Since Python 3.11 bytecode contains endline and column information, for each bytecode, we attribute the source code corresponding to the bytecode in a more accurate way. For example, we can highlight a function call in a series of nested function calls, or highlight a function call spanning multiple lines.
Sample:
```python
import torch
import torch._dynamo
from functorch.experimental.control_flow import cond
def h(x):
return x * 5
def true_fn(x):
return x * 2
def false_fn(x):
return x * 3
def f(pred, x):
x = h(
h(h(x))
)
x = x[1:][:2]
torch._dynamo.graph_break()
x = cond(pred, true_fn, false_fn, [x])
opt_f = torch.compile(f, backend="eager")
opt_f(torch.tensor(True), torch.randn(3, 3, 3, 3))
```
Output:
```
$ TORCH_LOGS="trace_call" python playground9.py
TRACE inlined call h from f /scratch/williamwen/work/pytorch/playground9.py:16
h(h(x))
~^^^
TRACE FX call mul from h /scratch/williamwen/work/pytorch/playground9.py:6 (inline depth: 1)
return x * 5
~~^~~
TRACE inlined call h from f /scratch/williamwen/work/pytorch/playground9.py:16
h(h(x))
~^^^^^^
TRACE FX call mul_1 from h /scratch/williamwen/work/pytorch/playground9.py:6 (inline depth: 1)
return x * 5
~~^~~
TRACE inlined call h from f /scratch/williamwen/work/pytorch/playground9.py:15
x = h(
~^
h(h(x))
^^^^^^^
)
^
TRACE FX call mul_2 from h /scratch/williamwen/work/pytorch/playground9.py:6 (inline depth: 1)
return x * 5
~~^~~
TRACE FX call getitem from f /scratch/williamwen/work/pytorch/playground9.py:18
x = x[1:][:2]
~^^^^
TRACE FX call getitem_1 from f /scratch/williamwen/work/pytorch/playground9.py:18
x = x[1:][:2]
~~~~~^^^^
TRACE inlined call true_fn from <resume in f> /scratch/williamwen/work/pytorch/playground9.py:20
x = cond(pred, true_fn, false_fn, [x])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TRACE FX call mul from true_fn /scratch/williamwen/work/pytorch/playground9.py:9 (inline depth: 1)
return x * 2
~~^~~
TRACE inlined call false_fn from <resume in f> /scratch/williamwen/work/pytorch/playground9.py:20
x = cond(pred, true_fn, false_fn, [x])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TRACE FX call mul from false_fn /scratch/williamwen/work/pytorch/playground9.py:12 (inline depth: 1)
return x * 3
~~^~~
TRACE FX call cond from <resume in f> /scratch/williamwen/work/pytorch/playground9.py:20
x = cond(pred, true_fn, false_fn, [x])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104676
Approved by: https://github.com/ezyang
This PR adds necessary plumbing through torchdynamo to allow tensor
subclasses with certain contract (i.e. with `__tensor_flatten__` and
`__tensor_unflatten__`) to goes through the dynamo fakification pass by
fakifying the tensor subclass internal components.
Some of the tensor subclass contract logic mostly borrowed from
https://github.com/pytorch/pytorch/pull/97540
Added some tests to verify simply passing through a tensor subclass
(i.e. DTensor) through dynamo eager works as expected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105308
Approved by: https://github.com/ezyang
Add similar semantics for creating a buffer object similar to creating a parameter. This is done by introducing a new `Buffer` class that can be used for type disambiguation. The underlying functionality of registering a buffer remains the same as the `register_buffer` method has not been changed. The `persistent` parameter in the `Buffer` type is to indicate whether a buffer object should be persistent or not. Other non-test changes have to do with getting the new `Buffer` type recognized by inductor and dynamo. Remaining changes are test changes to make sure that the `Buffer` type can be used as a drop in replacement for `register_buffer` as it just leads to `register_buffer` being called. The addition of this new functionality still allows for normal tensors to be used as buffers so these changes are intended to be backwards compatible.
