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perplexity : faster Winogrande via batching #5024

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Jan 19, 2024
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perplexity : faster Winogrande via batching #5024

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0f3ed78
perplexity : faster Winogrande via batching
ggerganov Jan 18, 2024
bb58b0e
perplexity : remove unused function
ggerganov Jan 18, 2024
9e4ad80
perplexity : only tokenize selected tasks for Winogrande
ggerganov Jan 19, 2024
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perplexity : faster Winogrande via batching 
ggml-ci
  • Loading branch information
@ggerganov
ggerganov committed Jan 18, 2024
commit 0f3ed789b32a239c034e34ee284ae3df280a1096
275 changes: 164 additions & 111 deletions examples/perplexity/perplexity.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -445,6 +445,33 @@ static std::vector<float> evaluate_tokens(llama_context * ctx, std::vector<int>
return result;
}

static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
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
};

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The llama_kv_cache_seq_rm call is no longer needed here?

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It's not needed because we clear the entire KV cache before each batch:

llama.cpp/examples/perplexity/perplexity.cpp

Lines 942 to 944 in 9e4ad80

llama_kv_cache_clear(ctx);

In the old implementation, it was reusing tokens from a previous batch, so the llama_kv_cache_seq_rm was used to evict the unused ones (i.e. the second sentence).

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;
}

memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
}

return true;
}

static void hellaswag_compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
const std::vector<std::pair<size_t, llama_token>>& eval_pairs, std::vector<float>& eval_results) {
constexpr int k_token_chunk = 4;
Expand Down Expand Up @@ -576,7 +603,6 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {

// 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] ||
Expand Down Expand Up @@ -609,45 +635,18 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
const int n_ctx = llama_n_ctx(ctx);
const int n_batch = params.n_batch;

const int max_tasks_per_batch = params.n_parallel;
const int max_tasks_per_batch = 32;
const int max_seq = 4*max_tasks_per_batch;

llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);

std::vector<float> tok_logits(n_vocab);
std::vector<float> batch_logits(n_ctx*n_vocab);
std::vector<float> batch_logits(n_vocab*n_ctx);

std::vector<std::pair<size_t, llama_token>> eval_pairs;
std::vector<float> eval_results;
std::vector<std::thread> workers(std::thread::hardware_concurrency());

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;
}

memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
}

return true;
};

for (size_t i0 = 0; i0 < hs_task_count; i0++) {
int n_cur = 0;

Expand Down Expand Up @@ -696,7 +695,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
llama_kv_cache_clear(ctx);

// decode all tasks [i0, i1)
if (!decode_helper(ctx, batch, n_batch)) {
if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
fprintf(stderr, "%s: llama_decode() failed\n", __func__);
return;
}
Expand Down Expand Up @@ -772,6 +771,13 @@ struct winogrande_entry {
std::string second;
std::array<std::string, 2> choices;
int answer;

size_t i_batch;
size_t common_prefix;
size_t required_tokens;
size_t n_base1; // number of tokens for context + choice 1
size_t n_base2; // number of tokens for context + choice 2
std::vector<llama_token> seq_tokens[2];
};

static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string& prompt) {
Expand Down Expand Up @@ -854,6 +860,29 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
return;
}

// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));

for (auto & task : data) {
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos);
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos);

task.common_prefix = 0;
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
if (task.seq_tokens[0][k] != task.seq_tokens[1][k]) {
break;
}
task.common_prefix++;
}

task.required_tokens = task.common_prefix +
task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix;

task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size();
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size();
}

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fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, data.size());

if (params.winogrande_tasks > 0 && params.winogrande_tasks < data.size()) {
Expand All @@ -875,115 +904,139 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
data = std::move(selected);
}

// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));

fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__);

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;

const int max_tasks_per_batch = 128;
const int max_seq = 2*max_tasks_per_batch;

llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);

std::vector<float> tok_logits(n_vocab);
std::vector<float> batch_logits(n_vocab*n_ctx);

int n_correct = 0;
int n_done = 0;

for (size_t task_idx = 0; task_idx < data.size(); task_idx++) {
const auto& task = data[task_idx];
for (size_t i0 = 0; i0 < data.size(); i0++) {
int n_cur = 0;

auto base_context = ::llama_tokenize(ctx, task.first, add_bos);
auto base_ctx_1st = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos);
auto base_ctx_2nd = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos);
size_t i1 = i0;
size_t i_batch = 0;

auto sentence_1st = task.first + task.choices[0] + task.second;
auto sentence_2nd = task.first + task.choices[1] + task.second;
auto query_1st = ::llama_tokenize(ctx, sentence_1st, add_bos);
auto query_2nd = ::llama_tokenize(ctx, sentence_2nd, add_bos);
llama_batch_clear(batch);

if (query_1st.size() > (size_t)n_ctx || query_2nd.size() > (size_t)n_ctx) {
fprintf(stderr, "%s : number of tokens in queries %zu, %zu > n_ctxl\n", __func__, query_1st.size(), query_2nd.size());
return;
}
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
const int s0 = 2*(i1 - i0);
if (s0 + 2 > max_seq) {
break;
}

auto query_1st_size = query_1st.size();
auto query_2nd_size = query_2nd.size();
for (size_t i = 0; i < data[i1].common_prefix; ++i) {
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1}, false);
}
batch.logits[batch.n_tokens - 1] = true;

for (int s = 0; s < 2; ++s) {
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
}
}

