vbatts/llama.cpp
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context : make output functions members

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ggml-ci
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Georgi Gerganov 2025-02-10 17:01:27 +02:00
parent d1d8d53008
commit 2cd8a903c8
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2 changed files with 122 additions and 124 deletions
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238
src/llama-context.cpp
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@ -9,121 +9,6 @@
#include <stdexcept>
#include <cinttypes>
// llama output (TMP)
// Make sure enough space is available for outputs.
// Returns max number of outputs for which space was reserved.
static size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs) {
const auto & cparams = lctx.cparams;
const auto & hparams = lctx.model.hparams;
const auto & vocab = lctx.model.vocab;
const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
const auto n_batch = cparams.n_batch;
const auto n_vocab = vocab.n_tokens();
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
const bool has_logits = !cparams.embeddings;
const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0;
if (lctx.output_ids.empty()) {
// init, never resized afterwards
lctx.output_ids.resize(n_batch);
}
const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0;
const size_t new_size = (logits_size + embd_size) * sizeof(float);
// alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer
if (!lctx.buf_output || prev_size < new_size) {
if (lctx.buf_output) {
#ifndef NDEBUG
// This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
lctx.buf_output = nullptr;
lctx.logits = nullptr;
lctx.embd = nullptr;
}
auto * buft = ggml_backend_cpu_buffer_type();
// try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory
auto * output_dev = lctx.model.dev_output();
auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr;
if (output_dev_host_buft) {
buft = output_dev_host_buft;
}
lctx.buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size));
if (lctx.buf_output == nullptr) {
LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
return 0;
}
}
float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output.get());
lctx.logits = has_logits ? output_base : nullptr;
lctx.embd = has_embd ? output_base + logits_size : nullptr;
lctx.output_size = n_outputs_max;
lctx.logits_size = logits_size;
lctx.embd_size = embd_size;
// set all ids as invalid (negative)
std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
ggml_backend_buffer_clear(lctx.buf_output.get(), 0);
lctx.n_outputs = 0;
return n_outputs_max;
}
// make the outputs have the same order they had in the user-provided batch
static void llama_output_reorder(struct llama_context & ctx) {
std::vector<size_t> & out_ids = ctx.sbatch.out_ids;
if (!out_ids.empty()) {
const uint32_t n_vocab = ctx.model.vocab.n_tokens();
const uint32_t n_embd = ctx.model.hparams.n_embd;
const int32_t n_outputs = ctx.n_outputs;
GGML_ASSERT((size_t) n_outputs == out_ids.size());
// TODO: is there something more efficient which also minimizes swaps?
// selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
for (int32_t i = 0; i < n_outputs - 1; ++i) {
int32_t j_min = i;
for (int32_t j = i + 1; j < n_outputs; ++j) {
if (out_ids[j] < out_ids[j_min]) {
j_min = j;
}
}
if (j_min == i) { continue; }
std::swap(out_ids[i], out_ids[j_min]);
if (ctx.logits_size > 0) {
for (uint32_t k = 0; k < n_vocab; k++) {
std::swap(ctx.logits[i*n_vocab + k], ctx.logits[j_min*n_vocab + k]);
}
}
if (ctx.embd_size > 0) {
for (uint32_t k = 0; k < n_embd; k++) {
std::swap(ctx.embd[i*n_embd + k], ctx.embd[j_min*n_embd + k]);
}
}
}
std::fill(ctx.output_ids.begin(), ctx.output_ids.end(), -1);
for (int32_t i = 0; i < n_outputs; ++i) {
ctx.output_ids[out_ids[i]] = i;
}
out_ids.clear();
}
}
static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
// TODO move to hparams if a T5 variant appears that uses a different value
const int64_t max_distance = 128;
@ -334,7 +219,7 @@ llama_context::llama_context(
// graph outputs buffer
{
// resized during inference when a batch uses more outputs
if (llama_output_reserve(*this, params.n_seq_max) < params.n_seq_max) {
if (reserve_outputs(params.n_seq_max) < params.n_seq_max) {
LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
throw std::runtime_error("failed to reserve initial output buffer");
}
@ -716,7 +601,7 @@ int llama_context::decode(llama_batch & inp_batch) {
// reserve output buffer
// TODO: move to batch manager?
if (llama_output_reserve(*this, bman->n_outputs_all) < (size_t) n_outputs_all) {
if (reserve_outputs(bman->n_outputs_all) < (size_t) n_outputs_all) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
return -2;
};
