RFC: https://github.com/pytorch/rfcs/pull/54
First commit is the contents of https://github.com/Quansight-Labs/numpy_pytorch_interop/
We have already been using this in core for the last few months as a external dependency. This PR pulls all these into core.
In the next commits, I do a number of things in this order
- Fix a few small issues
- Make the tests that this PR adds pass
- Bend backwards until lintrunner passes
- Remove the optional dependency on `torch_np` and simply rely on the upstreamed code
- Fix a number dynamo tests that were passing before (they were not tasting anything I think) and are not passing now.
Missing from this PR (but not blocking):
- Have a flag that deactivates tracing NumPy functions and simply breaks. There used to be one but after the merge stopped working and I removed it. @lezcano to investigate.
- https://github.com/pytorch/pytorch/pull/106431#issuecomment-1667079543. @voznesenskym to submit a fix after we merge.
All the tests in `tests/torch_np` take about 75s to run.
This was a work by @ev-br, @rgommers @honno and I. I did not create this PR via ghstack (which would have been convenient) as this is a collaboration, and ghstack doesn't allow for shared contributions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106211
Approved by: https://github.com/ezyang
Issue: #93684
In previous PRs #95849#99560 we redirect `numpy.*`, `<tensor>.numpy()` calls to `torch_np.*` methods and attributes, by creating `NumpyNdarrayVariable` for those calls.
We need to handle `NumpyNdarrayVariable` when graph break happens.
This PR did 2 things:
1. In `codegen.py` we made sure we can reconstruct the value wrapped by `NumpyNdarrayVariable`, to be `torch_np.ndarray` in the stack whenerver we recompiles the subgraph.
2. In `builder.py` we can wrap the value to be `NumpyNdarrayVariable` and save it as graph input.
-----
Starting from commit 6:
## A new design for supporting numpy in dynamo
In short the core concept doesn't change: we still convert `numpy` API calls to `torch_np` API calls. However, instead of wrapping a `torch_np.ndarray` in `NumpyNdarrayVariable`, the new design wraps a `torch.Tensor`.
The reason for doing this change is because we need to keep `torch.Tensor` everywhere in the captured graph, so that it works well with the backend of dynamo. See discussions in https://github.com/Quansight-Labs/numpy_pytorch_interop/issues/142 for details.
### Flow
This is an example showing how do we think about dynamo working on a simple function:
```python
def f(x: torch.Tensor, y: torch.Tensor):
a, b = x.numpy(), y.numpy()
c = np.add(x, y)
return torch.from_numpy(c)
```
```
+------------+ +------------+
torch.Tensor | |numpy.ndarray| |
-------------- .numpy() --------------| |
| | | | +------------------+
+------------+ | numpy.add |numpy.ndarray| |torch.Tensor
+------------+ | --------------| torch.from_numpy --------------
torch.Tensor | |numpy.ndarray| | | |
-------------- .numpy() --------------| | +------------------+
| | | |
+------------+ +------------+
+------------+ +----------------+
torch.Tensor | |torch.Tensor | |
-------------- .detach() --------------| |
| | | | +----------------+ +------------+
+------------+ | |torch_np.ndarray| |torch.Tensor| |torch.Tensor
| torch_np.add -----------------| util.to_tensor -------------| .detach() --------------
+------------+ | | | | | |
torch.Tensor | |torch.Tensor | | +----------------+ +------------+
-------------- .detach() --------------| |
| | | |
+------------+ | +----------------+ |
| wrapper on torch_np.add |
+--------------------------------------------------------+
```
### Approach
`torch_np` APIs can take both `torch_np.ndarray` as well as `torch.Tensor`. What we need to do is to have a wrapper for these APIs to convert the return value back to `torch.Tensor`. This way only the wrapper is showing up in the captured graph, with `torch.Tensor`s as input and `torch.Tensor` as output.
If we have a graph break or we've traced to the end of the program, we need to inspect all the `NumpyNdarrayVariable` in the stack and convert them back to `numpy.ndarray`, to make sure the compiled version is still behaving the same as the eager version.
