Internal changes

PiperOrigin-RevId: 844759322

BUILD.bazel

@@ -142,6 +142,7 @@ cc_test(
         ":mat",
         ":matmul",
         ":query",
+        ":test_util",
         ":threading_context",
         ":weights",
         "@googletest//:gtest_main",  # buildcleaner: keep

gemma/flash_attention.cc

@@ -20,7 +20,9 @@
 #include <array>
 #include <cmath>
 #include <cstdlib>
+#include <iostream>
 #include <limits>
+#include <vector>
 
 #include "compression/types.h"  // GEMMA_DISABLED_TARGETS
 #include "gemma/flash_structs.h"
@@ -438,6 +440,152 @@ float HWY_INLINE SingleFlashAttentionRowVector(DF df, VF& x, float& old_max,
   return scale;
 }
 
+// Reduces each of x and stores in following lanes of max (tested with float32)
+template <class DF, typename T = hn::TFromD<DF>,
+          class DF4 = hn::CappedTag<T, 4>, class VF4 = hn::Vec<DF4>,
+          class VF = hn::Vec<DF>, typename F>
+static VF4 HWY_INLINE Reduce4(DF df, VF x_0, VF x_1, VF x_2, VF x_3,
+                              F reducer) {
+  const DF4 df4;
+  constexpr size_t kMaxLanes = hn::MaxLanes(df);
+  HWY_LANES_CONSTEXPR size_t kLanes = hn::Lanes(df);
+  HWY_ALIGN T x_transposed[4 * kMaxLanes];
+  hn::StoreInterleaved4<DF>(x_0, x_1, x_2, x_3, df, x_transposed);
+  VF4 result = hn::Load(df4, x_transposed);
+  for (int i = 1; i < kLanes; ++i) {
+    result = reducer(result, hn::Load(df4, x_transposed + i * 4));
+  }
+  return result;
+}
+
+// Handles Up to 4 Q rows by NF*2 timesteps of flash attention.
+template <int kNumQueries, class DF, class VF = hn::Vec<DF>>
+static void HWY_INLINE FlashAttentionTileStepAndApplySoftCap(
+    DF df, float att_cap, float one_over_att_cap, VF& x_0_p0, VF& x_0_p1,
+    VF& x_1_p0, VF& x_1_p1, VF& x_2_p0, VF& x_2_p1, VF& x_3_p0, VF& x_3_p1,
+    float* HWY_RESTRICT old_max, float* HWY_RESTRICT old_d,
+    float* HWY_RESTRICT scales) {
+  using DF4 = hn::CappedTag<float, 4>;
+  const DF4 df4;
+  using VF4 = hn::Vec<DF4>;
+  static_assert(kNumQueries >= 1 && kNumQueries <= 4);
+  VF4 new_max = hn::Set(df4, -std::numeric_limits<float>::max() / 2.0f);
+  VF max_0, max_1, max_2, max_3 = hn::Zero(df);
+  max_0 = hn::Max(x_0_p0, x_0_p1);
+  if constexpr (kNumQueries >= 2) {
+    max_1 = hn::Max(x_1_p0, x_1_p1);
+  }
+  if constexpr (kNumQueries >= 3) {
+    max_2 = hn::Max(x_2_p0, x_2_p1);
+  }
+  if constexpr (kNumQueries >= 4) {
+    max_3 = hn::Max(x_3_p0, x_3_p1);
+  }
+  if constexpr (kNumQueries == 1) {
+    new_max = hn::InsertLane(new_max, 0, hn::ReduceMax(df, max_0));
+  } else {
+    new_max = Reduce4(df, max_0, max_1, max_2, max_3,
+                             [](auto a, auto b) { return hn::Max(a, b); });
+  }
+  if (att_cap > 0.0f) {
+    VF4 cap = hn::Set(df4, att_cap);
+    VF4 one_over_cap = hn::Set(df4, one_over_att_cap);
+    new_max = hn::Mul(cap, hn::Tanh(df4, hn::Mul(new_max, one_over_cap)));
+  }
+  VF4 old_max_vf = hn::Set(df4, -std::numeric_limits<float>::max() / 2.0f);
+  old_max_vf = hn::LoadU(df4, old_max);
+  new_max = hn::Max(new_max, old_max_vf);
+  // TODO figure out what was wrong with broadcasts and change to that.
+  HWY_ALIGN float tmp_max[4];
+  hn::Store(new_max, df4, tmp_max);
+  if constexpr (kNumQueries >= 1) {
+    const VF new_max_0 = hn::Set(df, tmp_max[0]);
+    x_0_p0 = hn::Exp(df, hn::Sub(x_0_p0 , new_max_0));
+    x_0_p1 = hn::Exp(df, hn::Sub(x_0_p1, new_max_0));
+  }
+  if constexpr (kNumQueries >= 2) {
+    const VF new_max_0 = hn::Set(df, tmp_max[1]);
+    x_1_p0 = hn::Exp(df, hn::Sub(x_1_p0, new_max_0));
+    x_1_p1 = hn::Exp(df, hn::Sub(x_1_p1, new_max_0));
+  }
+  if constexpr (kNumQueries >= 3) {
+    const VF new_max_0 = hn::Set(df, tmp_max[2]);
+    x_2_p0 = hn::Exp(df, hn::Sub(x_2_p0, new_max_0));
+    x_2_p1 = hn::Exp(df, hn::Sub(x_2_p1, new_max_0));
+  }
+  if constexpr (kNumQueries >= 4) {
+    const VF new_max_0 = hn::Set(df, tmp_max[3]);
+    x_3_p0 = hn::Exp(df, hn::Sub(x_3_p0, new_max_0));
+    x_3_p1 = hn::Exp(df, hn::Sub(x_3_p1, new_max_0));
+  }
+  VF4 old_d_vf = hn::Set(df4, 0.0f);
+  old_d_vf = hn::LoadU(df4, old_d);
+  VF4 scale = hn::Mul(old_d_vf, hn::Exp(df4, hn::Sub(old_max_vf, new_max)));
+
+  hn::StoreU(new_max, df4, old_max);
+
+  VF4 x_sum = hn::Zero(df4);
+  if constexpr (kNumQueries == 1) {
+    x_sum = hn::Set(df4, hn::ReduceSum(df, x_0_p0) + hn::ReduceSum(df, x_0_p1));
+  } else {
+    VF x_0_sum = hn::Add(x_0_p0, x_0_p1);
+    VF x_1_sum = hn::Add(x_1_p0, x_1_p1);
+    VF x_2_sum = hn::Add(x_2_p0, x_2_p1);
+    VF x_3_sum = hn::Add(x_3_p0, x_3_p1);
+    x_sum = Reduce4(df, x_0_sum, x_1_sum, x_2_sum, x_3_sum,
+                           [](auto a, auto b) { return hn::Add(a, b); });
+  }
+  old_d_vf = hn::Add(scale, x_sum);
+  auto non_zero_mask = hn::Gt(old_d_vf, hn::Set(df4, 0.0f));
+  const VF zero = hn::Zero(df);
+  const VF4 zero4 = hn::Zero(df4);
+  const VF4 one_over_d =
+      hn::MaskedDivOr(zero4, non_zero_mask, hn::Set(df4, 1.0f), old_d_vf);
+  float tmp_one_over_d[4];
+  hn::Store(one_over_d, df4, tmp_one_over_d);
+  hn::Store(old_d_vf, df4, old_d);
+  scale = hn::Mul(scale, one_over_d);
+  hn::Store(scale, df4, scales);
+  if (hn::ExtractLane(old_d_vf, 0) > 0.0f) {
+    const VF one_over_d_0 = hn::Set(df, tmp_one_over_d[0]);
+    x_0_p0 = hn::Mul(x_0_p0, one_over_d_0);
+    x_0_p1 = hn::Mul(x_0_p1, one_over_d_0);
+  } else {
+    x_0_p0 = zero;
+    x_0_p1 = zero;
+  }
+  if constexpr (kNumQueries >= 2) {
+    if (hn::ExtractLane(old_d_vf, 1) > 0.0f) {
+      const VF one_over_d_1 = hn::Set(df, tmp_one_over_d[1]);
+      x_1_p0 = hn::Mul(x_1_p0, one_over_d_1);
+      x_1_p1 = hn::Mul(x_1_p1, one_over_d_1);
+    } else {
+      x_1_p0 = zero;
+      x_1_p1 = zero;
+    }
+  }
+  if constexpr (kNumQueries >= 3) {
+    if (hn::ExtractLane(old_d_vf, 2) > 0.0f) {
+      const VF one_over_d_2 = hn::Set(df, tmp_one_over_d[2]);
+      x_2_p0 = hn::Mul(x_2_p0, one_over_d_2);
+      x_2_p1 = hn::Mul(x_2_p1, one_over_d_2);
+    } else {
+      x_2_p0 = zero;
+      x_2_p1 = zero;
+    }
+  }
+  if constexpr (kNumQueries >= 4) {
+    if (hn::ExtractLane(old_d_vf, 3) > 0.0f) {
+      const VF one_over_d_3 = hn::Set(df, tmp_one_over_d[3]);
+      x_3_p0 = hn::Mul(x_3_p0, one_over_d_3);
+      x_3_p1 = hn::Mul(x_3_p1, one_over_d_3);
+    } else {
+      x_3_p0 = zero;
+      x_3_p1 = zero;
+    }
+  }
+}
+
 // Implements flash attention for a strip of 4 query vectors.
 // It iterates through timesteps in K from `start_pos` up to `max_last_pos`.
 // Timesteps up to `min_last_pos` (*) are processed in tiles of shape 4 Q rows

