From f8835fe4a43d8ab612ca153de237c058bcb468f0 Mon Sep 17 00:00:00 2001 From: Daniel Keysers Date: Mon, 23 Sep 2024 10:09:10 -0700 Subject: [PATCH] Add support for PaliGemma Vision-LM (224x224) to gemma.cpp See https://arxiv.org/abs/2407.07726 for a description of the model. Because PaliGemma operates as a prefix-LM on the image+prompt, add support for that. PiperOrigin-RevId: 677841119 --- BUILD.bazel | 5 + CMakeLists.txt | 7 + README.md | 105 ++-- compression/compress_weights.cc | 3 + gemma/activations.h | 6 +- gemma/common.cc | 7 +- gemma/common.h | 17 +- gemma/configs.h | 51 +- gemma/gemma-inl.h | 590 +++++++++++++++++---- gemma/gemma.cc | 76 ++- gemma/gemma.h | 33 +- gemma/instantiations/paligemma_224_bf16.cc | 21 + gemma/instantiations/paligemma_224_f32.cc | 21 + gemma/instantiations/paligemma_224_sfp.cc | 21 + gemma/run.cc | 33 +- gemma/tokenizer.cc | 8 + gemma/weights.h | 106 ++++ paligemma/BUILD | 45 ++ paligemma/image.cc | 182 +++++++ paligemma/image.h | 59 +++ paligemma/image_test.cc | 72 +++ paligemma/paligemma_test.cc | 132 +++++ paligemma/testdata/image.ppm | 192 +++++++ util/app.h | 2 + 24 files changed, 1630 insertions(+), 164 deletions(-) create mode 100644 gemma/instantiations/paligemma_224_bf16.cc create mode 100644 gemma/instantiations/paligemma_224_f32.cc create mode 100644 gemma/instantiations/paligemma_224_sfp.cc create mode 100644 paligemma/BUILD create mode 100644 paligemma/image.cc create mode 100644 paligemma/image.h create mode 100644 paligemma/image_test.cc create mode 100644 paligemma/paligemma_test.cc create mode 100644 paligemma/testdata/image.ppm diff --git a/BUILD.bazel b/BUILD.bazel index 830cf20..51882ea 100644 --- a/BUILD.bazel +++ b/BUILD.bazel @@ -242,6 +242,9 @@ cc_library( "gemma/instantiations/gemma2_2b_bf16.cc", "gemma/instantiations/gemma2_2b_f32.cc", "gemma/instantiations/gemma2_2b_sfp.cc", + "gemma/instantiations/paligemma_224_bf16.cc", + "gemma/instantiations/paligemma_224_f32.cc", + "gemma/instantiations/paligemma_224_sfp.cc", ], hdrs = [ "gemma/activations.h", @@ -264,6 +267,7 @@ cc_library( ":weights", ":threading", "//compression:io", + "//paligemma:image", "@hwy//:hwy", "@hwy//:bit_set", "@hwy//:matvec", @@ -361,6 +365,7 @@ cc_binary( ":gemma_lib", ":threading", # Placeholder for internal dep, do not remove., + "//paligemma:image", "@hwy//:hwy", "@hwy//:profiler", "@hwy//:thread_pool", diff --git a/CMakeLists.txt b/CMakeLists.txt index c49bdf9..1671e54 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -93,6 +93,9 @@ set(SOURCES gemma/instantiations/gemma2_2b_bf16.cc gemma/instantiations/gemma2_2b_f32.cc gemma/instantiations/gemma2_2b_sfp.cc + gemma/instantiations/paligemma_224_bf16.cc + gemma/instantiations/paligemma_224_f32.cc + gemma/instantiations/paligemma_224_sfp.cc gemma/kv_cache.cc gemma/kv_cache.h gemma/tokenizer.cc @@ -103,6 +106,8 @@ set(SOURCES ops/matmul-inl.h ops/matvec-inl.h ops/ops-inl.h + paligemma/image.cc + paligemma/image.h util/allocator.h util/app.h util/args.h @@ -164,6 +169,8 @@ set(GEMMA_TEST_FILES ops/matmul_test.cc ops/gemma_matvec_test.cc evals/gemma_test.cc + paligemma/image_test.cc + paligemma/paligemma_test.cc ) foreach (TESTFILE IN LISTS GEMMA_TEST_FILES) diff --git a/README.md b/README.md index f988ed1..868b911 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ deployment-oriented C++ inference runtimes, which are not designed for experimentation, and Python-centric ML research frameworks, which abstract away low-level computation through compilation. -gemma.cpp provides a minimalist implementation of Gemma 2B and 7B models, +gemma.cpp provides a minimalist implementation of Gemma-1 and Gemma-2 models, focusing on simplicity and directness rather than full generality. This is inspired by vertically-integrated model implementations such as [ggml](https://github.com/ggerganov/ggml), @@ -78,17 +78,20 @@ winget install --id Microsoft.VisualStudio.2022.BuildTools --force --override "- ### Step 1: Obtain model weights and tokenizer from Kaggle or Hugging Face Hub Visit the -[Kaggle page for Gemma](https://www.kaggle.com/models/google/gemma/frameworks/gemmaCpp), -or [Gemma-2](https://www.kaggle.com/models/google/gemma-2/gemmaCpp), and select -`Model Variations |> Gemma C++`. +[Kaggle page for Gemma-2](https://www.kaggle.com/models/google/gemma-2/gemmaCpp) +[or Gemma-1](https://www.kaggle.com/models/google/gemma/frameworks/gemmaCpp), +and select `Model Variations |> Gemma C++`. On this tab, the `Variation` dropdown includes the options below. Note bfloat16 weights are higher fidelity, while 8-bit switched floating point weights enable faster inference. In general, we recommend starting with the `-sfp` checkpoints. +If you are unsure which model to start with, we recommend starting with the +smallest Gemma-2 model, i.e. `2.0-2b-it-sfp`. + Alternatively, visit the [gemma.cpp](https://huggingface.co/models?other=gemma.cpp) models on the Hugging -Face Hub. First go the the model repository of the model of interest (see +Face Hub. First go the model repository of the model of interest (see recommendations below). Then, click the `Files and versions` tab and download the model and tokenizer files. For programmatic downloading, if you have `huggingface_hub` installed, you can also download by running: @@ -98,7 +101,7 @@ huggingface-cli login # Just the first time huggingface-cli download google/gemma-2b-sfp-cpp --local-dir build/ ``` -2B instruction-tuned (`it`) and pre-trained (`pt`) models: +Gemma-1 2B instruction-tuned (`it`) and pre-trained (`pt`) models: | Model name | Description | | ----------- | ----------- | @@ -107,7 +110,7 @@ huggingface-cli download google/gemma-2b-sfp-cpp --local-dir build/ | `2b-pt` | 2 billion parameter pre-trained model, bfloat16 | | `2b-pt-sfp` | 2 billion parameter pre-trained model, 8-bit switched floating point | -7B instruction-tuned (`it`) and pre-trained (`pt`) models: +Gemma-1 7B instruction-tuned (`it`) and pre-trained (`pt`) models: | Model name | Description | | ----------- | ----------- | @@ -256,6 +259,53 @@ Step 1, and run the binary as follows: `./gemma --tokenizer tokenizer.spm --model gr2b-it --weights 2b-it-sfp.sbs` +### PaliGemma Vision-Language Model + +This repository includes a version of the PaliGemma VLM +([paper](https://arxiv.org/abs/2407.07726), +[code](https://github.com/google-research/big_vision/tree/main/big_vision/configs/proj/paligemma)). +We provide a C++ implementation of this model here. + +To use the version of PaliGemma included in this repository, build the gemma +binary as noted above in Step 3. Download the compressed weights and tokenizer +from //TODO(keysers) - update location// and run the binary as follows: + +```./gemma \ +--tokenizer paligemma_tokenizer.model \ +--model paligemma-224 \ +--weights paligemma-3b-mix-224-sfp.sbs \ +--image_file paligemma/testdata/image.ppm +``` + +Note that the image reading code is very basic to avoid depending on an image +processing library for now. We currently only support reading binary PPMs (P6). +So use a tool like `convert` to first convert your images into that format, e.g. + +`convert image.jpeg -resize 224x224^ image.ppm` + +(As the image will be resized for processing anyway, we can already resize at +this stage for slightly faster loading.) + +The interaction with the image (using the mix-224 checkpoint) may then look +something like this: + +``` +> Describe the image briefly +A large building with two towers in the middle of a city. +> What type of building is it? +church +> What color is the church? +gray +> caption image +A large building with two towers stands tall on the water's edge. The building +has a brown roof and a window on the side. A tree stands in front of the +building, and a flag waves proudly from its top. The water is calm and blue, +reflecting the sky above. A bridge crosses the water, and a red and white boat +rests on its surface. The building has a window on the side, and a flag on top. +A tall tree stands in front of the building, and a window on the building is +visible from the water. The water is green, and the sky is blue. +``` + ### Troubleshooting and FAQs **Running `./gemma` fails with "Failed to read cache gating_ein_0 (error 294) ..."** @@ -283,8 +333,8 @@ and not a pre-trained model (any model with a `-pt` suffix). **How do I convert my fine-tune to a `.sbs` compressed model file?** We're working on a python script to convert a standard model format to `.sbs`, -and hope have it available in the next week or so. Follow [this -issue](https://github.com/google/gemma.cpp/issues/11) for updates. +and hope have it available soon. Follow +[this issue](https://github.com/google/gemma.cpp/issues/11) for updates. **What are some easy ways to make the model run faster?** @@ -371,7 +421,7 @@ For using the `gemma` executable as a command line tool, it may be useful to create an alias for gemma.cpp with arguments fully specified: ```sh -alias gemma2b="~/gemma.cpp/build/gemma -- --tokenizer ~/gemma.cpp/build/tokenizer.spm --weights ~/gemma.cpp/build/2b-it-sfp.sbs --model 2b-it --verbosity 0" +alias gemma2b="~/gemma.cpp/build/gemma -- --tokenizer ~/gemma.cpp/build/tokenizer.spm --weights ~/gemma.cpp/build/gemma2-2b-it-sfp.sbs --model gemma2-2b-it --verbosity 0" ``` Replace the above paths with your own paths to the model and tokenizer paths @@ -381,7 +431,7 @@ Here is an example of prompting `gemma` with a truncated input file (using a `gemma2b` alias like defined above): ```sh -cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b +cat configs.h | tail -n 35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b ``` > [!NOTE] @@ -391,27 +441,11 @@ cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code The output of the above command should look like: ```console -$ cat configs.h | tail -35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b -[ Reading prompt ] ...