OK, so this PR used to be about reducing the number of constants we specialize on, but it turns out that unspecialization was ~essentially never used (because we still constant specialized way too aggressively) and I ended up having to fix a bunch of issues to actually get tests to pass. So this PR is now "make int unspecialization actually work". As part of this, I have to turn off unspecialization by default, as there are still latent bugs in inductor.
The general strategy is that an unspecialized int is represented as a SymInt. Representing it as a 0d tensor (which is what the code used to do) is untenable: (1) we often need unspecialized ints to participate in size computations, but we have no way of propagating sympy expressions through tensor compute, and (2) a lot of APIs work when passed SymInt, but not when passed a Tensor. However, I continue to represent Numpy scalars as Tensors, as they are rarely used for size computation and they have an explicit dtype, so they are more accurately modeled as 0d tensors.
* I folded in the changes from https://github.com/pytorch/pytorch/pull/95099 as I cannot represent unspecialized ints as SymInts without also turning on dynamic shapes. This also eliminates the necessity for test_unspec.py, as toggling specialization without dynamic shapes doesn't do anything. As dynamic shapes defaults to unspecializing, I just deleted this entirely; for the specialization case, I rely on regular static shape tests to catch it. (Hypothetically, we could also rerun all the tests with dynamic shapes, but WITH int/float specialization, but this seems... not that useful? I mean, I guess export wants it, but I'd kind of like our Source heuristic to improve enough that export doesn't have to toggle this either.)
* Only 0/1 integers get specialized by default now
* A hodgepodge of fixes. I'll comment on the PR about them.
Fixes https://github.com/pytorch/pytorch/issues/95469
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95621
Approved by: https://github.com/jansel, https://github.com/Chillee
Summary: When running the benchmark test with --accuracy, two eager runs
should return the same result. If not, we want to detect it early, but
comparing against fp64_output may hide the non-deterministism in eager.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95616
Approved by: https://github.com/ZainRizvi
I believe this fixes the AllenaiLongformerBase problem in periodic.
The longer version of the problem is here is we are currently optimistically converting all item() calls into unbacked SymInt/SymFloat, but sometimes this results in a downstream error due to a data-dependent guard. Fallbacks for this case are non-existent; this will just crash the model. This is bad. So we flag guard until we get working fallbacks.
What could these fallbacks look like? One idea I have is to optimistically make data-dependent calls unbacked, but then if it results in a crash, restart Dynamo analysis with the plan of graph breaking when the item() call immediately happened.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94987
Approved by: https://github.com/Skylion007, https://github.com/malfet
```
GuardOnDataDependentSymNode: It appears that you're trying to get a value out of symbolic int/float whose value is data-dependent (and thus we do not know the true value.) The expression we were trying to evaluate is Eq(i3, -1). Scroll up to see where each of these data-dependent accesses originally occurred.
While executing %as_strided : [#users=1] = call_method[target=as_strided](args = (%pad,), kwargs = {size: (12, %add, 768, 64), stride: (%getitem, %mul, %getitem_1, %getitem_2)})
Original traceback:
File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 928, in <graph break in _sliding_chunks_matmul_attn_probs_value>
chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94986
Approved by: https://github.com/albanD
Summary: It looks like setting torch.backends.cudnn.deterministic to
True is not enough for eliminating non-determinism when testing
benchmarks with --accuracy, so let's turn off cudnn completely.
With this change, mobilenet_v3_large does not show random failure on my
local environment. Also take this chance to clean up CI skip lists.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94363
Approved by: https://github.com/ezyang
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
The functorch setting still exists, but now it is no longer necessary:
we infer use of Python dispatcher by checking if the ambient
FakeTensorMode has a ShapeEnv or not. The setting still exists,
but it is for controlling direct AOTAutograd use now; for PT2,
it's sufficient to use torch._dynamo.config.dynamic_shapes.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94469
Approved by: https://github.com/Chillee, https://github.com/voznesenskym, https://github.com/jansel
Summary: It looks like setting torch.backends.cudnn.deterministic to
True is not enough for eliminating non-determinism when testing
benchmarks with --accuracy, so let's turn off cudnn completely.
