From 57f2664fa730fde4b7c7634063e9bf29ef49326e Mon Sep 17 00:00:00 2001 From: Martin Stolle Date: Fri, 5 Dec 2025 07:43:55 -0800 Subject: [PATCH] internal change PiperOrigin-RevId: 840724686 --- BUILD.bazel | 22 ++ gemma/attention_test.cc | 464 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 486 insertions(+) create mode 100644 gemma/attention_test.cc diff --git a/BUILD.bazel b/BUILD.bazel index 1a67f65..87c7b83 100644 --- a/BUILD.bazel +++ b/BUILD.bazel @@ -601,6 +601,28 @@ cc_library( ], ) +cc_test( + name = "gemma_lib_test", + srcs = ["gemma/attention_test.cc"], + deps = [ + ":configs", + ":gemma_args", + ":gemma_lib", + ":kv_cache", + ":mat", + ":matmul_env", + ":test_util", + ":threading_context", + ":weights", + "@googletest//:gtest_main", # buildcleaner: keep + "//third_party/absl/types:span", + "//compression:compress", + "//compression:types", + "@highway//:hwy", + "@highway//:hwy_test_util", + ], +) + cc_library( name = "cross_entropy", srcs = ["evals/cross_entropy.cc"], diff --git a/gemma/attention_test.cc b/gemma/attention_test.cc new file mode 100644 index 0000000..8bf8662 --- /dev/null +++ b/gemma/attention_test.cc @@ -0,0 +1,464 @@ +#include +#include // strcmp +#include +#include +#include +#include + +#include "gmock/gmock.h" +#include "gtest/gtest.h" +#include "third_party/absl/types/span.h" +#include "compression/types.h" // GEMMA_DISABLED_TARGETS +#include "gemma/activations.h" +#include "gemma/gemma.h" +#include "gemma/gemma_args.h" +#include "gemma/kv_cache.h" +#include "gemma/weights.h" +#include "ops/matmul.h" +#include "util/mat.h" +#include "util/threading_context.h" +#include "hwy/aligned_allocator.h" +#include "hwy/base.h" +#ifndef HWY_DISABLED_TARGETS +// These tests aren't designed to suss out instruction set specific problems. +// Disable most targets to keep the tests fast and simple and not have to +// worry about tolerances on floating point results. +#define HWY_DISABLED_TARGETS \ + (GEMMA_DISABLED_TARGETS | HWY_AVX2 | HWY_AVX3 | HWY_AVX3_SPR | HWY_AVX3_DL | \ + HWY_AVX3_ZEN4) +#endif // HWY_DISABLED_TARGETS + +// clang-format off +#undef HWY_TARGET_INCLUDE +#define HWY_TARGET_INCLUDE "gemma/attention_test.cc" // NOLINT +// clang-format on +#include "hwy/foreach_target.h" // IWYU pragma: keep +#include "hwy/highway.h" +// After highway.h +#include "compression/compress-inl.h" +#include "gemma/attention.h" +#include "gemma/configs.h" +#include "util/test_util.h" +#include "hwy/tests/test_util-inl.h" + +HWY_BEFORE_NAMESPACE(); +namespace gcpp { +namespace HWY_NAMESPACE { + +void FillRandom(MatPtrT& mat, uint64_t seed) { + hwy::RandomState rng(seed); + for (size_t r = 0; r < mat.Rows(); ++r) { + float* row = mat.Row(r); + for (size_t c = 0; c < mat.Cols(); ++c) { + row[c] = static_cast(RandomGaussian(rng)); + } + } +} + +void AllocateAndFillRandom(MatPtr& mat, const Allocator& allocator, + std::vector& mat_owners, uint64_t seed) { + if (mat.IsEmpty()) return; + if (mat.GetType() == Type::kUnknown) { + mat.SetType(Type::kF32); + } + mat_owners.emplace_back(); + mat_owners.back().AllocateFor(mat, allocator, MatPadding::kPacked); + MatPtrT