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Aug 20, 2023
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More efficient HellaSwag implementation #2677

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92 changes: 70 additions & 22 deletions examples/perplexity/perplexity.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
#include <cmath>
#include <ctime>
#include <sstream>
#include <cstring>

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
Expand Down Expand Up @@ -209,50 +210,97 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
double acc = 0.0f;
const int n_vocab = llama_n_vocab(ctx);

std::vector<float> tok_logits(n_vocab);

for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {

// Tokenize the context to count tokens
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
size_t context_size = context_embd.size();

for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
// Do the 1st ending
// In this case we include the context when evaluating
auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], prepend_bos);
auto query_size = query_embd.size();
//printf("First query: %d\n",(int)query_size);

// Stop if query wont fit the ctx window
if (query_size > (size_t)params.n_ctx) {
fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
return;
}

// Speedup small evaluations by evaluating atleast 32 tokens
if (query_size < 32) {
query_embd.resize(32);
}

// Evaluate the query
if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}

auto query_logits = llama_get_logits(ctx);

std::memcpy(tok_logits.data(), query_logits + (context_size-1)*n_vocab, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);

hs_data[task_idx].ending_logprob_count[0] = 1;
hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);

// Calculate the logprobs over the ending
for (size_t j = context_size; j < query_size - 1; j++) {

std::memcpy(tok_logits.data(), query_logits + j*n_vocab, n_vocab*sizeof(float));

const float prob = softmax(tok_logits)[query_embd[j + 1]];

hs_data[task_idx].ending_logprob[0] += std::log(prob);
hs_data[task_idx].ending_logprob_count[0]++;
}

// Calculate the mean token logprob for acc_norm
hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];

// Do the remaining endings
// For these, we use the bare ending with n_past = context_size
//
for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {

// Tokenize the query
std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
size_t query_size = query_embd.size();
query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
query_size = query_embd.size();
//printf("Second query: %d\n",(int)query_size);

// Stop if query wont fit the ctx window
if (query_size > (size_t)params.n_ctx) {
if (context_size + query_size > (size_t)params.n_ctx) {
fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
return;
}

// Speedup small evaluations by evaluating atleast 32 tokens
if (query_size < 32) {
query_embd.resize(32);
}
// No, resizing to 32 is actually slightly slower (at least on CUDA)
//if (query_size < 32) {
// query_embd.resize(32);
//}

// Evaluate the query
if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
if (llama_eval(ctx, query_embd.data(), query_embd.size(), context_size, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}

const auto query_logits = llama_get_logits(ctx);
std::vector<float> logits;
logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
query_logits = llama_get_logits(ctx);

hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
hs_data[task_idx].ending_logprob_count[ending_idx] = 1;
hs_data[task_idx].ending_logprob[ending_idx] = std::log(first_probs[query_embd[0]]);

// Calculate the logprobs over the ending
for (size_t j = context_size-1; j < query_size - 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);
for (size_t j = 0; j < query_size - 1; j++) {
std::memcpy(tok_logits.data(), query_logits + j*n_vocab, n_vocab*sizeof(float));

const float prob = softmax(tok_logits)[query_embd[ j + 1]];
const float prob = softmax(tok_logits)[query_embd[j + 1]];

hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
hs_data[task_idx].ending_logprob_count[ending_idx]++;
Expand All @@ -267,9 +315,9 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}

// Find the ending with maximum logprob
size_t ending_logprob_max_idx = -1;
double ending_logprob_max_val = -INFINITY;
for (size_t j=0; j < 4; j++) {
size_t ending_logprob_max_idx = 0;
double ending_logprob_max_val = hs_data[task_idx].ending_logprob[0];
for (size_t j = 1; j < 4; j++) {
if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
ending_logprob_max_idx = j;
ending_logprob_max_val = hs_data[task_idx].ending_logprob[j];
Expand Down