Add packed tensor format support for flex/sdpa/eager through the mask! (#39194)
* Add the necesary logic to mask_utils * add it everywhere * Update masking_utils.py * style * Update masking_utils.py * Update modeling_mimi.py * Update masking_utils.py * add support for more than batch size 1 * Update masking_utils.py * add test * style * Update test_masking_utils.py * Update masking_utils.py * add require_token * fix tests * fix
This commit is contained in:
Cyril Vallez
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tests/utils/test_masking_utils.py
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132
tests/utils/test_masking_utils.py
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# Copyright 2025 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers.testing_utils import is_torch_available, require_torch
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if is_torch_available():
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import torch
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from torch.nn.attention.flex_attention import create_block_mask
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from transformers import LlamaConfig
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from transformers.masking_utils import create_causal_mask
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# fmt: off
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EXPECTED_PACKED_MASK = torch.tensor([[[
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[ True, False, False, False, False, False, False, False, False, False],
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[ True, True, False, False, False, False, False, False, False, False],
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[ True, True, True, False, False, False, False, False, False, False],
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[ True, True, True, True, False, False, False, False, False, False],
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[False, False, False, False, True, False, False, False, False, False],
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[False, False, False, False, True, True, False, False, False, False],
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[False, False, False, False, False, False, True, False, False, False],
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[False, False, False, False, False, False, True, True, False, False],
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[False, False, False, False, False, False, True, True, True, False],
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[False, False, False, False, False, False, True, True, True, True]]],
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[[[ True, False, False, False, False, False, False, False, False, False],
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[ True, True, False, False, False, False, False, False, False, False],
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[ True, True, True, False, False, False, False, False, False, False],
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[ True, True, True, True, False, False, False, False, False, False],
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[ True, True, True, True, True, False, False, False, False, False],
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[ True, True, True, True, True, True, False, False, False, False],
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[False, False, False, False, False, False, True, False, False, False],
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[False, False, False, False, False, False, True, True, False, False],
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[False, False, False, False, False, False, True, True, True, False],
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[False, False, False, False, False, False, True, True, True, True]
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]]], dtype=torch.bool)
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# fmt: on
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@require_torch
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class MaskTest(unittest.TestCase):
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def test_packed_sequence_mask_sdpa(self):
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config = LlamaConfig()
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config._attn_implementation = "sdpa"
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batch_size = 2
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sequence_length = 10
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cache_position = torch.arange(sequence_length)
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# First batch has 3 packed sequences of 4, 2 and 4 tokens respectively, second has 2 of 6 and 4 tokens
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position_ids = torch.tensor([[0, 1, 2, 3, 0, 1, 0, 1, 2, 3], [0, 1, 2, 3, 4, 5, 0, 1, 2, 3]])
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causal_mask = create_causal_mask(
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config=config,
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# we only need batch size, seq_length and dtype here - we don't care about the values of the embeddings
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input_embeds=torch.empty((batch_size, sequence_length), dtype=torch.float16),
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attention_mask=None,
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cache_position=cache_position,
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past_key_values=None,
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position_ids=position_ids,
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)
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self.assertTrue((causal_mask == EXPECTED_PACKED_MASK).all())
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def test_packed_sequence_mask_eager(self):
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config = LlamaConfig()
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config._attn_implementation = "eager"
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batch_size = 2
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sequence_length = 10
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cache_position = torch.arange(sequence_length)
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# First batch has 3 packed sequences of 4, 2 and 4 tokens respectively, second has 2 of 6 and 4 tokens
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position_ids = torch.tensor([[0, 1, 2, 3, 0, 1, 0, 1, 2, 3], [0, 1, 2, 3, 4, 5, 0, 1, 2, 3]])
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causal_mask = create_causal_mask(
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config=config,
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# we only need batch size, seq_length and dtype here - we don't care about the values of the embeddings
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input_embeds=torch.empty((batch_size, sequence_length), dtype=torch.float16),
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attention_mask=None,
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cache_position=cache_position,
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past_key_values=None,
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position_ids=position_ids,
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)
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min_dtype = torch.finfo(torch.float16).min
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self.assertTrue((causal_mask == torch.where(EXPECTED_PACKED_MASK, 0.0, min_dtype)).all())
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def test_packed_sequence_mask_flex_attention(self):
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config = LlamaConfig()
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config._attn_implementation = "flex_attention"
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batch_size = 2
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sequence_length = 10
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cache_position = torch.arange(sequence_length)
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# First batch has 3 packed sequences of 4, 2 and 4 tokens respectively, second has 2 of 6 and 4 tokens
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position_ids = torch.tensor([[0, 1, 2, 3, 0, 1, 0, 1, 2, 3], [0, 1, 2, 3, 4, 5, 0, 1, 2, 3]])
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causal_mask = create_causal_mask(
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config=config,
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# we only need batch size, seq_length and dtype here - we don't care about the values of the embeddings
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input_embeds=torch.empty((batch_size, sequence_length), dtype=torch.float16),
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attention_mask=None,
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cache_position=cache_position,
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past_key_values=None,
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position_ids=position_ids,
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)
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def dummy_mask_mod(b, h, q, kv):
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return EXPECTED_PACKED_MASK[b, h, q, kv]
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EXPECTED_BLOCK_MASK = create_block_mask(dummy_mask_mod, 2, None, 10, 10, device="cpu")
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# We compatre the str representations, as the BlockMask objects themselves cannot easily be compared
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self.assertEqual(causal_mask.to_string(), EXPECTED_BLOCK_MASK.to_string())
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