Found this issue from [weekly running 7k github models](https://github.com/pytorch/torchdynamo/issues/1884). This caused regression on pass rate, there are 25 models failed due to this issue.
The reason is argument ```cx``` of ```aten._cudnn_rnn``` can be ```None```, but it doesn't handle well in meta registration, so throws the following error:
```
Traceback (most recent call last):
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/utils.py", line 1059, in run_node
return nnmodule(*args, **kwargs)
File "/scratch/ybliang/work/repos/pytorch/torch/nn/modules/module.py", line 1482, in _call_impl
return forward_call(*args, **kwargs)
File "/scratch/ybliang/work/repos/pytorch/torch/nn/modules/rnn.py", line 477, in forward
result = _VF.rnn_tanh(input, hx, self._flat_weights, self.bias, self.num_layers,
File "/scratch/ybliang/work/repos/pytorch/torch/_subclasses/fake_tensor.py", line 916, in __torch_dispatch__
r = func(*args, **kwargs)
File "/scratch/ybliang/work/repos/pytorch/torch/_ops.py", line 284, in __call__
return self._op(*args, **kwargs or {})
File "/scratch/ybliang/work/repos/pytorch/torch/_meta_registrations.py", line 2108, in _cudnn_rnn
cy = cx.new_empty(0 if cx is None else cell_shape)
AttributeError: 'NoneType' object has no attribute 'new_empty'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91333
Approved by: https://github.com/ezyang
It turns out that we *do* need to update *_scatter ops to return the exact same strides as their inputs. I added a test to `test/test_functionalization.py`, which now trips thanks to Ed's functionalization stride debugging check. It only actually ends up tripping silent correctness if you try to .backward() on that function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91029
Approved by: https://github.com/ezyang
## Summary
Torch.compile was previously not working for transformerencoder because torch.SDPA calls a native function on tensors that returns an int. This PR instead creates a dispatch stub for the function called in order to not create a separate fx node for this native function.
As well this pr adds meta functions for the fused kerenels.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90576
Approved by: https://github.com/cpuhrsch
Previously, we hackily wrapped unspecialized integers into
tensors and treated them as tensor inputs. Sometimes, downstream
operations would not be able to deal with the tensor input. Now,
we wrap them into SymInt, so more correct overload selection occurs.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89639
Approved by: https://github.com/anjali411
Fake tensor behaves pretty differently depending on if you have
symbolic shapes or not. This leads to bugs; for example, we
weren't getting correct convolution_backward strides because we
bypassed the correct stride logic in fake tensor on symbolic
shapes.
This PR attempts to unify the two codepaths. I don't manage to
unify everything, but I get most of it. The algorithm is delicate
and I'm still hosing down test failures.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89038
Approved by: https://github.com/anjali411
This improves the memory compression of resnet18 from .84 -> .94 on inductor no-cudagraphs. It does mean that any extern kernel which incorrectly computes strides will be a hard error at runtime, but that's an issue we are going to have to face with dynamic shapes anyway. CC @ezyang, @SherlockNoMad
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88248
Approved by: https://github.com/ezyang
Fixes https://github.com/pytorch/torchdynamo/issues/1802
There are a few problems,
1. torch.fused_moving_avg_obs_fake_quant doesn't have OpInfo test
2. self.empty_like() is not a valid call. it should be torch.empty_like(self)
3. python meta function has some unexplained behavior for arguments with default value of bool type?
In particular, problem 3 is the most concerning one.
**UPDATE: This is expected behavior, see discussion below for explanation.**
Without setting the default value for `per_row_fake_quant` and `symmetric_quant`, it gets the following error when running with meta tensor.
```
meta__fused_moving_avg_obs_fq_helper() missing 2 required positional arguments: 'per_row_fake_quant' and 'symmetric_quant'
```
I can fix this by adding the default values to these two args. However, I observer something strange when examining the actual value in meta function.
