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gemma.cpp/evals/benchmark_helper.cc

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// 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 "evals/benchmark_helper.h"
#include <stdio.h>
#include <time.h>
#include <iostream>
#include <ostream>
#include <random>
#include <string>
#include <vector>
#include "compression/types.h" // TypeName
#include "evals/cross_entropy.h"
#include "gemma/gemma.h"
#include "gemma/gemma_args.h"
#include "ops/matmul.h" // MatMulEnv
#include "util/threading_context.h"
#include "hwy/highway.h"
#include "hwy/per_target.h" // DispatchedTarget
#include "hwy/timer.h"
namespace gcpp {
void InitGenerator(const InferenceArgs& inference, std::mt19937& gen) {
if (inference.deterministic) {
// Nothing up my sleeve number, at least some upper bits set.
gen.seed(0x12345678);
} else {
// Depending on the library implementation, this may still be deterministic.
std::random_device rd; // NOLINT
gen.seed(rd());
}
}
GemmaEnv::GemmaEnv(const LoaderArgs& loader, const ThreadingArgs& threading,
const InferenceArgs& inference)
: env_(MakeMatMulEnv(threading)), gemma_(loader, inference, env_) {
const ModelConfig& config = gemma_.GetModelConfig();
// Only allocate one for starters because GenerateBatch might not be called.
kv_caches_.push_back(KVCache(config, inference));
if (inference.verbosity >= 2) {
ShowConfig(loader, threading, inference, config);
}
InitGenerator(inference, gen_);
runtime_config_ = {
.max_generated_tokens = inference.max_generated_tokens,
.temperature = inference.temperature,
.gen = &gen_,
.verbosity = inference.verbosity,
};
inference.CopyTo(runtime_config_);
}
GemmaEnv::GemmaEnv(int argc, char** argv)
: GemmaEnv(LoaderArgs(argc, argv), ThreadingArgs(argc, argv),
InferenceArgs(argc, argv)) {}
QueryResult GemmaEnv::QueryModel(const std::vector<int>& tokens) {
QueryResult result;
const BatchStreamFunc batch_stream_token =
[&result, &tokens, this](size_t /*query_index*/, size_t /*pos*/,
int token, float /*score*/) {
++result.tokens_generated;
result.response += StringFromTokens(std::vector<int>{token});
if (result.tokens_generated == tokens.size()) {
result.response_start_pos = result.response.size();
}
return true;
};
if (runtime_config_.verbosity >= 2) {
std::cout << "max generated tokens: "
<< runtime_config_.max_generated_tokens
<< "\ttemperature: " << runtime_config_.temperature << "\n";
}
gcpp::TimingInfo timing_info { .verbosity = runtime_config_.verbosity };
runtime_config_.batch_stream_token = batch_stream_token;
gemma_.Generate(runtime_config_, tokens, /*start_pos=*/0, kv_caches_[0],
timing_info);
return result;
}
void GemmaEnv::QueryModel(
const std::vector<int>& tokens, const StreamFunc& stream_token) {
gcpp::TimingInfo timing_info { .verbosity = runtime_config_.verbosity };
const StreamFunc previous_stream_token = runtime_config_.stream_token;
runtime_config_.stream_token = stream_token;
gemma_.Generate(runtime_config_, tokens, /*start_pos=*/0, kv_caches_[0],
timing_info);
runtime_config_.stream_token = previous_stream_token;
}
std::vector<QueryResult> GemmaEnv::BatchQueryModel(
const QueriesPromptTokens& queries_prompt,
const hwy::Span<const size_t>& prefix_end) {
const size_t num_queries = queries_prompt.size();
HWY_ASSERT(num_queries != 0);
std::vector<QueryResult> res(num_queries);
const BatchStreamFunc batch_stream_token = [&, this](const size_t query_index,
const size_t pos,
const int token, float) {
HWY_ASSERT(query_index < num_queries);
std::string token_text;
HWY_ASSERT(gemma_.Tokenizer().Decode(std::vector<int>{token}, &token_text));
res[query_index].response.append(token_text);
HWY_ASSERT(pos == res[query_index].tokens_generated);
res[query_index].tokens_generated += 1;
if (res[query_index].tokens_generated ==
queries_prompt[query_index].size()) {
res[query_index].response_start_pos = res[query_index].response.size();
}
return true;
};
runtime_config_.batch_stream_token = batch_stream_token;
if (runtime_config_.verbosity >= 2) {
fprintf(stderr, "Max gen: %zu temp: %f tbatch: %zu qbatch: %zu\n",
runtime_config_.max_generated_tokens, runtime_config_.temperature,
runtime_config_.prefill_tbatch_size,
runtime_config_.decode_qbatch_size);
}
// Ensure we have at least one KVCache per query.
