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[pre-commit.ci] pre-commit autoupdate (#5572)

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* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/PyCQA/autoflake: v2.2.1 → v2.3.1](https://github.com/PyCQA/autoflake/compare/v2.2.1...v2.3.1)
- [github.com/pycqa/isort: 5.12.0 → 5.13.2](https://github.com/pycqa/isort/compare/5.12.0...5.13.2)
- [github.com/psf/black-pre-commit-mirror: 23.9.1 → 24.4.2](https://github.com/psf/black-pre-commit-mirror/compare/23.9.1...24.4.2)
- [github.com/pre-commit/mirrors-clang-format: v13.0.1 → v18.1.7](https://github.com/pre-commit/mirrors-clang-format/compare/v13.0.1...v18.1.7)
- [github.com/pre-commit/pre-commit-hooks: v4.3.0 → v4.6.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.3.0...v4.6.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
pre-commit-ci[bot]
2024-07-01 17:16:41 +08:00
committed by GitHub
parent 936d0b0f7b
commit 7c2f79fa98
53 changed files with 157 additions and 100 deletions
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20
colossalai/auto_parallel/meta_profiler/meta_registry/conv.py
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@@ -107,20 +107,22 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
# NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
fwd_memory_cost = MemoryCost(
activation=compute_size_in_bytes([input_tensor, output_tensor]),
parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
if has_bias
else compute_size_in_bytes(weight_tensor),
parameter=(
compute_size_in_bytes([weight_tensor, bias_tensor]) if has_bias else compute_size_in_bytes(weight_tensor)
),
temp=0,
buffer=0,
)
bwd_memory_cost = MemoryCost(
activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
if has_bias
else compute_size_in_bytes([input_tensor, weight_tensor]),
parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
if has_bias
else compute_size_in_bytes(weight_tensor),
activation=(
compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
if has_bias
else compute_size_in_bytes([input_tensor, weight_tensor])
),
parameter=(
compute_size_in_bytes([weight_tensor, bias_tensor]) if has_bias else compute_size_in_bytes(weight_tensor)
),
temp=0,
buffer=0,
)