LLM-Inference/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2026-08-12 22:31:11 +04:00
Code Issues Packages Projects Releases Wiki Activity

llama : support multi-output backend sampling (#25532)

Browse Source 
* Enable backend sampling with token speculation

* Clamp the mask sum before converting it into the sampled index

* Add a numeric context parameter declaring the maximum outputs one sequence

* More fixes

* Don't reuse memory for output views.

* Match dist between CPU and GPU

* Fix CPU and backend sampling mismatches

* Simpify some of the changes

* Fix tests on Vulkan

* More test fixes

* Rebase changes

* Rebase and address review comments

* Address review comments

* Address review comments

* Update src/llama-sampler.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Gaurav Garg
2026-08-10 16:58:56 +03:00
committed by GitHub
co-authored by Georgi Gerganov
parent d2f83055d6
commit dd1ea52433
24 changed files with 1305 additions and 391 deletions
Download Patch File Download Diff File Expand all files Collapse all files
common/common.cpp
+1
View File
@@ -1639,6 +1639,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
cparams.n_seq_max = params.n_parallel;
cparams.n_rs_seq = params.speculative.need_n_rs_seq();
cparams.n_outputs_max = std::max(params.n_outputs_max, 0);
cparams.n_outputs_max_per_seq = std::max(params.n_outputs_max_per_seq, 0);
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.cpuparams.n_threads;
common/common.h
+1
View File
@@ -447,6 +447,7 @@ struct common_params {
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
int32_t n_outputs_max = 0; // max outputs in a batch (0 = n_batch)
int32_t n_outputs_max_per_seq = 1; // max outputs per sequence
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
common/llguidance.cpp
+2
View File
@@ -116,6 +116,8 @@ static llama_sampler_i llama_sampler_llg_i = {
/* .backend_accept = */ NULL,
/* .backend_apply = */ NULL,
/* .backend_set_input = */ NULL,
/* .backend_reset = */ NULL,
/* .copy_state = */ NULL,
};
static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
common/reasoning-budget.cpp
+2
View File
@@ -217,6 +217,8 @@ static struct llama_sampler_i common_reasoning_budget_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {
common/sampling.cpp
+20
View File
@@ -518,6 +518,26 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
};
}
void common_sampler_copy(const common_sampler * src, common_sampler * dst) {
if (!src || !dst || src == dst) {
return;
}
GGML_ASSERT((src->grmr == nullptr) == (dst->grmr == nullptr));
GGML_ASSERT((src->rbudget == nullptr) == (dst->rbudget == nullptr));
llama_sampler_copy(src->grmr, dst->grmr);
llama_sampler_copy(src->rbudget, dst->rbudget);
llama_sampler_copy(src->chain, dst->chain);
dst->params = src->params;
dst->prev = src->prev;
dst->cur = src->cur;
dst->cur_p = src->cur_p;
dst->cur_p.data = src->cur_p.data ? dst->cur.data() : nullptr; // re-point to dst's buffer
dst->t_total_us = src->t_total_us;
}
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
// TODO: measure grammar performance
common/sampling.h
+1
View File
@@ -47,6 +47,7 @@ void common_sampler_free(struct common_sampler * gsmpl);
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated);
void common_sampler_reset (struct common_sampler * gsmpl);
struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
void common_sampler_copy (const struct common_sampler * src, struct common_sampler * dst);
// arguments can be nullptr to skip printing
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
common/speculative.cpp
+12
View File
@@ -2299,6 +2299,7 @@ common_params common_base_params_to_speculative(const common_params & params) {
result.cache_type_k = params_spec.cache_type_k;
result.cache_type_v = params_spec.cache_type_v;
result.n_outputs_max = params.n_parallel;
result.n_outputs_max_per_seq = 1;
return result;
}
@@ -2384,6 +2385,17 @@ common_speculative_init_result_ptr common_speculative_init_from_params(common_pa
return std::make_unique<common_speculative_init_result>(params, model_tgt, ctx_tgt);
}
common_speculative_output_limits common_speculative_get_output_limits(
int32_t n_batch, int32_t n_parallel, int32_t n_draft) {
const int64_t per_seq = 1 + (int64_t) std::max(0, n_draft);
const int64_t total = (int64_t) n_parallel * per_seq;
return {
/* .total = */ (int32_t) std::min<int64_t>(n_batch, total),
/* .per_seq = */ (int32_t) std::min<int64_t>(n_batch, per_seq),
};
}
// initialization of the speculative decoding system
//
common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq) {
common/speculative.h
+9
View File
@@ -25,6 +25,15 @@ int32_t common_speculative_n_max(const common_params_speculative * spec);
common_params common_base_params_to_speculative(const common_params & params);
struct common_speculative_output_limits {
int32_t total;
int32_t per_seq;
};
// return the output limits needed for speculative decoding
common_speculative_output_limits common_speculative_get_output_limits(
int32_t n_batch, int32_t n_parallel, int32_t n_draft);
common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq);
void common_speculative_free(common_speculative * spec);
docs/speculative.md
+6
View File
@@ -202,6 +202,12 @@ Example Video:
If a draft model is combined with a draftless decoding the draftless decoding has higher precedence.
### Backend Sampling
Use `--backend-sampling` to run supported target-model samplers on the model backend. Draft-model sampling uses the backend by default and can be controlled with `--spec-draft-backend-sampling` and `--no-spec-draft-backend-sampling`.
Unsupported samplers and device layouts fall back to CPU sampling. Tensor split mode does not support backend sampling. A fixed seed produces repeatable random draws, but stochastic CPU and backend sampling can still select different tokens because floating-point operations can differ between implementations and devices. Use greedy sampling when exact output matching is required.
### General Speculative Parameters
```
examples/lookup/lookup.cpp
+6
View File
@@ -3,9 +3,11 @@
#include "common.h"
#include "ngram-cache.h"
#include "sampling.h"
#include "speculative.h"
#include "log.h"
#include "llama.h"
#include <algorithm>
#include <clocale>
#include <cstdint>
#include <cstdio>
@@ -27,6 +29,10 @@ int main(int argc, char ** argv){
// max. number of additional tokens to draft if match is found
const int n_draft = params.speculative.draft.n_max;
const auto output_limits = common_speculative_get_output_limits(params.n_batch, params.n_parallel, n_draft);
params.n_outputs_max = output_limits.total;
params.n_outputs_max_per_seq = output_limits.per_seq;
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
examples/speculative-simple/speculative-simple.cpp
+9
View File
@@ -5,6 +5,7 @@
#include "log.h"
#include "llama.h"
#include <algorithm>
#include <clocale>
#include <cstdio>
#include <cstring>
@@ -29,6 +30,11 @@ int main(int argc, char ** argv) {
return 1;
}
const auto output_limits = common_speculative_get_output_limits(
params.n_batch, params.n_parallel, common_speculative_n_max(&params.speculative));
params.n_outputs_max = output_limits.total;
params.n_outputs_max_per_seq = output_limits.per_seq;
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
@@ -55,6 +61,9 @@ int main(int argc, char ** argv) {
auto params_dft = params;
params_dft.n_outputs_max = params.n_parallel;
params_dft.n_outputs_max_per_seq = 1;
params_dft.devices = params_spec.devices;
params_dft.model = params_spec.mparams;
params_dft.n_gpu_layers = params_spec.n_gpu_layers;
examples/speculative/speculative.cpp
+8
View File
@@ -1,6 +1,7 @@
#include "arg.h"
#include "common.h"
#include "sampling.h"
#include "speculative.h"
#include "log.h"
#include "llama.h"
@@ -57,6 +58,11 @@ int main(int argc, char ** argv) {
// max number of parallel drafting sequences (i.e. tree branches)
const int n_seq_dft = params.n_parallel;
const auto output_limits = common_speculative_get_output_limits(
params.n_batch, params.n_parallel, params.speculative.draft.n_max);
params.n_outputs_max = output_limits.total;
params.n_outputs_max_per_seq = output_limits.per_seq;
// probability threshold for splitting a draft branch (only for n_seq_dft > 1)
const float p_draft_split = params.speculative.draft.p_split;
@@ -83,6 +89,8 @@ int main(int argc, char ** argv) {
params.devices = params.speculative.draft.devices;
params.model = params.speculative.draft.mparams;
params.n_gpu_layers = params.speculative.draft.n_gpu_layers;
params.n_outputs_max = params.n_parallel;
params.n_outputs_max_per_seq = 1;
if (params.speculative.draft.cpuparams.n_threads > 0) {
params.cpuparams.n_threads = params.speculative.draft.cpuparams.n_threads;
}
include/llama.h
+20 -2
View File
@@ -354,6 +354,7 @@ extern "C" {
uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
uint32_t n_rs_seq; // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
uint32_t n_outputs_max; // max outputs in a ubatch (0 = n_batch)
uint32_t n_outputs_max_per_seq; // max outputs per sequence (0 = n_outputs_max)
int32_t n_threads; // number of threads to use for generation
int32_t n_threads_batch; // number of threads to use for batch processing
@@ -1054,6 +1055,9 @@ extern "C" {
//
// Get the backend sampled token for the ith token.
// With multiple outputs, sampler state advances when the token is accepted,
// not when it is read through this function.
// When accepting multiple outputs, accept a contiguous prefix in output order.
// Returns LLAMA_TOKEN_NULL if no token was sampled.
LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i);
@@ -1270,9 +1274,12 @@ extern "C" {
// [EXPERIMENTAL]
// backend sampling interface:
// return true if the backend supports all ops needed by the sampler
// return true if the backend supports all ops needed by the sampler and can handle up to n_outputs_max_per_seq outputs per sequence
// note: call once per sampler
bool (*backend_init)(struct llama_sampler * smpl, ggml_backend_buffer_type_t buft);
bool (*backend_init)(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq);
// call after .backend_apply()
void (*backend_accept)(
@@ -1290,6 +1297,13 @@ extern "C" {
// called before graph execution to set inputs for the current ubatch
void (*backend_set_input)(struct llama_sampler * smpl);
// called before rebuilding a sampling graph to clear any internal sampler state
void (*backend_reset)(struct llama_sampler * smpl);
// copy mutable state from src into dst while keeping dst's references to the current sampling graph
// src and dst must have the same type and configuration
void (*copy_state)(const struct llama_sampler * src, struct llama_sampler * dst);
};
struct llama_sampler {
@@ -1310,6 +1324,9 @@ extern "C" {
