LiSsHhUuAaIi opened a new issue, #18476:
URL: https://github.com/apache/tvm/issues/18476
### Description
When converting a PyTorch model containing sparse matrix multiplication
(`torch.sparse.mm`) and random number generation (`torch.randn`) operations to
TVM Relax module via `torch.export`, an AssertionError occurs. TVM currently
does not support the `_sparse_mm.default` and `randn.default` operations.
### Expected behavior
The PyTorch model with sparse operations and random number generation should
be successfully converted to TVM Relax module, enabling deployment of models
that use sparse computations and stochastic components.
### Actual behavior
An AssertionError occurs during `from_exported_program` conversion with the
message `Unsupported function types ['_sparse_mm.default', 'randn.default']`,
indicating that TVM's PyTorch frontend lacks support for these operations.
```
AssertionError: Unsupported function types ['_sparse_mm.default',
'randn.default']
```
### Environment
- OS: Ubuntu 20.04.6 LTS
- TVM version: 0.23.dev0
- Python version: 3.11.14
### Steps to reproduce
```python
import torch
import torch.nn as nn
import tvm
from tvm import relax
class MinimalSparseAndRandomModel(nn.Module):
def __init__(self):
super(MinimalSparseAndRandomModel, self).__init__()
def forward(self, sparse_input):
# Unsupported operations
random_matrix = torch.randn(100, 50) # randn.default
result = torch.sparse.mm(sparse_input, random_matrix) #
_sparse_mm.default
return result
model = MinimalSparseAndRandomModel()
model.eval()
indices = torch.tensor([[0, 1, 2], [2, 0, 1]])
values = torch.tensor([1.0, 2.0, 3.0])
sparse_input = torch.sparse_coo_tensor(indices, values, size=(3, 100))
# PyTorch execution works
with torch.no_grad():
output = model(sparse_input)
# PyTorch export works
exported_program = torch.export.export(model, (sparse_input,))
# TVM conversion fails
from tvm.relax.frontend.torch import from_exported_program
mod = from_exported_program(exported_program) # AssertionError here
```
### Triage
* needs-triage
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