// Speedup small evaluations by evaluating atleast 32 tokens
// For Winogrande this seems to slow it down rather than speed it up.
//if (query_1st.size() < 32) query_1st.resize(32);
//if (query_2nd.size() < 32) query_2nd.resize(32);
data[i1].i_batch = i_batch;
i_batch += data[i1].required_tokens;

llama_kv_cache_clear(ctx);
auto logits_1st = evaluate_tokens(ctx, query_1st, 0, params.n_batch, n_vocab);
n_cur += data[i1].required_tokens;
if (++i1 == data.size()) {
break;
}
}

if (i0 == i1) {
fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
return;
}

llama_kv_cache_clear(ctx);
auto logits_2nd = evaluate_tokens(ctx, query_2nd, 0, params.n_batch, n_vocab);

if (logits_1st.empty() || logits_2nd.empty()) {
fprintf(stderr, "%s : failed to eval\n", __func__);
// decode all tasks [i0, i1)
if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
fprintf(stderr, "%s: llama_decode() failed\n", __func__);
return;
}

bool skip_choice = query_1st_size - base_ctx_1st.size() > k_min_trailing_ctx &&
query_2nd_size - base_ctx_2nd.size() > k_min_trailing_ctx;

float score_1st = 0;
bool is_nan_1st = false;
const auto& base_1 = skip_choice ? base_ctx_1st : base_context;
const int last_1st = query_1st_size - base_1.size() > 1 ? 1 : 0;
for (size_t j = base_1.size()-1; j < query_1st_size-1-last_1st; ++j) {
std::memcpy(tok_logits.data(), logits_1st.data() + j*n_vocab, n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[query_1st[j+1]];
if (std::isnan(prob) || !prob) {
fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
prob, j, sentence_1st.c_str(), base_context.size());
is_nan_1st = true;
break;
for (size_t i = i0; i < i1; ++i) {
auto & task = data[i];

const bool skip_choice =
task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;

float score_1st = 0;
bool is_nan_1st = false;
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
size_t li = n_base1 - 1;
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(task.i_batch + li++), n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[task.seq_tokens[0][j+1]];
if (std::isnan(prob) || !prob) {
fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
prob, j, (task.first + task.choices[0] + task.second).c_str(), n_base1);
is_nan_1st = true;
break;
}
score_1st += std::log(prob);
}
score_1st += std::log(prob);
}
score_1st /= (query_1st_size - base_1.size() - last_1st);

float score_2nd = 0;
bool is_nan_2nd = false;
const auto& base_2 = skip_choice ? base_ctx_2nd : base_context;
const int last_2nd = query_2nd_size - base_2.size() > 1 ? 1 : 0;
for (size_t j = base_2.size()-1; j < query_2nd_size-1-last_2nd; ++j) {
std::memcpy(tok_logits.data(), logits_2nd.data() + j*n_vocab, n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[query_2nd[j+1]];
if (std::isnan(prob) || !prob) {
fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
prob, j, sentence_2nd.c_str(), base_context.size());
is_nan_2nd = true;
break;
score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st);

float score_2nd = 0;
bool is_nan_2nd = false;
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(task.i_batch + li++), n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[task.seq_tokens[1][j+1]];
if (std::isnan(prob) || !prob) {
fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
prob, j, (task.first + task.choices[1] + task.second).c_str(), n_base2);
is_nan_2nd = true;
break;
}
score_2nd += std::log(prob);
}
score_2nd += std::log(prob);
}
score_2nd /= (query_2nd_size - base_2.size() - last_2nd);
score_2nd /= (task.seq_tokens[1].size() - n_base2 - last_2nd);

if (is_nan_1st || is_nan_2nd) {
continue;
}
if (is_nan_1st || is_nan_2nd) {
continue;
}

if (std::isnan(score_1st) || std::isnan(score_2nd)) {
printf("================== NaN score %g, %g) for:\n", score_1st, score_2nd);
printf("Q1: <%s> - %zu tokens\n", sentence_1st.c_str(), query_1st_size);
printf("Q2: <%s> - %zu tokens\n", sentence_2nd.c_str(), query_2nd_size);
printf("B : <%s> - %zu tokens\n", task.first.c_str(), base_context.size());
printf("base_1 has %zu tokens, base_2 has %zu tokens, skip_choice = %d\n", base_1.size(), base_2.size(), skip_choice);
continue;
}
if (std::isnan(score_1st) || std::isnan(score_2nd)) {
printf("================== NaN score %g, %g) for:\n", score_1st, score_2nd);
printf("Q1: <%s> - %zu tokens\n", (task.first + task.choices[0] + task.second).c_str(), task.seq_tokens[0].size());
printf("Q2: <%s> - %zu tokens\n", (task.first + task.choices[1] + task.second).c_str(), task.seq_tokens[1].size());
printf("B : <%s> - %zu tokens\n", task.first.c_str(), task.common_prefix);
printf("base_1 has %zu tokens, base_2 has %zu tokens, skip_choice = %d\n", n_base1, n_base2, skip_choice);
continue;
}

int result = score_1st > score_2nd ? 1 : 2;
int result = score_1st > score_2nd ? 1 : 2;

if (result == task.answer) {
++n_correct;
}
++n_done;

if (result == task.answer) {
++n_correct;
// Print the accumulated accuracy mean x 100
printf("%zu\t%.4lf\t%10.6f %10.6f %d %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer);
fflush(stdout);
}
++n_done;

// Print the accumulated accuracy mean x 100
printf("%zu\t%.4lf\t%10.6f %10.6f %d %d\n",task_idx+1, 100.0 * n_correct/n_done,score_1st,score_2nd,result,task.answer);
fflush(stdout);
i0 = i1 - 1;
}

printf("\n");
Expand Down
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