LLAMA_API void llama_sampler_apply ( struct llama_sampler * smpl, llama_token_data_array * cur_p);
LLAMA_API void llama_sampler_reset ( struct llama_sampler * smpl);
LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
// copy mutable sampler state without changing dst or its sampling graph bindings
// src and dst must have the same type and configuration
LLAMA_API void llama_sampler_copy (const struct llama_sampler * src, struct llama_sampler * dst);
// important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
LLAMA_API void llama_sampler_free ( struct llama_sampler * smpl);
@@ -1499,6 +1516,7 @@ extern "C" {
LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
/// @details Sample and accept a token from the idx-th output of the last evaluation
// For multiple outputs from one sampler, call this function in output order without gaps.
//
// Shorthand for:
// const auto * logits = llama_get_logits_ith(ctx, idx);
src/llama-context.cpp
+149 -134
View File
@@ -10,6 +10,7 @@
#include "llama-mmap.h"
#include "llama-model.h"
#include "llama-ext.h"
#include "llama-sampler.h"
#include "llama.h"
#include <cinttypes>
@@ -159,25 +160,6 @@ llama_context::llama_context(
}
}
// Initialize backend samplers here so they are part of the sampling graph
// before the reserve passes run later in this function. This avoids a later
// re-reserve when graph nodes change.
if (params.samplers != nullptr && params.n_samplers > 0) {
for (size_t i = 0; i < params.n_samplers; ++i) {
const auto & config = params.samplers[i];
if (llama_sampler_chain_get(config.sampler, -1) == nullptr) {
throw std::runtime_error("the backend samplers must be of type llama_sampler_chain");
}
if (set_sampler(config.seq_id, config.sampler)) {
const int n_samplers = llama_sampler_chain_n(config.sampler);
LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
}
}
}
auto rope_scaling_type = params.rope_scaling_type;
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
rope_scaling_type = hparams.rope_scaling_type_train;
@@ -265,6 +247,27 @@ llama_context::llama_context(
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
cparams.n_outputs_max = params.n_outputs_max == 0 || llama_model_has_encoder(&model) ? cparams.n_batch : params.n_outputs_max;
cparams.n_outputs_max_per_seq = params.n_outputs_max_per_seq == 0 ?
cparams.n_outputs_max : std::min(params.n_outputs_max_per_seq, cparams.n_outputs_max);
// Initialize backend samplers here so they are part of the sampling graph
// before the reserve passes run later in this function. This avoids a later
// re-reserve when graph nodes change.
if (params.samplers != nullptr && params.n_samplers > 0) {
for (size_t i = 0; i < params.n_samplers; ++i) {
const auto & config = params.samplers[i];
if (llama_sampler_chain_get(config.sampler, -1) == nullptr) {
throw std::runtime_error("the backend samplers must be of type llama_sampler_chain");
}
if (set_sampler(config.seq_id, config.sampler)) {
const int n_samplers = llama_sampler_chain_n(config.sampler);
LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
}
}
}
cparams.op_offload = params.op_offload;
cparams.kv_unified = params.kv_unified;
@@ -312,6 +315,7 @@ llama_context::llama_context(
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: n_rs_seq = %u\n", __func__, cparams.n_rs_seq);
LLAMA_LOG_INFO("%s: n_outputs_max = %u\n", __func__, cparams.n_outputs_max);
LLAMA_LOG_INFO("%s: n_outputs_max_per_seq = %u\n", __func__, cparams.n_outputs_max_per_seq);
if (cparams.n_ctx_seq < hparams.n_ctx_train) {
LLAMA_LOG_INFO("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
@@ -1231,7 +1235,7 @@ bool llama_context::set_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
if (sampler && can_offload) {
auto * buft = ggml_backend_dev_buffer_type(model.dev_output());
sampler->iface->backend_init(sampler, buft);
sampler->iface->backend_init(sampler, buft, cparams.n_outputs_max_per_seq);
sampling.samplers[seq_id] = sampler;
@@ -1576,109 +1580,39 @@ int llama_context::encode(const llama_batch & batch_inp) {
return 0;
}
static std::map<llama_seq_id, uint32_t> build_seq_to_output_row(const llama_ubatch & ubatch, uint32_t row_offset) {
std::map<llama_seq_id, uint32_t> seq_to_row;
// how many output tokens we have seen so far for this ubatch.
uint32_t local = 0;
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
// skip tokens that are not output.
if (!ubatch.output[i]) {
continue;
}
const llama_seq_id seq_id = ubatch.seq_id[i][0];
// row_offset is the number of output tokens before this ubatch.
seq_to_row[seq_id] = row_offset + local;
++local;
}
return seq_to_row;
}
static void copy_tensor_async_ints(
const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
const buffer_view<llama_token> & sampled,
const std::map<llama_seq_id, uint32_t> & seq_to_row,
ggml_backend_sched_t sched) {
if (!sampled.has_data()) {
return;
}
for (const auto & [seq_id, tensor] : tensor_map) {
auto it = seq_to_row.find(seq_id);
if (it == seq_to_row.end()) {
continue;
}
const uint32_t row = it->second;
GGML_ASSERT(row < sampled.size);
GGML_ASSERT(ggml_is_contiguous(tensor) && "sampled tokens tensor must be contiguous for async copy");
ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
ggml_backend_tensor_get_async(backend, tensor, sampled.data + row, 0, sizeof(sampled.data[row]));
}
}
static void copy_tensor_async_floats(
const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
const buffer_view<float> & dst,
template<typename T>
static void copy_tensor_async_rows(
const std::vector<ggml_tensor *> & tensors,
const buffer_view<T> & dst,
size_t stride,
std::vector<uint32_t> & counts,
const std::map<llama_seq_id, uint32_t> & seq_to_row,
ggml_backend_sched_t sched) {
uint32_t row_offset,
ggml_backend_sched_t sched,
std::vector<uint32_t> * counts = nullptr) {
if (!dst.has_data()) {
return;
}
for (const auto & [seq_id, tensor] : tensor_map) {
auto it = seq_to_row.find(seq_id);
if (it == seq_to_row.end()) {
for (size_t i = 0; i < tensors.size(); ++i) {
auto * tensor = tensors[i];
if (tensor == nullptr) {
continue;
}
const uint32_t row = it->second;
GGML_ASSERT(row < counts.size());
GGML_ASSERT(ggml_is_contiguous(tensor) && "logits/probs tensor must be contiguous for async copy");
const uint32_t row = row_offset + i;
const size_t n_elements = ggml_nelements(tensor);
GGML_ASSERT(ggml_is_contiguous(tensor) && "sampling tensor must be contiguous for async copy");
GGML_ASSERT(n_elements <= stride);
GGML_ASSERT((size_t) row * stride + n_elements <= dst.size);
ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
float * row_ptr = dst.data + (size_t) row * stride;
T * row_ptr = dst.data + (size_t) row * stride;
ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
// Update the actual number of logits/probabilities that were written for this row.
counts[row] = ggml_nelements(tensor);
if (counts) {
GGML_ASSERT(row < counts->size());
(*counts)[row] = n_elements;
}
}
static void copy_tensor_async_candidates(
const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
const buffer_view<llama_token> & dst,
size_t stride,
std::vector<uint32_t> & counts,
const std::map<llama_seq_id, uint32_t> & seq_to_row,
ggml_backend_sched_t sched) {
if (!dst.has_data()) {
return;
}
for (const auto & [seq_id, tensor] : tensor_map) {
auto it = seq_to_row.find(seq_id);
if (it == seq_to_row.end()) {
continue;
}
const uint32_t row = it->second;
GGML_ASSERT(row < counts.size());
GGML_ASSERT(ggml_is_contiguous(tensor) && "candidates tensor must be contiguous for async copy");
ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
llama_token * row_ptr = dst.data + (size_t) row * stride;
ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
// Update the actual number of candidates that were written.
counts[row] = ggml_nelements(tensor);
}
}
static bool needs_raw_logits(const llama_ubatch & ubatch, const std::map<llama_seq_id, llama_sampler *> & samplers) {
@@ -1726,12 +1660,12 @@ int llama_context::decode(const llama_batch & batch_inp) {
const uint32_t n_seq_max = cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max;
// TODO: avoid this workaround in the future
if (has_samplers && batch_inp.logits) {
// embedding contexts output every token even when batch.logits is not set
if (has_samplers && (output_all || batch_inp.logits)) {
std::vector<int32_t> seq_output_count(n_seq_max, 0);
for (int32_t i = 0; i < batch_inp.n_tokens; ++i) {
if (batch_inp.logits[i] == 0) {
if (!output_all && batch_inp.logits[i] == 0) {
continue;
}
@@ -1740,10 +1674,17 @@ int llama_context::decode(const llama_batch & batch_inp) {
for (int32_t s = 0; s < ns; ++s) {
const llama_seq_id seq_id = batch_inp.seq_id ? batch_inp.seq_id[i][s] : 0;
if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
continue;
}
seq_output_count[seq_id]++;
if (seq_output_count[seq_id] > 1) {
LLAMA_LOG_ERROR("%s: backend sampling requires at most one output token per sequence (seq_id %d had %d)\n",
__func__, seq_id, seq_output_count[seq_id]);
auto sampler = sampling.samplers.find(seq_id);
if (sampler != sampling.samplers.end() &&
seq_output_count[seq_id] > (int32_t) cparams.n_outputs_max_per_seq) {
LLAMA_LOG_ERROR("%s: backend sampling supports at most %u outputs per sequence "
"(seq_id %d had %d)\n", __func__, cparams.n_outputs_max_per_seq,
seq_id, seq_output_count[seq_id]);
return -1;
}
}
@@ -1843,6 +1784,11 @@ int llama_context::decode(const llama_batch & batch_inp) {
return -2;
};
// start a new sampling transaction for this logical batch
for (const auto & entry : sampling.samplers) {
llama_sampler_backend_begin(entry.second);
}
int64_t n_outputs_prev = 0;
int64_t n_tokens_prev = 0;
@@ -2009,17 +1955,14 @@ int llama_context::decode(const llama_batch & batch_inp) {
}
}
// Copy backend sampling output if this ubatch produced any sampling tensors.
if (has_samplers && (!res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty())) {
const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
if (has_samplers) {
const auto stride = n_vocab;
// async copy the sampling data from the backend to the host
copy_tensor_async_ints(res->t_sampled, sampling.sampled, seq_to_output_row, sched.get());
copy_tensor_async_floats (res->t_sampled_logits, sampling.logits, stride, sampling.logits_count, seq_to_output_row, sched.get());
copy_tensor_async_floats (res->t_sampled_probs, sampling.probs, stride, sampling.probs_count, seq_to_output_row, sched.get());
copy_tensor_async_candidates(res->t_candidates, sampling.candidates, stride, sampling.candidates_count, seq_to_output_row, sched.get());
copy_tensor_async_rows(res->t_sampled, sampling.sampled, 1, n_outputs_prev, sched.get());
copy_tensor_async_rows(res->t_sampled_logits, sampling.logits, stride, n_outputs_prev, sched.get(), &sampling.logits_count);
