prompt_lines.push_back(line);
}
- if( prompt_lines.size() % 6 != 0) {
+ if (prompt_lines.size() % 6 != 0) {
fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
return;
}
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
// Number of tasks to use when computing the score
- if ( params.hellaswag_tasks < hs_task_count ) {
+ if (params.hellaswag_tasks < hs_task_count) {
hs_task_count = params.hellaswag_tasks;
}
std::string ending[4];
size_t ending_logprob_count[4];
double ending_logprob[4];
+
+ size_t i_batch; // starting index in the llama_batch
+ size_t common_prefix; // max number of initial tokens that are the same in all sentences
+ size_t required_tokens; // needed number of tokens to evaluate all 4 endings
+ std::vector<llama_token> seq_tokens[4];
};
fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
// Select and read data from prompt lines
- hs_data_t *hs_data = new hs_data_t[hs_task_count];
- for (size_t i=0; i < hs_task_count; i++) {
+ std::vector<hs_data_t> hs_data(hs_task_count);
+ for (size_t i = 0; i < hs_task_count; i++) {
size_t idx = i;
+ auto & hs_cur = hs_data[i];
+
// Select a random example of those left in the prompt
if (randomize_tasks) {
std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
idx = dist(rng);
}
- hs_data[i].context = prompt_lines[idx*6];
- hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
- for (size_t j=0; j < 4; j++) {
- hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
+ hs_cur.context = prompt_lines[idx*6];
+ hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
+ for (size_t j = 0; j < 4; j++) {
+ hs_cur.ending[j] = prompt_lines[idx*6+2+j];
+ hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos);
}
+ // determine the common prefix of the endings
+ hs_cur.common_prefix = 0;
+ hs_cur.required_tokens = 0;
+ for (size_t k = 0; k < hs_cur.seq_tokens[0].size(); k++) {
+ if (hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[1][k] ||
+ hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[2][k] ||
+ hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[3][k]) {
+ break;
+ }
+ hs_cur.common_prefix++;
+ }
+ hs_cur.required_tokens = hs_cur.common_prefix +
+ hs_cur.seq_tokens[0].size() - hs_cur.common_prefix +
+ hs_cur.seq_tokens[1].size() - hs_cur.common_prefix +
+ hs_cur.seq_tokens[2].size() - hs_cur.common_prefix +
+ hs_cur.seq_tokens[3].size() - hs_cur.common_prefix;
+
+ //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size());
+
// Delete the selected random example from the prompt
if (randomize_tasks) {
prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );
}
fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
+
printf("\ntask\tacc_norm\n");
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_ctx = llama_n_ctx(ctx);
+ const int n_batch = params.n_batch;
- std::vector<std::vector<int>> ending_tokens(4);
+ const int max_tasks_per_batch = params.n_parallel;
+ const int max_seq = 4*max_tasks_per_batch;
- std::vector<float> tok_logits(n_vocab);
+ llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
- 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, 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;
- }
+ std::vector<float> tok_logits(n_vocab);
+ std::vector<float> batch_logits(n_ctx*n_vocab);
+
+ auto decode_helper = [&](llama_context * ctx, llama_batch & batch, int32_t n_batch) {
+ 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));
+
+ llama_batch batch_view = {
+ n_tokens,
+ batch.token + i,
+ nullptr,
+ batch.pos + i,
+ batch.n_seq_id + i,
+ batch.seq_id + i,
+ batch.logits + i,
+ 0, 0, 0, // unused
+ };
+
+ const int ret = llama_decode(ctx, batch_view);
+ if (ret != 0) {
+ LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
+ return false;
}
- }
- // 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 = ending_tokens[0];
- auto query_size = query_embd.size();
-
- // Stop if query wont fit the ctx window
- if (query_size > (size_t)n_ctx) {
- fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
- return;
+ memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
}
- // Speedup small evaluations by evaluating atleast 32 tokens
- if (query_size < 32) {
- query_embd.resize(32);
- }
+ return true;
+ };
- // clear the KV cache
- llama_kv_cache_clear(ctx);
+ for (size_t i0 = 0; i0 < hs_task_count; i0++) {
+ int n_cur = 0;
- auto logits = evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab);
- if (logits.empty()) {
- fprintf(stderr, "%s : failed to eval\n", __func__);
- return;
- }
+ size_t i1 = i0;
+ size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
+
+ llama_batch_clear(batch);
- std::memcpy(tok_logits.data(), logits.data() + (context_size-1)*n_vocab, n_vocab*sizeof(float));
- const auto first_probs = softmax(tok_logits);
+ // batch as much tasks as possible into the available context
+ // each task has 4 unique seuqnce ids - one for each ending
+ // the common prefix is shared among the 4 sequences to save tokens
+ // we extract logits only from the last common token and from all ending tokens of each sequence
+ while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