With this change, mobilenet_v3_large does not show random failure on my
local environment. Also take this chance to clean up CI skip lists.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94363
Approved by: https://github.com/ezyang
Change the dynamo benchmark timeout from hard code to a parameter with default value 1200ms, cause the hard code 1200ms timeout led some single thread mode model crashed on CPU platform. With the parameter, users can specify the timeout freely.
Fixes#94281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94284
Approved by: https://github.com/malfet
graph break != graph count - 1. Suppose you have a nested
inline function call f1 to f2 to f3. A graph break in f3
results in six graphs: f1 before, f2 before, f3 before, f3 after,
f2 after, f1 after.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94143
Approved by: https://github.com/voznesenskym
These backends have been broken for some time. I tried to get them
running again, but as far as I can tell they are not maintained.
Installing torch_tensorrt downgrades PyTorch to 1.12. If I manually
bypass that downgrade, I get import errors from inside fx2trt. Fixes that
re-add these are welcome, but it might make sense to move these wrappers
to the torch_tensorrt repo once PyTorch 2.0 support is added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93822
Approved by: https://github.com/frank-wei
As @peterbell10 pointed out, it was giving incorrect results for `compression_ratio`
and `compression_latency` when you used `--diff-branch`.
This fixes this by running a separate subprocess for each branch to make sure you are not being affected by run for other branch.
Also added a couple of more significant figures
to numbers in summary table.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93989
Approved by: https://github.com/jansel
--diff_main renamed to --diff-branch BRANCH and now works again
Summary table splits results per branch.
csv output now has column with branch name when run in this mode
Added --progress flag so you can track how many models are going to be
run.
Example output:
```
$ python benchmarks/dynamo/torchbench.py --quiet --performance --backend inductor --float16 --batch-size-file $(realpath benchmarks/dynamo/torchbench_models_list.txt) --filter 'alexnet|vgg16' --progress --diff viable/strict
Running model 1/2
batch size: 1024
cuda eval alexnet dynamo_bench_diff_branch 1.251x p=0.00
cuda eval alexnet viable/strict 1.251x p=0.00
Running model 2/2
batch size: 128
cuda eval vgg16 dynamo_bench_diff_branch 1.344x p=0.00
cuda eval vgg16 viable/strict 1.342x p=0.00
Summary for tag=dynamo_bench_diff_branch:
speedup gmean=1.30x mean=1.30x
abs_latency gmean=24.09x mean=25.26x
compilation_latency mean=2.0 seconds
compression_ratio mean=0.9x
Summary for tag=viable/strict:
speedup gmean=1.30x mean=1.30x
abs_latency gmean=24.11x mean=25.29x
compilation_latency mean=0.5 seconds
compression_ratio mean=1.0x
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92713
Approved by: https://github.com/jansel
Since the CI exclusions are hard-coded in our script, we might as well require them to match exactly. This solved some head scratching where I was like, "this model is not obviously excluded, why is it not showing up in CI."
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92761
Approved by: https://github.com/jansel
We've already shown some promising perf result by integrating dynamo with torchxla for inference. To provide consistent UX for training and for inference, in this PR we try to enable training for dynamo/torchxla.
Training is trickier than inference and we may not expect much perf gains since
1. in training case, torchxla only generate a single combined graph for fwd/bwd/optimizer while in `torchxla_trace_once` bridge we added in dynamo, due to how AOT_Autograd works, we will generate 3 graphs: one for forward, one for backward and one for the optimizer. XLA favors larger graph to do more optimizations.
2. in training case, tracing overhead can be overlapped with computation. Tracing overhead is not as a big deal for training as for inference. After all training cares more about throughput while inference cares more about latency.