mat_f32(mat); + FillRandom(mat_f32, seed); +} + +struct TestState { + TestState() : ctx({}), env(ctx) {} + ThreadingContext ctx; + std::vector mat_owners; + MatMulEnv env; +}; + +struct TestModelState { + TestModelState(TestState& state) + : config(Model::GEMMA2_2B, Type::kF32, PromptWrapping::GEMMA_PT), + tensor_info_registry(config), + layer_config(config.layer_configs[0]), + layers(0, layer_config, tensor_info_registry) { + config.att_cap = 1024.0f; + AllocateAndFillRandom(layers.qkv_einsum_w, state.ctx.allocator, + state.mat_owners, 42); + AllocateAndFillRandom(layers.attn_vec_einsum_w, state.ctx.allocator, + state.mat_owners, 43); + AllocateAndFillRandom(layers.gating_einsum_w, state.ctx.allocator, + state.mat_owners, 44); + AllocateAndFillRandom(layers.linear_w, state.ctx.allocator, + state.mat_owners, 45); + layers.Fixup(state.mat_owners, state.ctx); + } + + ModelConfig config; + TensorInfoRegistry tensor_info_registry; + const LayerConfig& layer_config; + LayerWeightsPtrs layers; +}; + +struct TestAttentionState { + TestAttentionState(TestState& state, TestModelState& model_state, + size_t num_tokens, size_t qbatch_size, + AttentionImpl attention_impl) + : num_tokens(num_tokens), + qbatch_size(qbatch_size), + batch_size(qbatch_size * num_tokens), + tokens(num_tokens), + attention_storage_(model_state.config, model_state.layer_config, + batch_size, num_tokens, attention_impl, + state.ctx.allocator, row_ptrs_), + attention(model_state.config, num_tokens, attention_storage_) { + for (size_t i = 0; i < qbatch_size; ++i) { + kv_caches.emplace_back(model_state.config, inference_args, + state.ctx.allocator); + } + activations.emplace( + runtime_config, model_state.config, runtime_config.prefill_tbatch_size, + kv_caches[0].SeqLen(), state.env.ctx, state.env.row_ptrs); + // Tokens don't matter, since we fill in pre_att_rms_out before calling + // GemmaAttention. + std::iota(tokens.begin(), tokens.end(), 1); + for (size_t i = 0; i < qbatch_size; ++i) { + prompts.emplace_back(tokens); + } + all_queries.emplace(prompts, + hwy::Span(kv_caches.data(), kv_caches.size())); + qbatch.emplace(/*start=*/0, /*max_size=*/qbatch_size, *all_queries); + FillRandom(attention.pre_att_rms_out, 46); + } + + const size_t num_tokens; + const size_t qbatch_size; + const size_t batch_size; + InferenceArgs inference_args; + RuntimeConfig runtime_config; + std::vector kv_caches; + std::optional activations; + std::vector tokens; + std::vector prompts; + std::optional all_queries; + std::optional qbatch; + std::vector> row_ptrs_; + AttentionActivations attention_storage_; + AttentionActivationsPtrs attention; +}; + +auto GetFloatNearMatcher() { + // TODO(mstoll): Consider removing this function and just disabling targets. + if (strcmp(hwy::TargetName(HWY_TARGET), "EMU128") == 0) { + // Do quasi exact match for EMU128 since we want to use it for golden value + // update. + return testing::FloatNear(1e-7f); + } + return testing::FloatEq(); // Note: