[Docs][MPS] Add mps environment variable table #129008
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This was referenced Jun 18, 2024
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/129008
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ghstack-source-id: 6905ac57e91d92d3c30d9be625cdfa7e403bdcf4 Pull Request resolved: #129008
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ghstack-source-id: 98f71f85ac9b9ea53c44cac317d22b2245b7db90 Pull Request resolved: #129008
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ghstack-source-id: e7fa7fbfd215d9727b61d530241a16ecd564cb49 Pull Request resolved: #129008
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ghstack-source-id: b7f196ebe495fd814d817a84b50cea35f48c0365 Pull Request resolved: #129008
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ghstack-source-id: 006b50c0d43befe8ec719905d6dba73b7f80b3e3 Pull Request resolved: #129008
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Allow users to decide whether they want to have fast math enabled via env var Pull Request resolved: #129007 Approved by: https://github.com/malfet ghstack dependencies: #129006, #129008
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This PR generalizes the multi_tensor_apply function for other fused optimizers Pull Request resolved: #129105 Approved by: https://github.com/malfet ghstack dependencies: #129006, #129008, #129007
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```
[-------------------------------------- Fused SGD --------------------------------------]
| Fused: True | Fused: False
1 threads: ------------------------------------------------------------------------------
numel: 1024, num_tensors: 100, momentum: True | 2 | 15
numel: 1024, num_tensors: 100, momentum: False | 2 | 5
numel: 65536, num_tensors: 100, momentum: True | 3 | 16
numel: 65536, num_tensors: 100, momentum: False | 2 | 5
numel: 1048576, num_tensors: 100, momentum: True | 11 | 16
numel: 1048576, num_tensors: 100, momentum: False | 8 | 6
numel: 1024, num_tensors: 500, momentum: True | 29 | 70
numel: 1024, num_tensors: 500, momentum: False | 20 | 24
numel: 65536, num_tensors: 500, momentum: True | 33 | 76
numel: 65536, num_tensors: 500, momentum: False | 22 | 26
numel: 1048576, num_tensors: 500, momentum: True | 70 | 80
numel: 1048576, num_tensors: 500, momentum: False | 43 | 40
numel: 1024, num_tensors: 1000, momentum: True | 108 | 139
numel: 1024, num_tensors: 1000, momentum: False | 72 | 48
numel: 65536, num_tensors: 1000, momentum: True | 116 | 150
numel: 65536, num_tensors: 1000, momentum: False | 77 | 52
numel: 1048576, num_tensors: 1000, momentum: True | 190 | 170
numel: 1048576, num_tensors: 1000, momentum: False | 120 | 50
```
```python
def profile_fused_sgd():
from torch.optim.sgd import sgd
import torch.utils.benchmark as benchmark
import itertools
def profile(fn, params, grads, momentum_buffer_list, fused):
fn(
params,
grads,
momentum_buffer_list,
momentum=True if len(momentum_buffer_list) > 0 else False,
dampening=0.0,
nesterov=False,
foreach=False,
fused=fused,
lr=1e-3,
weight_decay=.0,
maximize=False,
grad_scale=None,
found_inf=None,
)
torch.mps.synchronize()
device = "mps"
results = []
for num_tensors, numel, momentum in itertools.product([100, 500, 1000], [1024, 65536, 1048576], [True, False]):
sublabel = f"numel: {numel}, num_tensors: {num_tensors}, momentum: {momentum}"
print(sublabel)
params, grads = [[torch.arange(numel, dtype=torch.float32, device=device) + (numel * i) for i in range(num_tensors)] for _ in range(2)]
momentum_buffer_list = [torch.arange(numel, dtype=torch.float32, device=device) + (numel * i) for i in range(num_tensors)] if momentum else []
fn = sgd
for fused in [True, False]:
t = benchmark.Timer(
stmt='profile(fn, params, grads, momentum_buffer_list, fused)',
label='Fused SGD',
sub_label=sublabel,
globals=locals(),
description= f"Fused: {fused}",
).blocked_autorange(min_run_time=5)
results.append(t)
compare = benchmark.Compare(results)
compare.trim_significant_figures()
compare.colorize(rowwise=True)
compare.print()
```
Pull Request resolved: #129350
Approved by: https://github.com/janeyx99
ghstack dependencies: #129006, #129008, #129007, #129105
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