mirror of https://github.com/google/gemma.cpp.git
Fix Griffin model:
- use HalfRope position encodings - zero-initialize the caches for each Generate at position 0 The lack of the latter made the tests in gemma_test dependent on each other. PiperOrigin-RevId: 694509054
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d4050a2917
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@ -246,7 +246,7 @@ TEST_F(GemmaTest, CrossEntropySmall) {
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EXPECT_NEAR(entropy, 2.8f, 0.2f);
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break;
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case gcpp::Model::GRIFFIN_2B:
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EXPECT_NEAR(entropy, 1.57f, 0.02f);
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EXPECT_NEAR(entropy, 2.61f, 0.02f);
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break;
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case gcpp::Model::GEMMA2_2B:
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EXPECT_NEAR(entropy, 1.14f, 0.02f);
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@ -277,7 +277,7 @@ TEST_F(GemmaTest, CrossEntropyJingleBells) {
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EXPECT_NEAR(entropy, 1.07f, 0.05f);
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break;
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case gcpp::Model::GRIFFIN_2B:
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EXPECT_NEAR(entropy, 2.09f, 0.02f);
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EXPECT_NEAR(entropy, 1.62f, 0.02f);
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break;
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case gcpp::Model::GEMMA2_2B:
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EXPECT_NEAR(entropy, 0.49f, 0.02f);
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@ -308,7 +308,7 @@ TEST_F(GemmaTest, CrossEntropyGettysburg) {
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EXPECT_NEAR(entropy, 0.75f, 0.1f);
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break;
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case gcpp::Model::GRIFFIN_2B:
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EXPECT_NEAR(entropy, 0.86f, 0.02f);
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EXPECT_NEAR(entropy, 0.71f, 0.02f);
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break;
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case gcpp::Model::GEMMA2_2B:
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EXPECT_NEAR(entropy, 0.20f, 0.02f);
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@ -183,7 +183,7 @@ static ModelConfig ConfigGriffin2B() {
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.softmax_attn_output_biases = true,
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.type = LayerAttentionType::kGriffinRecurrentBlock,
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.activation = ActivationType::Gelu,
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.post_qk = PostQKType::Rope,
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.post_qk = PostQKType::HalfRope,
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};
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config.layer_configs = {26, layer_config};
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for (size_t i = 2; i < config.layer_configs.size(); i += 3) {
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@ -397,7 +397,11 @@ void AssertMatch(const ModelConfig& config) {
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ASSERT_EQ(TConfig::kPostNorm, config.layer_configs[i].post_norm);
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ASSERT_EQ(TConfig::kLayerConfig[i], config.layer_configs[i].type);
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ASSERT_EQ(TConfig::kActivation, config.layer_configs[i].activation);
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ASSERT_EQ(TConfig::kPostQK, config.layer_configs[i].post_qk);
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PostQKType post_qk = TConfig::kPostQK;
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if (TConfig::kUseHalfRope) {
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post_qk = PostQKType::HalfRope;
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}
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ASSERT_EQ(post_qk, config.layer_configs[i].post_qk);
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}
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ASSERT_EQ(TConfig::kAttentionWindowSizes.size(),
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@ -1240,8 +1240,12 @@ void GenerateT(const ModelWeightsStorage& model, Activations& activations,
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const QueriesPos& queries_prefix_end,
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const size_t query_idx_start, const KVCaches& kv_caches,
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TimingInfo& timing_info) {
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const size_t vocab_size = model.Config().vocab_size;
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const ModelWeightsPtrs<T>& weights = *model.GetWeightsOfType<T>();
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// Griffin assumes that the recurrent block cache is zero-initialized.
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for (size_t i = 0; i < kv_caches.size(); ++i) {
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if (queries_pos_in[i] == 0) {
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kv_caches[i].ZeroGriffinCache(); // No-op for non-Griffin models.
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}
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}
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// Copy so we can increment without requiring users to pass in a mutable span.