this does a 4ULP fuzzy match +} + +template +void CompareAttSumsWithGolden( + const AttentionActivationsPtrs& attention, + const float (&golden)[kNumTokens][kQBatchSize][kDims]) { + ASSERT_EQ(attention.att_sums.Rows(), kNumTokens * kQBatchSize); + ASSERT_LE(kDims, attention.att_sums.Cols()); + + auto float_near = GetFloatNearMatcher(); + hwy::AlignedFreeUniquePtr actual_row = + hwy::AllocateAligned(kDims); + for (size_t token_idx = 0; token_idx < kNumTokens; ++token_idx) { + for (size_t qi = 0; qi < kQBatchSize; ++qi) { + const size_t i = token_idx * kQBatchSize + qi; + for (size_t j = 0; j < kDims; ++j) { + actual_row[j] = hwy::F32FromBF16(attention.att_sums.Row(i)[j]); + } + EXPECT_THAT( + absl::MakeSpan(actual_row.get(), kDims), + testing::Pointwise(float_near, + absl::MakeSpan(golden[token_idx][qi], kDims))); + } + } +} + +template +void CompareKVCacheWithGolden( + const ModelConfig& config, hwy::Span kv_caches, const size_t layer, + const size_t kv_head, + const float (&k_golden)[kNumTokens][kQBatchSize][kDims], + const float (&v_golden)[kNumTokens][kQBatchSize][kDims]) { + const size_t qbatch_size = kv_caches.size(); + ASSERT_EQ(kQBatchSize, qbatch_size); + const size_t start_offset = 0; + const size_t qkv_dim = config.layer_configs[0].qkv_dim; + + auto float_near = GetFloatNearMatcher(); + + hwy::AlignedFreeUniquePtr actual_k_row = + hwy::AllocateAligned(kDims); + hwy::AlignedFreeUniquePtr actual_v_row = + hwy::AllocateAligned(kDims); + + const size_t cache_layer_size = config.layer_configs[layer].CacheLayerSize(); + const size_t head_offset = kv_head * qkv_dim * 2; + const size_t kv_offset = layer * cache_layer_size + head_offset; + + for (size_t token_idx = 0; token_idx < kNumTokens; ++token_idx) { + for (size_t qi = 0; qi < kQBatchSize; ++qi) { + const float* cache_row = + kv_caches[qi].kv_cache.Row(start_offset + token_idx); + for (size_t j = 0; j < kDims; ++j) { + actual_k_row[j] = cache_row[kv_offset + j]; + actual_v_row[j] = cache_row[kv_offset + qkv_dim + j]; + } + EXPECT_THAT( + absl::MakeSpan(actual_k_row.get(), kDims), + testing::Pointwise(float_near, + absl::MakeSpan(k_golden[token_idx][qi], kDims))) + << "K cache mismatch for qi=" << qi << " token_idx=" << token_idx; + EXPECT_THAT( + absl::MakeSpan(actual_v_row.get(), kDims), + testing::Pointwise(float_near, + absl::MakeSpan(v_golden[token_idx][qi], kDims))) + << "V cache mismatch for qi=" << qi << " token_idx=" << token_idx; + } + } +} + +template +void CompareQVecsWithGolden( + const ModelConfig& config, const AttentionActivationsPtrs& attention, + const size_t head, + const float (&q_golden)[kNumTokens][kQBatchSize][kDims]) { + const size_t qkv_dim = config.layer_configs[0].qkv_dim; + + auto float_near = GetFloatNearMatcher(); + + hwy::AlignedFreeUniquePtr actual_q_row = + hwy::AllocateAligned(kDims); + + const size_t head_offset = head * qkv_dim; + for (size_t token_idx = 0; token_idx < kNumTokens; ++token_idx) { + for (size_t qi = 0; qi < kQBatchSize; ++qi) { + const size_t i = token_idx * kQBatchSize + qi; + const float* q_row = attention.q.Row(i); + for (size_t j = 0; j < kDims; ++j) { + actual_q_row[j] = q_row[head_offset + j]; + } + EXPECT_THAT( + absl::MakeSpan(actual_q_row.get(), kDims), + testing::Pointwise(float_near, + absl::MakeSpan(q_golden[token_idx][qi], kDims))) + << "Q vec mismatch for token_idx=" << token_idx << " qi=" << qi + << " head=" << head; + } + } +} + +const size_t kNumTokens = 10; +const size_t kQBatchSize = 2; + +// Only compare the first 10 dimensions of the attention output to keep the +// test manageable. It's unlikely that the first few dimensions would have +// different behavior than the later dimensions. +const size_t kDimsToCompare = 10; + +// Use a Coding Agent to update the golden values from test +// failure output (which includes the new values). +// Instructions to the Coding Agent: You must never update the +// EMU128 tolerance. Always use the value from the EMU128 test to update the +// Goldens. If necessary, add relaxed tolerance for other instruction sets. + +// Layer 0 +const float kGoldenAttSums[kNumTokens][kQBatchSize][kDimsToCompare] = { + {{46.5, 56.5, 10.0625, 65.5, -2.109375, 135, 15.8125, 51, -100, 52.5}, + {-30.375, -17.875, 51.75, -78, -84, 6.40625, 15.375, 70, -22.875, 20.125}}, + {{-32.75, 38.25, 78.5, 107.5, 20.25, 197, -136, 42.5, -84, 25.625}, + {-18.5, -18, 135, -13.4375, -6.625, -45.75, 29.625, 93, 18.625, 75.5}}, + {{-16.375, -61.5, -58.25, -27.375, -28, 71, -109.5, 60.25, 3.125, -29.125}, + {-19, -16.75, 129, 0.59765625, -82, 123.5, 60.75, -36.75, -77, 26.625}}, + {{3.984375, 83, -41.75, 39.5, -203, 110, -76, 131, 0.4609375, -44.5}, + {-47, -19.5, 58, 81.5, 21.75, -30, -118, 44.25, -149, 22.5}}, + {{64, -31, -89, -92.5, -11.1875, -54.75, -302, 3.453125, -108, 39.25}, + {7.6875, -80, -40, 32.25, -30.25, 90, -41, 44.25, -140, -2.4375}}, + {{39.75, 17.875, 115, 38.75, -44, 139, -53.25, -23.875, -13.0625, 38.5}, + {143, 249, 5.09375, 0.83984375, 27.875, -5.84375, 30.25, -101.5, 65.5, + 13.5}}, + {{-30.125, -169, -150, 58, -35.75, 22.75, 36.5, -32.25, -8.9375, 55.25}, + {137, 5.25, 61.25, 37, -42.75, 240, 62, -164, 11.3125, 173}}, + {{-103, -47.5, 39, -48, -67.5, 121, -136, 99, 80, -47.5}, + {29.875, 7.34375, -36.75, -14.5, -27.5, 44.75, -67.5, -40.75, 71.5, 172}}, + {{-37.25, 109.5, -26.125, -115.5, 108, 57.25, 1.3671875, 72, -122.5, 59.25}, + {40.25, 53.25, -142, 78.5, 38, 4.3125, -27.75, -134, -85, 107.5}}, + {{-8.4375, -35, -35.5, 131, -33.25, 106, 109.5, -92, -135, 80}, + {-77, 40.75, -10.125, 33.25, -33, 104, -7.6875, 85.5, -40, 93}}, +}; + +// Layer 0, *K*V Head 0 +const float kGoldenK[kNumTokens][kQBatchSize][kDimsToCompare] = { + {{-4.51717567, 6.93118095, 6.48003578, 9.12825584, 2.38755274, 11.8121576, + 1.65376127, 5.04456615, -7.19549274, 2.57609844}, + {0.152208567, 3.14520073, -8.35154343, 5.44226503, -6.74000502, + -1.43484437, -4.72092056, -9.48932, -6.12409401, -1.55352509}}, + {{4.378829, 5.05565643, -7.63948059, -5.74608946, 2.90109587, 0.155819178, + 4.56115055, 1.37885749, 1.48427355, -1.07145202}, + {-0.702698231, 