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
