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# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import shutil
import pytest
import torch
from compressed_tensors import BitmaskCompressor, BitmaskConfig, BitmaskTensor
from safetensors.torch import save_file
@pytest.mark.parametrize(
"shape,sparsity,dtype",
[
[(512, 1024), 0.5, torch.float32],
[(830, 545), 0.8, torch.float32],
[(342, 512), 0.3, torch.bfloat16],
[(256, 700), 0.9, torch.float16],
],
)
def test_bitmask_sizes(shape, sparsity, dtype):
test_tensor = torch.rand(shape, dtype=dtype)
mask = (test_tensor.abs() < (1 - sparsity)).int()
test_tensor *= mask
dense_state_dict = {"dummy.weight": test_tensor}
sparsity_config = BitmaskConfig()
compressor = BitmaskCompressor(config=sparsity_config)
sparse_state_dict = compressor.compress(dense_state_dict)
# each dense tensor has 4 parameters for compression
assert len(dense_state_dict) * 4 == len(sparse_state_dict)
# bitmask should be 1 bit per dense element, rounded up to nearest int8
sparse_shape = sparse_state_dict["dummy.shape"]
assert torch.all(torch.eq(sparse_shape, torch.tensor(shape)))
bitmask_shape = sparse_state_dict["dummy.bitmask"].shape
assert bitmask_shape[0] == sparse_shape[0]
assert bitmask_shape[1] == int(math.ceil(sparse_shape[1] / 8.0))
# one value for each non-zero weight
values_shape = sparse_state_dict["dummy.compressed"].shape
assert values_shape[0] == torch.sum(test_tensor != 0)
row_offsets_shape = sparse_state_dict["dummy.row_offsets"].shape
assert row_offsets_shape[0] == test_tensor.shape[0]
@pytest.mark.parametrize(
"shape,sparsity,dtype",
[
[(256, 512), 0.5, torch.float32],
[(128, 280), 0.8, torch.float32],
[(1024, 256), 0.3, torch.bfloat16],
[(511, 350), 0.7, torch.float16],
],
)
def test_match(shape, sparsity, dtype):
test_tensor1 = torch.rand(shape, dtype=dtype)
mask = (test_tensor1.abs() < (1 - sparsity)).int()
test_tensor1 *= mask
test_tensor2 = torch.rand(shape, dtype=dtype)
mask = (test_tensor2.abs() < (1 - sparsity)).int()
test_tensor2 *= mask
dense_state_dict = {"dummy.weight": test_tensor1, "dummy2.weight": test_tensor2}
for key in dense_state_dict.keys():
dense_tensor = dense_state_dict[key]
sparse_tensor = BitmaskTensor.from_dense(dense_tensor)
decompressed = sparse_tensor.decompress()
assert decompressed.dtype == dense_tensor.dtype == dtype
assert torch.equal(dense_tensor, decompressed)
@pytest.mark.parametrize(
"sparsity,dtype",
[
[0.5, torch.float32],
[0.8, torch.float32],
[0.3, torch.bfloat16],
[0.7, torch.float16],
],
)
def test_reload_match(sparsity, dtype, tmp_path):
test_tensor1 = torch.rand((256, 512), dtype=dtype)
mask = (test_tensor1.abs() < (1 - sparsity)).int()
test_tensor1 *= mask
test_tensor2 = torch.rand((360, 720), dtype=dtype)
mask = (test_tensor2.abs() < (1 - sparsity)).int()
test_tensor2 *= mask
dense_state_dict = {"dummy.weight": test_tensor1, "dummy2.weight": test_tensor2}
sparsity_config = BitmaskConfig()
compressor = BitmaskCompressor(config=sparsity_config)
sparse_state_dict = compressor.compress(dense_state_dict)
save_file(sparse_state_dict, tmp_path / "model.safetensors")
reconstructed_dense = compressor.decompress(tmp_path)
for key, reconstructed_tensor in reconstructed_dense:
dense_tensor = dense_state_dict[key]
assert dense_tensor.dtype == reconstructed_tensor.dtype == dtype
assert torch.equal(dense_tensor, reconstructed_tensor)
shutil.rmtree(tmp_path)
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