Summary: Apparently if I just do `tensor + eps` this turns into add.Tensor, which is bad because the constant Tensor ends up getting hoisted into an input, which is a bozo thing to do. Just make sure it's exactly compatible.
Test Plan:
```
buck run 'fbcode//mode/opt' fbcode//bolt/nn/executorch/backends/tests:qnn_test_ar1g1 bolt.nn.executorch.backends.tests.qnn_test_ar1g1.QnnTestAR1G1.test_RMSNorm
```
Reviewed By: tugsbayasgalan
Differential Revision: D84613184
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165437
Approved by: https://github.com/tugsbayasgalan
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
ghstack dependencies: #164573
Switch from guard_size_oblivious to guard_or_false if you encounter a DDE, this would then avoid folding this 3d bmm into a mm.
806d9e3fe7/torch/_decomp/decompositions.py (L4506-L4512)
## DDE
```
File "/data/users/colinpeppler/pytorch/torch/_decomp/decompositions.py", line 4506, in matmul
elif should_fold(tensor1, tensor2, is_out):
File "/data/users/colinpeppler/pytorch/torch/_decomp/decompositions.py", line 4472, in should_fold
if guard_size_oblivious(t1.numel() == 0):
torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(12*((u0//2)), 0) (unhinted: Eq(12*((u0//2)), 0)). (Size-like symbols: none)
Caused by: (_decomp/decompositions.py:4472 in should_fold)
```
```
File "/data/users/colinpeppler/pytorch/torch/_decomp/decompositions.py", line 4506, in matmul
elif should_fold(tensor1, tensor2, is_out):
File "/data/users/colinpeppler/pytorch/torch/_decomp/decompositions.py", line 4483, in should_fold
return all(
torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(3*((u0//2)), 3) (unhinted: Eq(3*((u0//2)), 3)). (Size-like symbols: none)
Caused by: (_decomp/decompositions.py:4483 in should_fold)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159184
Approved by: https://github.com/ezyang
ghstack dependencies: #158894
### What
- Use `statically_known_true` over `guard_size_oblivious` in cases where we're checking an optimization path. Otherwise, it will DDE and we can't take the safe/slower path.
- For broadcast checks, use `fallback=False` if we encounter a DDE. Typically, unbackeds would be ≥2 and that falls inline with size-oblivious reasoning (i.e. when `size_oblivious=True`).
### Example DDE
```
torch._inductor.exc.InductorError: LoweringException: GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq((u0//387), 1) (unhinted: Eq((u0//387), 1)). (Size-like symbols: u0)
Caused by: (_inductor/lowering.py:488 in broadcast_symbolic_shapes)
```
```
torch._inductor.exc.InductorError: LoweringException: GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq((u0//387), 1) (unhinted: Eq((u0//387), 1)). (Size-like symbols: u0)
Caused by: (_inductor/ir.py:2797 in create)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155267
Approved by: https://github.com/eellison
Summary: guard_size_oblivious has side effects that'll result in invalid strides when slice nodes take negative index on dynamic input shapes.
Cause overflow error with a huge number “9223372036854776048”
Test Plan: CIs should pass.
Differential Revision: D74354663
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153131
Approved by: https://github.com/laithsakka
Summary: as title, the clamp type promotion should take min/max arg into consideration as well.
Test Plan:
```
buck run fbcode//caffe2/test/inductor:test_aot_inductor -- -r test_clamp_decomposition_cpu
python test/inductor/test_torchinductor.py -k test_clamp -v
```
Differential Revision: D75490124
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154471
Approved by: https://github.com/desertfire, https://github.com/chenyang78
This patch fixes a corner case of `torch.isin` decompisition when both
inputs are scalars. This pattern showed up from #141196.
Fixes#141196.
