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[webgpu] Apply Flash Attention if sliding window exceeds KV cache length #25594

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Merged
merged 5 commits into from
Aug 1, 2025
Merged

[webgpu] Apply Flash Attention if sliding window exceeds KV cache length #25594

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7a82784
Apply flash attention if sliding window exceeds KV cache length
daijh Jul 30, 2025
ade51b0
Fix typo
daijh Jul 30, 2025
5f00414
Check sequence length
daijh Jul 30, 2025
52ce1a1
Resolve comments
daijh Jul 31, 2025
4153d11
Minor update comment
daijh Jul 31, 2025
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4 changes: 3 additions & 1 deletion onnxruntime/contrib_ops/webgpu/bert/flash_attention.cc
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Expand Up @@ -910,7 +910,9 @@ Status ComputeFlashAttentionDecodeVxReduce(onnxruntime::webgpu::ComputeContext&

Status ApplyFlashAttention(const Tensor* Q, const Tensor* K, const Tensor* V, const Tensor* attention_bias,
Tensor* output, const Tensor* past_key, Tensor* present_key, const Tensor* past_value, Tensor* present_value,
const WebgpuAttentionParameters& parameters, onnxruntime::webgpu::ComputeContext& context) {
const WebgpuAttentionParameters& parameters, onnxruntime::webgpu::ComputeContext& context, int local_window_size) {
ORT_ENFORCE(local_window_size == -1, "Sliding window is not supported yet in FlashAttention.");

ORT_RETURN_IF_ERROR(CopyKVCache(context, parameters, K, past_key, present_key, V, past_value, present_value));
const int present_sequence_length = parameters.is_gqa_ ? parameters.seqlen_present_kv_cache_ : parameters.total_sequence_length_;
if (parameters.sequence_length_ > 1) {
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2 changes: 1 addition & 1 deletion onnxruntime/contrib_ops/webgpu/bert/flash_attention.h
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Expand Up @@ -134,7 +134,7 @@ class FlashAttentionDecodeVxReduceProgram final : public Program<FlashAttentionD

Status ApplyFlashAttention(const Tensor* Q, const Tensor* K, const Tensor* V, const Tensor* attention_bias,
Tensor* output, const Tensor* past_key, Tensor* present_key, const Tensor* past_value, Tensor* present_value,
const WebgpuAttentionParameters& parameters, onnxruntime::webgpu::ComputeContext& context);
const WebgpuAttentionParameters& parameters, onnxruntime::webgpu::ComputeContext& context, int local_window_size = -1);

bool CanApplyFlashAttention(const Tensor* bias, const Tensor* present_key, const Tensor* present_value,
const WebgpuAttentionParameters& parameters, onnxruntime::webgpu::ComputeContext& context);
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3 changes: 2 additions & 1 deletion onnxruntime/contrib_ops/webgpu/bert/group_query_attention.cc
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Expand Up @@ -198,9 +198,10 @@ Status GroupQueryAttention::ComputeInternal(onnxruntime::webgpu::ComputeContext&
Tensor* present_value = context.Output(2, present_kv_shape);
parameters.past_present_share_buffer_ = present_key != nullptr && present_value != nullptr && past_key != nullptr && past_value != nullptr && past_key->DataRaw() == present_key->DataRaw() && past_value->DataRaw() == present_value->DataRaw();

bool use_sliding_window = (local_window_size_ != -1 && local_window_size_ < parameters.seqlen_present_kv_cache_ && local_window_size_ < parameters.total_sequence_length_);
if (!do_rotary_ &&
head_sink == nullptr && !use_smooth_softmax_ &&
local_window_size_ == -1 &&
!use_sliding_window &&
CanApplyFlashAttention(attention_bias, present_key, present_value, parameters, context)) {
return ApplyFlashAttention(query, key, value, attention_bias, output, past_key, present_key, past_value,
present_value, parameters, context);
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