Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[feat] Added four blocksparsity layouts #320

Merged
merged 8 commits into from
Jun 3, 2022
Merged

[feat] Added four blocksparsity layouts #320

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
26fbdd9
added four blocksparsity layouts
igormolybogFB Jun 1, 2022
9a5f696
pre-commit linting fixed
igormolybogFB Jun 1, 2022
7a9c9a3
Added copyright, fixed block
igormolybogFB Jun 1, 2022
1c54759
Add to changelog, extra tests
igormolybogFB Jun 2, 2022
b942065
Fix block_size for Dense SC
igormolybogFB Jun 2, 2022
c5e7415
Fixed a unittest
igormolybogFB Jun 2, 2022
2aed52c
More tests for Variable, BigBird
igormolybogFB Jun 2, 2022
878ea72
removed .DS_Store
igormolybogFB Jun 2, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions CHANGELOG.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Removed dupliacated biases in the FusedMLP layers [#317]

### Added
- Four blocksparsity layouts from DeepSpeed [#320]

## [0.0.11] - 2022-05-30
### Fixed
Expand Down
263 changes: 263 additions & 0 deletions tests/test_attention_patterns.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,13 @@
import torch

import xformers.components.attention.attention_patterns as AP
from xformers.components.attention.sparsity_config import (
BigBirdSparsityConfig,
BSLongformerSparsityConfig,
DenseSparsityConfig,
FixedSparsityConfig,
VariableSparsityConfig,
)


# baseline implementations
Expand Down Expand Up @@ -210,3 +217,259 @@ def test_alibi_pattern():
mask = AP.alibi_pattern(1e-3, (16, 128, 128))
# Minor, check that all the top left corners are True
assert torch.sum(mask[:, 0, 0]) == 16


def test_quick_layouts():

seq_size = 128
block_size = 16
num_heads = 2

# Fixed
assert torch.allclose(
AP.quick_fixed_layout(num_heads, block_size, seq_size),
torch.Tensor(
[
[
[1, 1, 1, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
],
[
[1, 1, 1, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1, 1],
],
]
).long(),
)

# BSLongformer
assert torch.allclose(
AP.quick_bslongformer_layout(num_heads, block_size, seq_size),
torch.Tensor(
[
[
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 1, 1, 1, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 0],
[1, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 1, 1],
],
[
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 1, 1, 1, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 0],
[1, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 1, 1],
],
]
).long(),
)

# Variable
assert torch.allclose(
AP.quick_variable_layout(num_heads, block_size, seq_size),
torch.Tensor(
[
[
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
],
[
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
],
]
).long(),
)

# BigBird (just the shape)
assert AP.quick_bigbird_layout(num_heads, block_size, seq_size).shape == torch.Size(
[num_heads, seq_size // block_size, seq_size // block_size]
)


def test_layout_to_pattern():
torch.allclose(
AP.layout_to_pattern(
layout=torch.Tensor([[[0, 1], [1, 0]], [[1, 0], [0, 1]]]), block_size=2
),
torch.Tensor(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 0.0],
],
[
[1.0, 1.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
],
]
),
)


def test_dense_sparsity_config():
sc = DenseSparsityConfig(num_heads=1, block_size=16)
with pytest.raises(expected_exception=ValueError):
sc.setup_layout(seq_len=17)
assert torch.allclose(
sc.make_layout(seq_len=32), torch.Tensor([[[1, 1], [1, 1]]]).long()
)


def test_big_bird_sparsity_config():
sc = BigBirdSparsityConfig(
num_heads=1,
block_size=16,
num_random_blocks=2,
num_sliding_window_blocks=1,
num_global_blocks=1,
)
with pytest.raises(expected_exception=ValueError):
sc.make_layout(seq_len=16)
sc = BigBirdSparsityConfig(
num_heads=1,
block_size=16,
num_random_blocks=1,
num_sliding_window_blocks=2,
num_global_blocks=1,
)
with pytest.raises(expected_exception=ValueError):
sc.make_layout(seq_len=16)
sc = BigBirdSparsityConfig(
num_heads=1,
block_size=16,
num_random_blocks=1,
num_sliding_window_blocks=1,
num_global_blocks=2,
)
with pytest.raises(expected_exception=ValueError):
sc.make_layout(seq_len=16)
with pytest.raises(expected_exception=NotImplementedError):
BigBirdSparsityConfig(num_heads=1, attention="directional")


