[Feature] Add sliding window support to FlexAttention backend

[alhridoy]

Summary

Implements sliding window attention support for FlexAttention backend, resolving issue #24358.

Problem

FlexAttention backend was missing sliding window support, causing NotImplementedError when:

• Models with sliding window attention (like GptOss) were loaded
• Hardware limitations forced fallback to FlexAttention (RTX 2080, etc.)
• FlashAttention v2 was unavailable due to compute capability < 8.0

Solution

• Added sliding_window_causal_mask_mod() function for sliding window masks
• Updated FlexAttentionImpl to handle sliding window configuration
• Enhanced metadata pipeline to pass sliding window information
• Maintained full backward compatibility

Key Changes

Core Implementation

def sliding_window_causal_mask_mod(window_size: int):
    """Create a sliding window causal mask function."""
    def mask_mod(b, h, q_idx, kv_idx):
        causal_mask = q_idx >= kv_idx
        window_mask = q_idx - kv_idx < window_size
        return causal_mask & window_mask
    return mask_mod

Files Modified

• vllm/v1/attention/backends/flex_attention.py: Core implementation
• tests/v1/attention/backends/test_flex_attention_sliding_window.py: Unit tests
• tests/models/test_gpt_oss_sliding_window.py: Integration tests

Testing

• ✅ Unit tests for mask functions and metadata handling
• ✅ Integration tests with FlexAttentionImpl
• ✅ Edge cases and error conditions
• ✅ Backward compatibility verified

Validation

Before this change:

# This would fail with NotImplementedError
vllm serve unsloth/gpt-oss-20b-unsloth-bnb-4bit

After this change:

# This now works seamlessly
vllm serve unsloth/gpt-oss-20b-unsloth-bnb-4bit

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[gemini-code-assist]

Code Review

This pull request adds sliding window attention support to the FlexAttention backend. The core logic for the sliding window mask is correct. However, there is a critical issue in how the sliding window configuration is passed to the attention metadata, which will cause incorrect behavior for models with per-layer sliding window settings like GptOss. Additionally, the implementation contains some confusing code related to the sliding_window parameter, and the tests are insufficient to catch the critical bug. I've provided comments with details and suggestions for fixes.

[gemini-code-assist]

This change introduces a critical issue for models with per-layer sliding window configurations, such as GptOss which this PR aims to support. By reading sliding_window from the global model config here, the FlexAttentionMetadata will be built with a single sliding window setting that is applied to all layers. However, models like GptOss apply sliding window attention only to specific layers (e.g., even-numbered layers).

This will result in incorrect behavior: layers that should not have a sliding window will have one applied, leading to incorrect attention outputs and model predictions.

The FlexAttentionMetadata is shared across all layers for a forward pass, so it cannot hold layer-specific configurations like this. The decision to apply a sliding window mask should be made within the layer-specific FlexAttentionImpl.

A potential solution is to move the block_mask creation from FlexAttentionMetadata.__post_init__ into FlexAttentionImpl.forward. This would allow FlexAttentionImpl to use its own self.sliding_window value (which is correctly configured per-layer) to decide which masking function to use when building the block_mask just before the attention computation. This would align the FlexAttention backend with how other backends like FlashAttention handle per-layer settings.

[gemini-code-assist]

The current tests focus on the initialization of FlexAttentionImpl and don't include an end-to-end integration test that runs a forward pass on a model like GptOss. Such a test would involve the FlexAttentionMetadataBuilder and the multi-layer execution flow, which would have revealed the critical issue where sliding window attention is incorrectly applied to all layers.

It is highly recommended to add an integration test that:

1. Loads a model with per-layer sliding window (like GptOss).
2. Runs a forward pass.
3. Compares the output with a reference implementation (e.g., from transformers) to ensure correctness for both layers with and without sliding window.

[gemini-code-assist]

The conversion of sliding_window from an integer to a tuple (sliding_window - 1, 0) is unnecessary and confusing. The FlexAttentionImpl.sliding_window attribute (typed as a tuple on line 640) is never used. The actual sliding window logic is implemented via sliding_window_causal_mask_mod, which uses the integer sliding_window value passed to FlexAttentionMetadata.

This tuple conversion appears to be a remnant of a different implementation strategy and does not align with the masking-based approach used here. It introduces dead code and makes the implementation harder to follow.

To improve clarity, FlexAttentionImpl.sliding_window should store the integer value directly. This would require changing the type hint on line 640 to Optional[int] and updating this block to assign the integer value.

        if sliding_window is not None:
            self.sliding_window = sliding_window
        else:
            self.sliding_window = None

[DarkLight1337]

cc @drisspg

[Isotr0py]

Seems duplicate of #24089?