mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 00:20:18 +01:00
517aee5369
Test Plan:
test script:
```
def test_zhxchen17(self):
from libfb.py.pyinit import initFacebook
initFacebook()
class M(torch.nn.Module):
def forward(self, x):
return torch.add(x, x)
def tmptmp(x, y):
return torch.mul(x, y)
m = M()
n = torch.jit.script(m)
print(n(torch.tensor(1)))
print(torch.jit.script(tmptmp)(torch.tensor(1), torch.tensor(2)))
```
```
I0802 12:01:23.932929 4079081 init.cc:407] Logging to scuba: run __torch__.caffe2.test.export.test_export.M.forward sample rate: 1000000
```
Differential Revision: D60920867
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133484
Approved by: https://github.com/davidberard98
|
||
|---|---|---|
| .. | ||
| api | ||
| backends | ||
| codegen | ||
| cuda | ||
| docs | ||
| frontend | ||
| ir | ||
| mobile | ||
| operator_upgraders | ||
| passes | ||
| python | ||
| runtime | ||
| serialization | ||
| tensorexpr | ||
| testing | ||
| jit_log.cpp | ||
| jit_log.h | ||
| jit_opt_limit.cpp | ||
| jit_opt_limit.h | ||
| JIT-AUTOCAST.md | ||
| OVERVIEW.md | ||
| README.md | ||
| resource_guard.h | ||
PyTorch JIT
This folder contains (most of) the C++ code for the PyTorch JIT, a language and compiler stack for executing PyTorch models portably and efficiently. To learn more about the JIT from a user perspective, please consult our reference documentation and tutorials.
A brief summary of the source tree:
- OVERVIEW.md: High-level technical overview of the JIT.
- frontend/: Taking PyTorch modules in Python and translating them into the JIT IR.
- ir/: Core IR abstractions.
- runtime/: Interpreter, graph execution, and JIT operators.
- codegen/: Generating efficient, hardware-specific code for JIT subgraphs.
- serialization/: Saving and loading modules.
- api/: Any user-facing C++ or Python interfaces.
- python/: Binding stuff into Python or accessing information from the Python environment.
- testing/: Utilities and helpers for testing.
- mobile/: Mobile-specific implementations of runtime components.
- passes/: IR-to-IR passes, generally for optimization and lowering.
- generated/: This folder is generated by the PyTorch build, and contains bindings for native PyTorch operators into the JIT.
Refer to each folder for more in-depth documentation.
Other relevant parts of the codebase not contained here:
- aten/src/ATen/core: contains JIT code re-used by other elements of the runtime system (eager, mobile, etc.)