1.49563932, 6.42149782, -6.68306589, 1.85317755, + -7.70267582, 2.07357907, -7.60303402, -0.514724255, 0.308567047}}, + {{-1.20844436, 4.14724302, 6.04515219, 8.7753458, -0.975198627, 0.564640105, + 5.39941597, 4.64036179, 0.366614938, 3.48258138}, + {4.57078934, -4.60315752, -3.3364439, 1.29875994, -3.40833569, -6.95262, + -6.39040232, -6.60212612, 6.63269806, -0.815209687}}, + {{0.602011144, 2.22505236, 3.62411499, -4.07026958, 12.8036356, 3.76139069, + 6.99502087, 7.02500725, -2.51568675, 4.2489934}, + {-6.37204599, -3.34989691, 2.10935307, 4.23634195, 5.79134035, 13.502944, + -2.19158888, -1.55771351, -1.22244942, 3.36499929}}, + {{-8.85739231, -4.08585882, -0.618261, 6.52911091, 5.14922285, 7.6869874, + 0.750387549, -0.812200725, 2.7509625, 6.29693508}, + {-8.2555, 2.84032059, -1.03791106, 2.07648611, -4.94546843, 1.76888537, + -1.75901175, 11.2628574, 1.41086221, -3.58669901}}, + {{0.919141412, 1.97533965, -11.3202848, -3.3137629, -4.7161727, 5.07012081, + 1.76256621, 8.20588207, 6.05700159, -3.89765406}, + {9.11918, 2.11261511, -5.87290621, 11.6033278, -4.66597795, -7.13774204, + -9.10563755, -2.48294282, 3.35282946, -3.75122213}}, + {{4.17705393, -4.95192289, -10.5068378, 3.90004015, -3.51306129, 5.38068056, + 0.901511431, 11.222868, 2.67285442, 9.18779}, + {3.61795235, -7.00262165, 2.08284521, -6.70515728, 1.93205631, 2.84467721, + 3.94591737, -6.18882942, -1.78465152, -9.39100933}}, + {{-0.361971855, -1.57735932, 5.07296801, -1.55669761, -1.44996238, + 7.29838896, 5.23075104, -0.512441278, -3.59834242, 2.38584423}, + {-0.795628309, 7.30230665, -1.71035647, -16.6999454, 3.05102086, + -4.9243927, 4.28508186, -0.694577456, 6.58464718, 4.40330124}}, + {{2.57555652, -3.5651083, 0.784440041, -4.7043705, 2.37520599, -3.62385964, + -3.48913693, -7.28049421, -5.48726082, 1.95519221}, + {5.56260443, -5.7683115, 1.26402235, -17.507719, 4.18873024, -3.20694613, + -4.42512083, 1.78077614, -3.25167561, 0.864362717}}, + {{-9.43858051, 0.391518891, -2.74012518, 4.9842453, 7.48263216, -16.3434925, + -4.75156116, -1.99114823, 3.99918842, -5.95400572}, + {-1.22347319, 9.57339382, -1.31736016, -5.02770805, -4.81617355, + -1.96618557, -0.456317186, 12.6451035, -1.50221801, 6.7991147}}, +}; + +// Layer 0, K*V* Head 0 +const float kGoldenV[kNumTokens][kQBatchSize][kDimsToCompare] = { + {{2.77553034, -7.67514181, -1.60433948, 4.67795134, -1.75084186, 8.57896423, + -1.15065813, -3.75088787, -4.7442131, -1.68890858}, + {-4.79950905, -1.66658688, 4.14471292, -4.95649052, -5.4200325, 3.52626801, + -10.9432049, 0.338347554, -1.53204226, 0.473476171}}, + {{-10.6566734, 4.12785721, 4.54053593, -1.39667869, -1.55028772, 0.20508635, + -0.00620913506, 2.93214, -0.788117647, 2.78032446}, + {-0.322291255, 2.63848567, -2.30808377, -13.0153809, 2.74378228, + 3.21460533, 0.688529968, 2.37544608, 6.06825066, 4.57566404}}, + {{-3.34342527, 6.03099537, 6.335958, 0.993818045, 0.905343294, 6.93058586, + 3.9635396, 10.8044815, 7.8620863, -10.1157322}, + {16.416317, -1.62025332, 2.3161006, 