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std::vector<size_t> queries_pos_copy(queries_pos_in.cbegin(),
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@ -1268,7 +1272,7 @@ void GenerateT(const ModelWeightsStorage& model, Activations& activations,
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HWY_ASSERT(queries_pos_in.size() == num_queries);
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HWY_ASSERT(kv_caches.size() == num_queries);
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const hwy::Divisor div_seq_len(static_cast<uint32_t>(kv_caches[0].seq_len));
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const ModelWeightsPtrs<T>& weights = *model.GetWeightsOfType<T>();
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size_t max_prompt_size = MaxQueryLength(queries_prompt);
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size_t max_generated_tokens = runtime_config.max_generated_tokens;
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RangeChecks(weights.weights_config, max_generated_tokens, max_prompt_size);
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@ -1314,6 +1318,7 @@ void GenerateT(const ModelWeightsStorage& model, Activations& activations,
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0.0f);
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}
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const size_t vocab_size = model.Config().vocab_size;
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const double gen_start = hwy::platform::Now();
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for (size_t gen = 0; gen < max_generated_tokens; ++gen) {
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// Decode generates one token per query and increments queries_mutable_pos.
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@ -23,6 +23,17 @@
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namespace gcpp {
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void KVCache::ZeroGriffinCache() {
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if (conv1d_cache_size != 0) {
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hwy::ZeroBytes(conv1d_cache.get(),
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conv1d_cache_size * sizeof(conv1d_cache[0]));
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}
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if (rglru_cache_size != 0) {
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hwy::ZeroBytes(rglru_cache.get(),
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rglru_cache_size * sizeof(rglru_cache[0]));
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}
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}
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// prefill_tbatch_size is the maximum number of tokens from one query to
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// prefill at a time.
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KVCache KVCache::Create(const ModelConfig& weights_config,
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@ -37,9 +48,9 @@ KVCache KVCache::Create(const ModelConfig& weights_config,
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kv_cache.kv_cache =
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hwy::AllocateAligned<float>(kv_cache.seq_len * size_cache_pos);
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}
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size_t num_griffin_layers = weights_config.NumLayersOfType(
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LayerAttentionType::kGriffinRecurrentBlock);
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const size_t num_griffin_layers = weights_config.NumLayersOfType(
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LayerAttentionType::kGriffinRecurrentBlock);
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// TODO(patrickms): Add query batching support for Griffin.
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if (num_griffin_layers > 0) {
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size_t conv1d_width = 0;
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@ -49,20 +60,18 @@ KVCache KVCache::Create(const ModelConfig& weights_config,
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const size_t conv1d_cache_size =
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num_griffin_layers * (conv1d_width == 0 ? 0 : conv1d_width - 1) *
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weights_config.model_dim;
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kv_cache.conv1d_cache_size = conv1d_cache_size;
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if (conv1d_cache_size != 0) {
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kv_cache.conv1d_cache = hwy::AllocateAligned<float>(conv1d_cache_size);
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hwy::ZeroBytes(kv_cache.conv1d_cache.get(),
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conv1d_cache_size * sizeof(kv_cache.conv1d_cache[0]));
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}
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const size_t rglru_cache_size =
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num_griffin_layers * weights_config.model_dim;
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kv_cache.rglru_cache_size = rglru_cache_size;
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if (rglru_cache_size != 0) {
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kv_cache.rglru_cache = hwy::AllocateAligned<float>(rglru_cache_size);
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hwy::ZeroBytes(kv_cache.rglru_cache.get(),
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rglru_cache_size * sizeof(kv_cache.rglru_cache[0]));
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}
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} // kGriffinLayers
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} // num_griffin_layers
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return kv_cache;
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}
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@ -31,9 +31,15 @@ struct KVCache {
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// (kConv1dWidth - 1) * kModelDim * kGriffinLayers
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hwy::AlignedFreeUniquePtr<float[]> conv1d_cache;
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size_t conv1d_cache_size = 0;
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// kModelDim * kGriffinLayers
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hwy::AlignedFreeUniquePtr<float[]> rglru_cache;
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size_t rglru_cache_size = 0;
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// Zero-initialize the Griffin recurrent block cache, i.e. the conv1d_cache
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// and rglru_cache.
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void ZeroGriffinCache();
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static KVCache Create(const ModelConfig& weights_config,
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size_t prefill_tbatch_size);
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