```
print("per_row_fake_quant", per_row_fake_quant)
print("symmetric_quant", symmetric_quant)
```
When default values are False, printed value correctly reflect the args value populated from call site.
When default values are True, printed value is ALWAYS True, regardless of the populated value from call site.
When default Values are None, printed value is `None` when call site set the value to 'False', printed value is 'True' when call site sets the value to 'True'.
I also verify that this bug also affect for other meta function with default args....
My speculation is that this is something about pybind value packing when called from c++ dispatcher to python meta function, and default value parsing for python meta function (and other python dispatch functions) ?
I tried to find the c++ call stack, but gdb is missing symbols and C++ stacktrace is not working properly... Appreciate anyone who can point me to the source file for pybind value packing.
cc @ezyang
cc @bdhirsh. I know you had a fix in the symbolic shape branch...
cc @yanboliang who reported this bug
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88058
Approved by: https://github.com/bdhirsh, https://github.com/yanboliang
This is a policy update for meta registration. **We now prefer python meta implementation over C++ meta function.** This is a flip of the previous policy, where we prefer C++ meta function over python meta function if they both exist.
Here's the meta registration process:
1. register_meta and register_decomposition will place the python meta/decomp functions into the `global_decomp_table`. However, they will NOT register them into dispatcher.
2. After global_decomp_table is populated, we will compile an `active_meta_table`. For a given op, we pick the most specific decomp function from `global_decomp_table` in the preference order of Meta > PostAutograd > PreAutograd.
3. We will unconditionally register all of them into python dispatcher. And register them into C++ dispatcher, unless it one of the following 3 cases
- 1. the op is a CompositeImplicitAutograd, and should rely on decomposed op's meta
- 2. the op is a view op, as the MetaTensor doesn't support aliased storage
- 3. the op is in the blocklist (due to UT failures, and we will burn down this list op by op)
Over the long run, we wish to implement all meta functions in python. With this PR, 321 op_overloads will have cpp meta overridden by python meta. There are still 400 op_overloads is using cpp meta. The exact list can be found here https://gist.github.com/SherlockNoMad/d20bb736178df8eebd3b054c8bb7cdc5
cc @ngimel @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87426
Approved by: https://github.com/ezyang, https://github.com/jansel
`diag` was unnecessarily implemented as a kernel rather than as a composite
function, which made it unnecessarily difficult (explicit backward + all it entails).
We also change a few uses of `diag` on 2D tensors for `diagonal()`. The
latter returns a view rather than creating a new tensor.
We also upgrade its meta implementation to a fully-fledged
decomposition
I tried implementing the backwards of `diagonal()` via `diag_scatter` (or better `diag_scatter_` to keep the perf) but functionalisation was failing and I was not sure how to fix this, so I moved on. It may be possible to simplify that one as well if @soulitzer or someone knows how to do this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87180
Approved by: https://github.com/ngimel, https://github.com/albanD, https://github.com/mruberry
We recently fixed a bug on symbolic-shapes branch where
an isinstance(x, int) test failed when passed a SymIntNode.
To prevent this, I've added a lint for all the codepaths
where we may pass SymInt/SymFloat directly to reject
direct isinstance int/float tests, and instead use one of
the aliases. The lint rule explains the options. I then
go and fix all of them.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87345
Approved by: https://github.com/bdhirsh, https://github.com/albanD
Big-bang PR to symintify **all** .sizes() calls in derivatives.yaml, which will be needed for symbolic tracing.
* with the exception of `split()`, which is tougher to land because it requires internal changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86610
Approved by: https://github.com/albanD
This reverts commit 978b46d7c9.
