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Fix HellaSwag (#2805)

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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow 2023-08-26 16:48:53 +03:00 committed by GitHub
parent f305bad11e
commit 771551a793
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1 changed files with 17 additions and 3 deletions
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20
examples/perplexity/perplexity.cpp
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@ -351,6 +351,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
fprintf(stderr, "================================= is_spm = %d\n", is_spm);
// This is needed as usual for LLaMA models
const bool add_bos = is_spm;
@ -406,6 +407,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
double acc = 0.0f;
const int n_vocab = llama_n_vocab(ctx);
std::vector<std::vector<int>> ending_tokens(4);
std::vector<float> tok_logits(n_vocab);
for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
@ -413,11 +416,21 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
size_t context_size = context_embd.size();
for (int i = 0; i < 4; ++i) {
ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[i], add_bos);
for (int k = 0; k < int(context_size); ++k) {
if (ending_tokens[i][k] != context_embd[k]) {
fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
break;
}
}
}
// 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], add_bos);
//auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
auto query_embd = ending_tokens[0];
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) {
@ -462,7 +475,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
// Tokenize the query
query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
query_embd.resize(ending_tokens[ending_idx].size() - context_size);
std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));
query_size = query_embd.size();
// Stop if query wont fit the ctx window