3. in training case, people can increase batch size to 'mitigate' the tracing overhead. Increase batch size does not change tracing overhead, thus it shows like the tracing overhead 'per example' reduces.
But we still want to add training support to dynamo/torchxla to make the work complete.
We added '--iterations-per-run' argument to control how may iterations we do per measure/device sync. This is to understand the impact of item 2 above.
Results:
With '--iterations-per-run' equals to 1, here are the perf numbers:
```
+-------------------------+--------------------+-------------------------+
| Model | XLA (trace once) | XLA (trace everytime) |
+=========================+====================+=========================+
| resnet18 | 0.91 | 0.959 |
+-------------------------+--------------------+-------------------------+
| resnet50 | 0.917 | 0.932 |
+-------------------------+--------------------+-------------------------+
| resnext50_32x4d | 0.912 | 0.905 |
+-------------------------+--------------------+-------------------------+
| alexnet | 1.038 | 0.974 |
+-------------------------+--------------------+-------------------------+
| mobilenet_v2 | 0.881 | 0.835 |
+-------------------------+--------------------+-------------------------+
| mnasnet1_0 | 0.903 | 0.931 |
+-------------------------+--------------------+-------------------------+
| vgg16 | 0.914 | 0.967 |
+-------------------------+--------------------+-------------------------+
| BERT_pytorch | 1.359 | 0.84 |
+-------------------------+--------------------+-------------------------+
| timm_vision_transformer | 1.288 | 0.893 |
+-------------------------+--------------------+-------------------------+
| geomean | 1.0006 | 0.913794 |
+-------------------------+--------------------+-------------------------+
```
Overall it looks like graph break indeed cause perf loss. But for BERT_pytorch and timm_vision_transformer we still see perf gain. We need do more experiments with larger '--iterations-per-run'
NOTE:
In torchbench.py I added the following code to do a few workaround:
```
from myscripts import workaround # TODO will remove this line before landing
```
Here are the content of workaround.py:
```
import torch
from torch import nn
import os
# override max_pool2d with avg_pool2d
if os.environ.get("REPLACE_MAXPOOL", "0") == "1":
torch.nn.MaxPool2d = torch.nn.AvgPool2d
```
It work around a few issues we found
1. MaxPool2d does not work for training in dynamo/torchxla: https://github.com/pytorch/torchdynamo/issues/1837 . WIP fix from Brian in https://github.com/pytorch/pytorch/pull/90226 , https://github.com/pytorch/xla/pull/4276/files (WIP)
2. recent change ( this PR https://github.com/pytorch/pytorch/pull/88697 ) in op decomposition cause batch_norm ops to fallback in torchxla. Fix from jack in https://github.com/pytorch/xla/pull/4282#event-7969608134 . (confirmed the fix after adding Deduper to handle duplicated return from fx graph generated by AOTAutograd)
3. we have issue to handle dropout because of random seed out of sync issue. Here is the fix: https://github.com/pytorch/xla/pull/4293 (confirmed the fix)
Example command:
```
REPLACE_MAXPOOL=1 USE_FAKE_TENSOR=0 GPU_NUM_DEVICES=1 python benchmarks/dynamo/torchbench.py --randomize-input --performance --trace-on-xla --training --backend=aot_torchxla_trace_once --only vgg16
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88449
Approved by: https://github.com/wconstab, https://github.com/qihqi, https://github.com/malfet
Summary:
1) Setting torch.backends.cudnn.deterministic to True helps to
eliminate the eager_variance failures seen on CI
2) Skip Triton failure instead of retry
3) Some minor script cleanup is also included in this PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91283
Approved by: https://github.com/anijain2305
This will be the last disruptive functorch internals change.
Why are we moving these files?
- As a part of rationalizing functorch we are moving the code in
functorch/_src to torch/_functorch
- This is so that we can offer the functorch APIs as native PyTorch APIs
(coming soon) and resolve some internal build issues.
Why are we moving all of these files at once?