+ auto & hs_cur = hs_data[i1];
- hs_data[task_idx].ending_logprob_count[0] = 1;
- hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);
+ const int s0 = 4*(i1 - i0);
+ if (s0 + 4 > max_seq) {
+ break;
+ }
- // Calculate the logprobs over the ending
- for (size_t j = context_size; j < query_size - 1; j++) {
+ for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
+ llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
+ }
+ batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
- std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
+ for (int s = 0; s < 4; ++s) {
+ for (size_t i = hs_cur.common_prefix; i < hs_cur.seq_tokens[s].size(); ++i) {
+ llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, true);
+ }
+ }
- const float prob = softmax(tok_logits)[query_embd[j + 1]];
+ hs_cur.i_batch = i_batch;
+ i_batch += hs_cur.required_tokens;
- hs_data[task_idx].ending_logprob[0] += std::log(prob);
- hs_data[task_idx].ending_logprob_count[0]++;
+ n_cur += hs_data[i1].required_tokens;
+ if (++i1 == hs_task_count) {
+ break;
+ }
}
- // Calculate the mean token logprob for acc_norm
- hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];
+ if (i0 == i1) {
+ fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
+ return;
+ }
- // 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++) {
+ llama_kv_cache_clear(ctx);
- // Tokenize the query
- 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();
+ // decode all tasks [i0, i1)
+ if (!decode_helper(ctx, batch, n_batch)) {
+ fprintf(stderr, "%s: llama_decode() failed\n", __func__);
+ return;
+ }
- // Stop if query wont fit the ctx window
- if (context_size + query_size > (size_t)n_ctx) {
- fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
- return;
- }
+ // compute the logprobs for each ending of the decoded tasks
+ for (size_t i = i0; i < i1; ++i) {
+ auto & hs_cur = hs_data[i];
- // Speedup small evaluations by evaluating atleast 32 tokens
- // No, resizing to 32 is actually slightly slower (at least on CUDA)
- //if (query_size < 32) {
- // query_embd.resize(32);
- //}
+ std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + hs_cur.common_prefix - 1), n_vocab*sizeof(float));
- // Evaluate the query
- logits = evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab);
- if (logits.empty()) {
- fprintf(stderr, "%s : failed to eval\n", __func__);
- return;
- }
+ const auto first_probs = softmax(tok_logits);
- 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]]);
+ size_t li = hs_cur.common_prefix; // logits index in the batch
- // Calculate the logprobs over the ending
- for (size_t j = 0; j < query_size - 1; j++) {
- std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
+ for (int s = 0; s < 4; ++s) {
+ hs_cur.ending_logprob_count[s] = 1;
+ hs_cur.ending_logprob[s] = std::log(first_probs[hs_cur.seq_tokens[s][hs_cur.common_prefix]]);
- const float prob = softmax(tok_logits)[query_embd[j + 1]];
+ // Calculate the logprobs over the ending
+ for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
+ std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + li++), n_vocab*sizeof(float));
- hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
- hs_data[task_idx].ending_logprob_count[ending_idx]++;
- }
+ const float prob = softmax(tok_logits)[hs_cur.seq_tokens[s][j + 1]];
- // Calculate the mean token logprob for acc_norm
- hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];
+ hs_cur.ending_logprob[s] += std::log(prob);
+ hs_cur.ending_logprob_count[s]++;
+ }
+ // account that we skip the last token in the ending
+ ++li;
-// printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n",
-// task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] );
- }
+ // Calculate the mean token logprob for acc_norm
+ hs_cur.ending_logprob[s] /= hs_cur.ending_logprob_count[s];
+ }
- // Find the ending with maximum logprob
- 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];
+ // Find the ending with maximum logprob
+ size_t ending_logprob_max_idx = 0;
+ double ending_logprob_max_val = hs_cur.ending_logprob[0];
+ for (size_t s = 1; s < 4; s++) {
+ if (hs_cur.ending_logprob[s] > ending_logprob_max_val) {
+ ending_logprob_max_idx = s;
+ ending_logprob_max_val = hs_cur.ending_logprob[s];
+ }
}
- }
-// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
+ //printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_cur.gold_ending_idx);
+
+ // If the gold ending got the maximum logprobe add one accuracy point
+ if (ending_logprob_max_idx == hs_cur.gold_ending_idx) {
+ acc += 1.0;
+ }
- // If the gold ending got the maximum logprobe add one accuracy point
- if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
- acc += 1.0;
+ // Print the accumulated accuracy mean x 100
+ printf("%zu\t%.8lf\n", i + 1, acc/double(i + 1)*100.0);
+ fflush(stdout);
}
- // Print the accumulated accuracy mean x 100
- printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
- fflush(stdout);
+ i0 = i1 - 1;
}
- delete [] hs_data;
+ llama_batch_free(batch);
printf("\n");
}
overview / pkg / ggml / sources / llama.cpp / commitdiff