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Remove duplicate example + fix README typo

pull/346/head
Ramiro Leal-Cavazos 2021-09-30 20:00:46 +00:00 committed by Sean Silva
parent 05a01b10b3
commit 3aef3a9e30
2 changed files with 2 additions and 59 deletions
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4
README.md
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@ -121,7 +121,7 @@ The `examples` folder includes the Python package `torchfx`, which is a function
#### Example usage of `torchfx`
The `examples` folder includes scripts `torchfx_*.py` showing how to use the TorchFX to MLIR pipeline. In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-your-python-environment) instructions.
The `examples` folder includes scripts `torchfx_*.py` showing how to use the TorchFX to MLIR pipeline. In order to run the examples, make sure you've setup your `PYTHONPATH` by following the [Setup Python Environment](#setup-python-environment) instructions.
Then, run
@ -140,7 +140,7 @@ The `examples` folder includes the Python package `lazytensor`, which implements
The `examples` folder includes scripts `lazytensor_*.py` showing how to use the Lazy Tensor to MLIR pipeline. The examples depend on the Lazy Tensor Core (LTC) of PyTorch. For information on how to obtain LTC, see [here](https://github.com/pytorch/pytorch/blob/lazy_tensor_staging/lazy_tensor_core/QUICKSTART.md).
In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-your-python-environment) instructions, and also add `/path/to/pytorch/lazy_tensor_core` to your `PYTHONPATH` as shown below:
In order to run the examples, make sure you've setup your `PYTHONPATH` by following the [Setup Python Environment](#setup-python-environment) instructions, and also add `/path/to/pytorch/lazy_tensor_core` to your `PYTHONPATH` as shown below:
```
export PYTHONPATH=$PYTHONPATH:`/replace/with/path/to/pytorch/lazy_tensor_core`

57
examples/torchfx/examples/example_add_tanh_sigmoid.py
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@ -1,57 +0,0 @@
# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.
# From the torch-mlir root, run with:
# `python -m examples.torchfx.examples.example_add_tanh_sigmoid`
# (after setting up python environment with write_env_file.sh)
import torch
from torch.fx.experimental.fx_acc import acc_tracer
import torch_mlir
from torch_mlir.dialects.torch import register_dialect
from torch_mlir.passmanager import PassManager
from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend import RefBackendLinalgOnTensorsBackend
from ..builder import build_module
from ..annotator import annotate_forward_args
from ..torch_mlir_types import TorchTensorType
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, y):
return torch.tanh(x) + torch.sigmoid(y)
module = MyModule()
traced_module = acc_tracer.trace(module, [torch.Tensor(2,2),
torch.Tensor(2,2)])
print("TRACE")
arg_type = TorchTensorType(shape=[None, None], dtype=torch.float)
traced_module = annotate_forward_args(traced_module, [arg_type, arg_type])
print(traced_module.graph)
torch_mlir_module = build_module(traced_module)
print("\n\nTORCH MLIR")
torch_mlir_module.dump()
print(torch_mlir_module.operation.verify())
with torch_mlir_module.context:
pm = PassManager.parse('torchscript-module-to-linalg-on-tensors-backend-pipeline')
pm.run(torch_mlir_module)
print("\n\nLOWERED MLIR")
torch_mlir_module.dump()
backend = RefBackendLinalgOnTensorsBackend()
compiled = backend.compile(torch_mlir_module)
jit_module = backend.load(compiled)
print("\n\nRunning Forward Function")
t = torch.rand((2, 2), dtype=torch.float)
print("Compiled result:\n", jit_module.forward(t.numpy(), t.numpy()))
print("\nExpected result:\n", module.forward(t, t))