- It's better to break developers all at once rather than many times
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90091
Approved by: https://github.com/anijain2305, https://github.com/ezyang
- Add graph index to the profile information of the Inductor kernel for better debugability.
The generated code for different graphs could produce kernels with the same name. The side effect is that it is hard to identify the portion of E2E performance for these kernels because the profiler will aggregate the performance with the same kernel name regardless of different graphs. Hence, this PR added the graph index to the profile information to address this limitation.
- Label arbitrary code ranges for `eager` and `opt` modes for better debugability
The profile information of dynamo benchmarks mixes the eager mode and opt mode. It is hard to separate the range for different modes. This PR added eager and opt marks to the profile information to address this limitation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90008
Approved by: https://github.com/jgong5, https://github.com/jansel
Moving to train mode for TIMM models and also raising batch size for accuracy testing.
Raising batch size seems to remove a lot of noise/instability coming from batch_norm decomposition.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89780
Approved by: https://github.com/ngimel
This will be the last disruptive functorch internals change.
Why are we moving these files?
- As a part of rationalizing functorch we are moving the code in
functorch/_src to torch/_functorch
- This is so that we can offer the functorch APIs as native PyTorch APIs
(coming soon) and resolve some internal build issues.
Why are we moving all of these files at once?
- It's better to break developers all at once rather than many times
Test Plan:
- wait for tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88756
Approved by: https://github.com/ezyang
Disabling Gradscaler because
1) Benchmark setup runs 2 iterations of fwd-bwd. So, not useful.
2) Current setup shares grad_scaler for eager and dynamo model,
which is bad as Gradscaler has state and can adjust the scaling
factor between eager and dynamo run, making accuracy check
harder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89741
Approved by: https://github.com/ngimel
In #87741 we added the inference support for dynamo/torchxla integration. Later on in #88449 we attempt to add the training support. That attempt is not smooth because
- we try 2 things together
1. let dynamo trace the model on xla rather than eager
2. enable training
- It turns out neither of these two tasks are trivial enough.
Furthermore, item 2 (enable training) depends on item 1 (tracing on xla). We enable training via AOTAutograd. AOTAutograd lift all model parameters/buffers as graph inputs. Without item 1 being done, we would need copy all graph inputs (including model parameters/buffers) from eager device to xla devices. That hurts performance a lot. Have a cache to map eager parameter to XLA parameter does not solve the problem since the update on either will not sync automatically to the other. They will easily go out of sync.
This PR let dynamo trace the model on XLA rather than eager. This is a preparation step to enabling training.
Also, tracing on XLA makes the data movement more efficient. We see 1.5x geomean speedup compared to previous 1.38x.
```
+-------------------------+--------------------+-------------------------+
| Model | XLA (trace once) | XLA (trace everytime) |
+=========================+====================+=========================+
| resnet18 | 1.38 | 1.008 |
+-------------------------+--------------------+-------------------------+
| resnet50 | 1.227 | 0.998 |
+-------------------------+--------------------+-------------------------+
| resnext50_32x4d | 1.544 | 1.008 |
+-------------------------+--------------------+-------------------------+
| alexnet | 1.085 | 1.045 |
+-------------------------+--------------------+-------------------------+
| mobilenet_v2 | 2.028 | 1.013 |
+-------------------------+--------------------+-------------------------+
| mnasnet1_0 | 1.516 | 0.995 |
+-------------------------+--------------------+-------------------------+
| squeezenet1_1 | 0.868 | 1.01 |
+-------------------------+--------------------+-------------------------+
| vgg16 | 1.099 | 1.008 |
+-------------------------+--------------------+-------------------------+
| BERT_pytorch | 3.26 | 1.027 |
+-------------------------+--------------------+-------------------------+
| timm_vision_transformer | 2.182 | 1.015 |
+-------------------------+--------------------+-------------------------+
| geomean | 1.50389 | 1.01261 |
+-------------------------+--------------------+-------------------------+
```
Example command
```
GPU_NUM_DEVICES=1 python benchmarks/dynamo/torchbench.py --randomize-input --performance --trace-on-xla --only resnet18 --backend=torchxla_trace_once
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88904
Approved by: https://github.com/wconstab, https://github.com/JackCaoG, https://github.com/jansel
Summary:
This Diff ports the torchbench.py script from torchdynamo to torchbench to support the development of internal models.