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... -The code defines two C++ structs, `ConfigGemma7B` and `ConfigGemma2B`, which are used for configuring a deep learning model. +[ Reading prompt ] [...] +This C++ code snippet defines a set of **constants** used in a large language model (LLM) implementation, likely related to the **attention mechanism**. -**ConfigGemma7B**: - -* `kSeqLen`: Stores the length of the sequence to be processed. It's set to 7168. -* `kVocabSize`: Stores the size of the vocabulary, which is 256128. -* `kLayers`: Number of layers in the deep learning model. It's set to 28. -* `kModelDim`: Dimension of the model's internal representation. It's set to 3072. -* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 3072 / 2. - -**ConfigGemma2B**: - -* `kSeqLen`: Stores the length of the sequence to be processed. It's also set to 7168. -* `kVocabSize`: Size of the vocabulary, which is 256128. -* `kLayers`: Number of layers in the deep learning model. It's set to 18. -* `kModelDim`: Dimension of the model's internal representation. It's set to 2048. -* `kFFHiddenDim`: Dimension of the feedforward and recurrent layers' hidden representations. It's set to 16 * 2048 / 2. - -These structs are used to configure a deep learning model with specific parameters for either Gemma7B or Gemma2B architecture. +Let's break down the code: +[...] ``` ### Incorporating gemma.cpp as a Library in your Project @@ -496,4 +530,13 @@ Mikhaylov, Eugene Kliuchnikov, Jan Wassenberg, Jyrki Alakuijala, Lode Vandevenne, Luca Versari, Martin Bruse, Phil Culliton, Sami Boukortt, Thomas Fischbacher and Zoltan Szabadka. +Gemma-2 support was implemented in June/July 2024 with the help of several +people. + +PaliGemma support was implemented in September 2024 with contributions from +Daniel Keysers. + +[Jan Wassenberg](mailto:janwas@google.com) has continued to contribute many +improvements, including major gains in efficiency, since the initial release. + This is not an officially supported Google product. diff --git a/compression/compress_weights.cc b/compression/compress_weights.cc index a36b35c..41b46be 100644 --- a/compression/compress_weights.cc +++ b/compression/compress_weights.cc @@ -322,6 +322,9 @@ namespace gcpp { void Run(Args& args) { hwy::ThreadPool pool(args.num_threads); const Model model_type = args.ModelType(); + if (model_type == Model::PALIGEMMA_224) { + HWY_ABORT("PaliGemma is not supported in compress_weights."); + } const Type weight_type = args.WeightType(); GEMMA_EXPORT_AND_DISPATCH( model_type, weight_type, CompressWeights, diff --git a/gemma/activations.h b/gemma/activations.h index b2113da..d819d4a 100644 --- a/gemma/activations.h +++ b/gemma/activations.h @@ -97,7 +97,9 @@ struct Activations { x = RowVectorBatch(batch_size, kModelDim); q = RowVectorBatch(batch_size, kHeads * QStride()); - logits = RowVectorBatch(batch_size, kVocabSize); + if constexpr (kVocabSize > 0) { + logits = RowVectorBatch(batch_size, kVocabSize); + } pre_att_rms_out = RowVectorBatch(batch_size, kModelDim); att = RowVectorBatch(batch_size, kHeads * kSeqLen); @@ -109,7 +111,7 @@ struct Activations { C2 = RowVectorBatch(batch_size, kFFHiddenDim); ffw_out = RowVectorBatch(batch_size, kModelDim); - if (kGriffinLayers > 0) { + if constexpr (kGriffinLayers > 0) { griffin_x = RowVectorBatch(batch_size, kModelDim); griffin_y = RowVectorBatch(batch_size, kModelDim); griffin_gate_x = RowVectorBatch(batch_size, kModelDim); diff --git a/gemma/common.cc b/gemma/common.cc index 4161686..e68347b 100644 --- a/gemma/common.cc +++ b/gemma/common.cc @@ -36,6 +36,7 @@ constexpr const char* kModelFlags[] = { "gemma2-2b-pt", "gemma2-2b-it", // Gemma2 2B "9b-pt", "9b-it", // Gemma2 9B "27b-pt", "27b-it", // Gemma2 27B + "paligemma-224", // PaliGemma 224 }; constexpr Model kModelTypes[] = { Model::GEMMA_2B, Model::GEMMA_2B, // Gemma 2B @@ -43,8 +44,9 @@ constexpr Model kModelTypes[] = { Model::GRIFFIN_2B, Model::GRIFFIN_2B, // RecurrentGemma Model::GEMMA_TINY, // Gemma Tiny Model::GEMMA2_2B, Model::GEMMA2_2B, // Gemma2 2B - Model::GEMMA2_9B, Model::GEMMA2_9B, // Gemma2 9B - Model::GEMMA2_27B, Model::GEMMA2_27B, // Gemma2 27B + Model::GEMMA2_9B, Model::GEMMA2_9B, // Gemma2 9B + Model::GEMMA2_27B, Model::GEMMA2_27B, // Gemma2 27B + Model::PALIGEMMA_224, // PaliGemma 224 }; constexpr ModelTraining kModelTraining[] = { ModelTraining::GEMMA_PT, ModelTraining::GEMMA_IT, // Gemma 2B @@ -54,6 +56,7 @@ constexpr ModelTraining kModelTraining[] = { ModelTraining::GEMMA_PT, ModelTraining::GEMMA_IT, // Gemma2 2B ModelTraining::GEMMA_PT, ModelTraining::GEMMA_IT, // Gemma2 9B ModelTraining::GEMMA_PT, ModelTraining::GEMMA_IT, // Gemma2 27B + ModelTraining::PALIGEMMA, // PaliGemma 224 }; constexpr size_t kNumModelFlags = diff --git a/gemma/common.h b/gemma/common.h index 40b8c9f..2960b7c 100644 --- a/gemma/common.h +++ b/gemma/common.h @@ -37,10 +37,11 @@ enum class Model { GRIFFIN_2B, GEMMA_TINY, GEMMA2_2B, + PALIGEMMA_224, }; // Instruction-tuned models require extra 'turn structure' tokens in prompts. -enum class ModelTraining { GEMMA_IT, GEMMA_PT }; +enum class ModelTraining { GEMMA_IT, GEMMA_PT, PALIGEMMA }; // Tensor types for loading weights. When adding a new one, also // update GEMMA_FOREACH* and Call* below, and add instantiations/*.cc. @@ -93,7 +94,9 @@ decltype(auto) CallForModel(Model model, TArgs&&... args) { return FuncT>()(std::forward(args)...); case Model::GEMMA2_2B: return FuncT>()(std::forward(args)...); - + case Model::PALIGEMMA_224: + return FuncT>()( + std::forward(args)...); default: HWY_ABORT("Model type %d unknown.", static_cast(model)); } @@ -136,8 +139,9 @@ decltype(auto) CallForModelAndWeight(Model model, Type weight, GEMMA_FOREACH_WEIGHT(X, ConfigGemma7B) \ GEMMA_FOREACH_WEIGHT(X, ConfigGriffin2B) \ GEMMA_FOREACH_WEIGHT(X, ConfigGemma2_2B) \ - GEMMA_FOREACH_WEIGHT(X, ConfigGemma2_9B) \ - GEMMA_FOREACH_WEIGHT(X, ConfigGemma2_27B) \ + GEMMA_FOREACH_WEIGHT(X, ConfigGemma2_9B) \ + GEMMA_FOREACH_WEIGHT(X, ConfigGemma2_27B) \ + GEMMA_FOREACH_WEIGHT(X, ConfigPaliGemma_224) \ static_assert(true, "Allow trailing ;") // Used by GEMMA_EXPORT_AND_DISPATCH. For a given TWEIGHT (e.g. float), @@ -179,6 +183,11 @@ decltype(auto) CallForModelAndWeight(Model model, Type weight, ARGS; \ break; \ } \ + case Model::PALIGEMMA_224: { \ + HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC>)\ + ARGS; \ + break; \ + } \ default: \ HWY_ABORT("Model type %d unknown.", static_cast(MODEL)); \ } diff --git a/gemma/configs.h b/gemma/configs.h index cb903fd..d034da2 100644 --- a/gemma/configs.h +++ b/gemma/configs.h @@ -44,6 +44,7 @@ using EmbedderInputT = hwy::bfloat16_t; enum class LayerAttentionType { kGemma, kGriffinRecurrentBlock, + kVit, }; // Post attention and ffw normalization type. @@ -131,7 +132,22 @@ struct CachePosSize { } }; -struct ConfigNoSSM { +struct ConfigNoVit { + struct VitConfig { + static constexpr int kLayers = 0; + static constexpr std::array kLayerConfig = + FixedLayerConfig<0>(LayerAttentionType::kVit); + static constexpr int kModelDim = 0; + static constexpr int kFFHiddenDim = 0; + static constexpr int kHeads = 0; + static constexpr int kKVHeads = 0; + static constexpr int kQKVDim = 0; + static constexpr int kSeqLen = 0; + static constexpr ResidualType kResidual = ResidualType::Add; + }; +}; + +struct ConfigNoSSM : ConfigNoVit { static constexpr int kGriffinLayers = 0; static constexpr int kConv1dWidth = 0; @@ -247,6 +263,37 @@ struct ConfigGemma2B : public ConfigBaseGemmaV1 { static constexpr bool kAbsolutePE = false; }; +template +struct ConfigPaliGemma_224 : public ConfigGemma2B { + // On the LM side, the vocab size is one difference to Gemma1-2B in the + // architecture. PaliGemma adds 1024 and 128 tokens. + static constexpr int kVocabSize = 256000 + 1024 + 128; // = 257152 + + // Sub-config for the Vision-Transformer part. + struct VitConfig : public ConfigNoSSM { + using Weight = TWeight; + // The ViT parts. https://arxiv.org/abs/2305.13035 + // "SoViT-400m/14 [...] has a width of 1152, depth 27, and MLP dim 4304." + static constexpr std::array kLayerConfig = + FixedLayerConfig<27>(LayerAttentionType::kVit); + static constexpr int kLayers = kLayerConfig.size(); + static constexpr int kModelDim = 1152; + static constexpr int kFFHiddenDim = 4304; + static constexpr int kHeads = 16; + static constexpr int kKVHeads = 16; // standard MHA + static constexpr int kQKVDim = 72; + static constexpr int kSeqLen = 16 * 16; // 256 + static constexpr bool kFFBiases = true; + // The Vit part does not have a vocabulary, the image patches are embedded. + static constexpr int kVocabSize = 0; + // Dimensions related to image processing. + static constexpr int kPatchWidth = 14; + static constexpr int kImageSize = 224; + // Necessary constant for the layer configuration. + static constexpr PostNormType kPostNorm = PostNormType::None; + }; +}; + template struct ConfigGemma2_2B : public ConfigBaseGemmaV2 { using Weight = TWeight; // make accessible where we only have a TConfig @@ -297,7 +344,7 @@ struct ConfigGemmaTiny : public ConfigNoSSM { }; template -struct ConfigGriffin2B { +struct ConfigGriffin2B : ConfigNoVit { using Weight = TWeight; // make accessible where we only have a TConfig // Griffin uses local attention, so kSeqLen is actually the local attention diff --git a/gemma/gemma-inl.h b/gemma/gemma-inl.h index bfdb2d4..dec6449 100644 --- a/gemma/gemma-inl.h +++ b/gemma/gemma-inl.h @@ -41,6 +41,7 @@ #include "ops/matmul-inl.h" #include "ops/matvec-inl.h" #include "ops/ops-inl.h" +#include "paligemma/image.h" #include "util/allocator.h" #include "util/threading.h" #include "hwy/aligned_allocator.h" @@ -209,7 +210,7 @@ class GemmaAttention { static constexpr size_t kSeqLen = TConfig::kSeqLen; static constexpr bool kIsMHA = Activations::IsMHA(); - // The attention window usually starts at 0 unless unless `pos` is larger than + // The attention window usually starts at 0 unless `pos` is larger than // the attention window size, then it is `pos` - window_size + 1. static HWY_INLINE size_t StartPos(size_t pos, size_t layer) { const size_t att_window_size = TConfig::kAttentionWindowSizes[layer]; @@ -318,26 +319,26 @@ class GemmaAttention { } // Computes Q.K scores, which are "logits" (or scores) stored to head_att. - HWY_INLINE void QDotK(const size_t start_pos, const size_t pos, + HWY_INLINE void QDotK(const size_t start_pos, const size_t last_pos, const size_t head_offset, const float* HWY_RESTRICT q, const KVCache& kv_cache, float* HWY_RESTRICT head_att) { - if (HWY_LIKELY(pos < kSeqLen)) { + if (HWY_LIKELY(last_pos < kSeqLen)) { // Slightly faster: no wraparound. - for (size_t pos2 = start_pos; pos2 <= pos; ++pos2) { + for (size_t pos = start_pos; pos <= last_pos; ++pos) { const size_t kv_offset = - pos2 * kCachePosSize + layer_ * kCacheLayerSize + head_offset; + pos * kCachePosSize + layer_ * kCacheLayerSize + head_offset; const float* HWY_RESTRICT k = &kv_cache.kv_cache[kv_offset]; const float score = Dot(q, k, kQKVDim); - head_att[pos2] = score; + head_att[pos] = score; } } else { - for (size_t pos2 = start_pos; pos2 <= pos; ++pos2) { - const size_t cache_pos = div_seq_len_.Remainder(pos2); + for (size_t pos = start_pos; pos <= last_pos; ++pos) { + const size_t cache_pos = div_seq_len_.Remainder(pos); const size_t kv_offset = cache_pos * kCachePosSize + layer_ * kCacheLayerSize + head_offset; const float* HWY_RESTRICT k = &kv_cache.kv_cache[kv_offset]; const float score = Dot(q, k, kQKVDim); - head_att[pos2 % kSeqLen] = score; + head_att[pos % kSeqLen] = score; } } } @@ -345,32 +346,30 @@ class GemmaAttention { // Accumulates the sum of v (from `kv_cache`) * probability (`head_att`) into // `att_out`. Equivalent in gemma/modules.py: // encoded = jnp.einsum('BTNS,BSNH->BTNH', probs, value_proj) - static HWY_INLINE void WeightedSumV(const size_t start_pos, const size_t pos, - const float* HWY_RESTRICT head_att, - const size_t layer, - const size_t head_offset, - const hwy::Divisor& div_seq_len, - const KVCache& kv_cache, - float* HWY_RESTRICT att_out) { + static HWY_INLINE void WeightedSumV( + const size_t start_pos, const size_t last_pos, + const float* HWY_RESTRICT head_att, const size_t layer, + const size_t head_offset, const hwy::Divisor& div_seq_len, + const KVCache& kv_cache, float* HWY_RESTRICT att_out) { hwy::ZeroBytes(att_out, kQKVDim * sizeof(*att_out)); - if (HWY_LIKELY(pos < kSeqLen)) { + if (HWY_LIKELY(last_pos < kSeqLen)) { // Slightly faster: no wraparound. - for (size_t pos2 = start_pos; pos2 <= pos; ++pos2) { + for (size_t pos = start_pos; pos <= last_pos; ++pos) { const size_t kv_offset = - pos2 * kCachePosSize + layer * kCacheLayerSize + head_offset; + pos * kCachePosSize + layer * kCacheLayerSize + head_offset; const float* HWY_RESTRICT v = kv_cache.kv_cache.get() + kv_offset + kQKVDim; - MulByConstAndAdd(head_att[pos2], v, att_out, kQKVDim); + MulByConstAndAdd(head_att[pos], v, att_out, kQKVDim); } } else { - for (size_t pos2 = start_pos; pos2 <= pos; ++pos2) { - const size_t cache_pos = div_seq_len.Remainder(pos2); + for (size_t pos = start_pos; pos <= last_pos; ++pos) { + const size_t cache_pos = div_seq_len.Remainder(pos); const size_t kv_offset = cache_pos * kCachePosSize + layer * kCacheLayerSize + head_offset; const float* HWY_RESTRICT v = kv_cache.kv_cache.get() + kv_offset + kQKVDim; - MulByConstAndAdd(head_att[pos2 % kSeqLen], v, att_out, kQKVDim); + MulByConstAndAdd(head_att[pos % kSeqLen], v, att_out, kQKVDim); } } } @@ -402,20 +401,26 @@ class GemmaAttention { PositionalEncodingQK(q, pos, layer_, kQueryScale, q); const size_t start_pos = StartPos(pos, layer_); + size_t last_pos = pos; + const size_t prefix_end = queries_prefix_end_[query_idx]; + if (prefix_end > 0 && prefix_end - 1 > last_pos) { + // last_pos in QDotK and WeightedSumV is inclusive. + last_pos = prefix_end - 1; + } float* HWY_RESTRICT head_att = activations_.att.Batch(interleaved_idx) + head * kSeqLen; - QDotK(start_pos, pos, head_offset, q, kv_cache, head_att); + QDotK(start_pos, last_pos, head_offset, q, kv_cache, head_att); // SoftMax with optional SoftCap yields "probabilities" in // head_att. - const size_t head_att_len = std::min(pos + 1, kSeqLen); + const size_t head_att_len = std::min(last_pos + 1, kSeqLen); MaybeLogitsSoftCap(TConfig::kAttCap, head_att, head_att_len); Softmax(head_att, head_att_len); float* HWY_RESTRICT att_out = activations_.att_out.Batch(interleaved_idx) + head * kQKVDim; - WeightedSumV(start_pos, pos, head_att, layer_, head_offset, + WeightedSumV(start_pos, last_pos, head_att, layer_, head_offset, div_seq_len_, kv_cache, att_out); }); } @@ -435,16 +440,40 @@ class GemmaAttention { MatMul( num_interleaved, ConstMat(activations_.att_out.All(), kHeads * kQKVDim), ConstMat(layer_weights_.att_weights.data(), kHeads * kQKVDim), - layer_weights_.attn_vec_einsum_w.scale(), bias, activations_.env, + layer_weights_.att_weights.scale(), bias, activations_.env, MutableMat(activations_.att_sums.All(), kModelDim)); } public: + // Constructor with explicit initialization of queries_prefix_end. This is + // needed for the Prefix-LM style attention. For standard causal attention, + // the other constructor can be used. + GemmaAttention(const QueriesPos& queries_pos, + const QueriesPos& queries_prefix_end, size_t num_tokens, + size_t layer, Activations& activations, + const CompressedLayer* layer_weights, + const hwy::Divisor& div_seq_len, const KVCaches& kv_caches) + : queries_pos_(queries_pos), + queries_prefix_end_(queries_prefix_end), + num_queries_(queries_pos.size()), + num_tokens_(num_tokens), + layer_(layer), + activations_(activations), + layer_weights_(*layer_weights), + div_seq_len_(div_seq_len), + kv_caches_(kv_caches), + pool_(activations.env.Pool()) { + HWY_DASSERT(num_queries_ <= kv_caches_.size()); + } + // Constructor with default initialization to 0 for queries_prefix_end. GemmaAttention(const QueriesPos& queries_pos, size_t num_tokens, size_t layer, Activations& activations, const CompressedLayer* layer_weights, const hwy::Divisor& div_seq_len, const KVCaches& kv_caches) : queries_pos_(queries_pos), + queries_prefix_end_vec_(queries_pos.size(), 0), + queries_prefix_end_(queries_prefix_end_vec_.data(), + queries_prefix_end_vec_.size()), num_queries_(queries_pos.size()), num_tokens_(num_tokens), layer_(layer), @@ -456,6 +485,7 @@ class GemmaAttention { HWY_DASSERT(num_queries_ <= kv_caches_.size()); } + // Full attention computation in three steps. HWY_INLINE void operator()() { const size_t num_interleaved = num_tokens_ * num_queries_; ComputeQKV(num_interleaved); @@ -465,6 +495,8 @@ class GemmaAttention { private: const QueriesPos& queries_pos_; + const std::vector queries_prefix_end_vec_; + const QueriesPos queries_prefix_end_; const size_t num_queries_; const size_t num_tokens_; const size_t layer_; @@ -476,15 +508,15 @@ class GemmaAttention { }; template -HWY_NOINLINE void Attention(LayerAttentionType type, - const QueriesPos& queries_pos, size_t num_tokens, - size_t layer, Activations& activations, - const CompressedLayer* layer_weights, - const hwy::Divisor& div_seq_len, - const KVCaches& kv_caches) { +HWY_NOINLINE void Attention( + LayerAttentionType type, const QueriesPos& queries_pos, + const QueriesPos& queries_prefix_end, size_t num_tokens, size_t layer, + Activations& activations, const CompressedLayer* layer_weights, + const hwy::Divisor& div_seq_len, const KVCaches& kv_caches) { if (type == LayerAttentionType::kGemma) { - GemmaAttention(queries_pos, num_tokens, layer, activations, - layer_weights, div_seq_len, kv_caches)(); + GemmaAttention(queries_pos, queries_prefix_end, num_tokens, layer, + activations, layer_weights, div_seq_len, + kv_caches)(); } else { // Only reached if the model is Griffin. `if constexpr` prevents generating // this code for non-Griffin models. @@ -496,6 +528,115 @@ HWY_NOINLINE void Attention(LayerAttentionType type, } } +// Wrapper class; holds arguments in member variables to shorten call sites. +// The main differences to GemmaAttention are: +// - no KV Cache necessary, attention is always all-to-all and not causal. +// - no potential wrap-around, attention always goes from 0 to kSeqLen. +// - no need for batching, as we are always computing attention for kSeqLen +// tokens. +// This results in a much simpler implementation. However, to avoid duplicating +// code, we should still consider merging the two classes. +// TODO(keysers): Refactor to share code with GemmaAttention. +template +class VitAttention { + static constexpr size_t kHeads = TConfig::kHeads; + static constexpr size_t kKVHeads = TConfig::kKVHeads; + static constexpr size_t kModelDim = TConfig::kModelDim; + static constexpr size_t kQKVDim = TConfig::kQKVDim; + static constexpr size_t kQStride = 3 * kQKVDim; + static constexpr size_t kSeqLen = TConfig::kSeqLen; + + // Computes Q, K, V for all heads, stored in activations_.q. + HWY_NOINLINE void ComputeQKV() { + PROFILER_ZONE("Gen.VitAttention.QKV"); + const auto y = + ConstMat(activations_.pre_att_rms_out.All(), kModelDim); + auto& qkv = activations_.q; + HWY_ASSERT(qkv.BatchSize() == num_tokens_); + HWY_ASSERT(qkv.Len() == kHeads * kQStride); + MatMul( + num_tokens_, y, + ConstMat(layer_weights_.vit.qkv_einsum_w.data_scale1(), kModelDim), + /*scale=*/1.0f, layer_weights_.vit.qkv_einsum_b.data_scale1(), + activations_.env, MutableMat(qkv.All(), qkv.Len())); + } + + HWY_NOINLINE void DotSoftmaxWeightedSum() { + GEMMA_CONSTEXPR_SQRT float kQueryScale = + 1.0f / Sqrt(static_cast(TConfig::kQKVDim)); + PROFILER_ZONE("Gen.VitAttention.DotSoftmax"); + // A "head group" in the context of GQA refers to a collection of query + // heads that share the same key and value heads. + static_assert(kHeads == kKVHeads, "Vit expects MHA"); + + // Compute Q.K, softmax, and weighted V. + pool_.Run(0, kHeads * num_tokens_, + [&](uint64_t task, size_t /*thread*/) HWY_ATTR { + const size_t head = task % kHeads; + const size_t token = task / kHeads; + // Compute Q.K scores, which are "logits" stored in head_att. + float* HWY_RESTRICT q = + activations_.q.Batch(token) + head * kQStride; + MulByConst(kQueryScale, q, kQKVDim); + float* HWY_RESTRICT head_att = + activations_.att.Batch(token) + head * kSeqLen; + for (size_t i = 0; i < kSeqLen; ++i) { + float* HWY_RESTRICT k = + activations_.q.Batch(i) + head * kQStride + kQKVDim; + head_att[i] = Dot(q, k, kQKVDim); // score = q.k + } + // SoftMax yields "probabilities" in head_att. + Softmax(head_att, kSeqLen); + // Compute weighted sum of v into att_out. + float* HWY_RESTRICT att_out = + activations_.att_out.Batch(token) + head * kQKVDim; + hwy::ZeroBytes(att_out, kQKVDim * sizeof(*att_out)); + for (size_t i = 0; i < kSeqLen; ++i) { + float* HWY_RESTRICT v = + activations_.q.Batch(i) + head * kQStride + 2 * kQKVDim; + MulByConstAndAdd(head_att[i], v, att_out, kQKVDim); + } + }); + } + + // Sums encoded (`att_out`) over num_heads (`kHeads`) and head_dim (`kQKVDim`) + // into output (`att_sums`). + HWY_NOINLINE void SumHeads() { + PROFILER_ZONE("Gen.VitAttention.SumHeads"); + auto* bias = layer_weights_.vit.attn_out_b.data_scale1(); + auto att_out = ConstMat(activations_.att_out.All(), kHeads * kQKVDim); + auto att_weights = ConstMat(layer_weights_.vit.attn_out_w.data_scale1(), + kHeads * kQKVDim); + auto att_sums = MutableMat(activations_.att_sums.All(), kModelDim); + // att_weights and att_out are concatenated heads, each of length kQKVDim. + // Thus the [num_tokens_, kModelDim] matmul output is the sum over heads. + MatMul(num_tokens_, att_out, att_weights, /*scale=*/1.0f, + bias, activations_.env, att_sums); + } + + public: + VitAttention(size_t num_tokens, size_t layer, Activations& activations, + const CompressedLayer* layer_weights) + : num_tokens_(num_tokens), + layer_(layer), + activations_(activations), + layer_weights_(*layer_weights), + pool_(activations.env.Pool()) {} + + HWY_INLINE void operator()() { + ComputeQKV(); + DotSoftmaxWeightedSum(); + SumHeads(); + } + + private: + const size_t num_tokens_; + const size_t layer_; + Activations& activations_; + const CompressedLayer& layer_weights_; + hwy::ThreadPool& pool_; +}; + template HWY_NOINLINE void Activation(T* HWY_RESTRICT c1, T* HWY_RESTRICT c2, size_t count) { @@ -504,6 +645,11 @@ HWY_NOINLINE void Activation(T* HWY_RESTRICT c1, T* HWY_RESTRICT c2, using DF = hn::ScalableTag; using VF = hn::Vec; // ActivationType::Gelu + if (c2 == nullptr) { // No multiplier, just Gelu. + Gelu(c1, count); + return; + }; + // Has multiplier, Gelu(c1) * c2. hn::Transform1(DF(), c1, count, c2, [](DF df, VF v, VF mul) HWY_ATTR { return hn::Mul(mul, Gelu(df, v)); }); @@ -515,41 +661,66 @@ HWY_NOINLINE void FFW(Activations& activations, size_t num_interleaved, PROFILER_ZONE("Gen.FFW"); constexpr size_t kModelDim = TConfig::kModelDim; constexpr size_t kFFHiddenDim = TConfig::kFFHiddenDim; - - // MatMul expects col-major B, which is what we have: kModelDim consecutive - // elements in memory, repeated kFFHiddenDim times. - HWY_DASSERT(num_interleaved <= activations.bf_pre_ffw_rms_out.BatchSize()); - const auto A = ConstMat(activations.bf_pre_ffw_rms_out.All(), kModelDim); - const auto B1 = ConstMat(layer_weights->gating_einsum_w.data(), kModelDim); - const auto B2 = ConstMat(layer_weights->gating_einsum_w.data(), kModelDim, - kModelDim, kModelDim * kFFHiddenDim); - const float scale = layer_weights->gating_einsum_w.scale(); constexpr bool kAddBias = TConfig::kFFBiases; + constexpr bool kIsVit = TConfig::kLayerConfig[0] == LayerAttentionType::kVit; + using WeightType = + hwy::If::WeightF32OrBF16, + typename CompressedLayer::Weight>; + HWY_DASSERT(num_interleaved <= activations.bf_pre_ffw_rms_out.BatchSize()); + + // Define slightly more readable names for the weights and activations. + const auto x = ConstMat(activations.bf_pre_ffw_rms_out.All(), kModelDim); + Mat w1; const float* bias1 = nullptr; + Mat w2; const float* bias2 = nullptr; + float scale = 1.0f; + Mat