[js/webgpu] support FlashAttention-2 for attention operator

js/web/lib/wasm/jsep/webgpu/ops/attention.ts

@@ -1004,21 +1004,188 @@ const prepare = (context: ComputeContext, parameters: AttentionParameters) => {
   );
 };
 
+// The algorithmn refers the FlashAttention forward pass in the https://tridao.me/publications/flash2/flash2.pdf
+const createFlashAttentionV2ProgramInfo = (
+  q: TensorView,
+  k: TensorView,
+  v: TensorView,
+  parameters: AttentionParameters,
+  attributes: AttentionAttrs,
+) => {
+  const components = 4;
+  const Br = 32;
+  const Bc = Br;
+  const Tr = q.dims[2] / Br;
+  const Tc = k.dims[2] / Bc;
+  const d = q.dims[3] / components;
+  const numTiles = Math.ceil(q.dims[3] / Bc);
+  const workgroupSize: [number, number, number] = [8, 32, 1];
+  const qInner = numTiles * workgroupSize[0];
+  const colsPerThread = 4; // (Bc / workgroupSize[0])
+  const alpha = attributes.scale === 0 ? 1.0 / Math.sqrt(parameters.headSize) : attributes.scale;
+
+  const dispatch = {
+    x: Tr,
+    y: v.dims[1],
+    z: v.dims[0],
+  };
+
+  const headOffset = v.dims[2] * v.dims[3];
+  const batchOffset = v.dims[1] * headOffset;
+  const programUniforms: ProgramUniform[] = [
+    { type: DataType.uint32, data: batchOffset },
+    { type: DataType.uint32, data: headOffset },
+    { type: DataType.uint32, data: v.dims[1] },
+    { type: DataType.uint32, data: v.dims[3] },
+    { type: DataType.uint32, data: d },
+    { type: DataType.float, data: alpha },
+  ];
+
+  const outputDims = [v.dims[0], v.dims[2], v.dims[1] * v.dims[3]];
+  const outputs = [{ dims: outputDims, dataType: q.dataType, gpuDataType: GpuDataType.default }];
+  const inputDependencies: ProgramInputTensorInfoDependency[] = ['type', 'type', 'type'];
+
+  const getShaderSource = (shaderHelper: ShaderHelper) => {
+    const qInput = inputVariable('Q', q.dataType, q.dims, components);
+    const kInput = inputVariable('K', k.dataType, k.dims, components);
+    const vInput = inputVariable('V', k.dataType, k.dims, components);
+    const inputVars = [qInput, kInput, vInput];
+
+    const output = outputVariable('output', q.dataType, outputDims);
+    const outputVars = [output];
+    const type = tensorTypeToWsglValueType(v.dataType);
+
+    const uniforms: UniformsArrayType = [
+      { name: 'batchOffset', type: 'u32' },
+      { name: 'headOffset', type: 'u32' },
+      { name: 'headNum', type: 'u32' },
+      { name: 'headSize', type: 'u32' },
+      { name: 'd', type: 'u32' },
+      { name: 'alpha', type: 'f32' as UniformDataElementType },
+    ];
+
+    return `
+    var<workgroup> Q_i : array<array<${qInput.type.storage}, ${qInner}>, ${Br}>;
+    var<workgroup> KV_j : array<array<${qInput.type.storage}, ${workgroupSize[0]}>, ${Bc}>;
+    var<workgroup> S_i_j : array<array<${output.type.storage}, ${Bc}>, ${Br}>;
+  ${shaderHelper.registerUniforms(uniforms).declareVariables(...inputVars, ...outputVars)}
+  ${shaderHelper.mainStart(workgroupSize)}
+    let offset = (workgroup_id.z * uniforms.batchOffset + workgroup_id.y * uniforms.headOffset) / ${components} + workgroup_id.x * ${Br} * uniforms.d;
+
+    var O_i : array<${type}, ${numTiles * colsPerThread}>;
+    var l_i_j = ${type}(0);
+    var m_i_j = ${type === 'f32' ? 'f32(-3.402823e+38f)' : 'f16(-65504)'};
+
+    for (var tile = 0; tile < ${numTiles}; tile++) {
+      Q_i[local_id.y][u32(${workgroupSize[0]} * tile) + local_id.x] = Q[offset + local_id.y * uniforms.d + u32(${workgroupSize[0]} * tile) + local_id.x];
+    }
+
+    for (var j = 0; j < ${Tc}; j++) {
+      var acc : array<${type}, ${colsPerThread}>;
+