def test_bslongformer_sparsity_config():
sc = BSLongformerSparsityConfig(num_heads=1, global_block_end_indices=[1])
assert torch.allclose(
sc.make_layout(128),
torch.Tensor(
[
[
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 1, 1, 1, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 0],
[1, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 1, 1],
]
]
).long(),
)
with pytest.raises(expected_exception=ValueError):
BSLongformerSparsityConfig(num_heads=1, global_block_end_indices=[])
with pytest.raises(expected_exception=ValueError):
BSLongformerSparsityConfig(num_heads=1, global_block_end_indices=[-1])


def test_fixed_sparsity_config():
# chech that the case end < num_blocks is correct
sc = FixedSparsityConfig(num_heads=1, horizontal_global_attention=True)
assert torch.allclose(
sc.make_layout(112),
torch.Tensor(
[
[
[1, 1, 1, 1, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 1],
[1, 1, 1, 1, 0, 0, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
]
]
).long(),
)
with pytest.raises(expected_exception=ValueError):
FixedSparsityConfig(num_heads=1, num_local_blocks=3, num_global_blocks=2)
with pytest.raises(expected_exception=NotImplementedError):
FixedSparsityConfig(num_heads=1, attention="directional")
with pytest.raises(expected_exception=ValueError):
FixedSparsityConfig(
num_heads=1, attention="unidirectional", horizontal_global_attention=True
)
with pytest.raises(expected_exception=ValueError):
FixedSparsityConfig(
num_heads=1,
num_different_global_patterns=2,
different_layout_per_head=False,
)
with pytest.raises(expected_exception=ValueError):
FixedSparsityConfig(
num_heads=1,
num_different_global_patterns=10,
num_local_blocks=4,
num_global_blocks=1,
)


def test_variable_sparsity_config():
sc = VariableSparsityConfig(num_heads=1, global_block_end_indices=[1])
assert torch.allclose(
sc.make_layout(128),
torch.Tensor(
[
[
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
[1, 0, 0, 0, 1, 1, 1, 1],
]
]
).long(),
)
with pytest.raises(expected_exception=ValueError):
VariableSparsityConfig(num_heads=1, global_block_end_indices=[])
with pytest.raises(expected_exception=ValueError):
VariableSparsityConfig(num_heads=1, global_block_end_indices=[-1])
4 changes: 2 additions & 2 deletions xformers/benchmarks/LRA/code/config_nystrom.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@
"hidden_layer_multiplier": 2
}
}

],
"extra_settings": {
"attention": {
Expand Down Expand Up @@ -199,7 +199,7 @@
"hidden_layer_multiplier": 2
}
}

],
"extra_settings": {
"attention": {
Expand Down
35 changes: 35 additions & 0 deletions xformers/components/attention/attention_patterns.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,13 @@
import numpy as np
import torch

from xformers.components.attention.sparsity_config import (
BigBirdSparsityConfig,
BSLongformerSparsityConfig,
FixedSparsityConfig,
VariableSparsityConfig,
)


# generic nd cases
def _generate_nd_grid(*sizes):
Expand Down Expand Up @@ -258,3 +265,31 @@ def get_slopes_power_of_2(n: int) -> List[float]:

# Now threshold arbitrarily, report the mask
return alibi < threshold


def quick_fixed_layout(num_heads: int, block_size: int, seq_len: int):
config = FixedSparsityConfig(num_heads=num_heads, block_size=block_size)
return config.make_layout(seq_len)


def quick_variable_layout(num_heads: int, block_size: int, seq_len: int):
config = VariableSparsityConfig(num_heads=num_heads, block_size=block_size)
return config.make_layout(seq_len)


def quick_bigbird_layout(num_heads: int, block_size: int, seq_len: int):
config = BigBirdSparsityConfig(num_heads=num_heads, block_size=block_size)
return config.make_layout(seq_len)


def quick_bslongformer_layout(num_heads: int, block_size: int, seq_len: int):
config = BSLongformerSparsityConfig(num_heads=num_heads, block_size=block_size)
return config.make_layout(seq_len)


def layout_to_pattern(layout: torch.Tensor, block_size: int):
r"""
create a pattern of shape [heads, seq, seq] out of a blocksparse
layout of shape [heads, seq/block_size, seq/block_size]
"""
return torch.kron(layout, torch.ones(block_size, block_size))
Loading