Previously setting garbage_collection_threshold or max_split_size_mb along with expandable_segments:True could cause the allocator to hit assert failures when running nearly out of memory. This PR ensures garbage_collection and max_split freeing do not accidentally try to release expandable segments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134338
Approved by: https://github.com/ezyang
Fixes#133252
In strict mode, we have this routine for mapping traced parameters to their FQNs using tensor ids. Currently we assume there's at least 1 unique FQN for each traced parameter, but this seems to break with parameter reuse when call_module nodes are present. Adding a test case where this breaks.
Fixes this by assigning the same FQN to all traced parameters with the same tensor id. This is fine because we return the original state_dict for the EP, and the unflattener has its own routine of handling aliasing: https://github.com/pytorch/pytorch/pull/125758
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134500
Approved by: https://github.com/angelayi
In `collective()`, `pointToPoint()` and `collectiveCoalesced()`, CUDA guards were created with an unset (default) CUDA device. This is the reason for the IMA facing the NaN checker in issue https://github.com/pytorch/pytorch/issues/134062.
With this fix, `torch.cuda.set_device(device)` is not needed to work around the IMA.
Also refactored a couple places where the guard is created -- preferably we create the guard with a known device, rather than setting the device later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134357
Approved by: https://github.com/wconstab, https://github.com/shuqiangzhang
ghstack dependencies: #134345
Fix `test_logs_out` UT on Windows. make `test/dynamo/test_logging.py` all UTs pass on Windows.
Changes:
1. Close `NamedTemporaryFile` to release file handle to avoid PermissionError issue.
2. `PermissionError` setup as `delete=False`, let file not be auto deleted.
3. Open log file as "utf-8" to align with Linux.
4. Process wrap difference for Windows.
5. Delete tmp file manually.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134586
Approved by: https://github.com/jansel
Summary:
With training IR, we cannot rely on trapping `to()` in `FunctionalTensor` because the regular decomposition kicks it first, and that can cause it to be optimized away.
So instead we preserve it until we functionalize, and then replace it explicitly with `_to_copy()`.
Test Plan: expected test failures go away
Differential Revision: D61883878
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134622
Approved by: https://github.com/zhxchen17, https://github.com/tugsbayasgalan
aten.empty is almost always fusible into its consumer, so we never CSE
it. This fixes a bug that looks like the following:
```py
@torch.library.custom_op("_reinplacing::sin_cos", mutates_args={"out_sin", "out_cos"})
def sin_cos(x: torch.Tensor, out_sin: torch.Tensor, out_cos: torch.Tensor) -> None:
out_sin.copy_(x.sin())
out_cos.copy_(x.cos())
@torch.compile
def f(x):
out0 = torch.empty_like(x)
out1 = torch.empty_like(x)
sin_cos(x, out0, out1)
return x.clone(), out0, out1
x = torch.randn(3, requires_grad=True)
f(x)
```
- cse would de-duplicate the empty nodes
- reinplacing would add an additional clone (because it can't write to
both tensors at the same time)
- the clone lowers into a new buffer + a copy_ kernel
- the copy_ kernel is unnecessary because "empty" is special - all reinplacing needed was an additional
buffer, it doesn't matter what the values are.
We could attempt to fix this on the reinplacing side but this seemed
better as a partitioner heuristic and the reinplacing fix is a bit more
tricky (we'd need to identify that the op never reads from the empty
node).
Test Plan:
- new test (the old number was 27, the new number is 21, so this PR
helped).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134703
Approved by: https://github.com/yf225
ghstack dependencies: #134466, #134490, #134491
Fixes [134212](https://github.com/pytorch/pytorch/issues/134212)
Currently, when we use 2D FSDP with TP, `optimizer.step()` would fail if the model were not fully tensor parallelized. If we don't have the entire model tensor parallelized when doing 2D, we would have both 1D and 2D DTensor parameters. As foreach is turned on by default, `optimizer.step()` would fail as cross mesh op is not allowed. Error as follows:
```
NotImplementedError: aten._foreach_mul_.Scalar: DTensor does not support cross-mesh operation yet!Got meshes: DeviceMesh('cuda', [[0, 1], [2, 3]], mesh_dim_names=('dp', 'tp')) DeviceMesh('cuda', [1, 3], mesh_dim_names=('dp',))
```
In this PR, we extend implicit_replication to replicate DTensor in missing dimensions for foreach ops. If users don't want to fully tensor parallelize the model when using 2D, they have the option of using the `implicit_replication()` context manager for `optimizer.step()`. In this case, we would swap out the 1D DTensorSpec and replace it with 2D DTensorSpec. However, we don't want to turn this on by default yet, as we want the users to be aware that the tp dimension is replicated if a layer is not tp-ed.
With implicit implication turning on, try replicate dtensor spec in missing dimension would work for most cases for foreach case except when the first DTensor in the list is one that also need to be replicated. This is currently a limitation, which I don't have a good solution yet. Currently, with this change, we can handle most of the cases except the case that the first DTensor's ndim is not the largest.
```
[2D_DTensor, 1D_DTensor...] ---> Implicit_replication() can handle this.
[1D_DTensor, 2D_DTensor...] ---> Implicit_replication() can't handle this.
```
This change doesn't affect the existing default behavior, as `implicit_replication()` is not turned on by default.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134551
Approved by: https://github.com/tianyu-l
So that the tensor's lifetime management is the same as the management built for the NCCL, pre and post kernels.
Also so that on visualizers, they show up in the NCCL stream line. Otherwise if they show up in the compute line, user may get confused (my code does not have these kernels).
The check is thus moved after the point where we depend NCCL stream from the last compute kernel.
Also moved declaration of `checkForNan` from Utils.hpp to NCCLUtils.hpp, and renamed Utils.cu to NCCLUtils.cu.
Differential Revision: [D61957573](https://our.internmc.facebook.com/intern/diff/D61957573)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134300
Approved by: https://github.com/shuqiangzhang, https://github.com/wconstab
The previous PR https://github.com/pytorch/pytorch/pull/133532 caused stuck compilation issue on internal models. In this 2nd attempt PR, we gate the trace_rules.py changes with `if not torch._dynamo.config.skip_fsdp_hooks:`, so that they don't take effect for current graph-break FSDP2 (which relies on the default config value `skip_fsdp_hooks=True`), and will only take effect when we are using Traceable FSDP2 (in which case the user needs to proactively set `skip_fsdp_hooks=False`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134539
Approved by: https://github.com/ckluk2, https://github.com/yanboliang
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
There are two function variants to get accumulated dtype for a given dtype:
- Func1: `c10::ScalarType toAccumulateType(c10::ScalarType type, c10::DeviceType device)`
- Func2: `c10::ScalarType toAccumulateType(c10::ScalarType type, bool is_cuda)`
The Func1 is general enough to support different devices, while the Func2 only supports CUDA and CPU. This PR intends to add the Intel GPU path in the Func1. And we expect users to invoke the Func1 to ensure compatibility for different devices.
* __->__ #134465
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134465
Approved by: https://github.com/Skylion007, https://github.com/atalman
## Semantic
The semantic is
(1) By default `torch.serialization.skip_data(materialize_fake_tensors=False)` will make `torch.save` skip writing storages (but reserve space for them in the checkpoint).
```python
import torch
import torch.nn as nn
sd = nn.Linear(3, 5).state_dict()
with torch.serialization.skip_data():
torch.save(sd, 'foo.pt')
print(torch.load('foo.pt', weights_only=True))
```
(2) With `torch.serialization.skip_data(materialize_fake_tensors=True)`If FakeTensor is passed to `torch.save` the pickler will treat these FakeTensors as being "materialized" space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor)
```python
import torch
import torch.nn as nn
from torch._subclasses.fake_tensor import FakeTensorMode
with FakeTensorMode():
m = nn.Linear(3, 5, dtype=torch.float16, device='cuda')
sd = m.state_dict()
with torch.serialization.skip_data(materialize_fake_tensors=True):
torch.save(sd, 'bla.pt')
print(torch.load('bla.pt', weights_only=True))
# OrderedDict([('weight', tensor([[0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.]], device='cuda:0', dtype=torch.float16)), ('bias', tensor([0., 0., 0., 0., 0.], device='cuda:0', dtype=torch.float16))])
```
## Follow Ups
- [ ] `torch.load` semantic for skip_data context manager
- [ ] Mechanism for getting offsets of storages saved via this method (for writing in a separate pass)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134504
Approved by: https://github.com/albanD
Summary:
Encountered issues related to AMD build when working on https://www.internalfb.com/diff/D60739324?dst_version_fbid=2203158110057105 (see stack trace P1545717562)
Looking at the file history, seems that the flag is no longer used so I propose to remove it. Alternatively, I could change the `#ifdef` to check both `USE_C10D_NCCL` and `USE_ROCM` and include the corresponding AMD header files.
Let me know what is more preferred way.
Test Plan: Sandcastle
Differential Revision: D61762129
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134404
Approved by: https://github.com/malfet
A user wants to use the flop counter with meta devices. This previously caused problems for SDPA+NJT:
1. autocast check: `torch.is_autocast_enabled("meta")` fails because `meta` is not valid for autocasting. If we skip this, we run into the next error
2. math backend: conversion to NST requires getting concrete offsets in a list of python integers, which doesn't work on a meta tensor b2eb0e8c6a/torch/nested/_internal/sdpa.py (L809-L815)
3. (fixed in the previous PR, #134288) - if we force using flash attention backend for flop counting, `_flash_attention_forward` previously didn't support meta tensors.
In this PR, we check specifically for FlopCounterMode, and, if it's enabled and combined with meta tensors, (a) skip autocasting and (b) force it down the flash attention path. This isn't generally safe for tracing (e.g. if you actually care which kernels you are running), but in the absence of actual device information, we have to make some assumptions. By specifically checking for FlopCounterMode, this should reduce the chance of unintended side effects for other meta tensor users.
Note: fake tensor would solve a bunch of these issues, but it's not a viable solution right now for the user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134289
Approved by: https://github.com/soulitzer
ghstack dependencies: #134288
Fixes#130394
TorchInductor doesn't respect original strides of outputs. It opens up optimization opportunities like changing up memory layout. But for some cases, such as the case in https://github.com/pytorch/pytorch/issues/130394, we do need the output match the exact stride as required. The correctness is the first priority goal. So, this PR adds a new API `ir.ExternKernel.require_exact_strides(x, exact_strides, allow_padding=False)` to fix the issue. This PR enables dense and non-dense outputs' strides follow the strides required by semantics.
The comparison between the original and after this fix for the test is the below.
```python
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
- x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
tmp1 = tmp0 + tmp0
- tl.store(out_ptr0 + (x2), tmp1, xmask)
+ tl.store(out_ptr0 + (x0 + (16*x1)), tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (16, 8), (16, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
- buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
+ buf1 = empty_strided_cuda((16, 8), (16, 1), torch.float32)
stream0 = get_raw_stream(0)
triton_poi_fused_add_copy_0.run(arg0_1, buf1, 128, grid=grid(128), stream=stream0)
del arg0_1
return (buf1, )
```
The buf1 is created with exact stride required by users, and its values are written in same stride with the input.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130956
Approved by: https://github.com/eellison, https://github.com/blaine-rister, https://github.com/desertfire
```
compile time instruction count for iteration 1 is 10732129038
compile time instruction count for iteration 2 is 10719776783
compile time instruction count for iteration 3 is 10729546868
compile time instruction count for iteration 4 is 10737655132
compile time instruction count for iteration 5 is 10732564252
compile time instruction count for iteration 6 is 10728721234
compile time instruction count for iteration 7 is 10733354271
compile time instruction count for iteration 8 is 10719588972
compile time instruction count for iteration 9 is 10706311856
```
1. add torch.manual_seed(0), inputs was not the same across iterations
2. disable gc.
3. remove loop (not needed since compilation happen once only)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134649
Approved by: https://github.com/aorenste
ghstack dependencies: #133834, #134635
## Semantic
The semantic is
(1) By default `torch.serialization.skip_data(materialize_fake_tensors=False)` will make `torch.save` skip writing storages (but reserve space for them in the checkpoint).
```python
import torch
import torch.nn as nn
sd = nn.Linear(3, 5).state_dict()
with torch.serialization.skip_data():
torch.save(sd, 'foo.pt')
print(torch.load('foo.pt', weights_only=True))
```
(2) With `torch.serialization.skip_data(materialize_fake_tensors=True)`If FakeTensor is passed to `torch.save` the pickler will treat these FakeTensors as being "materialized" space will be reserved in the checkpoint for the associated storage bytes, and when loading the type will be Tensor instead of FakeTensor)
```python
import torch
import torch.nn as nn
from torch._subclasses.fake_tensor import FakeTensorMode
with FakeTensorMode():
m = nn.Linear(3, 5, dtype=torch.float16, device='cuda')
sd = m.state_dict()
with torch.serialization.skip_data(materialize_fake_tensors=True):
torch.save(sd, 'bla.pt')
print(torch.load('bla.pt', weights_only=True))
# OrderedDict([('weight', tensor([[0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.],
# [0., 0., 0.]], device='cuda:0', dtype=torch.float16)), ('bias', tensor([0., 0., 0., 0., 0.], device='cuda:0', dtype=torch.float16))])
```
## Follow Ups
- [ ] `torch.load` semantic for skip_data context manager
- [ ] Mechanism for getting offsets of storages saved via this method (for writing in a separate pass)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134504
Approved by: https://github.com/albanD
Summary: The default c_shim version was switched to 2 for HIP in D60674018. This results in some linking errors where shim function symbols are missing from the compiled .so file (eg. P1551186492) when building lowering benchmark scripts since the required files aren't included. Hipify the shim v2 generated header files as well since they're needed during codegen when the buck binaries are executed.
Reviewed By: frank-wei, zoranzhao, henryoier
Differential Revision: D61865202
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134689
Approved by: https://github.com/zoranzhao
Summary:
This is to fix the pytorch issue filed https://github.com/pytorch/pytorch/issues/133010
one way to fix this problem is to enable parallel start processes in mp.start_processes.
What else in the diff:
refactored a test case api_test which was repeating a lot of tests due to the inheritance.
added unit test for forkserver when parallel start is on.
Test Plan: Added unit tests
Differential Revision: D61878552
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134629
Approved by: https://github.com/d4l3k
Hi,
I noticed the `unfold` operator was missing on MaskedTensor.
I tested that my change works when calling unfold and backward on a `MaskedTensor` but I didn't find the tests for the dispatch of such operation. Where is it?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125262
Approved by: https://github.com/cpuhrsch
- This PR generates a more useful output log for users: P1552399180.
- It also fixes the logic when we check the all-gather size mismatch.
- Add dtype check for collective input/output
- We store more context information for error match_state so that we can report them in the file.
- Disable the size match for alltoall because we don't log the size for all inputs/outputs.
- Correct some types for func args specification.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134528
Approved by: https://github.com/c-p-i-o
This PR adds a basic Runtime Estimator for single-device models.
It estimates the GPU runtime in milliseconds using various estimation methods under the ``FakeTensorMode``.
It provides a ``TorchDispatchMode`` based context manager that can estimate the eager runtime of PyTorch functions. It supports two estimation modes, benchmarking (`operator-level-benchmark`) and roofline cost modeling (`operator-level-cost-model`).
For modules executed under this context manager, it agggregates the forward and backward operation runtimes and records their execution orders.
```
import torch
from torch import nn, optim
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.distributed._tools.runtime_estimator import RuntimeEstimator
from torch.testing._internal.distributed._tensor.common_dtensor import (
ModelArgs,
Transformer,
)
if __name__ == "__main__":
def _train_step(
model: nn.Module,
optimizer: optim.Optimizer,
inp: torch.Tensor,
):
out = model(inp)
loss = out.sum()
loss.backward()
optimizer.step()
optimizer.zero_grad()
dev = torch.cuda.current_device()
vocab_size = 8192
bsz, seq_len = 32, 1024
model_args = ModelArgs(
n_layers=4,
n_heads=12,
vocab_size=vocab_size,
max_seq_len=seq_len,
dim=768,
dropout_p=0.1,
)
runtime_estimator = RuntimeEstimator()
with FakeTensorMode():
with torch.device(dev):
model = Transformer(model_args)
optimizer = optim.Adam(model.parameters(), lr=1e-2, foreach=True)
inp = torch.randint(0, model_args.vocab_size, (bsz, model_args.max_seq_len), device=dev)
with runtime_estimator("operator-level-benchmark"):
_train_step(model, optimizer, inp)
with runtime_estimator("operator-level-cost-model"):
_train_step(model, optimizer, inp)
# Actual model runtime
with torch.device(dev):
model = Transformer(model_args)
optimizer = optim.Adam(model.parameters(), lr=1e-2, foreach=True)
inp = torch.randint(0, model_args.vocab_size, (bsz, model_args.max_seq_len), device=dev)
warmup_iters, actual_iters = 2, 5
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
for _ in range(warmup_iters):
_train_step(model, optimizer, inp)
start_event.record()
for _ in range(actual_iters):
_train_step(model, optimizer, inp)
end_event.record()
torch.cuda.synchronize()
measured_time = start_event.elapsed_time(end_event) / actual_iters
print(f"Actual total_time: {measured_time:.3f} ms")
```
<img width="506" alt="Screenshot 2024-08-26 at 11 27 15 PM" src="https://github.com/user-attachments/assets/04d243c9-21a6-4389-8c20-80958980788c">
@weifengpy @xuanzhang816 @gnadathur
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134243
Approved by: https://github.com/weifengpy
**Summary**
reland of https://github.com/pytorch/pytorch/pull/134294Fixes#131446Fixes#126852Fixes#126868Fixes#126493
The PR was reverted due to CI red signal in https://github.com/pytorch/pytorch/actions/runs/10537099590/job/29201744658. It seems that the `gaussian_nll_loss` test had been flaky before my original PR #134294 . Therefore this PR also removes the `xfail` mark on this specific test to make CI signal green.
See the error message below:
```
2024-08-24T13:42:01.3228990Z ==================================== RERUNS ====================================
2024-08-24T13:42:01.3229530Z [31m[1m_ TestDTensorOpsCPU.test_dtensor_op_db_nn_functional_gaussian_nll_loss_cpu_float32 _[0m
2024-08-24T13:42:01.3229710Z Unexpected success[90m[39;49;00m
2024-08-24T13:42:01.3230235Z [31m[1m_ TestDTensorOpsCPU.test_dtensor_op_db_nn_functional_gaussian_nll_loss_cpu_float32 _[0m
2024-08-24T13:42:01.3230407Z Unexpected success[90m[39;49;00m
2024-08-24T13:42:01.3230594Z =================================== FAILURES ===================================
2024-08-24T13:42:01.3231128Z [31m[1m_ TestDTensorOpsCPU.test_dtensor_op_db_nn_functional_gaussian_nll_loss_cpu_float32 _[0m
2024-08-24T13:42:01.3231296Z Unexpected success[90m[39;49;00m
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134509
Approved by: https://github.com/tianyu-l, https://github.com/wz337
The original DCP doesn't flattening all the containers, which can cause issues, https://github.com/pytorch/pytorch/pull/125335 intends to solve the issue by flattening all the dictionaries.
Unfortunately, it breaks the checkpoints that are saved before 2.4. This
also shows some issues of the DCP:
1. DCP should record version in the metadata.
2. DCP should have a nice way to load old state_dict.
3. DCP should unflatten all containers (map, list) not just map.
This PR only addresses issue 2 to unblock users. Issue 1 and issue 3 need to be addressed in the future.
@pradeepfn Please let me know if this summary matches our discussion.
Fixes https://github.com/pytorch/pytorch/issues/133923
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134158
Approved by: https://github.com/wz337, https://github.com/pradeepfn
Summary: benchmarks/dynamo/ci_expected_accuracy/update_expected.py expects a benchmark run config is named as {config}_{benchmark}, and CPU tests should follow the same naming convention.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134639
Approved by: https://github.com/huydhn
Summary: Recently https://github.com/pytorch/pytorch/pull/133620 added support for automatic dynamic shapes, where a new enum, `DIM`, was introduced to provide hints like `AUTO` and `STATIC`. This PR is a nominal change where we expose the hints via the existing public `Dim` API, and remove `DIM` from the public API. The main motivation is to avoid having users need to import too many things.
Test Plan: existing
Differential Revision: D61807361
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134484
Approved by: https://github.com/angelayi
The previous PR https://github.com/pytorch/pytorch/pull/133532 caused stuck compilation issue on internal models. In this 2nd attempt PR, we gate the trace_rules.py changes with `if not torch._dynamo.config.skip_fsdp_hooks:`, so that they don't take effect for current graph-break FSDP2 (which relies on the default config value `skip_fsdp_hooks=True`), and will only take effect when we are using Traceable FSDP2 (in which case the user needs to proactively set `skip_fsdp_hooks=False`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134539
Approved by: https://github.com/ckluk2, https://github.com/yanboliang
Fixes#134391, #124714
The above issues reported that `dist.barrier()` could hang in some cases.
The culprit is that ProcessGroupNCCL inferred a wrong device to perform the dummy all-reduce.
After the PR, the following will be the order of device selection:
- 1st choice: `opts.device_ids`, if provided by user via `barrier(opts)`.
- 2nd choice: bound device id, if provided to `init_process_group` via `device_id` arg.
- 3rd choice: `usedDeviceIdxs_` recorded in current PG. Will have a value from previous collectives.
- 4th choice: `globalRank() % localDeviceCount_`. This can only happen when `dist.barrier()` is the first call of the PG.
What's new:
- Added the 2nd choice.
- In the 4th choice, we use `globalRank()` instead of group-local rank, because the group-local rank can be offset wrt the device id if intra-node GPUs are sharded into multiple dimensions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134617
Approved by: https://github.com/yifuwang, https://github.com/shuqiangzhang
This adds logs if we can't acquire locks in NCCLUtils and ProcessGroupNCCL for 30s.
This is motivated by some deadlocks were seeing and it's unclear if it's in NCCL or on the PyTorch side of things.
This required replacing most `std::mutex` with `std::timed_mutex` and `std::condition_variable_any` as appropriate.
Test plan:
existing CI for regressions
will add unit tests on `C10D_LOCK_GUARD`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134131
Approved by: https://github.com/c-p-i-o, https://github.com/fduwjj
Summary:
There's 2 concepts of unsupported sympy.Functions in symbolic_shapes:
1) unsupported by the export solver, meaning the solver doesn't know how to provide useful fixes for those functions
2) unsupported by the sympy interpreter - meaning we can't reify them into FX nodes because the functions aren't present in PythonReferenceAnalysis
This splits the current call into a call for each version, with the Export solver the only user of 1). For 1), we enumerate the functions in _sympy/functions.py, and subtract the functions we know we can support. For 2) there's only 3 functions we've seen pop up in test cases.
cc jgong5 mingfeima XiaobingSuper sanchitintel ashokei jingxu10
Differential Revision: D61863394
Pulled By: pianpwk
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134598
Approved by: https://github.com/angelayi
PYTHONPATH=$(pwd) python benchmarks/update_hint_benchmark.py out
as of this diff, compile_time_instruction_count counts the number of instruction from within
convert_frame.compile_inner
```
update_hint_regression,compile_time_instruction_count,10522459165
```
will add result from CI once populated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133834
Approved by: https://github.com/aorenste
Summary: apparently DIM.AUTO leads to duck sizing, I didn't catch this. Doing the least intrusive fix possible by using `torch._dynamo.maybe_mark_dynamic()` under the hood.
Test Plan: added test
Differential Revision: D61809344
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134486
Approved by: https://github.com/avikchaudhuri
See #121528 for additional context.
In #120682, we moved the attention kernels from meta_registrations to fake_impls with the intent of fixing the device handling for seed/offset: these are typically on CPU. We needed to put the registrations in fake_impls to do this because meta_registrations doesn't have a way to specify device, whereas fake_impls does. But when we tried to actually fix the device types (#120839), we had to revert the PR because it broke cudagraph handling (during which seed/offset _are_ on CUDA).
Now, we want to put the registrations back in meta_registrations so that we can call these kernels with meta tensors. The use case is later in this stack - we want to be able to use the flop counter with these kernels.
Also - I specifically skip the `compare_tensor_meta()` check in test_fake / test_fake_autocast tests for the `_efficient_attention_forward` and `_flash_attention_forward` kernels, which fails because of the device mismatch from the seed/offset tensors. Then we can un-skip these opinfos. I verified that the efficient_attention_forward bug (#120842) is now caught by these opinfos if I revert the fix from this PR.
Differential Revision: [D61687369](https://our.internmc.facebook.com/intern/diff/D61687369)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134288
Approved by: https://github.com/drisspg
Maintainers have the links to their GitHub profiles, but the major contributors do not have them.
I added the links to the contributors' GitHub accounts in case anyone wants to follow them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133787
Approved by: https://github.com/albanD
Seeing failures like this:
```
#49 844.6 //build_scripts/manylinux1-check.py:6: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives
.....
[python 3/3] RUN bash build_scripts/build.sh && rm -r build_scripts:
846.9 ...it did, yay.
846.9 + for PYTHON in '/opt/python/*/bin/python'
846.9 + /opt/python/cpython-3.12.0/bin/python build_scripts/manylinux1-check.py
847.0 Traceback (most recent call last):
847.0 File "//build_scripts/manylinux1-check.py", line 55, in <module>
847.0 if is_manylinux1_compatible():
847.0 ^^^^^^^^^^^^^^^^^^^^^^^^^^
847.0 File "//build_scripts/manylinux1-check.py", line 6, in is_manylinux1_compatible
847.0 from distutils.util import get_platform
847.0 ModuleNotFoundError: No module named 'distutils'
------
```
PR: https://github.com/pytorch/pytorch/pull/134455
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134595
Approved by: https://github.com/kit1980, https://github.com/seemethere, https://github.com/malfet
**Summary**
This PR is a follow-up of #126924 to address reviewer's comments:
1) add a test case to show the use of `local_map` as a function decorator.
2) simplify the logic of handling different data types of `out_placements`.
3) correct variable naming in test cases to match math formulas.
**Test**
see #126924
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127752
Approved by: https://github.com/wanchaol
Which fixes BatchNorm behavior for if called with empty tensors on MPS backed. Removed `expectedFailureMPS` in test_nn.py, deleted expected failure in `test_mps.py` and adjusted `skipIfMPS` to `expectedFailureMPS` in BatchNorm2d OpInfo decorator, but restrict it only to the memory format tests
Test Plan: CI + `python3 -c "import torch; print(torch.nn.BatchNorm2d(3, device='mps')(torch.rand(0, 3, 2, 2, device='mps')))"`
Fixes https://github.com/pytorch/pytorch/issues/134423
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134540
Approved by: https://github.com/Skylion007, https://github.com/albanD
## Context
In some user Triton kernels, we have this set-up for whatever reason.
```
@triton.jit
def mykernel(
param0,
param1,
param2,
param3: tl.constexpr, # autotuned
param4, # non-constexpr
):
...
```
This is an edge case because it's a general practice to declare all constexprs params at the end.
And this will be an issue for AOTI because it fails to codegen all 4 params. That will surface as a device-side error: CUDA IMA, invalid argument...
```
> void* kernel_args_var_0[] = {&var_0, &var_1, &var_2};
---
< CUdeviceptr var_3;
< AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_get_data_ptr(buf0, reinterpret_cast<void**>(&var_3)));
< void* kernel_args_var_0[] = {&var_0, &var_1, &var_2, &var_3};
```
## Root-cause
* `kernel.constexpr` from the Kernel side-table contains the indices for all `constexpr` params that includes autotuned params.
* `raw_args`, that gets passed to wrapper codegen, excludes autotuned args.
* In the wrapper codegen, we try to find non-constexpr args using `kernel.constexpr` & `raw_args`. This is okay unless there's a `raw_arg` after an autotuned param in the function signature.
79b7fff188/torch/_inductor/codegen/cpp_wrapper_cuda.py (L118-L126)
## Fix
We try to fix this, by calculating the right constexprs wrt `raw_args`.
An illustration
```
raw_args: [arg0, arg1, arg2, arg4]
kernel.arg_names: [param0, param1, param2, param3, param4]
kernel.constexprs: [3] # param3 is autotuned; this is correct wrt kernel.arg_names
constexpr_indices: [] # this is correct wrt raw_args
```
Differential Revision: [D61831625](https://our.internmc.facebook.com/intern/diff/D61831625)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134520
Approved by: https://github.com/oulgen
This is designed to be a more ergonomic interface on top of justknob_feature (see https://github.com/pytorch/pytorch/pull/134151 for just the PR with the base commits).
The idea is that people stop having to think about this as much, and can just do JustkobsConfig("//the:thing", "FORCE_THING") and it'll do the right thing.
Primarily sending this to see how people feel about the API, and using it for new config changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134161
Approved by: https://github.com/ezyang
So that the tensor's lifetime management is the same as the management built for the NCCL, pre and post kernels.
Also so that on visualizers, they show up in the NCCL stream line. Otherwise if they show up in the compute line, user may get confused (my code does not have these kernels).
The check is thus moved after the point where we depend NCCL stream from the last compute kernel.
Also moved declaration of `checkForNan` from Utils.hpp to NCCLUtils.hpp, and renamed Utils.cu to NCCLUtils.cu.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134300
Approved by: https://github.com/shuqiangzhang, https://github.com/wconstab
Clarify that `add_safe_globals` will allow types for these instructions
Some types do not appear as `GLOBAL` and are only caught in `BUILD`, example from hf slack is `numpy.dtypes.UInt32DType`
```python
import torch
import numpy as np
from tempfile import TemporaryDirectory
from pathlib import Path
from codecs import encode
torch.serialization.add_safe_globals([encode, np.dtype, np.core.multiarray._reconstruct, np.ndarray])
with TemporaryDirectory() as tempdir:
p = Path(tempdir)
r2 = np.random.get_state()
torch.save(r2, p / "r2.pkl")
torch.load(p / "r2.pkl", weights_only=True)
```
Yields (error comes from BUILD)
```
UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
Please file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Can only build Tensor, parameter or OrderedDict objects, but got <class 'numpy.dtypes.UInt32DType'>
```
The reasoning is that `numpy.dtypes.UInt32DType` is constructed via `REDUCE` with `func =<class 'numpy.dtype'>` and `args= ('u4', False, True)`, clarify the error message that doing `add_safe_globals` on these will also allow them
After this PR error message becomes
```
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
Please file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Can only build Tensor, Parameter, OrderedDict or types allowlisted via `add_safe_globals`, but got <class 'numpy.dtypes.UInt32DType'>
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134346
Approved by: https://github.com/albanD
Changes jobs to go back to using the default AMI.
Note: This is only a cleanup PR. It does NOT introduce any behavior changes in CI
Now that the default variant uses the Amazon 2023 AMI and has been shown to be stable for a week, it's time to remove the explicit amz2023 references and go back to using the default variant.
After a week or two, when this is rolled out to most people, we can remove the variants from scale config as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134355
Approved by: https://github.com/jeanschmidt
Summary:
Currently the warning is printed when the cat inputs have same qparam, leading to a flood of warnings.
This diff emits the warning only when cat inputs don't have the same qparam.
Test Plan: CI
Reviewed By: aprotopopov
Differential Revision: D60638609
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133999
Approved by: https://github.com/tarun292
Fixes#127519
Currently in torchrun rendezvous, there are only two rendezvous backends supported out of the box: `C10d` and `Etcd`. The changes in this PR enables the distributed elastic users to bring their out-of-tree rendezvous backend implementations as Python packages.
#### AUTHORING NEW PLUGIN
Any new plugin will be a python package exposing entry-points. For example, the structure of redis plugin is as follows:
```
plugin_root
|_ pyproject.toml
|_ src
|_ redis
|_ __init__.py
|_ redis_store.py
|_ redis_backend.py
```
The contents of the `pyproject.toml` should indicate that this is exposes a torchrun entry-point by mentioning the group name `torchrun.plugins`. The `pyproject.toml` for redis plugin would be as follows:
```
[project]
name = "redis"
version = "0.0.1"
[project.entry-points.'torchrun.plugins']
redis = 'redis'
```
The `src/redis/__init__.py` file would contain functions that return the plugin name and plugin handler. The contents of `__init__.py` for redis would be as follows:
```
def getPluginHandler():
def _create_redis_handler(params: RendezvousParameters):
from redis_rendezvous_backend import create_backend
backend, store = create_backend(params)
return create_handler(store, backend, params)
return _create_redis_handler
```
The files `redis_store` and `redis_backend` contain the implementation of [Store](41189b0da4/torch/_C/_distributed_c10d.pyi (L171)) and [RendezvousBackend](e782918b8e/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py (L61)) respectively.
#### USER EXPERIENCE
Before using the plugin for the first time, the user has to install the plugin packages. For example, the published packages can be installed using `pip3 install <plugin-name>` and the plugin is in local file systemcan be installed using `pip3 install -e <plugin-location>`.
Once installed, the new backend can be used in torchrun as follows:
```
torchrun --rdzv-backend=redis --rdzv-endpoint=redis-container:6379 --nnodes=3 --nproc-per-node=1 --max-restarts=3 --rdzv-id=1 test.py
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132633
Approved by: https://github.com/wconstab
enable Windows inductor UTs for `test/inductor/test_torchinductor_codegen_dynamic_shapes.py`
Failed by depends on https://github.com/pytorch/pytorch/pull/134429, need to rebase after https://github.com/pytorch/pytorch/pull/134429 merged.
```cmd
2024-08-25T23:57:23.2747794Z Windows CI does not have necessary dependencies for test_torchinductor_dynamic_shapes yet
2024-08-25T23:57:23.2748541Z Traceback (most recent call last):
2024-08-25T23:57:23.2749593Z File "C:\actions-runner\_work\pytorch\pytorch\test\inductor\test_torchinductor_codegen_dynamic_shapes.py", line 30, in <module>
2024-08-25T23:57:23.2750688Z from inductor.test_torchinductor_dynamic_shapes import (
2024-08-25T23:57:23.2751877Z File "C:\actions-runner\_work\pytorch\pytorch\test\inductor\test_torchinductor_dynamic_shapes.py", line 46, in <module>
2024-08-25T23:57:23.2752876Z raise unittest.SkipTest("requires sympy/functorch/filelock")
2024-08-25T23:57:23.2753545Z unittest.case.SkipTest: requires sympy/functorch/filelock
2024-08-25T23:57:23.2754077Z Got exit code 1
2024-08-25T23:57:23.2754874Z No stepcurrent file found. Either pytest didn't get to run (e.g. import error) or file got deleted (contact dev infra)
```
Local test pass:
<img width="1892" alt="image" src="https://github.com/user-attachments/assets/241ab082-6026-4f33-b3ac-7e9ef7da744d">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134428
Approved by: https://github.com/jansel
Summary:
We want to add compile IDs and frames to each Torch-Compiled Region in order to help users cross reference the section they are checking alongside data obtained from tools, such as tlparse.
This diff operates on the assumption that each graph section will enter and exit a CompileContext before it is ran to either compile the graph or look it up in the cache. Based on this assuption, we can save the value of the graph section from the exited CompileContext in eval_frame.c using a Python C API. After this, we can create a new interface in cpp shim to wrap around the record_function in order to pass in the new keyword argument for "context".
Test Plan:
Enhance test_profiler_dynamo_compiled_region to look for kwinputs as well as a name to see that the context is now labeled. Also changed test to run graph with more contexts so that we test a wider range of profiling.
Differential Revision: D60803317
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132765
Approved by: https://github.com/anijain2305
This PR increases test coverage by including the tests in `test/test_nn.py` in the test suite of MPS.
Some of the tests are decorated with `@expectedFailureMPS` for various reasons. Either that the op is not implemented, or that the outputs do not align. Those tests that contain differing results should be investigated further to rule out any live bugs.
```bash
$ python test/run_test.py --mps --verbose -k TestNN
Running test batch 'tests to run' cost 84.76 seconds
```
Ref #133520
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134184
Approved by: https://github.com/albanD, https://github.com/malfet
There's 2 concepts of unsupported sympy.Functions in symbolic_shapes:
1) unsupported by the export solver, meaning the solver doesn't know how to provide useful fixes for those functions
2) unsupported by the sympy interpreter - meaning we can't reify them into FX nodes because the functions aren't present in PythonReferenceAnalysis
This splits the current call into a call for each version, with the Export solver the only user of 1). For 1), we enumerate the functions in _sympy/functions.py, and subtract the functions we know we can support. For 2) there's only 3 functions we've seen pop up in test cases.
