvm.so(tvm::runtime::MicroSession::AllocateInSection(tvm::runtime::SectionKind,
unsigned long)+0x1a5) [0x7f0b6f5f5795]
[bt] (0) /home/tgall/tvm/tvm/build/libtvm.so(+0x18a4898) [0x7f0b6f5f2898]
File "/home/tgall/tvm/tvm/src/runtime/micro/micro_section_allocator.h",
line 68
TVMError: Check failed: size_ + size < capacity_: cannot alloc 208 bytes in
section "rodata" (start_addr=0x20004650, used=0, capacity=100)
I think we've seen this before haven't we?
code
import os
import numpy as np
import tvm
import tvm.micro as micro
from tvm.contrib import graph_runtime, util
from tvm import relay
from tvm.contrib.download import download_testdata
target = "c --system-lib --runtime=c"
model_dir ="/home/tgall/tvm/utvm-exp/"
tflite_model_file = os.path.join(model_dir, "sine_model.tflite")
tflite_model_buf = open(tflite_model_file, "rb").read()
try:
import tflite
tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)
version = tflite_model.Version()
print ("Model Version: " + version)
except AttributeError:
import tflite.Model
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)
version = tflite_model.Version()
print ("Model Version: " + str(version))
input_tensor = "dense_4_input"
input_shape = (1,)
input_dtype = "float32"
dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
mod, params = relay.frontend.from_tflite(tflite_model,
shape_dict={input_tensor:
input_shape},
dtype_dict={input_tensor:
input_dtype})
with micro.Session(dev_config) as sess:
ctx = tvm.micro_dev(0)
with tvm.transform.PassContext(disabled_pass={'FuseOps'},
config={"tir.disable_vectorize": True}):
graph, c_mod, params = relay.build(mod, target=target, params=params)
micro_mod = micro.create_micro_mod(c_mod, dev_config)
mod = graph_runtime.create(graph, micro_mod, ctx)
mod.set_input(**params)
#throw a simple single bogus number at the model
mod.set_input(input_tensor, tvm.nd.array(np.array([0.5],
dtype="float32")))
mod.run()
tvm_output = mod.get_output(0).asnumpy()
print("result is: "+str(tvm_output))
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