w_output; const float* output_bias = nullptr; - if constexpr (kAddBias) { + float output_scale = 1.0f; + auto hidden_activations = MutableMat(activations.C1.All(), kFFHiddenDim); + auto multiplier = MutableMat(activations.C2.All(), kFFHiddenDim); + auto ffw_out = MutableMat(activations.ffw_out.All(), kModelDim); + + // For some of the weights and activations, it depends on the config where to + // get them from or whether to use them at all. + if constexpr (kAddBias && !kIsVit) { bias1 = layer_weights->ffw_gating_biases.data_scale1(); bias2 = bias1 + kFFHiddenDim; output_bias = layer_weights->ffw_output_biases.data_scale1(); } - auto C1 = MutableMat(activations.C1.All(), kFFHiddenDim); - auto C2 = MutableMat(activations.C2.All(), kFFHiddenDim); + if constexpr (!kIsVit) { + w1 = ConstMat(layer_weights->gating_einsum_w.data(), kModelDim); + w2 = ConstMat(layer_weights->gating_einsum_w.data(), kModelDim, kModelDim, + kModelDim * kFFHiddenDim); + scale = layer_weights->gating_einsum_w.scale(); + w_output = ConstMat(layer_weights->linear_w.data(), kFFHiddenDim); + output_scale = layer_weights->linear_w.scale(); + } else { + w1 = ConstMat(layer_weights->vit.linear_0_w.data_scale1(), kModelDim); + bias1 = layer_weights->vit.linear_0_b.data_scale1(); + multiplier.ptr = nullptr; + w_output = + ConstMat(layer_weights->vit.linear_1_w.data_scale1(), kFFHiddenDim); + output_bias = layer_weights->vit.linear_1_b.data_scale1(); + } - // Will go through GELU. - MatMul(num_interleaved, A, B1, scale, bias1, activations.env, C1); - // What to multiply by. - MatMul(num_interleaved, A, B2, scale, bias2, activations.env, C2); + // Compute the hidden layer activations. + MatMul(num_interleaved, x, w1, scale, bias1, activations.env, + hidden_activations); + if constexpr (!kIsVit) { + MatMul(num_interleaved, x, w2, scale, bias2, activations.env, + multiplier); + } - // Activation (Gelu) and multiply by gate. Store activations in C1. - Activation(C1.ptr, C2.ptr, kFFHiddenDim * num_interleaved); + // Activation (Gelu) and maybe multiply by gate. Store activations in act. + Activation(hidden_activations.ptr, multiplier.ptr, + kFFHiddenDim * num_interleaved); // Hidden layer -> output layer. - MatMul(num_interleaved, ConstMat(C1), - ConstMat(layer_weights->linear_w.data(), kFFHiddenDim), - layer_weights->linear_w.scale(), output_bias, - activations.env, - MutableMat(activations.ffw_out.All(), kModelDim)); + MatMul(num_interleaved, ConstMat(hidden_activations), w_output, + output_scale, output_bias, activations.env, ffw_out); } // `batch_idx` indicates which row of `x` to write to. @@ -557,8 +728,17 @@ HWY_NOINLINE void FFW(Activations& activations, size_t num_interleaved, // called for batches of tokens in prefill, but batches of queries in decode. template HWY_NOINLINE void EmbedToken(int token, size_t batch_idx, size_t pos, + size_t pos_in_prompt, const CompressedWeights& weights, - RowVectorBatch& x) { + RowVectorBatch& x, + const ImageTokens* image_tokens) { + // Image tokens just need to be copied. + if (image_tokens != nullptr && pos_in_prompt < image_tokens->BatchSize()) { + hwy::CopyBytes(image_tokens->Batch(pos_in_prompt), x.Batch(batch_idx), + x.Len() * sizeof(x.Const()[0])); + return; + } + constexpr size_t kModelDim = TConfig::kModelDim; constexpr size_t kVocabSize = TConfig::kVocabSize; GEMMA_CONSTEXPR_EMBSCALING const float kEmbScaling = @@ -598,7 +778,8 @@ void PostNorm(size_t num_interleaved, const WeightT& weights, InOutT* inout) { template HWY_NOINLINE void TransformerLayer( - const QueriesPos& queries_pos, size_t num_tokens, size_t layer, + const QueriesPos& queries_pos, const QueriesPos& queries_prefix_end, + size_t num_tokens, size_t layer, const CompressedLayer* layer_weights, Activations& activations, const hwy::Divisor& div_seq_len, const KVCaches& kv_caches) { constexpr size_t kModelDim = TConfig::kModelDim; @@ -611,8 +792,9 @@ HWY_NOINLINE void TransformerLayer( layer_weights->pre_attention_norm_scale.data_scale1(), activations.pre_att_rms_out.All(), kModelDim); - Attention(type, queries_pos, num_tokens, layer_of_type, activations, - layer_weights, div_seq_len, kv_caches); + Attention(type, queries_pos, queries_prefix_end, num_tokens, + layer_of_type, activations, layer_weights, div_seq_len, + kv_caches); PostNorm(num_interleaved, layer_weights->post_attention_norm_scale, activations.att_sums.All()); @@ -635,6 +817,51 @@ HWY_NOINLINE void TransformerLayer( /*is_attention=*/false); } +// Vit transformer layer. Some comments below refer to the Vit implementation in +// the Big Vision codebase. See +// github.com/google-research/big_vision/blob/main/big_vision/models/vit.py +// TODO(keysers): consider adding a wrapper for both LayerNorm with RMSNorm and +// try mergig this with TransformerLayer. +template +HWY_NOINLINE void VitTransformerLayer( + size_t num_tokens, size_t layer, + const CompressedLayer* layer_weights, Activations& activations) { + constexpr size_t kModelDim = TConfig::kModelDim; + auto type = TConfig::kLayerConfig[layer]; + HWY_ASSERT(type == LayerAttentionType::kVit); + + auto& x = activations.x; + HWY_ASSERT(x.BatchSize() == num_tokens); + HWY_ASSERT(x.Len() == kModelDim); + + // y = nn.LayerNorm()(x) + // y ~ pre_att_rms_out + LayerNormBatched(num_tokens, x.All(), + layer_weights->vit.layer_norm_0_scale.data_scale1(), + layer_weights->vit.layer_norm_0_bias.data_scale1(), + activations.pre_att_rms_out.All(), kModelDim); + // y = out["sa"] = nn.MultiHeadDotProductAttention(...)(y, y) + // y ~ att_sums + VitAttention(num_tokens, layer, activations, layer_weights)(); + + // x = out["+sa"] = x + y + AddFromBatched(num_tokens, activations.att_sums.All(), x.All(), kModelDim); + + // y = nn.LayerNorm()(x) + // y ~ bf_pre_ffw_rms_out + LayerNormBatched(num_tokens, x.All(), + layer_weights->vit.layer_norm_1_scale.data_scale1(), + layer_weights->vit.layer_norm_1_bias.data_scale1(), + activations.bf_pre_ffw_rms_out.All(), kModelDim); + + // y = out["mlp"] = MlpBlock(...)(y) + // y ~ ffw_out + FFW(activations, num_tokens, layer_weights); + + // x = out["+mlp"] = x + y + AddFromBatched(num_tokens, activations.ffw_out.All(), x.All(), kModelDim); +} + // Prefill() and Transformer() increment positions in-place. using QueriesMutablePos = hwy::Span; @@ -642,13 +869,14 @@ using QueriesMutablePos = hwy::Span; template HWY_NOINLINE void Prefill( const QueriesPromptTokens& queries_prompt, - const QueriesMutablePos& queries_pos, const size_t query_idx_start, - const CompressedWeights& weights, Activations& activations, - const RuntimeConfig& runtime_config, const hwy::Divisor& div_seq_len, - const KVCaches& kv_caches) { + const QueriesMutablePos& queries_pos, const QueriesPos& queries_prefix_end, + const size_t query_idx_start, const CompressedWeights& weights, + Activations& activations, const RuntimeConfig& runtime_config, + const hwy::Divisor& div_seq_len, const KVCaches& kv_caches) { PROFILER_ZONE("Gen.Prefill"); const size_t num_queries = queries_prompt.size(); HWY_ASSERT(queries_pos.size() == num_queries); + HWY_ASSERT(queries_prefix_end.size() == num_queries); HWY_ASSERT(kv_caches.size() == num_queries); // Batches are important for amortizing loading weights over multiple tokens. @@ -667,27 +895,50 @@ HWY_NOINLINE void Prefill( // Single query at a time, so pass slices of the spans because // GemmaAttention will only access the first KV cache and position. QueriesPos single_query_pos(&queries_pos[qi], 1); + QueriesPos single_query_prefix_end(&queries_prefix_end[qi], 1); KVCaches single_kv_cache(&kv_caches[qi], 1); - const size_t prefill_per_query = queries_prompt[qi].size() - 1; + const size_t prompt_size = queries_prompt[qi].size(); + // In autoregressive mode, we don't need to prefill the last token, so - 1. + size_t prefill_this_query = prompt_size - 1; + const size_t prefix_end_this_query = queries_prefix_end[qi]; + // We can't attend beyond the prompt_size. + HWY_ASSERT(prefix_end_this_query <= prompt_size); + // Special case: if the prefix includes the last token, we need to prefill + // the last token, too. However, we need to rewind this for the generation + // of the first token. So we need to keep track of this. + // TODO: consider implementing masking instead of this logic? + bool attend_to_last_token = (prefill_this_query < prefix_end_this_query); + if (attend_to_last_token) { + // The difference can be at most 1. + prefill_this_query += 1; + HWY_ASSERT(prefill_this_query == prefix_end_this_query); + } + // In prefix-LM mode, we need to look at all the tokens for the prefix in + // one iteration through the layers, so we need a large enough batch size. + HWY_ASSERT(max_tbatch_size >= prefill_this_query); + // For each batch of tokens in the query: - for (size_t tbatch_start = 0; tbatch_start < prefill_per_query; + for (size_t tbatch_start = 0; tbatch_start < prefill_this_query; tbatch_start += max_tbatch_size) { - // Fill activations.x (much faster than TransformerLayer). const size_t tbatch_size = - HWY_MIN(max_tbatch_size, prefill_per_query - tbatch_start); + HWY_MIN(max_tbatch_size, prefill_this_query - tbatch_start); + + // Fill activations.x (much faster than TransformerLayer). for (size_t ti = 0; ti < tbatch_size; ++ti) { - const int token = queries_prompt[qi][tbatch_start + ti]; const size_t pos = queries_pos[qi] + ti; - EmbedToken(token, ti, pos, weights, activations.x); + const size_t pos_in_prompt = tbatch_start + ti; + const int token = queries_prompt[qi][pos_in_prompt]; + EmbedToken(token, ti, pos, pos_in_prompt, weights, + activations.x, runtime_config.image_tokens); } // Transformer with one batch of tokens from a single query. for (size_t layer = 0; layer < TConfig::kLayers; ++layer) { const auto* layer_weights = weights.GetLayer(layer); - TransformerLayer(single_query_pos, tbatch_size, layer, - layer_weights, activations, div_seq_len, - single_kv_cache); + TransformerLayer(single_query_pos, single_query_prefix_end, + tbatch_size, layer, layer_weights, + activations, div_seq_len, single_kv_cache); } // NOTE: we unconditionally call StreamToken, even if EOS. @@ -695,19 +946,111 @@ HWY_NOINLINE void Prefill( const size_t pos = queries_pos[qi] + ti; const size_t pos_in_prompt = tbatch_start + ti; const int token = queries_prompt[qi][pos_in_prompt]; - runtime_config.StreamToken(query_idx_start + qi, pos, token, 0.0f); + if (pos_in_prompt < prompt_size - 1) { + runtime_config.StreamToken(query_idx_start + qi, pos, token, 0.0f); + } else { + // The last token will be streamed later and we should only get here + // if we need to attend to the last token because it is in the prefix. + HWY_ASSERT(attend_to_last_token); + } } queries_pos[qi] += tbatch_size; } // for tbatch_start + if (attend_to_last_token) { + // We need to rewind the position for the last token that we only + // attended to to make sure the prefix LM sees everything. + // This means we duplicate work on the last prompt token in autoregressive + // decoding. Alternatives: (1) real masking; (2) always prefill the last + // token and only generate the next one from the already prefilled + // activations. + queries_pos[qi] -= 1; + } } } +// Gets the patches of the image and embeds them with the image embedding +// kernel. The result is stored in activations.x. +template +HWY_NOINLINE void