Fixes#35735
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104069
Approved by: https://github.com/mikaylagawarecki
Fixes#95900
Using the following repro as guide:
```python
import torch
import torch._dynamo
from torch._subclasses import fake_tensor
from torch.fx.experimental.symbolic_shapes import ShapeEnv
from torch._dynamo.output_graph import config
class Model(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(2, 2)
self.linear2 = torch.nn.Linear(2, 2)
def forward(self, x):
out = self.linear(x)
out = self.linear2(out)
return out
fake_mode = fake_tensor.FakeTensorMode(allow_non_fake_inputs=False,
allow_fallback_kernels=True,
shape_env=ShapeEnv(
allow_scalar_outputs=config.capture_scalar_outputs,
allow_dynamic_output_shape_ops=config.capture_dynamic_output_shape_ops,
frame_id=0
),
)
# Fakefying input/model before calling torch._dynamo.export
with fake_mode:
fake_x = torch.rand(5, 2, 2)
model = Model()
# Calling torch._dynamo.export without active fake mode
graph_module, guards = torch._dynamo.export(
model,
fake_x,
aten_graph=True,
fake_mode=fake_mode
)
graph_module.print_readable()
graph_module.graph.print_tabular()
```
Summary of changes:
* Plumb fake_mode through torch.export API. When specified, it
replaces the creation of a new FaketendorMode at InstructionTranslator on behalf of OutputGraph
Hacks FakeTensor.__new__ to prevent a
torch.tensor._make_subclass call for inputs that are already fakefied by
user. This probably need to be fixed in a nicer way. Any idea?
* Removed a few asserts that didn't want faked tensors coming
from user script
* Added torch._subclasses.fake_tensor.FakeTensor to type list on a few
asserts check to allow fake inputs
The changes above allowed symbolic tracing with both static and dynamic shapes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100017
Approved by: https://github.com/ezyang
These are the numbers with this PR
There are 3 main followups
* A naive partitioner gives better memory footprint than min-cut partitioner here. Currently, we are using min-cut partitioner. Waiting for @Chillee to discuss this further to either modify min-cut or add a naive partitioner.
* aot_eager is < 1x memory footprint. This is true even for non AC models. This could hide some inefficiency somewhere.
* inductor is giving very different memory numbers between AOT-traced-AC (duplicate early) vs this implementation. This leads to some inefficiency in inductor that we need to resolve.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102935
Approved by: https://github.com/jansel
This PR adds universal support for ndarray methods. After #100839 each `NumpyNdarrayVariable` should wrap a `torch.Tensor`. This PR adds a `numpy_method_wrapper` which converts the `torch.Tensor` to `torch_np.ndarray` and then call the numpy ndarray method. Then we also try to return a `torch.Tensor` (return as-is if the value is not ndarray-like)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97537
Approved by: https://github.com/ezyang
On calls to `_init_group` rather than tracing through it, extract python values from the arguments, and call the initialization. This avoids having to trace this function which is very slow with large parameters, and also avoids graph breaking on it. This is sound in this case because the state is only initialized once in the eager case. Guards on the state and params are generated explicitly rather than via tracing the initialization.
Caveats:
`_init_group` also gathers various state tensors into lists via mutating list arguments to pass to the functional optimizer implementation. These state tensors exist on the optimizer itself, but we don't know exactly how the gathering is done and which tensors correspond to which attributes of the optimizer module (each optimizer has different states). To rectify this, we keep weak_ptrs to all of the tensors collected in the lists in globals (similar to how parameter keys are stored for dictionaries). These pointers are guaranteed to be alive as long as the optimizer object is alive if the internal state is not interfered with and they are guarded with weakref guards
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102640
Approved by: https://github.com/jansel
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
Summary:
This PR adds support for folding bn weights into conv for QAT flow, this is equivalent
to the QAT branch of `from_float` in eager mode quantized conv module: https://github.com/pytorch/pytorch/blob/main/torch/ao/nn/quantized/modules/conv.py#L223
Items that needs followup:
* there are some workaround I did because quantize_per_tensor is using float/int args and dynamo does not support these args, need to fix after we change the quantized model representation and also change these args to Tensor
Test Plan: buck2 test @//mode/opt //caffe2/test:quantization_pt2e -- --exact 'caffe2/test:quantization_pt2e - test_convert_qat_conv_bn_fusion (quantization.pt2e.test_quantize_pt2e.TestQuantizePT2E)'
Reviewed By: andrewor14
Differential Revision: D45344281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100442
Approved by: https://github.com/kimishpatel
This adds a new operator debugprims::load_storage which does the unusual thing of loading a tensor from disk (via ContentStoreReader). This will be used in a later PR to implement delta debugging in the minifier, even when the repro is too big to fit into memory. The way it works is that you specify a name of the tensor you want to load, as well as enough metadata to reconstruct the tensor, if the store isn't available. If there is an active content store, we read and return the tensor from that store; otherwise we use `rand_strided` to create it.
