The PyTorch model with hardswish operator and input_dtype=float64 crashed when load to relay.
Actual behavior
data types float64 and float32 do not match in BroadcastRel
data types float64 and float32 do not match in BroadcastRel
Traceback (most recent call last):
File "19_crash_hardswish.py", line 19, in <module>
mod, params = relay.frontend.from_pytorch(trace, input_shapes)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 4970, in from_pytorch
outputs = converter.convert_operators(operator_nodes, outputs, ret_name)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 4243, in convert_operators
self.record_output_type(relay_out)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 238, in record_output_type
self.infer_type_with_prelude(output)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 174, in infer_type_with_prelude
body = self.infer_type(val, self.prelude.mod)
File "/workplace/software/tvm/tvm/python/tvm/relay/frontend/pytorch.py", line 167, in infer_type
new_mod = transform.InferType()(new_mod)
File "/workplace/software/tvm/tvm/python/tvm/ir/transform.py", line 160, in __call__
return _ffi_transform_api.RunPass(self, mod)
File "/workplace/software/tvm/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 238, in __call__
raise get_last_ffi_error()
tvm.error.DiagnosticError: Traceback (most recent call last):
7: TVMFuncCall
6: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::transform::Pass, tvm::IRModule)>::AssignTypedLambda<tvm::transform::$_6>(tvm::transform::$_6, 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*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
5: tvm::transform::Pass::operator()(tvm::IRModule) const
4: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
3: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
2: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::$_2>(tvm::relay::transform::InferType()::$_2)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
1: tvm::DiagnosticContext::Render()
0: _ZN3tvm7runtime6detail
File "/workplace/software/tvm/tvm/src/ir/diagnostic.cc", line 131
DiagnosticError: one or more error diagnostics were emitted, please check diagnostic render for output.
What actually happened
Environment
Any environment details, such as: Operating System, TVM version, etc
Steps to reproduce
import torch
from tvm import relay
import tvm
import numpy as np
from torch.nn import Module
input_data = torch.randn([2, 4, 4], dtype=torch.float64) # crash when dtype=float64
class hardswish(Module):
def forward(self, *args):
return torch.nn.functional.hardswish(args[0], )
m = hardswish().float().eval()
torch_outputs = m(input_data)
trace = torch.jit.trace(m, input_data)
input_shapes = [('input0', torch.Size([2, 4, 4]))]
mod, params = relay.frontend.from_pytorch(trace, input_shapes)
Environments
TVM: 0.13.dev0'
PyTorch: 1.13.1+cu117
Triage
needs-triage
front:pytorch
Analysis
This bug is related to input_dtype.
If the input_dtype was set as 'float32', TVM can run well.
If the input_dtype was set as 'float64', TVM will crash as before.
The PyTorch model with
hardswish
operator and input_dtype=float64 crashed when load to relay.Actual behavior
What actually happened
Environment
Any environment details, such as: Operating System, TVM version, etc
Steps to reproduce
Environments
Triage
Analysis
This bug is related to input_dtype. If the input_dtype was set as 'float32', TVM can run well. If the input_dtype was set as 'float64', TVM will crash as before.
cc @echuraev @Hzfengsy @shingjan