EmbedImagePatches(const Image& image, + const CompressedWeights& weights, + Activations& activations) { + static constexpr size_t kModelDim = TConfig::VitConfig::kModelDim; + static constexpr size_t kPatchWidth = TConfig::VitConfig::kPatchWidth; + static constexpr size_t kSeqLen = TConfig::VitConfig::kSeqLen; + constexpr size_t kPatchSize = kPatchWidth * kPatchWidth * 3; + HWY_ASSERT(weights.vit_img_embedding_kernel.NumElements() == + kPatchSize * kModelDim); + HWY_ASSERT(activations.x.Len() == kModelDim); + std::vector> image_patches(kSeqLen); + for (size_t i = 0; i < kSeqLen; ++i) { + image_patches[i] = hwy::AllocateAligned(kPatchSize); + image.GetPatch(i, image_patches[i].get()); + } + // img/embedding/kernel has original shape (14, 14, 3, 1152) + // H x W x C x D transposed to D x (H x W x C) so here (1152, 14 * 14 * 3) + // image_patches is (256, 14 * 14 * 3) + // This could be done as one MatMul like: + // RowVectorBatch image_patches(kSeqLen, kPatchSize); + // [Get patches] + // MatMul( + // kVitSeqLen, ConstMat(image_patches.All(), kPatchSize), + // ConstMat(weights.vit_img_embedding_kernel.data_scale1(), kPatchSize), + // /*scale=*/1.0f, weights.vit_img_embedding_bias.data_scale1(), + // activations.env, MutableMat(activations.x.All(), kVitModelDim)); + // However, MatMul currently requires that + // A.cols % (2 * hn::Lanes(hn::ScalableTag())) == 0 + // which is not the case here. We should relax that requirement on MatMul and + // then use the above. For now, we rely on MatVecAdd instead. + for (size_t i = 0; i < kSeqLen; ++i) { + MatVecAdd( + weights.vit_img_embedding_kernel, 0, image_patches[i].get(), + weights.vit_img_embedding_bias.data_scale1(), activations.x.Batch(i), + activations.env.Pools().Outer()); + } + // Add position embeddings. + AddFrom(weights.vit_img_pos_embedding.data_scale1(), activations.x.All(), + kSeqLen * kModelDim); +} + +// Prefills the image tokens with the ViT encoder. +template +HWY_NOINLINE void PrefillVit(const CompressedWeights& weights, + const RuntimeConfig& runtime_config, + const Image& image, ImageTokens& image_tokens, + Activations& activations) { + PROFILER_ZONE("Gen.PrefillVit"); + const size_t num_tokens = TConfig::VitConfig::kSeqLen; + const size_t kVitModelDim = TConfig::VitConfig::kModelDim; + HWY_ASSERT(num_tokens == activations.x.BatchSize()); + // Embed the image patches. + EmbedImagePatches(image, weights, activations); + // Go through all layers. + for (size_t layer = 0; layer < TConfig::VitConfig::kLayers; ++layer) { + const auto* layer_weights = weights.GetVitLayer(layer); + VitTransformerLayer( + num_tokens, layer, layer_weights, activations); + } + // Final Layernorm. + LayerNormBatched(num_tokens, activations.x.All(), + weights.vit_encoder_norm_scale.data_scale1(), + weights.vit_encoder_norm_bias.data_scale1(), + activations.x.All(), kVitModelDim); + + // Apply head embedding into image_tokens of size of the LLM kModelDim. + MatMul( + num_tokens, ConstMat(activations.x.All(), kVitModelDim), + ConstMat(weights.vit_img_head_kernel.data_scale1(), kVitModelDim), + /*scale=*/1.0f, weights.vit_img_head_bias.data_scale1(), activations.env, + MutableMat(image_tokens.All(), TConfig::kModelDim)); +} + // Generates one token for each query. `queries_token` is the previous token // from each query, and `queries_pos` are their position in the sequence. template HWY_NOINLINE void Transformer( const QueriesToken& queries_token, const QueriesMutablePos& queries_pos, + const QueriesPos& queries_prefix_end, const CompressedWeights& weights, Activations& activations, const hwy::Divisor& div_seq_len, const KVCaches& kv_caches, const LayersOutputFunc& layers_output, @@ -715,6 +1058,7 @@ HWY_NOINLINE void Transformer( constexpr size_t kModelDim = TConfig::kModelDim; const size_t num_queries = queries_token.size(); HWY_DASSERT(queries_pos.size() == num_queries); + HWY_DASSERT(queries_prefix_end.size() == num_queries); if (layers_output) { for (size_t query_idx = 0; query_idx < num_queries; ++query_idx) { @@ -726,13 +1070,14 @@ HWY_NOINLINE void Transformer( for (size_t query_idx = 0; query_idx < num_queries; ++query_idx) { EmbedToken(queries_token[query_idx], query_idx, - queries_pos[query_idx], weights, activations.x); + queries_pos[query_idx], /*pos_in_prompt=*/0, weights, + activations.x, /*image_tokens=*/nullptr); } for (size_t layer = 0; layer < TConfig::kLayers; ++layer) { const CompressedLayer* layer_weights = weights.GetLayer(layer); - TransformerLayer(queries_pos, /*num_tokens=*/1, layer, - layer_weights, activations, div_seq_len, + TransformerLayer(queries_pos, queries_prefix_end, /*num_tokens=*/1, + layer, layer_weights, activations, div_seq_len, kv_caches); if (activations_observer) { @@ -834,8 +1179,10 @@ template void GenerateT(const ByteStorageT& weights_u8, Activations& activations, const RuntimeConfig& runtime_config, const QueriesPromptTokens& queries_prompt, - const QueriesPos& queries_pos_in, const size_t query_idx_start, - const KVCaches& kv_caches, TimingInfo& timing_info) { + const QueriesPos& queries_pos_in, + const QueriesPos& queries_prefix_end, + const size_t query_idx_start, const KVCaches& kv_caches, + TimingInfo& timing_info) { constexpr size_t kModelDim = TConfig::kModelDim; constexpr size_t kVocabSize = TConfig::kVocabSize; const CompressedWeights& weights = @@ -888,8 +1235,8 @@ void GenerateT(const ByteStorageT& weights_u8, Activations& activations, prefill_activations.Allocate(runtime_config.prefill_tbatch_size, activations.env.Pools()); } - Prefill(queries_prompt, queries_mutable_pos, query_idx_start, - weights, + Prefill(queries_prompt, queries_mutable_pos, queries_prefix_end, + query_idx_start, weights, use_prefill_activations ? prefill_activations : activations, runtime_config, div_seq_len, kv_caches); // Compute the number of tokens that were prefilled and notify timing_info. @@ -918,10 +1265,10 @@ void GenerateT(const ByteStorageT& weights_u8, Activations& activations, const double gen_start = hwy::platform::Now(); for (size_t gen = 0; gen < HWY_MIN(max_tokens, max_generated_tokens); ++gen) { // Decode generates one token per query and increments queries_mutable_pos. - Transformer(QueriesToken(gen_tokens.data(), num_queries), - queries_mutable_pos, weights, activations, div_seq_len, - kv_caches, runtime_config.layers_output, - runtime_config.activations_observer); + Transformer( + QueriesToken(gen_tokens.data(), num_queries), queries_mutable_pos, + queries_prefix_end, weights, activations, div_seq_len, kv_caches, + runtime_config.layers_output, runtime_config.activations_observer); // queries_pos are incremented by Transformer. bool all_queries_eos = true; @@ -954,8 +1301,9 @@ void GenerateT(const ByteStorageT& weights_u8, Activations& activations, template void GenerateSingleT(const ByteStorageT& weights_u8, const RuntimeConfig& runtime_config, - const PromptTokens& prompt, size_t pos, KVCache& kv_cache, - PerClusterPools& pools, TimingInfo& timing_info) { + const PromptTokens& prompt, size_t pos, size_t prefix_end, + KVCache& kv_cache, PerClusterPools& pools, + TimingInfo& timing_info) { constexpr size_t kNumQueries = 1; const size_t qbatch_start = 0; @@ -963,20 +1311,24 @@ void GenerateSingleT(const ByteStorageT& weights_u8, Activations activations; activations.Allocate(kNumQueries, pools); - const QueriesPromptTokens prompt_span(&prompt, kNumQueries); - QueriesPos pos_span(&pos, kNumQueries); + const QueriesPromptTokens queries_prompt(&prompt, kNumQueries); + QueriesPos queries_pos(&pos, kNumQueries); + const QueriesPos queries_prefix_end(&prefix_end, kNumQueries); const KVCaches kv_caches{&kv_cache, kNumQueries}; - GenerateT(weights_u8, activations, runtime_config, prompt_span, - pos_span, qbatch_start, kv_caches, timing_info); + GenerateT(weights_u8, activations, runtime_config, queries_prompt, + queries_pos, queries_prefix_end, qbatch_start, kv_caches, + timing_info); } template void GenerateBatchT(const ByteStorageT& weights_u8, const RuntimeConfig& runtime_config, const QueriesPromptTokens& queries_prompt, - const QueriesPos& queries_pos, const KVCaches& kv_caches, - PerClusterPools& pools, TimingInfo& timing_info) { + const QueriesPos& queries_pos, + const QueriesPos& queries_prefix_end, + const KVCaches& kv_caches, PerClusterPools& pools, + TimingInfo& timing_info) { const size_t num_queries = queries_prompt.size(); HWY_ASSERT(queries_pos.size() == num_queries); HWY_ASSERT(kv_caches.size() == num_queries); @@ -995,9 +1347,33 @@ void GenerateBatchT(const ByteStorageT& weights_u8, const QueriesPromptTokens qbatch_prompts(&queries_prompt[qbatch_start], qbatch_size); QueriesPos qbatch_pos(&queries_pos[qbatch_start], qbatch_size); + const QueriesPos qbatch_prefix_end(&queries_prefix_end[qbatch_start], + qbatch_size); const KVCaches qbatch_kv(&kv_caches[qbatch_start], qbatch_size); GenerateT(weights_u8, activations, runtime_config, qbatch_prompts, - qbatch_pos, qbatch_start, qbatch_kv, timing_info); + qbatch_pos, qbatch_prefix_end, qbatch_start, qbatch_kv, + timing_info); + } +} + +template +void GenerateImageTokensT(const ByteStorageT& weights_u8, + const RuntimeConfig& runtime_config, + const Image& image, ImageTokens& image_tokens, + PerClusterPools& pools) { + if constexpr (TConfig::VitConfig::kLayers == 0) { + return; + } else { + Activations prefill_activations; + RuntimeConfig prefill_runtime_config = runtime_config; + prefill_runtime_config.prefill_tbatch_size = TConfig::VitConfig::kSeqLen; + prefill_activations.Allocate( + prefill_runtime_config.prefill_tbatch_size, pools); + // Weights are for the full PaliGemma model, not just the ViT part. + const CompressedWeights& weights = + *reinterpret_cast*>(weights_u8.get()); + PrefillVit(weights, prefill_runtime_config, image, image_tokens, + prefill_activations); } } @@ -1010,20 +1386,30 @@ void GenerateBatchT(const ByteStorageT& weights_u8, void GenerateSingle( // NOLINT(misc-definitions-in-headers) GEMMA_CONFIG, const ByteStorageT& weights_u8, const RuntimeConfig& runtime_config, const PromptTokens& prompt, size_t pos, - KVCache& kv_cache, PerClusterPools& pools, TimingInfo& timing_info) { + size_t prefix_end, KVCache& kv_cache, PerClusterPools& pools, + TimingInfo& timing_info) { HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(GenerateSingleT) - (weights_u8, runtime_config, prompt, pos, kv_cache, pools, timing_info); + (weights_u8, runtime_config, prompt, pos, prefix_end, kv_cache, pools, + timing_info); } void GenerateBatch( // NOLINT(misc-definitions-in-headers) GEMMA_CONFIG, const ByteStorageT& weights_u8, const RuntimeConfig& runtime_config, const QueriesPromptTokens& queries_prompt, const QueriesPos& queries_pos, - const KVCaches& kv_caches, PerClusterPools& pools, - TimingInfo& timing_info) { + const QueriesPos& queries_prefix_end, const KVCaches& kv_caches, + PerClusterPools& pools, TimingInfo& timing_info) { HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(GenerateBatchT) - (weights_u8, runtime_config, queries_prompt, queries_pos, kv_caches, pools, - timing_info); + (weights_u8, runtime_config, queries_prompt, queries_pos, queries_prefix_end, + kv_caches, pools, timing_info); +} + +void GenerateImageTokens( // NOLINT(misc-definitions-in-headers) + GEMMA_CONFIG, const ByteStorageT& weights_u8, + const