while (kv_caches_.size() < num_queries) {
kv_caches_.push_back(KVCache(gemma_.GetModelConfig(), gemma_.Inference()));
}
const hwy::Span<KVCache> kv_caches(&kv_caches_[0], num_queries);
gcpp::AllQueries all_queries(queries_prompt, kv_caches, prefix_end);
gcpp::TimingInfo timing_info = {.verbosity = runtime_config_.verbosity};
gemma_.GenerateBatch(runtime_config_, all_queries, timing_info);
return res;
}
QueryResult GemmaEnv::QueryModel(std::string& input) {
const std::vector<int> prompt = WrapAndTokenize(input);
return QueryModel(prompt);
}
std::vector<QueryResult> GemmaEnv::BatchQueryModel(
const std::vector<std::string>& inputs) {
std::vector<std::vector<int>> prompts;
prompts.reserve(inputs.size());
for (auto& input : inputs) {
std::string mutable_prompt = input;
prompts.push_back(WrapAndTokenize(mutable_prompt));
}
std::vector<PromptTokens> prompt_vector;
prompt_vector.reserve(prompts.size());
for (auto& prompt : prompts) {
prompt_vector.push_back(PromptTokens(prompt.data(), prompt.size()));
}
QueriesPromptTokens prompt_span(prompt_vector.data(), prompt_vector.size());
return BatchQueryModel(prompt_span);
}
float GemmaEnv::CrossEntropy(const std::string& input) {
std::vector<int> prompt = Tokenize(input);
prompt.insert(prompt.begin(), BOS_ID);
return ComputeCrossEntropy(*GetGemma(), /*max_generated_tokens=*/3072, prompt,
MutableKVCache(),
/*verbosity=*/0) /
static_cast<int>(input.size());
}
void LogSpeedStats(double time_start, size_t total_tokens) {
const double time_end = hwy::platform::Now();
const double time_elapsed = time_end - time_start;
const double tok_sec = total_tokens / time_elapsed;
std::cout << total_tokens << " tokens in " << time_elapsed << " seconds"
<< " [" << tok_sec << " tokens / sec" << "]\n";
}
std::string CacheString() {
const hwy::Cache* caches = hwy::DataCaches();
if (caches == nullptr) return "cache unknown";
char buf[200];
// Do not print cores_sharing because that is visible from the topology.
const int len =
snprintf(buf, sizeof(buf), "L1 %uK=%u*%u@%u, L2 %uK=%u*%u@%u ",
caches[1].size_kib, caches[1].sets, caches[1].bytes_per_line,
caches[1].associativity, caches[2].size_kib, caches[2].sets,
caches[2].bytes_per_line, caches[2].associativity);
HWY_ASSERT(len >= 24);
if (caches[3].size_kib != 0) {
snprintf(buf + len, sizeof(buf) - len, "L3 %uK=%u*%u@%u",
caches[3].size_kib, caches[3].sets, caches[3].bytes_per_line,
caches[3].associativity);
}
return buf;
}
static constexpr const char* CompiledConfig() {
if constexpr (HWY_IS_ASAN) {
return "asan";
} else if constexpr (HWY_IS_MSAN) {
return "msan";
} else if constexpr (HWY_IS_TSAN) {
return "tsan";
} else if constexpr (HWY_IS_HWASAN) {
return "hwasan";
} else if constexpr (HWY_IS_UBSAN) {
return "ubsan";
} else if constexpr (HWY_IS_DEBUG_BUILD) {
return "dbg";
} else {
return "opt";
}
}
void ShowConfig(const LoaderArgs& loader, const ThreadingArgs& threading,
const InferenceArgs& inference, const ModelConfig& config) {
threading.Print(inference.verbosity);
loader.Print(inference.verbosity);
inference.Print(inference.verbosity);
fprintf(stderr, "Model : %s, to_bf16 %d, mmap %d\n",
config.Specifier().c_str(), static_cast<int>(loader.to_bf16),
static_cast<int>(loader.map));
if (inference.verbosity >= 2) {
time_t now = time(nullptr);
char* dt = ctime(&now); // NOLINT
char cpu100[100] = "unknown";
(void)hwy::platform::GetCpuString(cpu100);
const ThreadingContext& ctx = ThreadingContext::Get();
fprintf(stderr,
"Date & Time : %s" // dt includes \n
"CPU : %s, bind %d\n"
"CPU topology : %s, %s, %s\n"
"Instruction set : %s (%zu bits)\n"
"Compiled config : %s\n"
"Memory MiB : %4zu, %4zu free\n",
dt, cpu100, static_cast<int>(threading.bind),
ctx.topology.TopologyString(), ctx.pools.PinString(),
CacheString().c_str(), hwy::TargetName(hwy::DispatchedTarget()),
ctx.allocator.VectorBytes() * 8, CompiledConfig(),
ctx.allocator.TotalMiB(), ctx.allocator.FreeMiB());
}
}
void ShowHelp(const LoaderArgs& loader, const ThreadingArgs& threading,
const InferenceArgs& inference) {
std::cerr
<< "\n\ngemma.cpp : a lightweight, standalone C++ inference engine\n"
"==========================================================\n\n"
"To run with pre-2025 weights, specify --tokenizer and --weights.\n"
"With the single-file weights format, specify just --weights.\n";
std::cerr << "\n*Example Usage*\n\n./gemma --tokenizer tokenizer.spm "
"--weights gemma2-2b-it-sfp.sbs\n";
std::cerr << "\n*Model Loading Arguments*\n\n";
loader.Help();
std::cerr << "\n*Threading Arguments*\n\n";
threading.Help();
std::cerr << "\n*Inference Arguments*\n\n";
inference.Help();
std::cerr << "\n";
}
} // namespace gcpp
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