Currently, only works with the `--only` option, and can only test one model at a time.
Note that the noisy logs are from upstream model code, not the benchmark code.
In the internal environment, `torch._dynamo.config.base_dir` is not writable, so we add an option to specify the output directory.
Test Plan:
```
$ buck2 run mode/opt //caffe2/benchmarks/dynamo:torchbench -- --performance --only ads_dhen_5x --part over --output-directory /tmp/tb-test/
cuda eval ads_dhen_5x
1/ 1 +0 frames 2s 1 graphs 1 graph calls 412/ 411 = 100% ops 100% time
```
```
$ buck2 run mode/opt //caffe2/benchmarks/dynamo:torchbench -- --performance --only cmf_10x --part over --output-directory /tmp/tb-test/
cuda eval cmf_10x
1/ 1 +0 frames 1s 1 graphs 1 graph calls 306/ 305 = 100% ops 100% time
```
Reviewed By: jansel
Differential Revision: D41294311
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89239
Approved by: https://github.com/jansel
Sometimes it's really convenient to run simple models thru the torchbench.py script rather than those from pytorch/benchmark. This PR add the ability to run any model from a specified path by overloading the --only argument.
This PR is split out from #88904
Here is the usage:
Specify the path and class name of the model in format like:
--only=path:<MODEL_FILE_PATH>,class:<CLASS_NAME>
Due to the fact that dynamo changes current working directory,
the path should be an absolute path.
The class should have a method get_example_inputs to return the inputs
for the model. An example looks like
```
class LinearModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(10, 10)
def forward(self, x):
return self.linear(x)
def get_example_inputs(self):
return (torch.randn(2, 10),)
```
Test command:
```
# python benchmarks/dynamo/torchbench.py --performance --only=path:/pytorch/myscripts/model_collection.py,class:LinearModel --backend=eager
WARNING:common:torch.cuda.is_available() == False, using CPU
cpu eval LinearModel 0.824x p=0.00
```
Content of model_collection.py
```
from torch import nn
import torch
class LinearModel(nn.Module):
"""
AotAutogradStrategy.compile_fn ignore graph with at most 1 call nodes.
Make sure this model calls 2 linear layers to avoid being skipped.
"""
def __init__(self, nlayer=2):
super().__init__()
layers = []
for _ in range(nlayer):
layers.append(nn.Linear(10, 10))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
def get_example_inputs(self):
return (torch.randn(2, 10),)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89028
Approved by: https://github.com/jansel
Add absolute latency to dashboard, as requested by https://github.com/pytorch/torchdynamo/issues/1833#issuecomment-1302742914
Tested by setting `run.sh` to
```
# Setup the output directory
rm -rf ../test-dynamo-runner-logs-7/
mkdir ../test-dynamo-runner-logs-7/
# Commands for torchbench for device=cuda, dtype=float32 for training and for performance testing
python benchmarks/dynamo/torchbench.py --performance --float32 -dcuda --output=../test-dynamo-runner-logs-7//inductor_torchbench_float32_training_cuda_performance.csv --training --inductor --no-skip --dashboard --only mobilenet_v2 --cold_start_latency
# Commands for torchbench for device=cuda, dtype=float32 for training and for accuracy testing
python benchmarks/dynamo/torchbench.py --accuracy --float32 -dcuda --output=../test-dynamo-runner-logs-7//inductor_torchbench_float32_training_cuda_accuracy.csv --training --inductor --no-skip --dashboard --only mobilenet_v2
```
and running `python benchmarks/dynamo/runner.py --output-dir ../test-dynamo-runner-logs-7/ --dashboard-archive-path /data/home/williamwen/dynamo-runner-logs-copy --training --run --compilers inductor --flag-compilers inductor --suites torchbench --update-dashboard` (need to comment out the `generate_commands` line and change the github issue ID from 681 to something else).
