kazimuth opened a new issue #7565:
URL: https://github.com/apache/tvm/issues/7565
The bring-your-own-codegen c compiler demo fails to handle the following
relay IR:
```
def @main(%x: Tensor[(10, 10), float32], %y: Tensor[(10, 10), float32]) {
%0 = (%x, %y);
%5 = fn (%xy: (Tensor[(10, 10), float32], Tensor[(10, 10), float32]),
Primitive=1, Compiler="ccompiler", global_symbol="tuple_thingamabob") {
%1 = %xy.0;
%2 = %xy.1;
%3 = add(%1, %2);
%4 = multiply(%1, %2);
(%3, %4)
};
%6 = %5(%0);
%6.0
}
```
Trying to compile this with the Relay VM throws an exception: `Check failed:
res.size() > static_cast<size_t>(op->index) (1 vs. 1)`,
[here](https://github.com/apache/tvm/blob/b52267e/src/relay/backend/contrib/codegen_c/codegen.cc#L72).
However, if the external codegen attributes (`Compiler` and `global_symbol`)
are removed, then it works fine.
Repro python script (based on `tests/python/relay/test_external_codegen.py`):
```python
import os
import sys
import numpy as np
import tvm
import tvm.relay.testing
import tvm.relay.transform
from tvm import relay
from tvm import runtime
from tvm.contrib import utils
def set_external_func_attr(func, compiler, ext_symbol):
func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
func = func.with_attr("Compiler", compiler)
func = func.with_attr("global_symbol", ext_symbol)
return func
def check_result(mod, map_inputs, out_shape, result, tol=1e-5,
target="llvm", ctx=tvm.cpu()):
if sys.platform == "win32":
print("Skip test on Windows for now")
return
def update_lib(lib):
test_dir =
os.path.dirname(os.path.realpath(os.path.expanduser(__file__)))
source_dir = os.path.join(test_dir, "..", "..", "..")
contrib_path = os.path.join(source_dir, "src", "runtime", "contrib")
for m in lib.imported_modules:
if m.type_key == 'c':
print(m.get_source())
kwargs = {}
kwargs["options"] = ["-O2", "-std=c++14", "-I" + contrib_path]
tmp_path = utils.tempdir()
lib_name = "lib.so"
lib_path = tmp_path.relpath(lib_name)
lib.export_library(lib_path, fcompile=False, **kwargs)
lib = tvm.runtime.load_module(lib_path)
return lib
def check_vm_result():
with tvm.transform.PassContext(opt_level=3,
disabled_pass=["AlterOpLayout"]):
exe = relay.vm.compile(mod, target=target)
code, lib = exe.save()
lib = update_lib(lib)
exe = runtime.vm.Executable.load_exec(code, lib)
vm = runtime.vm.VirtualMachine(exe, ctx)
out = vm.run(**map_inputs)
tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol,
atol=tol)
def check_graph_runtime_result():
with tvm.transform.PassContext(opt_level=3,
disabled_pass=["AlterOpLayout"]):
json, lib, _ = relay.build(mod, target=target)
lib = update_lib(lib)
rt_mod = tvm.contrib.graph_runtime.create(json, lib, ctx)
for name, data in map_inputs.items():
rt_mod.set_input(name, data)
rt_mod.run()
out = tvm.nd.empty(out_shape, ctx=ctx)
out = rt_mod.get_output(0, out)
tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol,
atol=tol)
check_vm_result()
#check_graph_runtime_result() # tuples don't work in graph runtime
anyway afaik
def test_tuple():
x = relay.var("x", shape=(10, 10))
y = relay.var("y", shape=(10, 10))
tuple = relay.Tuple([x, y])
xy = relay.var("xy", relay.TupleType([
relay.TensorType((10, 10), dtype='float32'),
relay.TensorType((10, 10), dtype='float32'),
]))
x_ = relay.TupleGetItem(xy, 0)
y_ = relay.TupleGetItem(xy, 1)
sum = x_ + y_
prod = x_ * y_
result = relay.Tuple([sum, prod])
subgraph = relay.Function([xy], result)
subgraph = set_external_func_attr(subgraph, "ccompiler",
"tuple_thingamabob")
call = relay.Call(subgraph, [tuple])
result = relay.TupleGetItem(call, 0)
f = relay.Function([x, y], result)
mod = tvm.IRModule()
mod["main"] = f
print(mod)
x_data = np.random.rand(10, 10).astype("float32")
y_data = np.random.rand(10, 10).astype("float32")
check_result(
mod,
{
'x': x_data,
'y': y_data
},
(10, 10),
(x_data + y_data)
)
if __name__ == '__main__':
test_tuple()
```
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