Error stack befor this patch:
```
File "/home/ryanguo99/repos/pytorch/test/dynamo/test_misc.py", line 12503, in test_scalar_isin_decomposition
res = opt_f()
^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/eval_frame.py", line 691, in _fn
raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/output_graph.py", line 1618, in _call_user_compiler
raise BackendCompilerFailed(
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/output_graph.py", line 1593, in _call_user_compiler
compiled_fn = compiler_fn(gm, self.example_inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/repro/after_dynamo.py", line 150, in __call__
compiled_gm = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/__init__.py", line 2365, in __call__
return compile_fx(model_, inputs_, config_patches=self.config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_inductor/compile_fx.py", line 2317, in compile_fx
return aot_autograd(
^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/backends/common.py", line 106, in __call__
cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/aot_autograd.py", line 1179, in aot_module_simplified
compiled_fn = AOTAutogradCache.load(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/_aot_autograd/autograd_cache.py", line 923, in load
compiled_fn = dispatch_and_compile()
^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/aot_autograd.py", line 1164, in dispatch_and_compile
compiled_fn, _ = create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/aot_autograd.py", line 576, in create_aot_dispatcher_function
return _create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/aot_autograd.py", line 826, in _create_aot_dispatcher_function
compiled_fn, fw_metadata = compiler_fn(
^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 180, in aot_dispatch_base
fw_module, updated_flat_args, maybe_subclass_meta = aot_dispatch_base_graph( # type: ignore[misc]
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 2199, in _trace_inner
t = dispatch_trace(
^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_compile.py", line 51, in inner
return disable_fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/eval_frame.py", line 872, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 1223, in dispatch_trace
graph = tracer.trace(root, concrete_args) # type: ignore[arg-type]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_dynamo/eval_frame.py", line 872, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/_symbolic_trace.py", line 850, in trace
(self.create_arg(fn(*args)),),
^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 1278, in wrapped
out = f(*tensors) # type:ignore[call-arg]
^^^^^^^^^^^
File "<string>", line 1, in <lambda>
File "/home/ryanguo99/repos/pytorch/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 720, in inner_fn
outs = fn(*args)
^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 419, in _functionalized_f_helper
f_outs = fn(*f_args)
^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 81, in inner_fn
outs = fn(*args)
^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 902, in functional_call
out = PropagateUnbackedSymInts(mod).run(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/interpreter.py", line 171, in run
self.env[node] = self.run_node(node)
^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/symbolic_shapes.py", line 7387, in run_node
result = super().run_node(n)
^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/interpreter.py", line 240, in run_node
return getattr(self, n.op)(n.target, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/interpreter.py", line 320, in call_function
return target(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 1326, in __torch_function__
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_subclasses/functional_tensor.py", line 511, in __torch_dispatch__
outs_unwrapped = func._op_dk(
^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/utils/_stats.py", line 27, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 1428, in __torch_dispatch__
return proxy_call(self, func, self.pre_dispatch, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 797, in proxy_call
r = maybe_handle_decomp(proxy_mode, func, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/fx/experimental/proxy_tensor.py", line 2358, in maybe_handle_decomp
out = CURRENT_DECOMPOSITION_TABLE[op](*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_prims_common/wrappers.py", line 309, in _fn
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_decomp/decompositions.py", line 5108, in isin
return isin_default(elements, test_elements, invert=invert)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ryanguo99/repos/pytorch/torch/_decomp/decompositions.py", line 5137, in isin_default
x = elements.view(*elements.shape, *((1,) * test_elements.ndim))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
TypeError: view() received an invalid combination of arguments - got (), but expected one of:
* (torch.dtype dtype)
* (tuple of ints size)
While executing %isin : [num_users=1] = call_function[target=torch.isin](args = (%x, %x), kwargs = {})
GraphModule: class GraphModule(torch.nn.Module):
def forward(self):
# File: /home/ryanguo99/repos/pytorch/test/dynamo/test_misc.py:12498 in f, code: x = torch.tensor(0)
x: "i64[][]" = torch.tensor(0)
# File: /home/ryanguo99/repos/pytorch/test/dynamo/test_misc.py:12499 in f, code: return torch.isin(x, x)
isin: "b8[][]" = torch.isin(x, x); x = None
return (isin,)
Original traceback:
File "/home/ryanguo99/repos/pytorch/test/dynamo/test_misc.py", line 12499, in f
return torch.isin(x, x)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153216
Approved by: https://github.com/williamwen42, https://github.com/peterbell10
Currently, `torch._chunk_cat` only supports contiguous inputs (due to `.view()` usage in `_pad_chunk()` supporting only contiguous tensor). This doesn't work for internal models where there can be non-contiguous input tensors:
- size=[8192, 16416], stride=[16448, 1] # stride[0] is larger than size[1]
- size=[1152, 384], stride=[1, 1152] # column-major tensor
In this PR, we relax the assumption on contiguous input tensor, by switching from `.view()` to `.reshape()`. Note that since `.reshape()` will try to use `.view()` under the hood whenever possible, this should not cause regression to existing use cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151263
Approved by: https://github.com/BoyuanFeng
This is a resubmission of my previous PR that I accidentally deleted, apology in advance if any inconvenience caused. Below are details of this PR.
Fix an issue when torch.addmv behaves inconsistent between torch.compile mode and eager mode. Here is the code to reproduce:
```
import torch
import numpy as np
@torch.compile
def test_optimized(input, mat, vec):
return torch.addmv(input, mat, vec)
def test(input, mat, vec):
return torch.addmv(input, mat, vec)
input = torch.tensor([2], dtype=torch.int32)
mat = torch.tensor(np.random.randn(0, 0), dtype=torch.int32)
vec = torch.tensor([])
origin_out = test(input, mat, vec)
optimized_out = test_optimized(input, mat, vec)
print(origin_out) # tensor([2.])
print(optimized_out) # tensor([])
```
According to the equation (https://pytorch.org/docs/stable/generated/torch.addmv.html), when matrix and vector is empty, returning `[2.]` seems more reasonable to me.
Following the cpu implementation of this API:e97b97af56/aten/src/ATen/native/Blas.cpp (L62)
I add an additional branch to handle empty matrix
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143792
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/jansel
