mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
Strided perplexity (#2714)
* Implementing strided computation of perplexity * Alternative way to output PPL results --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
GitHub
parent
7f7ddd5002
commit
62959e740e
@ -417,6 +417,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
||||
params.antiprompt.push_back(argv[i]);
|
||||
} else if (arg == "--perplexity") {
|
||||
params.perplexity = true;
|
||||
} else if (arg == "--ppl-stride") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.ppl_stride = std::stoi(argv[i]);
|
||||
} else if (arg == "--ppl-output-type") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.ppl_output_type = std::stoi(argv[i]);
|
||||
} else if (arg == "--hellaswag") {
|
||||
params.hellaswag = true;
|
||||
} else if (arg == "--hellaswag-tasks") {
|
||||
|
||||
@ -64,6 +64,10 @@ struct gpt_params {
|
||||
std::string lora_adapter = ""; // lora adapter path
|
||||
std::string lora_base = ""; // base model path for the lora adapter
|
||||
|
||||
int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
|
||||
int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
|
||||
// (which is more convenient to use for plotting)
|
||||
//
|
||||
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
|
||||
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
|
||||
|
||||
|
||||
@ -27,7 +27,121 @@ std::vector<float> softmax(const std::vector<float>& logits) {
|
||||
return probs;
|
||||
}
|
||||
|
||||
void perplexity_v2(llama_context * ctx, const gpt_params & params) {
|
||||
|
||||
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
|
||||
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
|
||||
// Output: `perplexity: 13.5106 [114/114]`
|
||||
// BOS tokens will be added for each chunk before eval
|
||||
|
||||
if (params.ppl_stride <= 0) {
|
||||
fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
|
||||
return;
|
||||
}
|
||||
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
|
||||
|
||||
const int calc_chunk = params.n_ctx;
|
||||
|
||||
fprintf(stderr, "%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk);
|
||||
|
||||
if (int(tokens.size()) <= calc_chunk) {
|
||||
fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
|
||||
tokens.size(), params.n_ctx, params.ppl_stride);
|
||||
return;
|
||||
}
|
||||
|
||||
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(ctx);
|
||||
const int n_batch = params.n_batch;
|
||||
|
||||
int count = 0;
|
||||
double nll = 0.0;
|
||||
|
||||
fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
|
||||
|
||||
for (int i = 0; i < n_chunk; ++i) {
|
||||
const int start = i * params.ppl_stride;
|
||||
const int end = start + calc_chunk;
|
||||
|
||||
const int num_batches = (calc_chunk + n_batch - 1) / n_batch;
|
||||
//fprintf(stderr, "%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches);
|
||||
|
||||
std::vector<float> logits;
|
||||
|
||||
const auto t_start = std::chrono::high_resolution_clock::now();
|
||||
|
||||
for (int j = 0; j < num_batches; ++j) {
|
||||
const int batch_start = start + j * n_batch;
|
||||
const int batch_size = std::min(end - batch_start, n_batch);
|
||||
|
||||
//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
|
||||
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
|
||||
//fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return;
|
||||
}
|
||||
|
||||
// save original token and restore it after eval
|
||||
const auto token_org = tokens[batch_start];
|
||||
|
||||
// add BOS token for the first batch of each chunk
|
||||
if (j == 0) {
|
||||
tokens[batch_start] = llama_token_bos(ctx);
|
||||
}
|
||||
|
||||
const auto batch_logits = llama_get_logits(ctx);
|
||||
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
|
||||
|
||||
if (j == 0) {
|
||||
tokens[batch_start] = token_org;
|
||||
}
|
||||
}
|
||||
|
||||
const auto t_end = std::chrono::high_resolution_clock::now();
|
||||
|
||||
if (i == 0) {
|
||||
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
|
||||
fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
||||
int total_seconds = (int)(t_total * n_chunk);
|
||||
if (total_seconds >= 60*60) {
|
||||
fprintf(stderr, "%d hours ", total_seconds / (60*60));
|
||||
total_seconds = total_seconds % (60*60);
|
||||
}
|
||||
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
|
||||
}
|
||||
|
||||
//fprintf(stderr, "%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
|
||||
for (int j = params.n_ctx - params.ppl_stride - 1; j < params.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);
|
||||
|
||||
const float prob = softmax(tok_logits)[tokens[start + j + 1]];
|
||||
|
||||
nll += -std::log(prob);
|
||||
++count;
|
||||
}
|
||||
// perplexity is e^(average negative log-likelihood)
|
||||
if (params.ppl_output_type == 0) {
|
||||
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
|
||||
} else {
|
||||
printf("%8d %.4lf\n", i*params.ppl_stride, std::exp(nll / count));
|
||||
}
|
||||
fflush(stdout);
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
void perplexity(llama_context * ctx, const gpt_params & params) {
|
||||
|
||||
if (params.ppl_stride > 0) {
|
||||
perplexity_v2(ctx, params);
|
||||
return;
|
||||
}
|
||||
|
||||
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
|
||||
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
|
||||
// Output: `perplexity: 13.5106 [114/114]`
|
||||
@ -116,7 +230,11 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
||||
++count;
|
||||
}
|
||||
// perplexity is e^(average negative log-likelihood)
|
||||
if (params.ppl_output_type == 0) {
|
||||
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
|
||||
} else {
|
||||
printf("%8d %.4lf\n", i*params.n_ctx, std::exp(nll / count));
|
||||
}
|
||||
fflush(stdout);
|
||||
}
|
||||
printf("\n");
|
||||
@ -369,6 +487,12 @@ int main(int argc, char ** argv) {
|
||||
params.perplexity = true;
|
||||
params.n_batch = std::min(params.n_batch, params.n_ctx);
|
||||
|
||||
if (params.ppl_stride > 0) {
|
||||
fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
|
||||
params.n_ctx, params.n_ctx + params.ppl_stride/2);
|
||||
params.n_ctx += params.ppl_stride/2;
|
||||
}
|
||||
|
||||
if (params.n_ctx > 2048) {
|
||||
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
|
||||
"expect poor results\n", __func__, params.n_ctx);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user