Differential Revision: D61677956
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134271
Approved by: https://github.com/avikchaudhuri
enable Windows inductor UTs of `test/inductor/test_binary_folding.py`
Failed UT depends on https://github.com/pytorch/pytorch/pull/134427
Need to rebase after https://github.com/pytorch/pytorch/pull/134427 merged.
```cmd
2024-08-25T23:32:23.0905727Z Traceback (most recent call last):
2024-08-25T23:32:23.0906516Z File "C:\actions-runner\_work\pytorch\pytorch\test\inductor\test_binary_folding.py", line 18, in <module>
2024-08-25T23:32:23.0908200Z from inductor.test_inductor_freezing import TestCase
2024-08-25T23:32:23.0909883Z File "C:\actions-runner\_work\pytorch\pytorch\test\inductor\test_inductor_freezing.py", line 39, in <module>
2024-08-25T23:32:23.0911128Z raise unittest.SkipTest("requires sympy/functorch/filelock")
2024-08-25T23:32:23.0911801Z unittest.case.SkipTest: requires sympy/functorch/filelock
2024-08-25T23:32:23.0912370Z Got exit code 1
2024-08-25T23:32:23.0913155Z No stepcurrent file found. Either pytest didn't get to run (e.g. import error) or file got deleted (contact dev infra)
```
Local test pass:
<img width="1898" alt="image" src="https://github.com/user-attachments/assets/4a6e3f66-4bbc-4aab-8f0d-2e2318046e53">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134425
Approved by: https://github.com/ezyang, https://github.com/jansel
Windows file path use `\` as delimiter, it is also a escape character. We need translate all path `\` to `/`. which like Linux.
Reproduce UTs:
```cmd
pytest test\dynamo\test_minifier.py -v -k test_after_dynamo_cpu_accuracy_error
```
Error message:
```cmd
____________________________________________________________________________________________________________ MinifierTests.test_after_dynamo_cpu_accuracy_error _____________________________________________________________________________________________________________
Traceback (most recent call last):
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_minifier.py", line 40, in test_after_dynamo_cpu_accuracy_error
self._test_after_dynamo(
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_minifier.py", line 27, in _test_after_dynamo
self._run_full_test(run_code, "dynamo", expected_error, isolate=False)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\_dynamo\test_minifier_common.py", line 235, in _run_full_test
self.assertIn(expected_error, test_proc.stderr.decode("utf-8"))
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 1112, in assertIn
self.fail(self._formatMessage(msg, standardMsg))
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 675, in fail
raise self.failureException(msg)
AssertionError: 'AccuracyError' not found in 'Traceback (most recent call last):\n File "C:\\Users\\Xuhan\\.conda\\envs\\win_mkl_static\\lib\\site-packages\\torch\\_dynamo\\test_minifier_common.py", line 114, in _maybe_subprocess_run\n exec(code, {"__name__": "__main__", "__compile_source__": code})\n File "<string>", line 9\n torch._dynamo.config.debug_dir_root = "C:\\Users\\Xuhan\\AppData\\Local\\Temp\\tmpufu9t3pc"\n ^\nSyntaxError: (unicode error) \'unicodeescape\' codec can\'t decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n'
To execute this test, run the following from the base repo dir:
python test\dynamo\test_minifier.py MinifierTests.test_after_dynamo_cpu_accuracy_error
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
--------------------------------------------------------------------------------------------------------------------------- Captured stdout call ----------------------------------------------------------------------------------------------------------------------------
test stdout:
test stderr: Traceback (most recent call last):
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\_dynamo\test_minifier_common.py", line 114, in _maybe_subprocess_run
exec(code, {"__name__": "__main__", "__compile_source__": code})
File "<string>", line 9
torch._dynamo.config.debug_dir_root = "C:\Users\Xuhan\AppData\Local\Temp\tmpufu9t3pc"
^
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape
--------------------------------------------------------------------------------------------------------------------------- Captured stderr call ----------------------------------------------------------------------------------------------------------------------------
running test
```
Local test passed:
<img width="849" alt="image" src="https://github.com/user-attachments/assets/4a4eecc2-7c08-4de6-9395-546b69803b16">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134365
Approved by: https://github.com/jansel, https://github.com/jgong5
Optimize memory cost at [PR#129635](https://github.com/pytorch/pytorch/pull/129635)
There are 2 main part of the optimization here:
1. optimize the tensor distributing part, postpone the full_tensor generation, which avoids the memory overlap, saves around 50% peak memory at 2 param test case.
2. apply `assign=True` for the `load_state_dict`, saves memory cost at the state dict loading by assigning the input param, around 50% peak memory at loading part.
Future work:
Memory optimization to the opt will be conducted in the next PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134025
Approved by: https://github.com/fegin
Co-authored-by: Rachel Guo <guorachel@meta.com>
Summary: Fixes https://github.com/pytorch/pytorch/issues/134133
Test Plan:
Tested on the small repro in the linked issue with different lengths N (replacing 100), recording N vs. time taken in nanoseconds:
10 127268319
20 220839662
30 325463125
40 429259441
50 553136055
60 670799769
70 999170514
80 899014103
90 997168902
100 1168202035
110 1388556619
120 1457488235
130 1609816470
140 2177889877
150 1917560313
160 2121096113
170 2428502334
180 4117450755
190 4003068224
So N ~ 200 takes ~5s. Previously even smaller N would go for >1 min.
Didn't add a perf test because ezyang is planning to build a benchmark.
Also tested on https://www.internalfb.com/diff/D61560171, which now gets past the stuck point.
Differential Revision: D61619660
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134150
Approved by: https://github.com/ezyang
Because aten.poisson doesn't have meta function registered, there is one additional eager execution of this op during compilation phase of torch.compile.
There are more ops without meta registration. Is there any reason for it?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134103
Approved by: https://github.com/ezyang
I had a night mare rewriting tests in test_misc.py specifically :
1. graphs can have comments that refers to my files "/lsakka/.." we really dont care about comments add option to ignore comments.
2. empty lines added when EXPECTTEST_ACCEPT=1 are changed with linter causing tests to fail or linter fail!
add flag to ignore empty lines.
3. EXPECTTEST_ACCEPT fails when the text have some not readable characters. those should not effect comparing strings, also those causes weird diffs comments when tests fails. I removed ansi_escape chars https://github.com/pytorch/pytorch/pull/133045
this is used in
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134248
Approved by: https://github.com/aorenste
ghstack dependencies: #133639, #134364
This UT actual code only one empty line wrap difference(`linear` and `add`) between Windows/Linux, and the context is right.
Reproduce UTs:
```cmd
pytest test\dynamo\test_higher_order_ops.py -v -k test_functional_call_sequential_params_and_buffers
```
We can add `empty_line_normalizer` to fix it.
```cmd
______________________________________________________________________________________________ FuncTorchHigherOrderOpTests.test_functional_call_sequential_params_and_buffers _______________________________________________________________________________________________
Traceback (most recent call last):
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_higher_order_ops.py", line 3676, in test_functional_call_sequential_params_and_buffers
self.assertExpectedInline(
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_utils.py", line 2871, in assertExpectedInline
return super().assertExpectedInline(actual if isinstance(actual, str) else str(actual), expect, skip + 1)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\expecttest\__init__.py", line 271, in assertExpectedInline
self.assertMultiLineEqualMaybeCppStack(expect, actual, msg=help_text)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\expecttest\__init__.py", line 292, in assertMultiLineEqualMaybeCppStack
self.assertMultiLineEqual(expect, actual, *args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 1226, in assertMultiLineEqual
self.fail(self._formatMessage(msg, standardMsg))
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 675, in fail
raise self.failureException(msg)
AssertionError: 'clas[509 chars]one\n add: "f32[1, 1]" = linear + l_buf[69 chars],)\n' != 'clas[509 chars]one\n\n add: "f32[1, 1]" = linear + l_b[71 chars],)\n'
class GraphModule(torch.nn.Module):
def forward(self, L_params_l1_weight_: "f32[1, 1]", L_params_l1_bias_: "f32[1]", L_buffers_buffer_: "f32[1]", L_inputs_: "f32[1, 1]"):
l_params_l1_weight_ = L_params_l1_weight_
l_params_l1_bias_ = L_params_l1_bias_
l_buffers_buffer_ = L_buffers_buffer_
l_inputs_ = L_inputs_
linear: "f32[1, 1]" = torch._C._nn.linear(l_inputs_, l_params_l1_weight_, l_params_l1_bias_); l_inputs_ = l_params_l1_weight_ = l_params_l1_bias_ = None
+ <<<< (difference is here )
add: "f32[1, 1]" = linear + l_buffers_buffer_; linear = l_buffers_buffer_ = None
return (add,)
: To accept the new output, re-run test with envvar EXPECTTEST_ACCEPT=1 (we recommend staging/committing your changes before doing this)
To execute this test, run the following from the base repo dir:
python test\dynamo\test_higher_order_ops.py FuncTorchHigherOrderOpTests.test_functional_call_sequential_params_and_buffers
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
========================================================================================================================== short test summary info ==========================================================================================================================
FAILED [0.4275s] test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_functional_call_sequential_params_and_buffers - AssertionError: 'clas[509 chars]one\n add: "f32[1, 1]" = linear + l_buf[69 chars],)\n' != 'clas[509 chars]one\n\n add: "f32[1, 1]" = linear + l_b[71 chars],)\n'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134394
Approved by: https://github.com/jansel
Co-authored-by: Jason Ansel <jansel@jansel.net>
After this I think all `using namespace` will have been eliminated from PyTorch header files. Internally, `-Wheader-hygiene` will prevent more from being added.
Test Plan: Sandcastle
Differential Revision: D61679037
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134336
Approved by: https://github.com/Skylion007
Summary:
This enables patching extern modules to provide compatibility with serialized code depending on different versions of those extern modules.
The main motivation is to enable Numpy upgrade. In the recent release many alias to builtin types were deprecated and removed [1]. This breaks loading pickled modules that reference the removed aliases. While the proper solution is to re-generate pickled modules, it's not always feasible.
This proposes a way to define mapping with a new type, for a module member. It is only set if it's not present in the loaded module, thus removes the need to check for exact versions.
https://numpy.org/doc/stable/release/1.20.0-notes.html#using-the-aliases-of-builtin-types-like-np-int-is-deprecated
Differential Revision: D61556888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134376
Approved by: https://github.com/SherlockNoMad
If a mesh_dim_name is given, we will use the given mesh_dim_name to name the new flattened dim.
Otherwise, the default is a string concatentaing the mesh_dim_names of the given submesh with each mesh_dim_name separated by "_".
For example, if we have a 3D mesh DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")), calling mesh_3d["dp", "cp"]._flatten() will create a 1D submesh DeviceMesh([0, 1, 2, 3], mesh_dim_names=("dp_cp",)) on rank 0, 1, 2, 3 and a 1D submesh DeviceMesh([4, 5, 6, 7], mesh_dim_names=("dp_cp",)) on rank 4, 5, 6, 7.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134048
Approved by: https://github.com/fegin
ghstack dependencies: #133838, #133839
Sympy's implementation of Min/Max displays asymptotically bad behavior on `TORCH_COMPILE_CPROFILE=1 python torchrec/distributed/tests/test_pt2_multiprocess.py TestPt2Train.test_compile_multiprocess`. Evidence profile:
On this test case, we spend 42% of all time compiling the network on ShapeEnv.replace, which in turn spends all of its time in xreplace.
The problem appears to be find_localzeros call. By vendoring the implementations of Min/Max, we can potentially reduce the cost of this operation.
The implementation is copy-pasted sympy/functions/elementary/miscellaneous.py but with some adjustments:
* I deleted logic related to differentatiation, evalf and heaviside, as it's not relevant to PyTorch reasoning
* There's some massaging to appease PyTorch's linters, including a lot of noqa and type: ignore (which I could potentially refactor away with substantive changes, but that's better as its own change)
* I deleted the second loop iteration for is_connected, as an attempt at initial optimization (this also simplifies the port, since I can omit some code). I'll comment at that point what the exact difference is.
Before this change, the test in question takes 100s with 40 features; post this change, afterwards, it takes only 69s.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133319
Approved by: https://github.com/Skylion007
Summary:
Today there is no good mechanism to detect progress of non-strict export line-by-line in user code. This caused some pain recently in trying to find the exact line of user code that was triggering a bug where the process appeared stuck because deep down something was calling some symbolic shapes code that was suffering some exponential blowup.
This PR adds a environment variable for extended debugging that will log the line of user code corresponding to every torch function call. It only works in non-strict export for now. Prefix setting this environment variable with `TORCH_LOGS` enabled for `export` logs at `DEBUG` level (i.e., with a `+` prefix), i.e.,.:
```
TORCHEXPORT_EXTENDED_DEBUG_CURRENT_LOC=1 TORCH_LOGS="+export" ...
```
This will show logs with something like:
```
...
prim::device called at .../example.py:4284 in foo
TensorBase.item called at .../example.py:4277 in bar
...
```
We already have an existing place to intercept torch functions where we process data-dependent errors in non-strict, so parking the logging there. An alternative place we could be doing this is where we add `stack_trace` metadata when generating code, but unfortunately at least the example that motivated this gets stuck before generating code, so that would be too late.
Test Plan: ran it on some sample commands
Differential Revision: D61692156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134298
Approved by: https://github.com/angelayi
Summary: Create simple test that checks that FunctionEvent build tree happens lazily by checking that the metrics for it changes before and after call.
Test Plan: Make sure test passes in CI
Reviewed By: briancoutinho
Differential Revision: D61685429
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134359
Approved by: https://github.com/briancoutinho
Fixes#133338
Test Plan:
```
TORCH_LOGS=dynamic python
import torch
torch._dynamo.config.capture_scalar_outputs = True
@torch.compile()
def f(x):
y = x.item()
torch._check_is_size(y)
r = torch.arange(y, dtype=torch.float32)
torch._check(r.size(0) == y)
return r
f(torch.tensor([300]))
```
Before and after diff. Verify the following line
```
I0813 11:05:44.890000 652898 torch/fx/experimental/symbolic_shapes.py:5198] [0/0] runtime_assert Eq(CeilToInt(IntTrueDiv(u0, 1)), u0) [guard added] at aa.py:10 in f (_dynamo/utils.py:2092 in run_node), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED="Eq(CeilToInt(IntTrueDiv(u0, 1)), u0)"
```
no longer shows in the logs. Also verify CI passes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134296
Approved by: https://github.com/aorenste
Current temporary directory path is hard code. Fixed by get temporary directory path by API.
Reproduce UTs:
```cmd
python test/dynamo/test_dynamic_shapes.py -v -k test_torch_package_working_with_trace_dynamic_shapes
```
Error message:
```cmd
________________________________________________________________________________________________ DynamicShapesMiscTests.test_torch_package_working_with_trace_dynamic_shapes ________________________________________________________________________________________________
Traceback (most recent call last):
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_misc.py", line 7199, in test_torch_package_working_with_trace
with package.PackageExporter(path) as exp:
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\package\package_exporter.py", line 237, in __init__
self.zip_file = torch._C.PyTorchFileWriter(f)
RuntimeError: Parent directory /tmp does not exist.
To execute this test, run the following from the base repo dir:
python test\dynamo\test_dynamic_shapes.py DynamicShapesMiscTests.test_torch_package_working_with_trace_dynamic_shapes
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
========================================================================================================================== short test summary info ==========================================================================================================================
FAILED [0.0080s] test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_package_working_with_trace_dynamic_shapes - RuntimeError: Parent directory /tmp does not exist.
==================================================================================================================== 1 failed, 1665 deselected in 4.00s =====================================================================================================================
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134397
Approved by: https://github.com/ezyang
Fixes#130394
TorchInductor doesn't respect original strides of outputs. It opens up optimization opportunities like changing up memory layout. But for some cases, such as the case in https://github.com/pytorch/pytorch/issues/130394, we do need the output match the exact stride as required. The correctness is the first priority goal. So, this PR adds a new API `ir.ExternKernel.require_exact_strides(x, exact_strides, allow_padding=False)` to fix the issue. This PR enables non-dense outputs' strides follow the strides required by semantics.
The comparison between the original and after this fix for the test is the below.
```python
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
- x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
tmp1 = tmp0 + tmp0
- tl.store(out_ptr0 + (x2), tmp1, xmask)
+ tl.store(out_ptr0 + (x0 + (16*x1)), tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (16, 8), (16, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
- buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
+ buf1 = empty_strided_cuda((16, 8), (16, 1), torch.float32)
stream0 = get_raw_stream(0)
triton_poi_fused_add_copy_0.run(arg0_1, buf1, 128, grid=grid(128), stream=stream0)
del arg0_1
return (buf1, )
```
The buf1 is created with exact stride required by users, and its values are written in same stride with the input.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130956
Approved by: https://github.com/eellison, https://github.com/blaine-rister
torch.cuda.amp.autocast / torch.cpu.amp.autocast are deprecated and spew a ton of warnings when these tests run. This PR: Update to just use torch.amp.autocast(device).
Note: this uncovers a bug in the test: when `device` is CUDA, it actually shows up as "cuda:0" - so previously, this test was _always_ using `torch.cpu.amp.autocast` even for `cuda` device. This PR fixes this, and uncovers additional bugs in `pinverse` and `linalg.pinv`; `linalg.pinv` was already failing before on CPU, but now the test also catches failures on CUDA, (and this PR adds to the skipped-test list).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134291
Approved by: https://github.com/YuqingJ
Summary:
# context
* when fixing the graph break in _maybe_compute_kjt_to_jt_dict, we encountered this issue P1539489731:
```
[rank0]: ATTENTION: guard_size_oblivious would fix the error, evaluating expression to False.
[rank0]: Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
[rank0]:
[rank0]: Potential framework code culprit (scroll up for full backtrace):
[rank0]: File "/data/users/hhy/fbsource/buck-out/v2/gen/fbcode/61f992c26f3f2773/aps_models/ads/icvr/__icvr_launcher_live__/icvr_launcher_live#link-tree/torch/_inductor/fx_passes/post_grad.py", line 671, in slice_noop
[rank0]: if start == 0 and end >= 2**63 - 1 and step == 1:
```
* change the condition logic to be compatible with SymInt
Test Plan:
# commands
* run test
```
TORCH_SHOW_CPP_STACKTRACES=1 TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 TORCH_LOGS="+graph_code,output_code,dynamic,aot,guards,verbose_guards,recompiles,graph_breaks" TORCH_TRACE=/var/tmp/tt buck2 run fbcode//mode/opt fbcode//aps_models/ads/icvr:icvr_launcher_live -- mode=fmc/local_ig_fm_v4_mini training.pipeline_type=pt2 2>&1 | tee -a `date +"%Y.%m.%d.%H.%M"`.`sl whereami`.log
```
* tlparse
```
ls -thl /var/tmp/tt | head -9 && tlparse `ls -t /var/tmp/tt/* | head -1`
```
Differential Revision: D61677207
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134270
Approved by: https://github.com/ezyang
Summary:
This diff will decompose torch.ops._quantized.wrapped_quantized_linear into torch.ops._quantized.wrapped_linear_prepack and torch.ops._quantized.wrapped_quantized_linear_prepacked for AOTI, and added the corresponding impl into shim
The way it works will be similar to what we did previously for fbgemm fp16 dynamic qlinear. We will do constant folding for packed weight during runtime (warm up) to achieve the speed up
Reviewed By: desertfire
Differential Revision: D61396144
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134368
Approved by: https://github.com/houseroad
Windows file path use `\` as delimiter, it is also a escape character. We need translate all path `\` to `/`. which like Linux.
Reproduce UT:
```cmd
pytest test\dynamo\test_higher_order_ops.py -v -k test_vmap_grad_vmap_guard_fail
```
Error msg:
```cmd
________________________________________________________________________________________________________ HigherOrderOpVmapGuardTests.test_vmap_grad_vmap_guard_fail _________________________________________________________________________________________________________
Traceback (most recent call last):
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\logging_utils.py", line 89, in test_fn
fn(self, records)
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_higher_order_ops.py", line 2714, in test_vmap_grad_vmap_guard_fail
munge_exc(record.getMessage()),
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_utils.py", line 5252, in munge_exc
s = re.sub(file, os.path.basename(file), s)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\re.py", line 209, in sub
return _compile(pattern, flags).sub(repl, string, count)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\re.py", line 303, in _compile
p = sre_compile.compile(pattern, flags)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\sre_compile.py", line 788, in compile
p = sre_parse.parse(p, flags)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\sre_parse.py", line 955, in parse
p = _parse_sub(source, state, flags & SRE_FLAG_VERBOSE, 0)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\sre_parse.py", line 444, in _parse_sub
itemsappend(_parse(source, state, verbose, nested + 1,
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\sre_parse.py", line 526, in _parse
code = _escape(source, this, state)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\sre_parse.py", line 370, in _escape
raise source.error("incomplete escape %s" % escape, len(escape))
re.error: incomplete escape \x at position 2
To execute this test, run the following from the base repo dir:
python test\dynamo\test_higher_order_ops.py HigherOrderOpVmapGuardTests.test_vmap_grad_vmap_guard_fail
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
--------------------------------------------------------------------------------------------------------------------------- Captured stdout call ----------------------------------------------------------------------------------------------------------------------------
frames [('total', 2), ('ok', 2)]
inductor []
inline_call []
stats [('calls_captured', 38), ('unique_graphs', 2)]
--------------------------------------------------------------------------------------------------------------------------- Captured stderr call ----------------------------------------------------------------------------------------------------------------------------
V0824 01:29:00.148000 27840 torch\_dynamo\guards.py:2787] [0/1] [__recompiles] Recompiling function fn in D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_higher_order_ops.py:2699
V0824 01:29:00.148000 27840 torch\_dynamo\guards.py:2787] [0/1] [__recompiles] triggered by the following guard failure(s):
V0824 01:29:00.148000 27840 torch\_dynamo\guards.py:2787] [0/1] [__recompiles] - 0/0: torch._functorch.pyfunctorch.compare_functorch_state([('Vmap', 1, 'error')]) # _dynamo\output_graph.py:479 in init_ambient_guards
========================================================================================================================== short test summary info ==========================================================================================================================
FAILED [0.7452s] test/dynamo/test_higher_order_ops.py::HigherOrderOpVmapGuardTests::test_vmap_grad_vmap_guard_fail - re.error: incomplete escape \x at position 2
```
Local test passed:
<img width="860" alt="image" src="https://github.com/user-attachments/assets/90f0d780-0639-4c03-8d7c-6f227c93a3fc">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134348
Approved by: https://github.com/jansel
Fixes#133499
### The issue
Testing a variety of TP `requires_grad` patterns (validating maximally flexible finetuning) revealed `DTensor` sharding propagation of `aten.native_layer_norm_backward` (default) fails with an `IndexError` for certain `requires_grad` patterns (pattern 1) (e.g. `output_mask` `[True, False, False]`) and an `AssertionError` for others (pattern 2) (e.g. output mask `[False, True, *]`). Please see issue #133499 for a full description of the observed failure patterns along with reproduction.
### Use Cases and Remediation
Failure pattern 1 is potentially problematic for a variety of finetuning scenarios. Though failure pattern 2 is really an xfail right now since it's not fully supported, IMHO there are use cases (e.g. especially wrt to mechanistic interpretability research, but certain finetuning scenarios too potentially) that justify supporting this output mask (especially since supporting it is fairly straightforward I think).
In this PR I propose some modest changes that:
* Address the aforementioned failure modes.
* Add a couple tests that I'm hopeful will help ensure `DTenso`r op dispatch (which is so well implemented and such a pleasure working with btw! 🚀🎉) accommodates a wide variety of (potentially unanticipated) `requires_grad` patterns as it evolves.
To address both failure modes, I'm proposing the following changes:
1. To [`torch.distributed._tensor.ops._math_ops.layer_norm_bwd_strategy`](7b269cc484/torch/distributed/_tensor/ops/_math_ops.py (L873)):
- Refactor conditional `output_mask` handling such that the input and output specs in the`PlacementStrategy`s of the returned `output_strategy.strategies` list remain aligned with the `op_schema.args_spec` (whose definition does not change at runtime based upon unused optional args).
2. To [`torch.distributed._tensor._sharding_prop.propagate_op_sharding_non_cached`](7b269cc484/torch/distributed/_tensor/_sharding_prop.py (L256-L262)):
- When iterating through the active `op_schema.args_spec` to build the relevant `expected_input_specs` list, filter any `None` `desired_specs`.
3. To [`torch/distributed/_tensor/_op_schema.OpSchema._inplace_rewrap_schema_suggestion`](7b269cc484/torch/distributed/_tensor/_op_schema.py (L418))
- When inputs need a redistribute, for runtime-unrequired (`None` arguments in the aligned `suggestion_args_schema`), ignore the associated `suggestion_args_spec`
### Implementation considerations:
- Regarding `1`, to avoid changing the op strategy return args ([`op_strategy`](cf81180007/torch/distributed/_tensor/_sharding_prop.py (L234))), the change in `1` allows `None` elements to exist temporarily in `PlacementStrategy.input_specs` (treating it as `Sequence[DTensorSpec | None] | None` when it's `Sequence[DTensorSpec] | None`. This could be addressed in any number of ways but I thought it best to leave that for a subsequent PR since it could have broader ramifications (e.g. allowing op_strategies to return an output_strategy.input_specs` mask explicitly, explicitly allowing `None`s in `PlacementStrategy.input_specs`, creating a `Null` DTensorSpec etc.). That's why I'm using an ignore arg-type directive there for now.
- Regarding `2` and `3` above, I don't introspect `op_schema.op._schema.arguments` to verify any `None` arguments are `torch.OptionalType`, leaving adherence to the schema contract the responsibility of the given op. Regarding `2`, I assume any `desired_spec` will be either a `DTensorSpec` or `None`, so only `None` can be Falsy in this context.
- I considered altering the active `args_schema`, which could be inspected and aligned with the active `output_strategy.input_specs` in some cases and avoid the changes in `3`, but I think that would rely on one of (among other possibilities):
- all supported op signatures having optional Tensors (`DTensorSpec`) args after required tensors (which isn't a planned required as far as I know),
- (somewhat brittle) heuristic-driven arg alignment
- only supporting kwargs etc.
### Added Tests
To facilitate detection of future `requires_grad` pattern op failure modes as `DTensor` evolves, I added the following two tests:
1. `test/distributed/_tensor/test_math_ops.py DistMathOpsTest.test_layer_norm_bwd_req_grad`
- Tests `native_layer_norm_backward` specifically with 20 subtests that sweep valid `output_mask` patterns along in different LayerNorm dimensionality and `elementwise_affine` configurations.
2. `test/distributed/tensor/parallel/test_tp_examples.py DistTensorParallelExampleTest.test_transformer_req_grad`
- Samples a subset of `requires_grad` patterns in a more realistic (relative to the `LayerNorm`-specific test) Transformer usage context with different `dtype` and `is_seq_parallel` configurations. Note since there was substantial overlap with the existing `test_transformer_training` test, I took the opportunity to refactor that test to allow relevant code-sharing. I also added an `ExpCommCounts` `NamedTuple` to facilitate the addition of additional `requires_grad` patterns that we may want to test in the future which may result in different comm counts. I created the separate `requires_grad` test to allow decoupling the multi-iteration `test_transformer_training` test and allow addition of new `requires_grad` scenarios as desired while being mindful of resources.
Thanks again to the PyTorch distributed team for your immensely valuable contributions to the open-source ML community!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133502
Approved by: https://github.com/XilunWu
For `aten.any`, we can use `reduce_op="sum"` as the linear reduction op.
When we do `all_reduce` with `reduce_op="sum"` on bool tensor, if one rank returns `torch.Tensor([True]) `, then the reduction result is `torch.Tensor([True]) `. Only when all ranks return `torch.Tensor([False]) ` would the reduction result be `torch.Tensor([False]) `. This matches with `any`'s behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134206
Approved by: https://github.com/tianyu-l, https://github.com/chuanhaozhuge
Add DeviceMesh slicing support such that we could do the following:
```
mesh_3d = init_device_mesh(
self.device_type, (2, 2, 2), mesh_dim_names=("replicate", "shard", "cp")
)
shard_cp_mesh = mesh_3d["shard", "cp"]._flatten()
hsdp_mesh = mesh_3d["replicate", "shard_cp"]
# we can get the corresponding group of the flatten mesh through
group = shard_cp_mesh.get_group()
# or
group = mesh_3d["shard_cp"].get_group()
# or
mesh_3d.get_group(mesh_dim="shard_cp")
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133839
Approved by: https://github.com/fegin
ghstack dependencies: #133838
### Description
This PR extends the `VecISA` class to include support for VSX on the `ppc64le` architecture within the Inductor backend. This enhancement enables vectorization support, resulting in performance improvements when using `torch.compile()` on `ppc64le`.
### Fixes
- Resolved the `test_acosh_with_negative_large_input` test case in `test_cpu_repro.py` by implementing `acosh` for VSX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132746
Approved by: https://github.com/jansel
Summary: Pass process group info into NcclWork
Test Plan: buck2 run mode/dev-nosan kineto/libkineto/fb/integration_tests:pytorch_execution_trace_integration_test
Differential Revision: D61677160
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134269
Approved by: https://github.com/wconstab
The pattern matcher runs DCE and remove_noop_ops on the replacement
graph by default. Previously we had a switch for the DCE. This PR
changes that switch to also control if we run remove_noop_ops.
The context was that there is silent incorrectness with
auto_functionalized. We use the Pattern matcher to decompose
auto_functionalized into a mutable op + clones; remove_noop_ops were
deleting the clones.
Future: can try #134363
Test Plan:
- new test. I wasn't able to produce a silently incorrect example so I
settled for asserting that clones still exist in the post-grad graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134364
Approved by: https://github.com/eellison
ghstack dependencies: #133639
This adds logs if we can't acquire locks in NCCLUtils and ProcessGroupNCCL for 30s.
This is motivated by some deadlocks were seeing and it's unclear if it's in NCCL or on the PyTorch side of things.
This required replacing most `std::mutex` with `std::timed_mutex` and `std::condition_variable_any` as appropriate.
Test plan:
existing CI for regressions
will add unit tests on `C10D_LOCK_GUARD`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134131
Approved by: https://github.com/c-p-i-o, https://github.com/fduwjj
Refactors construction of ExportGraphSignature object for export & training IR, explicitly creating AOTAutograd signature for training IR. This will be helpful for upcoming refactors for placeholder naming & runtime asserts prettifying.
Changes:
- dedups `make_argument_spec` call, moved to export/graph_signature.py
- `_sig_to_specs` wrapped into new function `_convert_to_export_graph_signature`, directly converts GraphSignature -> ExportGraphSignature
- `_make_fx_helper` explicitly creates AOTAutograd GraphSignature object
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134059
Approved by: https://github.com/angelayi, https://github.com/ydwu4
**Summary**
When check the vectorization status among 3 test suit, we found some operators disabled vectorization with message `Disabled vectorization: op: remainder`. In this PR, we add vectorization support of this op.
**Test Plan**
```
python -u -m pytest -s -v test/inductor/test_cpu_repro.py -k test_vec_remainder
python -u -m pytest -s -v test/inductor/test_cpu_repro.py -k test_int_div_vec
```
Differential Revision: [D61147014](https://our.internmc.facebook.com/intern/diff/D61147014)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129849
Approved by: https://github.com/jgong5, https://github.com/lezcano
Starter version of automatic dynamic shapes for export.
Creates enums `DIM.AUTO`, `DIM.STATIC`, allowing user to specify `AUTO` for dims in dynamic_shapes specs, meaning that corresponding dims are treated as dynamic, and relevant guards will do what's necessary (e.g. refine ValueRanges, set replacements based on equality, or even set static) without raising ConstraintViolationErrors. Basically allows the user to say, "a bunch of these dims can be dynamic, let export do model analysis and return the program with maximum possible dynamism, without complaining".
The usage for specifying `dynamic_shapes` is now:
```
AUTO -> dynamic by default, return whatever produce_guards() says, even if it's static
None/int/STATIC -> static
Dim/DerivedDim -> same as before - will complain if the min/max range is invalid, or if dims related to this are unspecified.
```
Caveat 1: specifying `AUTO` for a dim won't guarantee it'll be dynamic:
- specifying `AUTO` for a dim will return the maximum possible dynamism given your program and other specified constraints, but this can still mean you'll get a static program. For example, with the program below, x is specified dynamic, but it's equal to y, which is specified static, and with how we currently do things we won't promote y to dynamic, but will demote(?) x to static. So this can be surprising if you don't fully know your model, and/or missed one of your other inputs when specifying auto-dynamic shapes.
```
class Foo(torch.nn.Module):
def forward(self, x, y):
return x + y
inputs = (torch.randn(6), torch.randn(6))
export(Foo(), inputs, dynamic_shapes={"x": (DIM.AUTO,), "y": None})
```
Caveat 2: specifying `AUTO` and Dims in the same spec is still problematic:
- The way Dims/DerivedDims are currently handled is very strict. A Dim represents a symbol, and we require a user to specify the symbol for all dims governed by the symbol - that's why we've seen errors in the past like `The values of x must always be related to y by ...`, asking the user to specify the exact relation as in the program. We also require the specified min/max range to be a subset of the valid range from model analysis. All this doesn't compose well with specifying `AUTO` just yet - for example in the program below, ideal behavior could be to return a dynamic program, where `dx = x.size(0) = y.size(0)` has range (3,6). Unfortunately this crashes, and correct behavior is to specify `dx` for both inputs. So currently we raise a UserError and crash if both Dims + `AUTO` are present in the spec.
```
class Foo(torch.nn.Module):
def forward(self, x, y):
return x + y
inputs = (torch.randn(6), torch.randn(6))
export(Foo(), inputs, dynamic_shapes={"x": (DIM.AUTO,), "y": {0: Dim("dx", min=3, max=6)}}) # this doesn't work, because x & y and related
```
Implementation details:
This is done by setting `assume_static_by_default=False`, and doing a transform on the `dynamic_shapes` spec to preserve semantics. `assume_static_by_default=False` will treat unspecified dims or Nones as dynamic. This is the opposite of what `export.export()` currently does - unspecified Dims/Nones are treated as static. Historically this static-by-default behavior, where the user deals with fewer guards, has been desirable, and we would like to respect that in this implementation. So this internal spec transformation is added, `_transform_shapes_for_default_dynamic()`, does the spec conversion necessary to be compatbile with dynamic by default. Specifically, AUTOs are converted into Nones, and Nones/unspecified dims are filled in with explicitly static constraints.
For example, this would look like, for a 3-d tensor: `{0: DIM.AUTO, 1: None, 2: Dim("dx")} -> {0: None, 1: 32, 2: Dim("dx")}`
This does seem overly complicated, but it's done to preserve dynamic shapes semantics for `torch._dynamo.export()`, which already uses `assume_static_by_default=False`, and follows the same process for generating shape constraints , via `_process_dynamic_shapes`. There the semantics are:
```
None/unspecified: dynamic by default
Dim/DerivedDim: also a strict assertion
```
If we don't care about BC for `_dynamo.export(dynamic_shapes)`, then we can just modify semantics for `_process_dynamic_shapes()` and change all the relevant tests in `test/dynamo/test_export.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133620
Approved by: https://github.com/avikchaudhuri
The function expects a Tensor of type LongTensor. It currently throws the following error: "one_hot is only applicable to index tensor." which, imo, does not provide the user with enough information on what the problem is.
PR simply adds extra information to the error message on this specific scenario.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134209
Approved by: https://github.com/mikaylagawarecki
`nn_module_stack` was previously serialized to string by adding commas between the module_path and module_type. This error prone when the `nn_module_stack` itself contains commas.