Sample comment: https://github.com/pytorch/torchdynamo/issues/1831#issuecomment-1309645562
NOTE: this change breaks processing old logs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88790
Approved by: https://github.com/anijain2305
- DLRM requires special configuration of embedding layers which are sparse
and not compatible with DDP.
- I could mark the embedding params as ignored in DDP
to make the benchmark pass, but this isn't a representative benchmark.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88712
Approved by: https://github.com/ezyang
- find_unused_parameters adds a slight overhead, but is required
in cases where users do not manually specify parameters to ignore
which will not receive grads. In some models, some parameters
do not receive grads, and this causes DDP to throw an exception
as it waits for a grad for each parameter
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88645
Approved by: https://github.com/soumith
- does not intend to support multi-process, as that is more complex
and we have torchbench scripts for that
- currently only works in accuracy mode as this was the main goal,
but could be extended for measuring single-gpu perf impact of
graph breaks
Run with
`python benchmarks/dynamo/torchbench.py --inductor --training --accuracy --only hf_Bert --ddp`
Example output
```
cuda train hf_Bert
[2022-11-04 18:52:08,304] torch._inductor.compile_fx: [WARNING] skipping cudagraphs due to complex input striding
PASS
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88511
Approved by: https://github.com/davidberard98, https://github.com/aazzolini
# Motivation
- torchdynamo and torchxla uses different strategies to be a sound graph capture technique. The former relies on guards; the latter relies on retracing
- guard system is quite low overhead but torchxla tracing overhead is quite high
The main idea is to leverage guard system in torchdynamo to avoid retracing in torchxla so that
- we can integration torchdynamo with XLA
- we reduce or even completely avoid tracing overhead of torchxla
# Technique details
## XLA baseline
We found that different frameworks do not generate numerically identical results for the SAME model with the SAME input. By default, torchdynamo uses eager as baseline so the model will run with PyTorch. It would be tricky to compare a model running on XLA with this baseline: it's hard to check correctness. To make the comparison easier, we add a flag `--use-xla-baseline`. When it's enabled, the baseline will be run on XLA.
## New dynamo backends added
We add 2 new dynamo backends torchxla_trivial and trochxla_trace_once to control the optimization targets.
torchxla_trivial simply moves inputs/model parameters to XLA and run the model on XLA. There is tracing overhead for each run. We should expect that result to be mostly neutral compared to the XLA baseline.
torchxla_trace_once only traces once during AOT compiling time. Here are the steps:
1. dynamo capture guards and the subgraph
2. torchxla_trace_once backend trace the graph with torchxla, lowering the graph and record a hash of the graph for later lookup
3. at inference time, the hash is used directly to lookup the optimized graph and run it.
# Limitations
We can not handle LTC/torchxla fall back right now. If a op misses LTC kernel, we raise and exception and that will results in dynamo fallback (or try another compiler). People have brainstormed the idea of graph breaking and stitching the subgraphs together. But maybe it's easier to add those missing LTC kernels for those models.
# Results
The models we tested are those not causing LTC fallback. We run the tests on **GPU**. We see **1.38x** geomean speedup for trochxla_trace_once and torchxla_trivial is mostly neutral as expected.