copy_tensor_async_rows(res->t_sampled_probs, sampling.probs, stride, n_outputs_prev, sched.get(), &sampling.probs_count);
copy_tensor_async_rows(res->t_candidates, sampling.candidates, stride, n_outputs_prev, sched.get(), &sampling.candidates_count);
}
n_outputs_prev += n_outputs;
@@ -2349,6 +2292,7 @@ void llama_context::output_reorder() {
//
uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
uint32_t res;
if (model.arch == LLM_ARCH_QWEN3NEXT ||
model.arch == LLM_ARCH_KIMI_LINEAR ||
model.arch == LLM_ARCH_QWEN35 ||
@@ -2357,12 +2301,32 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
(model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
model.arch == LLM_ARCH_NANBEIGE ||
model.arch == LLM_ARCH_MINIMAX_M3) {
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
}
uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
res = std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
} else {
res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
for (const auto & lora : model.loras) {
res += lora->get_n_nodes();
}
}
uint32_t n_sampling_nodes = 0;
uint32_t n_sampling_nodes_max = 0;
for (const auto & [seq_id, sampler] : sampling.samplers) {
const uint32_t n_nodes = llama_sampler_backend_n_nodes(sampler);
n_sampling_nodes += n_nodes;
if (cparams.n_outputs_max_per_seq > 1) {
n_sampling_nodes_max = std::max(n_sampling_nodes_max, n_nodes);
}
}
const uint32_t n_sampling_outputs_max = std::min<uint64_t>(
std::min(n_tokens, cparams.n_outputs_max),
(uint64_t) cparams.n_seq_max * cparams.n_outputs_max_per_seq);
res += n_sampling_nodes;
if (n_sampling_outputs_max > 1) {
res += (n_sampling_outputs_max - 1) * n_sampling_nodes_max;
}
return res;
}
@@ -2370,6 +2334,63 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
return static_cast<llm_graph_result *>(gf_res_reserve.get());
}
// pack sampler outputs into as few sequences as possible before using sequences without samplers
static void ubatch_prepare_reserve(
llama_ubatch & ubatch,
uint32_t n_outputs,
const std::map<llama_seq_id, llama_sampler *> & samplers,
uint32_t n_outputs_max_per_seq) {
const uint32_t n_seqs = ubatch.n_seqs;
const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
for (uint32_t s = 0; s < n_seqs; ++s) {
for (uint32_t t = 0; t < n_seq_tokens; ++t) {
const uint32_t i = s * n_seq_tokens + t;
ubatch.n_seq_id[i] = 1;
ubatch.seq_id[i] = &ubatch.seq_id_unq[s];
}
}
// sequences with a sampler that fit in this ubatch
std::vector<uint32_t> sampler_seqs;
std::vector<bool> has_sampler(n_seqs, false);
for (const auto & entry : samplers) {
const llama_seq_id seq_id = entry.first;
if (seq_id < 0 || (uint32_t) seq_id >= n_seqs) {
continue;
}
sampler_seqs.push_back(seq_id);
has_sampler[seq_id] = true;
}
uint32_t n_outputs_set = 0;
const uint32_t n_outputs_per_seq = std::min(n_seq_tokens, n_outputs_max_per_seq);
for (uint32_t s : sampler_seqs) {
if (n_outputs_set >= n_outputs) {
break;
}
for (uint32_t t = 0; t < n_outputs_per_seq && n_outputs_set < n_outputs; ++t) {
ubatch.output[s * n_seq_tokens + t] = true;
++n_outputs_set;
}
}
// use sequences without samplers for any remaining outputs
for (uint32_t t = 0; t < n_seq_tokens && n_outputs_set < n_outputs; ++t) {
for (uint32_t s = 0; s < n_seqs && n_outputs_set < n_outputs; ++s) {
if (has_sampler[s]) {
continue;
}
ubatch.output[s * n_seq_tokens + t] = true;
++n_outputs_set;
}
}
}
ggml_cgraph * llama_context::graph_reserve(
uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) {
LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
@@ -2394,14 +2415,7 @@ ggml_cgraph * llama_context::graph_reserve(
llama_batch_allocr balloc(model.hparams.n_pos_per_embd());
llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
// set one output token per sequence in order to activate all backend samplers
std::vector<llama_seq_id> seq_ids(n_seqs);
for (uint32_t i = 0; i < n_seqs; ++i) {
seq_ids[i] = i;
ubatch.n_seq_id[i] = 1;
ubatch.seq_id[i] = &seq_ids[i];
ubatch.output[i] = true;
}
ubatch_prepare_reserve(ubatch, n_outputs, sampling.samplers, cparams.n_outputs_max_per_seq);
auto * res = gf_res_reserve.get();
@@ -3488,6 +3502,7 @@ llama_context_params llama_context_default_params() {
/*.n_seq_max =*/ 1,
/*.n_rs_seq =*/ 0,
/*.n_outputs_max =*/ 0,
/*.n_outputs_max_per_seq =*/ 1,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.ctx_type =*/ LLAMA_CONTEXT_TYPE_DEFAULT,
src/llama-cparams.h
+1
View File
@@ -15,6 +15,7 @@ struct llama_cparams {
uint32_t n_seq_max;
uint32_t n_rs_seq; // number of recurrent-state snapshots per seq for rollback
uint32_t n_outputs_max; // max outputs supported by the context
uint32_t n_outputs_max_per_seq;
int32_t n_threads; // number of threads to use for generation
int32_t n_threads_batch; // number of threads to use for batch processing
src/llama-graph.cpp
+62 -36
View File
@@ -4,6 +4,7 @@
#include "llama-model.h"
#include "llama-batch.h"
#include "llama-cparams.h"
#include "llama-sampler.h"
#include "llama-kv-cache.h"
#include "llama-kv-cache-iswa.h"
@@ -1353,24 +1354,24 @@ void llm_graph_result::set_outputs(const llm_graph_params & params) {
}
}
}
for (auto & [seq_id, t] : t_sampled) {
if (t != nullptr) {
ggml_set_output(t);
for (auto * tensor : t_sampled) {
if (tensor != nullptr) {
ggml_set_output(tensor);
}
}
for (auto & [seq_id, t] : t_sampled_probs) {
if (t != nullptr) {
ggml_set_output(t);
for (auto * tensor : t_sampled_probs) {
if (tensor != nullptr) {
ggml_set_output(tensor);
}
}
for (auto & [seq_id, t] : t_sampled_logits) {
if (t != nullptr) {
ggml_set_output(t);
for (auto * tensor : t_sampled_logits) {
if (tensor != nullptr) {
ggml_set_output(tensor);
}
}
for (auto & [seq_id, t] : t_candidates) {
if (t != nullptr) {
ggml_set_output(t);
for (auto * tensor : t_candidates) {
if (tensor != nullptr) {
ggml_set_output(tensor);
}
}
}
@@ -3649,34 +3650,45 @@ void llm_graph_context::build_sampling() const {
auto inp_sampling = std::make_unique<llm_graph_input_sampling>(samplers);
res->add_input(std::move(inp_sampling));
std::map<llama_seq_id, int32_t> seq_to_logit_row;
int32_t logit_row_idx = 0;
for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
std::map<llama_seq_id, std::vector<uint32_t>> sampling_rows;
uint32_t n_rows = 0;
for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
if (ubatch.output[i]) {
llama_seq_id seq_id = ubatch.seq_id[i][0];
seq_to_logit_row[seq_id] = logit_row_idx;
logit_row_idx++;
sampling_rows[ubatch.seq_id[i][0]].push_back(n_rows++);
}
}
res->t_sampled.resize(n_rows, nullptr);
res->t_sampled_probs.resize(n_rows, nullptr);
res->t_sampled_logits.resize(n_rows, nullptr);
res->t_candidates.resize(n_rows, nullptr);
// res->t_logits will contain logits for all tokens that want the logits calculated (logits=1 or output=1)
GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
// add a dummy row of logits
// this trick makes the graph static, regardless of which samplers are activated
// this is important in order to minimize graph reallocations
// add a dummy row to keep the single-output graph static regardless of active samplers
// multi-output graphs can still vary with the number of output rows
ggml_tensor * logits_t = ggml_pad(ctx0, res->t_logits, 0, 1, 0, 0);
for (const auto & entry : samplers) {
if (entry.second->iface->backend_reset) {
entry.second->iface->backend_reset(entry.second);
}
}
static const std::vector<uint32_t> dummy_row = { 0 };
for (const auto & [seq_id, sampler] : samplers) {
const auto it = seq_to_logit_row.find(seq_id);
const auto it = sampling_rows.find(seq_id);
// inactive samplers always work on the first row
const auto row_idx = it != seq_to_logit_row.end() ? it->second : 0;
const int i_out = it != seq_to_logit_row.end() ? 1 : 0;
const bool active = it != sampling_rows.end();
const auto & rows = active ? it->second : dummy_row;
const int i_out = active ? 1 : 0;
ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], row_idx * logits_t->nb[1]);
ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
for (uint32_t i = 0; i < rows.size(); ++i) {
ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], rows[i] * logits_t->nb[1]);
ggml_format_name(logits_seq, "logits_seq_%d_%u", seq_id, i);
struct llama_sampler_data data = {
/*.logits =*/ logits_seq,
@@ -3689,37 +3701,51 @@ void llm_graph_context::build_sampling() const {
sampler->iface->backend_apply(sampler, ctx0, gf, &data);
if (data.sampled != nullptr) {
res->t_sampled[seq_id] = data.sampled;
if (active) {
res->t_sampled[rows[i]] = data.sampled;
}
outs[1] = data.sampled;
ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
}
if (data.probs != nullptr) {
res->t_sampled_probs[seq_id] = data.probs;
if (active) {
res->t_sampled_probs[rows[i]] = data.probs;
}
outs[1] = data.probs;
ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
}
if (data.logits != nullptr) {
res->t_sampled_logits[seq_id] = data.logits;
if (active) {
res->t_sampled_logits[rows[i]] = data.logits;
}
outs[1] = data.logits;
ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
}
if (data.candidates != nullptr) {
res->t_candidates[seq_id] = data.candidates;
if (active) {
res->t_candidates[rows[i]] = data.candidates;
}
outs[1] = data.candidates;
ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
}
}
}
// TODO: Call llama_sampler_accept_ggml after all samplers have been applied.
// TODO: Call backend_accept after all samplers have been applied.
/*
for (const auto & [seq_id, sampler] : samplers) {
if (auto it = res->t_sampled.find(seq_id); it != res->t_sampled.end()) {
ggml_tensor * selected_token = it->second;
if (selected_token != nullptr) {
llama_sampler_accept_ggml(sampler, ctx0, gf, selected_token);
const auto it = sampling_rows.find(seq_id);
if (it == sampling_rows.end()) {
continue;
}
for (uint32_t row : it->second) {
ggml_tensor * selected_token = res->t_sampled[row];
if (selected_token != nullptr && sampler->iface->backend_accept) {
sampler->iface->backend_accept(sampler, ctx0, gf, selected_token);
}
}
}
src/llama-graph.h
+4 -4
View File
@@ -904,10 +904,10 @@ public:
std::vector<ggml_tensor *> t_layer_inp;
std::map<llama_seq_id, ggml_tensor *> t_sampled_logits;
std::map<llama_seq_id, ggml_tensor *> t_candidates;
std::map<llama_seq_id, ggml_tensor *> t_sampled;
std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
std::vector<ggml_tensor *> t_sampled;
std::vector<ggml_tensor *> t_sampled_probs;
std::vector<ggml_tensor *> t_sampled_logits;
std::vector<ggml_tensor *> t_candidates;
std::vector<llm_graph_input_ptr> inputs;
std::vector<llm_graph_fused_node> fused_nodes;
src/llama-sampler.cpp
+360 -77
View File
@@ -467,9 +467,11 @@ static void llama_sampler_empty_free(struct llama_sampler * smpl) {
static bool llama_sampler_empty_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
GGML_UNUSED(smpl);