This PR fixes this by creating a dictionary to store the `nn_module_stack` and serialize it to string via `json.dumps()`
Fixes#131941
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134049
Approved by: https://github.com/angelayi
Summary: Currently, for sequential mode, minimizer search terminates after a node is excluded via the user defined exclusion_fn. However, on some occasions we would like the search to continue past that for the remaining nodes. In this diff I am changing the termination criteria to respect the find_all setting, where we continue sequential search if it is set.
Test Plan: CI
Differential Revision: D61720262
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134339
Approved by: https://github.com/jfix71
Fixes#134050
### The issue
The current `DTensor` sharding propagation caching policy for `aten.scaled_dot_product_efficient_attention` (default) can result in silently incorrect gradients or trigger an IMA after cuda kernel launch in mixed `require_grad` configurations. Please see issue #134050 for a full description of the observed failure patterns along with reproduction. Note `aten.scaled_dot_product_flash_attention` presents a similar concern so this PR addresses both [as discussed here.](https://github.com/pytorch/pytorch/issues/134050#issuecomment-2299887602)
### Remediation
While there are a number of ways this could be addressed, the most straightforward remediation is to modify the sharding propagation caching policy of [`aten._scaled_dot_product_efficient_attention.default`](b03381cac2/torch/distributed/_tensor/ops/_matrix_ops.py (L337-L340)), registering it with `schema_info=RuntimeSchemaInfo(4)` to prevent cache sharing between differing `compute_log_sumexp` values i.e.
```python
@register_op_strategy(aten._scaled_dot_product_efficient_attention.default, schema_info=RuntimeSchemaInfo(4))
def scaled_dot_product_efficient_attention_strategy(
...
```
[As discussed here](https://github.com/pytorch/pytorch/issues/134050#issuecomment-2299887602), since `aten::_scaled_dot_product_flash_attention` could be affected by a similar issue wrt `return_debug_mask`, this PR adjusts the sharding propagation caching policy for that op as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134146
Approved by: https://github.com/tianyu-l
Summary:
This PR updated cuSPARSELt to v0.6.2. I think we should land
https://github.com/pytorch/pytorch/pull/128534 first though.
Most of this PR is just enabling tests to run when cuSPARSELt v0.6.2 is
available.
Unfortunately was running into a bug with fp32 support on Hopper, so I
removed fp32 support from the cuSPARSELt backend. I think this should be
fine since almost everybody uses the bfloat/float16/int8 kernels.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134022
Approved by: https://github.com/jerryzh168, https://github.com/malfet
ghstack dependencies: #128534
Summary:
Added support for more custom op input types, now only missing dtype,
layout, memory format as input type, since we need to add some more testing for
mapping the types to their integer values
([previous
comment](https://github.com/pytorch/pytorch/pull/126215#discussion_r1617428066)).
This PR also replaces the `DynamicArg` struct's `serialized_arg_val` with
`list_item_types`, which stores an optional list of strings, where each string
represents the type of the value within this list. This is only used for
parsing lists of optional tensors, where we need to know if a specific value in
the list should be a tensor, or a None. Replacing with a list of strings is
also better than storing the actual json format because then we don't need to
parse the json string during the runtime, and can just loop over a preprocessed
list of strings.
Test Plan: `buck2 run @//mode/dev-nosan //caffe2/test/inductor:test_aot_inductor -- -r "test_custom_"`
Reviewed By: desertfire
Differential Revision: D60295995
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132454
Approved by: https://github.com/desertfire
Summary:
We should always emit an end event in a finally block so that if a unit test or job fails, the stack is still correct.
Also, we use thread local storage for the stack, so that in multithreaded scenarios the stack will still be correctly added.
Test Plan:
Run benchmark and see that everything still works
Run
```
TORCH_LOGS=dynamo buck run test/functorch:test_aotdispatch -- -r test_backward_mutation_on_grad_out
```
With some extra logging to see that start events with the correct stack are emitted, and the end events are also emitted even though the test fails at runtime.
Differential Revision: D61682556
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134279
Approved by: https://github.com/aorenste
Fixes#128084
The approach is option 2 of what Elias suggested in the comment
thread:
- We require tensors to have the correct stride at usage. This may
involve a clone; if there was a clone and then a mutation into it
then we copy_ back the result of the mutation.
The reason why I went this approach was because it was the easiest and
Inductor already works really hard to remove additional clones/copy_.
There are some cases that this doesn't generate efficient code for; for
example, if the tensor is a view, we don't change the base of the view
to have the right stride order, instead we do a clone.
The view case isn't very common so I'm ignoring it for now but we could
improve this in the future.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133639
Approved by: https://github.com/eellison
Support of effectful operations in backward:
1/ AOTD collects metadata from forward fn only, so we can have usage of effectful ops in backward, that were not used in forward => Allowing tokens discovery during joint function .
FunctionalTensorMode holds _tokens, in Joint function after tracing forward we memoize _tokens as `_tokens_forward_output`.
2/ Tokens are added as primals inputs (forward) in EffectTokensWrapper.
Tokens that will be used in backward are in partitioner saved values. We do not have control on which positions they are saved in forward outputs.
2/ If new tokens discovered in backward after tracing joint_fn, the result graph will be manually added in the end of primals.
_aot_autograd/utils.py
3/ All effectful ops during backward are marked with 'must_be_in_backward' partitioner_tag, to prevent partiitoner to place them in forward.
For that functional_tensor_mode got new optional state `self._effects_partitioner_tag` for effectful ops, to set after tracing forward.
There are additional changes in partitioner to improve functionality of 'must_be_in_backward'
4/ Unlift tokens now should run for both forward and backward.
- As saved for backward tokens are placed on non static places - we identify input and output tokens to erase, by input and output of `with_effects` operation
- In forward we can have input tokens, discovered in backward, that are not used in with_effects ops in forward, but saved for backward. We identify them by position in forward inputs.
5/ Adding aot debug logging for graphs before unlifting and before adding additional primal for backward tokens.
Tests:
```
python test/higher_order_ops/test_with_effects.py
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132638
Approved by: https://github.com/bdhirsh
MSVC don't support dynamic array.
Ref: https://stackoverflow.com/questions/56555406/creating-dynamic-sized-array-using-msvc-c-compiler
We tried to solutions:
1. use std::vector to instead of it in previous PR: https://github.com/pytorch/pytorch/pull/134140, but it changed variable's type and failed at UTs.
2. Use `std::unique_ptr` to instead of it in PR: https://github.com/pytorch/pytorch/pull/134156, @jansel reviewed and give comments: https://github.com/pytorch/pytorch/pull/134156#pullrequestreview-2253091693. It is make sense, allocation memory maybe make code run slower.
3. Use fixed size array to instead of it in PR: https://github.com/pytorch/pytorch/pull/134210, fixed size is hard to process the situlation, reserved size if small than CPU number.
> a. Use min() function limited is local test failed: https://github.com/pytorch/pytorch/pull/134210#issuecomment-2304447729
> b. Dynamic select fixed size or dynamic array: https://github.com/pytorch/pytorch/pull/134210#issuecomment-2304128666 . It makes code too complex to maintains.
Discussed with origin PR(https://github.com/pytorch/pytorch/pull/115620) author @zhuhaozhe, we think:
1. MSVC it the only one compiler, which not support VLA.
2. MSVC it worse performance than other compilers, use `std::unique_ptr` for MSVC and make it works.
3. For other compilers, keep using current `VLA` code.
4. For Windows users, they can use `clang-cl` or `icx` to get better performance than MSVC.
5. Discussed with @jansel , we need to move compiler check to python side, and make output code cleaner.
Reproduce UT:
```cmd
pytest test/inductor/test_cpu_repro.py -v -k test_reduction_with_dynamic_threads
```
Error msg:
```cmd
C:/Users/Xuhan/AppData/Local/Temp/tmpncykej5v/a4/ca4534cazplidnf7vopaaxaifqkjiyhxm3h2gsylgztputbaeybx.cpp(13): error C2131: expression did not evaluate to a constant
C:/Users/Xuhan/AppData/Local/Temp/tmpncykej5v/a4/ca4534cazplidnf7vopaaxaifqkjiyhxm3h2gsylgztputbaeybx.cpp(13): note: failure was caused by a read of a variable outside its lifetime
C:/Users/Xuhan/AppData/Local/Temp/tmpncykej5v/a4/ca4534cazplidnf7vopaaxaifqkjiyhxm3h2gsylgztputbaeybx.cpp(13): note: see usage of 'max_threads'
C:/Users/Xuhan/AppData/Local/Temp/tmpncykej5v/a4/ca4534cazplidnf7vopaaxaifqkjiyhxm3h2gsylgztputbaeybx.cpp(16): error C3863: array type 'float [max_threads]' is not assignable
```
Genarated code:
```c++
#include "C:/Users/Xuhan/AppData/Local/Temp/tmpt6mxcjzi/j2/cj22tgrdgh42wbunl7gdptg2lintcziox2kmr7rdbcc6n2njrhgx.h"
extern "C" __declspec(dllexport) void kernel(const float* in_ptr0,
const float* in_ptr1,
float* out_ptr0,
float* out_ptr1)
{
{
{
float tmp_acc0 = 0;
at::vec::Vectorized<float> tmp_acc0_vec = at::vec::Vectorized<float>(0);
int max_threads = omp_get_max_threads();
float tmp_acc0_arr[max_threads];
for (int tid = 0; tid < max_threads; tid++)
{
tmp_acc0_arr[tid] = 0;
}
at::vec::Vectorized<float> tmp_acc0_vec_arr[max_threads];
for (int tid = 0; tid < max_threads; tid++)
{
tmp_acc0_vec_arr[tid] = at::vec::Vectorized<float>(0);
}
#pragma omp parallel
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134221
Approved by: https://github.com/zhuhaozhe, https://github.com/jansel
Summary:
This diff adds two new operators torch.ops._quantized.wrapped_linear_prepack and torch.ops._quantized.wrapped_quantized_linear_prepacked. It is a decomposition of the op torch.ops._quantized.wrapped_quantized_linear added in the previous diff.
We decomposed in this way as packed weight could be computed early so we don;t need to do it in every forward in AOTI
Reviewed By: jerryzh168
Differential Revision: D61395887
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134232
Approved by: https://github.com/houseroad
Summary:
As title.
Add a test case in test_aot_inductor to check for codegen (i.e. `aoti_torch_print_tensor_handle` is inserted as expected for debugging printer) for both cpu and cuda based on a simple `addmm` test model.
Test Plan:
```
AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=1 TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+graph, inductor, +schedule, output_code" buck2 run -c fbcode.enable_gpu_sections=true -c fbcode.nvcc_arch=h100 @//mode/opt fbcode//caffe2/test/inductor:test_aot_inductor -- -r test_aoti_debug_printer_codegen_abi_compatible_{cuda/cpu}
```
Differential Revision: D61169068
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133326
Approved by: https://github.com/ColinPeppler
Summary: Add tests that check function events for dynamic activity toggling for both GPU and CPU events. Also added comments from previous GH comments
Test Plan: Make sure all tests pass
Differential Revision: D61617514
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134149
Approved by: https://github.com/aaronenyeshi
Summary: Reduce the aarch64 dashboard run to only test the default config, until we solve the timeout issue. Also increase the frequency from nightly to 6 times a day, to see if we can reproduce the perf instability Nikita has observed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134265
Approved by: https://github.com/malfet
Switch installation of the pytorch package to be installed from our download.pytorch.org sources which are better maintained.
As well, switching over the miniconda installation to a miniforge installation in order to ensure backwards compat for users expecting to have the conda package manager installed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134274
Approved by: https://github.com/malfet, https://github.com/atalman
Co-authored-by: atalman <atalman@fb.com>
Summary:
Make quantization tests compatible with the new training IR.
With the new batch norm node `torch.ops.aten.batch_norm.default`, we don't need an additional getitem node after the bn node, so tests need to be fixed to not check for the getitem node.
We added a capture_pre_autograd_graph_using_training_ir() function, which returns True when we are using the training ir, and False otherwise. This way, the code supports both training ir and the old ir.
For now, we are just rolling out the training ir for fbcode internal tests.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_preserve_source_fn_stack
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_update_shared_qspec
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_conv2d
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv_bn_relu_fusion
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv_bn_fusion
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv_bn_fusion_literal_args
```
Reviewed By: andrewor14, tugsbayasgalan
Differential Revision: D61292102
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134259
Approved by: https://github.com/tugsbayasgalan
This patch makes two changes:
1. Whenever ncclCommSplit accepts groupRanks in its config, we should
populate it. This is independent of using PMI or not. For example,
non-PMI NCCL can also use this information, if it chooses to.
2. Provide a user flag to decide when to do a uniqueId broadcast and
when to skip it. This is a performance optimization, and not a
correctness requirement. If the user forgets to set this, we will
do the uniqueId broadcast, which is wasteful (because it will be
ignored by NCCL), but not incorrect.
@exported-using-ghexport
Differential Revision: [D60966774](https://our.internmc.facebook.com/intern/diff/D60966774/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133960
Approved by: https://github.com/shuqiangzhang
Reland of #128143 but added `alpha` and `bias` initialization to `launchTunableGemmAndBias`
Thus far TunableOp was implemented for gemm, bgemm, and scaled_mm. gemm_and_bias was notably missing. This PR closes that gap.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128919
Approved by: https://github.com/malfet
Summary:
In the new training ir, we produce `torch.ops.aten.batch_norm.default` instead of `torch.ops.aten._native_batch_norm_legit.default` or `torch.ops.aten._native_batch_norm_legit_no_training.default`.
So we need to change the pattern match to accomodate the new op.
- Add `torch.ops.aten.batch_norm.default` to pattern matcher list so it's identified as a batch norm node
- `torch.ops.aten.batch_norm.default` doesn't have a getitem user anymore, so when removing the bn norm, we need to do `bn_node.replace_all_uses_with(conv_node)` instead of `getitem_node.replace_all_uses_with(conv_node)`
The behavior of capture_pre_autograd_graph is consistent for each run.
If the run is a fbcode test, then capture_pre_autograd_graph uses training IR. This means both _get_aten_graph_module_for_pattern and replace_pattern_with_filters see the same training IR.
If the run is not a fbcode test, then both would see the old IR.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_conv2d_binary2
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_conv2d_unary
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_linear_unary
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_dynamic_quant_linear
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_dynamic_quant_linear
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_flatten_recipe
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_linear_unary
```
Reviewed By: andrewor14, tugsbayasgalan
Differential Revision: D61291077
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134157
Approved by: https://github.com/tugsbayasgalan
Part of #134054.
This corresponds to the pytorch mypy changes from D61493706. Updating takes so
long and touches so many files that it's impossible to land as a whole without conflicting with some other intermediate change.
So landing these 'type: ignore' for pytorch in advance of them actually being needed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134202
Approved by: https://github.com/Skylion007
Changes:
1. Move `polyfill.py` -> `polyfills/__init__.py`. It can be used as `polyfill.xxx` -> `polyfills.xxx`.
2. Move submodule loading from `polyfills/__init__.py` to `polyfills/loader.py`.
Merge `polyfill.py` and `polyfills/` packages. Each polyfill module have its own namespace for better code organization.
The ultimate goal is make `polyfills/__init__.py` empty and all polyfill functions move to its own namespace.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133977
Approved by: https://github.com/jansel
Summary: When deepcopy a proxy, we first try the default deepcopy behavior.
Test Plan: buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:fx -- -r proxy_deepcopy
Differential Revision: D61398418
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133706
Approved by: https://github.com/angelayi
Summary:
This diff implements a bunch of views for internal scuba viewing.
TODOS that I might punt to another diff:
- Saving cache stats via counter is definitely sus here, but there's not really a good way to track "fx graph cache hit for this compile phase" right now. Will think about this more.
- We should definitely log frame id, compile id, etc
- We should definitely be logging configs. That way, we can A/B test based on whether a config is turned on.
- idk what I'm doing with compile_uuid yet, but it's useful when you want to look at samples for a single run. I think if we had mast job info this field is not needed, but it's nice to be able to drill down to a single run and get its chrome trace view or icicle view, so idk
Test Plan:
All of the above views are run with nanogpt benchmark:
```
buck run mode/opt caffe2/benchmarks/dynamo:torchbench -- --training --backend=inductor --only nanogpt --performance
```
Differential Revision: D61603243
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134118
Approved by: https://github.com/oulgen
As per title, this PR adds proper casting to fuse_linear_bn_weights in the same style as the conv case above. This previously caused numerical issues on my end, so that is why I am fixing it.
Also cleans up the docstring.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134105
Approved by: https://github.com/mikaylagawarecki
Update cudnn_frontend submodule to 1.6.1 to patch some minor bugfixes and compiler fixes.
# Bug fix
* Fixed an issue where custom dropout mask was not correctly applied.
* Added -fvisibility=hidden for the pip wheels generated to avoid symbol conflicts with other modules that use cudnn frontend.
* Fixed an issue in sdpa operation which when deserialized will lead to numerical mismatches.
* Fixed an issue in sdpa fp8 fprop operation (in inference mode).
# Samples
* Added a new sample to showcase how a custom dropout mask can be applied to a sdpa operation.
* Added a sample to showcase convolutions on large (c * d * h * w > 2 **31) tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134007
Approved by: https://github.com/eqy
Zero bubble can be expressed through `ScheduleFlexibleInterleaved1F1B` by setting `enable_zero_bubble=True`. But instead of having to include this flag in schedule initialization we should create a separate ZeroBubbleSchedule and also transition `Interleaved1F1B` to derive from `ScheduleFlexibleInterleaved1F1B`. Then we dont need to expose `ScheduleFlexibleInterleaved1F1B` since the naming is not obvious
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133467
Approved by: https://github.com/wconstab
ghstack dependencies: #132691
Just something I noticed while implementing a new DeviceInterface
I had to add `# type: ignore[assignment]` because mypy thinks
DeviceInterface.get_raw_stream is a `Callable` and therefore
incompatible with a `staticmethod`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134187
Approved by: https://github.com/jansel
CUTLASS automatically skips a stage in the epilogue if we provide a nullptr. Thus, instead of building a special kernel for bias=None, we can reuse one of the other ones.
This also considerably simplifies the code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134113
Approved by: https://github.com/drisspg
ghstack dependencies: #134110, #134111, #134112
The compute dtype for the bias addition was set to ElementBias. Thus, for a bf16 bias, we would cast the fp32 accum to bf16 and _then_ add the bias. It is however (slightly?) more accurate to first add the bias in fp32 and only cast at the end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134112
Approved by: https://github.com/drisspg
ghstack dependencies: #134110, #134111
Bugfixings for PyTorch 2.5,
1. Using SYCL group algorithm API instead of old style for sub group shift utilities.
2. Add preprocess in reduction kernel for cases requiring data type cast.
3. Make group norm memory format compatible.
4. ZeroTensor: a. Remove unnecessary aten operators registration, or ZeroTensor process is bypassed. b. Align preprocess with intree implementation in aten::copy_.
5. Rebase checkIndexTensorTypes usage.
6. Align latest semantics of PyTorch foreach operators. Return multiple tensors with offset=0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133850
Approved by: https://github.com/EikanWang
As you can see, 'privateuse1' appears many times in out-of-tree extension codebase. I think that everything about the device type should be as same as other in-tree backends after registering the privateuse1 backend.
For example, after registering a privateuse1 backend named "foo", you should allow "foo" to be passed in as a valid device type.
```diff
- instantiate_device_type_tests(TestIndexing, globals(), only_for='privateuse1')
- instantiate_device_type_tests(NumpyTests, globals(), only_for='privateuse1')
+ instantiate_device_type_tests(TestIndexing, globals(), only_for='foo')
+ instantiate_device_type_tests(NumpyTests, globals(), only_for='foo')
```
> https://github.com/Ascend/pytorch/blob/master/test/test_indexing.py#L1654-L1655
The change is to map privateuse1 backend name to 'privateuse1' when calling `filter_desired_device_types()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133082
Approved by: https://github.com/albanD
Summary:
Previously, reuse of the same `Dim` was encoded by "sharing" internal constraints among constraint targets. This kind of sharing, implemented using `shared` fields between `_Constraint`s, was originally motivated by `dynamic_dim`, specifically to support `==` between `dynamic_dim`s, but we no longer need to maintain this overcomplicated structure: we can simply use names of `Dims` to directly encode sharing information.
Thus this PR vastly simplifies the structure of `_Constraint` by removing `shared` fields. As a result, both `_Constraint` and its moral subclass, `_DerivedConstraint`, are 1-1 with `Dim` and its moral subclass, `DerivedDim`.
Note that this will break `==` over `dynamic_dim`, so an immediate follow-up will be to remove `dynamic_dim` entirely from our public API. (It's been more than 6 months since the deprecation warning anyway.) I just didn't want to deal with that process in the same PR.
Test Plan: existing
Differential Revision: D61559413
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134045
Approved by: https://github.com/pianpwk
Currently, `fully_shard` will create a new `FSDPMyModuleClass` class for each `MyModuleClass` module **object**, which causes Dynamo to guard-fail on every module object's type checking. This PR fixes the issue by caching and reusing previously created FSDP wrapper class.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134135
Approved by: https://github.com/awgu
Fixes#128084
The approach is option 2 of what Elias suggested in the comment
thread:
- We require tensors to have the correct stride at usage. This may
involve a clone; if there was a clone and then a mutation into it
then we copy_ back the result of the mutation.
The reason why I went this approach was because it was the easiest and
Inductor already works really hard to remove additional clones/copy_.
There are some cases that this doesn't generate efficient code for; for
example, if the tensor is a view, we don't change the base of the view
to have the right stride order, instead we do a clone.
The view case isn't very common so I'm ignoring it for now but we could
improve this in the future.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133639
Approved by: https://github.com/eellison
Summary:
This PR adds in cuSPARSELt as a backend to PyTorch.
It is now possible to see if cuSPARSELt is available and the version if
it is with
```
torch.backends.cusparselt.is_available()
torch.backends.cusparselt.version()
```
Test Plan:
```
python test/test_sparse_semi_structured.py -k test_cusparselt_backend
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128534
Approved by: https://github.com/cpuhrsch, https://github.com/eqy, https://github.com/syed-ahmed
As in the title. In addition, the PR introduces `_int_bsr_dense_addmm` that is equivalent to `bsr_dense_addmm` except for int8 inputs the operation result is int32 tensor (similar to existing `_int_mm`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133855
Approved by: https://github.com/cpuhrsch
Fixes#133690
The naming was added in #121170 to allow performance debugging of latency critical threads. However the `pt_main_thread` name gets inherited every time a new process or thread is created from the parent one, which defeats the purpose. We need a better way to name the thread that launches kernels on accelerators but for the time being we can let users name the threads in the application code, using: `torch.multiprocessing._set_thread_name("insert_name")`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134066
Approved by: https://github.com/soulitzer, https://github.com/d4l3k
The functorch partitioners use network flow to split the joint graph into a forward and backward graph. Internally, we've found that upgrading to networkx 2.8.8 (from 2.5) results in some hard-to-debug failures (internal reference: https://fburl.com/workplace/jrqwagdm). And I'm told that there's interest to remove the python dependency.
So this PR introduces a C++ implementation that mirrors the API provided by networkx. We'll need to add python bindings and do some additional testing to verify correctness.
Differential Revision: [D61550977](https://our.internmc.facebook.com/intern/diff/D61550977)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132188
Approved by: https://github.com/Chillee
Add a way of generating a FunctionSchema from example values because hop's schema varies even for the same hop.
We didn't use torch._C.FunctionSchema because we cannot construct the classes directly (e.g. "__init__" cannot be used for torch._C.FunctionSchema). Also extending the Basic types in c++ seems not that easy.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133521
Approved by: https://github.com/zou3519
Summary:
In export, we will generate many redundant getitem nodes branching from the same source, inserted by runtime assertions or any passes. This is causing issues with any downstream system relying on any value being uniquely defined by a single node.
I don't think it hurt to remove a bunch of getitem nodes only, so I just added to the ctor.
Test Plan:
rebase on D61256937
```
buck2 run scripts/bearzx:pt2_export_playground
```
Differential Revision: D61351578
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133618
Approved by: https://github.com/tugsbayasgalan
Add `stage_backward_input` and `stage_backward_weight` functions to perform the weight updates for inputs and weights independently.
We still support `self.dw_builder` argument for a custom backward, but it has become optional. It takes a separate code path and cannot be used in conjuction with the native zero backward.
Added tests:
`python test/distributed/pipelining/test_schedule_multiproc.py -k test_schedule_with_native_zero_bubble`
`python test/distributed/pipelining/test_backward.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132691
Approved by: https://github.com/wconstab
**Summary**
Implement the complete vectorization of `index_expr` functionally. We also add heuristic from performance perspective to resolve the regressions posted below: https://github.com/pytorch/pytorch/pull/122961#issuecomment-2041336265 by disabling vectorization of specific (Fused) scheduler Node:
- Heuristic 1: when the num of non-contiguous `index_expr/load/store` exceeds the threshold, we disable the vectorization.
- Heuristic 2: when the total number of elements along the vec dim is less than `tiling_factor/2`, we disable the vectorization.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122961
Approved by: https://github.com/jansel
Co-authored-by: leslie-fang-intel <leslie.fang@intel.com>
Summary:
This diff adds a new operator wrapped_quantized_linear (torch.ops._quantized.wrapped_quantized_linear) and takes the following input argument: input (in fp32) , input_scale, input_zero_point, weight (in fp32), weight_scale, weight_zero_point, bias (in fp32), output_scale, output_zero_point, and out_channel. It does the following
1. Use quantize_per_tensor(input, input_scale, input_zero_point) to quantize the input tensor to int8
2. Use quantized::linear_prepack(weight, weight_scale, weight_zero_point, bias) to pack the weight and bias
3. Use quantized::linear to perform int8 quantized linear
4. dequantize
This new op is essentially a wrapper of mutiple ops. We do this as torch.export cannot handle models where it has old quantize apis.
Reviewed By: jerryzh168
Differential Revision: D61377266
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134024
Approved by: https://github.com/houseroad
Add decorator `torch.compiler.substitute_in_graph` to register polyfill for unsupported C++ function to avoid graph break. This API provides an official way to add support for dynamo for third-party C extensions. Also, it can be used to simplify our implementation for `torch._dynamo.polyfill`.
5ee070266f/torch/_dynamo/variables/builtin.py (L97-L107)
Example:
```python
>>> import operator
>>> operator.indexOf([1, 2, 3, 4, 5], 3)
2
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
Unsupported: ...
>>> @torch.compiler.substitute_in_graph(operator.indexOf)
... def indexOf(sequence, x):
... for i, item in enumerate(sequence):
... if item is x or item == x:
... return i
... raise ValueError("sequence.index(x): x not in sequence")
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
2
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133712
Approved by: https://github.com/jansel
Summary:
* TLDR:
`getenv` is not thread safe w.r.t `setenv`. Environment variables are kept as a per-process "dictionary" by libc. `setenv` can essentially realloc the whole thing move this list to a completely different location. If there is a concurrent `getenv` happening as the same time, it is possible that it might end up reading stale memory and segfault.
`getenv` is thread safe w.r.t other `getenv`.
* Details:
Inside PTD init:
```
ProcessGroupNCCL ctor
...
ncclCommWatchdogThread_ =
std::thread(&ProcessGroupNCCL::ncclCommWatchdog, this); (https://fburl.com/code/terf9ai7)
```
Inside ncclCommWatchdog thread:
```
...
ncclHeartbeatMonitorThread_ =
std::thread(&ProcessGroupNCCL::heartbeatMonitor, this); (https://fburl.com/code/fv9camg2)
...
```
Inside heartbeatMonitor thread:
```
...
std::optional<DumpPipe> dumpPipe = std::nullopt; (https://fburl.com/code/qdvahzbu)
dumpPipe.emplace(rank_);
...
```
Inside DumpPipe ctor (https://fburl.com/code/wvixlqcz)
```
getCvarString
getenv <=== SIGSEGV
```
On the main thread:
We go on to initialize NCCL:
Inside getNCCLComm, we call: `getNcclVersion` -> `initEnv` (https://fburl.com/code/j312pccu)
`initEnv` inside NCCL does this: `initEnv` -> `setEnvFile`
This guy, reads the /etc/nccl.conf file, and sets values of env variables with "setenv" (https://fburl.com/code/cq4r0y0h)
This "setenv" can race with "getenv" in heartbeatMonitor thread.
Ideally, all `setenv` should be done by a single thread before launching other threads. This diff moves getNCCLVersion before launching watchdog thread to make sure all setenvs are done beforehand.
I think we are just getting lucky that we are not hitting it in production. IIRC in fact we saw getenv segfault once in one of the large scale runs, but now I dont remember the details.
Test Plan: A lot of testing done as part of D61411062 & CI
Differential Revision: D61421292
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133744
Approved by: https://github.com/wconstab, https://github.com/fduwjj
Summary:
Change ReorderConvertTest to work with the new `capture_pre_autograd_graph` implementation using D61175223.
Note that now `ReorderConvertTest` doesn't work with the old `capture_pre_autograd_graph` anymore.
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/passes/tests:optimize_test -- -r ReorderConvertTest
```
Differential Revision: D61507772
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134010
Approved by: https://github.com/tugsbayasgalan
Link various classes and functions of the `optim.swa.util` to make doc content accessible from the `torch.optim` doc.
Currently, if you click the link,
https://pytorch.org/docs/stable/optim.html#module-torch.optim.swa_utils it goes to a blank, bottom of the page section of `torch.optim`.
Also,
`torch.optim.swa_utils.AveragedModel` and `torch.optim.swa_utils.SWALR` classes as well as `torch.optim.swa_utils.update_bn()` and `optim.swa_utils.get_ema_multi_avg_fn` are not linked to doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133393
Approved by: https://github.com/janeyx99
https://github.com/pytorch/pytorch/pull/132990 introduced dependency on `torch.version`, which might not be imported yet, and can result in `AttributeError: partially initialized module 'torch' has no attribute 'version' (most likely due to a circular import)` if user starts its code with `import torch.cuda`
Fix it by importing `torch.version` explicitly
Test Plan: CI
Differential Revision: D61549284
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134019
Approved by: https://github.com/seemethere
Summary:
Skip re-exporting modules with the duplicated types to speed up the exportability tests.
In real models, there are many duplicated modules, and mostly have the same export issues.
Test Plan: Existing CI
Differential Revision: D61504630
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133930
Approved by: https://github.com/angelayi
Add decorator `torch.compiler.substitute_in_graph` to register polyfill for unsupported C++ function to avoid graph break. This API provides an official way to add support for dynamo for third-party C extensions. Also, it can be used to simplify our implementation for `torch._dynamo.polyfill`.
5ee070266f/torch/_dynamo/variables/builtin.py (L97-L107)
Example:
```python
>>> import operator
>>> operator.indexOf([1, 2, 3, 4, 5], 3)
2
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
Unsupported: ...
>>> @torch.compiler.substitute_in_graph(operator.indexOf)
... def indexOf(sequence, x):
... for i, item in enumerate(sequence):
... if item is x or item == x:
... return i
... raise ValueError("sequence.index(x): x not in sequence")
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
2
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133712
Approved by: https://github.com/jansel
```
# supposed we have a 3d mesh
mesh_3d = init_device_mesh("cuda", (2,2,2), mesh_dim_names=("dp", "cp", "tp")
dp_cp_mesh = mesh_3d["dp", "cp"]._flatten()
"""
then we would have
flatten_name_to_root_dims[mesh_3d]: {
"dp_cp": (0, 1)
}
"""
```
We need this information to validate the order mesh slice including flatten mesh dim.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133838
Approved by: https://github.com/fegin
Summary:
Skip re-exporting modules with the duplicated types to speed up the exportability tests.
In real models, there are many duplicated modules, and mostly have the same export issues.
Test Plan: Existing CI
Differential Revision: D61504630
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133930
Approved by: https://github.com/angelayi
Co-authored-by: bearzx <bearzx@fb.com>
It is parallel PR to https://github.com/pytorch/pytorch/pull/133819 , and it is append change for @jansel 's comments.
1. For `torch/_inductor/codegen/cpp_wrapper_cpu.py`, revert to origin code to append LL on MacOS and Windows: bdc14ad89a
2. For `torch/_inductor/codegen/cpp_utils.py`, append LL on MacOS and Windows forlarge constants. And fix its UTs: 3a56b76ce0
------------------------------
Another solution for https://github.com/pytorch/pytorch/pull/133615, use `int64_t` as index type for all plartform.
### Development notes:
The metioned PR( https://github.com/pytorch/pytorch/pull/133615) is fix the index type not match to parse_arg args types. As reviewed with @jansel , Jason think we need to unificate `INDEX_TYPE` for all platforms.
Current code is make code cumbersome:
```python
INDEX_TYPE = "int64_t" if _IS_WINDOWS else "long"
```
So, I have some attempts to unificate `INDEX_TYPE` as `long` or `int64_t`.
For use `long` as index type: https://github.com/pytorch/pytorch/pull/133768
For use `int64_t` as index type: https://github.com/pytorch/pytorch/pull/133782
Since that, we still discussed which type we will select as final solution.
`long` type is different define and size in different OSs and different compilers. So, @jansel make decision that, we need to select `int64_t` for all platforms. So, I would comtine my work based on https://github.com/pytorch/pytorch/pull/133782.
As https://github.com/pytorch/pytorch/pull/133782 still has two issues:
1. std::min/std::max could not match function instances by arg types. It as fixed and validated in PR: https://github.com/pytorch/pytorch/pull/133812
4. Cuda TestMemoryPlanning::test_cpp_wrapper issue by wrong index type. It is fixing in this PR.
So, we made final solution in this PR.
### Changes:
**1. Use `int64_t` type as index type for all OSs: `Windows`, `Linux` and `MacOS`.**
**2. Use static_cast<int64_t>(`constant`) to convert constant to `div_floor_integer` with args type(`int64_t`).**
**3. Update `parse_arg` function signature to `int64_t`, which follow the index type.**
**4. Append double L(`LL`) to constant on Windows and MacOS, because of their int64_t are are long long.**
**5. Fix `std::min/std::max` type miss match by static_cast to `INDEX_TYPE`.**
**6. Fix UTs, containts: cuda `TestMemoryPlanning::test_cpp_wrapper`, and `test_indexing.py`.**
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133892
Approved by: https://github.com/jansel
Another attempt to update NVTX to NVTX3. We now avoid changing NVTX header inclusion of existing code. The advantage of NVTX3 over NVTX is that it is a header-only library so that linking with NVTX3 can greatly simplify our CMake and other building scripts for finding libraries in user environments. In addition, NVTX are indeed still present in the latest CUDA versions, but they're no longer a compiled library: It's now a header-only library. That's why there isn't a .lib file anymore.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109843
Approved by: https://github.com/peterbell10, https://github.com/eqy
Co-authored-by: Ivan Zaitsev <108101595+izaitsevfb@users.noreply.github.com>
Summary:
- exir.capture + to_edge is deprecated. We need to use the export + to_edge.
- Fix quantization pass to be compatible with the new export IR. In the quantization pass, some nodes might have side-effects, so they don't have users, but still are not removed by the DCE pass. We need to consider it.
- now export_rle_model works with the default `capture_pre_autograd_graph`, it should also work with the new training it
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/export:export_rle_model -- -r export_rle_model
```
Differential Revision: D61485834
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133937
Approved by: https://github.com/tugsbayasgalan
Summary:
The existing tests didn't cover a case where we had multiple autotunes in a single graph. Add a test to demonstrate that case.
Also added a test dependency on redis and removed the "fake redis" from the previous PR (#133579)
Test Plan: unit tests
Reviewed By: oulgen
Differential Revision: D61178861
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133868
Approved by: https://github.com/oulgen
Adds guards checking whether torch function mode is in the all disabled state.
There are three torch function enablement states:
* All torch function disabled (modes + subclasses)
* Torch function subclass disabled
* All enabled
We now have guards checking if the state is All enabled and if state is All disabled.
All of the above ternary states are assigned to a unique pair of these two flags.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133135
Approved by: https://github.com/anijain2305
ghstack dependencies: #133130, #133729, #133131, #133132, #133133, #133134, #133136
This PR adds a C function to check if all torch function is disabled.