I needed some infra improvements to do this:
* `custom_op` now supports factory functions. Factory functions have to be registered specially via `impl_factory`
* I modified `clone_input` to also support dtype conversion, which I use to change the dtype of a loaded tensor if necessary.
* ContentStore needs to work with a device argument, so we torch.load directly to the correct device. This is for fake tensor support.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100519
Approved by: https://github.com/zou3519, https://github.com/anijain2305
The changes:
* Add config knob `same_two_models_use_fp64` for toggling whether or not to use fp64
* Add a test showing that RMSE is superior to atol/rtol
* Add `--strict-accuracy` options, which allows for testing against integral/boolean accuracy. Regular accuracy by default now ONLY. There's a test which exercises this, it's a little delicate but I had trouble thinking of a good test otherwise.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100447
Approved by: https://github.com/voznesenskym
This PR splits OutputGraph into two classes:
- SubgraphTracer (handles FX-tracing)
- OutputGraph (handles Dynamo-specific output graph logic, like
tracking graph inputs, compiling the graph, and executing it).
The motivation behind this is in the next PR up in the stack.
TL;DR is: in order to do higher-order operators, we need nested
SubgraphTracer, one for each level of nesting of the higher-order
operators.
I'm happy to flatten the stack into a single PR, but this separate made
it easier for me to test. Lmk if you want the stack flattened.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99987
Approved by: https://github.com/anijain2305, https://github.com/voznesenskym
This is a two part PR; I can split it if you really want me to.
The first part is a refactor of the after aot repro/minifier scripts to come with a command line interface. I maintain exact BC with the previous interface (so, e.g., you still get a repro.py and a run_minifier.py that do the same thing as before), but each of these scripts also take command line arguments now which you can use to customize what actually happens. Check `run_repro` for full documentation on the arguments.
The second part of this is an implementation of `analyze` subcommand on the new CLI for any repro.
<img width="1277" alt="image" src="https://user-images.githubusercontent.com/13564/235045677-8545aab7-5e83-4813-bbec-47783dc60122.png">
This facility is oriented towards accuracy debugging. It does several things:
1. It will run your model twice and check for nondeterminism in inductor/float64, *even* on intermediate inputs (our benchmarking nondeterminism test only checks for nondeterminism on the final output). This makes localizing which operator is nondeterministic easy.
2. It will run your compiled model side-by-side with eager and float64 variants, and then report when things diverge too far from RMSE delta from float64.
Importantly, it does all this without requiring every intermediate to be held in memory (which will cause an OOM on large repros, such as the one I tested this on.)
Some other minor improvements:
* MinifierTestBase now has an easy to comment out spot that you can use to retain the temporary directory; good for debugging
* We print "running minifier" and "running repro" in MinifierTestBase to make it easier to orient where logs are coming from
* same takes a `log_error` optional argument which you can use to reroute the error logs when things mismatch
* counters["inductor"]["intermediate_hooks"] tracks the number of intermediate hooks we've codegen'ed; good for populate the tqdm interface
* torch.fx.interpreter gets an official `boxed_run` interface which uses the boxed arguments calling convention and doesn't retain inputs unnecessarily long
* torch.utils._content_store gets compute_tensor_metadata/read_tensor_metadata helper functions for computing tensor information without serializing it
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100226
Approved by: https://github.com/bertmaher, https://github.com/bdhirsh, https://github.com/anijain2305