@ -940,7 +825,7 @@ int llama_context::encode(llama_batch & inp_batch) {
const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
// reserve output buffer
if (llama_output_reserve(*this, n_tokens) < n_tokens) {
if (reserve_outputs(n_tokens) < n_tokens) {
LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
return -2;
};
@ -1555,6 +1440,113 @@ void llama_context::set_inputs(const llama_ubatch & ubatch) {
}
}
void llama_context::reorder_outputs() {
std::vector<size_t> & out_ids = sbatch.out_ids;
if (!out_ids.empty()) {
const uint32_t n_vocab = model.vocab.n_tokens();
const uint32_t n_embd = model.hparams.n_embd;
GGML_ASSERT((size_t) n_outputs == out_ids.size());
// TODO: is there something more efficient which also minimizes swaps?
// selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort)
for (int32_t i = 0; i < n_outputs - 1; ++i) {
int32_t j_min = i;
for (int32_t j = i + 1; j < n_outputs; ++j) {
if (out_ids[j] < out_ids[j_min]) {
j_min = j;
}
}
if (j_min == i) { continue; }
std::swap(out_ids[i], out_ids[j_min]);
if (logits_size > 0) {
for (uint32_t k = 0; k < n_vocab; k++) {
std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]);
}
}
if (embd_size > 0) {
for (uint32_t k = 0; k < n_embd; k++) {
std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]);
}
}
}
std::fill(output_ids.begin(), output_ids.end(), -1);
for (int32_t i = 0; i < n_outputs; ++i) {
output_ids[out_ids[i]] = i;
}
out_ids.clear();
}
}
size_t llama_context::reserve_outputs(size_t n_outputs) {
const auto & hparams = model.hparams;
const auto & vocab = model.vocab;
const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
const auto n_batch = cparams.n_batch;
const auto n_vocab = vocab.n_tokens();
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
const bool has_logits = !cparams.embeddings;
const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
logits_size = has_logits ? n_vocab*n_outputs_max : 0;
embd_size = has_embd ? n_embd*n_outputs_max : 0;
if (output_ids.empty()) {
// init, never resized afterwards
output_ids.resize(n_batch);
}
const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
const size_t new_size = (logits_size + embd_size) * sizeof(float);
// alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer
if (!buf_output || prev_size < new_size) {
if (buf_output) {
#ifndef NDEBUG
// This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
buf_output = nullptr;
logits = nullptr;
embd = nullptr;
}
auto * buft = ggml_backend_cpu_buffer_type();
// try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory
auto * output_dev = model.dev_output();
auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr;
if (output_dev_host_buft) {
buft = output_dev_host_buft;
}
buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size));
if (buf_output == nullptr) {
LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
return 0;
}
}
float * output_base = (float *) ggml_backend_buffer_get_base(buf_output.get());
logits = has_logits ? output_base : nullptr;
embd = has_embd ? output_base + logits_size : nullptr;
output_size = n_outputs_max;
// set all ids as invalid (negative)
std::fill(output_ids.begin(), output_ids.end(), -1);
ggml_backend_buffer_clear(buf_output.get(), 0);
n_outputs = 0;
return n_outputs_max;
}
// do mat_mul, while optionally apply lora
ggml_tensor * llama_context::build_lora_mm(
ggml_context * ctx0,
@ -2827,8 +2819,7 @@ float * llama_get_logits(struct llama_context * ctx) {
llama_synchronize(ctx);
// reorder logits for backward compatibility
// TODO: maybe deprecate this
llama_output_reorder(*ctx);
ctx->reorder_outputs();
return ctx->logits;
}
@ -2877,8 +2868,7 @@ float * llama_get_embeddings(struct llama_context * ctx) {
llama_synchronize(ctx);
// reorder embeddings for backward compatibility
// TODO: maybe deprecate this
llama_output_reorder(*ctx);
ctx->reorder_outputs();
return ctx->embd;
}
@ -3187,7 +3177,7 @@ struct llama_data_write {
//}
void write_output_ids(struct llama_context * ctx) {
llama_output_reorder(*ctx);
ctx->reorder_outputs();
const uint32_t n_outputs = ctx->n_outputs;
@ -3281,7 +3271,7 @@ struct llama_data_read {
uint32_t n_outputs;
read_to(&n_outputs, sizeof(n_outputs));
if (n_outputs > llama_output_reserve(*ctx, n_outputs)) {
if (n_outputs > ctx->reserve_outputs(n_outputs)) {
throw std::runtime_error("could not reserve outputs");
}

8
src/llama-context.h
View file

@ -114,6 +114,14 @@ struct llama_context {
void set_inputs(const llama_ubatch & ubatch);
// make the outputs have the same order they had in the user-provided batch
// TODO: maybe deprecate this
void reorder_outputs();
// Make sure enough space is available for outputs.
// Returns max number of outputs for which space was reserved.
size_t reserve_outputs(size_t n_outputs);
ggml_tensor * build_lora_mm(
ggml_context * ctx0,
ggml_tensor * w,