3.32571959, -1.79581594, -10.2925539, + -5.86338425, -6.36642933, 9.18872166, 5.95524168}}, + {{6.58254147, -6.96165133, -4.97437, -2.33467388, 5.83671236, -0.794236898, + -2.03117108, -3.93387103, -5.96872902, 5.83316422}, + {7.13243389, -8.04649162, 2.53055143, 2.0771277, -0.667295456, -13.0285645, + 0.960428238, -2.11983275, 8.18105602, -6.72609901}}, + {{9.52539158, -4.3840766, -6.94514465, -2.75913763, -10.8364506, + -3.95606327, 2.43603897, -5.78482246, -0.801304817, 8.23436832}, + {6.89768124, 2.36394405, -2.08569574, -0.682706833, 3.38872, -6.28313875, + 4.79594612, 4.93417454, -6.40791416, -10.7355442}}, + {{1.64614546, -3.93421197, -0.48935914, 5.48284435, -7.69781828, 11.8203125, + 1.81672478, -1.42535269, -5.26496315, -5.31612349}, + {7.52353811, 3.56836724, 0.414305687, 0.340799928, 2.44263697, 7.52111912, + 0.246491909, -11.1172791, -3.82061529, 3.24794388}}, + {{-2.48950672, -8.55112743, 8.04663277, -5.77116871, -0.637019753, + -7.65882111, -7.49037457, 3.8041625, -3.57038307, 9.37715435}, + {-2.95554614, -5.18210888, 1.00015664, -4.03864431, -7.14954519, + 5.99929142, 5.86350155, 2.03810191, -4.23009968, 9.39885902}}, + {{2.86876941, -0.836064458, -0.374509573, -0.277966499, 3.20654631, + -3.68510771, -7.76134634, 2.23905277, -8.35530376, 5.25071716}, + {-4.37938213, 4.78577232, 2.03453469, 5.48564529, -1.05589461, -1.65940428, + 4.0130887, 5.26074123, 4.67537832, 0.791350365}}, + {{0.289200783, 7.06031752, -1.15099299, -5.29136801, -1.343642, -8.36283112, + 4.13158274, -1.93137062, 3.16199875, 2.21854591}, + {-3.04716778, -2.52233481, -5.99031925, 2.80152273, 0.340899587, + 0.667474508, -2.39674735, 8.83768654, -5.45613146, -1.55994594}}, + {{-3.1383903, -7.71573353, 3.38072681, 6.07642221, -2.39587545, -7.84178352, + -1.60108304, -8.6121521, -5.151721, 4.17612457}, + {4.77026033, -5.51171303, -7.38155365, -5.38462543, 2.95842505, 5.18372536, + 0.521988213, 7.23966122, -4.90852165, 7.18465281}}, +}; + +// Layer 0, QHead 0 +const float kGoldenQ[kNumTokens][kQBatchSize][kDimsToCompare] = { + {{-0.574402, 0.370210946, -0.426894128, -0.543187499, -0.0266762525, + -0.177960426, -0.00839614868, 0.411925435, 0.536462843, 0.528389931}, + {0.270543516, -0.109283224, -0.58602041, -0.358663559, -0.393124342, + -0.0895933211, -0.632167816, 0.386703, 0.314152211, 0.0554139167}}, + {{-0.0331106335, -0.100827977, 0.322449774, 0.225943685, -0.384854138, + -0.208085626, 0.0206767023, 0.287796348, -0.139513299, 0.255447835}, + {-0.026678158, -0.453293741, 0.560033202, 0.105156109, -0.35259974, + 0.711447656, -0.253611118, 0.0487166606, -0.0861926, -0.0338740163}}, + {{-0.321974963, -0.466039389, 0.207254, -0.126807243, -0.192775548, + -0.0953653902, 0.209789664, 0.405356169, -0.00627979636, -0.059096083}, + {-0.345578849, 0.394073665, 0.299743384, -0.00751776947, -0.288939655, + 0.127782926, -0.20755057, 0.0655022934, -0.705084443, -0.241842657}}, + {{0.216089681, 0.0918798074, 0.0560657121, -0.157523692, -0.00141696166, + 0.51770097, 0.596379519, -0.271057934, 0.241035387, -0.275827795}, + {-0.0744233802, 0.180814296, 