Recall that there are three torch function enablement states:
* All disabled
* Torch Function Subclass disabled
* All enabled
The API before this change provides two functions:
* `_is_torch_function_enabled` - returns True iff the current TF state is All enabled
* `_is_torch_function_mode_enabled` - returns True iff the state is not All disabled and the torch function mode stack is non-empty.
The crux of why a new API is needed is the following: If dynamo enters a frame with the torch function mode stack empty, `_is_torch_function_enabled` == False, it is impossible to determine if after a new mode is pushed whether we should enter the mode or not. This is because we don't know if the enablement state is All disabled or only subclass disabled. Adding this API to check if All disabled is True allows us to disambiguate this case.
In the next PR, Dynamo InstructionTranslator will have clearer flags than the underlying C API:
* A flag to indicate if subclasses are disabled (ie All disabled or Subclass Disabled is the current state)
* A flag to indicate if modes are disabled (ie if All disabled is the current state)
* A symbolic stack which can be checked if any modes are present
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133136
Approved by: https://github.com/bdhirsh
ghstack dependencies: #133130, #133729, #133131, #133132, #133133, #133134
This PR adds support `torch._C._push_on_torch_function_stack()` by updating `torch.py` to push onto the symbolic torch function mode stack when a push is encountered. The same side effects infra used in the previous PR is used to track the mutation of the torch function mode stack and add bytecode to update it if it is mutated.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133132
Approved by: https://github.com/williamwen42
ghstack dependencies: #133130, #133729, #133131
This PR adds support for tracing `torch._C._pop_torch_function_stack()` without graph breaking and in order to verify the state change also adds replay of mutations to the torch function mode stack via side_effects appending supplemental bytecode as we do for other python mutable objects.
Details:
To represent the torch function mode stack symbolically a deque field is added to the instruction translator. When the InstructionTranslator is initialized, all modes are read from the current torch function mode stack, and stashed in a global weak ref for later access (using existing sources) without needing to push/pop the python/cpp torch function mode stack.
During tracing, when `_pop_torch_function_stack` is encountered a value is popped from this deque and the variable tracker representing the mode is returned. To ensure the true torch function mode stack matches this state, `TorchFunctionModeStackVariable`, a singleton, is marked as mutated, this adds it to side effects, where during final codegen, side effects will codegen a call to a python helper which will update the python torch function mode stack.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133131
Approved by: https://github.com/jansel
ghstack dependencies: #133130, #133729
This PR adds a guard on the torch function mode stack state at the beginning of tracing. The way this is implemented is via a new leaf guard which is passed the initial stack state at construction and compares it to the stack state at the time the guard is run.
Details:
The stack state is extracted via popping all modes, appending them to a list, and pushing all modes back. This list is stored on the output graph and read during guard construction to pass to the stack mode guard. There the length and types of the modes are recorded. Next time the guard is run it compares this recorded state to the current mode stack state.
To implement this in python a helper function was added to utils.py and this is used if cpp guards are not enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133130
Approved by: https://github.com/anijain2305
Summary: Defaulting TORCH_NCCL_DUMP_ON_TIMEOUT to "true" and adding a kilswitch in case we need to kill this feature in production.
Test Plan: Tests pass manually but need futher testing before this is rolled out fully everywhere.
Differential Revision: D61136320
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133237
Approved by: https://github.com/c00w
Summary: This diff fixed many lint issues in qlinear_prepack.cpp. I'am fixing them as I want to add more ops/funcs into this file later.
Test Plan: Sandcastle
Differential Revision: D61425436
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133797
Approved by: https://github.com/Skylion007
Summary: `_ConstraintTarget` is an internal data structure that has some redundancy: tensors are identified by their id but also carry a weak reference. The weak reference was probably useful a year back but everything is done with ids right now, and the lifetime of these tensors ensures that using their ids is OK.
Test Plan: existing tests
Differential Revision: D61488816
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133890
Approved by: https://github.com/tugsbayasgalan
Summary: When generating CUDA kernel load and launch, certain Triton kernel meta data are needed, but those meta data only exist after kernel auto-tune is done. DeferredCudaKernelLine is a deferred line which can backfill a string template after kernel auto-tune. This is to prepare for one-pass AOTI codegen implementation.
Differential Revision: [D61018114](https://our.internmc.facebook.com/intern/diff/D61018114)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129135
Approved by: https://github.com/angelayi
Summary:
Remove the early exit for padding when padding = [0, 0, 0, 0].
This prevents export from specializing when all padding=0, allowing export when all padding >= 0. Specialization will still happen for negative padding.
This change will be used to export image preprocess for multimodal models, where images of dynamic shape are padded. As images are of dynamic shape, we can't be sure if padding will be required or not. Padding is guaranteed to be non-negative.
Preprocess code: https://github.com/pytorch/torchtune/pull/1242
Note: the alternative is to wrap padding in a custom op, which isn't ideal given the custom op will contain the same impl as constant_pad_nd.
Test Plan: ci
Differential Revision: D60687727
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132679
Approved by: https://github.com/ezyang
The regex in the script is too restrictive, as it excludes examples with parentheses in args, like the following:
```
triton_poi_fused_add_0.run(arg0_1.item(), arg1_1.item(), buf0, 1, grid=grid(1), stream=streamNone)
^
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130837
Approved by: https://github.com/Chillee
Fixes the observed graph breaks in https://github.com/pytorch/pytorch/issues/121349 and https://github.com/pytorch/pytorch/issues/121350.
But there are still graph breaks since a random output is being used as a seed, e.g.
```python
import random
import torch
def fn(x):
seed = random.randint(0, 100)
rand = random.Random(seed)
return x + rand.randrange(10)
opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
opt_fn(torch.ones(1))
```
fails with
```
torch._dynamo.exc.InternalTorchDynamoError: UnspecializedPythonVariable() is not a constant
```
when tracing the line
```
rand = random.Random(seed)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133725
Approved by: https://github.com/jansel
Add decorator `torch.compiler.substitute_in_graph` to register polyfill for unsupported C++ function to avoid graph break. This API provides an official way to add support for dynamo for third-party C extensions. Also, it can be used to simplify our implementation for `torch._dynamo.polyfill`.
5ee070266f/torch/_dynamo/variables/builtin.py (L97-L107)
Example:
```python
>>> import operator
>>> operator.indexOf([1, 2, 3, 4, 5], 3)
2
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
Unsupported: ...
>>> @torch.compiler.substitute_in_graph(operator.indexOf)
... def indexOf(sequence, x):
... for i, item in enumerate(sequence):
... if item is x or item == x:
... return i
... raise ValueError("sequence.index(x): x not in sequence")
>>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
2
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133712
Approved by: https://github.com/jansel
# UPDATE:
This is take 3 of https://github.com/pytorch/pytorch/pull/131863 which was landed via co dev but not applying correclty
# Summary
Changes the stance of SDPA on what to do for fully masked out rows
## Current Behavior
Several PyTorch users have expressed frustration over this issue:
- https://github.com/pytorch/pytorch/issues/41508
- https://github.com/pytorch/pytorch/issues/103749
- https://github.com/pytorch/pytorch/issues/103963
These are significant issues with extensive discussion but no satisfactory resolution. The PyTorch team's consensus, as stated here:
https://github.com/pytorch/pytorch/issues/24816#issuecomment-524415617
Can be paraphrased as follows:
When passing in fully masked out rows, attention becomes ambiguous. We have two main options:
1. Uniformly attend to all values:
```python
scores[masked_out_rows] = 1 / len(row)
out[masked_out_rows] = 1 / len(row) * value
```
2. Decide that attention between no queries (masked) and no keys (masked) is meaningless:
```python
output[fully_masked_rows] = NaN
```
We went with option 2. Partially because it was easier to implement, but also people argued that users can slice the output to remove the NaNs:
``` Python
>fill_value = -float("inf")
>row0 = torch.randn(4)
>row1 = torch.tensor([(fill_value for _ in range(4)])
>matrix = torch.stack([row0, row1]).requires_grad_(True)
>out = torch.softmax(matrix, 1)
>out = out[0]
>print(out)
tensor([0.5377, 0.2729, 0.0692, 0.1201])
```
Cool, problem solved. But what happends when you call backwards..
```Python
>out.backward(torch.ones_like(out))
>print(matrix.grad)
tensor([[3.0957e-08, 1.4157e-08, 7.7802e-10, 1.3713e-08],
[ nan, nan, nan, nan]])
```
Those pesky NaNs are back!
## Why do we see NaNs today?
The core of the problem revolves around using softmax function in sdpa:
```python
> row = torch.tensor([(-float("inf")) for _ in range(4)])
> torch.softmax(row, 0)
tensor([nan, nan, nan, nan])
```
## Quick Aside: Masking in Attention
Attention itself doesn't have a concept of masking. The `sdpa` function has an argument called `attn_mask`, which would be more accurately named `attn_bias`. This is because we don't actually "mask" entries when computing attention. Instead, due to implementation details([performance](https://github.com/pytorch/pytorch/issues/25110#issuecomment-524519087)), we add a value to the masked-out query/key pairs.
We use a large negative number (typically -inf) to decrease the attention weight, as softmax assigns more weight to larger values.
## Alternative Approaches
If we use a very large negative number instead of -inf:
```python
> row = torch.tensor([(-1e6) for _ in range(4)])
> torch.softmax(row, 0)
tensor([0.2500, 0.2500, 0.2500, 0.2500])
```
However if users always remembered to "slice" out their outputs i.e.:
```Python
>fill_value = -1e6
>...
>out.backward(torch.ones_like(out))
>print(matrix.grad)
tensor([[-0.0563, -0.0564, 0.1613, -0.0486],
[ 0.0000, 0.0000, 0.0000, 0.0000]])
```
This would bring us back into a better state.
## A Third Option
We don't necessarily need to alter the behavior of softmax for -inf or very large negative numbers. The fundamental goal is to exclude certain query/key pairs from attention, regardless of the underlying implementation.
This PR implements the new semantic for masking w/ attention in fully masked-out rows:
```python
out[masked_out_rows] = 0
```
**Important Note**: This idea isn't entirely new. The [MaskedTensor](https://pytorch.org/tutorials/prototype/maskedtensor_overview#safe-softmax) prototype, a tensor subclass, was designed to handle such cases. However, it remains a prototype feature and hasn't gained widespread adoption.
## Details
This PR stack does 3 things:
1. Adds a PRIVATE _safe_softmax op
2. Updates semantic for flash_cpu fused kernel
3. Updates semantic for efficient_cuda fused kernel
_safe_softmax is not supposed to be used generically and is only meant to be used within the context of SDPA. Due to this fact instead of decomposing softmax and checking for -inf rows we instead "cheat" and use nan_to_num.
Why I think this is okay? (please find a counter point if avail)
There are multiple ways NaNs can emerge. For the fully masked out rows case nan_to_num works. But what if there were other NaNs, wouldn't this silently remove them?
The only case that this can happen is if the input itself had a NaN or an Inf
For example:
```Python
a = torch.ones([4], requires_grad=False, dtype=torch.float16)
a[1] = torch.finfo(torch.float16).max
print(a.softmax(-1))
```
Will return
`tensor([0., 1., 0., 0.], dtype=torch.float16)`
Where
```Python
a = torch.ones([4], requires_grad=False, dtype=torch.float16)
a[1] = float("inf")
a.softmax(-1)
```
returns:
`tensor([nan, nan, nan, nan], dtype=torch.float16)`
If we dont want to even allow for the possibility of "inf" or "NaN" attention scores to be converted to 0 then we can implemented it something like this
```Python
max = torch.max(a, dim=-1, keepdim=True)
exp = torch.exp(a - max.values)
denom = torch.sum(exp, dim=-1, keepdim=True)
softmax = exp / denom
softmax = torch.where(max.values == float('-inf'), 0.0, softmax)
```
however we would be paying for this in math performance.
## Why Now
I think one point that has substantially changed where PyTorch should lie on this argument is the fact that we have fused implementations for SDPA now. And these fused implementations allow us to easily and performantly support this new semantic.
Differential Revision: [D61418679](https://our.internmc.facebook.com/intern/diff/D61418679)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133882
Approved by: https://github.com/soulitzer
When exporting a training model for Executorch (which requires all ops to be core aten) with cross entropy loss (`torch.nn.CrossEntropyLoss`), we ran into the following error from the fx verifier in `to_edge`:
```
torch._export.verifier.SpecViolationError: Operator torch._ops.aten.nll_loss2d_forward.default is not Aten Canonical.
```
The aten [implementation](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/LossNLL.cpp#L624) of `torch.nn.CrossEntropyLoss` uses `nll_loss2d_forward` for inference and `nll_loss2d_backward` for training, so we need to add the decompositions for both (which already exist) to the list of core aten decompositions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133534
Approved by: https://github.com/JacobSzwejbka
## Description
Create decomposition of _unsafe_index_put (non-core aten) that turns it into index_put (core aten)
## Testing
Phi3 mini + LoRA model successfully passed `to_edge` after failing due to a non-core aten `unsafe_index_put` getting introduced in a decomposition during joint graph calculations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133365
Approved by: https://github.com/pianpwk
Summary: migrate to aten IR, `reshape` -> `view.default`, not covering `flatten` as there are already optimazation done in PT2, see the example here P1506057533
Differential Revision: D60476525
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132183
Approved by: https://github.com/frank-wei
Creates a new runtime that shifts complexity from runtime to
ahead-of-time.
The existing runtime (PipelineScheduleMulti) accepts a
compute-only schedule (forward, backward, weight) actions only are
specified, and it infers the communication operations at runtime.
Compared to that runtime, PipelineScheduleRuntime has less logic that
happens at runtime and relies on lowering passes to transform the
compute-only schedule to add communications.
Advantages include
- easier to verify the correctness by dumping a compute+comm schedule
- posible to manually edit the compute+comm schedule if the lowering
heuristics are insufficient
Functionality included inside the PipelineScheduleRuntime is limited to
- accepting a compute-only schedule and lowering it to add comms
- executing the compute or comm operations specified by the given
schedule
- handling work.wait() automatically by calling it just before the
matching compute operation (for RECV ops) or at the end of step (for
SEND ops)
Follow ups for later PRs
- Some refactoring should be done to replace PipelineScheduleMulti with
this runtime
- Optimizer execution is not considered (e.g. for zero-bubble cases)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130488
Approved by: https://github.com/H-Huang
Summary: Previously we were mocking out FbRemoteFxGraphCacheBackend which meant that we were missing testing a whole bunch of the cache code. Cache at a lower level (CacheClient, LocalAutotuneCacheBackend, ManifoldClient, Redis) so we cover a larger amount of the caching code.
Test Plan: unit tests
Reviewed By: oulgen
Differential Revision: D60937966
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133579
Approved by: https://github.com/oulgen
This is the first step to make sure we have a basic function of analyzer for FR in production.
- We want to use this script to find out abnormalities in collectives and report it to users.
- We also fixed some type errors.
- [Ongoing] Also we will add more unit tests to this script and make it modularized so that we can better maintain it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133412
Approved by: https://github.com/c-p-i-o, https://github.com/atalman
This is a bugfix that was recently encountered in ROCm/Deepspeed. Currently if a library installs pynvml and runs on ROCm pytorch will break as _HAS_PYNVML is set to true and it will attempt to use amdsmi library for the device_count call which will not be installed.
This fix will set _HAS_PYNVML to false on ROCm if amdsmi is not installed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132990
Approved by: https://github.com/pruthvistony, https://github.com/eqy, https://github.com/malfet
This fixes an issue on AArch64 cpus supporting BF16, caused when torch.set_float32_matmul_precision("highest") does not disable the bf16 downconversion in mkldnn_matmul.
This was discovered from a unit test failure where the decorator `torch.testing._internal.common_mkldnn.bf32_on_and_off`, which internally switches the float32_matmul_precision between "medium" and "highest" was not having the desired effect.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130919
Approved by: https://github.com/jgong5
Upgrades the LF scale configs to change the default AMI in accordance with the Amazon 2023 rollout plan.
This PR will be merged on Monday Aug 19 in the morning, and over the next 2-3 days as new linux runners are spun up (and old ones spun down) they'll start using this new AMI
This PR will be paired with https://github.com/pytorch/test-infra/pull/5558, which will be merged after this one
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133641
Approved by: https://github.com/jeanschmidt
FIXES https://github.com/pytorch/pytorch/issues/123949https://github.com/pytorch/pytorch/issues/124376
torch.cuda.memory_allocated returns the amount of memory allocated in the current process, so if it isn't 0 it means another test didn't properly clean up after itself. I'm keeping the memory check and isolating these tests in subprocess as we don't have a good way to test for activation refcount
e.g. https://github.com/pytorch/pytorch/runs/28838386083
```
_______________ TestCompiledAutograd.test_free_activation_memory _______________
Traceback (most recent call last):
File "/var/lib/jenkins/workspace/test/inductor/test_compiled_autograd.py", line 1892, in test_free_activation_memory
self.assertTrue(torch.cuda.memory_allocated() == 0)
File "/opt/conda/envs/py_3.10/lib/python3.10/unittest/case.py", line 687, in assertTrue
raise self.failureException(msg)
AssertionError: False is not true
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133733
Approved by: https://github.com/jansel
This threads through all of the necessary parts into aot autograd from the FXGraphCache changes so that we can run cudagraphs properly on a AOTAutograd cache hit.
Specifics:
- AOTAutograd needs access to the `cudagraphs` boxedbool in order to properly set the backward to not use cudagraphs on a cache hit from the forward.
- We have lots of tests that test this already from the previous PR, so I just added an extra test and made the previous test work with both AOTAutogradCache and FXGraphCache at the same time.
```
TORCH_LOGS=torch._functorch._aot_autograd.autograd_cache,cudagraphs ENABLE_AOT_AUTOGRAD_CACHE=1 TORCHINDUCTOR_FX_GRAPH_CACHE=1 tlp python benchmarks/gpt_fast/benchmark.py --output ~/gpt_fast_benchmark.csv
```
Twice, once on cache miss and once and cache hit.
Here is the perfetto trace for each(FB only link):
**Cache Miss:**
Logs:
```
Loading model Llama-2-7b-chat-hf
Time to load model: 0.66 seconds
I0813 10:53:34.416000 911030 torch/_functorch/_aot_autograd/autograd_cache.py:479] [0/0] AOTAutograd cache miss for key alqchc7zw6ynsxj2bzktcsngu4cajwcb3tmhvwlyqkuinx3zhmey
I0813 10:53:51.395000 911030 torch/_functorch/_aot_autograd/autograd_cache.py:558] [0/0] Writing AOTAutograd cache entry to /tmp/torchinductor_jjwu/aotautograd/alqchc7zw6ynsxj2bzktcsngu4cajwcb3tmhvwlyqkuinx3zhmey/entry
I0813 10:54:17.579000 911030 torch/_functorch/_aot_autograd/autograd_cache.py:479] [1/0] AOTAutograd cache miss for key a3nq2ywjxku342c6ag7rsqkalnxfshlcgve3tb2bigg7a45uz6pt
I0813 10:54:38.636000 911030 torch/_functorch/_aot_autograd/autograd_cache.py:558] [1/0] Writing AOTAutograd cache entry to /tmp/torchinductor_jjwu/aotautograd/a3nq2ywjxku342c6ag7rsqkalnxfshlcgve3tb2bigg7a45uz6pt/entry
I0813 10:54:39.228000 911030 torch/_inductor/cudagraph_trees.py:385] [__cudagraphs] recording cudagraph tree for graph without symints
V0813 10:54:39.939000 911030 torch/_inductor/cudagraph_trees.py:2160] [__cudagraphs] Running warmup of function 0
V0813 10:55:10.615000 911030 torch/_inductor/cudagraph_trees.py:2119] [__cudagraphs] Recording function 0 of graph recording id 0
Compilation time: 101.24 seconds
Average tokens/sec: 147.96 tokens/sec
Average bandwidth achieved: 1955.22 GB/s
Memory used: 14.51 GB
```
Chromium Event(fb only):
https://interncache-all.fbcdn.net/manifold/perfetto-artifacts/tree/ui/index.html?url=https%3A%2F%2Finterncache-all.fbcdn.net%2Fmanifold%2Ftlparse_reports%2Ftree%2Flogs%2Fjjwu%2Fcustom%2Fchromium_events.json#!/viewer?url=https%3A%2F%2Finterncache-all.fbcdn.net%2Fmanifold%2Ftlparse_reports%2Ftree%2Flogs%2Fjjwu%2Fcustom%2Fchromium_events.json&local_cache_key
**Cache Hit:**
Logs:
```
Loading model Llama-2-7b-chat-hf
Time to load model: 0.67 seconds
I0813 10:55:51.821000 944420 torch/_functorch/_aot_autograd/autograd_cache.py:474] [0/0] AOTAutograd cache hit for key alqchc7zw6ynsxj2bzktcsngu4cajwcb3tmhvwlyqkuinx3zhmey
I0813 10:55:55.465000 944420 torch/_functorch/_aot_autograd/autograd_cache.py:474] [1/0] AOTAutograd cache hit for key a3nq2ywjxku342c6ag7rsqkalnxfshlcgve3tb2bigg7a45uz6pt
I0813 10:55:56.030000 944420 torch/_inductor/cudagraph_trees.py:385] [__cudagraphs] recording cudagraph tree for graph without symints
V0813 10:55:56.192000 944420 torch/_inductor/cudagraph_trees.py:2160] [__cudagraphs] Running warmup of function 0
V0813 10:55:56.426000 944420 torch/_inductor/cudagraph_trees.py:2119] [__cudagraphs] Recording function 0 of graph recording id 0
Compilation time: 9.40 seconds
Average tokens/sec: 147.94 tokens/sec
Average bandwidth achieved: 1954.98 GB/s
Memory used: 14.51 GB
```
Chromium Event(fb only):
https://interncache-all.fbcdn.net/manifold/perfetto-artifacts/tree/ui/index.html?url=https%3A%2F%2Finterncache-all.fbcdn.net%2Fmanifold%2Ftlparse_reports%2Ftree%2Flogs%2Fjjwu%2Fcustom2%2Fchromium_events.json#!/viewer?url=https%3A%2F%2Finterncache-all.fbcdn.net%2Fmanifold%2Ftlparse_reports%2Ftree%2Flogs%2Fjjwu%2Fcustom2%2Fchromium_events.json&local_cache_key
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132294
Approved by: https://github.com/eellison
**Summary**
Implement the complete vectorization of `index_expr` functionally. We also add heuristic from performance perspective to resolve the regressions posted below: https://github.com/pytorch/pytorch/pull/122961#issuecomment-2041336265 by disabling vectorization of specific (Fused) scheduler Node:
- Heuristic 1: when the num of non-contiguous `index_expr/load/store` exceeds the threshold, we disable the vectorization.
- Heuristic 2: when the total number of elements along the vec dim is less than `tiling_factor/2`, we disable the vectorization.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122961
Approved by: https://github.com/jansel
Co-authored-by: leslie-fang-intel <leslie.fang@intel.com>
Summary:
# context
* when running an IG FM training with PT2 we found there are a few graph break due to torch.diff call in [jagged_tensor.py](https://fburl.com/code/cwssxabc)
```
_length: List[int] = (
_length_per_key_from_stride_per_key(torch.diff(offsets), stride_per_key)
if variable_stride_per_key
else torch.sum(torch.diff(offsets).view(-1, stride), dim=1).tolist()
)
```
* look into the failure, we found the TORCH_CHECK in diff should be TORCH_SYM_CHECK
* slice_forward error: df3d7729e, [tlparse](https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpxXZ2em/index.html)
```
RestartAnalysis
Tried to use data-dependent value in the subsequent computation. This can happen when we encounter unbounded dynamic value that is unknown during tracing time. You will need to explicitly give hint to the compiler. Please take a look at torch._check OR torch._check_is_size APIs. Could not guard on data-dependent expression ((5*u37 + u38)//(u37 + u38)) < 0 (unhinted: ((5*u37 + u38)//(u37 + u38)) < 0). (Size-like symbols: u38, u37)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to False.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
File "/data/users/hhy/fbsource/buck-out/v2/gen/fbcode/e99934938a0abe90/aps_models/ads/icvr/__icvr_launcher_live__/icvr_launcher_live#link-tree/torch/_decomp/decompositions.py", line 771, in slice_forward
if end_val < 0:
```
* after this diff: [tlparse](https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpAhv2Sh/failures_and_restarts.html)
Test Plan:
# command
* run model
```
TORCH_SHOW_CPP_STACKTRACES=1 TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 TORCH_LOGS="+graph_code,output_code,dynamic,aot,guards,verbose_guards,recompiles,graph_breaks" TORCH_TRACE=/var/tmp/tt buck2 run fbcode//mode/opt fbcode//aps_models/ads/icvr:icvr_launcher_live -- mode=fmc/local_ig_fm_v4_mini training.pipeline_type=pt2
```
* generate tlparse
```
tlparse `ls -t /var/tmp/tt/* | head -1`
```
Reviewed By: ezyang
Differential Revision: D56339251
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133740
Approved by: https://github.com/ezyang
Moving DTensor to be in the public namespace, to formally add the
documentation page that includes all the public APIs. This includes:
* many path renames and path import fixes
* a dedicated doc page without too much content yet (adding in the next
PRs)
* To preserve the BC for users still using the `torch.distributed._tensor`,
I added a shim script to redirect old path calls to the new module
The BC preserving is evidented by the fact that all DTensor tests are still
working without changing the public imports. So it's safe to land the
changes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133113
Approved by: https://github.com/XilunWu
ghstack dependencies: #133305, #133306
Summary:
These tests aren't running internally because the outer test harness is crashing without listing the tests. To fix we need:
* Add a target for the tools/stats/ folder since this test imports it
* Add a dependence to that target so it's included in the par
* Fix up the relative import syntax, which is somehow different internally vs. fbcode (not sure why this works, but many other tests are doing it)
Test Plan: `buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/inductor:cudagraph_trees_expandable_segments -- --run-disabled`
Differential Revision: D61396711
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133698
Approved by: https://github.com/xuzhao9
**Summary**
After enabling more vectorization, we found that vectorization does not always bring performance benefits. For example, a kernel with several non-contiguous index computations or non-contiguous buffer load/store operations can experience performance regression. A typical case is what we observed in the next PR: after fully enabling vectorization of `index_expr`, we saw a performance regression of `hf_BigBird`.
In this PR, we refactor the tiling select into a standalone module to enhance its extensibility for further advanced tiling select heuristic. A standalone class `TilingSelect` with its method `select_tiling` has been added. `select_tiling` accepts the inputs of `fn_list`, `var_sizes_list` and return `tiling_factors`, `tiling_indices`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130892
Approved by: https://github.com/jgong5
Summary:
This diff aims to fix the GPU Test skips in the quantization tests under the `caffe2/test/quantization` directory. The changes made in the `TARGETS` files include adding the `should_use_remote_gpu` flag to enable remote GPU testing. This should help to resolve the skipped tests and improve the overall test coverage.
[This diff] Fixed skip count: 4
[Running total] Fixed skip count: 4
Note: Creating separate diffs for each test-group.
Test Plan:
**281475054644766**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_compare_per_channel_device_numerics (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/5629499773981783
**281475054644780**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_compare_per_tensor_device_numerics (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/11540474087422107
**281475054644853**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_quant_pin_memory (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/11540474087422477
**844425008078016**: buck2 test 'fbcode//mode/opt' fbcode//caffe2/test/quantization:test_quantization -- --exact 'caffe2/test/quantization:test_quantization - test_cuda_quantization_does_not_pin_memory (caffe2.test.quantization.core.test_quantized_tensor.TestQuantizedTensor)'
https://www.internalfb.com/intern/testinfra/testrun/1407375259845199
Differential Revision: D60055277
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133158
Approved by: https://github.com/jovianjaison
Summary: Recently we observed in AI CMF, enabling decompose_mm pass will lead to mixed dtype for aten.mm and aten.addmm errors. By investigation, we figure out that the error comes from torch.sum, which has an implicit type casting to avoid the possible overflow (a similar discussion in github: https://github.com/pytorch/pytorch/issues/115832). Thus we do the output cast to avoid the error.
Test Plan:
# unit test
```
buck2 test mode/dev-nosan //caffe2/test/inductor:decompose_mem_bound_mm -- test_decompose_mm_mixed_precision
```
Buck UI: https://www.internalfb.com/buck2/00dc168e-4d65-40f8-b169-f4a58206f641
Test UI: https://www.internalfb.com/intern/testinfra/testrun/17169973624867151
Network: Up: 25KiB Down: 44KiB (reSessionID-b7e2ecc7-16ca-476d-95b2-09ea74645eb0)
Jobs completed: 19. Time elapsed: 1:07.6s.
Cache hits: 0%. Commands: 2 (cached: 0, remote: 0, local: 2)
Tests finished: Pass 6. Fail 0. Fatal 0. Skip 0. Build failure 0
# e2e
ads_dper3:68464f2dc5e849ba2670482079cecaaa
training_platform:2c41d916ad5dd82f196372a8c7bd37a0
### build training_platform
```
buck2 run fbcode//fblearner/flow/projects/training_platform:training_platform
```
### register training_platform
```
buck2 run mode/opt fblearner/flow/projects/training_platform:workflow -- register-workflows --project-name training_platform --flow_version training_platform:2c41d916ad5dd82f196372a8c7bd37a0
```
### build ads_dper 3
```
fbpkg build -E ads_dper3 --yes --expire 14d
```
### register ads_dper 3
```
buck2 run //pyper/core/eval_app_utils:flow_utils_script -- register --pkg-version ads_dper3:68464f2dc5e849ba2670482079cecaaa
```
### extend package (optional)
```
fbpkg expire --extend-only training_platform:2c41d916ad5dd82f196372a8c7bd37a0 30d
```
### before fix
f591360990
### after fix
baseline
f591395056
proposal
Differential Revision: D61351815
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133626
Approved by: https://github.com/jackiexu1992
If the scalar tensor is an output tensor, it shouldn't be unwrapped (i.e. `.item()` called) since `tl.store` requires a pointer type for outputs. This issue only occurs for mutated buffers: the input tensor is also used as an output tensor.
Fixes #ISSUE_NUMBER
@yanboliang @jansel @ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132859
Approved by: https://github.com/jansel
`torch.cuda.Event` objects are different from `torch.cuda.Stream` in that events are not pooled, meaning we can't look up a previously created CUDA event object by ID. This prevents CUDA event object created outside of the Dynamo graph from being used within the graph (since Dynamo needs a way to emit a `call_function` line in the graph that does the retrieval of the event object for downstream op use). This PR adds a simple object pool within Dynamo utility, to support looking up CUDA event object by ID from within the Dynamo graph.
After this PR, if a user creates a CUDA event object outside of the graph and use that event within the graph, the behavior will exactly match eager.
Test commands:
- `pytest -rA test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_created_outside_of_graph`
- `pytest -rA test/dynamo/test_ctx_manager.py::CtxManagerTests::test_cuda_event_across_graph_break`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133635
Approved by: https://github.com/yifuwang
ghstack dependencies: #133532, #133531, #133636
During Inductor lowering, layout constraints for an op is applied before the op's lowering is called. Currently `add_layout_constraint(aten._scaled_mm.default, constrain_to_fx_strides)` is called inside `aten._scaled_mm.default`'s lowering. This means that if the first `_scaled_mm` to be lowered relies on the layout constraint, it won't be applied and the generated code would fail. The issue won't manifest if the first `_scaled_mm` doesn't rely on the layout constraint.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133669
Approved by: https://github.com/drisspg, https://github.com/yangsiyu007
Updates CUDNN_frontend header only library to make the most of the newest CUDNN features and decrease the overhead of the library.
Copied from commit:
New API
- Graph Slice Operation: Introduced the graph.slice operation for slicing input tensors. Refer to docs/operations/Slice.md for detailed documentation and samples/cpp/misc/slice.cpp for a C++ sample. Pybinds for this operation have also been added.
- SM Carveout Feature: Added the set_sm_count(int32_t type) graph property to support the SM Carveout feature introduced in Ampere and Hopper GPUs. Engines that do not support SM_COUNT will return NOT_SUPPORTED.
Bug Fixes
- Convolution Mode Attribute: Added the missing set_convolution_mode attribute to convolution attributes in forward propagation (fprop), data gradient (dgrad), and weight gradient (wgrad). Previously, this was hardcoded to CUDNN_CROSS_CORRELATION in the 1.x API.
- SDPA FP8 Backward Node: Fixed an issue with the deserialization of the sdpa_fp8_backward node.
Enhancements
- Graph Execution Overhead: Reduced the overhead of graph.execute() by optimizing sub-node tree traversal, collected UIDs, workspace modifications, and workspace size.
- Graph Validation Performance: Significantly improved (~10x) the performance of graph.validate() by deferring graph expansion to a later stage (build_operation_graph).
- Optional Running Stats for BatchNorm: Made the running statistics for the batch normalization operation optional, supported by cuDNN backend version 9.3.0 and later.
- Shape and Stride Inferencing: Enhanced shape and stride inferencing to preserve the stride order of the input.
- Diagnostic Error Message: Added a diagnostic error message to create_execution_plans if called without the preceding build_operation_graph.
- JSON Schema and Deserialization: Improved the JSON schema and deserialization logic with additional checks.
- Logging Overhead: Reduced logging overhead, resulting in faster graph.build() calls.
- CMake Integration: Replaced CMAKE_SOURCE_DIR with PROJECT_SOURCE_DIR in CMake files for better integration. See the relevant pull request for more details.
Samples
- Jupyter Notebooks: Added Jupyter notebooks for RMSNorm, InstanceNorm, and LayerNorm. Refer to the samples/python folder for more information.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133687
Approved by: https://github.com/eqy, https://github.com/malfet
During distributed training if all ranks except one hit the cache, the rank that did not hit the cache will cause a NCCL timeout since rest of the ranks will enter the collective and start the timer. This PR uses the new PTD API to increase timeout for the ranks that hit the cache by the amount of time the cache would save.
Differential Revision: [D61363722](https://our.internmc.facebook.com/intern/diff/D61363722)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133374
Approved by: https://github.com/ezyang
This is a low-risk short-term fix for
https://github.com/pytorch/pytorch/issues/128084, for the purposes of
2.4.1. The actual fix for that issue is more risky and we'll target 2.5.
needs_fixed_stride_order is silently incorrect with args that are
mutable because it creates clones of those args, writes into them, and
doesn't update the original args.
This PR makes it so that needs_fixed_stride_order doesn't apply to
inputs that are being mutated.
This PR doesn't completely fix the problem, but it makes it less
incorrect: most of the time the input already has the correct strides
but inductor fails to recognize it, and in those cases writing directly
to the input is fine.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133452
Approved by: https://github.com/eellison
Fix https://github.com/pytorch/pytorch/issues/132716
The triton template for convolution does not work when the stride or padding contains dynamic shape. Use the hint and add guards to handle that. An alternative is to fallback to eager, but since I've seen the lowering rule for convolution use the hint in other cases, I'll just follow the convention.
I don't really know how to add a unit test here since I need create symbolic strides (not strides of a tensor but the stride parameter for convolution) and paddings. I can try harder if reviewer swants me to add unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132938
Approved by: https://github.com/jansel, https://github.com/eellison
ghstack dependencies: #132952
Setting `torch._dynamo.config.skip_fsdp_hooks = True` is required for graph-break compiled FSDP2, thus setting it to default will make this adoption easier. If users want to use Traceable FSDP2, they can set this to False manually (which will allow FSDP2 hooks to be traced through).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133531
Approved by: https://github.com/awgu
ghstack dependencies: #133532
Fixes#128059
I'm not sure if this is the right way, since Inductor doesn't always respect the device id set by users, so probably we should just wrap it as null context manager and print a warning. cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames @jansel @anijain2305 @mlazos @williamwen42
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133385
Approved by: https://github.com/jansel
Summary:
We saw ncclCommAbort was called and hang during the NCCLComm:create.
