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perplexity : fix integer overflow (#9783)

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* perplexity : fix integer overflow

ggml-ci

* perplexity : keep n_vocab as int and make appropriate casts

ggml-ci
This commit is contained in:
Georgi Gerganov
2024-10-09 17:00:18 +03:00
committed by GitHub
parent 3dc48fe75a
commit e7022064ab
1 changed files with 49 additions and 36 deletions
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85
examples/perplexity/perplexity.cpp
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@@ -169,7 +169,7 @@ static void process_logits(
break;
}
lock.unlock();
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const results_log_softmax results = log_softmax(n_vocab, logits + size_t(i)*n_vocab, tokens[i+1]);
const double v = -results.log_softmax;
local_nll += v;
local_nll2 += v*v;
@@ -203,7 +203,7 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits,
break;
}
lock.unlock();
const double v = log_softmax(n_vocab, logits + i*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
const double v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, log_probs.data() + i*nv, tokens[i+1]);
local_nll += v;
local_nll2 += v*v;
}
@@ -281,7 +281,9 @@ static std::pair<double, float> log_softmax(int n_vocab, const float * logits, c
kld.sum_kld += sum;
kld.sum_kld2 += sum*sum;
++kld.count;
if (imax == imax_base) ++kld.n_same_top;
if (imax == imax_base) {
++kld.n_same_top;
}
const float p_base = expf(-nll_base);
const float p = expf(-nll);
@@ -323,7 +325,7 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
break;
}
lock.unlock();
std::pair<double, float> v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
std::pair<double, float> v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
kld_values[i] = (float)v.first;
p_diff_values[i] = v.second;
}
@@ -383,9 +385,10 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
int count = 0;
double nll = 0.0;
@@ -424,8 +427,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
const auto batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab);
if (j == 0) {
tokens[batch_start] = token_org;
@@ -447,11 +450,10 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
//LOG_DBG("%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
for (int j = n_ctx - params.ppl_stride - 1; j < n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
const std::vector<float> tok_logits(
logits.begin() + (j + 0) * n_vocab,
logits.begin() + (j + 1) * n_vocab);
logits.begin() + size_t(j + 0) * n_vocab,
logits.begin() + size_t(j + 1) * n_vocab);
const float prob = softmax(tok_logits)[tokens[start + j + 1]];
logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
@@ -521,9 +523,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
const int n_chunk_max = tokens.size() / n_ctx;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
int count = 0;
double nll = 0.0;
double nll2 = 0.0;
@@ -538,7 +541,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
std::vector<float> logits;
if (num_batches > 1) {
logits.reserve((size_t)n_ctx * n_vocab);
logits.reserve(size_t(n_ctx) * n_vocab);
}
LOG_INF("%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
@@ -620,7 +623,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
if (num_batches > 1 && n_outputs > 0) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
logits.insert(logits.end(), batch_logits, batch_logits + size_t(n_outputs) * n_vocab);
}
}
@@ -661,7 +664,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
} else {
double av = nll/count;
double av2 = nll2/count - av*av;
if (av2 > 0) av2 = sqrt(av2/(count-1));
if (av2 > 0) {
av2 = sqrt(av2/(count-1));
}
LOG("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
}
}
@@ -686,10 +691,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
return {tokens, ppl, logit_history, prob_history};
}
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int n_batch, int n_vocab) {
int prev_outputs = 0;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
for (int i = 0; i < (int) batch.n_tokens; i += n_batch) {
const int n_tokens = std::min<int>(n_batch, batch.n_tokens - i);
llama_batch batch_view = {
n_tokens,
@@ -713,7 +718,7 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
n_outputs += batch_view.logits[i] != 0;
}
memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
memcpy(batch_logits.data() + size_t(prev_outputs)*n_vocab, llama_get_logits(ctx), size_t(n_outputs)*n_vocab*sizeof(float));
prev_outputs += n_outputs;
}
@@ -728,7 +733,9 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
if (eval_results.size() != eval_pairs.size()) {
eval_results.resize(eval_pairs.size());
}
if (eval_pairs.empty()) return;
if (eval_pairs.empty()) {
return;
}
size_t max_threads = std::min((eval_pairs.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK, workers.size());
@@ -736,11 +743,13 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
auto compute = [&counter, &eval_pairs, &eval_results, batch_logits, n_vocab] () {
float local_logprobs[K_TOKEN_CHUNK];
while (true) {
size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed);
if (first >= eval_results.size()) break;
size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size());
const size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed);
if (first >= eval_results.size()) {
break;
}
const size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size());
for (size_t i = first; i < last; ++i) {
auto logits = batch_logits + eval_pairs[i].first * n_vocab;
const auto * logits = batch_logits + eval_pairs[i].first * n_vocab;
float max_logit = logits[0];
for (int j = 1; j < n_vocab; ++j) {
max_logit = std::max(max_logit, logits[j]);
@@ -877,10 +886,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
double acc = 0.0f;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@@ -888,7 +898,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
@@ -975,7 +985,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
auto & hs_cur = hs_data[i];
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
std::memcpy(tok_logits.data(), batch_logits.data() + hs_cur.i_logits*n_vocab, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@@ -1158,10 +1168,11 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int max_tasks_per_batch = 128;
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
@@ -1169,7 +1180,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
@@ -1509,17 +1520,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
LOG("\ntask\tacc_norm\n");
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
std::vector<float> tok_logits(n_vocab);
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<float> batch_logits(size_t(n_ctx)*n_vocab);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
@@ -1627,7 +1639,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
//LOG("\n common_prefix: %zu\n", cur_task.common_prefix);
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*cur_task.i_logits, n_vocab*sizeof(float));
std::memcpy(tok_logits.data(), batch_logits.data() + cur_task.i_logits*n_vocab, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@@ -1709,7 +1721,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
__func__, params.logits_file.c_str(), n_ctx, params.n_ctx);
}
int n_vocab, n_chunk;
int n_vocab;
int n_chunk;
in.read((char *)&n_vocab, sizeof(n_vocab));
in.read((char *)&n_chunk, sizeof(n_chunk));
if (in.fail()) {
@@ -1720,7 +1733,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_n_vocab(llama_get_model(ctx)));
}
std::vector<llama_token> tokens(n_ctx * n_chunk);
std::vector<llama_token> tokens(size_t(n_ctx) * n_chunk);
if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) {
LOG_ERR("%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str());
return;
@@ -1737,7 +1750,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> logits;
if (num_batches > 1) {
logits.reserve(n_ctx * n_vocab);
logits.reserve(size_t(n_ctx) * n_vocab);
}
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
@@ -1801,7 +1814,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
if (num_batches > 1) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab);
}
}
@@ -1822,7 +1835,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
const int first = n_ctx/2;
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
process_logits(n_vocab, all_logits + size_t(first)*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr);
p_diff_ptr += n_ctx - 1 - first;
kld_ptr += n_ctx - 1 - first;