GGML_UNUSED(buft);
GGML_UNUSED(n_outputs_max_per_seq);
return true;
}
@@ -511,6 +513,8 @@ static struct llama_sampler_i llama_sampler_empty_i = {
/* .backend_accept = */ llama_sampler_empty_backend_accept,
/* .backend_apply = */ llama_sampler_empty_backend_apply,
/* .backend_set_input = */ llama_sampler_empty_backend_set_input,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_empty(const char * name) {
@@ -551,6 +555,12 @@ struct llama_sampler_backend {
this->support = support;
}
// copy the state that is not tied to the current sampling graph
// samplers that hold only immutable configuration can use this as is
void copy_state(const llama_sampler_backend & src) {
GGML_UNUSED(src);
}
private:
std::string name;
std::string name_ext;
@@ -559,6 +569,71 @@ private:
bool support;
};
// .copy_state for samplers deriving from llama_sampler_backend
template<typename T>
static void llama_sampler_backend_copy_state(const struct llama_sampler * src, struct llama_sampler * dst) {
((T *) dst->ctx)->copy_state(*(const T *) src->ctx);
}
struct llama_sampler_backend_probe {
ggml_context_ptr ctx;
ggml_cgraph * gf;
};
static llama_sampler_backend_probe llama_sampler_backend_probe_graph(
llama_sampler * sampler,
int64_t n_candidates,
uint32_t max_nodes,
bool with_candidates) {
ggml_init_params params = {
/*.mem_size =*/ max_nodes * ggml_tensor_overhead() + ggml_graph_overhead_custom(max_nodes, false),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
ggml_context_ptr ctx_ptr { ggml_init(params) };
if (!ctx_ptr) {
throw std::runtime_error(format("failed to create ggml context"));
}
auto * ctx = ctx_ptr.get();
auto * gf = ggml_new_graph_custom(ctx, max_nodes, false);
llama_sampler_data data = {
/*.logits =*/ ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_candidates),
/*.probs =*/ nullptr,
/*.sampled =*/ nullptr,
/*.candidates =*/ with_candidates ? ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_candidates) : nullptr,
};
if (sampler->iface->backend_reset) {
sampler->iface->backend_reset(sampler);
}
sampler->iface->backend_apply(sampler, ctx, gf, &data);
for (auto * output : { data.logits, data.probs, data.sampled, data.candidates }) {
if (output) {
ggml_build_forward_expand(gf, output);
}
}
if (sampler->iface->backend_reset) {
sampler->iface->backend_reset(sampler);
}
return { std::move(ctx_ptr), gf };
}
static uint32_t llama_sampler_backend_probe_n_nodes(const llama_sampler_backend_probe & probe) {
uint32_t n_tensors = 0;
for (auto * tensor = ggml_get_first_tensor(probe.ctx.get()); tensor;
tensor = ggml_get_next_tensor(probe.ctx.get(), tensor)) {
++n_tensors;
}
return std::max<uint32_t>(ggml_graph_n_nodes(probe.gf), n_tensors);
}
// check if all ggml ops used by the sampler are supported by the backend
static bool llama_sampler_backend_support(
llama_sampler * smpl,
@@ -569,50 +644,10 @@ static bool llama_sampler_backend_support(
return true;
}
ggml_init_params params = {
/*.mem_size =*/ 128*ggml_tensor_overhead() + ggml_graph_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
auto probe = llama_sampler_backend_probe_graph(smpl, 1024*1024, GGML_DEFAULT_GRAPH_SIZE, true);
ggml_context_ptr ctx_ptr { ggml_init(params) };
if (!ctx_ptr) {
throw std::runtime_error(format("failed to create ggml context"));
}
ggml_context * ctx = ctx_ptr.get();
const int64_t n = 1024*1024;
llama_sampler_data data = {
/*.logits = */ ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n),
/*.probs = */ nullptr,
/*.sampled = */ nullptr,
/*.candidates = */ ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n),
};
ggml_cgraph * gf = ggml_new_graph(ctx);
smpl->iface->backend_apply(smpl, ctx, gf, &data);
if (data.logits) {
ggml_build_forward_expand(gf, data.logits);
}
if (data.probs) {
ggml_build_forward_expand(gf, data.probs);
}
if (data.sampled) {
ggml_build_forward_expand(gf, data.sampled);
}
if (data.candidates) {
ggml_build_forward_expand(gf, data.candidates);
}
for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
struct ggml_tensor * op = ggml_graph_node(gf, i);
for (int i = 0; i < ggml_graph_n_nodes(probe.gf); i++) {
struct ggml_tensor * op = ggml_graph_node(probe.gf, i);
if (!ggml_backend_dev_supports_op(device, op)) {
LLAMA_LOG_WARN("%s: device '%s' does not have support for op %s needed for sampler '%s'\n",
@@ -697,7 +732,8 @@ static void llama_sampler_chain_free(struct llama_sampler * smpl) {
static bool llama_sampler_chain_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
GGML_ASSERT(chain->is_init == false && "llama_sampler_chain_backend_init() called twice");
@@ -705,26 +741,32 @@ static bool llama_sampler_chain_backend_init(
chain->is_init = true;
bool res = true;
bool backend_prefix = true;
for (auto & smpl : chain->samplers) {
bool res_cur = true;
bool cur_prefix = backend_prefix;
// to be able to run a sampler on the backend, it has to:
// - have the .backend_init() API implemented
// - return true during .backend_init()
if (smpl.ptr->iface->backend_init) {
if (!smpl.ptr->iface->backend_init(smpl.ptr, buft)) {
res_cur = false;
// - support the requested per-sequence output limit
if (cur_prefix && smpl.ptr->iface->backend_init) {
if (!smpl.ptr->iface->backend_init(smpl.ptr, buft, n_outputs_max_per_seq)) {
cur_prefix = false;
}
} else {
res_cur = false;
cur_prefix = false;
}
smpl.is_backend = res_cur;
smpl.is_backend = cur_prefix;
backend_prefix = cur_prefix;
res = res && res_cur;
res = res && cur_prefix;
}
auto probe = llama_sampler_backend_probe_graph(smpl, 1024*1024, GGML_DEFAULT_GRAPH_SIZE, false);
chain->n_nodes = llama_sampler_backend_probe_n_nodes(probe);
return res;
}
@@ -780,6 +822,36 @@ static void llama_sampler_chain_backend_set_input(struct llama_sampler * smpl) {
}
}
static void llama_sampler_chain_backend_reset(struct llama_sampler * smpl) {
auto * chain = (llama_sampler_chain *) smpl->ctx;
for (auto & entry : chain->samplers) {
if (!entry.is_backend) {
break;
}
if (entry.ptr->iface->backend_reset) {
entry.ptr->iface->backend_reset(entry.ptr);
}
}
}
static void llama_sampler_chain_copy_state(const struct llama_sampler * src, struct llama_sampler * dst) {
const auto * src_chain = (const llama_sampler_chain *) src->ctx;
auto * dst_chain = (llama_sampler_chain *) dst->ctx;
GGML_ASSERT(src_chain->samplers.size() == dst_chain->samplers.size());
for (size_t i = 0; i < src_chain->samplers.size(); ++i) {
llama_sampler_copy(src_chain->samplers[i].ptr, dst_chain->samplers[i].ptr);
}
// note: is_init, n_nodes and is_backend belong to the current sampling graph
dst_chain->params = src_chain->params;
dst_chain->cur = src_chain->cur;
dst_chain->t_sample_us = src_chain->t_sample_us;
dst_chain->n_sample = src_chain->n_sample;
}
static struct llama_sampler_i llama_sampler_chain_i = {
/* .name = */ llama_sampler_chain_name,
/* .accept = */ llama_sampler_chain_accept,
@@ -791,6 +863,8 @@ static struct llama_sampler_i llama_sampler_chain_i = {
/* .backend_accept = */ llama_sampler_chain_backend_accept,
/* .backend_apply = */ llama_sampler_chain_backend_apply,
/* .backend_set_input = */ llama_sampler_chain_backend_set_input,
/* .backend_reset = */ llama_sampler_chain_backend_reset,
/* .copy_state = */ llama_sampler_chain_copy_state,
};
struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
@@ -799,6 +873,7 @@ struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_param
/* .ctx = */ new llama_sampler_chain {
/* .params = */ params,
/* .is_init = */ false,
/* .n_nodes = */ 0,
/* .samplers = */ {},
/* .cur = */ {},
/* .t_sample_us = */ 0,
@@ -807,6 +882,16 @@ struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_param
);
}
uint32_t llama_sampler_backend_n_nodes(const llama_sampler * sampler) {
GGML_ASSERT(sampler != nullptr);
GGML_ASSERT(sampler->iface == &llama_sampler_chain_i);
const auto * chain = (const llama_sampler_chain *) sampler->ctx;
GGML_ASSERT(chain->is_init);
return chain->n_nodes;
}
llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx) {
const llama_token sampled_token = llama_get_sampled_token_ith (ctx, idx);
const float * sampled_probs = llama_get_sampled_probs_ith (ctx, idx);
@@ -816,6 +901,7 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
// If a backend sampler has already sampled a token, return it.
if (sampled_token != LLAMA_TOKEN_NULL) {
LLAMA_LOG_DEBUG("%s: Backend sampler selected token for idx %d. Skipping CPU samplers\n", __func__, idx);
llama_sampler_accept(smpl, sampled_token);
return sampled_token;
}
@@ -975,8 +1061,10 @@ static void llama_sampler_greedy_apply(struct llama_sampler * /*smpl*/, llama_to
static bool llama_sampler_greedy_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_greedy *) smpl->ctx;
GGML_UNUSED(n_outputs_max_per_seq);
const bool res = llama_sampler_backend_support(smpl, buft);
@@ -1012,6 +1100,8 @@ static struct llama_sampler_i llama_sampler_greedy_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_greedy_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_greedy>,
};
struct llama_sampler * llama_sampler_init_greedy() {
@@ -1031,7 +1121,25 @@ struct llama_sampler_dist : public llama_sampler_backend {
std::mt19937 rng;
ggml_tensor * inp_uniform;
// TODO: refactor + fix naming
// https://github.com/ggml-org/llama.cpp/pull/25532/changes#r3749906719
// use a temporary RNG for multi-output sampling so rejected tokens do not advance rng
bool backend_transactional;
std::mt19937 rng_backend;
size_t n_backend_draws_generated;
size_t n_backend_draws_committed;
// inputs for the current sampling graph
std::vector<ggml_tensor *> inp_uniforms;
void copy_state(const llama_sampler_dist & src) {
// note: inp_uniforms and backend_transactional belong to the current sampling graph
seed_cur = src.seed_cur;
rng = src.rng;
rng_backend = src.rng_backend;
n_backend_draws_generated = src.n_backend_draws_generated;
n_backend_draws_committed = src.n_backend_draws_committed;
}
};
static const char * llama_sampler_dist_name(const struct llama_sampler * smpl) {
@@ -1050,7 +1158,11 @@ static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_da
cur_p->selected = 0;
std::uniform_real_distribution<double> dist(0.0f, 1.0f);
if (cur_p->size == 1) {
// keep the RNG state aligned with backend sampling, which draws once per output
dist(ctx->rng);
cur_p->data[0].p = 1.0f;
return;
}
@@ -1075,7 +1187,6 @@ static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_da
// sample from the obtained probabilities and normalize the probs in a single pass
// this is ~3x faster on Mac with full gpt-oss vocab than the version below
//
std::uniform_real_distribution<double> dist(0.0f, 1.0f);
const double rnd = dist(ctx->rng);
double sum_run = 0.0f;
@@ -1115,6 +1226,9 @@ static void llama_sampler_dist_reset(struct llama_sampler * smpl) {
auto * ctx = (llama_sampler_dist *) smpl->ctx;
ctx->seed_cur = get_rng_seed(ctx->seed);
ctx->rng.seed(ctx->seed_cur);
ctx->rng_backend = ctx->rng;
ctx->n_backend_draws_generated = 0;
ctx->n_backend_draws_committed = 0;
}
static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sampler * smpl) {
@@ -1125,7 +1239,12 @@ static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sample
{
auto * result_ctx = (llama_sampler_dist *) result->ctx;
result_ctx->seed_cur = ctx->seed_cur;
result_ctx->rng = ctx->rng;
result_ctx->backend_transactional = ctx->backend_transactional;
result_ctx->rng_backend = ctx->rng_backend;
result_ctx->n_backend_draws_generated = ctx->n_backend_draws_generated;
result_ctx->n_backend_draws_committed = ctx->n_backend_draws_committed;
}
return result;
@@ -1137,12 +1256,17 @@ static void llama_sampler_dist_free(struct llama_sampler * smpl) {
static bool llama_sampler_dist_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_dist *) smpl->ctx;
const bool res = llama_sampler_backend_support(smpl, buft);
sctx->init(res);
sctx->backend_transactional = n_outputs_max_per_seq > 1;
sctx->rng_backend = sctx->rng;
sctx->n_backend_draws_generated = 0;
sctx->n_backend_draws_committed = 0;
return res;
}
@@ -1156,9 +1280,10 @@ static void llama_sampler_dist_backend_apply(
auto * sctx = (llama_sampler_dist *) smpl->ctx;
sctx->inp_uniform = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_set_name (sctx->inp_uniform, "uniform");
ggml_set_input(sctx->inp_uniform);
ggml_tensor * inp_uniform = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_format_name(inp_uniform, "uniform_%zu", sctx->inp_uniforms.size());
ggml_set_input(inp_uniform);
sctx->inp_uniforms.push_back(inp_uniform);
// flatten
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
@@ -1174,7 +1299,7 @@ static void llama_sampler_dist_backend_apply(
// Recall that each entry in cumsum is the cumulative probability up to that
// index so values stay negative while the cumulative total is below the
// random value, and become zero/positive once the threshold is crossed.
struct ggml_tensor * diff = ggml_sub(ctx, cumsum, sctx->inp_uniform);
struct ggml_tensor * diff = ggml_sub(ctx, cumsum, inp_uniform);
ggml_set_name(diff, "dist_cumsum");
// The ggml_step function produces a tensor where entries are 1 if the
@@ -1189,6 +1314,9 @@ static void llama_sampler_dist_backend_apply(
struct ggml_tensor * idxf = ggml_sum(ctx, mask);
ggml_set_name(idxf, "dist_index_f32");
// Clamp to prevent out-of-bounds access when computing the index.
idxf = ggml_clamp(ctx, idxf, 1.0f, mask->ne[0]);
// Use ggml_scale_bias to scale the index value by -1 and then add the size
// of the mask to that value so we get the correct index ((-1 * idxf) + n).
struct ggml_tensor * idx = ggml_cast(ctx, ggml_scale_bias(ctx, idxf, -1.0f, mask->ne[0]), GGML_TYPE_I32);
@@ -1210,22 +1338,52 @@ static void llama_sampler_dist_backend_apply(
static void llama_sampler_dist_backend_set_input(struct llama_sampler * smpl) {
auto * sctx = (llama_sampler_dist *) smpl->ctx;
GGML_ASSERT(sctx->inp_uniform != nullptr);
GGML_ASSERT(!sctx->inp_uniforms.empty());
// We sample in double precision and cast to float to match rnd numbers of
// llama_dampler_dist which uses double precision (sampling from
// llama_sampler_dist which uses double precision (sampling from
// std::uniform_real_distribution<double> and
// std::uniform_real_distribution<float> with same rng will produce
// different sequences).
std::uniform_real_distribution<double> dist(0.0f, 1.0f);
const float rnd = dist(sctx->rng);
ggml_backend_tensor_set(sctx->inp_uniform, &rnd, 0, sizeof(float));
auto & rng = sctx->backend_transactional ? sctx->rng_backend : sctx->rng;
for (auto * inp_uniform : sctx->inp_uniforms) {
GGML_ASSERT(inp_uniform != nullptr);
const float rnd = dist(rng);
ggml_backend_tensor_set(inp_uniform, &rnd, 0, sizeof(float));
if (sctx->backend_transactional) {
++sctx->n_backend_draws_generated;
}
}
}
static void llama_sampler_dist_backend_reset(struct llama_sampler * smpl) {
auto * sctx = (llama_sampler_dist *) smpl->ctx;
sctx->inp_uniforms.clear();
}
static void llama_sampler_dist_accept(struct llama_sampler * smpl, llama_token token) {
GGML_UNUSED(token);
auto * sctx = (llama_sampler_dist *) smpl->ctx;
if (!sctx->backend_transactional ||
sctx->n_backend_draws_committed >= sctx->n_backend_draws_generated) {
return;
}
std::uniform_real_distribution<double> dist(0.0f, 1.0f);
dist(sctx->rng);
++sctx->n_backend_draws_committed;
}
static struct llama_sampler_i llama_sampler_dist_i = {
/* .name = */ llama_sampler_dist_name,
/* .accept = */ nullptr,
/* .accept = */ llama_sampler_dist_accept,
/* .apply = */ llama_sampler_dist_apply,
/* .reset = */ llama_sampler_dist_reset,
/* .clone = */ llama_sampler_dist_clone,
@@ -1234,6 +1392,8 @@ static struct llama_sampler_i llama_sampler_dist_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_dist_backend_apply,
/* .backend_set_input = */ llama_sampler_dist_backend_set_input,
/* .backend_reset = */ llama_sampler_dist_backend_reset,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_dist>,
};
struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
@@ -1245,11 +1405,36 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
/* .seed = */ seed,
/* .seed_cur = */ seed_cur,
/* .rng = */ std::mt19937(seed_cur),
/* .inp_uniform = */ nullptr,
/* .backend_transactional = */ false,
/* .rng_backend = */ std::mt19937(seed_cur),
/* .n_backend_draws_generated = */ 0,
/* .n_backend_draws_committed = */ 0,
/* .inp_uniforms = */ {},
}
);
}
void llama_sampler_backend_begin(llama_sampler * sampler) {
GGML_ASSERT(sampler != nullptr);
if (sampler->iface == &llama_sampler_chain_i) {
auto * chain = (llama_sampler_chain *) sampler->ctx;
for (auto & entry : chain->samplers) {
if (!entry.is_backend) {
break;
}
llama_sampler_backend_begin(entry.ptr);
}
} else if (sampler->iface == &llama_sampler_dist_i) {
auto * ctx = (llama_sampler_dist *) sampler->ctx;
if (ctx->backend_transactional) {
ctx->rng_backend = ctx->rng;
ctx->n_backend_draws_generated = 0;
ctx->n_backend_draws_committed = 0;
}
}
}
// top-k
struct llama_sampler_top_k : public llama_sampler_backend {
@@ -1277,8 +1462,10 @@ static void llama_sampler_top_k_free(struct llama_sampler * smpl) {
static bool llama_sampler_top_k_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_top_k *) smpl->ctx;
GGML_UNUSED(n_outputs_max_per_seq);
const bool res = llama_sampler_backend_support(smpl, buft);
@@ -1325,6 +1512,8 @@ static struct llama_sampler_i llama_sampler_top_k_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_top_k_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_top_k>,
};
struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
@@ -1423,8 +1612,10 @@ static void llama_sampler_top_p_free(struct llama_sampler * smpl) {
static bool llama_sampler_top_p_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_top_p *) smpl->ctx;
GGML_UNUSED(n_outputs_max_per_seq);
const bool res = llama_sampler_backend_support(smpl, buft);
@@ -1521,6 +1712,8 @@ static struct llama_sampler_i llama_sampler_top_p_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_top_p_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_top_p>,
};
struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
@@ -1618,8 +1811,10 @@ static void llama_sampler_min_p_free(struct llama_sampler * smpl) {
static bool llama_sampler_min_p_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_min_p *) smpl->ctx;
GGML_UNUSED(n_outputs_max_per_seq);
const bool res = llama_sampler_backend_support(smpl, buft);
@@ -1680,6 +1875,8 @@ static struct llama_sampler_i llama_sampler_min_p_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_min_p_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_min_p>,
};
struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
@@ -1790,6 +1987,8 @@ static struct llama_sampler_i llama_sampler_typical_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_typical(float p, size_t min_keep) {
@@ -1866,8 +2065,10 @@ static void llama_sampler_backend_temp_sampling(
static bool llama_sampler_temp_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_temp *) smpl->ctx;
GGML_UNUSED(n_outputs_max_per_seq);
const bool res = llama_sampler_backend_support(smpl, buft);
@@ -1896,6 +2097,8 @@ static struct llama_sampler_i llama_sampler_temp_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_temp_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_temp>,
};
struct llama_sampler * llama_sampler_init_temp(float temp) {
@@ -2009,8 +2212,10 @@ static void llama_sampler_temp_ext_free(struct llama_sampler * smpl) {
static bool llama_sampler_temp_ext_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_temp_ext *) smpl->ctx;
GGML_UNUSED(n_outputs_max_per_seq);
const bool res = llama_sampler_backend_support(smpl, buft);
@@ -2095,6 +2300,8 @@ static struct llama_sampler_i llama_sampler_temp_ext_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_temp_ext_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_temp_ext>,
};
struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, float exponent) {
@@ -2202,6 +2409,8 @@ static struct llama_sampler_i llama_sampler_xtc_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
@@ -2290,7 +2499,7 @@ static struct llama_sampler * llama_sampler_mirostat_clone(const struct llama_sa
// copy the state
{
auto * result_ctx = (llama_sampler_mirostat *) smpl->ctx;
auto * result_ctx = (llama_sampler_mirostat *) result->ctx;
result_ctx->mu = ctx->mu;
result_ctx->rng = ctx->rng;
@@ -2321,6 +2530,8 @@ static struct llama_sampler_i llama_sampler_mirostat_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
@@ -2425,6 +2636,8 @@ static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
@@ -2546,6 +2759,8 @@ static struct llama_sampler_i llama_sampler_grammar_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
static struct llama_sampler * llama_sampler_init_grammar_impl(
@@ -2661,6 +2876,12 @@ struct llama_sampler_penalties : public llama_sampler_backend {
std::vector<int32_t> host_token_ids;
std::vector<int32_t> host_counts;
void copy_state(const llama_sampler_penalties & src) {
// note: inp_token_ids/inp_counts belong to the current sampling graph
prev = src.prev;
token_count = src.token_count;
}
static bool is_disabled(
int32_t penalty_last_n,
float penalty_repeat,
@@ -2790,9 +3011,15 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) {
static bool llama_sampler_penalties_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
auto * sctx = (llama_sampler_penalties *) smpl->ctx;
if (n_outputs_max_per_seq > 1) {
sctx->init(false);
return false;
}
const bool res = llama_sampler_backend_support(smpl, buft);
sctx->init(res);
@@ -2952,6 +3179,12 @@ static void llama_sampler_penalties_backend_set_input(struct llama_sampler * smp