0.170143232, -0.337861359, -0.175804436, + 0.213403717, -0.173699334, 0.109528266, -0.385727286, 0.109684013}}, + {{-0.350524545, -0.142550975, -0.212269634, -0.0589753427, -0.434021264, + 0.384472728, 0.445421219, -0.635599554, -0.246593416, 0.120986834}, + {-0.227436215, 0.357608676, -0.253396749, -0.0683219433, -0.179259315, + 0.236576155, 0.559984267, 0.165754244, -0.0402980633, -0.101906672}}, + {{-0.246389568, -0.266164333, -0.0967710093, -0.401160359, 0.242542222, + 0.0869856179, 0.201580375, 0.207793966, -0.0875666812, -0.242263734}, + {0.703284621, 0.0382430181, 0.43997851, -0.858277559, 0.342218578, + 0.414044619, 0.403636098, -0.579880178, -1.12243, -0.112913512}}, + {{0.138267457, 0.483219147, 0.230450034, -0.568304598, 0.204461277, + -0.286731184, -0.416590065, -0.483460307, -0.561008453, 0.395195067}, + {0.282603294, 0.0723766834, -0.206548199, 0.561849773, 0.482716858, + 0.135281503, -0.438842, 0.472577244, -0.346201897, -0.0211652946}}, + {{-0.0537007526, -0.227346301, -0.28714636, 0.24774681, -0.0975415707, + -0.0123391608, 0.0612513833, -0.374673873, 0.283457726, 0.40945363}, + {0.0688965, -0.149037778, -0.246169269, 0.0599289, -0.456733376, + 0.0808929652, 0.115154937, 0.0997389406, -0.408117682, 0.576600909}}, + {{0.402076691, -0.11815206, 0.542395, 0.0382412523, -0.614984, 0.286177, + 0.318540573, -0.299300939, -0.177486524, 0.394140184}, + {0.105372488, -0.145784974, 0.0695323348, -0.106080391, -0.755512118, + 0.975362539, -0.15056029, 0.58882606, -0.059625227, -0.810613}}, + {{0.243249148, 0.0904035419, -0.472183734, -0.176162, 0.314925164, + -0.191137731, 0.492265761, -0.0120046511, 0.824757636, 0.298175}, + {0.0523641109, 0.224086404, 0.0143201668, 0.0090854, 0.304901183, + -0.391372293, 0.267655343, 0.117368169, 0.645064473, 0.336050332}}, +}; + +void RunAttentionTest(AttentionImpl attention_impl) { + TestState state; + TestModelState model_state(state); + TestAttentionState attention_state(state, model_state, kNumTokens, + kQBatchSize, attention_impl); + + GemmaAttention(attention_state.tokens.size(), 0, model_state.layers, + attention_state.attention, *attention_state.qbatch, state.env, + AttentionImplToFlags(attention_impl, HWY_NATIVE_DOT_BF16)); + + CompareAttSumsWithGolden(attention_state.attention, kGoldenAttSums); + CompareKVCacheWithGolden(model_state.config, + hwy::Span(attention_state.kv_caches.data(), + attention_state.kv_caches.size()), + /*layer=*/0, /*kv_head=*/0, kGoldenK, kGoldenV); + CompareQVecsWithGolden(model_state.config, attention_state.attention, + /*head=*/0, kGoldenQ); +} + +void TestGemmaAttentionOld() { RunAttentionTest(AttentionImpl::kOld); } + +void TestGemmaAttentionFlash() { RunAttentionTest(AttentionImpl::kFlash); } + +} // namespace HWY_NAMESPACE +} // namespace gcpp +HWY_AFTER_NAMESPACE(); + +#if HWY_ONCE + +namespace gcpp { +HWY_BEFORE_TEST(AttentionTest); +HWY_EXPORT_AND_TEST_P(AttentionTest, TestGemmaAttentionOld); +HWY_EXPORT_AND_TEST_P(AttentionTest, TestGemmaAttentionFlash); +HWY_AFTER_TEST(); + +} // namespace gcpp + +#endif