llama : greatly reduce output buffer memory usage (#6122)
* llama : greatly reduce logits memory usage * llama : more compact state saving and reloading * llama : fix lctx.n_outputs not being set before building graph * perplexity : adapt to the logits API changes * perplexity : fix Winogrande, use correct logits for second choice start The first logits used to evaluate the second choice were not from the end of the common prefix; instead, they were the logits from the end of the first choice. This has been corrected. The previous implementation sometimes had outliers in the scores of choices for some tasks, and the logic to skip choices words in the log-likelihood evaluation probably was an attempt to reduce those, but it was complex and didn't quite seem to be the right thing. This is simpler now, and the outlier scores aren't there anymore. * perplexity : normalize spaces and punctuation in Winogrande sentences * llama : fix embedding conditions * llama : fix llama_get_embeddings_ith when the resulting id is 0 * llama : fix wrong n_outputs in llama_set_inputs A mismatch happened when using a smaller n_ubatch than n_batch and then using llama_batch_get_one(). The decision of what n_outputs should be now almost fully depends on how lctx.n_outputs is set in llama_decode_internal. The conditions are simpler this way. * llama : when saving the state, recalculate n_outputs This ensures the correct number of outputs for the entire previous batch is stored in the session file, even when n_ubatch is smaller than n_batch. * llama : fix not-skipping outputs of non-causal models * llama : fix running a batch with n_outputs == 0 It previously worked because lctx.inp_out_ids was not initialized, so it pointed to some garbage address which was somehow still valid when I ran my tests. * llama : keep same graph topology even when n_outputs == 0 * ggml : saner ggml_can_repeat with empty tensors * ggml : future-proof ggml_is_empty by using GGML_MAX_DIMS - 1 * ggml : do not multi-thread ops returning empty tensors * ggml : make ggml_is_empty public and work with views * llama : use a vector for ctx->output_ids * llama : rework reallocation logic for llama_output_reserve Now comparing the actual size with the new total size of the output buffer to allow more efficient enabling and disabling of the embeddings and/or logits output in the future. * ggml : skip empty tensors in all backends * llama : fix llama_output_reserve nullptr deref when new_size is 0 * perplexity : make Winogrande work as it does on master The problems with the Winogrande implementation will need to be fixed in a separate PR to ease review. * llama : clearer error messages for invalid logits or embeddings ids * llama : assert all models that can have inp_out_ids Since the graph topology is now constant, this presence check can be done even when there are no outputs. * llama : assert logits and embd buffers exist before writing to them * llama : handle errors from llama_output_reserve at call sites * perplexity : make hellaswag and multiple-choice outputs identical to master Due to how the KV cache is updated, the logprobs for tokens in a batch are very slightly affected by the other tokens present in the batch, so to make hellaswag and multiple-choice return exactly the same results as on master, the last token of each sequence needs to be evaluated even though its output is not used at all. This will probably be changed back in the future to make these benchmarks a tiny bit faster. * perplexity : fix division by zero when using less than 100 multiple-choice tasks * llama : allow loading state saved with a different ctx size When loading a session file, the context size is now only required to be at least enough to load the KV cells contained in that session file, instead of requiring to use exactly the same context size as when saving. Doing this enables the use-case of extending or shrinking the context size of a saved session. This breaks existing session files because the meaning of kv_buf_size is slightly changed (previously it was the size of the whole KV cache, now it's only the size of the saved part of it). This allows for finer-grained sanity checks when loading in an effort to keep kv_buf_size useful even when the kv_size is changed. * llama : minor ggml-ci * readme : update recent API changes, and warn about Vulkan --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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16 changed files with 704 additions and 197 deletions
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@ -380,6 +380,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
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const int batch_size = std::min(end - batch_start, n_batch);
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//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
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// TODO: use llama_batch.logits instead of relying on logits_all == true
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if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
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//fprintf(stderr, "%s : failed to eval\n", __func__);
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return {tokens, -1, logit_history, prob_history};
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@ -552,6 +553,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
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const int batch_start = start + j * n_batch;
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const int batch_size = std::min(end - batch_start, n_batch);
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int n_outputs = 0;
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batch.n_tokens = 0;
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for (int seq = 0; seq < n_seq_batch; seq++) {
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int seq_start = batch_start + seq*n_ctx;
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@ -566,11 +569,13 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
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for (int k = 0; k < batch_size; ++k) {
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const int idx = seq*n_ctx + k;
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batch.token[idx] = tokens[seq_start + k];
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batch.pos[idx] = j*n_batch + k;
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batch.n_seq_id[idx] = 1;
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batch.seq_id[idx][0] = seq;
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batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0;
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batch.token [idx] = tokens[seq_start + k];
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batch.pos [idx] = j*n_batch + k;
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batch.n_seq_id[idx] = 1;
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batch.seq_id [idx][0] = seq;
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batch.logits [idx] = batch.pos[idx] >= first ? 1 : 0;
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n_outputs += batch.logits[idx] != 0;
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}
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batch.n_tokens += batch_size;
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@ -583,9 +588,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
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return {tokens, -1, logit_history, prob_history};
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}
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if (num_batches > 1) {
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if (num_batches > 1 && n_outputs > 0) {
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const auto * batch_logits = llama_get_logits(ctx);
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logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
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logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
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}
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}
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@ -604,14 +609,15 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
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}
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for (int seq = 0; seq < n_seq_batch; seq++) {
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const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
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const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first);
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llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
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if (!params.logits_file.empty()) {
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process_logits(logits_stream, n_vocab, all_logits + first*n_vocab,
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process_logits(logits_stream, n_vocab, all_logits,
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tokens_data, n_ctx - 1 - first,
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workers, log_probs, nll, nll2);
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} else {
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process_logits(n_vocab, all_logits + first*n_vocab,
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process_logits(n_vocab, all_logits,
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tokens_data, n_ctx - 1 - first,
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workers, nll, nll2,
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logit_history.data() + start + seq*n_ctx + first,
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@ -652,6 +658,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
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}
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static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
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int prev_outputs = 0;
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for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
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const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
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@ -672,7 +679,14 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
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return false;
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}
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memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
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int n_outputs = 0;