ggml_backend_tensor_set(sctx->inp_counts, sctx->host_counts.data(), 0, sctx->n_max * sizeof(int32_t));
}
static void llama_sampler_penalties_backend_reset(struct llama_sampler * smpl) {
auto * sctx = (llama_sampler_penalties *) smpl->ctx;
sctx->inp_token_ids = nullptr;
sctx->inp_counts = nullptr;
}
static struct llama_sampler_i llama_sampler_penalties_i = {
/* .name = */ llama_sampler_penalties_name,
/* .accept = */ llama_sampler_penalties_accept,
@@ -2963,6 +3196,8 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_penalties_backend_apply,
/* .backend_set_input = */ llama_sampler_penalties_backend_set_input,
/* .backend_reset = */ llama_sampler_penalties_backend_reset,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_penalties>,
};
struct llama_sampler * llama_sampler_init_penalties(
@@ -3058,6 +3293,8 @@ static struct llama_sampler_i llama_sampler_top_n_sigma_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_top_n_sigma(float n) {
@@ -3395,6 +3632,8 @@ static struct llama_sampler_i llama_sampler_dry_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
@@ -3614,6 +3853,8 @@ static struct llama_sampler_i llama_sampler_adaptive_p_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_adaptive_p(
@@ -3715,6 +3956,9 @@ static void llama_sampler_logit_bias_backend_apply(
const size_t n = sctx->logit_bias.size();
if (sctx->inp_logit_bias == nullptr) {
GGML_ASSERT(sctx->inp_logit_idxs == nullptr);
sctx->inp_logit_bias = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n);
ggml_set_name(sctx->inp_logit_bias, "logit_bias");
ggml_set_input(sctx->inp_logit_bias);
@@ -3722,6 +3966,7 @@ static void llama_sampler_logit_bias_backend_apply(
sctx->inp_logit_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n);
ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
ggml_set_input(sctx->inp_logit_idxs);
}
ggml_tensor * cur = ggml_fill(ctx, data->logits, 0.0f);
@@ -3756,10 +4001,18 @@ static void llama_sampler_logit_bias_backend_set_input(struct llama_sampler * sm
ggml_backend_tensor_set(sctx->inp_logit_idxs, data_logit_idxs.data(), 0, ggml_nbytes(sctx->inp_logit_idxs));
}
static void llama_sampler_logit_bias_backend_reset(struct llama_sampler * smpl) {
auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
sctx->inp_logit_bias = nullptr;
sctx->inp_logit_idxs = nullptr;
}
static bool llama_sampler_logit_bias_backend_init(
struct llama_sampler * smpl,
ggml_backend_buffer_type_t buft) {
ggml_backend_buffer_type_t buft,
uint32_t n_outputs_max_per_seq) {
GGML_UNUSED(buft);
GGML_UNUSED(n_outputs_max_per_seq);
auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
@@ -3783,6 +4036,8 @@ static struct llama_sampler_i llama_sampler_logit_bias_i = {
/* .backend_accept = */ nullptr,
/* .backend_apply = */ llama_sampler_logit_bias_backend_apply,
/* .backend_set_input = */ llama_sampler_logit_bias_backend_set_input,
/* .backend_reset = */ llama_sampler_logit_bias_backend_reset,
/* .copy_state = */ llama_sampler_backend_copy_state<llama_sampler_logit_bias>,
};
struct llama_sampler * llama_sampler_init_logit_bias(
@@ -4022,10 +4277,12 @@ static struct llama_sampler_i llama_sampler_infill_i = {
/* .reset = */ nullptr,
/* .clone = */ llama_sampler_infill_clone,
/* .free = */ llama_sampler_infill_free,
/* .backend_apply = */ nullptr,
/* .backend_accept = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_init = */ nullptr,
/* .backend_accept = */ nullptr,
/* .backend_apply = */ nullptr,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
@@ -4039,6 +4296,32 @@ struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * voca
);
}
void llama_sampler_copy(const struct llama_sampler * src, struct llama_sampler * dst) {
if (!src || !dst || src == dst) {
return;
}
GGML_ASSERT(src->iface == dst->iface && "llama_sampler_copy: cannot copy between different sampler types");
if (dst->iface->copy_state) {
dst->iface->copy_state(src, dst);
return;
}
// build a temporary sampler carrying src's current state
llama_sampler * tmp = llama_sampler_clone(src);
// free dst's old state (frees dst->ctx, including children for a chain)
if (dst->iface->free) {
dst->iface->free(dst);
}
// transplant tmp's state into dst, then destroy the (now empty) temp shell
dst->ctx = tmp->ctx;
tmp->ctx = nullptr;
delete tmp;
}
// utils
uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) {
src/llama-sampler.h
+5
View File
@@ -15,6 +15,8 @@ struct llama_sampler_chain {
// has .backend_init() been called?
bool is_init = false;
uint32_t n_nodes = 0;
struct info {
bool is_backend;
@@ -33,6 +35,9 @@ struct llama_sampler_chain {
mutable int32_t n_sample;
};
uint32_t llama_sampler_backend_n_nodes(const llama_sampler * sampler);
void llama_sampler_backend_begin(llama_sampler * sampler);
struct llama_sampler * llama_sampler_init_dry_testing(
float dry_multiplier,
float dry_base,
tests/test-arg-parser.cpp
+30
View File
@@ -2,7 +2,9 @@
#include "common.h"
#include "download.h"
#include "llama.h"
#include "speculative.h"
#include <limits>
#include <string>
#include <vector>
#include <sstream>
@@ -14,6 +16,34 @@
static void test(void) {
common_params params;
auto assert_output_limits = [](int32_t n_batch, int32_t n_parallel, int32_t n_draft,
int32_t total, int32_t per_seq) {
const auto limits = common_speculative_get_output_limits(n_batch, n_parallel, n_draft);
assert(limits.total == total);
assert(limits.per_seq == per_seq);
};
assert_output_limits(16, 2, 3, 8, 4);
assert_output_limits(16, 2, -1, 2, 1);
assert_output_limits( 6, 2, 3, 6, 4);
assert_output_limits( 2, 1, 3, 2, 2);
assert_output_limits(
std::numeric_limits<int32_t>::max(),
std::numeric_limits<int32_t>::max(),
std::numeric_limits<int32_t>::max(),
std::numeric_limits<int32_t>::max(),
std::numeric_limits<int32_t>::max());
{
common_params base;
base.n_parallel = 4;
base.n_outputs_max_per_seq = 8;
const auto draft = common_base_params_to_speculative(base);
assert(draft.n_outputs_max == 4);
assert(draft.n_outputs_max_per_seq == 1);
}
printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
try {
tests/test-backend-sampler.cpp
+464 -33
View File
@@ -14,6 +14,7 @@
#include <fstream>
#include <functional>
#include <map>
#include <random>
#include <string>
#include <unordered_map>
#include <unordered_set>
@@ -80,7 +81,13 @@ struct test_context {
std::unordered_map<llama_seq_id, int32_t> seq_positions;
std::unordered_map<llama_seq_id, int32_t> last_batch_info;
test_context(const test_params & params, std::vector<llama_sampler_seq_config> & configs, int32_t n_seq_max = -1) {
test_context(
const test_params & params,
std::vector<llama_sampler_seq_config> & configs,
int32_t n_seq_max = -1,
uint32_t n_outputs_max = 0,
uint32_t n_ubatch = 0,
uint32_t n_outputs_max_per_seq = 1) {
auto * model = params.model.get();
GGML_ASSERT(model);
@@ -89,6 +96,11 @@ struct test_context {
llama_context_params cparams = llama_context_default_params();
cparams.n_ctx = 512;
cparams.n_batch = 512;
if (n_ubatch > 0) {
cparams.n_ubatch = n_ubatch;
}
cparams.n_outputs_max = n_outputs_max;
cparams.n_outputs_max_per_seq = n_outputs_max_per_seq;
cparams.samplers = configs.data();
cparams.n_samplers = configs.size();
cparams.kv_unified = true;
@@ -262,6 +274,66 @@ struct test_context {
}
};
struct test_single_output_backend_sampler {
bool backend_initialized = false;
uint32_t backend_outputs_max_per_seq = 0;
int backend_apply_count = 0;
int apply_count = 0;
};
static const char * test_single_output_backend_sampler_name(const llama_sampler * /*smpl*/) {
return "single-output-backend";
}
static void test_single_output_backend_sampler_apply(
llama_sampler * smpl, llama_token_data_array * /*cur_p*/) {
auto * ctx = (test_single_output_backend_sampler *) smpl->ctx;
ctx->apply_count++;
}
static void test_single_output_backend_sampler_free(llama_sampler * smpl) {
delete (test_single_output_backend_sampler *) smpl->ctx;
}
static bool test_single_output_backend_sampler_backend_init(
llama_sampler * smpl, ggml_backend_buffer_type_t /*buft*/, uint32_t n_outputs_max_per_seq) {
auto * ctx = (test_single_output_backend_sampler *) smpl->ctx;
ctx->backend_outputs_max_per_seq = n_outputs_max_per_seq;
if (n_outputs_max_per_seq > 1) {
return false;
}
ctx->backend_initialized = true;
return true;
}
static void test_single_output_backend_sampler_backend_apply(
llama_sampler * smpl, ggml_context * /*ctx*/, ggml_cgraph * /*gf*/, llama_sampler_data * /*data*/) {
auto * ctx = (test_single_output_backend_sampler *) smpl->ctx;
ctx->backend_apply_count++;
}
static llama_sampler_i test_single_output_backend_sampler_i = {
/* .name = */ test_single_output_backend_sampler_name,
/* .accept = */ nullptr,
/* .apply = */ test_single_output_backend_sampler_apply,
/* .reset = */ nullptr,
/* .clone = */ nullptr,
/* .free = */ test_single_output_backend_sampler_free,
/* .backend_init = */ test_single_output_backend_sampler_backend_init,
/* .backend_accept = */ nullptr,
/* .backend_apply = */ test_single_output_backend_sampler_backend_apply,
/* .backend_set_input = */ nullptr,
/* .backend_reset = */ nullptr,
/* .copy_state = */ nullptr,
};
static llama_sampler * test_single_output_backend_sampler_init(
test_single_output_backend_sampler ** sampler_ctx) {
auto * ctx = new test_single_output_backend_sampler;
*sampler_ctx = ctx;
return llama_sampler_init(&test_single_output_backend_sampler_i, ctx);
}
static void test_backend_greedy_sampling(const test_params & params) {
const int seq_id = 0;
@@ -661,7 +733,7 @@ static void test_backend_multi_sequence_sampling(const test_params & params) {
}
static void test_backend_dist_sampling(const test_params & params) {
const int seq_id = 189;
const int seq_id = 0;
const int32_t seed = 88;
struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
@@ -1527,43 +1599,398 @@ static void test_backend_cpu_mixed_batch(const test_params & params) {
printf("backend-cpu mixed batch test PASSED\n");
}
static void test_backend_max_outputs(const test_params & params) {
const int seq_id = 0;
const int32_t seed = 88;
static void test_backend_multi_output_limit(const test_params & params) {
const llama_seq_id seq_id = 0;
llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(seed));
std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
llama_sampler_ptr chain(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(88));
std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
test_context test_ctx(params, configs, 1, 3, 0, 2);
test_context test_ctx(params, backend_sampler_configs);
llama_batch batch = llama_batch_init(512, 0, 1);
std::string prompt = "Hello";
std::vector<llama_token> tokens;
tokens.push_back(llama_vocab_bos(test_ctx.vocab));
std::vector<llama_token> prompt_tokens(32);
int n_tokens = llama_tokenize(test_ctx.vocab, prompt.c_str(), prompt.length(),
