Gemma sdpa squash

tests/torchtune/models/gemma2/test_sliding_attention_mask.py

@@ -0,0 +1,126 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pytest
+import torch
+
+from torchtune.models.gemma2._attention_mask import get_sliding_attention_mask
+
+
+class TestGetSlidingAttentionMask:
+    @pytest.fixture
+    def basic_params(self):
+        return {"bsz": 2, "seq_len": 4, "sliding_window_size": 2, "device": None}
+
+    def test_get_sliding_attention_mask(self, basic_params):
+        """Test that when mask is None, a causal mask is created and sliding window is applied."""
+        bsz = 2
+        seq_len = 4
+        sliding_window_size = 2
+        mask = get_sliding_attention_mask(
+            mask=None,
+            sliding_window_size=basic_params["sliding_window_size"],
+            bsz=basic_params["bsz"],
+            seq_len=basic_params["seq_len"],
+            device=basic_params["device"],
+        )
+
+        assert mask.shape == (
+            basic_params["bsz"],
+            basic_params["seq_len"],
+            basic_params["seq_len"],
+        )
+        assert mask.dtype == torch.bool
+
+        # Check that the mask has the expected sliding window pattern
+        # True positions can be attended to, False positions are masked
+        expected_pattern = torch.tensor(
+            [
+                [True, False, False, False],
+                [True, True, False, False],
+                [False, True, True, False],
+                [False, False, True, True],
+            ],
+            dtype=torch.bool,
+        )
+
+        # Check first batch element
+        torch.testing.assert_close(mask[0], expected_pattern)
+        # All batch elements should be identical
+        torch.testing.assert_close(mask[0], mask[1])
+
+    def test_get_sliding_attention_mask_different_window_sizes(self):
+        """Test sliding window with different window sizes."""
+        bsz, seq_len = 1, 5
+
+        # Test window size 1 (only current position)
+        mask = get_sliding_attention_mask(
+            mask=None,
+            sliding_window_size=1,
+            bsz=bsz,
+            seq_len=seq_len,
+            device=None,
+        )
+
+        expected_window_1 = torch.tensor(
+            [
+                [True, False, False, False, False],
+                [False, True, False, False, False],
+                [False, False, True, False, False],
+                [False, False, False, True, False],
+                [False, False, False, False, True],
+            ],
+            dtype=torch.bool,
+        )
+
+        torch.testing.assert_close(mask[0], expected_window_1)
+
+        # Test window size 3
+        mask = get_sliding_attention_mask(
+            mask=None,
+            sliding_window_size=3,
+            bsz=bsz,
+            seq_len=seq_len,
+            device=None,
+        )
+
+        expected_window_3 = torch.tensor(
+            [
+                [True, False, False, False, False],
+                [True, True, False, False, False],
+                [True, True, True, False, False],
+                [False, True, True, True, False],
+                [False, False, True, True, True],
+            ],
+            dtype=torch.bool,
+        )
+
+        torch.testing.assert_close(mask[0], expected_window_3)
+
+    def test_get_sliding_attention_mask_large_window(self):
+        """Test sliding window larger than sequence length."""
+        bsz, seq_len = 1, 3
+        sliding_window_size = 5  # Larger than seq_len
+
+        mask = get_sliding_attention_mask(
+            mask=None,
+            sliding_window_size=sliding_window_size,
+            bsz=bsz,
+            seq_len=seq_len,
+            device=None,
+        )
+
+        # Should behave like a regular causal mask when window is larger than seq_len
+        expected_causal = torch.tensor(
+            [
+                [True, False, False],
+                [True, True, False],
+                [True, True, True],
+            ],
+            dtype=torch.bool,
+        )
+
+        torch.testing.assert_close(mask[0], expected_causal)

tests/torchtune/modules/test_attention_utils.py

@@ -122,7 +122,7 @@ def test_flex_attention(self, mock_sdpa, mock_flex):
             _attention_call = _sdpa_or_flex_attention()
             _ = _attention_call(q, k, v, attn_mask, dropout_p, is_causal)
             mock_sdpa.assert_not_called()
-            mock_flex.assert_called_with(q, k, v, block_mask=attn_mask)
+            mock_flex.assert_called_with(q, k, v, block_mask=attn_mask, scale=None)
         # If mask is not a BlockMask, then we should call SDPA
         _attention_call = _sdpa_or_flex_attention()
         _ = _attention_call(q, k, v, attn_mask, dropout_p, is_causal)

torchtune/models/gemma2/_attention_mask.py

@@ -0,0 +1,52 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional
+
+import torch
+
+from torchtune.modules.attention_utils import _MaskType
+
+
+def get_sliding_attention_mask(
+    mask: Optional[_MaskType],
+    sliding_window_size: int,
+    bsz: int,
+    seq_len: int,
+    device: Optional[torch.device] = None,
+) -> _MaskType:
+    """
+    Args:
+        mask (Optional[_MaskType]): Mask to apply to the attention scores.
+        sliding_window_size (int): Sliding window size to apply to the attention mask.
+        bsz (int): Batch size. Argument is unused, but listed for consistency.
+        seq_len (int): Sequence length.
+        device (Optional[torch.device]): Device to use for the mask. Defaults to None.
+
+    Returns:
+        A tensor mask that applies sliding window masking.
+
+    Raises:
+        ValueError: If the input mask is not a Tensor
+    """
+
+    if mask is None:
+        mask = torch.tril(
+            torch.ones(size=(bsz, seq_len, seq_len), dtype=torch.bool).to(device)
+        )
+
+    if not isinstance(mask, torch.Tensor):
+        raise ValueError(
+            f"For non-flex attention, mask must be a Tensor. Got: {type(mask)}"
+        )
+
+    all_ones = torch.ones_like(mask, dtype=torch.bool)
+    sliding_mask = torch.triu(all_ones, -1 * sliding_window_size + 1) & torch.tril(
+        all_ones, sliding_window_size - 1
+    )
+    mask = mask & sliding_mask
+
+    return mask