gemma/flash_attention_test.cc

@@ -14,6 +14,8 @@
 // limitations under the License.
 
 #include <cstring>
+#include <iostream>
+#include <limits>
 #include <numeric>
 #include <vector>
 
@@ -24,6 +26,7 @@
 #include "gemma/kv_cache.h"
 #include "gemma/weights.h"
 #include "ops/matmul.h"
+#include "util/test_util.h"
 #ifndef HWY_DISABLED_TARGETS
 #define HWY_DISABLED_TARGETS GEMMA_DISABLED_TARGETS
 #endif  // HWY_DISABLED_TARGETS

util/mat.h

@@ -454,6 +454,21 @@ decltype(auto) CallUpcastedActivation(const MatPtr* base, const Func& func,
   }
 }
 
+// Like CallUpcasted, but only for kv_cache types: kBF16 and kF32.
+template <class Func, typename... Args>
+decltype(auto) CallUpcastedKV(const MatPtr* base, const Func& func,
+                                      Args&&... args) {
+  if (base->GetType() == Type::kF32) {
+    const MatPtrT<float> mat(*base);
+    return func(&mat, std::forward<Args>(args)...);
+  } else if (base->GetType() == Type::kBF16) {
+    const MatPtrT<BF16> mat(*base);
+    return func(&mat, std::forward<Args>(args)...);
+  } else {
+    HWY_ABORT("Unhandled type %s.", TypeName(base->GetType()));
+  }
+}
+
 void CopyMat(const MatPtr& from, MatPtr& to);
 void ZeroInit(MatPtr& mat);