Reverted https://github.com/pytorch/pytorch/pull/86488 on behalf of https://github.com/osalpekar due to Broke executorch builds internally with the following message: RuntimeError: Missing out variant for functional op: aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[] . Make sure you have loaded your custom_ops_generated_lib
symintify split_with_sizes, dropout, fused_fake_obs_quant. meta for padding_2d ops
add meta_bernoulli_
meta kernel for at::gather
get pytorch_struct to pass: meta for scatter_add, fix backward
symintify split ops
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86488
Approved by: https://github.com/ezyang
symintify split_with_sizes, dropout, fused_fake_obs_quant. meta for padding_2d ops
add meta_bernoulli_
meta kernel for at::gather
get pytorch_struct to pass: meta for scatter_add, fix backward
symintify split ops
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86334
Approved by: https://github.com/ezyang
The output striding channels-last preservation logic differs between cuda and cpu. For the meta kernel, we can peek at the fake tensor device and use that to determine whether to do cpu or cuda.
You could argue there's a leaking of abstraction here but this seems like a pretty minimal leak and I'm not sure there's a much cleaner way forward for device-specific striding tracing logic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82846
Approved by: https://github.com/ezyang
Fixes#79512
This PR adds support for convolutional meta modules and computes the output shape correctly for some meta input tensor.
Currently in progress, no tests written so far.
**Feature implementations**:
- [x] `Conv1d`
- [x] `Conv2d`
- [x] `Conv3d`
**Tests**:
- [x] `Conv1d`
- [x] `Conv2d`
- [x] `Conv3d`
cc @albanD @anjali411
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79834
Approved by: https://github.com/ezyang, https://github.com/albanD
When a function returns multiple parameters in PyTorch, the `out`
parameter takes a tuple of tensors (see `linalg.svd` for example).
The current implementation in `out_wrapper_multi` modelled this wrong,
as it assumed that it would take a number of different named
parameters.
This PR implements the correct behaviour in `out_wrapper`. As a small
side-effect, we now need to call `@out_wrapper()` when the output is
just one tensor.
This PR also implements an additional optional parameter that checks
whether the dtype of the given `out` is exactly the dtype that the meta
function requires. This is the behaviour that we currently have in
PyTorch, and this check is necessary in eager when we call with these
tensors into external libraries.
We also make the functions with several outputs return a namedtuple,
similar to what we do in PyTorch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79941
Approved by: https://github.com/mruberry, https://github.com/ezyang
This PR simplifies the logic of `linalg.qr` using structured kernels. I
also took this chance and merged a few `copy_` operations with other
ops.
This PR removes a the previous magma implementation as is never faster
than that of cusolver and it's rather buggy. This has the side-effect
that now `qr` is not supported in Rocm. Ivan confirmed that this is
fine, given how incredibly slow was QR on Rocm anyway (we were marking
some tests as slow because of this...).
This PR also corrects the dispatch in geqrf. Before, if we called it
with a matrix for which `input.size(-2) <= 256 && batchCount(input) >= std::max<int64_t>(2, input.size(-2) / 16)` is false, and we have cublas but not cusolver, we would end up calling magma rather than cublas. This is not what the heuristic suggested.
Probaly we should benchmark these heuristics again, but that's beyond the scope of this PR.
Note. It looks like `torch.geqrf` maybe broken in MAGMA as per the
previous comment in `linalg_qr_helper_magma`. IvanYashchuk wdyt?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79054
Approved by: https://github.com/IvanYashchuk, https://github.com/ezyang
This PR adds `linalg.lu_solve`. While doing so, I found a bug in MAGMA
when calling the batched MAGMA backend with trans=True. We work around
that by solving the system solving two triangular systems.
We also update the heuristics for this function, as they were fairly
updated. We found that cuSolver is king, so luckily we do not need to
rely on the buggy backend from magma for this function.
We added tests testing this function left and right. We also added tests
for the different backends. We also activated the tests for AMD, as
those should work as well.
Fixes https://github.com/pytorch/pytorch/issues/61657
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77634
Approved by: https://github.com/malfet
Decompositions can be used to fill in meta support where necessary,
assuming the operations they decompose to support meta key.
This PR adds register_meta kwarg to register_decomposition that
optionally lets you register the meta to the C++ dispatch table
for meta tensors. I use this to then get the meta function for
where and huber_loss for free.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77353
Approved by: https://github.com/mruberry
