gemma.cpp/gemma/gemma.cc

// 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.

// Defines Gemma member functions; the actual implementations are in
// gemma-inl.h, included from instantiations/*.cc.

#include "gemma/gemma.h"

#include <stddef.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include <utility>  // std::move
#include <vector>

#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"

namespace gcpp {

Gemma::Gemma(const Path& tokenizer_path, const Path& weights,
             const ModelInfo& info, PerClusterPools& pools)
    : pools_(pools), tokenizer_(tokenizer_path), info_(info) {
  weights_u8_ =
      LoadCompressedWeights(weights, info.model, info.weight, pools_.Inner(0));
}

Gemma::Gemma(GemmaTokenizer&& tokenizer, const ModelInfo& info,
             PerClusterPools& pools)
    : pools_(pools), tokenizer_(std::move(tokenizer)), info_(info) {
  HWY_ASSERT(info.weight == Type::kF32);
  weights_u8_ = CallForModel<float, AllocateCompressedWeights>(info.model,
                                                               pools_.Inner(0));
}

Gemma::~Gemma() {
  CallForModelAndWeight<DeleteCompressedWeights>(info_.model, info_.weight,
                                                 weights_u8_);
}

// There are >100 instantiations of the inference code. To reduce compile time,
// we shard them across multiple translation units in instantiations/*.cc.
// This declares the functions defined there. We use overloading because
// explicit instantiations are still too slow to compile.
#define GEMMA_DECLARE(CONFIGT, TWEIGHT)                                        \
  extern void GenerateSingle(CONFIGT<TWEIGHT>, const ByteStorageT& weights_u8, \
                             const RuntimeConfig& runtime_config,              \
                             const PromptTokens& prompt, size_t pos,           \
                             size_t prefix_end, KVCache& kv_cache,             \
                             PerClusterPools& pools, TimingInfo& timing_info); \
  extern void GenerateBatch(                                                   \
      CONFIGT<TWEIGHT>, const ByteStorageT& weights_u8,                        \
      const RuntimeConfig& runtime_config, const QueriesPromptTokens& prompts, \
      const QueriesPos& queries_pos,                                           \
      const QueriesPos& queries_prefix_end, const KVCaches& kv_caches,   \
      PerClusterPools& pools, TimingInfo& timing_info);                        \
  extern void GenerateImageTokens(                                             \
      CONFIGT<TWEIGHT>, 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.
template <class TConfig>
struct GenerateSingleT {
  void operator()(const ByteStorageT& weights_u8,
                  const RuntimeConfig& runtime_config,
                  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);
  }
};

template <class TConfig>
struct GenerateBatchT {
  void operator()(const ByteStorageT& weights_u8,
                  const RuntimeConfig& runtime_config,
                  const QueriesPromptTokens& queries_prompt,
                  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, queries_prefix_end, kv_caches, pools,
                  timing_info);
  }
};

template <class TConfig>
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, size_t prefix_end,
                     KVCache& kv_cache, TimingInfo& timing_info) {
  if (runtime_config.use_spinning) pools_.StartSpinning();

  CallForModelAndWeight<GenerateSingleT>(
      info_.model, info_.weight, weights_u8_, runtime_config, prompt, pos,
      prefix_end, kv_cache, pools_, timing_info);

  if (runtime_config.use_spinning) pools_.StopSpinning();
}

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<size_t> 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<GenerateBatchT>(
      info_.model, info_.weight, weights_u8_, runtime_config, queries_prompt,
      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<GenerateImageTokensT>(info_.model, info_.weight,
                                              weights_u8_, runtime_config,
                                              image, image_tokens, pools_);

  if (runtime_config.use_spinning) pools_.StopSpinning();
}

template <typename TConfig>
struct GetModelConfig {
  ModelConfigInfo operator()() const {
    return ModelConfigInfo{
        .layers = TConfig::kLayers,
        .model_dim = TConfig::kModelDim,
        .heads = TConfig::kHeads,
        .kv_heads = TConfig::kKVHeads,
        .qkv_dim = TConfig::kQKVDim,
    };
  }
};

ModelConfigInfo Gemma::ModelConfig() const {
  return CallForModel<float, GetModelConfig>(info_.model);
}

}  // namespace gcpp