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for (int i = 0; i < n_tokens; ++i) {
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n_outputs += batch_view.logits[i] != 0;
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}
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memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
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prev_outputs += n_outputs;
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}
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return true;
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@ -779,7 +793,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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size_t ending_logprob_count[4];
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double ending_logprob[4];
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size_t i_batch; // starting index in the llama_batch
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size_t i_logits; // starting index of logits in the llama_batch
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size_t common_prefix; // max number of initial tokens that are the same in all sentences
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size_t required_tokens; // needed number of tokens to evaluate all 4 endings
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std::vector<llama_token> seq_tokens[4];
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@ -844,9 +858,10 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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const int max_tasks_per_batch = 32;
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const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
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llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
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llama_batch batch = llama_batch_init(n_ctx, 0, 4);
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std::vector<float> tok_logits(n_vocab);
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// TODO: this could be made smaller; it's currently the worst-case size
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std::vector<float> batch_logits(n_vocab*n_ctx);
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std::vector<std::pair<size_t, llama_token>> eval_pairs;
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@ -857,16 +872,17 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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int n_cur = 0;
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size_t i1 = i0;
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size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
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size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
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llama_batch_clear(batch);
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// batch as much tasks as possible into the available context
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// each task has 4 unique seuqnce ids - one for each ending
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// each task has 4 unique sequence ids - one for each ending
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// the common prefix is shared among the 4 sequences to save tokens
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// we extract logits only from the last common token and from all ending tokens of each sequence
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while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
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auto & hs_cur = hs_data[i1];
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int n_logits = 0;
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const int s0 = 4*(i1 - i0);
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if (s0 + 4 > max_seq) {
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@ -874,18 +890,23 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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}
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for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
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llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
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llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
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}
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batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
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n_logits += 1;
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for (int s = 0; s < 4; ++s) {
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for (size_t i = hs_cur.common_prefix; i < hs_cur.seq_tokens[s].size(); ++i) {
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llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, true);
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const size_t seq_tokens_size = hs_cur.seq_tokens[s].size();
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// TODO: don't evaluate the last token of each sequence
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for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
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const bool needs_logits = i < seq_tokens_size - 1;
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llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
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n_logits += needs_logits;
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}
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}
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hs_cur.i_batch = i_batch;
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i_batch += hs_cur.required_tokens;
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hs_cur.i_logits = i_logits;
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i_logits += n_logits;
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n_cur += hs_data[i1].required_tokens;
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if (++i1 == hs_task_count) {
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@ -911,12 +932,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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eval_pairs.clear();
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for (size_t i = i0; i < i1; ++i) {
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auto & hs_cur = hs_data[i];
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size_t li = hs_cur.common_prefix;
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size_t li = 1; // skip the last logit of the common prefix (computed separately below)
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for (int s = 0; s < 4; ++s) {
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for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
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eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]);
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eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]);
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}
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++li;
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}
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}
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// Then we do the actual calculation
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@ -928,7 +948,8 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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for (size_t i = i0; i < i1; ++i) {
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auto & hs_cur = hs_data[i];
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std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + hs_cur.common_prefix - 1), n_vocab*sizeof(float));
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// get the logits of the last token of the common prefix
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std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
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const auto first_probs = softmax(tok_logits);
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@ -978,7 +999,7 @@ struct winogrande_entry {
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std::array<std::string, 2> choices;
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int answer;
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size_t i_batch;
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size_t i_logits;
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size_t common_prefix;
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size_t required_tokens;
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size_t n_base1; // number of tokens for context + choice 1
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@ -1104,6 +1125,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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task.common_prefix++;
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}
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// TODO: the last token of each of the sequences don't need to be evaluated
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task.required_tokens = task.common_prefix +
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task.seq_tokens[0].size() - task.common_prefix +
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task.seq_tokens[1].size() - task.common_prefix;
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@ -1121,9 +1143,10 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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const int max_tasks_per_batch = 128;
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const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
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llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
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llama_batch batch = llama_batch_init(n_ctx, 0, 2);
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std::vector<float> tok_logits(n_vocab);
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// TODO: this could be made smaller; it's currently the worst-case size
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std::vector<float> batch_logits(n_vocab*n_ctx);
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std::vector<std::pair<size_t, llama_token>> eval_pairs;
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@ -1137,29 +1160,33 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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int n_cur = 0;