prompt_tokens.data(), prompt_tokens.size(),
false, false);
for (int i = 0; i < n_tokens; i++) {
tokens.push_back(prompt_tokens[i]);
llama_batch batch = llama_batch_init(3, 0, 1);
for (int i = 0; i < 3; ++i) {
common_batch_add(batch, llama_vocab_bos(test_ctx.vocab), i, { seq_id }, true);
}
for (size_t i = 0; i < tokens.size(); i++) {
// set all tokens as output to trigger error
common_batch_add(batch, tokens[i], i, { seq_id }, true);
}
printf(">>> test_max_outputs expected error start:\n");
printf(">>> test_backend_multi_output_limit expected error start:\n");
const int ret = llama_decode(test_ctx.ctx.get(), batch);
GGML_ASSERT(ret != 0 && "llama_decode should not succeed multiple outputs per sequence");
printf("<<< test_max_outputs expected error end.\n");
GGML_ASSERT(ret != 0 && "llama_decode should reject outputs above the per-sequence limit");
printf("<<< test_backend_multi_output_limit expected error end.\n");
llama_batch_free(batch);
printf("backend max outputs test PASSED\n");
printf("backend multi-output limit test PASSED\n");
}
static void test_backend_multi_sequence_multi_output_dist(const test_params & params) {
const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
const int32_t n_vocab = llama_vocab_n_tokens(vocab);
const uint32_t seeds[] = { 88, 1337 };
// reduce the chance that swapped random inputs select the same token
const float temp = 10.0f;
llama_sampler_ptr chain_0(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_ptr chain_1(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_chain_add(chain_0.get(), llama_sampler_init_temp(temp));
llama_sampler_chain_add(chain_0.get(), llama_sampler_init_dist(seeds[0]));
llama_sampler_chain_add(chain_1.get(), llama_sampler_init_temp(temp));
llama_sampler_chain_add(chain_1.get(), llama_sampler_init_dist(seeds[1]));
std::vector<llama_sampler_seq_config> configs = {
{ 0, chain_0.get() },
{ 1, chain_1.get() },
};
test_context test_ctx(params, configs, 2, 4, 0, 2);
std::vector<llama_sampler_seq_config> reference_configs;
test_context reference_ctx(params, reference_configs, 2, 4);
const llama_token seq_tokens[2][2] = {
{ llama_vocab_bos(vocab), llama_vocab_eos(vocab) },
{ llama_vocab_eos(vocab), llama_vocab_bos(vocab) },
};
llama_batch batch = llama_batch_init(4, 0, 1);
for (int pos = 0; pos < 2; ++pos) {
common_batch_add(batch, seq_tokens[0][pos], pos, { 0 }, true);
common_batch_add(batch, seq_tokens[1][pos], pos, { 1 }, true);
}
GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
GGML_ASSERT(llama_decode(reference_ctx.ctx.get(), batch) == 0);
std::mt19937 reference_rngs[] = {
std::mt19937(seeds[0]),
std::mt19937(seeds[1]),
};
std::uniform_real_distribution<double> reference_dist(0.0, 1.0);
for (int i = 0; i < batch.n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
GGML_ASSERT(seq_id == 0 || seq_id == 1);
llama_sampler * chain = seq_id == 0 ? chain_0.get() : chain_1.get();
const llama_token backend_token = llama_sampler_sample(chain, test_ctx.ctx.get(), i);
const float * sampled_logits = llama_get_sampled_logits_ith(test_ctx.ctx.get(), i);
const float * sampled_probs = llama_get_sampled_probs_ith(test_ctx.ctx.get(), i);
const uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), i);
const uint32_t n_probs = llama_get_sampled_probs_count_ith(test_ctx.ctx.get(), i);
const float * reference_logits = llama_get_logits_ith(reference_ctx.ctx.get(), i);
GGML_ASSERT(backend_token >= 0 && backend_token < n_vocab);
GGML_ASSERT(sampled_logits != nullptr);
GGML_ASSERT(sampled_probs != nullptr);
GGML_ASSERT(reference_logits != nullptr);
GGML_ASSERT(n_logits == (uint32_t) n_vocab);
GGML_ASSERT(n_probs == (uint32_t) n_vocab);
float prob_sum = 0.0f;
float cumsum_before = 0.0f;
for (llama_token token = 0; token < n_vocab; ++token) {
const float expected_logit = reference_logits[token] / temp;
const float tolerance = 1e-4f * std::max(1.0f, std::fabs(expected_logit));
GGML_ASSERT(std::fabs(sampled_logits[token] - expected_logit) <= tolerance);
GGML_ASSERT(std::isfinite(sampled_probs[token]));
GGML_ASSERT(sampled_probs[token] >= 0.0f);
prob_sum += sampled_probs[token];
if (token < backend_token) {
cumsum_before += sampled_probs[token];
}
}
GGML_ASSERT(std::fabs(prob_sum - 1.0f) <= 1e-3f);
const float rnd = reference_dist(reference_rngs[seq_id]);
const float cumsum_sampled = cumsum_before + sampled_probs[backend_token];
GGML_ASSERT(rnd >= cumsum_before - 1e-4f);
GGML_ASSERT(rnd <= cumsum_sampled + 1e-4f);
}
llama_batch_free(batch);
printf("backend multi-sequence multi-output dist test PASSED\n");
}
static void test_backend_multi_output_dist_transaction(const test_params & params) {
const llama_seq_id seq_id = 0;
const uint32_t seed = 95;
const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
llama_sampler_ptr chain(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_chain_add(chain.get(), llama_sampler_init_temp(10.0f));
llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(seed));
std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
test_context test_ctx(params, configs, 1, 3, 2, 3);
auto verify_random = [&](int32_t row, float rnd, bool accept = true) {
const llama_token token = accept ?
llama_sampler_sample(chain.get(), test_ctx.ctx.get(), row) :
llama_get_sampled_token_ith(test_ctx.ctx.get(), row);
const float * probs = llama_get_sampled_probs_ith(test_ctx.ctx.get(), row);
GGML_ASSERT(token >= 0 && token < llama_vocab_n_tokens(vocab));
GGML_ASSERT(probs != nullptr);
float cumsum_before = 0.0f;
for (llama_token i = 0; i < token; ++i) {
cumsum_before += probs[i];
}
const float cumsum_sampled = cumsum_before + probs[token];
GGML_ASSERT(rnd >= cumsum_before - 1e-4f);
GGML_ASSERT(rnd <= cumsum_sampled + 1e-4f);
};
std::mt19937 rng(seed);
std::uniform_real_distribution<double> dist(0.0, 1.0);
float randoms[3];
for (float & rnd : randoms) {
rnd = dist(rng);
}
int32_t pos = 0;
auto decode = [&]() {
llama_batch batch = llama_batch_init(3, 0, 1);
for (int32_t i = 0; i < 3; ++i) {
common_batch_add(batch, llama_vocab_bos(vocab), pos++, { seq_id }, true);
}
GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
return batch;
};
llama_batch batch = decode();
verify_random(0, randoms[0], false);
llama_batch_free(batch);
batch = decode();
verify_random(0, randoms[0]);
verify_random(1, randoms[1]);
llama_batch_free(batch);
batch = decode();
llama_sampler_ptr saved(llama_sampler_clone(chain.get()));
verify_random(0, randoms[2]);
llama_batch_free(batch);
llama_sampler_copy(saved.get(), chain.get());
batch = decode();
verify_random(0, randoms[2]);
llama_batch_free(batch);
printf("backend multi-output dist transaction test PASSED\n");
}
static void test_backend_multi_output_sampling_chain(const test_params & params) {
const llama_seq_id seq_id = 0;
const uint32_t seed = 88;
const float p = 0.9f;
const float temp = 0.8f;
const float cdf_epsilon = 1e-4f;
const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
const int32_t n_vocab = llama_vocab_n_tokens(vocab);
const uint32_t k = std::min<uint32_t>(512, n_vocab);
const llama_logit_bias bias = { llama_vocab_bos(vocab), -0.1f };
auto make_filter_chain = [&]() {
llama_sampler_ptr result(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_chain_add(result.get(), llama_sampler_init_logit_bias(n_vocab, 1, &bias));
llama_sampler_chain_add(result.get(), llama_sampler_init_top_k(k));
llama_sampler_chain_add(result.get(), llama_sampler_init_top_p(p, 1));
llama_sampler_chain_add(result.get(), llama_sampler_init_min_p(0.01f, 1));
llama_sampler_chain_add(result.get(), llama_sampler_init_temp(temp));
return result;
};
llama_sampler_ptr chain = make_filter_chain();
llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(seed));
std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
test_context test_ctx(params, configs, 1, 2, 2, 2);
std::vector<llama_sampler_seq_config> reference_configs;
test_context reference_ctx(params, reference_configs, 1, 2, 2);
llama_sampler_ptr reference_bias(llama_sampler_init_logit_bias(n_vocab, 1, &bias));
llama_sampler_ptr reference_top_k(llama_sampler_init_top_k(k));
llama_sampler_ptr reference_top_p(llama_sampler_init_top_p(p, 1));
llama_sampler_ptr reference_min_p(llama_sampler_init_min_p(0.01f, 1));
llama_sampler_ptr reference_temp(llama_sampler_init_temp(temp));
std::vector<llama_token_data> reference_data(n_vocab);
auto make_batch = [&](int32_t pos) {
llama_batch batch = llama_batch_init(2, 0, 1);
for (int i = 0; i < 2; ++i) {
common_batch_add(batch, llama_vocab_bos(vocab), pos + i, { seq_id }, true);
}
return batch;
};
llama_batch batch = make_batch(0);
GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
GGML_ASSERT(llama_decode(reference_ctx.ctx.get(), batch) == 0);
for (int i = 0; i < batch.n_tokens; ++i) {
const llama_token backend_token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), i);
const float * sampled_logits = llama_get_sampled_logits_ith(test_ctx.ctx.get(), i);
const float * sampled_probs = llama_get_sampled_probs_ith(test_ctx.ctx.get(), i);
const llama_token * sampled_candidates = llama_get_sampled_candidates_ith(test_ctx.ctx.get(), i);
const uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), i);
const uint32_t n_probs = llama_get_sampled_probs_count_ith(test_ctx.ctx.get(), i);
const uint32_t n_candidates = llama_get_sampled_candidates_count_ith(test_ctx.ctx.get(), i);
const float * reference_logits = llama_get_logits_ith(reference_ctx.ctx.get(), i);
GGML_ASSERT(backend_token >= 0 && backend_token < n_vocab);
GGML_ASSERT(sampled_logits != nullptr);
GGML_ASSERT(sampled_probs != nullptr);
GGML_ASSERT(sampled_candidates != nullptr);
GGML_ASSERT(reference_logits != nullptr);
GGML_ASSERT(n_logits == k);
GGML_ASSERT(n_probs == n_logits);
GGML_ASSERT(n_candidates == n_logits);
for (llama_token token = 0; token < n_vocab; ++token) {
reference_data[token] = { token, reference_logits[token], 0.0f };
}
llama_token_data_array reference = {
/* .data = */ reference_data.data(),
/* .size = */ reference_data.size(),
/* .selected = */ LLAMA_TOKEN_NULL,
/* .sorted = */ false,
};
llama_sampler_apply(reference_bias.get(), &reference);
llama_sampler_apply(reference_top_k.get(), &reference);
llama_sampler_apply(reference_top_p.get(), &reference);
GGML_ASSERT(reference.size > 0);
float cdf = 0.0f;
for (size_t j = 0; j < reference.size; ++j) {
cdf += reference.data[j].p;
}
const float cdf_before = cdf - reference.data[reference.size - 1].p;
const float boundary_distance = std::min(std::fabs(cdf_before - p), std::fabs(cdf - p));
llama_sampler_apply(reference_min_p.get(), &reference);
llama_sampler_apply(reference_temp.get(), &reference);
std::unordered_map<llama_token, float> reference_by_id;
for (size_t j = 0; j < reference.size; ++j) {