RuntimeConfig& runtime_config, const Image& image, + ImageTokens& image_tokens, PerClusterPools& pools) { + HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(GenerateImageTokensT) + (weights_u8, runtime_config, image, image_tokens, pools); } #endif // HWY_ONCE diff --git a/gemma/gemma.cc b/gemma/gemma.cc index 9197e3e..94cffc1 100644 --- a/gemma/gemma.cc +++ b/gemma/gemma.cc @@ -24,10 +24,12 @@ #include #include // std::move +#include #include "compression/io.h" // Path #include "gemma/common.h" #include "gemma/weights.h" +#include "paligemma/image.h" #include "util/threading.h" #include "hwy/contrib/thread_pool/thread_pool.h" #include "hwy/highway.h" @@ -62,13 +64,18 @@ Gemma::~Gemma() { extern void GenerateSingle(CONFIGT, const ByteStorageT& weights_u8, \ const RuntimeConfig& runtime_config, \ const PromptTokens& prompt, size_t pos, \ - KVCache& kv_cache, PerClusterPools& pools, \ - TimingInfo& timing_info); \ + size_t prefix_end, KVCache& kv_cache, \ + PerClusterPools& pools, TimingInfo& timing_info); \ extern void GenerateBatch( \ CONFIGT, const ByteStorageT& weights_u8, \ const RuntimeConfig& runtime_config, const QueriesPromptTokens& prompts, \ - const QueriesPos& queries_pos, const KVCaches& kv_caches, \ - PerClusterPools& pools, TimingInfo& timing_info); + const QueriesPos& queries_pos, \ + const QueriesPos& queries_prefix_end, const KVCaches& kv_caches, \ + PerClusterPools& pools, TimingInfo& timing_info); \ + extern void GenerateImageTokens( \ + CONFIGT, const ByteStorageT& weights_u8, \ + const RuntimeConfig& runtime_config, const Image& image, \ + ImageTokens& image_tokens, PerClusterPools& pools); GEMMA_FOREACH_CONFIG_AND_WEIGHT(GEMMA_DECLARE); // Adapters to select from the above overloads via CallForModelAndWeight. @@ -76,10 +83,11 @@ template struct GenerateSingleT { void operator()(const ByteStorageT& weights_u8, const RuntimeConfig& runtime_config, - const PromptTokens& prompt, size_t pos, KVCache& kv_cache, - PerClusterPools& pools, TimingInfo& timing_info) const { - GenerateSingle(TConfig(), weights_u8, runtime_config, prompt, pos, kv_cache, - pools, timing_info); + const PromptTokens& prompt, size_t pos, size_t prefix_end, + KVCache& kv_cache, PerClusterPools& pools, + TimingInfo& timing_info) const { + GenerateSingle(TConfig(), weights_u8, runtime_config, prompt, pos, + prefix_end, kv_cache, pools, timing_info); } }; @@ -88,21 +96,34 @@ struct GenerateBatchT { void operator()(const ByteStorageT& weights_u8, const RuntimeConfig& runtime_config, const QueriesPromptTokens& queries_prompt, - const QueriesPos& queries_pos, const KVCaches& kv_caches, - PerClusterPools& pools, TimingInfo& timing_info) const { + const QueriesPos& queries_pos, + const QueriesPos& queries_prefix_end, + const KVCaches& kv_caches, PerClusterPools& pools, + TimingInfo& timing_info) const { GenerateBatch(TConfig(), weights_u8, runtime_config, queries_prompt, - queries_pos, kv_caches, pools, timing_info); + queries_pos, queries_prefix_end, kv_caches, pools, + timing_info); + } +}; + +template +struct GenerateImageTokensT { + void operator()(const ByteStorageT& weights_u8, + const RuntimeConfig& runtime_config, const Image& image, + ImageTokens& image_tokens, PerClusterPools& pools) const { + GenerateImageTokens(TConfig(), weights_u8, runtime_config, image, + image_tokens, pools); } }; void Gemma::Generate(const RuntimeConfig& runtime_config, - const PromptTokens& prompt, size_t pos, KVCache& kv_cache, - TimingInfo& timing_info) { + const PromptTokens& prompt, size_t pos, size_t prefix_end, + KVCache& kv_cache, TimingInfo& timing_info) { if (runtime_config.use_spinning) pools_.StartSpinning(); - CallForModelAndWeight(info_.model, info_.weight, weights_u8_, - runtime_config, prompt, pos, kv_cache, - pools_, timing_info); + CallForModelAndWeight( + info_.model, info_.weight, weights_u8_, runtime_config, prompt, pos, + prefix_end, kv_cache, pools_, timing_info); if (runtime_config.use_spinning) pools_.StopSpinning(); } @@ -110,12 +131,33 @@ void Gemma::Generate(const RuntimeConfig& runtime_config, void Gemma::GenerateBatch(const RuntimeConfig& runtime_config, const QueriesPromptTokens& queries_prompt, const QueriesPos& queries_pos, + const QueriesPos& queries_prefix_end, const KVCaches& kv_caches, TimingInfo& timing_info) { + // If we did not get passed prefix ends (size 0), assume 0 and pass that on. + QueriesPos mutable_queries_prefix_end = queries_prefix_end; + std::vector prefix_end_vec; + if (queries_prefix_end.size() == 0) { + prefix_end_vec.resize(queries_prompt.size(), 0); + mutable_queries_prefix_end = + QueriesPos(prefix_end_vec.data(), prefix_end_vec.size()); + } + if (runtime_config.use_spinning) pools_.StartSpinning(); CallForModelAndWeight( info_.model, info_.weight, weights_u8_, runtime_config, queries_prompt, - queries_pos, kv_caches, pools_, timing_info); + queries_pos, mutable_queries_prefix_end, kv_caches, pools_, timing_info); + + if (runtime_config.use_spinning) pools_.StopSpinning(); +} + +void Gemma::GenerateImageTokens(const RuntimeConfig& runtime_config, + const Image& image, ImageTokens& image_tokens) { + if (runtime_config.use_spinning) pools_.StartSpinning(); + + CallForModelAndWeight(info_.model, info_.weight, + weights_u8_, runtime_config, + image, image_tokens, pools_); if (runtime_config.use_spinning) pools_.StopSpinning(); } diff --git a/gemma/gemma.h b/gemma/gemma.h index 42f2034..f05cab5 100644 --- a/gemma/gemma.h +++ b/gemma/gemma.h @@ -27,6 +27,7 @@ #include "gemma/common.h" #include "gemma/kv_cache.h" #include "gemma/tokenizer.h" +#include "paligemma/image.h" #include "util/allocator.h" #include "util/threading.h" #include "hwy/contrib/thread_pool/thread_pool.h" @@ -75,6 +76,10 @@ using LayersOutputFunc = std::function; +// ImageTokens are represented as a RowVectorBatch, where each "batch" index +// corresponds to a token for an image patch as computed by the image encoder. +using ImageTokens = RowVectorBatch; + // RuntimeConfig holds configuration for a single generation run. struct RuntimeConfig { // If not empty, batch_stream_token is called for each token in the batch, @@ -110,6 +115,10 @@ struct RuntimeConfig { LayersOutputFunc layers_output; // if not empty, called after each layer. ActivationsObserverFunc activations_observer; // if set, called per-layer. + // If not empty, these point to the image tokens and are used in the + // PaliGemma prefix-LM style attention. + const ImageTokens *image_tokens = nullptr; + // Whether to use thread spinning to reduce barrier synchronization latency. bool use_spinning = true; @@ -198,14 +207,34 @@ class Gemma { // `pos` is the position in the KV cache. Users are responsible for // incrementing it in the `*StreamFunc`, or setting to zero for single-turn. void Generate(const RuntimeConfig& runtime_config, const PromptTokens& prompt, - size_t pos, KVCache& kv_cache, TimingInfo& timing_info); + size_t pos, KVCache& kv_cache, TimingInfo& timing_info) { + Generate(runtime_config, prompt, pos, /*prefix_end=*/0, kv_cache, + timing_info); + } + // For prefix-LM style attention, we can pass the end of the prefix. + void Generate(const RuntimeConfig& runtime_config, const PromptTokens& prompt, + size_t pos, size_t prefix_end, KVCache& kv_cache, + TimingInfo& timing_info); // `queries_pos` are the positions in the KV cache. Users are responsible for // incrementing them in `BatchStreamFunc`, or setting to zero for single-turn. void GenerateBatch(const RuntimeConfig& runtime_config, const QueriesPromptTokens& queries_prompt, const QueriesPos& queries_pos, const KVCaches& kv_caches, - TimingInfo& timing_info); + TimingInfo& timing_info) { + GenerateBatch(runtime_config, queries_prompt, queries_pos, + /*queries_prefix_end=*/{}, kv_caches, timing_info); + } + // For prefix-LM style attention, we can pass the ends of the prefixes. + void GenerateBatch(const RuntimeConfig& runtime_config, + const QueriesPromptTokens& queries_prompt, + const QueriesPos& queries_pos, + const QueriesPos& queries_prefix_end, + const KVCaches& kv_caches, TimingInfo& timing_info); + + // Generates the image tokens by running the image encoder ViT. + void GenerateImageTokens(const RuntimeConfig& runtime_config, + const Image& image, ImageTokens& image_tokens); private: PerClusterPools& pools_; diff --git a/gemma/instantiations/paligemma_224_bf16.cc b/gemma/instantiations/paligemma_224_bf16.cc new file mode 100644 index 0000000..8d508e7 --- /dev/null +++ b/gemma/instantiations/paligemma_224_bf16.cc @@ -0,0 +1,21 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#undef HWY_TARGET_INCLUDE +#define HWY_TARGET_INCLUDE \ + "gemma/instantiations/paligemma_224_bf16.cc" +#include "hwy/foreach_target.h" // IWYU pragma: keep +#define GEMMA_CONFIG ConfigPaliGemma_224 +#include "gemma/gemma-inl.h" diff --git a/gemma/instantiations/paligemma_224_f32.cc b/gemma/instantiations/paligemma_224_f32.cc new file mode 100644 index 0000000..97107e9 --- /dev/null +++ b/gemma/instantiations/paligemma_224_f32.cc @@ -0,0 +1,21 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#undef HWY_TARGET_INCLUDE +#define HWY_TARGET_INCLUDE \ + "gemma/instantiations/paligemma_224_f32.cc" +#include "hwy/foreach_target.h" // IWYU pragma: keep +#define GEMMA_CONFIG ConfigPaliGemma_224 +#include "gemma/gemma-inl.h" diff --git a/gemma/instantiations/paligemma_224_sfp.cc b/gemma/instantiations/paligemma_224_sfp.cc new file mode 100644 index 0000000..aff27a0 --- /dev/null +++ b/gemma/instantiations/paligemma_224_sfp.cc @@ -0,0 +1,21 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#undef HWY_TARGET_INCLUDE +#define HWY_TARGET_INCLUDE \ + "gemma/instantiations/paligemma_224_sfp.cc" +#include "hwy/foreach_target.h" // IWYU pragma: keep +#define GEMMA_CONFIG ConfigPaliGemma_224 +#include "gemma/gemma-inl.h" diff --git a/gemma/run.cc b/gemma/run.cc index 3c7ec1e..2e8b0cb 100644 --- a/gemma/run.cc +++ b/gemma/run.cc @@ -25,6 +25,7 @@ #include "evals/benchmark_helper.h" #include "gemma/common.h" #include "gemma/gemma.h" // Gemma +#include "paligemma/image.h" #include "util/app.h" #include "util/args.h" // HasHelp #include "util/threading.h" @@ -87,6 +88,17 @@ void ReplGemma(Gemma& model, KVCache& kv_cache, const InferenceArgs& args, std::mt19937 gen; InitGenerator(args, gen); + const bool have_image = !args.image_file.path.empty(); + Image image; + ImageTokens image_tokens(256, 2048); + if (have_image) { + HWY_ASSERT(model.Info().model == Model::PALIGEMMA_224); + HWY_ASSERT(image.ReadPPM(args.image_file.path)); + image.Resize(); + RuntimeConfig runtime_config = {.verbosity = verbosity, .gen = &gen}; + model.GenerateImageTokens(runtime_config, image, image_tokens); + } + // callback function invoked for each generated token. auto stream_token = [&](int token, float) { ++abs_pos; @@ -132,7 +144,12 @@ void ReplGemma(Gemma& model, KVCache& kv_cache, const InferenceArgs& args, } } - const std::vector prompt = WrapAndTokenize( + if (have_image && abs_pos != 0) { + // This occurs when we have hit max_generated. + abs_pos = 0; + } + + std::vector prompt = WrapAndTokenize( model.Tokenizer(), model.Info(), abs_pos, prompt_string); prompt_size = prompt.size(); std::cerr << "\n" @@ -151,7 +168,19 @@ void ReplGemma(Gemma& model, KVCache& kv_cache, const InferenceArgs& args, .accept_token = accept_token, }; args.CopyTo(runtime_config); - model.Generate(runtime_config, prompt, abs_pos, kv_cache, timing_info); + size_t prefix_end = 0; + if (have_image) { + runtime_config.image_tokens = &image_tokens; + prompt.insert(prompt.begin(), image_tokens.BatchSize(), 0); + prompt_size = prompt.size(); + // The end of the prefix for prefix-LM style attention in Paligemma. + // See Figure 2 of https://arxiv.org/abs/2407.07726. + prefix_end = prompt_size; + // We need to look at all the tokens for the prefix. + runtime_config.prefill_tbatch_size = prompt_size; + } + model.Generate(runtime_config, prompt, abs_pos, prefix_end, kv_cache, + timing_info); std::cout << "\n\n"; } std::cout diff --git a/gemma/tokenizer.cc b/gemma/tokenizer.cc index 07b3f98..fbc55cd 100644 --- a/gemma/tokenizer.cc +++ b/gemma/tokenizer.cc @@ -107,6 +107,14 @@ std::vector WrapAndTokenize(const GemmaTokenizer& tokenizer, if (pos == 0) { tokens.insert(tokens.begin(), BOS_ID); } + + // PaliGemma separator. The SEP token "\n" is always tokenized separately. + if (info.model == Model::PALIGEMMA_224) { + std::vector sep_tokens; + HWY_ASSERT(tokenizer.Encode("\n", &sep_tokens)); + tokens.insert(tokens.end(), sep_tokens.begin(), sep_tokens.end()); + } + return tokens; } diff --git a/gemma/weights.h b/gemma/weights.h index 09cbbcb..818ae8e 100644 --- a/gemma/weights.h +++ b/gemma/weights.h @@ -48,6 +48,7 @@ struct CompressedLayer { static constexpr size_t kAttVecEinsumWSize = kHeads * kQKVDim * kModelDim; static constexpr size_t kQKVEinsumWSize = (kHeads + 2 * kKVHeads) * kQKVDim * kModelDim; + static constexpr size_t kQKVEinsumBSize = (kHeads + 2 * kKVHeads) * kQKVDim; // 2x for (gelu gating vector, gated vector) static constexpr size_t kGatingEinsumWSize = 2 * kFFHiddenDim * kModelDim; static constexpr size_t kConv1dWidth = TConfig::kConv1dWidth; @@ -81,6 +82,24 @@ struct CompressedLayer { ArrayT gate_biases; ArrayT a; } griffin; + + struct { + // MultiHeadDotProductAttention. + ArrayT attn_out_w; + ArrayT attn_out_b; + ArrayT qkv_einsum_w; + ArrayT qkv_einsum_b; + // MlpBlock. + ArrayT linear_0_w; + ArrayT linear_0_b; + ArrayT linear_1_w; + ArrayT linear_1_b; + // LayerNorm. + ArrayT layer_norm_0_bias; + ArrayT layer_norm_0_scale; + ArrayT layer_norm_1_bias; + ArrayT layer_norm_1_scale; + } vit; }; ArrayT gating_einsum_w; @@ -121,6 +140,7 @@ struct CompressedLayer { out_row + h * kQKVDim, kQKVDim * sizeof(Weight)); } } + att_weights.set_scale(attn_vec_einsum_w.scale()); } }; @@ -133,10 +153,21 @@ struct CompressedLayerPointers { pool.Run(0, TConfig::kLayers, [this](uint64_t task, size_t /*thread*/) { this->c_layers[task] = hwy::AllocateAligned>(1); }); + if constexpr (TConfig::VitConfig::kLayers > 0) { + pool.Run(0, TConfig::VitConfig::kLayers, + [this](uint64_t task, size_t /*thread*/) { + this->c_vit_layers[task] = hwy::AllocateAligned< + CompressedLayer>(1); + }); + } } using CLayer = CompressedLayer; std::array, TConfig::kLayers> c_layers; + using CVitLayer = CompressedLayer; + std::array, + TConfig::VitConfig::kLayers> + c_vit_layers; }; template @@ -159,6 +190,23 @@ struct CompressedWeights { hwy::If(), float, hwy::bfloat16_t>; CompressedArray final_norm_scale; + // Vit parts. + CompressedArray + vit_encoder_norm_bias; + CompressedArray + vit_encoder_norm_scale; + CompressedArray vit_img_embedding_bias; + CompressedArray + vit_img_embedding_kernel; + CompressedArray + vit_img_pos_embedding; + // The head maps from VitConfig::kModelDim (Vit final layer) to + // kModelDim (LLM input). + CompressedArray vit_img_head_bias; + CompressedArray + vit_img_head_kernel; + // Must be last so that the other arrays remain aligned. CompressedLayerPointers c_layer_ptrs; @@ -174,9 +222,21 @@ struct CompressedWeights { void ZeroInit() { hwy::ZeroBytes(&embedder_input_embedding, sizeof(embedder_input_embedding)); hwy::ZeroBytes(&final_norm_scale, sizeof(final_norm_scale)); + hwy::ZeroBytes(&vit_encoder_norm_bias, sizeof(vit_encoder_norm_bias)); + hwy::ZeroBytes(&vit_encoder_norm_scale, sizeof(vit_encoder_norm_scale)); + hwy::ZeroBytes(&vit_img_embedding_bias, sizeof(vit_img_embedding_bias)); + hwy::ZeroBytes(&vit_img_embedding_kernel, sizeof(vit_img_embedding_kernel)); + hwy::ZeroBytes(&vit_img_head_bias, sizeof(vit_img_head_bias)); + hwy::ZeroBytes(&vit_img_head_kernel, sizeof(vit_img_head_kernel)); + hwy::ZeroBytes(&vit_img_pos_embedding, sizeof(vit_img_pos_embedding)); for (int i = 0; i < TConfig::kLayers; ++i) { hwy::ZeroBytes(GetLayer(i), sizeof(*GetLayer(i))); } + if constexpr (TConfig::VitConfig::kLayers > 0) { + for (int i = 0; i < TConfig::VitConfig::kLayers; ++i) { + hwy::ZeroBytes(GetVitLayer(i), sizeof(*GetVitLayer(i))); + } + } } const CompressedLayer* GetLayer(size_t layer) const { @@ -185,6 +245,13 @@ struct CompressedWeights { CompressedLayer* GetLayer(size_t layer) { return c_layer_ptrs.c_layers[layer].get(); } + const CompressedLayer* GetVitLayer( + size_t layer) const { + return c_layer_ptrs.c_vit_layers[layer].get(); + } + CompressedLayer* GetVitLayer(size_t layer) { + return c_layer_ptrs.c_vit_layers[layer].get(); + } }; // ---------------------------------------------------------------------------- @@ -288,6 +355,16 @@ void ForEachTensor(RawWeightsPtr raw_weights, GEMMA_CALL_TOP_FUNC("c_embedding", embedder_input_embedding); GEMMA_CALL_TOP_FUNC("c_final_norm", final_norm_scale); + if constexpr (TConfig::VitConfig::kLayers > 0 && !kHaveRaw) { + GEMMA_CALL_TOP_FUNC("enc_norm_bias", vit_encoder_norm_bias); + GEMMA_CALL_TOP_FUNC("enc_norm_scale", vit_encoder_norm_scale); + GEMMA_CALL_TOP_FUNC("img_emb_bias", vit_img_embedding_bias); + GEMMA_CALL_TOP_FUNC("img_emb_kernel", vit_img_embedding_kernel); + GEMMA_CALL_TOP_FUNC("img_head_bias", vit_img_head_bias); + GEMMA_CALL_TOP_FUNC("img_head_kernel", vit_img_head_kernel); + GEMMA_CALL_TOP_FUNC("img_pos_emb", vit_img_pos_embedding); + } + char name_buf[16]; for (int layer_idx = 0; layer_idx < TConfig::kLayers; ++layer_idx) { auto type = TConfig::kLayerConfig[layer_idx]; @@ -334,6 +411,35 @@ void ForEachTensor(RawWeightsPtr raw_weights, GEMMA_CALL_FUNC("attn_ob", attention_output_biases); } } + + // Vit layers. Not supported for compress_weights. + if constexpr (TConfig::VitConfig::kLayers > 0 && !kHaveRaw) { + for (int layer_idx = 0; layer_idx < TConfig::VitConfig::kLayers; + ++layer_idx) { + auto type = TConfig::VitConfig::kLayerConfig[layer_idx]; + HWY_ASSERT(type == LayerAttentionType::kVit); + const size_t idx = static_cast(layer_idx); + const RawLayer* raw_layer = nullptr; + CompressedLayer* c_layer = + c_weights.GetVitLayer(idx); + + // MHA. + GEMMA_CALL_FUNC("attn_out_w", vit.attn_out_w); + GEMMA_CALL_FUNC("attn_out_b", vit.attn_out_b); + GEMMA_CALL_FUNC("qkv_ein_w", vit.qkv_einsum_w); + GEMMA_CALL_FUNC("qkv_ein_b", vit.qkv_einsum_b); + // MlpBlock. + GEMMA_CALL_FUNC("linear_0_w", vit.linear_0_w); + GEMMA_CALL_FUNC("linear_0_b", vit.linear_0_b); + GEMMA_CALL_FUNC("linear_1_w", vit.linear_1_w); + GEMMA_CALL_FUNC("linear_1_b", vit.linear_1_b); + // LayerNorm. + GEMMA_CALL_FUNC("ln_0_bias", vit.layer_norm_0_bias); + GEMMA_CALL_FUNC("ln_0_scale", vit.layer_norm_0_scale); + GEMMA_CALL_FUNC("ln_1_bias", vit.layer_norm_1_bias); + GEMMA_CALL_FUNC("ln_1_scale", vit.layer_norm_1_scale); + } + } #undef GEMMA_CALL_FUNC #undef GEMMA_CALL_TOP_FUNC } // ForEachTensor diff --git a/paligemma/BUILD b/paligemma/BUILD new file mode 100644 index 0000000..335e531 --- /dev/null +++ b/paligemma/BUILD @@ -0,0 +1,45 @@ +package( + default_applicable_licenses = [ + "//:license", # Placeholder comment, do not modify + ], + default_visibility = [ + "//:__subpackages__", # Placeholder, do not modify + ], +) + +cc_library( + name = "image", + srcs = ["image.cc"], + hdrs = ["image.h"], + deps = ["@hwy//:hwy"], +) + +cc_test( + name = "image_test", + srcs = ["image_test.cc"], + data = ["testdata/image.ppm"], + deps = [ + ":image", + "@googletest//:gtest_main", # buildcleaner: keep + ], +) + +cc_test( + name = "paligemma_test", + srcs = ["paligemma_test.cc"], + # Requires model files + tags = [ + "local", + "manual", + "no_tap", + ], + deps = [ + "@googletest//:gtest_main", + "//:benchmark_helper", + "//:common", + "//:gemma_lib", + "//:tokenizer", + "@hwy//:hwy", + "@hwy//:hwy_test_util", + ], +) diff --git a/paligemma/image.cc b/paligemma/image.cc new file mode 100644 index 0000000..716436b --- /dev/null +++ b/paligemma/image.cc @@ -0,0 +1,182 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paligemma/image.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "hwy/base.h" + +namespace gcpp { +namespace { +// Hardcoded for PaliGemma-224 ViT input. +constexpr size_t kPatchSize = 14; +constexpr size_t kImageSize = 224; +constexpr size_t kNumPatches = kImageSize / kPatchSize; // 16 + +// Returns the linearly scaled index in [0, to_size) closest to the +// value in [0, from_size). +int NearestNeighbor(int value, int from_size, int to_size) { + float scale_factor = static_cast(to_size - 1) / (from_size - 1); + // Apply nearest neighbor rounding. + int nn = static_cast(std::round(value * scale_factor)); + // Ensure the value is within the new range. + nn = std::clamp(nn, 0, to_size - 1); + return nn; +} + +// Returns value in [0,1] mapped linearly to [-1,1]. +float StretchToSigned(float value) { + // = out_min + (value - in_min) * (out_max - out_min) / (in_max - in_min); + return value * 2.0f - 1.0f; +} + +bool IsLineBreak(int c) { return c == '\r' || c == '\n'; } + +void SkipWhitespaceAndComments(std::ifstream& file) { + int value = file.get(); + while (std::isspace(value)) value = file.get(); + while (value == '#') { // Skip comment lines. + while (!IsLineBreak(value)) value = file.get(); + while (std::isspace(value)) value = file.get(); + } + file.unget(); // Rewind last byte. +} +} // namespace + +bool Image::ReadPPM(const std::string& filename) { + std::ifstream file(filename); + if (!file.is_open()) { + std::cerr << "Failed to open " << filename << "\n"; + return false; + } + std::string format; + file >> format; + if (format != "P6") { + std::cerr << "We only support binary PPM (P6) but got: " << format << "\n"; + return false; + } + int width, height, max_value; + SkipWhitespaceAndComments(file); + file >> width; + SkipWhitespaceAndComments(file); + file >> height; + SkipWhitespaceAndComments(file); + file >> max_value; + if (max_value <= 0 || max_value > 255) { + std::cerr << "Unsupported max value " << max_value << "\n"; + return false; + } + // P6 requires exactly one whitespace character after the header. + int value = file.get(); + if (!std::isspace(value)) { + std::cerr << "Missing whitespace after header\n"; + return false; + } + width_ = width; + height_ = height; + int data_size = width * height * 3; + data_.resize(data_size); + std::vector data_bytes(data_size); + file.read(data_bytes.data(), data_size); + if (file.gcount() != data_size) { + std::cerr << "Failed to read " << data_size << " bytes\n"; + return false; + } + for (int i = 0; i < data_size; ++i) { + data_[i] = StretchToSigned(static_cast(data_bytes[i]) / max_value); + } + if (file.get() != EOF) { + std::cerr << "Extra data in file\n"; + return false; + } + file.close(); + return true; +} + +void Image::Resize() { + int new_width = 224; + int new_height = kImageSize; + std::vector