If NCCL comm is not properly initialized, ncclCommAbort behavior is
'undefined', avoid calling it would allow the process to properly throw
exception
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133630
Approved by: https://github.com/wconstab
This PR fixes the accuracy issues when template_buffer has users other than the epilogue nodes. This will fix the accuracy failure of the below models using max-autotune:
- MobileBertForMaskedLM
- MobileBertForQuestionAnswering
- convnext_base
- swin_base_patch4_window7_224
## Issue 1:
Previously we always add `template_buffer` as an alias of `Y`. In case the `template_buffer` has users other than the epilogue nodes, we shouldn't set it as an alias of `Y`. This PR adds the check in such case.
Wrong code before the fix where `tmp4` and `tmp9` are both stored to `Y` while we need 2 different buffers for them since `tmp4` will be used by nodes other than the epilogue node:
```cpp
Y[static_cast<long>(n_start + x1 + (32L*m_start) + (32L*x0))] = tmp4; // tmp4 is the output of the template
Y[static_cast<long>(n_start + x1 + (32L*m_start) + (32L*x0))] = tmp9; // tmp9 is the output of the epilogue node
```
Correct code after the fix:
```cpp
out_ptr2[static_cast<long>(n_start + x1 + (32L*m_start) + (32L*x0))] = tmp4;
Y[static_cast<long>(n_start + x1 + (32L*m_start) + (32L*x0))] = tmp9;
```
## Issue 2:
When fixing the above issue, we found that there's correctness issue when `bias` is `False`. The root cause is that in the case where `bias` is `False`, the `template_buffer` has users other than the epilogue nodes and the GEMM output buffer is localized, we need to add an extra copy epilogue to ensure that the GEMM output (a local buffer) is stored to the `template_buffer` that will be used later by other nodes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133073
Approved by: https://github.com/jgong5
ghstack dependencies: #133070
Summary: Some symbols (unbacked symints?) can have upper bound that is `sys.maxsize - 1` but our code for runtime assertions assumes that such upper bounds would come in as `sympy.oo` (like backed symints?) in order to drop them. So we weren't dropping them, which this PR fixes.
Test Plan: added test
Differential Revision: D61352056
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133627
Approved by: https://github.com/SherlockNoMad
Updating the source matcher to also accept pattern matching on the torch_fn metadata, which exists in both strict and non-strict export. We want to replace the use of source_fn_stack with torch_fn, as it's not possible for us to get source_fn_stack in non-strict export.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133642
Approved by: https://github.com/ydwu4
This PR enables dynamic shapes for the CK backend for gemm max autotune (see #125453).
This is achieved via unhardcoding the problem sizes from the template body and passing them as parameters instead.
We handle passing the problem sizes for the kernel call as well as for the benchmark call.
# Testing
`pytest test/inductor/test_ck_backend.py [-k dynamic]`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133285
Approved by: https://github.com/ColinPeppler
Summary: Recently we observed more missing example values in nodes introduced in Optimus, which causes problem to have further optimization when this node info needs to be used. Thus we add the meta for these nodes in the diff.
Test Plan:
# unit test
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 test //caffe2/test/inductor:split_cat_fx_passes
```
Buck UI: https://www.internalfb.com/buck2/c0ad506f-ce9d-4b80-947a-cb79074b72f0
Test UI: https://www.internalfb.com/intern/testinfra/testrun/2251800058834808
Network: Up: 1.4GiB Down: 2.0GiB (reSessionID-fb781425-f29b-44b5-8a5b-daffe7274f86)
Jobs completed: 300289. Time elapsed: 13:19.5s.
Cache hits: 99%. Commands: 119360 (cached: 118494, remote: 824, local: 42)
Tests finished: Pass 9. Fail 0. Fatal 0. Skip 1. Build failure 0
# benchmark
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 run mode/opt //scripts/jackiexu0313/pt2:local_model_with_pt2 -- --test_mode batch-split --model_type "cmf_shrink" --flow_id 587303213
```
P1520691492
Differential Revision: D61039772
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133414
Approved by: https://github.com/jackiexu1992
This is the first step to make sure we have a basic function of analyzer for FR in production.
- We want to use this script to find out abnormalities in collectives and report it to users.
- We also fixed some type errors.
- [Ongoing] Also we will add more unit tests to this script and make it modularized so that we can better maintain it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133412
Approved by: https://github.com/c-p-i-o
Summary: Switch to set_proxy_slot instead of set the proxy directly on the Tensor. We do not want to add Proxy to tensor objects, because Proxy cannot be deepcopied or pickeled and can cause problems when users want to deepcopy or pickle models.
Test Plan: CI
Differential Revision: D61277650
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133470
Approved by: https://github.com/zou3519
This PR adds support in train_decision if one wants to learn a heuristic for ranking. The main idea is that the user has to provide a number of choices the heuristic should return. I added a way to prune the learned decision tree such that it always returns the number of choices provided by the user.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131705
Approved by: https://github.com/eellison
Improve the cache blocking by reducing Mc_blocks to make A reside in L2 and reused by B as much as possible. This improves large bs perf for both scenarios: 1) N is large and K is of medium sizes; 2) K is large. Different strategies are used to handle these scenarios. Check the notes in `get_cache_blocking` in the changes.
Measured with 56-core Intel (R) Xeon (R) CPU Max 9480, jemalloc 5.1 and intel omp, bf16. Run with code cache of B matrix (weights).
Model Shapes | Before Optimization | After Optimization | Speedup | onednn linear | Speedup over onednn
-- | -- | -- | -- | -- | --
M=1024, N=12288, K=4096 (Llama2-8b) | 5.69 ms | 3.71 ms | 1.53 | 4.53 ms | 1.22
M=1024, N=4096, K=4096 (Llama2-8b) | 1.69 ms | 1.63 ms | 1.04 | 2.05 ms | 1.26
M=1024, N=22016, K=4096 (Llama2-8b) | 10.32 ms | 6.57 ms | 1.57 | 8.46 ms | 1.29
M=1024, N=4096, K=11008 (Llama2-8b) | 5.21 ms | 3.26 ms | 1.60 | 4.65 ms | 1.43
M=1024, N=5120, K=4096 (Llama3-8b) | 1.99 ms | 1.78 ms | 1.12 | 2.31 ms | 1.30
M=1024, N=28672, K=4096 (Llama3-8b) | 13.41 ms | 8.56 ms | 1.57 | 10.96 ms | 1.28
M=1024, N=4096, K=14336 (Llama3-8b) | 6.93 ms | 4.31 ms | 1.61 | 6.24 ms | 1.45
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132729
Approved by: https://github.com/leslie-fang-intel, https://github.com/chunyuan-w, https://github.com/jansel
Summary:
Fix quantization pass to be compatible with the new export IR.
Some nodes might have side-effects, so they don't have users, but still are not removed by the DCE pass.
Test Plan:
CI
buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/export:export_rle_model -- -r export_rle_model
Differential Revision: D61223356
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133587
Approved by: https://github.com/tugsbayasgalan
Summary: with acc_tracer disabled, the nodes generated use `args` instead of `kwargs` like before, in the current passes there are a mixed usage of `args` and `kwargs` and normalize nodes to switch between them can cause following passes to work/not work, in this diff we create a pass to normalize all the nodes to use `kwargs` at the beginning and changed all the passes to follow the same
Reviewed By: frank-wei
Differential Revision: D61049898
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133518
Approved by: https://github.com/frank-wei
Some recommendation models have a high number of `nn.Parameter`s. This exacerbates per-tensor CPU overheads in FSDP2 compared to FSDP1.
This PR adds a fast-path for the common bf16/fp32 mixed precision case for the casting the parameters from fp32 to bf16 to reduce CPU overhead and possibly have more efficient copy.
- Old: `for` loop + `.to(torch.bfloat16)`, incurring dispatcher overhead per parameter
- New: `torch.empty` + `torch.split` + `torch._foreach_copy_`, incurring three dispatches
---
Example on Llama3-8B which does not have many `nn.Parameter`s (compared to recommendation models):
(Old) on Llama3-8B (0.46 ms CPU overhead for all-gather):
(New) on Llama3-8B (0.37 ms CPU overhead for all-gather):
---
Same example as above but now with float8 all-gather:
(Old) on Llama3-8B with float8 (0.996 ms CPU overhead for all-gather):
(New) on Llama3-8B with float8 (1.014 ms CPU overhead for all-gather):
The times are relatively comparable for float8 with the new one possibly slightly slower, but this is mainly because for Llama's transformer blocks, there are only two norm weights that need to cast to bf16. These screenshots are mainly to show that the optimization still works in the mixed case.
Differential Revision: [D61236983](https://our.internmc.facebook.com/intern/diff/D61236983)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133369
Approved by: https://github.com/weifengpy
ghstack dependencies: #133498
Summary: Some element of tensor list output doesn't not have a user. In such case, create a name as `{node_name}_unused_{index}` for it.
Test Plan: OSS CI
Differential Revision: D61309011
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133539
Approved by: https://github.com/zhxchen17
These tests keep failing on the Linux Amazon 2023 AMI. The distributed team is looking into them, but until then, disabling the tests in order to unblock the AMI upgrade
Examples of the failures:
Failure 1: https://github.com/pytorch/pytorch/actions/runs/10047579686/job/27770963175
```
FAILED [90.0880s] distributed/test_c10d_nccl.py::NCCLTraceTestDumpOnTimeout::test_timeout_dumps_timing_enabled_False - AssertionError: None mismatch: None is not -6
```
Failure 2: https://github.com/pytorch/pytorch/actions/runs/10047579686/job/27770963494
```
____ NCCLTraceTestTimeoutDumpOnStuckRanks.test_timeout_dumps_on_stuck_ranks ____
Traceback (most recent call last):
File "/var/lib/jenkins/workspace/test/distributed/test_c10d_nccl.py", line 4214, in test_timeout_dumps_on_stuck_ranks
self.assertEqual(self._wait_process(0, timeout=90), -6)
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 3721, in assertEqual
raise error_metas.pop()[0].to_error(
AssertionError: None mismatch: None is not -6
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133355
Approved by: https://github.com/kit1980, https://github.com/wconstab
It is possible to write to Meta's internal in-memory database Scuba via the Scribe Graph API: https://www.internalfb.com/intern/wiki/Scribe/users/Knowledge_Base/Interacting_with_Scribe_categories/Graph_API/ This is currently being used by pytorch/benchmark repo to upload torchbench performance results.
I want to make this API generally available to all jobs running on CI in a semi-trusted context. To talk to Scribe, you need a secret access token. I have initially configured an environment prod-branch-main which contains `SCRIBE_GRAPHQL_ACCESS_TOKEN`, and switched a single class of jobs (linux-test) to use this environment when they are running on the main branch. Because we require approvals for running CI on untrusted contributions, we could potentially allow all jobs to run in this environment, including jobs on PRs, but I don't need this for my use case (per-PR benchmark result reporting, and miscellaneous statistics on main.)
If this works, I'll push out this environment to the rest of our test jobs.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133536
Approved by: https://github.com/xuzhao9, https://github.com/malfet, https://github.com/albanD
Summary:
Logging C++ stack traces occasionally races with shutdown processes on exception. It isn't safe and we've seen SIGSEGVs in the field.
These crashes prevent flight recorder dumps from completing.
For now, default this dumping to `true` and provide a knob if we need to control things in production.
Test Plan:
Tested locally on a job named `torchx-chirag_test_run` to make sure that the JK was honored by the code.
It was correctly disabled on my test job.
see (TORCH_NCCL_LOG_CPP_STACK_ON_EXCEPTION: 0) below.
```
] [trainer2]:I0814 11:21:20.152419 3708 ProcessGroupNCCL.cpp:874] [PG ID 0PG GUID 0 Rank 10] ProcessGroupNCCL environments: NCCL version: 2.20.3, TORCH_NCCL_ASYNC_ERROR_HANDLING: 1, TORCH_NCCL_DUMP_ON_TIMEOUT: 1, TORCH_NCCL_WAIT_TIMEOUT_DUMP_MILSEC: 60000, TORCH_NCCL_DESYNC_DEBUG: 0, TORCH_NCCL_ENABLE_TIMING: 0, TORCH_NCCL_BLOCKING_WAIT: 0, TORCH_DISTRIBUTED_DEBUG: OFF, TORCH_NCCL_USE_TENSOR_REGISTER_ALLOCATOR_HOOK: 0, TORCH_NCCL_ENABLE_MONITORING: 0, TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC: 480, TORCH_NCCL_TRACE_BUFFER_SIZE: 2000, TORCH_NCCL_COORD_CHECK_MILSEC: 1000, TORCH_NCCL_NAN_CHECK: 0, TORCH_NCCL_LOG_CPP_STACK_ON_EXCEPTION: 0
```
Differential Revision: D61283335
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133490
Approved by: https://github.com/fduwjj
Summary:
It seems we have multiple places deserializing torchbind objects. Moving the code around so that every load essentially share the same implementation.
Also added a test case "package_reader_testing" which load back the archive file in Python and eagerly validate the numerical result.
Test Plan: buck test mode/opt sigmoid/inference/test:e2e_test_cpu
Reviewed By: SherlockNoMad
Differential Revision: D61235770
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133463
Approved by: https://github.com/ydwu4
Fixes#124550
Also moves `graph.eliminate_dead_code()` call to a few lines after
`_inline_module(...)` in `const_fold.py`
* Test plan:
Add a new test on `test_eager_transforms.py` to ensure the reported
issue was indeed fixed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133364
Approved by: https://github.com/zou3519
This PR adds the foreach impl for Adafactor knowing that there are many ways to improve its runtime perf today (by adding more foreach support). After this PR:
- we have a foreach flag for Adafactor
- It is NOT the default. Why not? It is only slightly faster + uses O(n) more memory where n is the number of params in your max param group. People tend to use Adafactor for memory efficiency.
Next steps:
- make torch.compile possible on it
- make it faster (by adding more foreach apis)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132336
Approved by: https://github.com/albanD
ghstack dependencies: #133360
**What does this PR achieve**
1. This PR rewrite ring attention backward algorithm to fuse the alltoall and overlap the gradient communication with computation.
2. Enables memory efficient attention with CP by templating the ring attention backward to verify the accuracy as fp32 gives us higher confident about the implementation correctness.
3. Provides some experimental APIs to enable context parallelism.
4. Ensures CP work with torch.compiler. The combination of causal masking and torch.compiler has not
yet worked.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131351
Approved by: https://github.com/wanchaol
Sorryyyyy for another refactor. This splits `_process_dynamic_shapes` into 3 parts:
1. `_combine_args` - mostly the same thing
2. `_check_dynamic_shapes`, which is responsible for raising 99% of UserErrors if the dynamic shapes spec is invalid (minus 1 UserError with DerivedDims)
3. `_process_dynamic_shapes`, which for now, is the same thing, minus the stuff in 2.
This refactor is helpful for incoming automatic dynamic shapes work, because, we're switching to `assume_static_by_default=False`, which is what `_dynamo.export` currently does. This means any unspecified dims are allocated a symbol, in contrast to export today which keeps unspecified dims static. Historically this has been desirable - export users don't want too much dynamism. So we want to change how the spec is translated into constraints.
This means when we switch over to automatic dynamic shapes, we want to plug in something in between steps 2. and 3. which patches up the spec for `assume_static_by_default=False`, filling in static shapes for any unspecified dims, and potentially clearing out the auto-dynamic dims (since they're no-ops). We would do this in-between 2. and 3. to keep `_process_dynamic_shapes` semantically the same, since it's used with `_dynamo.export`.
We could do this without a refactor, plugging in this transform before `_process_dynamic_shapes`, but since that function's responsible for both spec checking + constraint production, moving spec checking to before we transform the specs helps guarantee we're raising errors on what the user's specified, and not an internal export bug.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133391
Approved by: https://github.com/avikchaudhuri
Counting `elapsed_time` immediately after `start_time`, not reflect real execution time of `test_batch`.
Move `elapsed_time` and print method after `run_tests` method call to fix it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133199
Approved by: https://github.com/clee2000
This PR introduces scripts that make it easier to use autoheuristic:
- `collect_data.sh`: The user can specify things like the number of GPUs to be used and the number of training samples to collect. This script will open one tmux pane per GPU and collect num_training_samples/num_gpus samples per GPU.
- `merge_data.py`: This script can be used to merge multiple training data files into a single file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133409
Approved by: https://github.com/Chillee
The function argument is A, not V.
Remaining inconsistency is the matrix $A$ with columns $v_i$.
It seems, a better solution would be to rename the argument $A \rightarrow V$, but this might lead to backward compatibility issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124279
Approved by: https://github.com/lezcano
When I did profiling using the "TORCHINDUCTOR_PROFILE" option, some kernel shows less bandwidth than expected. So, added the option to exclude the CPU overheads from the profiling time:
```
# With the option:
(pytorch-3.10) [shuqiyangdevgpu001.lla3 ~/local/pytorch (gh/shunting314/144/head)]$ TORCHINDUCTOR_PROFILE=1 TORCHINDUCTOR_PROFILE_WITH_DO_BENCH_USING_PROFILING=1 TORCHINDUCTOR_PROFILE_OUTPUT=/tmp/profile.txt python ../test_pt/a.py
0.038ms 0.067 GB 1777.11GB/s triton_poi_fused__to_copy_clamp_clone_mul_0
SUMMARY (/tmp/torchinductor_shuqiyang/tmp03wdg8e4/m6/cm6vdqp62ofwsone3u3fmb42vs3fti5omseo3qn4ddh2bhalsvbn.py)
0.04ms 0.07 GB 1777.11GB/s
# Without the option:
(pytorch-3.10) [shuqiyangdevgpu001.lla3 ~/local/pytorch (gh/shunting314/144/head)]$ TORCHINDUCTOR_PROFILE=1 TORCHINDUCTOR_PROFILE_OUTPUT=/tmp/profile.txt python ../test_pt/a.py
0.040ms 0.067 GB 1663.09GB/s triton_poi_fused__to_copy_clamp_clone_mul_0
SUMMARY (/tmp/torchinductor_shuqiyang/tmpwr6rraao/s4/cs4npkh77myatwpcmsizyduyfm6ne6o4pg4n3eodejdvvg2j3xzd.py)
0.04ms 0.07 GB 1663.09GB/s
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133523
Approved by: https://github.com/nmacchioni
The functorch partitioners use network flow to split the joint graph into a forward and backward graph. Internally, we've found that upgrading to networkx 2.8.8 (from 2.5) results in some hard-to-debug failures (internal reference: https://fburl.com/workplace/jrqwagdm). And I'm told that there's interest to remove the python dependency.
So this PR introduces a C++ implementation that mirrors the API provided by networkx. We'll need to add python bindings and do some additional testing to verify correctness.
Differential Revision: [D61284135](https://our.internmc.facebook.com/intern/diff/D61284135)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132188
Approved by: https://github.com/Chillee
**Summary**
When check the vectorization status among 3 test suit, we found some operators disabled vectorization with message `Disabled vectorization: op: remainder`. In this PR, we add vectorization support of this op.
**Test Plan**
```
python -u -m pytest -s -v test/inductor/test_cpu_repro.py -k test_vec_remainder
python -u -m pytest -s -v test/inductor/test_cpu_repro.py -k test_int_div_vec
```
Differential Revision: [D61147014](https://our.internmc.facebook.com/intern/diff/D61147014)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129849
Approved by: https://github.com/jgong5, https://github.com/lezcano
During distributed training if all ranks except one hit the cache, the rank that did not hit the cache will cause a NCCL timeout since rest of the ranks will enter the collective and start the timer. This PR uses the new PTD API to increase timeout for the ranks that hit the cache by the amount of time the cache would save.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133374
Approved by: https://github.com/ezyang
ghstack dependencies: #133362, #133363
For the next phase of the Amazon 2023 migration we'll be bulk migrating the remaining jobs over to the new AMI by changing the default AMI that we use.
In preparation for that, we're adding the old Linux Amazon 2 ami as a fixed variant for runners, so that if any of the less frequently jobs breaks on Amazon 2023 AMI then they can shift to explicitly using the Amazon 2 AMI temporarily while the underlying problem is debugged and fixed.
This PR is part 1, and there's a corresponding scale config PR in test-infra: https://github.com/pytorch/test-infra/pull/5551
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133469
Approved by: https://github.com/clee2000
We realized the fix for (https://github.com/pytorch/pytorch/pull/129683) loading the learning rate in place actually broke the meta tensor initialization. After the PR #129683, the learning rate is loading correctly, the param with meta tensors are still un-initialized.
We cannot use `tree_map_only_` to iterate over state_dict for initialization in-place, as `empty_like` and `to("cuda")` are both not in-place option. More context in https://github.com/pytorch/pytorch/issues/130709 Therefore, with changes in (https://github.com/pytorch/pytorch/pull/129683), the tensor after loading are still meta tensors. We previously did not catch that since `self.assertEqual()` does not distinguish a DTensor with meta DTensor.
In this PR, we added a _iterate_state_dict() function to implement in-place update for state_dict and updated the test to make sure that the params are no longer meta tensors after loading.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133256
Approved by: https://github.com/fegin
Before, having arbitrary depth nested configs like
```
class Foo:
foo: List[int] = [1, 2, 3]
class Bar:
bar: str = "1"
class Baz:
baz: int = 1
```
would cause problems beyond the first layer. For example, if we tried
```
from torch._inductor import config as inductor_config
print(inductor_config.Foo)
print(repr(inductor_config.Foo.foo))
print(inductor_config.Foo.Bar)
print(repr(inductor_config.Foo.Bar.bar))
print(inductor_config.Foo.Bar.Baz)
print(repr(inductor_config.Foo.Bar.Baz.baz))
```
we would get some output like
```
<torch.utils._config_module.SubConfigProxy object at 0x7fac65de00a0>
[1, 2, 3]
...
AttributeError: torch._inductor.config.Foo.Bar does not exist
```
Obviously, this is not what we want. With these changes, we get the right values
```
<torch.utils._config_module.SubConfigProxy object at 0x7f840d05bf40>
[1, 2, 3]
<torch.utils._config_module.SubConfigProxy object at 0x7f840cedc940>
'1'
<torch.utils._config_module.SubConfigProxy object at 0x7f840cedc100>
1
```
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133418
Approved by: https://github.com/oulgen
Fixes segmentation fault during model load via C++ API.
An `Assign` statement (`TK_ASSIGN` type) have 3 fields: `lhs`, `rhs` and `type`. Field `type` is of type `Maybe`, which means it could be not presented. During model load in `import_source.cpp` field `type` is dereferenced without validation.
It is similar error that have been fixed in #106041.
Fixes#127877
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127878
Approved by: https://github.com/malfet
This PR fixes the accuracy of jx_nest_base and part of the accuracy issue of convnext_base of the max-autotune path. Another fix (https://github.com/pytorch/pytorch/pull/133073 in this ghstack) is needed to make convnext_base fully pass the accuracy check.
The index calculated via the reindexer was wrong before this PR. Both the shape of the reshape reindexer and the stride order of the stride reindexer needs to be fixed.
Index calculated before this PR:
```
# in_ptr4 points to arg4_1: size = (1, 32, 18, 18), stride = (10368, 1, 576, 32))
auto tmp7 = in_ptr4[static_cast<long>((32L*(static_cast<long>((n_start + x1 + (32L*m_start) + (32L*x0))) % static_cast<long>(18L))) + (576L*(static_cast<long>(c10::div_floor_integer((n_start + x1 + (32L*m_start) + (32L*x0)), 324L)) % static_cast<long>(32L))))];
```
The correct one after the fix is:
```
auto tmp7 = in_ptr4[static_cast<long>(n_start + x1 + (32L*(static_cast<long>((m_start + x0)) % static_cast<long>(324L))))];
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133070
Approved by: https://github.com/jgong5
Summary:
Fixes https://github.com/pytorch/pytorch/issues/133336
When we fail to suggest fixes for a data dependent error because some symbols couldn't be mapped to sources, we print out those symbols but there was a silly bug in the printing code.
New error:
```
...
raise self._make_data_dependent_error(
torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(u0 + 1, CeilToInt(IntTrueDiv(u0 + 1, 1))) (unhinted: Eq(u0 + 1, CeilToInt(IntTrueDiv(u0 + 1, 1)))). (Size-like symbols: u0)
Potential framework code culprit (scroll up for full backtrace):
File "/data/users/avik/fbsource/buck-out/v2/gen/fbcode/6ef5f323b6193f0f/pyspeech/fb/tools/__export_speech_llama__/export_speech_llama#link-tree/torch/_refs/__init__.py", line 2972, in expand
guard_size_oblivious(requested_length == x)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
The following call raised this error:
File "/data/users/avik/fbsource/buck-out/v2/gen/fbcode/6ef5f323b6193f0f/pyspeech/fb/tools/__export_speech_llama__/export_speech_llama#link-tree/pyspeech/nn/utils.py", line 271, in lengths_to_padding_mask
).expand(batch_size, max_length)
```
Test Plan: Repro gets past reported error, hits new error
Differential Revision: D61221994
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133345
Approved by: https://github.com/ezyang
Original issue:
https://github.com/pytorch/pytorch/issues/129486
Before subclass_wrapper() got inputs containing additional effect tokens and failed as this did not match SubclassMeta indexes.
This happened as functionalization was responsible to add / remove those tokens.
Functionalization can not be run above Subclasses, as args/outs are duplicated in case of mutations.
The main design thought is to keep logic of EffectTokens, Subclasses, Functionalization to know as less as possible about each others transformations.
For that extracting EffectTokens manipulation to a separate wrapper, which will be processed above SubclassWrapper, while functionalization will happen below SubclassWrapper as before.
In that case subclass wrap/unwrap works without information of additional arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131672
Approved by: https://github.com/bdhirsh, https://github.com/zou3519
Summary: Model owners can set the lower_settings with max_acc_splits=2, and lowering will fail during model iteration, to alert them of possible performance degradation from increased fragmentation.
Test Plan: Added unit tests
Differential Revision: D60133589
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133041
Approved by: https://github.com/hl475
## Summary
As part of #125683, this PR modifies existing CPU GEMM cpp template & micro-kernel template to enable int8 WoQ GEMM auto-tuning with AVX2, AVX512 & AMX ISAs (the latter is only available on Xeon 4th generation & beyond).
WoQ GEMM takes FP16/BF16 activations, int8 weights, and scale of the same dtype as activations.
The operation is equivalent to `torch.nn.functional.linear(x, w.to(x.dtype)) * scale`, which is essentially what the ATen op `torch.ops.aten._weight_int8pack_mm` currently does (except that weights are not cached by it). Weights will be considered constant & cached, so this implementation is suitable for inference, and not QAT. `scale` is supported as a `mul` epilogue.
Only BF16 activations have been supported in this PR because for FP16 & FP32, weight is dequantized during constant-folding pass of freezing, and then after auto-tuning, performance with a large `M` dimension may be better than either torch.ops.aten._weight_int8pack_mm, or the WoQ micro-kernel support introduced in this PR, which dequantizes `w` within the micro-kernel.
While even BF16 activations with a large `M` dimension may benefit from dequantizing `w` beforehand, for now, they would use WoQ support in GEMM templates for auto-tuning, and then a subsequent PR would add logic for deciding whether or not to dequantize weights beforehand.
### Performance
#### AMX
Op-level speedup due to AMX micro-kernel (selected during auto-tuning) on 32 physical cores of Intel(R) Xeon(R) Platinum 8468H (of Xeon 4th generation series, codenamed Sapphire Rapids) vs. ATen kernel `torch.ops.aten._weight_int8pack_mm`. Intel OpenMP & tcmalloc were preloaded.
In a few cases with an odd `K`, the implementation being added in this PR may not perform as well as the ATen kernel, which is unrelated to this PR, though, since `test_linear_amx` also exhibits similar datapoints. In those cases, the AMX micro-kernel might be slower than AVX512 micro-kernel, so if such sets of shapes are used for auto-tuning, either the AVX512 micro-kernel implementation, or the ATen kernel would be chosen instead.
Benchmarked with unit-tests.
Tabular data at https://gist.github.com/sanchitintel/294811a86c8ff6b867c668ae2107c405?permalink_comment_id=5142442#gistcomment-5142442
The AVX512 micro-kernel was disabled to collect data for AMX micro-kernel.
#### AVX2/AVX512 micro-kernels
Tabular data at at https://gist.github.com/sanchitintel/52b5fa9c66f791be19e48e2aa6423dc4?permalink_comment_id=5142437#gistcomment-5142437
### Follow-up
1. int4 WoQ GEMM micro-kernel will also be added in a separate PR.
2. A subsequent PR would add logic for deciding whether or not to dequantize weights beforehand.
E2E perf measurement should be done with #131310.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131887
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/jansel
Summary:
Add a special field in Graph and Node level metadata called "custom" which should be mapped to a json-serializable object, and we guarantee this field should be always preversed across the following transformations:
1. copy/deepcopy
2. run_decompositions()
3. serialization
4. re-exporting
Test Plan: :test_export -- -r custom_tag
Reviewed By: angelayi
Differential Revision: D60291839
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131912
Approved by: https://github.com/angelayi
Forward fix after #132464 because TuningContext had been created during static library init, which creates the TuningResultsValidator, which tries to query HIP device properties before the HIP runtime has initialized.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133347
Approved by: https://github.com/zixi-qi
Summary:
Follow up small diff to fix a couple issues:
- add condition for cuda/gpu case to only print kernel name list in the second pass i.e. when we do the cpp wrapper codegen
- other minor fixes around `AOT_INDUCTOR_FILTERED_KERNELS_TO_PRINT` option
Test Plan:
```
AOT_INDUCTOR_FILTERED_KERNELS_TO_PRINT="triton_poi_fused_0" AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=1 TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+graph, inductor, +schedule, output_code" buck2 run -c fbcode.enable_gpu_sections=true -c fbcode.nvcc_arch=h100 @//mode/opt fbcode//caffe2/test/inductor:test_aot_inductor -- -r test_addmm_abi_compatible_cuda
```
Differential Revision: D60954888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133016
Approved by: https://github.com/ColinPeppler
This features is not yet supported on ROCm.
Skipping:
distributed/test_symmetric_memory.py::SymmetricMemoryTest::test_low_contention_all_gather_symm_mem_input_False
With the errors:
RuntimeError: CUDASymmetricMemory requires PYTORCH_C10_DRIVER_API_SUPPORTED
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133241
Approved by: https://github.com/pruthvistony, https://github.com/malfet
when we call benchmarker.benchmark(fn, (), {}) it attempts to infer the device from the args and kwargs, which are both empty. in this case the default behavior is to assume CPU, since `is_cpu_device` is implemented as `all([x.device == "cpu" for x in ... if x is Tensor])`, and `all([]) == True`. I've added a PR that makes this raise an error, but we should just fix this one callsite first
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133290
Approved by: https://github.com/eellison
To avoid high overheads of constructing datastructure in python when the user is simply saving trace to a file, we only process things lazily.
## Details
1. Delay function event parsing, add a flag to denote when needed.
1. Make profiler.function_events a computed property so code using `prof.function_events` does not need to change.
1. Fix coverage for `str(prof)` in profiler tests.
## Test run
Test program
```
import torch
from torch.profiler import profile, record_function, ProfilerActivity
def payload(use_cuda=False):
x = torch.randn(10, 10)
if use_cuda:
x = x.cuda()
y = torch.randn(10, 10)
if use_cuda:
y = y.cuda()
z = torch.mm(x, y)
z = z + y
if use_cuda:
z = z.cpu()
with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:
with record_function("model_inference"):
payload()
prof.export_chrome_trace("/tmp/test_trace.json")
#print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))
```
The print "this is computing events" will happen lazily.
```
>]$ python3 profiler_test.py
Brian: this is computing function events
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
model_inference 6.77% 441.628us 100.00% 6.523ms 6.523ms 1
aten::randn 1.86% 121.108us 46.93% 3.061ms 1.530ms 2
aten::mm 45.36% 2.959ms 45.44% 2.964ms 2.964ms 1
aten::normal_ 44.72% 2.917ms 44.72% 2.917ms 1.458ms 2
aten::add 0.87% 56.646us 0.87% 56.646us 56.646us 1
aten::empty 0.35% 22.808us 0.35% 22.808us 11.404us 2
aten::resolve_conj 0.08% 5.173us 0.08% 5.173us 1.724us 3
---------------------- ------------ ------------ ------------ ------------ ------------ ------------
Self CPU time total: 6.523ms
$> python3 profiler_test.py
(pytorch) [bcoutinho@devgpu038.ftw6 /data/users/bcoutinho/pytorch (profiler_optimize_parsing)]$
$>ls -a profiler_test.py
$> ls -l /tmp/test_trace.json
-rw-r--r-- 1 bcoutinho users 16471 Aug 5 16:10 /tmp/test_trace.json
```
## Unit test
Updates some tests and they all pass now.
`pytest test/profiler/test_profiler.py`
Also
`python test/test_autograd.py TestAutogradWithCompiledAutograd.test_record_function`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132713
Approved by: https://github.com/sraikund16
Summary: This test is flakey internally, but it's not a great test in the first place since it's relying on the max-autotune step to bump a related counter. Instead of doing that, directly install a mock that bumps a counter specifically for this test. Additionally, test that the caching logic correctly accommodates an arbitrary counter delta (previously the relevant counter is only bumped by +1).
Differential Revision: [D61141164](https://our.internmc.facebook.com/intern/diff/D61141164)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133244
Approved by: https://github.com/eellison
Reland by reverting commit 844103197d3e8cf6b4b59176e473365113f4f962. #131675 failed a few internal tests because it imported a diff version which wasn't rebased on the proper dependent diffs. Reland from OSS only to avoid the out-of-sync issue.
Original description from #131675
Summary:
A ComboKernel combines independent Inductor Triton kernels into a single one.
This is part 2 pull request which 1) adds automatic horizontal fusion in the end of the inductor operator fusion process, 2) adds type annotation for trition_combo_kernel.py
ComboKernel is used in two cases: 1) for existing foreach kernels, combo kernels are used as the backend kernel. the front-end kernel generation logic remains the same. 2) Added an extra optimization phase to the end of the scheduler to generate extra combo kernels if combo_kernels is True in config.py
This is part 2 pull request which deals with the 2nd case above:
The combo kernel generation in the added optimization phase is done in two steps: 1) in the front end inside the scheduler, it topologically sort the schedule nodes to find all the nodes with no data dependency and create a frond end schedule node for them. We currently limit the maximal number of sub-nodes for each combo kernel to 8 (but we still need to find what is the optimal number). 2) then, these sub-nodes are combined in the codegen phase to generate the combo kernel code for them based on a few rules. For example, 1d and 2d kernels are separated into different combo kernels, as mixing them is not supported yet. Note these algorithms we provide are very basic, and the users can register their customized combo kernel generation algorithms for both steps.
Performance wise, combining small kernels is about always to see performance gain. however, combining very large kernels may not see any perf gain, sometimes even regression possibly due to improper block sizes. Thus, a benchmark function is implemented to avoid such perf regression, and it is recommended to turn it on by setting benchmark_combo_kernels to True whenever combo_kernels is True.
Please refer to part 1 pull request https://github.com/pytorch/pytorch/pull/124969 for more details.
Test Plan: buck2 test mode/dev-nosan caffe2/test/inductor:combo_kernels
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133291
Approved by: https://github.com/wdvr
Summary: Should skip C++ warmup `unwind::unwind();` if there is no context set. This call is sometimes causing hanging issues since C++ stack collection is not robust.
Test Plan: CI
Differential Revision: D60965985
Pulled By: aaronenyeshi
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133038
Approved by: https://github.com/eqy
Optimize `compile_only` logical. Origin code only apply for `CppTorchCudaOptions`, this PR make it apply for all build option classes.
Changes:
1. Remove `libraries_dirs` and `libraries` settings, when `compile_only`.
2. Remove compile_only from CppTorchCudaOptions.
3. Make the `compile_only` apply for all classes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129975
Approved by: https://github.com/henrylhtsang
The `LRScheduler` class provides methods to adjusts the learning rate during optimization (as updated in this PR). Also, as a note, all the classes of lr_scheduluer are already provided in the `How to adjust learning rate` section.
Fixes#127884
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133243
Approved by: https://github.com/janeyx99
This PR refactors process_inputs so that it occurs earlier outside of create_aot_dispatcher_function for the purpose of calculating a cache key with the inputs after they have been processed.
