mirror of https://github.com/google/gemma.cpp.git
106 lines
3.6 KiB
C++
106 lines
3.6 KiB
C++
// Copyright 2024 Google LLC
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// SPDX-License-Identifier: Apache-2.0
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// https://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "gemma/kv_cache.h"
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#include <algorithm>
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#include "gemma/common.h" // CallForModel
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#include "hwy/aligned_allocator.h"
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#include "hwy/base.h" // ZeroBytes
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namespace gcpp {
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void KVCache::ZeroGriffinCache() {
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if (conv1d_cache_size != 0) {
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hwy::ZeroBytes(conv1d_cache.get(),
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conv1d_cache_size * sizeof(conv1d_cache[0]));
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}
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if (rglru_cache_size != 0) {
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hwy::ZeroBytes(rglru_cache.get(),
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rglru_cache_size * sizeof(rglru_cache[0]));
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}
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}
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// prefill_tbatch_size is the maximum number of tokens from one query to
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// prefill at a time.
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KVCache KVCache::Create(const ModelConfig& weights_config,
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size_t prefill_tbatch_size) {
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KVCache kv_cache = {};
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const size_t size_cache_pos = weights_config.CachePosSize();
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if (size_cache_pos != 0) {
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// Allocate more so that prefill can always access one batch, even if
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// near the end of the sequence.
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kv_cache.seq_len = weights_config.seq_len + prefill_tbatch_size;
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kv_cache.kv_cache =
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hwy::AllocateAligned<float>(kv_cache.seq_len * size_cache_pos);
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}
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const size_t num_griffin_layers = weights_config.NumLayersOfType(
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LayerAttentionType::kGriffinRecurrentBlock);
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// TODO(patrickms): Add query batching support for Griffin.
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if (num_griffin_layers > 0) {
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uint32_t conv1d_width = 0;
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for (const auto& layer_config : weights_config.layer_configs) {
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conv1d_width = std::max(conv1d_width, layer_config.conv1d_width);
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}
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const size_t conv1d_cache_size =
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num_griffin_layers * (conv1d_width == 0 ? 0 : conv1d_width - 1) *
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weights_config.model_dim;
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kv_cache.conv1d_cache_size = conv1d_cache_size;
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if (conv1d_cache_size != 0) {
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kv_cache.conv1d_cache = hwy::AllocateAligned<float>(conv1d_cache_size);
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}
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const size_t rglru_cache_size =
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num_griffin_layers * weights_config.model_dim;
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kv_cache.rglru_cache_size = rglru_cache_size;
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if (rglru_cache_size != 0) {
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kv_cache.rglru_cache = hwy::AllocateAligned<float>(rglru_cache_size);
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}
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} // num_griffin_layers
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return kv_cache;
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}
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KVCache KVCache::Copy(const ModelConfig& weights_config,
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size_t prefill_tbatch_size) {
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KVCache kv_cache_copy = Create(weights_config, prefill_tbatch_size);
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const size_t size_cache_pos = weights_config.CachePosSize();
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if (size_cache_pos != 0) {
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std::copy(kv_cache.get(), kv_cache.get() + size_cache_pos * seq_len,
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kv_cache_copy.kv_cache.get());
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}
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const size_t num_griffin_layers = weights_config.NumLayersOfType(
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LayerAttentionType::kGriffinRecurrentBlock);
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if (num_griffin_layers > 0) {
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if (conv1d_cache_size != 0) {
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std::copy(conv1d_cache.get(), conv1d_cache.get() + conv1d_cache_size,
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kv_cache_copy.conv1d_cache.get());
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}
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if (rglru_cache_size != 0) {
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std::copy(rglru_cache.get(),
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rglru_cache.get() + rglru_cache_size * sizeof(rglru_cache[0]),
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kv_cache_copy.rglru_cache.get());
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}
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}
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return kv_cache_copy;
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}
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} // namespace gcpp
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