reference_by_id.emplace(reference.data[j].id, reference.data[j].logit);
}
size_t n_backend_only = 0;
int32_t sampled_index = -1;
float prob_sum = 0.0f;
for (uint32_t j = 0; j < n_logits; ++j) {
GGML_ASSERT(sampled_candidates[j] >= 0 && sampled_candidates[j] < n_vocab);
GGML_ASSERT(std::isfinite(sampled_probs[j]));
GGML_ASSERT(sampled_probs[j] >= 0.0f);
prob_sum += sampled_probs[j];
if (sampled_candidates[j] == backend_token) {
sampled_index = j;
}
if (!std::isfinite(sampled_logits[j])) {
GGML_ASSERT(std::isinf(sampled_logits[j]) && sampled_logits[j] < 0.0f);
GGML_ASSERT(sampled_probs[j] == 0.0f);
continue;
}
const auto match = reference_by_id.find(sampled_candidates[j]);
if (match == reference_by_id.end()) {
++n_backend_only;
continue;
}
const float tolerance = 1e-4f * std::max(1.0f, std::fabs(match->second));
GGML_ASSERT(std::fabs(sampled_logits[j] - match->second) <= tolerance);
reference_by_id.erase(match);
}
const size_t n_reference_only = reference_by_id.size();
if (n_backend_only != 0 || n_reference_only != 0) {
GGML_ASSERT(n_backend_only <= 1);
GGML_ASSERT(n_reference_only <= 1);
GGML_ASSERT(boundary_distance <= cdf_epsilon);
}
GGML_ASSERT(sampled_index >= 0);
GGML_ASSERT(std::isfinite(sampled_logits[sampled_index]));
GGML_ASSERT(sampled_probs[sampled_index] > 0.0f);
GGML_ASSERT(std::fabs(prob_sum - 1.0f) <= 1e-3f);
}
llama_batch_free(batch);
batch = make_batch(2);
GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
llama_batch_free(batch);
printf("backend multi-output sampling chain test PASSED\n");
}
static void test_backend_multi_output_cpu_suffix(const test_params & params) {
const llama_seq_id seq_id = 0;
const int32_t k = 8;
const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
auto make_chain = [&](test_single_output_backend_sampler ** sampler_ctx) {
llama_sampler_ptr result(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_chain_add(result.get(), llama_sampler_init_top_k(k));
llama_sampler_chain_add(result.get(), test_single_output_backend_sampler_init(sampler_ctx));
llama_sampler_chain_add(result.get(), llama_sampler_init_dist(88));
return result;
};
{
test_single_output_backend_sampler * sampler_ctx = nullptr;
llama_sampler_ptr chain = make_chain(&sampler_ctx);
std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
test_context test_ctx(params, configs, 1, 1, 0, 4);
llama_batch batch = llama_batch_init(1, 0, 1);
common_batch_add(batch, llama_vocab_bos(vocab), 0, { seq_id }, true);
GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
GGML_ASSERT(sampler_ctx->backend_initialized);
GGML_ASSERT(sampler_ctx->backend_outputs_max_per_seq == 1);
GGML_ASSERT(sampler_ctx->backend_apply_count > 0);
GGML_ASSERT(sampler_ctx->apply_count == 0);
GGML_ASSERT(llama_get_sampled_token_ith(test_ctx.ctx.get(), 0) != LLAMA_TOKEN_NULL);
llama_batch_free(batch);
}
{
test_single_output_backend_sampler * sampler_ctx = nullptr;
llama_sampler_ptr chain = make_chain(&sampler_ctx);
std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
test_context test_ctx(params, configs, 1, 2, 0, 0);
llama_batch batch = llama_batch_init(2, 0, 1);
for (int i = 0; i < 2; ++i) {
common_batch_add(batch, llama_vocab_bos(vocab), i, { seq_id }, true);
}
GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
GGML_ASSERT(!sampler_ctx->backend_initialized);
GGML_ASSERT(sampler_ctx->backend_outputs_max_per_seq == 2);
GGML_ASSERT(sampler_ctx->backend_apply_count == 0);
for (int i = 0; i < batch.n_tokens; ++i) {
GGML_ASSERT(llama_get_sampled_token_ith(test_ctx.ctx.get(), i) == LLAMA_TOKEN_NULL);
GGML_ASSERT(llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), i) == (uint32_t) k);
GGML_ASSERT(llama_get_sampled_candidates_count_ith(test_ctx.ctx.get(), i) == (uint32_t) k);
const llama_token token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), i);
GGML_ASSERT(token >= 0 && token < llama_vocab_n_tokens(vocab));
}
GGML_ASSERT(sampler_ctx->apply_count == batch.n_tokens);
llama_batch_free(batch);
}
printf("backend multi-output CPU suffix test PASSED\n");
}
struct backend_test_case {
@@ -1583,7 +2010,11 @@ static const backend_test_case BACKEND_TESTS[] = {
{ "dist", test_backend_dist_sampling, true },
{ "dist_and_cpu", test_backend_dist_sampling_and_cpu, true },
{ "set_sampler", test_backend_set_sampler, true },
{ "max_outputs", test_backend_max_outputs, true },
{ "multi_output_limit", test_backend_multi_output_limit, true },
{ "multi_sequence_multi_output_dist", test_backend_multi_sequence_multi_output_dist, true },
{ "multi_output_dist_transaction", test_backend_multi_output_dist_transaction, true },
{ "multi_output_sampling_chain", test_backend_multi_output_sampling_chain, true },
{ "multi_output_cpu", test_backend_multi_output_cpu_suffix, true },
{ "mixed", test_backend_mixed_sampling, true },
{ "min_p", test_backend_min_p_sampling, true },
{ "cpu_mixed", test_backend_cpu_mixed_batch, true },
tests/test-sampling.cpp
+31
View File
@@ -61,6 +61,35 @@ private:
std::vector<llama_token_data> cur;
};
static llama_token sample_dist(llama_sampler * sampler, const std::vector<float> & logits) {
std::vector<llama_token_data> cur;
for (llama_token token_id = 0; token_id < (llama_token) logits.size(); ++token_id) {
cur.push_back({ token_id, logits[token_id], 0.0f });
}
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_sampler_apply(sampler, &cur_p);
GGML_ASSERT(cur_p.selected >= 0);
GGML_ASSERT((size_t) cur_p.selected < cur_p.size);
return cur_p.data[cur_p.selected].id;
}
static void test_dist_singleton_rng() {
llama_sampler * singleton = llama_sampler_init_dist(4242);
llama_sampler * control = llama_sampler_init_dist(4242);
sample_dist(singleton, { 0.0f });
sample_dist(control, { 0.0f, 0.0f });
const std::vector<float> logits(256, 0.0f);
for (int i = 0; i < 4; ++i) {
GGML_ASSERT(sample_dist(singleton, logits) == sample_dist(control, logits));
}
llama_sampler_free(singleton);
llama_sampler_free(control);
}
static void test_temp(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp) {
sampler_tester tester(probs, probs_expected);
@@ -308,6 +337,8 @@ static void test_perf() {
int main(void) {
ggml_time_init();
test_dist_singleton_rng();
test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f);
tools/server/server-context.cpp
+16 -20
View File
@@ -39,19 +39,18 @@ using json = nlohmann::ordered_json;
constexpr int HTTP_POLLING_SECONDS = 1;
static uint32_t server_n_outputs_max(const common_params & params) {
const uint32_t n_batch = params.n_batch;
static common_speculative_output_limits server_output_limits(const common_params & params) {
if (params.embedding ||
(params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED && params.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
return n_batch;
return { params.n_batch, 1 };
}
const uint32_t n_outputs_per_seq = 1 + common_speculative_n_max(&params.speculative);
auto result = common_speculative_get_output_limits(
params.n_batch, params.n_parallel, common_speculative_n_max(&params.speculative));
const uint64_t n_outputs = (uint64_t) params.n_parallel * n_outputs_per_seq;
return std::max<uint32_t>(1, std::min<uint64_t>(n_batch, n_outputs));
result.total = std::max<int32_t>(1, result.total);
result.per_seq = std::max<int32_t>(1, result.per_seq);
return result;
}
// state diagram: https://github.com/ggml-org/llama.cpp/pull/9283
@@ -1063,7 +1062,9 @@ private:
const bool is_resume = sleeping;
params_base = params;
params_base.n_outputs_max = server_n_outputs_max(params_base);
const auto output_limits = server_output_limits(params_base);
params_base.n_outputs_max = output_limits.total;
params_base.n_outputs_max_per_seq = output_limits.per_seq;
const bool has_mmproj = !params.mmproj.path.empty();
const bool has_draft = params.speculative.has_dft();
@@ -1832,18 +1833,13 @@ private:
const bool need_pre_sample_logits = task.params.sampling.n_probs > 0 && !task.params.post_sampling_probs;
bool backend_sampling = true;
backend_sampling &= task.params.sampling.backend_sampling;
// TODO: speculative decoding requires multiple samples per batch - not supported yet
backend_sampling &= !(slot.can_speculate());
bool use_backend_sampling = task.params.sampling.backend_sampling;
// TODO: getting pre sampling logits is not yet supported with backend sampling
backend_sampling &= !need_pre_sample_logits;
use_backend_sampling &= !need_pre_sample_logits;
// TODO: tmp until backend sampling is fully implemented
if (backend_sampling) {
if (use_backend_sampling) {
llama_set_sampler(ctx_tgt, slot.id, common_sampler_get(slot.smpl.get()));
} else {
llama_set_sampler(ctx_tgt, slot.id, nullptr);
@@ -3865,7 +3861,8 @@ private:
// speculative decoding - main model sample and accept
iterate(slots, [&](server_slot & slot) {
if (slot.state != SLOT_STATE_GENERATING || !slot.can_speculate() || slot.spec_draft.empty()) {
if (slot.state != SLOT_STATE_GENERATING || !slot.can_speculate() ||
slot.spec_draft.empty() || slot.spec_i_batch.empty()) {
return;
}
@@ -3876,7 +3873,6 @@ private:
// verify and try to accept the draft
{
// save the sampler sampler state in case we need to restore it
common_sampler_ptr smpl_save(common_sampler_clone(slot.smpl.get()));
GGML_ASSERT(slot.spec_i_batch.size() == n_draft + 1);
@@ -3915,7 +3911,7 @@ private:
slot.mem.seq_rm(slot.id, ckpt.pos_max + 1, -1);
slot.prompt.tokens.keep_first(ckpt.n_tokens);
slot.smpl = std::move(smpl_save);
common_sampler_copy(smpl_save.get(), slot.smpl.get());
return;
}
tools/server/tests/unit/test_speculative.py
+16 -15
View File
@@ -25,33 +25,34 @@ def fixture_create_server():
def test_with_and_without_draft():
global server
request = {
"prompt": "I believe the meaning of life is",
"temperature": 0.8,
"top_k": 40,
"seed": 4242,
"n_predict": 16,
"return_tokens": True,
}
server.model_draft = None # disable draft model
server.spec_type = None
server.backend_sampling = True
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "I believe the meaning of life is",
"temperature": 0.0,
"top_k": 1,
"n_predict": 16,
})
res = server.make_request("POST", "/completion", data=request)
assert res.status_code == 200
content_no_draft = res.body["content"]
tokens_no_draft = res.body["tokens"]
server.stop()
# create new server with draft model
create_server()
server.backend_sampling = True
server.start()
res = server.make_request("POST", "/completion", data={
"prompt": "I believe the meaning of life is",
"temperature": 0.0,
"top_k": 1,
"n_predict": 16,
})
res = server.make_request("POST", "/completion", data=request)
assert res.status_code == 200
assert res.body["timings"]["draft_n"] > 0
content_draft = res.body["content"]
tokens_draft = res.body["tokens"]
assert content_no_draft == content_draft
assert tokens_no_draft == tokens_draft
def test_different_draft_min_draft_max():