new_data(new_width * new_height * 3); + // TODO: go to bilinear interpolation, or antialias. + // E.g. consider WeightsSymmetric3Lowpass and SlowSymmetric3 from + // jpegxl/lib/jxl/convolve_slow.cc + // For now, just do nearest neighbor. + for (int i = 0; i < new_height; ++i) { + for (int j = 0; j < new_width; ++j) { + int old_i = NearestNeighbor(i, new_height, height_); + int old_j = NearestNeighbor(j, new_width, width_); + for (int k = 0; k < 3; ++k) { + new_data[(i * new_width + j) * 3 + k] = + data_[(old_i * width_ + old_j) * 3 + k]; + } + } + } + data_ = std::move(new_data); + height_ = new_height; + width_ = new_width; +} + +bool Image::WriteBinary(const std::string& filename) const { + // Writes the floating point values as float32 in binary format. + std::ofstream file(filename); + if (!file.is_open()) { + std::cerr << "Failed to open " << filename << "\n"; + return false; + } + for (int i = 0; i < data_.size(); ++i) { + file.write(reinterpret_cast(&data_[i]), sizeof(float)); + } + file.close(); + return true; +} + +// Image.data() is kImageSize x kImageSize x 3, H x W x C. +// We want the N-th patch (of 256) of size kPatchSize x kPatchSize x 3. +// Patches are numbered in usual "pixel-order". +void Image::GetPatch(size_t patch_num, float* patch) const { + constexpr size_t kDataSize = kImageSize * kImageSize * 3; + HWY_ASSERT(size() == kDataSize); + constexpr size_t kPatchDataSize = kPatchSize * kPatchSize * 3; + int i_offs = patch_num / kNumPatches; + int j_offs = patch_num % kNumPatches; + HWY_ASSERT(0 <= i_offs && i_offs < kNumPatches); + HWY_ASSERT(0 <= j_offs && j_offs < kNumPatches); + i_offs *= kPatchSize; + j_offs *= kPatchSize; + // This can be made faster, but let's first see whether it matters. + const float* image_data = data(); + for (int i = 0; i < kPatchSize; ++i) { + for (int j = 0; j < kPatchSize; ++j) { + for (int k = 0; k < 3; ++k) { + const int patch_index = (i * kPatchSize + j) * 3 + k; + HWY_ASSERT(patch_index < kPatchDataSize); + const int image_index = + ((i + i_offs) * kImageSize + (j + j_offs)) * 3 + k; + HWY_ASSERT(image_index < kDataSize); + patch[patch_index] = image_data[image_index]; + } + } + } +} +} // namespace gcpp diff --git a/paligemma/image.h b/paligemma/image.h new file mode 100644 index 0000000..280a324 --- /dev/null +++ b/paligemma/image.h @@ -0,0 +1,59 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef THIRD_PARTY_GEMMA_CPP_PALIGEMMA_IMAGE_H_ +#define THIRD_PARTY_GEMMA_CPP_PALIGEMMA_IMAGE_H_ + +#include +#include +#include + +namespace gcpp { + +// Very basic image loading and processing for PaliGemma-224. Does not try to be +// generic at the moment, e.g. the size to normalize to is hardcoded. +class Image { + public: + Image() = default; + // Reads a file in PPM format (P6, binary), normalizes to [-1, 1]. + // Returns true on success. + bool ReadPPM(const std::string& filename); + // Resizes to 224x224 (nearest-neighbor for now, bilinear or antialias would + // be better). + void Resize(); + // Writes the file as plain floats in binary. Useful to e.g. load in a colab. + bool WriteBinary(const std::string& filename) const; + // Stores the patch for the given patch number [0, 256) in `patch`. + // As sizes are hardcoded, the patch number is sufficient here. + // `patch` should have space for at least 14 * 14 * 3 = 588 floats. + // Requires that Normalize() has been called. + void GetPatch(size_t patch_num, float* patch) const; + + float *data() { return data_.data(); } + const float *data() const { return data_.data(); } + int width() const { return width_; } + int height() const { return height_; } + size_t size() const { return data_.size(); } + operator bool() const { return data_.size() > 0; } + + private: + int width_ = 0; + int height_ = 0; + std::vector data_; // r, g, b +}; + +} // namespace gcpp + +#endif // THIRD_PARTY_GEMMA_CPP_PALIGEMMA_IMAGE_H_ diff --git a/paligemma/image_test.cc b/paligemma/image_test.cc new file mode 100644 index 0000000..8c7a46a --- /dev/null +++ b/paligemma/image_test.cc @@ -0,0 +1,72 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paligemma/image.h" + +#include + +#include "gtest/gtest.h" + +namespace gcpp { +namespace { + +float Normalize(int value) { return 2.0f * (value / 255.0f) - 1.0f; } + +TEST(ImageTest, BasicFunctionality) { + return; // Need to figure out how to get the external path for the test file. + std::string path; + Image image; + EXPECT_EQ(image.width(), 0); + EXPECT_EQ(image.height(), 0); + EXPECT_EQ(image.size(), 0); + ASSERT_TRUE(image.ReadPPM(path)); + EXPECT_EQ(image.width(), 256); + EXPECT_EQ(image.height(), 341); + EXPECT_EQ(image.size(), 256 * 341 * 3); + // Spot check a few values. + EXPECT_EQ(image.data()[0], Normalize(160)); + EXPECT_EQ(image.data()[1], Normalize(184)); + EXPECT_EQ(image.data()[2], Normalize(188)); + EXPECT_EQ(image.data()[3], Normalize(163)); + EXPECT_EQ(image.data()[4], Normalize(185)); + EXPECT_EQ(image.data()[5], Normalize(189)); + EXPECT_EQ(image.data()[30], Normalize(164)); + EXPECT_EQ(image.data()[31], Normalize(185)); + EXPECT_EQ(image.data()[32], Normalize(191)); + EXPECT_EQ(image.data()[33], Normalize(164)); + EXPECT_EQ(image.data()[34], Normalize(185)); + EXPECT_EQ(image.data()[35], Normalize(191)); + image.Resize(); + // Check first and last pixel. + EXPECT_EQ(image.data()[0], Normalize(160)); + EXPECT_EQ(image.data()[1], Normalize(184)); + EXPECT_EQ(image.data()[2], Normalize(188)); + EXPECT_EQ(image.data()[image.size() - 3], Normalize(90)); + EXPECT_EQ(image.data()[image.size() - 2], Normalize(132)); + EXPECT_EQ(image.data()[image.size() - 1], Normalize(122)); + // Extract two patches. + float patch[588]; + image.GetPatch(0, patch); + EXPECT_EQ(patch[0], Normalize(160)); + EXPECT_EQ(patch[1], Normalize(184)); + EXPECT_EQ(patch[2], Normalize(188)); + image.GetPatch(18, patch); + for (int i = 0; i < 10; ++i) { + EXPECT_EQ(patch[i], image.data()[(14 * 224 + 2 * 14) * 3 + i]); + } +} + +} // namespace +} // namespace gcpp diff --git a/paligemma/paligemma_test.cc b/paligemma/paligemma_test.cc new file mode 100644 index 0000000..3e2e9e8 --- /dev/null +++ b/paligemma/paligemma_test.cc @@ -0,0 +1,132 @@ +// Copyright 2024 Google LLC +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "gemma/gemma.h" + +#include +#include +#include +#include + +#include "evals/benchmark_helper.h" +#include "gemma/common.h" +#include "hwy/base.h" +#include "hwy/tests/hwy_gtest.h" + +// This test can be run manually with the downloaded PaliGemma weights. +// To run the test, pass the following flags: +// --model paligemma-224 --tokenizer --weights +// It should pass for the following models: +// paligemma-3b-mix-224 + +namespace gcpp { +namespace { + +// Shared state. Requires argc/argv, so construct in main and use the same raw +// pointer approach as in benchmarks.cc. Note that the style guide forbids +// non-local static variables with dtors. +GemmaEnv* s_env = nullptr; + +class PaliGemmaTest : public ::testing::Test { + protected: + void InitVit(const std::string& path); + std::string GemmaReply(const std::string& prompt_text) const; + void TestQuestions(const char* kQA[][2], size_t num_questions); + + std::unique_ptr image_tokens_; +}; + +void PaliGemmaTest::InitVit(const std::string& path) { + ASSERT_NE(s_env->GetModel(), nullptr); + Gemma& model = *(s_env->GetModel()); + image_tokens_ = std::make_unique(256, 2048); + Image image; + HWY_ASSERT(model.Info().model == Model::PALIGEMMA_224); + HWY_ASSERT(image.ReadPPM(path)); + image.Resize(); + RuntimeConfig runtime_config = {.verbosity = 0, .gen = &s_env->MutableGen()}; + model.GenerateImageTokens(runtime_config, image, *image_tokens_); +} + +std::string PaliGemmaTest::GemmaReply(const std::string& prompt_text) const{ + Gemma& model = *(s_env->GetModel()); + s_env->MutableGen().seed(0x12345678); + RuntimeConfig runtime_config = {.max_tokens = 1024, + .max_generated_tokens = 512, + .verbosity = 0, + .gen = &s_env->MutableGen()}; + runtime_config.image_tokens = image_tokens_.get(); + size_t abs_pos = 0; + std::string mutable_prompt = prompt_text; + std::vector tokens = + WrapAndTokenize(model.Tokenizer(), model.Info(), abs_pos, mutable_prompt); + std::string response; + auto stream_token = [&](int token, float) { + std::string token_text; + HWY_ASSERT(model.Tokenizer().Decode(std::vector{token}, &token_text)); + response += token_text; + return true; + }; + runtime_config.stream_token = stream_token, + tokens.insert(tokens.begin(), image_tokens_->BatchSize(), 0); + size_t num_tokens = tokens.size(); + size_t prefix_end = num_tokens; + runtime_config.prefill_tbatch_size = num_tokens; + TimingInfo timing_info = {.verbosity = 0}; + model.Generate(runtime_config, tokens, abs_pos, prefix_end, + s_env->MutableKVCache(), timing_info); + return response; +} + +void PaliGemmaTest::TestQuestions(const char* kQA[][2], size_t num_questions) { + ASSERT_NE(s_env->GetModel(), nullptr); + return; // Need to figure out how to get the external path for the test file. + std::string path; + InitVit(path); + for (size_t i = 0; i < num_questions; ++i) { + fprintf(stderr, "Question %zu\n\n", i + 1); + std::string response = GemmaReply(kQA[i][0]); + fprintf(stderr, "'%s'\n\n", response.c_str()); + EXPECT_TRUE(response.find(kQA[i][1]) != std::string::npos); // NOLINT + } +} + +TEST_F(PaliGemmaTest, General) { + static const char* kQA[][2] = { + {"describe this image", + "A large building with two towers stands tall on the water's edge."}, + {"describe image briefly", + "A large building with two towers in the middle of a city."}, + {"What kind of building is it?", "church"}, + {"How many towers does the church have?", "2"}, + {"detect water", " water"}, + {"segment water", " water"}, + {"Which city is this more likely? Tokio or Zurich?", "zurich"}, + }; + static const size_t kNum = sizeof(kQA) / sizeof(kQA[0]); + TestQuestions(kQA, kNum); +} + +} // namespace +} // namespace gcpp + +int main(int argc, char** argv) { + gcpp::GemmaEnv env(argc, argv); + gcpp::s_env = &env; + + testing::InitGoogleTest(&argc, argv); + + return RUN_ALL_TESTS(); +} diff --git a/paligemma/testdata/image.ppm b/paligemma/testdata/image.ppm new file mode 100644 index 0000000..756ff1b --- /dev/null +++ b/paligemma/testdata/image.ppm @@ -0,0 +1,192 @@ +P6 +# Source (CC0): +# https://images.pexels.com/photos/17868110/pexels-photo-17868110/free-photo-of-grossmunster-church-near-river-in-zurich.jpeg +256 341 +255 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*+:(5O>HvfWDwiK€sGvb7Q8MudW€Uˆ{V‰|U‡zP…xP†xWˆ|Z€V‹~VŠ}VŠ}QˆzK„uN…wTˆ{WŠ}aŽƒb„`ƒaŽ„XŠ~UŠ}f‘…n•Šg’ˆ_Žƒ\Z€XŒWŠ}TˆxO„sF}jHrG€rD}nC€pCo>}l;yh{kF‚rJƒuPˆyM…wHƒsF‚qD€nC€nB~lE€nHpFnI‚qH‚qFmBnG‚sT‰{WŒ~[‚a…Z€[Œ€VŠ}R‡zQ…xK‚t=xg9ra6o^1jY5n]8q`7rc5rd6p`DyhF{nN„vP‡yLƒvL}oQzq_Œ‚f“‰i“Še’ˆY‚T‹~P…wGƒtBpAp7xk9xn=zrA{tE|uJ~v]‡\‡M~yK~zN|@upqk>rl=pk { float temperature; bool deterministic; bool multiturn; + Path image_file; // Returns error string or nullptr if OK. const char* Validate() const { @@ -221,6 +222,7 @@ struct InferenceArgs : public ArgsBase { "interaction\n 1 = continue KV cache after every interaction\n " " Default : 0 (conversation " "resets every turn)"); + visitor(image_file, "image_file", Path(), "Image file to load."); } void CopyTo(RuntimeConfig& runtime_config) const {