```
| Model | XLA (trace once) | XLA (trace everytime) |
+=========================+====================+=========================+
| resnet18 | 1.346 | 1.045 |
+-------------------------+--------------------+-------------------------+
| resnet50 | 1.153 | 1.007 |
+-------------------------+--------------------+-------------------------+
| resnext50_32x4d | 1.381 | 1.039 |
+-------------------------+--------------------+-------------------------+
| alexnet | 1.045 | 1.018 |
+-------------------------+--------------------+-------------------------+
| mobilenet_v2 | 1.562 | 1.021 |
+-------------------------+--------------------+-------------------------+
| mnasnet1_0 | 1.303 | 1.069 |
+-------------------------+--------------------+-------------------------+
| squeezenet1_1 | 1.278 | 1.025 |
+-------------------------+--------------------+-------------------------+
| vgg16 | 1.076 | 1.008 |
+-------------------------+--------------------+-------------------------+
| BERT_pytorch | 2.224 | 0.978 |
+-------------------------+--------------------+-------------------------+
| timm_vision_transformer | 1.81 | 1.025 |
+-------------------------+--------------------+-------------------------+
| geomean | 1.38101 | 1.02324 |
+-------------------------+--------------------+-------------------------+
```
The speedup is similar to what we see from previous work for LTC's TorchScript backend (we see 1.40 geomean speedup there):
https://docs.google.com/presentation/d/1G09X8v41u_cLKLtSdf7v6R8G19-iZTPcW_VAdOnvYBI/edit#slide=id.g11bf989cb6b_1_5
# Next steps
- Use AOT autograd to enable training
- Share results on XLA devices
- Do more extensive tests on torchbench models
Example command
```
GPU_NUM_DEVICES=1 python benchmarks/dynamo/torchbench.py --randomize-input --performance --use-xla-baseline --only resnet18 --backend=torchxla_trace_once
```
Thanks @JackCaoG from torchxla team to help debugging various perf issues and merging the torchxla PR! That's super critical for us to get the results above. torchxla side PR: https://github.com/pytorch/xla/pull/4119
topic: not user facing
cc @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @chunyuan-w @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @jansel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87741
Approved by: https://github.com/wconstab
Parallel compilation warms the Threadpool when we call `torch._dynamo.optimize()`. In current benchmarks, we were setting up the TRITON_CACHE_DIR much later. Because of this parallel compilation artifacts were not used and compilation latency improvements were not visible in dashboard. This PR just prepones the setup of TRITON_CACHE_DIR.
cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @EikanWang @jgong5 @Guobing-Chen @chunyuan-w @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87913
Approved by: https://github.com/wconstab
We witnessed slow compilation times last week. Earlier, I thought it was due to parallel compilation. But, after git bisect, I found the source of extra time to be my PR - https://github.com/pytorch/pytorch/pull/87049
For 1x1 kernel, the current striding check incorrectly declares channels-first 1x1 convs to channels last. I am not sure why it caused so much compilation time jump. Or why it did not fail? There was no change in performance speedup. cc @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang @penguinwu to identify what could be source of this compilation time increase, so that we can manually check that part of the stack.
With this `res2next50` compilation time went back to 96 seconds (which was raised to 900 seconds with my earlier PR) for single thread. And parallel-compilation brings it down to ~30 seconds.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87588
Approved by: https://github.com/soumith, https://github.com/jansel, https://github.com/ngimel
- this `--cold_start` experiment didn't end up being used
- there is a new `--cold_start_latency` flag that is used
- this experiment was only hooked up for nvfuser anyway
cc @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87470
Approved by: https://github.com/anijain2305
I noticed that a lot of bugs are being suppressed by torchdynamo's default
error suppression, and worse yet, there's no way to unsuppress them. After
discussion with voz and soumith, we decided that we will unify error suppression
into a single option (suppress_errors) and default suppression to False.
If your model used to work and no longer works, try TORCHDYNAMO_SUPPRESS_ERRORS=1
to bring back the old suppression behavior.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
cc @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87440
Approved by: https://github.com/voznesenskym, https://github.com/albanD
Inductor internally models any `size=1` dimension as having `stride=0` to simplify indexing formulas (sympy will remove these terms from the expression).
This caused a bug in our generate stride assert in detectron2_maskrcnn_r_50_fpn, where we asserted the wrong stride of a size==1 dimension.
This fixes that bug, and moves size/stride assert logic to C++ which should be a small perf gain.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87028
Approved by: https://github.com/anijain2305