This way, if tensors have symint sizes/strides, we successfully factor that into the cache key instead of specializing on every possible size and stride. Test that utilizes this incoming.
# Guard behavior
Note that it's technically possible for tensors with symint arguments to introduce guards in aot_dispatch, if they trace through decompositions that branch on tensor size/stride. This can result in multiple graph modules with differing guards having the same key in the cache.
FXGraphCache has this same issue, and the remote FXGraphCache intentionally does not handle this: instead it only saves the first result in the cache, and cache misses if guards miss. The local FXGraphCache does handle this by storing multiple files and iterating through them, but we opt not to introduce that complexity just yet for AOTAutogradCache until we deem it necessary (i.e., models appear where saving multiple cache results with different guards but the same cache key becomes important). Instead, AOTAutogradCache will save a single entry per result, overriding it if it cache misses due to guards.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130962
Approved by: https://github.com/bdhirsh
In joint-graph export we have a `copy.deepcopy(ep.graph_module)` call. This turns out to be an imperfect deepcopy, because deepcopy allows objects to overwrite their `__deepcopy__` methods. For fx.Graph, this ends up deferring to `Graph.create_node()`, which checks the graph namespace, and can avoiding copying the exact name in niche examples, like where the name is a Python keyword (e.g. `input` gets renamed to `input_1`).
Names like `input` happen because export's placeholder naming pass overwrites what the namespace creates, based on the model's `forward()` signature. So we can either 1) avoid overwriting such cases, which requires rewriting the naming pass logic, or 2) force another overwrite after deepcopying. This goes with 2).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133269
Approved by: https://github.com/zhxchen17, https://github.com/dvorjackz, https://github.com/ydwu4
The process of inlining HOO subgraphs (e.g. set_grad_enabled) seems to break node.users when a node is present in multiple subgraphs, for example:
```
class SetGradCase(torch.nn.Module):
def forward(self, x):
_x = x.shape[0] + 2
_xx = _x + 2
with torch.no_grad():
y = _x * 4
return _xx, y
```
The `_x` node contains 2 users (_xx and y) after being inlined, but with inspection it seems to only contain y as a user.
Previously we were completely clearing node.users for output nodes in HOO subgraphs before inlining them - we should just be deleting the subgraph output nodes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133144
Approved by: https://github.com/larryliu0820, https://github.com/ydwu4
Fixes#133163
Debugged in collaboration with @hariveliki
The `byte` type is demanding the global `_codecs.encode`. That means, the following currently works:
```python
import torch
torch.save(b'hello', '/tmp/dummy.pth')
torch.serialization.add_safe_globals([_codecs.encode])
torch.load('/tmp/dummy.pth', weights_only=True)
```
Similarly, `bytearray` needs `builtins.bytearray`.
Following the `torch.loads` docs promise, both types should be supported without `add_safe_globals` as they are both primitive types:
> weights_only: Indicates whether unpickler should be restricted to
> loading only tensors, primitive types, dictionaries
> and any types added via :func:`torch.serialization.add_safe_globals`.
This PR adds both `_codecs.encode` and `builtins.bytearray` to `_get_allowed_globals` and test for saving and loading of both types with and without `weights_only`.
Co-authored-by: hariveliki <98284163+hariveliki@users.noreply.github.com>
Co-authored-by: mikaylagawarecki <mikaylagawarecki@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133189
Approved by: https://github.com/mikaylagawarecki
Makes it possible to run `test/profiler/test_profiler.py#test_profiler_pattern_matcher_json_report` on CI environments where the test runner doesn't have write permissions to the current-working-directory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133009
Approved by: https://github.com/zou3519
We promise the user that these custom ops (and their kernels) are black
boxes w.r.t. torch.compile. Unfortunately Dynamo can turn itself back
on in the implementation of the custom operator, so we force it off by
disabling Dynamo
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133125
Approved by: https://github.com/ezyang
For workloads that only exercised scaled_mm, the csv result file would not contain the same set of validators as a gemm workload. Trying to reuse the same csv file between workloads would discard the file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132464
Approved by: https://github.com/zixi-qi
Summary:
The Quantizer subclass can return a new model from `transform_for_annotation`,
and this is common if it uses any ExportPass subclass which does not mutate in-place.
Use the returned model instead of assuming its the same.
Differential Revision: D60869676
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132893
Approved by: https://github.com/jerryzh168
Summary:
Add back the change in 19897a1647.
The change was lost in refactoring due to a bad rebase.
Test Plan:
CI
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//torchrec/distributed/tests:test_pt2 -- --filter-text test_sharded_quant_fpebc_non_strict_export
```
Differential Revision: D61052687
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133142
Approved by: https://github.com/ydwu4
This PR only adds the execution of the benchmarks on this PR and print results, following diffs will add checking out head~1 and running it and comparing.
to access results goto test pr_time_benchmarks and inspect logs:
you should see
```
+ echo 'benchmark results on current PR: '
benchmark results on current PR:
+ cat /var/lib/jenkins/workspace/test/test-reports/pr_time_benchmarks_before.txt
update_hint_regression,instruction_count,27971461254
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131475
Approved by: https://github.com/ezyang
Summary:
Add back the change in 19897a1647.
The change was lost in refactoring due to a bad rebase.
Test Plan:
CI
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//torchrec/distributed/tests:test_pt2 -- --filter-text test_sharded_quant_fpebc_non_strict_export
```
Differential Revision: D61052687
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133142
Approved by: https://github.com/ydwu4
- Reduced number of skipped test cases
- Merged redundant test cases
**Benchmark:**
| | Original | New |
| ----- | ----- | ----- |
| Run time | 60 mins | 35 mins |
| Total tests | 75k | 18k |
| Skipped tests | 20k | 4k |
_These are approximate numbers from running test_transformers.py on a single H100, and can change based on the device._
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133049
Approved by: https://github.com/drisspg
I have worked with @henrylhtsang to switch the cpp_builder to new one. We have removed the dependency to the old implementation.
So, it is time to remove the old implementation now. This PR is done the change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133161
Approved by: https://github.com/ezyang
Partially addresses https://github.com/pytorch/pytorch/issues/130170 for float scalars saved from forward pass of a custom c++ autograd function. With this PR, compiled autograd no longer recaptures when the float value changes, but downstream support isn't there yet: 4bdb4bbd86/torch/_dynamo/config.py (L58-L61)
Currently, any non-tensors passed in ctx->saved_data is specialized on by compiled autograd. To stop specializing on float values, we lift the float. We also require user code to use IValue::toSymFloat instead of IValue::toDouble in order to swap the SymFloat to proxy during compiled autograd tracing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133048
Approved by: https://github.com/jansel
ghstack dependencies: #132771
Addresses https://github.com/pytorch/pytorch/issues/130170 for int scalars saved from forward pass of a custom c++ autograd function
Currently, any non-tensors passed in ctx->saved_data is specialized on by compiled autograd. To stop specializing on int values, we lift the ints. We also require user code to use IValue::toSymInt instead of IValue::toInt in order to swap the SymInt to proxy during compiled autograd tracing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132771
Approved by: https://github.com/jansel
Summary: Pessimisticly assume that things are being torn down if TCPStore is not available and do not attempt to dump stack traces.
Test Plan:
Seeing crashes in production when Flight Recorder is enabled.
Here's the relevant mast link: https://fburl.com/mlhub/qia257xh
Reviewed By: fduwjj
Differential Revision: D61055124
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133150
Approved by: https://github.com/fduwjj
## Summary
As part of #125683, this PR modifies existing CPU GEMM cpp template & micro-kernel template to enable int8 WoQ GEMM auto-tuning with AVX2, AVX512 & AMX ISAs (the latter is only available on Xeon 4th generation & beyond).
WoQ GEMM takes FP16/BF16 activations, int8 weights, and scale of the same dtype as activations.
The operation is equivalent to `torch.nn.functional.linear(x, w.to(x.dtype)) * scale`, which is essentially what the ATen op `torch.ops.aten._weight_int8pack_mm` currently does (except that weights are not cached by it). Weights will be considered constant & cached, so this implementation is suitable for inference, and not QAT. `scale` is supported as a `mul` epilogue.
Only BF16 activations have been supported in this PR because for FP16 & FP32, weight is dequantized during constant-folding pass of freezing, and then after auto-tuning, performance with a large `M` dimension may be better than either torch.ops.aten._weight_int8pack_mm, or the WoQ micro-kernel support introduced in this PR, which dequantizes `w` within the micro-kernel.
While even BF16 activations with a large `M` dimension may benefit from dequantizing `w` beforehand, for now, they would use WoQ support in GEMM templates for auto-tuning, and then a subsequent PR would add logic for deciding whether or not to dequantize weights beforehand.
### Performance
#### AMX
Op-level speedup due to AMX micro-kernel (selected during auto-tuning) on 32 physical cores of Intel(R) Xeon(R) Platinum 8468H (of Xeon 4th generation series, codenamed Sapphire Rapids) vs. ATen kernel `torch.ops.aten._weight_int8pack_mm`. Intel OpenMP & tcmalloc were preloaded.
In a few cases with an odd `K`, the implementation being added in this PR may not perform as well as the ATen kernel, which is unrelated to this PR, though, since `test_linear_amx` also exhibits similar datapoints. In those cases, the AMX micro-kernel might be slower than AVX512 micro-kernel, so if such sets of shapes are used for auto-tuning, either the AVX512 micro-kernel implementation, or the ATen kernel would be chosen instead.
Benchmarked with unit-tests.
Tabular data at https://gist.github.com/sanchitintel/294811a86c8ff6b867c668ae2107c405?permalink_comment_id=5142442#gistcomment-5142442
The AVX512 micro-kernel was disabled to collect data for AMX micro-kernel.
#### AVX2/AVX512 micro-kernels
Tabular data at at https://gist.github.com/sanchitintel/52b5fa9c66f791be19e48e2aa6423dc4?permalink_comment_id=5142437#gistcomment-5142437
### Follow-up
1. int4 WoQ GEMM micro-kernel will also be added in a separate PR.
2. A subsequent PR would add logic for deciding whether or not to dequantize weights beforehand.
E2E perf measurement should be done with #131310.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131887
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/jansel
Summary:
# context
* use FakeProcessGroup to mimic the multi-process tests
* can use `_test_compile_fake_pg_fn` as the single-process VB compile test
```
from torchrec.distributed.tests.test_pt2_multiprocess import _test_compile_fake_pg_fn
_test_compile_fake_pg_fn(
rank=0,
world_size=2,
)
```
reference: D59637444
Test Plan:
# run test
* run command and results: P1519228952, [tlparse](https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpwMCK1E/index.html)
```
TORCH_TRACE=/var/tmp/tt TORCH_SHOW_CPP_STACKTRACES=1 TORCH_LOGS="+all" buck2 run fbcode//mode/opt fbcode//torchrec/distributed/tests:test_pt2_multiprocess
```
Differential Revision: D56124045
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133039
Approved by: https://github.com/ezyang
This PR adds back 10 configs for tuned_mm that were previously removed in https://github.com/pytorch/pytorch/pull/126570. The main idea is that we use 30 configs to autotune only when data is collected with AutoHeuristic. The learned heuristic will prune these 30 configs down to 10 configs, which reduces compilation time and at the same time might improve performance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131616
Approved by: https://github.com/eellison
ghstack dependencies: #131615
Optional option to detect missing ranks (that can be mapped to host info via `rank_tracing_decoder` lambda argument) in store barrier operation.
This approach has been used in some form already, moving it to collectives API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132818
Approved by: https://github.com/d4l3k
### tl;dr
This PR adds GQA support to higher order op `flex_attention`.
## Details
When `enable_gqa` is set to True, HOP `flex_attention(score_mod, query, key, value, block_mask, enable_gqa)` runs Group Query Attention(GQA), where the number of query heads (Hq) is a multiple of number of key/value heads (Hkv). Each group of query heads (`Hq//Hkv` heads) attends to a shared kv head.
Otherwise, `flex_attention` assumes Multi Head Attention (MHA) where the number of query heads is equal the number of kv heads.
The `score_mod` and `mask_mod` API are adapted accordingly to take `q_head` as head index.
```
def score_mod(score: torch.Tensor, batch: torch.Tensor, q_head: torch.Tensor, token_q: torch.Tensor, token_kv: torch.Tensor) -> torch.Tensor
def mask_mod(batch: torch.Tensor, q_head: torch.Tensor, token_q: torch.Tensor, token_kv: torch.Tensor) -> torch.Tensor
```
## Example
```python
import torch
from torch.nn.attention.flex_attention import flex_attention
from torch.nn.attention.flex_attention import create_block_mask
torch.manual_seed(0)
def query_key_value_clones(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
dtype: torch.dtype = None,
):
"""Clones the query, key, and value tensors and moves them to the specified dtype."""
if dtype is None:
dtype = query.dtype
query_ref = query.clone().detach().to(dtype).requires_grad_(query.requires_grad)
key_ref = key.clone().detach().to(dtype).requires_grad_(key.requires_grad)
value_ref = value.clone().detach().to(dtype).requires_grad_(value.requires_grad)
return query_ref, key_ref, value_ref
# Lets create some input tensors
# The input tensor has shape (batch_size, num_heads, seq_len, head_dim).
# query and key/value can have different num_heads and seq_len
# Here 8 query heads share one KV head.
query = torch.randn(2, 8, 2048, 64, device="cuda", dtype=torch.float32, requires_grad=True)
key = torch.randn(2, 2, 2048, 64, device="cuda", dtype=torch.float32, requires_grad=True)
value = torch.randn(2, 2, 2048, 64, device="cuda", dtype=torch.float32, requires_grad=True)
query1, key1, value1 = query_key_value_clones(query, key, value)
# Lets create a score_modification. We take alibi_bias as an example.
# score_mod takes batch index, query head index, query index, and key/value index.
def _generate_alibi_bias(num_kv_heads: int, num_q_heads: int):
def _alibi_bias(
score: torch.Tensor,
b: torch.Tensor,
hq: torch.Tensor,
token_q: torch.Tensor,
token_kv: torch.Tensor,
) -> torch.Tensor:
# Let's calculate kv head from query head index
group = num_q_heads // num_kv_heads
hkv = hq // group
scale = torch.exp2(-((hkv + 1) * 8.0 / num_kv_heads))
return score + (token_kv - token_q) * scale
return _alibi_bias
# Let's apply a casual mask on top of it
def causal_mask(b, h, q, kv):
return q >= kv
# Generate a block mask for our new mask_mod function.
# The mask is broadcasted long head & batch dimensions.
block_mask = create_block_mask(causal_mask, B=1, H=1, Q_LEN=2048, KV_LEN=2048)
# Lets call flex_attention with our new score modification and block mask under eager mode.
output = flex_attention(query, key, value, score_mod=_generate_alibi_bias(2, 8), block_mask=block_mask, enable_gqa=True)
# Now lets compile flex_attention and run the flex_attention kernel.
compiled_flex_attention = torch.compile(flex_attention)
out_compiled = compiled_flex_attention(query1, key1, value1, score_mod=_generate_alibi_bias(2, 8), block_mask=block_mask, enable_gqa=True)
torch.testing.assert_close(output, out_compiled, atol=5e-2, rtol=2e-2)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131559
Approved by: https://github.com/drisspg
Summary: D56956245 added the ability to accumulate FunctionEvents across multiple cycles in order to perform statistical analysis on them all together. Although this can be useful, it uses too many CPU resources especially for long running jobs. For this reason, lets add a flag to the profiler to turn off this behavior by default, but still allow users to turn it on if they wish.
Test Plan: Changed function count test to have acc_events passed in and check the amount of function events based on if flag is true or not
Differential Revision: D61021490
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133095
Approved by: https://github.com/briancoutinho, https://github.com/LucasLLC, https://github.com/aaronenyeshi
Summary:
Fixes T198245910.
In previous diff D60532628 that causes the test failure, we fix the in-consistency caused by constant tensors is accidentally reigistered as buffer by deleting the buffer and re assign them as constant.
However, this broke several existing tests in pyspeech when the exported program is re-traced with torch.jit.trace (which is an anti-pattern we probably should have some alignment), the jit tracer finds this constant tensor requiring grad and errors out.
This PR force constant attr not requiring grad, which is the correct behavior. A better fix is finding out where the constants are created in user code and why it requires grad. But this has low roi so we warn user about it.
Test Plan: See failures in T198245910.
Differential Revision: D60974869
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133031
Approved by: https://github.com/angelayi
motivated by FSDP2 + DoRA https://github.com/pytorch/pytorch/issues/132721
after meta init, we need a user-defined function to move DoRALinear.magnitude from device=meta to device=cuda
The problem is how to trigger reset_sharded_param or _apply to update FSDPParam. Otherwise lazy_init complains that DoRALinear.magnitude are still on device=meta
credit to @awgu for chasing after DDP lazy_init to unblock the PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132954
Approved by: https://github.com/awgu
ghstack dependencies: #133059
**Description**
**_[BUG FIX]_**
This PR fixes a bug which happens during compilation with GCC 11.4 compiler in the FlashAttentionKernel.cpp file. This issue doesn't seem to be with PyTorch main branch but gets introduced with our SVE PR changes (https://github.com/pytorch/pytorch/pull/119571 ) + PyTorch main.
See the CI Pipeline failing in our PR:
https://github.com/pytorch/pytorch/actions/runs/9895714768/job/27336251795?pr=119571
```
/var/lib/jenkins/workspace/build/aten/src/ATen/native/cpu/FlashAttentionKernel.cpp.SVE256.cpp
during RTL pass: expand
In file included from /var/lib/jenkins/workspace/build/aten/src/ATen/native/cpu/FlashAttentionKernel.cpp.SVE256.cpp:1:
/var/lib/jenkins/workspace/aten/src/ATen/native/cpu/FlashAttentionKernel.cpp: In lambda function:
/var/lib/jenkins/workspace/aten/src/ATen/native/cpu/FlashAttentionKernel.cpp:290:57: internal compiler error: in emit_move_insn, at expr.c:3821
290 | at::parallel_for(0, batchSize * num_head * qSlice, 1, [&](int64_t begin, int64_t end) {
| ^
0xffffb03f73fb __libc_start_call_main
../sysdeps/nptl/libc_start_call_main.h:58
0xffffb03f74cb __libc_start_main_impl
../csu/libc-start.c:392
Please submit a full bug report,
with preprocessed source if appropriate.
Please include the complete backtrace with any bug report.
See <file:///usr/share/doc/gcc-11/README.Bugs> for instructions.
[5731/6839] Building CXX object caffe2/CMakeFiles/torch_cpu.dir/__/aten/src/ATen/native/cpu/CatKernel.cpp.SVE256.cpp.o
[5732/6839] Building CXX object caffe2/CMakeFiles/torch_cpu.dir/__/aten/src/ATen/native/cpu/GridSamplerKernel.cpp.SVE256.cpp.o
```
This issue with compilation only happens with GCC 11.4 and works well with the latest GCC 12.3 compiler and also the Clang compiler. The issue is related to the check for `is_b_stride_zero` introduced as a template parameter (compile time check complexity) in the following commit: 5da428d9eb which was added recently into FlashAttentionKernel.cpp file.
This PR fixes the above compilation failure with GCC 11.4 compiler.
cc : @Valentine233 @yanbing-j @mingfeima @malfet @jgong5 @r-barnes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132434
Approved by: https://github.com/jgong5
When checking the logs of c10d, I found it showed that "[PG 7 rank 7]" which it actually means "[PG 1 rank 7]". So we need to use pg_id(aka, uid_) rather than pg_name_ because when creating subpgs, currently we need to call it multiple times, so this makes PG names are based on bumped up numbers (e.g, 7 rather than 1). Using pg_id is more accurate and consistent with other logging tools.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132058
Approved by: https://github.com/shengbao-zheng, https://github.com/shuqiangzhang
Summary:
TunableOp logging improvements:
1. PYTORCH_TUNABLEOP_VERBOSE=1: print out the expected value vs actual value for TunableOp validators, so that if validation fails, we know exactly how to fix it
2. PYTORCH_TUNABLEOP_VERBOSE=3: print out the exact kernel signature for both successful and failure cases in kernel lookup
Test Plan:
> PYTORCH_TUNABLEOP_VERBOSE=3 buck
2 run mode/{opt,amd-gpu} -c fbcode.enable_gpu_sections=true //scripts/xdwang/example:fc_llama -- --enab
le-tuning
```
reading tuning results from hipblas_tuning_pt_llama0.csv
Validator PT_VERSION=2.5.0
Validator ROCBLAS_VERSION=4.0.0-72e57364-dirty
Validator HIPBLASLT_VERSION=800-a15e4178
Validator ROCM_VERSION=6.0.0.0-12969-1544e39
Validator GCN_ARCH_NAME=gfx942:sramecc+:xnack-
GCN_ARCH_NAME validation: expect gfx942:sramecc+:xnack- to match gfx942:sramecc+:xnack-
ROCM_VERSION validation: expect 6.0.0.0-12969-1544e39 to match 6.0.0.0-12969-1544e39
HIPBLASLT_VERSION validation: expect 800-a15e4178 to match 800-a15e4178
ROCBLAS_VERSION validation: expect 4.0.0-72e57364-dirty to match 4.0.0-72e57364-dirty
PT_VERSION validation: expect 2.5.0 to match 2.5.0
Loading results
GemmTunableOp_BFloat16_TN(tn_8192_2_1024) -> Gemm_Hipblaslt_TN_61169,0.0171694
GemmTunableOp_BFloat16_TN(tn_7168_2_8192) -> Gemm_Hipblaslt_TN_61089,0.036138
GemmTunableOp_BFloat16_TN(tn_8192_2_3584) -> Gemm_Hipblaslt_TN_61169,0.0240673
missing params_signature, returning null ResultEntry for GemmTunableOp_BFloat16_TN,tn_1280_2_8192
finding fastest for GemmTunableOp_BFloat16_TN(tn_1280_2_8192) out of 2818 candidates
Rotating buffer 4 MiB. Needed Size: 20 MiB. Needed number of param copies: 1
├──tuning using warmup iters 0 [0 ms] and tuning iters 1 [0.208254 ms] instance id=0, GemmTunableOp_BFloat16_TN(tn_1280_2_8192) Default
├──offset at 3
......
ResultEntry found for GemmTunableOp_BFloat16_TN,tn_8192_2_3584
ResultEntry found for GemmTunableOp_BFloat16_TN,tn_8192_2_3584
ResultEntry found for GemmTunableOp_BFloat16_TN,tn_8192_2_3584
Avg time: 16.42832040786743 us, Achieved 7.15 TFLOPS, 3578.07 GB/s
2x1280x8192-torch.bfloat16,16.260499954223633,2.5794434438103107,1294.0669757533708
2x8192x1024-torch.bfloat16,16.15394949913025,2.0771658350056508,1041.11852032876
2x7168x8192-torch.bfloat16,25.691540241241455,9.14234887416194,4574.841325057144
2x8192x3584-torch.bfloat16,16.42832040786743,7.1486621324818085,3578.0709494714856
```
Differential Revision: D60468273
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132173
Approved by: https://github.com/mxz297, https://github.com/jeffdaily, https://github.com/eqy
What we found recently is that:
1. Monitoring detect watchdog hang(no heartbeat) at same time as nccl timeout. This race leads to less useful debug info gets dumped to logs (such as CudaEventDestroy and GIL checker)
2. We don't kill the program if monitoring thread has not enabled but somehow still silently run the monitoring thread. Plus for users who feel this is too short, they should config TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC themselves.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133028
Approved by: https://github.com/shuqiangzhang, https://github.com/wconstab
Addresses a common misconception about safety of using multiple NCCL
process groups from PyTorch.
Notably, it IS safe to use multiple process groups, so long as
communication operations from different groups are not allowed to
overlap. (Overlap of communication operations from one group with
compute operations IS ok).
TODO: after getting feedback on the text, update other copies of the warning on other APIs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131895
Approved by: https://github.com/fduwjj
Currently if storage_offset is unbacked symbol and is_align can not be computed compiletime - it hard fails.
Doing the best we can: adding guard_size_oblivious and fallback on False if can not be evaluated compiletime
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132423
Approved by: https://github.com/ezyang
Summary: Otherwise it will break FSDP code paths
Test Plan:
unit test
see next diff for print message
```
sh ./scripts/lufang/amd/small_repro.sh
ROCM_GET_SCALAR_ITEM_SYNC=1 sh ./scripts/lufang/amd/small_repro.sh
```
It will log "====== Async mode ======" or "====== Sync mode ======" correspondingly
Differential Revision: D60995134
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133054
Approved by: https://github.com/houseroad
Summary:
A ComboKernel combines independent Inductor Triton kernels into a single one.
This is part 2 pull request which 1) adds automatic horizontal fusion in the end of the inductor operator fusion process, 2) adds type annotation for trition_combo_kernel.py
ComboKernel is used in two cases: 1) for existing foreach kernels, combo kernels are used as the backend kernel. the front-end kernel generation logic remains the same. 2) Added an extra optimization phase to the end of the scheduler to generate extra combo kernels if combo_kernels is True in config.py
This is part 2 pull request which deals with the 2nd case above:
- The combo kernel generation in the added optimization phase is done in two steps: 1) in the front end inside the scheduler, it topologically sort the schedule nodes to find all the nodes with no data dependency and create a frond end schedule node for them. We currently limit the maximal number of sub-nodes for each combo kernel to 8 (but we still need to find what is the optimal number). 2) then, these sub-nodes are combined in the codegen phase to generate the combo kernel code for them based on a few rules. For example, 1d and 2d kernels are separated into different combo kernels, as mixing them is not supported yet. Note these algorithms we provide are very basic, and the users can register their customized combo kernel generation algorithms for both steps.
- Performance wise, combining small kernels is about always to see performance gain. however, combining very large kernels may not see any perf gain, sometimes even regression possibly due to improper block sizes. Thus, a benchmark function is implemented to avoid such perf regression, and it is recommended to turn it on by setting benchmark_combo_kernels to True whenever combo_kernels is True.
Please refer to part 1 pull request https://github.com/pytorch/pytorch/pull/124969 for more details.
Test Plan: buck2 test mode/dev-nosan caffe2/test/inductor:combo_kernels
Differential Revision: D60067757
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131675
Approved by: https://github.com/mlazos
Summary: When PyTree detects a structural mismatch between inputs and dynamic shapes, the error messages are quite horrible. This PR fixes these error messages by adding, for each kind of error, the path to the point where the error happens and an actionable reason for the error.
Test Plan: added test with several cases
Differential Revision: D60956976
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132982
Approved by: https://github.com/yushangdi
#### Description
Transform quantized operation properly. Add de/quantization before and after the quantized operation.
#### Test Plan
`pytest test/export/test_converter.py -s -k test_ts2ep_convert_quantized_model`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133026
Approved by: https://github.com/angelayi
# Summary
Changes the stance of SDPA on what to do for fully masked out rows
## Current Behavior
Several PyTorch users have expressed frustration over this issue:
- https://github.com/pytorch/pytorch/issues/41508
- https://github.com/pytorch/pytorch/issues/103749
- https://github.com/pytorch/pytorch/issues/103963
These are significant issues with extensive discussion but no satisfactory resolution. The PyTorch team's consensus, as stated here:
https://github.com/pytorch/pytorch/issues/24816#issuecomment-524415617
Can be paraphrased as follows:
When passing in fully masked out rows, attention becomes ambiguous. We have two main options:
1. Uniformly attend to all values:
```python
scores[masked_out_rows] = 1 / len(row)
out[masked_out_rows] = 1 / len(row) * value
```
2. Decide that attention between no queries (masked) and no keys (masked) is meaningless:
```python
output[fully_masked_rows] = NaN
```
We went with option 2. Partially because it was easier to implement, but also people argued that users can slice the output to remove the NaNs:
``` Python
>fill_value = -float("inf")
>row0 = torch.randn(4)
>row1 = torch.tensor([(fill_value for _ in range(4)])
>matrix = torch.stack([row0, row1]).requires_grad_(True)
>out = torch.softmax(matrix, 1)
>out = out[0]
>print(out)
tensor([0.5377, 0.2729, 0.0692, 0.1201])
```
Cool, problem solved. But what happends when you call backwards..
```Python
>out.backward(torch.ones_like(out))
>print(matrix.grad)
tensor([[3.0957e-08, 1.4157e-08, 7.7802e-10, 1.3713e-08],
[ nan, nan, nan, nan]])
```
Those pesky NaNs are back!
## Why do we see NaNs today?
The core of the problem revolves around using softmax function in sdpa:
```python
> row = torch.tensor([(-float("inf")) for _ in range(4)])
> torch.softmax(row, 0)
tensor([nan, nan, nan, nan])
```
## Quick Aside: Masking in Attention
Attention itself doesn't have a concept of masking. The `sdpa` function has an argument called `attn_mask`, which would be more accurately named `attn_bias`. This is because we don't actually "mask" entries when computing attention. Instead, due to implementation details([performance](https://github.com/pytorch/pytorch/issues/25110#issuecomment-524519087)), we add a value to the masked-out query/key pairs.
We use a large negative number (typically -inf) to decrease the attention weight, as softmax assigns more weight to larger values.
## Alternative Approaches
If we use a very large negative number instead of -inf:
```python
> row = torch.tensor([(-1e6) for _ in range(4)])
> torch.softmax(row, 0)
tensor([0.2500, 0.2500, 0.2500, 0.2500])
```
However if users always remembered to "slice" out their outputs i.e.:
```Python
>fill_value = -1e6
>...
>out.backward(torch.ones_like(out))
>print(matrix.grad)
tensor([[-0.0563, -0.0564, 0.1613, -0.0486],
[ 0.0000, 0.0000, 0.0000, 0.0000]])
```
This would bring us back into a better state.
## A Third Option
We don't necessarily need to alter the behavior of softmax for -inf or very large negative numbers. The fundamental goal is to exclude certain query/key pairs from attention, regardless of the underlying implementation.
This PR implements the new semantic for masking w/ attention in fully masked-out rows:
```python
out[masked_out_rows] = 0
```
**Important Note**: This idea isn't entirely new. The [MaskedTensor](https://pytorch.org/tutorials/prototype/maskedtensor_overview#safe-softmax) prototype, a tensor subclass, was designed to handle such cases. However, it remains a prototype feature and hasn't gained widespread adoption.
## Details
This PR stack does 3 things:
1. Adds a PRIVATE _safe_softmax op
2. Updates semantic for flash_cpu fused kernel
3. Updates semantic for efficient_cuda fused kernel
_safe_softmax is not supposed to be used generically and is only meant to be used within the context of SDPA. Due to this fact instead of decomposing softmax and checking for -inf rows we instead "cheat" and use nan_to_num.
Why I think this is okay? (please find a counter point if avail)
There are multiple ways NaNs can emerge. For the fully masked out rows case nan_to_num works. But what if there were other NaNs, wouldn't this silently remove them?
The only case that this can happen is if the input itself had a NaN or an Inf
For example:
```Python
a = torch.ones([4], requires_grad=False, dtype=torch.float16)
a[1] = torch.finfo(torch.float16).max
print(a.softmax(-1))
```
Will return
`tensor([0., 1., 0., 0.], dtype=torch.float16)`
Where
```Python
a = torch.ones([4], requires_grad=False, dtype=torch.float16)
a[1] = float("inf")
a.softmax(-1)
```
returns:
`tensor([nan, nan, nan, nan], dtype=torch.float16)`
If we dont want to even allow for the possibility of "inf" or "NaN" attention scores to be converted to 0 then we can implemented it something like this
```Python
max = torch.max(a, dim=-1, keepdim=True)
exp = torch.exp(a - max.values)
denom = torch.sum(exp, dim=-1, keepdim=True)
softmax = exp / denom
softmax = torch.where(max.values == float('-inf'), 0.0, softmax)
```
however we would be paying for this in math performance.
## Why Now
I think one point that has substantially changed where PyTorch should lie on this argument is the fact that we have fused implementations for SDPA now. And these fused implementations allow us to easily and performantly support this new semantic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131060
Approved by: https://github.com/jbschlosser
Fixes#125077
**Feature**
This PR creates a new Inductor config, `config.triton.prefer_nd_tiling`, which is disabled by default. When enabled, this encourages the Triton code to use as many tiling dimensions as possible. This simplifies indexing expressions for discontiguous tensors, resulting in expressions like `5 * x + 8 * y` as opposed to `5 * (x // 7) + 8 * (y % 9)`. This allows us to find more block pointers than we normally would. We should now see simplified indexing expressions as long as:
1. All discontiguous reads/writes have the same shape.
2. The number of discontiguous dimensions is less than `config.triton.max_tiles`.
Here's an example kernel (elementwise add of views) with ND tiling disabled:
```
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 21
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 7
x1 = (xindex // 7)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (9*x1)), xmask)
tmp1 = tl.load(in_ptr1 + (x0 + (9*x1)), xmask)
tmp2 = tmp0 + tmp1
tl.store(tl.make_block_ptr(out_ptr0, shape=[21], strides=[1], block_shape=[XBLOCK], order=[0], offsets=[xoffset]), tl.broadcast_to(tmp2, [XBLOCK]).to(tl.float32), boundary_check=[0])
''', device_str='cuda')
```
And here's the version with it enabled:
```
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 3
xnumel = 7
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(tl.make_block_ptr(in_ptr0, shape=[7, 3], strides=[1, 9], block_shape=[XBLOCK, YBLOCK], order=[1, 0], offsets=[xoffset, yoffset]), boundary_check=[0, 1], eviction_policy='evict_last')
tmp1 = tl.load(tl.make_block_ptr(in_ptr1, shape=[7, 3], strides=[1, 9], block_shape=[XBLOCK, YBLOCK], order=[1, 0], offsets=[xoffset, yoffset]), boundary_check=[0, 1], eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(tl.make_block_ptr(out_ptr0, shape=[7, 3], strides=[1, 7], block_shape=[XBLOCK, YBLOCK], order=[1, 0], offsets=[xoffset, yoffset]), tl.broadcast_to(tmp2, [XBLOCK, YBLOCK]).to(tl.float32), boundary_check=[0, 1])
''', device_str='cuda')
```
With this feature enabled, we get a discontiguous strided block pointer. Previously, this would only have worked for specific shapes, like powers of 2 or multiples of the maximum block size. With this PR, we can support arbitrary shapes so long as we have enough tiles to cover all discontiguous dimensions.
**Test plan**
This PR adds some tests for pointwise ops with discontiguous tensors.
- Test that we can generate block pointers for views with odd shapes like `(5,7)`, `(9,3,5)`, etc.
- Test that we can generate block pointers for a single discontiguous dim in 3D and 4D tensors.
- Test that we generate a 2D tiling for a 5D tensor with two discontiguous dims. This case doesn't generate a block pointer, but it checks that the output code is at least correct.
This PR also parametrizes some existing tests to run with and without `triton.prefer_nd_tiling`. That way, we ensure this feature doesn't break existing usage.
Since this setting isn't enabled on most tests, I also created https://github.com/pytorch/pytorch/pull/132935 to test what happens when `triton.prefer_nd_tiling=True` by default. None of the failures seem related to invalid tiling, so I think this feature is safe to merge.
**Limitations and follow-ups**
I can see two main improvements which would expand the usefulness of this feature:
1. This feature currently only works for pointwise kernels, since reductions are never tiled. As a follow-up, we could enable tiled reductions to extend these benefits to reduction kernels.