+      let kvOffset = (workgroup_id.z * uniforms.batchOffset + workgroup_id.y * uniforms.headOffset) / ${components} + u32(j * ${Bc}) * uniforms.d;
+      for (var tile = 0; tile < ${numTiles}; tile++) {
+        KV_j[local_id.y][local_id.x] = K[kvOffset + local_id.y * uniforms.d + local_id.x + u32(tile * 8)];
+        workgroupBarrier();
+        for (var col = 0; col < ${colsPerThread}; col++) {
+          for (var k = 0; k < ${workgroupSize[0]}; k++) {
+            acc[col] += dot(Q_i[local_id.y][k + tile * 8], KV_j[local_id.x + u32(col * 8)][k]);
+          }
+        }
+        workgroupBarrier();
+      }
+
+      for (var col = 0; col < ${colsPerThread}; col++) {
+        S_i_j[local_id.y][u32(col * 8) + local_id.x] = acc[col] *  ${type}(uniforms.alpha);
+      }
+
+      workgroupBarrier();
+      let m_i_j_1 = m_i_j;
+      for (var m = 0; m < ${Bc}; m++) {
+        m_i_j = max(m_i_j, S_i_j[local_id.y][m]);
+      }
+
+      let exp_j_j_1 = exp(m_i_j_1 - m_i_j);
+      l_i_j *= exp_j_j_1;
+      for (var o = 0; o < ${colsPerThread * numTiles}; o++) {
+        O_i[o] *= exp_j_j_1;
+      }
+
+      for (var tile = 0; tile < ${numTiles}; tile++) {
+        KV_j[local_id.y][local_id.x] = V[kvOffset + local_id.y * uniforms.d + local_id.x + u32(tile * 8)];
+        workgroupBarrier();
+
+        for (var d = 0; d < ${Bc}; d++) {
+          let p_i_j = exp(S_i_j[local_id.y][d] - m_i_j);
+          if (tile == 0) {
+            l_i_j += p_i_j;
+          }
+
+          for (var col = 0; col < ${colsPerThread}; col++) {
+            let v_i_j = KV_j[d][(u32(8 * col) + local_id.x) / 4][(u32(8 * col) + local_id.x) % 4];
+            O_i[col * ${numTiles} + tile] += p_i_j * v_i_j;
+          }
+        }
+        workgroupBarrier();
+      }
+    }
+
+    let outputOffset = workgroup_id.z * uniforms.batchOffset + (workgroup_id.x * 32 + local_id.y) * uniforms.headNum
+                       * uniforms.headSize + workgroup_id.y * uniforms.headSize + local_id.x;
+    for (var tile = 0; tile < ${numTiles}; tile++) {
+      for (var col = 0; col < ${colsPerThread}; col++) {
+        let outputIndx = outputOffset + u32(tile * ${Bc}) + u32(col * 8);
+        output[outputIndx] = O_i[col * ${numTiles} + tile] / l_i_j;
+      }
+    }
+  }`;
+  };
+  return {
+    name: 'FlashAttentionV2',
+    shaderCache: { hint: `${numTiles}`, inputDependencies },
+    getRunData: () => ({ outputs, dispatchGroup: dispatch, programUniforms }),
+    getShaderSource,
+  };
+};
+
+const applyFlashAttentionV2 = (
+  context: ComputeContext,
+  q: TensorView,
+  k: TensorView,
+  v: TensorView,
+  parameters: AttentionParameters,
+  attributes: AttentionAttrs,
+) => {
+  const inputs = [q, k, v];
+  context.compute(createFlashAttentionV2ProgramInfo(q, k, v, parameters, attributes), { inputs });
+};
+
 export const attention = (context: ComputeContext, attributes: AttentionAttrs): void => {
   const params = validateAttentionInputs(context.inputs, attributes);
 
   const [q, k, v] = prepare(context, params);
 
-  return applyAttention(
-    context,
-    q,
-    k,
-    v,
-    context.inputs[4],
-    undefined,
-    undefined,
-    undefined,
-    context.inputs[5],
-    params,
-  );
+  if (
+    params.sequenceLength >= 1024 &&
+    params.sequenceLength % 32 === 0 &&
+    params.headSize <= 128 &&
+    params.headSize % 32 === 0 &&
+    context.inputs[4] === undefined &&
+    context.inputs[5] === undefined
+  ) {
+    return applyFlashAttentionV2(context, q, k, v, params, attributes);
+  } else {
+    return applyAttention(
+      context,
+      q,
+      k,
+      v,
+      context.inputs[4],
+      undefined,
+      undefined,
+      undefined,
+      context.inputs[5],
+      params,
+    );
+  }
 };