### Examples
Here's an example of the graph generated:
```python
def fn(x: np.ndarray, y: np.ndarray):
a = x.real
b = y.real
torch._dynamo.graph_break()
return np.add(a, 1), np.add(b, 1)
```
Graph generated:
```
[2023-05-16 10:31:48,737] torch._dynamo.output_graph.__graph: [DEBUG] TRACED GRAPH
__compiled_fn_0 <eval_with_key>.0 opcode name target args kwargs
------------- -------------- ---------------------------------------------------------- ---------------------- --------
placeholder l_x_ L_x_ () {}
placeholder l_y_ L_y_ () {}
call_function from_numpy <built-in method from_numpy of type object at 0x12b1fdc80> (l_x_,) {}
call_function from_numpy_1 <built-in method from_numpy of type object at 0x12b1fdc80> (l_y_,) {}
call_function attr_wrapper <function attr_wrapper at 0x12e8693a0> (from_numpy, 'real') {}
call_function attr_wrapper_1 <function attr_wrapper at 0x12e8693a0> (from_numpy_1, 'real') {}
output output output ((),) {}
[2023-05-16 10:31:48,908] torch._dynamo.output_graph.__graph: [DEBUG] TRACED GRAPH
__compiled_fn_2 <eval_with_key>.1 opcode name target args kwargs
------------- ------------- ---------------------------------------------------------- ------------------------------- --------
placeholder l_a_ L_a_ () {}
placeholder l_b_ L_b_ () {}
call_function from_numpy <built-in method from_numpy of type object at 0x12b1fdc80> (l_a_,) {}
call_function from_numpy_1 <built-in method from_numpy of type object at 0x12b1fdc80> (l_b_,) {}
call_function wrapped_add <Wrapped function <original add>> (from_numpy, 1) {}
call_function wrapped_add_1 <Wrapped function <original add>> (from_numpy_1, 1) {}
output output output ((wrapped_add, wrapped_add_1),) {}
```
### Changes
* `codegen.py`: reconstruct `numpy.ndarray` from `NumpyNdarrayVariable` by adding bytecode to call `utils.to_numpy_helper()`.
* `output_graph.py`: getting rid of legacy code that does exactly what `codegen.py` does, which only handling return case but not graph break case.
* `utils.py`: added helpers to convert `numpy.ndarray` to `torch.Tensor` and vice versa. Also adding a wrapper class that takes in a function. In `__call__` it calls the function and converts its out to `torch.Tensor` (or a list of it).
* `builder.py`: add method to wrap `numpy.ndarray` graph inputs into `NumpyNdarrayVariable`, by calling `torch.numpy` in the proxy.
* `misc.py`: `numpy` API calls goes into `NumpyVariable` and we find the function with the same name in `torch_np` module, then wrap it with the wrapper defined in `utils.py`.
* `tensor.py`, `torch.py`: proxy `tensor.numpy()` to be `torch.detach()` but wrap it with `NumpyNdarrayVariable`. Similarly, `torch.from_numpy()` -> `torch.detach()` but wrap it with `TensorVariable`. In `NumpyNdarrayVariable`, do the similar `torch_np.ndarray` to `torch.Tensor` wrapping for attributes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100839
Approved by: https://github.com/ezyang
Issue: #93684
# Problem
Reduce graph breaks when dynamo compiles python functions containing numpy functions and ndarray operations.
# Design (as I know it)
* Use torch_np.ndarray(a wrapper of tensor) to back a `VariableTracker`: `NumpyTensorVariable`.
* Translate all attributes and methods calls, on ndarray, to torch_np.ndarray equivalent.
This PR adds `NumpyTensorVariable` and supports:
1. tensor to ndarray, ndarray to tensor
2. numpy functions such as numpy.meshgrid()
3. ndarray attributes such as `itemsize`, `stride`
Next PR will handle returning `np.ndarray` and add support for ndarray methods
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95849
Approved by: https://github.com/ezyang