2. The usefulness of this feature depends on `triton.config.max_tiles`. This is currently restricted to 2 by default, although it can be increased to 3 in certain cases. To support more discontiguous dims, we might consider expanding support for 3D tiling, or even supporting ND tiling, by mapping an ND "virtual" launch grid onto Triton's 3D launch grid.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132937
Approved by: https://github.com/jansel, https://github.com/eellison
Summary:
TunableOp logging improvements:
1. PYTORCH_TUNABLEOP_VERBOSE=1: print out the expected value vs actual value for TunableOp validators, so that if validation fails, we know exactly how to fix it
2. PYTORCH_TUNABLEOP_VERBOSE=3: print out the exact kernel signature for both successful and failure cases in kernel lookup
Test Plan:
> PYTORCH_TUNABLEOP_VERBOSE=3 buck
2 run mode/{opt,amd-gpu} -c fbcode.enable_gpu_sections=true //scripts/xdwang/example:fc_llama -- --enab
le-tuning
```
reading tuning results from hipblas_tuning_pt_llama0.csv
Validator PT_VERSION=2.5.0
Validator ROCBLAS_VERSION=4.0.0-72e57364-dirty
Validator HIPBLASLT_VERSION=800-a15e4178
Validator ROCM_VERSION=6.0.0.0-12969-1544e39
Validator GCN_ARCH_NAME=gfx942:sramecc+:xnack-
GCN_ARCH_NAME validation: expect gfx942:sramecc+:xnack- to match gfx942:sramecc+:xnack-
ROCM_VERSION validation: expect 6.0.0.0-12969-1544e39 to match 6.0.0.0-12969-1544e39
HIPBLASLT_VERSION validation: expect 800-a15e4178 to match 800-a15e4178
ROCBLAS_VERSION validation: expect 4.0.0-72e57364-dirty to match 4.0.0-72e57364-dirty
PT_VERSION validation: expect 2.5.0 to match 2.5.0
Loading results
GemmTunableOp_BFloat16_TN(tn_8192_2_1024) -> Gemm_Hipblaslt_TN_61169,0.0171694
GemmTunableOp_BFloat16_TN(tn_7168_2_8192) -> Gemm_Hipblaslt_TN_61089,0.036138
GemmTunableOp_BFloat16_TN(tn_8192_2_3584) -> Gemm_Hipblaslt_TN_61169,0.0240673
missing params_signature, returning null ResultEntry for GemmTunableOp_BFloat16_TN,tn_1280_2_8192
finding fastest for GemmTunableOp_BFloat16_TN(tn_1280_2_8192) out of 2818 candidates
Rotating buffer 4 MiB. Needed Size: 20 MiB. Needed number of param copies: 1
├──tuning using warmup iters 0 [0 ms] and tuning iters 1 [0.208254 ms] instance id=0, GemmTunableOp_BFloat16_TN(tn_1280_2_8192) Default
├──offset at 3
......
ResultEntry found for GemmTunableOp_BFloat16_TN,tn_8192_2_3584
ResultEntry found for GemmTunableOp_BFloat16_TN,tn_8192_2_3584
ResultEntry found for GemmTunableOp_BFloat16_TN,tn_8192_2_3584
Avg time: 16.42832040786743 us, Achieved 7.15 TFLOPS, 3578.07 GB/s
2x1280x8192-torch.bfloat16,16.260499954223633,2.5794434438103107,1294.0669757533708
2x8192x1024-torch.bfloat16,16.15394949913025,2.0771658350056508,1041.11852032876
2x7168x8192-torch.bfloat16,25.691540241241455,9.14234887416194,4574.841325057144
2x8192x3584-torch.bfloat16,16.42832040786743,7.1486621324818085,3578.0709494714856
```
Differential Revision: D60468273
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132173
Approved by: https://github.com/mxz297, https://github.com/jeffdaily
Summary: We found recent CMF and IGCTR has more complicated patterns to optimize in order to remove as many stack/cat nodes as possible, we thus design such patterns
Test Plan:
# unit test
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 test //caffe2/test/inductor:split_cat_fx_passes
```
Test UI: https://www.internalfb.com/intern/testinfra/testrun/3659174939423652
Network: Up: 113KiB Down: 112KiB (reSessionID-11c9b598-af3a-4727-8f02-ccb1471d092b)
Jobs completed: 27. Time elapsed: 5:45.8s.
Cache hits: 0%. Commands: 2 (cached: 0, remote: 0, local: 2)
Tests finished: Pass 9. Fail 0. Fatal 0. Skip 1. Build failure 0
# benchmark
### cmf
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 run mode/opt //scripts/jackiexu0313/pt2:local_model_with_pt2 -- --test_mode batch-split --model_type "cmf_shrink" --flow_id 587303213 -n
```
P1515072258
Counter({'pattern_matcher_nodes': 2170, 'pattern_matcher_count': 1766, 'normalization_pass': 402, 'remove_split_with_size_one_pass': 269, 'extern_calls': 193, 'merge_splits_pass': 74, 'normalization_aten_pass': 51, 'fxgraph_cache_miss': 9, 'batch_aten_mul': 6, 'scmerge_split_sections_removed': 5, 'scmerge_split_removed': 3, 'scmerge_cat_removed': 3, 'unbind_stack_pass': 3, 'batch_sigmoid': 2, 'batch_linear': 2, 'batch_aten_sub': 2, 'batch_layernorm': 1, 'scmerge_split_added': 1, 'scmerge_cat_added': 1, 'split_stack_to_cats_pass': 1, 'split_cat_to_slices_pass': 1, 'batch_aten_add': 1, 'batch_relu': 1})
### ig_ctr
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 run mode/opt //scripts/jackiexu0313/pt2:local_model_with_pt2 -- --test_mode batch-split --model_type "ig_ctr" --flow_id 584880697 -n
```
P1515087739
Counter({'pattern_matcher_nodes': 1832, 'pattern_matcher_count': 1564, 'extern_calls': 378, 'normalization_pass': 345, 'normalization_aten_pass': 49, 'fxgraph_cache_miss': 18, 'batch_aten_mul': 6, 'scmerge_cat_removed': 5, 'scmerge_cat_added': 4, 'batch_linear_post_grad': 4, 'scmerge_split_removed': 3, 'unbind_stack_pass': 3, 'unbind_cat_to_view_pass': 3, 'batch_tanh': 2, 'scmerge_split_sections_removed': 2, 'scmerge_split_added': 2, 'split_stack_to_cats_pass': 2, 'split_cat_to_slices_pass': 1})
# e2e
testing the following new patterns
```
"split_stack_to_cats_pass": {},
"split_cat_to_slices_pass": {},
"unbind_cat_to_view_pass": {},
```
Note that you can tune the hyper-parameter "threshold_to_cat " for these patterns, and the minimum value you give should be at least 2. The larger the value, the less aggressive to do the node slicing but to keep the cat, and the default value is 10. You can tune the parameters by setting threshold_to_cat. For example
```
"split_stack_to_cats_pass": {"threshold_to_cat": 10},
"split_cat_to_slices_pass": {"threshold_to_cat": 10},
"unbind_cat_to_view_pass": {"threshold_to_cat": 10},
```
Note that the default value may not be optimal, it's based on my experiments on CMF and IGCTR, you are more than welcome to tune the value to find the best threashold for you. For example, in the cmf local run,
- when "threshold_to_cat" is 2
P1515072258
=============Print full analysis for cmf_shrink================
| Metric | Value |
|:-------------------|:----------------|
| Batch size | 10 |
| Latency | 156.07 ms |
| Model size | 844357184 bytes |
| Flops/example | 583.53 G |
| TFLOPS | 37.39 |
| MFU | 4.67% |
| Activation/example | 1707.49 MB |
- when "threshold_to_cat" is 10
P1515912635
=============Print full analysis for cmf_shrink================
| Metric | Value |
|:-------------------|:----------------|
| Batch size | 10 |
| Latency | 155.09 ms |
| Model size | 844357184 bytes |
| Flops/example | 583.53 G |
| TFLOPS | 37.63 |
| MFU | 4.70% |
| Activation/example | 1707.49 MB |
ads_dper3:164562cbe29f6c5aea4546cf3d463b87
training_platform:5e455c643c52940bb4567017f4c7ba83
## cmf
baseline
f588717948
proposal
f588719502
### QPS and NE results
{F1793304642}
{F1793304664}
{F1793304689}
{F1793304683}
### Compilation time reduction
zoomer link: https://www.internalfb.com/intern/zoomer/?profiling_run_fbid=1045728747213538&tab=pt2_metrics
Compile time for that frame is reduced to 1 min from 9 min.
### trace analysis
baseline trace link
https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree%2Ftraces%2Fdynocli%2Ff588722004-TrainingApplication%2F0%2Frank-1.Aug_06_00_03_46.3617.pt.trace.json.gz&bucket=pyper_traces
proposal trace link
https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree%2Ftraces%2Fdynocli%2Ff588723545-TrainingApplication%2F0%2Frank-1.Aug_05_23_54_56.3647.pt.trace.json.gz&bucket=pyper_traces
{F1793312804} {F1793312867}
From the trace, we can see that the green part (introduced by split cat) has been reduced significantly with our new patterns.
Differential Revision: D60750275
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132831
Approved by: https://github.com/jackiexu1992
Summary:
Re-enable testHelperPrefix test that was erroneously disabled in CI.
Fixes#50701
Test Plan:
Test passes locally:
```
❯ ./TCPStoreTest --gtest_filter=TCPStoreTest.testHelperPrefix
Running main() from
/data/users/cpio/pytorch/third_party/googletest/googletest/src/gtest_main.cc
Note: Google Test filter = TCPStoreTest.testHelperPrefix
[==========] Running 1 test from 1 test suite.
[----------] Global test environment set-up.
[----------] 1 test from TCPStoreTest
[ RUN ] TCPStoreTest.testHelperPrefix
[W807 12:01:31.531576727 socket.cpp:462] [c10d] waitForInput: poll for
socket SocketImpl(fd=6, addr=[localhost]:37984,
remote=[localhost]:37171) returned 0, likely a timeout
[W807 12:01:31.531663710 socket.cpp:487] [c10d] waitForInput: socket
SocketImpl(fd=6, addr=[localhost]:37984, remote=[localhost]:37171) timed
out after 100ms
[ OK ] TCPStoreTest.testHelperPrefix (314 ms)
[----------] 1 test from TCPStoreTest (314 ms total)
[----------] Global test environment tear-down
[==========] 1 test from 1 test suite ran. (314 ms total)
[ PASSED ] 1 test.
╭─ ~/local/pytorch/build/bin main *1 +1 ···················· ✔
/home/cpio/local/a/pytorch-env cpio@devgpu011 ─╮
╰─
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132916
Approved by: https://github.com/Skylion007
This PR fixes flaky internal tests:
- The AutoHeuristic test was sometimes failing because it required autotuning to happen for mixed_mm which didn't end up happening when there was a fx graph cache hit.
- The tests inside pattern_matcher failed because in some cases pad_mm decided to pad which made the mixed_mm pattern not match anymore (instead of cast -> mm, it was cast -> pad -> mm), and the tests also fail when is_big_gpu is false (which I haven't found an explanation for).
Differential Revision: [D60972176](https://our.internmc.facebook.com/intern/diff/D60972176)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133015
Approved by: https://github.com/Chillee, https://github.com/eellison
Partially fixes#122980
- change cpp type mapping for complex64 to std::complex<float>
- add `aoti_torch_item_complex64` and `aoti_torch_scalar_to_tensor_complex64`.
- add `expensiveCopyToTensor()` to convert `ArrayRefTensor<T>` type to `AtenTensorHandle` type.
- if we want to fully fix#122980, we still need to let ArrayRef and MiniArrayRef to consider underlying storage number of elements. See more details in https://github.com/pytorch/pytorch/pull/132347 (#132347 broke some internal tests, so we need more work before landing it).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132810
Approved by: https://github.com/desertfire
It's possible to construct an NJT with "holes" by specifying both `offsets` and `lengths` metadata. When `nt.clone(memory_format=torch.contiguous_format)` is called on such an NJT, the result should be an NJT without holes.
This PR fixes this in simplistic way using `unbind()`, which isn't really supported in `torch.compile`. The longer term solution involves writing a proper kernel to support this.
NB: Another limitation is that the returned NJT does not have the same ragged structure as the input. While we could manually hack the nested int registry (or update the union find when that lands), this is the first instance where a NJT with holes and an NJT without holes could have the same ragged structure, and getting those to play nicely together requires some fairly involved updates. For now, this PR punts on these updates until we can clean this up.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132776
Approved by: https://github.com/ani300, https://github.com/soulitzer
ghstack dependencies: #131898, #131704, #131937
Summary: These tests are failing stress tests internally because of remote caching. Most already have local cache disabled; disable remote cache as well
Test Plan: Ran stress tests locally for each of the affected tests
Differential Revision: D60940081
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132955
Approved by: https://github.com/leslie-fang-intel
Summary: When HOPs live out of tree, it makes it impossible to make breaking changes to the HOP API. But HOP implementations are deeply entwined with PyTorch internals. Move the HOP into PyTorch tree so that changes are possible.
Test Plan: sandcastle, ossci
Differential Revision: D60674615
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132525
Approved by: https://github.com/zou3519, https://github.com/Skylion007
The capture_triton decorator returns a function that goes through the
triton kernel wrapper HOP. This is useful for make_fx tracing and
non-strict export. However, the HOP dispatch is slow (~1ms) and not
necessary in certain situations.
This PR skips going through the HOP dispatch for any
capture_triton-wrapped triton kernels that are registered as
implementations to a `@triton_op` custom operator. We do this by
creating a new thread-local flag that controls if the
capture_trition-wrapped triton kernel goes through HOP dispatch or not.
Test Plan:
- new test and existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132822
Approved by: https://github.com/SherlockNoMad
Summary:
Modify `softmax` on the ragged dimension, where `ragged_idx == 1`, to allow for 2D nested tensors. This diff now enables a `softmax` operation on tensors of shape `(B, *)`, where `*` is the ragged dimension.
Extend existing `softmax` unit tests to include 2D nested tensors using the `include_2d_tensor=True` keyword argument.
Test Plan:
Verify that existing and modified unit tests pass using the following commands:
```
buck2 run mode/{opt,inplace} //caffe2/test:nested -- --regex test_softmax
```
```
buck2 run mode/{opt,inplace} //caffe2/test:nested -- --regex test_jagged_op
```
Reviewed By: davidberard98
Differential Revision: D60780975
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132812
Approved by: https://github.com/davidberard98
The goal of this PR is to avoid stack overflow when we create extremely long chains of thunks, and then evaluate them (e.g., as occurs if you sum(long list of symint)). The basic idea behind this PR is to only thunkify proxies if they're being created in places where they may or may not be used--crucially, symint operations that occur in user code we are tracing are eagerly placed into the graph, even if they may eventually be dead.
I annotated the PR with explanation of changes.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132421
Approved by: https://github.com/Skylion007, https://github.com/zou3519
ghstack dependencies: #132674, #132675
Instead of having a separate context variable for SymDispatchMode, we
now simply delegate to the current active proxy tensor mode when we
need to trace a SymInt. We maintain a separate `__sym_dispatch__` magic
method as the calling convention is different than `__torch_dispatch__`.
Consolidating the modes in this ways means that we can consistently
disable both of these modes in tandem simply by removing the mode
from the proxy mode infra slot.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132674
Approved by: https://github.com/zou3519, https://github.com/bdhirsh
Introduces enhancement for SortingKernel.cpp for cases where both the values and indices tensors have a stride 1, indicating contiguous memory layouts.
The changes include:
1. A new function `sort_kernel_impl`, encapsulating the core sorting logic for distinct types of tensor accessors.
2. Modifications to the `sort_kernel` function to utilize `sort_kernel_impl`. It now checks for tensor strides and optimally handles contiguous and non-contiguous tensor scenarios.
3. The optimization aims to improve cache locality and efficiency in memory access for contiguous tensor sorts.
4. Enhanced Code Readability and Structure: The restructuring of the sorting process improves clarity and maintenance by clearly defining how different stride scenarios are handled, making the code more transparent and easier to understand.
Tests have been conducted across various tensor sizes and shapes to ensure stability and reliability of the change.
The result of running the `test/test_sort_and_select.py` test suite is consistent between the main branch, and this modified branch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132236
Approved by: https://github.com/jgong5
As titled, this PR rewrite the current redistribute algorithm to make
the multi-mesh dim redistribute logic more sound. The previous algorithm
works numerically but it could incur additional non-necessary steps
when transforming shardings in the multi-dimesnion device mesh, i.e.
Let's say we want to transform from (S(1), S(1)) -> (S(1), S(2)). The
previous algorithm yield the following steps:
* mesh_dim 1: S(1) -> R, mesh_dim 0: S(1) -> R
* mesh_dim 0: R -> S(1), mesh_dim 1: R -> S(2)
Although it works semantically but it incurs two allgather
transformations, where it should really only incur a S(1) -> S(2) on the
mesh dim 1.
The rewrite algorithm basically take it in a more principled way:
1. we check if src_spec have sharding, if not, we don't need to worry about nested sharding case, as sharding would always be in order, so we just go from left to right in the placements and add the transform steps
2. if src_spec have sharding, this potentially means that there would be either nested or mis-aligned shardings. So we first tranverse from right to left to check if there's mis-aligned sharding as the above example showed, if there is, we replicate that mesh dimension so that it unshard the nested sharding
3. we tranverse again from left to right to generate the transformation
after we unshard the nested sharding
should also fix https://github.com/pytorch/pytorch/issues/132751
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131210
Approved by: https://github.com/tianyu-l
#### Description
Transform quantized operation properly. Add de/quantization before and after the quantized operation.
#### Test Plan
`pytest test/export/test_converter.py -s -k test_ts2ep_convert_quantized_model`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131915
Approved by: https://github.com/angelayi
Summary:
When fixing https://github.com/pytorch/pytorch/issues/130810, we suspected FSDP1 optimizer state_dict cannot handle foreach optimizer, which is not correct. For FSDP1, whether optimizer uses foreach or not does not matter. Since we already have tests for non-foreach version optimizer, this PR changes the distributed state_dict tests for FSDP1 to use foreach optimizer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132933
Approved by: https://github.com/c-p-i-o
ghstack dependencies: #132908
There are still some differences between CUDA and non-CUDA custom devices when
construct FSDP because CUDA is selected as the default device. For example,
when construct FSDP from CPU model and device_id is not passed, device_handle
will choose CUDA as default device. This PR will autoselect the real device
as the default device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127609
Approved by: https://github.com/awgu
Summary: Dynamo doesn't trace through sparse tensors in fbcode. So we should disable tests that run sparse tensors in export. We should do this to make the CI green internally.
Test Plan:
Before:
Tests finished: Pass 1409. Fail 71. Fatal 0. Skip 90. Build failure 0
After:
Tests finished: Pass 1408. Fail 0. Fatal 0. Skip 162. Build failure 0
Differential Revision: D60870543
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132824
Approved by: https://github.com/BoyuanFeng
Summary:
**Context:**
Currently we have a helper to print out AtenTensor in [shim_common.cpp](https://github.com/pytorch/pytorch/blob/v2.4.0-rc4/torch/csrc/inductor/aoti_torch/shim_common.cpp#L866)
The way we were using this function was a “manual” process. We inject this function into the generated output.cpp file, and recompile and reload the file. This diff automates the printing value process.
**Changes:**
1. Added a simple initial debug printer helper to print out tensor values
2. Added a filter option to selectively dump tensor values.
**Usage:**
Sample cmd :
```
AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=1 TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+inductor, +schedule, output_code" python test/inductor/test_aot_inductor.py -k test_addmm_abi_compatible_cuda
```
Sample outputs :
```
[ before_launch - triton_poi_fused_0 - buf0 ]:
0.6331
1.6358
-0.3459
1.0196
-0.4122
1.4279
[ CUDAFloatType{6} ]
Min value: -0.412198
Max value: 1.63582
Device: cuda:0
Size: [6]
Stride: [1]
Dtype: float
Layout: Strided
Number of elements: 6
Is contiguous: 1
Requires grad: 0
[ after_launch - triton_poi_fused_0 - buf0 ]:
0.6331
1.6358
-0.3459
1.0196
-0.4122
1.4279
[ CUDAFloatType{6} ]
Min value: -0.412198
Max value: 1.63582
Device: cuda:0
Size: [6]
Stride: [1]
Dtype: float
Layout: Strided
Number of elements: 6
Is contiguous: 1
Requires grad: 0
[ before_launch - aoti_torch_cuda_addmm_out - buf1 ]:
Min value: -2.25655
Max value: 2.32996
Device: cuda:0
Size: [16, 6]
Stride: [6, 1]
Dtype: float
Layout: Strided
Number of elements: 96
Is contiguous: 1
Requires grad: 0
[ before_launch - aoti_torch_cuda_addmm_out - buf0 ]:
0.6331
1.6358
-0.3459
1.0196
-0.4122
1.4279
[ CUDAFloatType{6} ]
Min value: -0.412198
Max value: 1.63582
Device: cuda:0
Size: [6]
Stride: [1]
Dtype: float
Layout: Strided
Number of elements: 6
Is contiguous: 1
Requires grad: 0
[ after_launch - aoti_torch_cuda_addmm_out - buf1 ]:
Min value: -12.0839
Max value: 11.6878
Device: cuda:0
Size: [16, 6]
Stride: [6, 1]
Dtype: float
Layout: Strided
Number of elements: 96
Is contiguous: 1
Requires grad: 0
[ after_launch - aoti_torch_cuda_addmm_out - buf0 ]:
0.6331
1.6358
-0.3459
1.0196
-0.4122
1.4279
[ CUDAFloatType{6} ]
Min value: -0.412198
Max value: 1.63582
Device: cuda:0
Size: [6]
Stride: [1]
Dtype: float
Layout: Strided
Number of elements: 6
Is contiguous: 1
Requires grad: 0
stats [('calls_captured', 1), ('unique_graphs', 1)]
inductor [('pattern_matcher_count', 2), ('pattern_matcher_nodes', 2), ('extern_calls', 2)]
.
----------------------------------------------------------------------
Ran 1 test in 10.867s
OK
```
The user is able to filter kernel names to print out values by specifying env var `AOT_INDUCTOR_FILTERED_KERNELS_TO_PRINT` and see choices of kernel names in a log message like below:
```
torch/_inductor/graph.py:1642] Finished codegen for all nodes. The list of kernel names available: ['triton_poi_fused_0', 'aoti_torch_cuda_addmm_out']
```
In the follow-up diff, will add `torch.save()` to dump/save the intermediate tensors into individual `.pt` files that can be further `torch.load()`.
Test Plan:
Run Unit Tests in OSS: (similar cmd as mentioned above in the usage part)
`AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=1 TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCH_COMPILE_DEBUG=1 TORCH_LOGS="+inductor, output_code" python test/inductor/test_aot_inductor.py -k test_addmm_abi_compatible_cuda`
Differential Revision: D60538496
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132323
Approved by: https://github.com/ColinPeppler
When autocasting is turned on, right now SDPA w/ NJT won't be autocasted. This PR adds manual "autocasting" logic in sdpa.py - at the beginning, it just checks if autocasting is enabled, and if so, it casts the inputs in the way you would expect if autocasting was actually running.
Why normal autocasting won't work:
* NJT intercepts the `__torch_function__` call for scaled_dot_product_attention, which, AFAIK, happens before we get to any dispatcher logic, and then calls efficient attention or flash attention. So autocasting the scaled_dot_product_attention op won't work; we never call the aten op for scaled_dot_product_attention, so we won't ever run autocasting for it.
* If we try to add autocasting handling for `_flash_attention_forward` or `_efficient_attention_forward`, then autocasting will _run_, but it will have the wrong semantics: sdpa.py's handling will run first, and it will do backend selection based on the uncasted inputs to SDPA. This also means that if the inputs to the SDPA call don't have uniform types, the sdpa.py implementation will fail checks (this is the specific issue we're targeting).
Alternative: "just change the backend selection logic for NJT to be autocast aware, but don't actually do the autocast; then, add `_(flash|efficient)_attention_forward` to autocasting rules". I think this would work too. But it's arguably better to make the backend-selection logic and actual-autocast-behavior use the same implementation, in case the implementations are different.
Differential Revision: [D60879916](https://our.internmc.facebook.com/intern/diff/D60879916)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132835
Approved by: https://github.com/soulitzer
Summary:
A re-land of D60006710.
Fixed TrainingIRToRunDecomp failures for test_tensor_attribute_zero_args and also a few re-tracability failures because run_decomposition does a retracing.
edit: also remove the eliminate_dead_code() in _unlift because of one onnx test failure:
a constant tensor attr was lifted as constant_tensor input but it's not used in the graph after aot_autograd due to a short cut in its decomposition. This causes the setattr to be removed by eliminate_dead_code but the graph signature still contains the name of that buffer, which causes an inconsitency between the transformed graph and ep's original signature after _unlift. And it seems that this has happened a few times where some nodes are accidentally removed and we're in an inconsistent state.
The alternative of removing it would be: every time we call elimiate_dead_code, we verify the consistency of the graph with 1. the graph before transformation and 2. all the meta datas but i think this deserves a complete design
edit 2: Also fix the inconsistency of graph signatures when param_constant is marked as lifted_tensor_constants but it's registered as parameters in the output of ep.module().
Differential Revision: D60532628
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132307
Approved by: https://github.com/zhxchen17
move benchmarking out of `torch._inductor.runtime.runtime_utils` and into `torch._inductor.runtime.benchmarking`, and prefer this path over directly accessing Triton's benchmarking
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132827
Approved by: https://github.com/eellison
Debuged with @leslie-fang-intel , and we found that: https://github.com/pytorch/pytorch/issues/132561 and https://github.com/pytorch/pytorch/issues/132569 are all failed by `capture_pre_autograd_graph` not work well on Windows.
So, we added some code to raise message and let end user known that.
Detailed:
For https://github.com/pytorch/pytorch/issues/132561
```cmd
Traceback (most recent call last):
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 59, in testPartExecutor
yield
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 591, in run
self._callTestMethod(testMethod)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 549, in _callTestMethod
method()
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_utils.py", line 2918, in wrapper
method(*args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_utils.py", line 1515, in wrapper
fn(*args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_quantization.py", line 399, in wrapper
fn(*args, **kwargs)
File "D:\xu_git\dnnl_cb\pytorch\test\quantization\pt2e\test_x86inductor_quantizer.py", line 1737, in test_qat_conv2d
self._test_quantizer(
File "D:\xu_git\dnnl_cb\pytorch\test\quantization\pt2e\test_x86inductor_quantizer.py", line 553, in _test_quantizer
m = capture_pre_autograd_graph(
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\_export\__init__.py", line 121, in capture_pre_autograd_graph
raise RuntimeError("capture_pre_autograd_graph not yet supported on Windows")
RuntimeError: capture_pre_autograd_graph not yet supported on Windows
To execute this test, run the following from the base repo dir:
python test\quantization\pt2e\test_x86inductor_quantizer.py -k TestQuantizePT2EX86Inductor.test_qat_conv2d
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
```
For https://github.com/pytorch/pytorch/issues/132569
```cmd
Traceback (most recent call last):
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 59, in testPartExecutor
yield
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 591, in run
self._callTestMethod(testMethod)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 549, in _callTestMethod
method()
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_utils.py", line 2918, in wrapper
method(*args, **kwargs)
File "D:\xu_git\dnnl_cb\pytorch\test\inductor\test_torchinductor.py", line 11218, in new_test
return value(self)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\_dynamo\testing.py", line 312, in _fn
return fn(*args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\contextlib.py", line 79, in inner
return func(*args, **kwds)
File "D:\xu_git\dnnl_cb\pytorch\test\inductor\test_cpu_cpp_wrapper.py", line 155, in fn
_, code = test_torchinductor.run_and_get_cpp_code(
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\_inductor\utils.py", line 1863, in run_and_get_cpp_code
result = fn(*args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_quantization.py", line 415, in wrapper
fn(*args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_quantization.py", line 367, in wrapper
fn(*args, **kwargs)
File "D:\xu_git\dnnl_cb\pytorch\test\inductor\test_mkldnn_pattern_matcher.py", line 1668, in test_qlinear_gelu_cpu
self._qlinear_unary_cpu_test_helper((torch.randn((2, 4)),), gelu)
File "D:\xu_git\dnnl_cb\pytorch\test\inductor\test_mkldnn_pattern_matcher.py", line 1615, in _qlinear_unary_cpu_test_helper
self._test_common(
File "D:\xu_git\dnnl_cb\pytorch\test\inductor\test_mkldnn_pattern_matcher.py", line 165, in _test_common
convert_model = _generate_qdq_quantized_model(
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_quantization.py", line 2949, in _generate_qdq_quantized_model
export_model = capture_pre_autograd_graph(
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\_export\__init__.py", line 121, in capture_pre_autograd_graph
raise RuntimeError("capture_pre_autograd_graph not yet supported on Windows")
RuntimeError: capture_pre_autograd_graph not yet supported on Windows
To execute this test, run the following from the base repo dir:
python test\inductor\test_cpu_cpp_wrapper.py -k DynamicShapesCppWrapperCpuTests.test_qlinear_gelu_cpu_dynamic_shapes_cpp_wrapper
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
--------------------------------------------------------------------------------------------------------------------------- Captured stderr call ----------------------------------------------------------------------------------------------------------------------------
W0807 13:24:34.291000 11228 torch\_export\__init__.py:64] +============================+
W0807 13:24:34.291000 11228 torch\_export\__init__.py:65] | !!! WARNING !!! |
W0807 13:24:34.291000 11228 torch\_export\__init__.py:66] +============================+
W0807 13:24:34.291000 11228 torch\_export\__init__.py:67] capture_pre_autograd_graph() is deprecated and doesn't provide any function guarantee moving forward.
W0807 13:24:34.291000 11228 torch\_export\__init__.py:68] Please switch to use torch.export instead.
```
Co-authored-by: Jiong Gong <jiong.gong@intel.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132841
Approved by: https://github.com/jgong5, https://github.com/ezyang
See title. Until now, calling `torch.as_tensor` on a CuPy array would return a CPU tensor, when not providing a device. This is most likely not desired.
Fixes#132553
```python3
import torch
import cupy as cp
cupy_arr = cp.asarray([1, 2, 3])
# Default case
t = torch.as_tensor(cupy_arr)
# New behavior, same device as cupy_arr now, was cpu before
print(t.device) # cuda:0
# Explicitly set device
t = torch.as_tensor(cupy_arr, device='cpu')
print(t.device) # cpu
# Implicit default device
torch.set_default_device('cpu')
t = torch.as_tensor(cupy_arr)
print(t.device) # cpu
# Default device via context manager
torch.set_default_device('cuda')
with torch.device('cpu'):
t = torch.as_tensor(cupy_arr)
print(t.device) # cpu
# Unset default device
torch.set_default_device(None)
t = torch.as_tensor(cupy_arr)
# New behavior, same device as cupy_arr now, was cpu before
print(t.device) # cuda:0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132595
Approved by: https://github.com/ezyang
Summary:
By default, performance tests (speedup experiments) will run the baseline and test backend alternately.
However, this does not work for the torchao backend, which will change the model in-place, therefore the baseline run will also run with torchao backend since the model has already been quantized.
Add a new experiment "latency_experiment" to run performance tests non-alternately (first run baseline for a few iterations, then run the test backend).
other changes:
need to add torch.compiler.cudagraph_mark_step_begin() to avoid the
slowdown from # Unable to hit fast path of CUDAGraphs because of pending, uninvoked backwards
also updated the torchao APIs to the current versions
X-link: https://github.com/pytorch/benchmark/pull/2394
Test Plan:
python run_benchmark.py torchao --only AlbertForMaskedLM --quantization noquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune python run_benchmark.py torchao --only BartForCausalLM --quantization noquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune python run_benchmark.py torchao --only timm_efficientnet --quantization noquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune
(should all be ~1.0
0.997x
1.006x
0.994x
Reviewed By: xuzhao9
Differential Revision: D60252821
Pulled By: HDCharles
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131935
Approved by: https://github.com/xuzhao9
Creates a new runtime that shifts complexity from runtime to
ahead-of-time.
The existing runtime (PipelineScheduleMulti) accepts a
compute-only schedule (forward, backward, weight) actions only are
specified, and it infers the communication operations at runtime.
Compared to that runtime, PipelineScheduleRuntime has less logic that
happens at runtime and relies on lowering passes to transform the
compute-only schedule to add communications.
Advantages include
- easier to verify the correctness by dumping a compute+comm schedule
- posible to manually edit the compute+comm schedule if the lowering
heuristics are insufficient
Functionality included inside the PipelineScheduleRuntime is limited to
- accepting a compute-only schedule and lowering it to add comms
- executing the compute or comm operations specified by the given
schedule
- handling work.wait() automatically by calling it just before the
matching compute operation (for RECV ops) or at the end of step (for
SEND ops)
Follow ups for later PRs
- Some refactoring should be done to replace PipelineScheduleMulti with
this runtime
- Optimizer execution is not considered (e.g. for zero-bubble cases)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130488
Approved by: https://github.com/H-Huang
Summary:
PDB allows to do conditional breakpoint but the ability won't work in the distributed environment. We can still do conditional breakpoint by doing the following:
```
counter = 0
global counter
count += 1
if counter > 100:
dist.breakpoint()
```
This PR makes dist.breakpoint() support this feature as a syntax sugar.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129511
Approved by: https://github.com/wconstab, https://github.com/c-p-i-o
Summary:
A couple of improvements to the generated comments in inductor kernels:
1. Makes the nodes in the comment topologically sorted, I think having them
alphabetically sorted is a gotcha. I was always confused on why the
sorting in the comments did not match the code.
2. Adds a printout of the aten graph fragment corresponding to the
current inductor kernel, to make it easier to map from aten
code to inductor code
Example float8-overhead-related inductor kernel comment after this PR:
```
# kernel path: /tmp/torchinductor_vasiliy/27/c27ts3rdw56ns7od5j6ovdnhxphished2lcu3adclzzixoo7khg5.py
# Source Nodes: [weight_fp8], Original ATen: [aten.mul, aten.clamp, aten._to_copy]
# Source node to ATen node mapping:
# weight_fp8 => clamp_max_1, clamp_min_3, convert_element_type_10, convert_element_type_11, convert_element_type_9, mul_3
# Graph fragment:
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %convert_element_type_8), kwargs = {})
# %convert_element_type_9 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_3, torch.float32), kwargs = {})
# %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%convert_element_type_9, -448.0), kwargs = {})
# %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_3, 448.0), kwargs = {})
# %convert_element_type_10 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%clamp_max_1, torch.bfloat16), kwargs = {})
# %convert_element_type_11 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%convert_element_type_10, torch.float8_e4m3fn), kwargs = {})
triton_poi_fused__to_copy_clamp_mul_5 = async_compile.triton('triton_', '''
```
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126698
Approved by: https://github.com/ezyang
ghstack dependencies: #126573
Summary:
Uses the `seq_nr` field (introduced to aot_autograd nodes in
https://github.com/pytorch/pytorch/pull/103129) to map the aot_autograd
fx bw nodes to the corresponding fw nodes, and copy the metadata over.
I am trusting the `seq_nr` mapping in the linked PR here. I did
some validation with a toy LLaMa 3 8b training run and the mapping seemed
correct.
I am also trusting that the forward is single threaded, since `seq_nr` is thread local. If this isn't always true, we'll need to also plumb `thread_id` through the same machinery which is populating `seq_nr`.
I'd like to use this data in a future PR to make inductor kernels easily
attributable to the nn.Module path in modeling land, to make it easier
to do performance debugging.
Test Plan:
```
// 1. unit test
python test/dynamo/test_aot_autograd.py -k test_aot_sequence_nr
// 2. manual test
// run LLaMa 3 8B fw + bw with torch.compile, print out the inductor graphs
// seen in `torch/_inductor/utils.py::get_kernel_metadata`, they seemed
// right to me.
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126573
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
I find myself occasionally trying to modify this to get additional debug info. Recompiling takes forever after modifying these lines, because the .h file is depended on by a huge number of files.
If we move this logic into a helper function and put it in the .cpp file, recompilation will be a lot faster when adding debug here.
Tested with a local DEBUG=1 build (which is needed to use `TORCH_SHOW_DISPATCH_TRACE=1`) and verified basic sanity - i.e. it still prints `[call]`, etc.
Differential Revision: [D60804331](https://our.internmc.facebook.com/intern/diff/D60804331)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132717
Approved by: https://github.com/soulitzer, https://github.com/bdhirsh
Overloads so that you can get more specific type info based on how you are indexing.
