liquanfeng commented on issue #15101:
URL: https://github.com/apache/tvm/issues/15101#issuecomment-1779026435
A very useful job! And I try it on
tests/python/relax/test_codegen_cudnn.py::test_conv2d_offload according to
tests/python/relax/test_vm_multi_device.py::test_multi_device as shown below
```python
import numpy as np
import tvm
import tvm.testing
import tvm.topi.testing
from tvm import relax
from tvm.relax.backend.contrib.cudnn import partition_for_cudnn
from tvm.script import relax as R, ir as I
from tvm.script.ir_builder import IRBuilder
from tvm.script.ir_builder import relax as relax_builder
data_shape, weight_shape, dtype = (
(16, 32, 32, 16),
(32, 3, 3, 16),
"float32",
)
input_np = np.random.randn(*data_shape).astype(dtype)
weight_np = np.random.randn(*weight_shape).astype(dtype)
oc = weight_shape[0]
bias_np = np.random.randn(1, 1, 1, oc).astype(dtype)
args = (input_np, weight_np, bias_np)
with IRBuilder() as builder:
with relax_builder.function():
R.func_name("main")
data = R.arg("data", R.Tensor(data_shape, dtype))
weight = R.arg("weight", R.Tensor(weight_shape, dtype))
bias = R.arg("bias", R.Tensor((1, 1, 1, weight_shape[0]), dtype))
with R.dataflow() as frame:
output = R.emit(
R.nn.conv2d(
data,
weight,
out_dtype=dtype,
padding=(1, 1),
data_layout="NHWC",
kernel_layout="OHWI",
)
)
output = R.emit(output + bias)
output = R.emit(relax.op.to_vdevice(output, I.vdevice("llvm")))
output = R.emit(R.multiply(output, R.const(2, "float32")))
R.output(output)
R.func_ret_value(frame.output_vars[0])
func = builder.get()
mod = tvm.IRModule(
{"main": func},
global_infos={
"vdevice": [
I.vdevice("cuda", 0),
I.vdevice("llvm"),
]
},
)
mod = partition_for_cudnn(mod)
mod = relax.transform.RunCodegen()(mod)
devs = [tvm.device("cuda", 0), tvm.device("llvm")]
mod = relax.transform.RealizeVDevice()(mod)
mod = relax.transform.LegalizeOps()(mod)
mod = tvm.tir.transform.DefaultGPUSchedule()(mod)
with tvm.transform.PassContext(config={"relax.backend.use_cuda_graph":
False}):
ex = relax.build(mod)
vm = relax.VirtualMachine(ex, devs)
f = vm["main"]
inputs = [tvm.nd.array(inp, tvm.device("cuda", 0)) for inp in input_np]
print(f(*inputs).numpy())
```
raise following error
```
Traceback (most recent call last):
File "/workspace/yongwww/tvm/tests/byoc.py", line 77, in <module>
ex = relax.build(mod)
File "/workspace/yongwww/tvm/python/tvm/relax/vm_build.py", line 334, in
build
new_mod = lowering_passes(mod)
File "/workspace/yongwww/tvm/python/tvm/ir/transform.py", line 238, in
__call__
return _ffi_transform_api.RunPass(self, mod)
File "/workspace/yongwww/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line
239, in __call__
raise_last_ffi_error()
File "/workspace/yongwww/tvm/python/tvm/_ffi/base.py", line 476, in
raise_last_ffi_error
raise py_err
tvm._ffi.base.TVMError: Traceback (most recent call last):
24:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::transform::Pass,
tvm::IRModule)>::AssignTypedLambda<tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#7}>(tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#7}, std::__cxx11::basic_string<char, std::char_traits<char>,
std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*,
std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>
>, tvm::runtime::TVMRetValue)
23: tvm::transform::Pass::operator()(tvm::IRModule) const
22: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
21: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
20: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
19: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
18: _ZN3tvm7runtime13PackedFuncObj
17: tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule,
tvm::transform::PassContext)>::AssignTypedLambda<tvm::relax::transform::CallTIRRewrite()::{lambda(tvm::IRModule,
tvm::transform::PassContext)#1}>(tvm::relax::transform::CallTIRRewrite()::{lambda(tvm::IRModule,
tvm::transform::PassContext)#1})::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}::operator()(tvm::runtime::TVMArgs const,
tvm::runtime::TVMRetValue) const
16: tvm::relax::CallTIRMutator::Run()
15: tvm::relax::ExprMutator::VisitExpr(tvm::RelayExpr const&)
14:
_ZZN3tvm5relax11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
13: tvm::relax::ExprMutator::VisitExpr_(tvm::relax::FunctionNode const*)
12: tvm::relax::ExprMutator::VisitWithNewScope(tvm::RelayExpr const&,
tvm::runtime::Optional<tvm::runtime::Array<tvm::relax::Var, void> >)
11: tvm::relax::ExprMutator::VisitExpr(tvm::RelayExpr const&)
10:
_ZZN3tvm5relax11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
9: tvm::relax::ExprMutator::VisitExpr_(tvm::relax::SeqExprNode const*)
8: tvm::relax::ExprMutator::VisitBindingBlock(tvm::relax::BindingBlock
const&)
7:
tvm::relax::ExprMutator::VisitBindingBlock_(tvm::relax::BindingBlockNode const*)
6: tvm::relax::ExprMutator::VisitBinding(tvm::relax::Binding const&)
5: tvm::relax::ExprMutator::VisitBinding_(tvm::relax::VarBindingNode
const*)
4: tvm::relax::ExprMutator::VisitBinding_(tvm::relax::VarBindingNode
const*, tvm::relax::CallNode const*)
3: tvm::relax::ExprMutator::VisitExpr(tvm::RelayExpr const&)
2:
_ZZN3tvm5relax11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
1: tvm::relax::CallTIRMutator::VisitExpr_(tvm::relax::CallNode const*)
0: tvm::relax::GetDeviceIndex(tvm::IRModule const&, tvm::VDevice const&)
File "/workspace/yongwww/tvm/src/relax/transform/utils.h", line 384
TVMError: The vdevice is not in the ir_module.
```
Is there any problem with byoc?
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