mirror of https://github.com/llvm/torch-mlir
55 lines
1.6 KiB
Markdown
55 lines
1.6 KiB
Markdown
# Torch-MLIR Lazy Tensor Core Backend Examples
|
|
|
|
Refer to the main documentation [here](ltc_backend.md).
|
|
|
|
## Example Usage
|
|
```python
|
|
import torch
|
|
import torch._lazy
|
|
import torch_mlir._mlir_libs._REFERENCE_LAZY_BACKEND as lazy_backend
|
|
|
|
# Register the example LTC backend.
|
|
lazy_backend._initialize()
|
|
|
|
device = 'lazy'
|
|
|
|
# Create some tensors and perform operations.
|
|
inputs = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.float32, device=device)
|
|
outputs = torch.tanh(inputs)
|
|
|
|
# Mark end of training/evaluation iteration and lower traced graph.
|
|
torch._lazy.mark_step()
|
|
print('Results:', outputs)
|
|
|
|
# Optionally dump MLIR graph generated from LTC trace.
|
|
computation = lazy_backend.get_latest_computation()
|
|
if computation:
|
|
print(computation.debug_string())
|
|
```
|
|
|
|
```
|
|
Received 1 computation instances at Compile!
|
|
Received 1 arguments, and returned 2 results during ExecuteCompile!
|
|
|
|
Results: tensor([[0.7616, 0.9640, 0.9951, 0.9993, 0.9999]], device='lazy:0')
|
|
|
|
JIT Graph:
|
|
graph(%p0 : Float(1, 5)):
|
|
%1 : Float(1, 5) = aten::tanh(%p0)
|
|
return (%p0, %1)
|
|
|
|
MLIR:
|
|
func.func @graph(%arg0: !torch.vtensor<[1,5],f32>) -> (!torch.vtensor<[1,5],f32>, !torch.vtensor<[1,5],f32>) {
|
|
%0 = torch.aten.tanh %arg0 : !torch.vtensor<[1,5],f32> -> !torch.vtensor<[1,5],f32>
|
|
return %arg0, %0 : !torch.vtensor<[1,5],f32>, !torch.vtensor<[1,5],f32>
|
|
}
|
|
|
|
Input/Output Alias Mapping:
|
|
Output: 0 -> Input param: 0
|
|
|
|
In Mark Step: true
|
|
```
|
|
|
|
## Example Models
|
|
There are also examples of a [HuggingFace BERT](../examples/ltc_backend_bert.py) and [MNIST](../examples/ltc_backend_mnist.py) model running on the example LTC backend.
|