```python
from torch import nn
module_list = nn.ModuleList(32 * [nn.Linear(2, 2)])
# before:
reveal_type(module_list[0]) # Type of "module_list[0]" is "Module | ModuleList"
reveal_type(module_list[:1]) # Type of "module_list[: 1]" is "Module | ModuleList"
# now:
reveal_type(module_list[0]) # Type of "module_list[0]" is "Module"
reveal_type(module_list[:1]) # Type of "module_list[: 1]" is "ModuleList"
```
Co-authored-by: Skylion007 <Skylion007@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132834
Approved by: https://github.com/Skylion007, https://github.com/albanD
Move the slow test json to be in the pytorch/pytorch repo and make a job that will update it weekly. The job uses the same environment as the commit hash. It uses similar code to the hash updates, but the hash update contains a lot of code that is specific to the hash update, so I chose to pick out the parts that are relevant
Remove references to the old file and set up testing to read from the new file instead
The old update cadence was every day, the new one is every week
The auto slow test infra + the lack of pinning between pytorch and test-infra makes it really hard to tell if a test started failing because of a change or because of the slow test json changing. While this can have benefits, like disable test issues being effective everywhere immediately, it can also be very confusing, especially since we don't have the same insight into slow tests like we do for disable issues.
Example PR made: https://github.com/pytorch/pytorch/pull/132383 (with all the changes from this PR because it was working on top of this)
We should just get rid of this at some point in favor of the slowTest decorator, but there are some tests that take 5+ minutes to run and I don't want to track them down right now
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132379
Approved by: https://github.com/huydhn
As XPU became a PyTorch built-in device, the profiler support is indispensable part of functionality completeness. This PR is associated with the PR to introduce XPU profiler plugin into the kineto. When USE_XPU is enabled, the LIBKINETO_NOXPUPTI option will be suppressed accordingly, which allows kineto to build with XPU profiler plugin.
Associated PR to introduce kineto-based XPU profiler into kineto:
https://github.com/pytorch/kineto/pull/961
Also updates the Kineto Submodule to include XPU changes.
Co-authored-by: Aaron Enye Shi <enye.shi@gmail.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130811
Approved by: https://github.com/aaronenyeshi
**Summary**
1. change `compute_local_shape_and_global_offset` to correctly compute shape and offset for strided sharding placement (currently it only handles 2D and some 3D+ sharding).
2. Add a new property `num_shards_map` to `DTensorSpec` denoting how many shards each tensor dimension has. This is necessary for constructing `_StridedShard` placement when we call `distribute_tensor(dtensor_tp, dp_device_mesh, [Shard(0)])` and the `split_factor` argument will just be the number of shards on that sharding tensor dim.
**Test**
`test/distributed/_tensor/test_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132391
Approved by: https://github.com/wanchaol
ghstack dependencies: #126697, #130239
**Summary**
This PR adds a new private placement type `_StridedShard` for FSDP2 + TP style tensor sharding. The previously used `Shard` placement type cannot produce correct `full_tensor()` result because it assumes the tensor to be first sharded over `dp` mesh dimension then `tp` mesh dimension which does not hold true in FSDP2 + TP case.
**Test**
`pytest test/distributed/_tensor/test_utils.py -s -k strided_sharding`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126697
Approved by: https://github.com/wanchaol
This tries to fix https://github.com/pytorch/pytorch/issues/120961.
This is a similar situation as https://github.com/pytorch/pytorch/pull/132116. The overlap tests were written strictly based on a precise calculation of what compute/communication should be non-overlapped vs. overlapped. This is done via `torch.cuda._sleep()`, which takes inputs in cycles, so we must convert from milliseconds to cycles via `get_cycles_per_ms()`, which is computed once and cached. Variation in CI can cause this `get_cycles_per_ms()` value to be inaccurate when the FSDP overlap tests run. Thus, we decide to relax the overlap tests to just make sure the overlapped runs are faster than a baseline without overlap.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132869
Approved by: https://github.com/weifengpy
More context in [#132471](https://github.com/pytorch/pytorch/issues/132471) and https://github.com/pytorch/pytorch/issues/132366.
TLDR:
When cuda is available and users move tensors to cuda, we cannot really reuse the default pg if default pg is gloo, as lots of collectives are not supported on gloo for cuda tensors. For example, `dtensor.full_tensor()` would result in a mysterious SIGTERM when all_gather a cuda tensor using gloo. Without the change in this PR, users would have to know the context and explicitly move the cuda tensor to cpu before invoking most collectives, which I think is not so ideal UX.
Therefore, given most collectives are not supported on gloo for cuda tensors, we should init a new pg if the default pg is gloo when torch.cuda.is_available() and device_type is cuda.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132709
Approved by: https://github.com/awgu, https://github.com/wanchaol
Fixes#10536
Reattempt of #61467. Thank you so much to @mskoh52 for your excellent work!
As I was trying to create a more efficient LLM data collator, I realized that `pad_sequence` only supports right padding, even though left padding is a very common format for LLMs, like Llama and Mistral.
The proposed alternative implementation was to use multiple flips, which tends to be 1.5x-2x slower. Instead we can add a [`padding_side` parameter as there is for for Hugging Face tokenizers](9d6c0641c4/src/transformers/tokenization_utils_base.py (L1565)), which requires only a very small change in the C++ code.
Here are the benchmarks of the new implementation!
`float32`:
`bool`:
Code:
```python
from __future__ import annotations
import random
import time
from typing import Literal
import numpy as np
import torch
def pad_sequence_with_flips(
sequences: list[torch.Tensor],
batch_first: bool = False,
padding_value: int | float | bool = 0.0,
padding_side: Literal["left", "right"] | str = "left",
) -> torch.Tensor:
if padding_side == 'right':
padded_sequence = torch._C._nn.pad_sequence([t.flatten() for t in sequences], batch_first=batch_first, padding_value=padding_value)
elif padding_side=='left':
padded_sequence = torch._C._nn.pad_sequence([t.flatten().flip(0) for t in sequences], batch_first=batch_first, padding_value=padding_value) # pyright: ignore[reportArgumentType]
padded_sequence = padded_sequence.flip(int(batch_first))
else:
raise ValueError(f"padding_side should be either 'right' or 'left', but got {padding_side}")
return padded_sequence
sequence_lengths: list[int] = []
flip_left_pad_times: list[float] = []
flip_left_pad_times_std: list[float] = []
left_pad_times: list[float] = []
left_pad_times_std: list[float] = []
RUNS_PER_LOOP: int = 100
for i in range(1, 7):
sequence_length = i * int(1e6) // 6
sequence_lengths.append(sequence_length)
sequences = [torch.randint(0, 2, (random.randint(1, sequence_length),), dtype=torch.bool) for _ in range(64)]
inner_left_pad_times: list[float] = []
inner_right_pad_times: list[float] = []
inner_flip_left_pad_times: list[float] = []
inner_flip_right_pad_times: list[float] = []
for _ in range(RUNS_PER_LOOP):
start = time.perf_counter()
torch._C._nn.pad_sequence(sequences, batch_first=True, padding_value=False, padding_side="left")
end = time.perf_counter()
inner_left_pad_times.append(end - start)
start = time.perf_counter()
pad_sequence_with_flips(sequences, batch_first=True, padding_value=False, padding_side="left")
end = time.perf_counter()
inner_flip_left_pad_times.append(end - start)
left_pad_times.append(sum(inner_left_pad_times) / len(inner_left_pad_times))
left_pad_times_std.append(np.std(inner_left_pad_times))
flip_left_pad_times.append(sum(inner_flip_left_pad_times) / len(inner_flip_left_pad_times))
flip_left_pad_times_std.append(np.std(inner_flip_left_pad_times))
print(f"Sequence Length: {sequence_length}, Left Pad Time: {left_pad_times[-1]}, Left with Flips Pad Time: {flip_left_pad_times[-1]}")
import matplotlib.pyplot as plt
plt.plot(sequence_lengths, left_pad_times, label="new pad_sequence left")
plt.scatter(sequence_lengths, left_pad_times)
plt.errorbar(sequence_lengths, left_pad_times, yerr=left_pad_times_std, linestyle='None', marker='^')
plt.plot(sequence_lengths, flip_left_pad_times, label="old pad_sequence left (2 flips)")
plt.scatter(sequence_lengths, flip_left_pad_times)
plt.errorbar(sequence_lengths, flip_left_pad_times, yerr=flip_left_pad_times_std, linestyle='None', marker='^')
plt.xlabel("Sequence Length")
plt.ylabel("Time (s)")
plt.legend(loc="upper right")
# Sequence Length: 166666, Left Pad Time: 0.06147645162009212, Left with Flips Pad Time: 0.09842291727001794
# Sequence Length: 333333, Left Pad Time: 0.08933195920990329, Left with Flips Pad Time: 0.15597836187991562
# Sequence Length: 500000, Left Pad Time: 0.08863158334006585, Left with Flips Pad Time: 0.15224887342999863
# Sequence Length: 666666, Left Pad Time: 0.10524682551997103, Left with Flips Pad Time: 0.18177212480995877
# Sequence Length: 833333, Left Pad Time: 0.11801802741003485, Left with Flips Pad Time: 0.20821274195001024
# Sequence Length: 1000000, Left Pad Time: 0.131894061660023, Left with Flips Pad Time: 0.23223503091008751
```
Co-authored-by: mskoh52 <mskoh52@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131884
Approved by: https://github.com/ezyang
This PR does 3 things:
1. Adds a copy-free strided->jagged layout conversion for NT
2. Adds a copy-free jagged->strided layout conversion for NT
3. Modifies and expands the .to() API to support the layout argument for the specific case of NT layout conversion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115749
Approved by: https://github.com/jbschlosser
Fixes#132290
This PR attempts a more invasive / complete solution than the one from #132338, which removes immediate tensor fields from the `tensor_dict` copy stored in node meta. The approach taken here is to store only those fields of the `tensor_dict` which are absolutely utilized somewhere else.
So far, this appears to be limited to:
* `_dynamo_static_input_type`
* `tag` (at least in the tests). Discussion at #94080 appears to indicate this is depended on for export
(CI may point out more)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132805
Approved by: https://github.com/mlazos
The goal of this PR is to avoid stack overflow when we create extremely long chains of thunks, and then evaluate them (e.g., as occurs if you sum(long list of symint)). The basic idea behind this PR is to only thunkify proxies if they're being created in places where they may or may not be used--crucially, symint operations that occur in user code we are tracing are eagerly placed into the graph, even if they may eventually be dead.
I annotated the PR with explanation of changes.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132421
Approved by: https://github.com/Skylion007, https://github.com/zou3519
ghstack dependencies: #132674, #132675
Previously, when we slice out a submesh from a mesh, we assign the mesh as the parent mesh of the submesh. In this case, when we have a 3D mesh topology, the parent mesh of a 1D mesh sliced out from the 3D mesh is different from the parent mesh of the same 1D mesh sliced out from the 2D submesh of the 3D mesh. For example:
```
mesh_3d = init_device_mesh("cuda", (2,2,2), ("dim0", "dim1", "dim2"))
mesh_dim0 = mesh_3d["dim0"]
mesh_2d = mesh_2d["dim0", "dim1"]
mesh_dim0_2 = mesh_2d["dim0_2"]
# This would evaluate to be True
print(_mesh_resources.get_parent_mesh(mesh_dim0) != _mesh_resources.get_parent_mesh(mesh_dim0))
```
We can always reconstruct the mesh needed from the mesh dim names, as long as two dims come from the same root. For simplicity, we do not see the necessity of building a tree structure to represent child-parent relationship. Therefore, we are replacing the parent mesh concept with a root mesh concept in `_MeshEnv` so we would have:
```
mesh_3d = init_device_mesh("cuda", (2,2,2), ("dim0", "dim1", "dim2"))
mesh_dim0 = mesh_3d["dim0"]
mesh_2d = mesh_2d["dim0", "dim1"]
mesh_dim0_2 = mesh_2d["dim0_2"]
# This would evaluate to be True
print(_mesh_resources.get_root_mesh(mesh_dim0) == _mesh_resources.get_root_mesh(mesh_dim0))
```
With this change, we will have two types of meshes in an environment.
1. `device_mesh != _mesh_resources.get_root_mesh(device_mesh)` means that the device_mesh is created by slicing.
2. `device_mesh == _mesh_resources.get_root_mesh(device_mesh)` means that the device_mesh is a root mesh not created through slicing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132339
Approved by: https://github.com/wanchaol
ghstack dependencies: #132310, #132311
Summary: We observe the stack mpde can be transformed to cat node to elimiate split nodes, which could further enable the unbind cat optimization, thus we add a more advanced pattern to do the graph transformation
Test Plan:
# unit test
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 test //caffe2/test/inductor:split_cat_fx_passes
```
Buck UI: https://www.internalfb.com/buck2/de6c1cda-3d74-4a30-8980-7b209b6fe5dc
Test UI: https://www.internalfb.com/intern/testinfra/testrun/12103424042268125
Network: Up: 485KiB Down: 728KiB (reSessionID-2f2c01c3-79bb-4e37-b5be-fb77ec09b264)
Jobs completed: 29. Time elapsed: 5:19.8s.
Cache hits: 0%. Commands: 4 (cached: 0, remote: 0, local: 4)
Tests finished: Pass 9. Fail 0. Fatal 0. Skip 1. Build failure 0
# benchmark
```
CUDA_VISIBLE_DEVICES=3 OC_CAUSE=1 buck2 run mode/opt //scripts/jackiexu0313/pt2:local_model_with_pt2 -- --test_mode batch-split --model_type "ig_ctr" --flow_id 584880697
```
P1503698962
before and after graph transformation
https://www.internalfb.com/intern/diffing/?paste_number=1504050718
Differential Revision: D60411560
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132542
Approved by: https://github.com/jackiexu1992
Summary:
- We add Inductor logs for what tensors we tried to reinplace, what
tensors we were unable to reinplace, and of those tensors, which of
those might be bugs (the "missed reinplacing opportunities"). You can
tell this by reading the Inductor output graph but the logs make it
easier to figure out.
- Add a dynamo_compile counter for missed reinplacing opportunities. The
goal is to see how widespread existing problems (if any) are. We've had
trouble getting all of the edge cases for the reinplacing pass; the
counter will help us hunt down issues.
Test Plan:
- tested locally
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132758
Approved by: https://github.com/eellison
Summary:
- make default DCE pass check schema,
- need to rebase onto https://github.com/pytorch/pytorch/pull/131651 after it's in phabricator (for now the change is manually added).
- mark Proxy dump as NotImplemented for better error msg
- Remove Proxy from tensors when dumping models, as Proxy cannot be dumped.
More details in https://docs.google.com/document/d/1G5vmTXjzxoyVGRI2kpA1gQukK_Glyg2NrE0Oh6Nlg9A/edit?usp=sharing.
Test Plan:
CI
```
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r qat_conv2d
- test_export.py
- buck2 run 'fbcode//mode/dev-nosan' fbcode//modai/test:test_modai -- -r test_qat_stinson_htp_export
- buck2 run 'fbcode//mode/dev-nosan' fbcode//vizard_projects/ml_depth/tests:test_model -- -r test_qat_model_et
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:fx -- -r dce
- buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/backends/tests:qnn_test -- -r test_qat_bias=False,use_3d_input=False
- buck2 run 'fbcode//mode/dev-nosan' fbcode//bolt/nn/executorch/backends/tests:qnn_test -- -r test_qat_bias=True,use_3d_input=False
- buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_fold_bn_erases_bn_node
```
Reviewed By: angelayi
Differential Revision: D60319175
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132764
Approved by: https://github.com/angelayi
More context in [#132471](https://github.com/pytorch/pytorch/issues/132471) and https://github.com/pytorch/pytorch/issues/132366.
TLDR:
When cuda is available and users move tensors to cuda, we cannot really reuse the default pg if default pg is gloo, as lots of collectives are not supported on gloo for cuda tensors. For example, `dtensor.full_tensor()` would result in a mysterious SIGTERM when all_gather a cuda tensor using gloo. Without the change in this PR, users would have to know the context and explicitly move the cuda tensor to cpu before invoking most collectives, which I think is not so ideal UX.
Therefore, given most collectives are not supported on gloo for cuda tensors, we should init a new pg if the default pg is gloo when torch.cuda.is_available() and device_type is cuda.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132709
Approved by: https://github.com/awgu, https://github.com/wanchaol
This PR makes sure all current tests in the sparsity export test suite pass. Note that there will probably be anecdotal cases that need fixing after this, but the general idea of preserving sparsity metadata has been completed.
Fixes: https://github.com/pytorch/pytorch/issues/117188
```
$ PYTORCH_TEST_WITH_DYNAMO=0 python test/export/test_sparse.py ........................................................................................................................................................
----------------------------------------------------------------------
Ran 152 tests
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132690
Approved by: https://github.com/ezyang
Bumps [rexml](https://github.com/ruby/rexml) from 3.2.8 to 3.3.3.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/ruby/rexml/releases">rexml's releases</a>.</em></p>
<blockquote>
<h2>REXML 3.3.3 - 2024-08-01</h2>
<h3>Improvements</h3>
<ul>
<li>
<p>Added support for detecting invalid XML that has unsupported
content before root element</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/184">GH-184</a></li>
<li>Patch by NAITOH Jun.</li>
</ul>
</li>
<li>
<p>Added support for <code>REXML::Security.entity_expansion_limit=</code> and
<code>REXML::Security.entity_expansion_text_limit=</code> in SAX2 and pull
parsers</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/187">GH-187</a></li>
<li>Patch by NAITOH Jun.</li>
</ul>
</li>
<li>
<p>Added more tests for invalid XMLs.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/183">GH-183</a></li>
<li>Patch by Watson.</li>
</ul>
</li>
<li>
<p>Added more performance tests.</p>
<ul>
<li>Patch by Watson.</li>
</ul>
</li>
<li>
<p>Improved parse performance.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/186">GH-186</a></li>
<li>Patch by tomoya ishida.</li>
</ul>
</li>
</ul>
<h3>Thanks</h3>
<ul>
<li>
<p>NAITOH Jun</p>
</li>
<li>
<p>Watson</p>
</li>
<li>
<p>tomoya ishida</p>
</li>
</ul>
<h2>REXML 3.3.2 - 2024-07-16</h2>
<h3>Improvements</h3>
<ul>
<li>
<p>Improved parse performance.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/160">GH-160</a></li>
<li>Patch by NAITOH Jun.</li>
</ul>
</li>
<li>
<p>Improved parse performance.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/169">GH-169</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/170">GH-170</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/171">GH-171</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/172">GH-172</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/173">GH-173</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/174">GH-174</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/175">GH-175</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/176">GH-176</a></li>
</ul>
</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/ruby/rexml/blob/master/NEWS.md">rexml's changelog</a>.</em></p>
<blockquote>
<h2>3.3.3 - 2024-08-01 {#version-3-3-3}</h2>
<h3>Improvements</h3>
<ul>
<li>
<p>Added support for detecting invalid XML that has unsupported
content before root element</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/184">GH-184</a></li>
<li>Patch by NAITOH Jun.</li>
</ul>
</li>
<li>
<p>Added support for <code>REXML::Security.entity_expansion_limit=</code> and
<code>REXML::Security.entity_expansion_text_limit=</code> in SAX2 and pull
parsers</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/187">GH-187</a></li>
<li>Patch by NAITOH Jun.</li>
</ul>
</li>
<li>
<p>Added more tests for invalid XMLs.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/183">GH-183</a></li>
<li>Patch by Watson.</li>
</ul>
</li>
<li>
<p>Added more performance tests.</p>
<ul>
<li>Patch by Watson.</li>
</ul>
</li>
<li>
<p>Improved parse performance.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/186">GH-186</a></li>
<li>Patch by tomoya ishida.</li>
</ul>
</li>
</ul>
<h3>Thanks</h3>
<ul>
<li>
<p>NAITOH Jun</p>
</li>
<li>
<p>Watson</p>
</li>
<li>
<p>tomoya ishida</p>
</li>
</ul>
<h2>3.3.2 - 2024-07-16 {#version-3-3-2}</h2>
<h3>Improvements</h3>
<ul>
<li>
<p>Improved parse performance.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/160">GH-160</a></li>
<li>Patch by NAITOH Jun.</li>
</ul>
</li>
<li>
<p>Improved parse performance.</p>
<ul>
<li><a href="https://redirect.github.com/ruby/rexml/issues/169">GH-169</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/170">GH-170</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/171">GH-171</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/172">GH-172</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/173">GH-173</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/174">GH-174</a></li>
<li><a href="https://redirect.github.com/ruby/rexml/issues/175">GH-175</a></li>
</ul>
</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="e4a067e112"><code>e4a067e</code></a> Add 3.3.3 entry</li>
<li><a href="17ff3e7874"><code>17ff3e7</code></a> test: add a performance test for attribute list declaration</li>
<li><a href="be86b3de0a"><code>be86b3d</code></a> test: fix wrong test name</li>
<li><a href="b93d790b36"><code>b93d790</code></a> test: use double quote for string literal</li>
<li><a href="0fbe7d5a0e"><code>0fbe7d5</code></a> test: don't use abbreviated name</li>
<li><a href="1599e8785f"><code>1599e87</code></a> test: add a performance test for PI with many tabs</li>
<li><a href="e2546e6eca"><code>e2546e6</code></a> parse pi: improve invalid case detection</li>
<li><a href="73661ef281"><code>73661ef</code></a> test: fix a typo</li>
<li><a href="850488abf2"><code>850488a</code></a> test: use double quote for string literal</li>
<li><a href="46c6397d5c"><code>46c6397</code></a> test: add performance tests for entity declaration</li>
<li>Additional commits viewable in <a href="https://github.com/ruby/rexml/compare/v3.2.8...v3.3.3">compare view</a></li>
</ul>
</details>
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/132469
Approved by: https://github.com/ezyang
Summary:
## Why
utils.checkpoint doesn't support meta device:
```
File "/Users/lyu1/torchdev/lib/python3.9/site-packages/torch/utils/checkpoint.py", line 490, in checkpoint
next(gen)
File "/Users/lyu1/torchdev/lib/python3.9/site-packages/torch/utils/checkpoint.py", line 1359, in _checkpoint_without_reentrant_generator
device_module = _get_device_module(device)
File "/Users/lyu1/torchdev/lib/python3.9/site-packages/torch/utils/checkpoint.py", line 98, in _get_device_module
device_module = getattr(torch, device)
File "/Users/lyu1/torchdev/lib/python3.9/site-packages/torch/__init__.py", line 1938, in __getattr__
raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
AttributeError: module 'torch' has no attribute 'meta'
```
This blocks us from running model with checkpoint enabled in meta mode.
## What
This diff handles the case of meta device in checkpoint.py.
(in checkpoint.py, device module is manily used when preserve_rng_state=true, which doesn't apply to meta case. So a more elgant fix might be set preserve_rng_state=false when detecting args are on meta device. But I didn't find where to do this check in the minimum way. Let me know if you have ideas.)
Test Plan: Tested with toy model which has checkpoint on its module: P1513716944
Differential Revision: D60749427
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132684
Approved by: https://github.com/kit1980
This extends the runner determinator to be able to opt-in to keywords
to provide additional options when determining which systems to run
jobs on. This enables us to support opt-in users to Amazon Linux 2023.
This change creates a generic get_optin_feature() which hopefully will
be useful to handle additional future features that we might want to
experiment with.
This change has kept backwards compatability with the existing issue
userlist format and adds support for the comma-separated list of users
in a backwards compatible way.
The user list has the following rules:
- Users are GitHub usernames with the @ prefix
- If the first line is a "*" then all users will use the new runners
- If the first line is a "!" then all users will use the old runners
- Each user is also a comma-separated list of features/experiments to enable
- A "#" prefix indicates the user is opted out of the new runners but is opting
into features/experiments.
Example user list:
```
@User1
@User2,amz2023
#@UserOptOutOfNewRunner,amz2023
```
This closespytorch/ci-infra#249.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131792
Approved by: https://github.com/jeanschmidt, https://github.com/ZainRizvi
The regression from https://github.com/pytorch/pytorch/issues/132281 pinpoints e4ace1a396 as the cause. The main delta that commit introduces is that we now manually check `is_inference()` and call `increment_version()` (a pybind call) on every mutated input tensor to the graph.
This PR attempts to reduce overhead a bit by bundling up all of those checks into a single pybind call, by:
(1) updating `torch.autograd.graph.increment_version()` to accept a `Union[Tensor, List[Tensor]]`
(2) updating its semantics to no-op if you pass in a tensor with no version counter, instead of erroring
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132652
Approved by: https://github.com/albanD
Summary:
FIx exportdb test for tensor_setattr.
copy.deepcopy(deepcopy) can fail if tensor inputs have attribute (i.e. __dict__).
We remove it before deepcopy.
Before the fix, we have
```
inputs[0].__dict__
{'attr': FakeTensor(..., size=(3, 2))}
```
the test errors out with
```
======================================================================
ERROR: test_exportdb_supported_case_tensor_setattr (caffe2.test.export.test_serialize.TestDeserialize)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/a915c8ae5cba5b70/caffe2/test/__test_export__/test_export#link-tree/torch/testing/_internal/common_utils.py", line 529, in instantiated_test
test(self, **param_kwargs)
File "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/a915c8ae5cba5b70/caffe2/test/__test_export__/test_export#link-tree/caffe2/test/export/test_serialize.py", line 878, in test_exportdb_supported
self.check_graph(model, case.example_args, _check_meta=_check_meta)
File "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/a915c8ae5cba5b70/caffe2/test/__test_export__/test_export#link-tree/caffe2/test/export/test_serialize.py", line 548, in check_graph
_check_graph(pre_dispatch=True)
File "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/a915c8ae5cba5b70/caffe2/test/__test_export__/test_export#link-tree/caffe2/test/export/test_serialize.py", line 506, in _check_graph
copy.deepcopy(inputs),
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 211, in _deepcopy_tuple
y = [deepcopy(a, memo) for a in x]
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 211, in <listcomp>
y = [deepcopy(a, memo) for a in x]
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 153, in deepcopy
y = copier(memo)
File "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/a915c8ae5cba5b70/caffe2/test/__test_export__/test_export#link-tree/torch/_tensor.py", line 206, in __deepcopy__
new_tensor.__dict__ = deepcopy(self.__dict__, memo)
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 146, in deepcopy
y = copier(x, memo)
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "/usr/local/fbcode/platform010/lib/python3.10/copy.py", line 153, in deepcopy
y = copier(memo)
File "/data/users/shangdiy/fbsource/buck-out/v2/gen/fbcode/a915c8ae5cba5b70/caffe2/test/__test_export__/test_export#link-tree/torch/_tensor.py", line 108, in __deepcopy__
or (type(self) is not Tensor and self.data_ptr() == 0)
RuntimeError: Cannot access data pointer of Tensor (e.g. FakeTensor, FunctionalTensor). If you're using torch.compile/export/fx, it is likely that we are erroneously tracing into a custom kernel. To fix this, please wrap the custom kernel into an opaque custom op. Please see the following for details: https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html
```
Test Plan:
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test:test_export -- -r test_exportdb_supported_case_tensor_setattr
```
Differential Revision: D60610860
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132678
Approved by: https://github.com/zhxchen17
Combines contributions from https://github.com/pytorch/pytorch/pull/130505
Some context can be found in this large comment block:
a5b64d39fd/test/dynamo/test_subclasses.py (L1667-L1681)
Changes in this PR
- For each tensor fakified, check the nested int registry in eager, and eagerly symbolicize if that tensor has already been associated with nested int in eager.
- Adds a separate counter stored on FakeTensorMode as a fake analog to _tensor_id_counter (which keeps track of unique tensors). This counter is initialized to the global eager tensor id counter upon creation of the FakeTensorMode, and needs to be reset when the same FakeTensorMode is reused to trace again (in this PR, we piggyback on the epoch incrementing logic).
- (refactor) Today, we store FakeTensor -> symbolic nested int in the global registry. With this PR, symbolic nested int is stored directly on the FakeTensor. (Eager still caches nested int in the registry, though we should avoid this at some point.)
Basically unchanged, but worth noting:
- `__tensor_unflatten__` is still responsible for determining whether we should cache for now. The logic is somewhat simplified.
- to_copy is still using the trick of updating two different tensors in the registry to point to the same nested int. This is kind of broken, but we try to leave it as is, and plan a better fix with the UnionFind stack.
Differential Revision: [D60406772](https://our.internmc.facebook.com/intern/diff/D60406772)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130292
Approved by: https://github.com/bdhirsh
ghstack dependencies: #131916, #131803
Rewrite of original PR in https://github.com/pytorch/pytorch/pull/130291
To answer review comments from https://github.com/pytorch/pytorch/pull/130291#pullrequestreview-2166671953:
> At a higher level, do we need this?
Today, this should not change the behavior of anything. But an invariant of "same tensor always corresponds to the same FakeTensor" is nice (from discussion with @bdhirsh).
> Why does this happen?
Today, both dynamo and meta_utils do some recursion when it comes to FakeTensors. So whenever we fakify a subclass, the process would roughly like:
```
wrap_to_fake (subclass)
meta_utils (subclass)
meta_utils (values) -> not cached because we use callback
meta_utils(offsets) -> not cached because we use callback
wrap_to_fake (values)
wrap_to_fake (offsets) -> cached because we rely on top-level meta_utils
```
However, we know that:
- Caching only occurs at the top-level of meta_utils.
- The return value of the top-level wrap_to_fake is returned.
This means that after all of this:
- The fakified subclass holds inner FakeTensors that are NOT part of the cache
- values/offsets are Fakified a second time, and those instances are cached.
Differential Revision: [D60406773](https://our.internmc.facebook.com/intern/diff/D60406773)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131803
Approved by: https://github.com/ezyang
ghstack dependencies: #131916
Instead of having a separate context variable for SymDispatchMode, we
now simply delegate to the current active proxy tensor mode when we
need to trace a SymInt. We maintain a separate `__sym_dispatch__` magic
method as the calling convention is different than `__torch_dispatch__`.
Consolidating the modes in this ways means that we can consistently
disable both of these modes in tandem simply by removing the mode
from the proxy mode infra slot.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132674
Approved by: https://github.com/zou3519, https://github.com/bdhirsh
https://github.com/pytorch/pytorch/pull/130775 recently killed forced specializations for export on complex guards, so the only way we now get a specialized value is if we're able to solve for it. For example, if we have guards `s0 * 2 = s1`, `s0 + 6 = s1`, we specialize `s0 = 6; s1 = 12`.
That might look like this:
```
class Foo(torch.nn.Module):
def forward(self, x, y):
return x.reshape([-1]) + y
dy = Dim("dy", min=6)
x, y = torch.randn(6, 2), torch.randn(12)
dynamic_shapes = {
"x": (dy - 6, 2),
"y": (dy,),
}
```
Our current error message is:
`{symbol} must be specialized to {value} because the guards generated for it are too complex`
This is now misleading, so we change it to:
`solving the guards generated for {symbol} resulted in a specialized value of {value}`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132698
Approved by: https://github.com/avikchaudhuri
I found that when using TorchDynamo (torch.compile) with dynamic shape on H100, there are some extra guards added to check the sequence length of inputs of `scaled_dot_product_attention` to be divisible by 64. These guards cause unwanted recompilations when the input shape changes.
In fact these guards are not necessary if our CUDNN version is higher enough, So I change the order of those checks to use short-circuit rules to skip those checks and avoid unnecessary guards.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132384
Approved by: https://github.com/eqy, https://github.com/Skylion007
Summary: Previously, when folding BN into conv, we rely on DCE
to clean up the unused BN node from the graph. This works if
the model is already in eval mode, but fails if the model is
still in train mode because DCE doesn't remove nodes with
potential side effects (in this case `_native_batch_norm_legit`).
This required users to move the model to eval mode before calling
convert in order to get a properly DCE'd graph.
To solve this, we manually erase the BN node after folding
instead of relying on DCE. This relaxes the ordering constraints
between `move_exported_model_to_eval` and `convert_pt2e`.
Test Plan:
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn1d.test_fold_bn_erases_bn_node
python test/test_quantization.py TestQuantizePT2EQAT_ConvBn2d.test_fold_bn_erases_bn_node
Reviewers: jerryzh168, yushangdi
Subscribers: jerryzh168, yushangdi, supriyar
Differential Revision: [D60520149](https://our.internmc.facebook.com/intern/diff/D60520149)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131651
Approved by: https://github.com/yushangdi, https://github.com/leslie-fang-intel
`torch.cuda.memory.mem_get_info` allows device strings given the current type hints. However, `device = torch.device('cuda')` leads to `device.index = None`, which results in downstream problems. Setting `optional=True` will insert the default device index in such cases.
Fixes#132583
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132616
Approved by: https://github.com/soulitzer
Summary: When the preprocessor check we leave an unused constexpr around, so when `-Wunused-const-variable` is enabled we get an error. Let's inline these values since they're not used anywhere else in order to avoid this.
Test Plan: CI
Differential Revision: D60723823
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132582
Approved by: https://github.com/houseroad
Preventative fix of a test failure with oneDNN v3.5 upgrade where order of float32 arithmetic may change in torch.admm ( bias term can be at the start or end of the arithmetic ) resulting in slightly different output due to float32 precision loss.
Replaced occurrences of torch.allclose with ~~torch._dynamo.testing.same~~ torch.testing.assert_close which is the recommended approach as per this issue https://github.com/pytorch/pytorch/issues/56544 ,the default tolerance is more relaxed than torch.allclose which satisfies the test with upcoming oneDNN change.
This should fix aarch64 ci failures in #129932
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130618
Approved by: https://github.com/jgong5, https://github.com/malfet
We provide an API for user to add ephemeral timeout across all PGs within one rank and the timeout will reset when the first collective issued after the timeout added finishes.
Each extension only covers collectives after the issue and before the first collective finished. The diagram below shows how the timeout changes:
<img width="1174" alt="image" src="https://github.com/user-attachments/assets/354923b7-581c-40de-ae0f-1cd3da273ccc">
While this feature provides flexibility in specific scenarios, it introduces statefulness to timeout setting. Therefore, it is advisable to use this API sparingly and consider alternative approaches, such as directly setting the timeout or utilizing a barrier collective (one can set any timeout to the barrier), whenever feasible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130905
Approved by: https://github.com/ezyang
Current AOTI model runner has supported CUDA and CPU. However, in terms of a particular out-of-tree backend, it is not easier to support the feature.
This PR intends to provide a registration mechanism to support this case by providing two: `RegisterAOTIModelRunner` and `getAOTIModelRunnerRegistry`.
- `RegisterAOTIModelRunner` is used to register a function(`AOTIModelRunnerABC`) to create a `AOTIModelContainerRunner`. The function signature is as follows.
```C++
using AOTIModelRunnerABC = std::shared_ptr<AOTIModelContainerRunner> (*)(
const std::string& model_so_path,
size_t num_models,
const std::string& device_str,
const std::string& bin_dir);
```
- `getAOTIModelRunnerRegistry` is used to get all the registered backends.
In terms of a new backend, it needs to define its `AOTIModelContainerRunner` class and then register a `AOTIModelRunnerABC` function to `aoti` to create its `AOTIModelContainerRunner`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131638
Approved by: https://github.com/desertfire, https://github.com/jansel
`return_and_correct_aliasing` is used by FunctionalTensor today to ensure that when we call view/inplace ops, the input and output `FunctionalTensors` share the same storage.
This was previously done with a dispatcher call to `aten.set_`. In this PR I swap it out with a util that just manually does the storage swap. Benefits:
(1) we know this is safe in the specific way it is used by FunctionalTensor: avoiding the extra assertions in `aten.set_` is necessary to avoid some unbacked symint errors
(2) this should improve compile times a bit
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132524
Approved by: https://github.com/ezyang
ghstack dependencies: #132243, #132337, #132322
2024-08-06 00:44:35 +00:00
1728 changed files with 71629 additions and 24875 deletions
If you are adding a new function or defaulted argument to native_functions.yaml, you cannot use it from pre-existing Python frontend code until our FC window passes (two weeks). Split your PR into two PRs, one which adds the new C++ functionality, and one that makes use of it from Python, and land them two weeks apart. See https://github.com/pytorch/pytorch/wiki/PyTorch's-Python-Frontend-Backward-and-Forward-Compatibility-Policy#forwards-compatibility-fc for more info.
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