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size_t i1 = i0;
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size_t i_batch = 0;
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size_t i_logits = 0;
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llama_batch_clear(batch);
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while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
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int n_logits = 0;
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const int s0 = 2*(i1 - i0);
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if (s0 + 2 > max_seq) {
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break;
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}
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for (size_t i = 0; i < data[i1].common_prefix; ++i) {
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llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1}, false);
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llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
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}
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batch.logits[batch.n_tokens - 1] = true;
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n_logits += 1;
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for (int s = 0; s < 2; ++s) {
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// TODO: end before the last token, no need to predict past the end of the sequences
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for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
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llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
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n_logits += 1;
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}
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}
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data[i1].i_batch = i_batch;
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i_batch += data[i1].required_tokens;
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data[i1].i_logits = i_logits;
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i_logits += n_logits;
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n_cur += data[i1].required_tokens;
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if (++i1 == data.size()) {
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@ -1190,15 +1217,16 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
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const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
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size_t li = n_base1 - 1;
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size_t li = n_base1 - task.common_prefix;
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for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
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eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]);
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eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]);
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}
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const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
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const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
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li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
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// FIXME: this uses the wrong first logits when not skipping the choice word
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li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix;
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for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
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eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]);
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eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]);
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}
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}
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compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
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@ -1287,7 +1315,7 @@ struct multiple_choice_task {
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}
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// For evaluation
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size_t i_batch; // starting index in the llama_batch
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size_t i_logits; // starting index of logits in the llama_batch
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size_t common_prefix; // max number of initial tokens that are the same in all sentences
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size_t required_tokens; // needed number of tokens to evaluate all answers
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std::vector<std::vector<llama_token>> seq_tokens;
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@ -1366,7 +1394,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
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std::vector<uint32_t> task_pos(n_task);
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strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
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if (strstream.fail()) {
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printf("%s: failed to raad task positions from prompt\n", __func__);
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printf("%s: failed to read task positions from prompt\n", __func__);
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return;
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}
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@ -1447,7 +1475,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
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return;
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}
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} else {
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int n_dot = n_task/100;
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int n_dot = std::max((int) n_task/100, 1);
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int i_task = 0;
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for (auto& task : tasks) {
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++i_task;
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@ -1491,17 +1519,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
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int n_cur = 0;
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size_t i1 = i0;
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size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
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size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
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llama_batch_clear(batch);
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// batch as much tasks as possible into the available context
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// each task has 4 unique seuqnce ids - one for each ending
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// each task has 4 unique sequence ids - one for each ending
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// the common prefix is shared among the 4 sequences to save tokens
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// we extract logits only from the last common token and from all ending tokens of each sequence
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int s0 = 0;
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while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
|
||||
auto& cur_task = tasks[i1];
|
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int n_logits = 0;
|
||||
|
||||
int num_answers = cur_task.seq_tokens.size();
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if (s0 + num_answers > max_seq) {
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@ -1518,17 +1547,22 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
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llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
|
||||
}
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batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
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n_logits += 1;
|
||||
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||||
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
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||||
for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) {
|
||||
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true);
|
||||
const size_t seq_tokens_size = cur_task.seq_tokens[s].size();
|
||||
// TODO: don't evaluate the last token of each sequence
|
||||
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
|
||||
const bool needs_logits = i < seq_tokens_size - 1;
|
||||
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
|
||||
n_logits += needs_logits;
|
||||
}
|
||||
}
|
||||
|
||||
s0 += num_answers;
|
||||
|
||||
cur_task.i_batch = i_batch;
|
||||
i_batch += cur_task.required_tokens;
|
||||
cur_task.i_logits = i_logits;
|
||||
i_logits += n_logits;
|
||||
|
||||
n_cur += cur_task.required_tokens;
|
||||
if (++i1 == tasks.size()) {
|
||||
|
|
@ -1554,12 +1588,11 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
|
|||
eval_pairs.clear();
|
||||
for (size_t i = i0; i < i1; ++i) {
|
||||
auto& cur_task = tasks[i];
|
||||
size_t li = cur_task.common_prefix;
|
||||
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
|
||||
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
|
||||
for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
|
||||
eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]);
|
||||
eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]);
|
||||
}
|
||||
++li;
|
||||
}
|
||||
}
|
||||
// Then we do the actual calculation
|
||||
|
|
@ -1578,7 +1611,8 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
|
|||
//}
|
||||
//printf("\n common_prefix: %zu\n", cur_task.common_prefix);
|
||||
|
||||
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float));
|
||||
// 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));
|
||||
|
||||
const auto first_probs = softmax(tok_logits);
|
||||
|
||||
|
|
@ -1730,6 +1764,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
|
|||
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
|
||||
}
|
||||
|
||||
// TODO: use llama_batch.logits instead of relying on logits_all == true
|
||||
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue