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IBM granite/granitemoe architecture support (#6760)

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* fix(ext_server): Port llama.cpp sampling refactors to ext_server

This was a fairly large changeset. I closely followed the changes here:
df270ef745

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(server.cpp): Refactor server.cpp logging for llama.cpp overhaul

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Bump llama.cpp to the latest master with `granite` support

This does not yet have granite MoE support, but that can come in a
follow up PR

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(patches): Update all patches (except solar-pro) to work with bumped llama.cpp

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(solar): Update solar patch for llama.cpp bump

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(llama.cpp): Bump llama.cpp for granitemoe support

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(llama.cpp): Bump llama.cpp for granitemoe support

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(solar): Update the solar-pro patch for latest llama.cpp bump

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(llama.cpp): Bump to the latest master of llama.cpp

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(patches): Update all patches for latest bump

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(llama): Always run sync.sh from the right directory

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama/patches): Update llama patches

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat(llama)!: Rough sync with llama.cpp submodule

There are a number of changes that will need to be propagated to llama.go
before any of this works!

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama/patches): Add a patch and update for missing ggml-impl.h include

This include is where the ggml_cgraph struct is defined. It is included in
many of the .c files to define the forward declartion in ggml.h. It seems
that with the subset of code included here, the import was somehow lost (or
out-of-order) when building, so adding this include to llama.cpp fixes the
missing definition.

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama/sync): Add missing ggml-cpu-impl.h copy-over in sync.sh

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Add missing log.cpp

This was added as part of the logging overhaul done in llama.cpp

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Overhaul use of sampling module for llama.cpp changes

The changes here reflect the changes made in the big llama.cpp sampling PR
https://github.com/ggerganov/llama.cpp/pull/9294

The sampling functionality is now broken into the base interface
(llama_sampler) and the generation implementation (gpt_sampler). The
changes here reflect that. Since the sampling.h/sampling.cpp code uses c++
STL headers, the sampling_ext.[h|cpp] wrapper is maintained to allow go to
access a pure-C interface.

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Fix the impl of SampleTokenGreedy for new sampling

I don't think this method is currently used, so it could probably just be
removed so that all sampling goes through the GPT interface, but in the
interest of doing no harm, this should keep the method working as expected.

Branch: IBMGraniteArchitectureSupport

* fix(llama): Remove unused SampleTokenGreedy

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(sync): Remove bash-specific change to sync.sh

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* chore(gofumpt): Format on llama.go to pass linting

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llm): Fix missing <thread> include in ext_server

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Remove TODO about grammar_first

This feature was not used/needed previously so should be fine without
plumbing it through now.

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Better naming for sampling wrapper and args

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Fix patch 05 to use new wrapper api and re-sync

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* runner: Flush pending responses before returning

If there are any pending reponses (such as from potential stop
tokens) then we should send them back before ending the sequence.
Otherwise, we can be missing tokens at the end of a response.

Fixes #6707

* fix(llama/sampling): Use gpt_sampler with a forward declaration

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llama): Remove unnecessary patch for gguf impl header

This was caused by an earlier mistake in the embeddings patch that was
dereferencing the pointer instead of using the wrapper API.

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(llm): Remove use of deprecated --log-disable flag

Branch: IBMGraniteArchitectureSupport

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart
2024-10-17 12:59:52 -06:00
committed by GitHub
parent 05cd82ef94
commit f2890a4494
263 changed files with 14255 additions and 10867 deletions
Download Patch File Download Diff File Expand all files Collapse all files

166
llm/ext_server/server.cpp vendored
View File

@@ -22,7 +22,8 @@
#include "common.h"
#include "llama.h"
#include "grammar-parser.h"
#include "log.h"
#include "sampling.h"
#include "utils.hpp"
#include "../llava/clip.h"
@@ -137,7 +138,6 @@ struct server_slot {
json prompt;
std::string generated_text;
llama_token sampled;
std::vector<llama_token> cache_tokens;
std::vector<completion_token_output> generated_token_probs;
@@ -151,8 +151,9 @@ struct server_slot {
std::string stopping_word;
// sampling
struct llama_sampling_params sparams;
llama_sampling_context *ctx_sampling = nullptr;
struct gpt_sampler_params sparams;
struct gpt_sampler * smpl = nullptr;
llama_token sampled;
int32_t ga_i = 0; // group-attention state
int32_t ga_n = 1; // group-attention factor
@@ -416,7 +417,7 @@ struct llama_server_context
const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
const int n_embd_llm = llama_n_embd(model);
if (n_embd_clip != n_embd_llm) {
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
LOG_WRN("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
llama_free(ctx);
llama_free_model(model);
return false;
@@ -551,7 +552,7 @@ struct llama_server_context
bool launch_slot_with_data(server_slot* &slot, json data) {
slot_params default_params;
llama_sampling_params default_sparams;
gpt_sampler_params default_sparams;
slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
@@ -560,7 +561,7 @@ struct llama_server_context
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
slot->sparams.typ_p = json_value(data, "typ_p", default_sparams.typ_p);
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
@@ -605,47 +606,11 @@ struct llama_server_context
slot->prompt = "";
}
slot->sparams.penalty_prompt_tokens.clear();
slot->sparams.use_penalty_prompt_tokens = false;
const auto &penalty_prompt = data.find("penalty_prompt");
if (penalty_prompt != data.end())
{
if (penalty_prompt->is_string())
{
const auto penalty_prompt_string = penalty_prompt->get<std::string>();
auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
if (slot->params.n_predict > 0)
{
slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
}
slot->sparams.use_penalty_prompt_tokens = true;
}
else if (penalty_prompt->is_array())
{
const auto n_tokens = penalty_prompt->size();
slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
const int n_vocab = llama_n_vocab(model);
for (const auto &penalty_token : *penalty_prompt)
{
if (penalty_token.is_number_integer())
{
const auto tok = penalty_token.get<llama_token>();
if (tok >= 0 && tok < n_vocab)
{
slot->sparams.penalty_prompt_tokens.push_back(tok);
}
}
}
slot->sparams.use_penalty_prompt_tokens = true;
}
}
slot->sparams.logit_bias.clear();
if (json_value(data, "ignore_eos", false))
{
slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
slot->sparams.logit_bias.push_back({llama_token_eos(model), -INFINITY});
}
const auto &logit_bias = data.find("logit_bias");
@@ -675,7 +640,7 @@ struct llama_server_context
llama_token tok = el[0].get<llama_token>();
if (tok >= 0 && tok < n_vocab)
{
slot->sparams.logit_bias[tok] = bias;
slot->sparams.logit_bias.push_back({tok, bias});
}
}
else if (el[0].is_string())
@@ -683,7 +648,7 @@ struct llama_server_context
auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
for (auto tok : toks)
{
slot->sparams.logit_bias[tok] = bias;
slot->sparams.logit_bias.push_back({tok, bias});
}
}
}
@@ -704,22 +669,22 @@ struct llama_server_context
}
}
const auto &samplers_sequence = data.find("samplers");
if (samplers_sequence != data.end() && samplers_sequence->is_array())
const auto &samplers = data.find("samplers");
if (samplers != data.end() && samplers->is_array())
{
std::vector<std::string> sampler_names;
for (const auto &sampler_name : *samplers_sequence)
for (const auto &name : *samplers)
{
if (sampler_name.is_string())
if (name.is_string())
{
sampler_names.emplace_back(sampler_name);
sampler_names.emplace_back(name);
}
}
slot->sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false);
slot->sparams.samplers = gpt_sampler_types_from_names(sampler_names, false);
}
else
{
slot->sparams.samplers_sequence = default_sparams.samplers_sequence;
slot->sparams.samplers = default_sparams.samplers;
}
if (multimodal)
@@ -777,12 +742,12 @@ struct llama_server_context
}
}
if (!found) {
LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
LOG_WRN("ERROR: Image with id: %i, not found.\n", img_id);
slot->images.clear();
return false;
}
} catch (const std::invalid_argument& e) {
LOG_TEE("Invalid image number id in prompt\n");
LOG_WRN("Invalid image number id in prompt\n");
slot->images.clear();
return false;
}
@@ -795,11 +760,11 @@ struct llama_server_context
}
}
if (slot->ctx_sampling != nullptr)
if (slot->smpl != nullptr)
{
llama_sampling_free(slot->ctx_sampling);
gpt_sampler_free(slot->smpl);
}
slot->ctx_sampling = llama_sampling_init(slot->sparams);
slot->smpl = gpt_sampler_init(model, slot->sparams);
slot->command = LOAD_PROMPT;
all_slots_are_idle = false;
@@ -847,7 +812,7 @@ struct llama_server_context
};
if (llama_decode(ctx, batch_view) != 0)
{
LOG_TEE("%s: llama_decode() failed\n", __func__);
LOG_WRN("%s: llama_decode() failed\n", __func__);
return;
}
}
@@ -859,7 +824,7 @@ struct llama_server_context
}
}
LOG_TEE("system prompt updated\n");
LOG_INF("system prompt updated\n");
system_need_update = false;
}
@@ -918,12 +883,6 @@ struct llama_server_context
slot.has_next_token = true;
if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
{
// we can change penalty_prompt_tokens because it is always created from scratch each request
slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
}
// check if there is incomplete UTF-8 character at the end
bool incomplete = false;
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
@@ -1040,7 +999,7 @@ struct llama_server_context
}
if (!llava_image_embed_make_with_clip_img(clp_ctx, params.cpuparams.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) {
LOG_TEE("Error processing the given image");
LOG_WRN("Error processing the given image");
return false;
}
@@ -1053,7 +1012,7 @@ struct llama_server_context
void send_error(task_server& task, const std::string &error)
{
LOG_TEE("task %i - error: %s\n", task.id, error.c_str());
LOG_WRN("task %i - error: %s\n", task.id, error.c_str());
task_result res;
res.id = task.id;
res.multitask_id = task.multitask_id;
@@ -1065,13 +1024,10 @@ struct llama_server_context
json get_formated_generation(server_slot &slot)
{
const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
eos_bias->second < 0.0f && std::isinf(eos_bias->second);
std::vector<std::string> samplers_sequence;
for (const auto &sampler_type : slot.sparams.samplers_sequence)
{
samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type));
std::vector<std::string> samplers;
samplers.reserve(slot.sparams.samplers.size());
for (const auto & sampler : slot.sparams.samplers) {
samplers.emplace_back(gpt_sampler_type_to_str(sampler));
}
return json {
@@ -1086,13 +1042,11 @@ struct llama_server_context
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
{"tfs_z", slot.sparams.tfs_z},
{"typical_p", slot.sparams.typical_p},
{"typical_p", slot.sparams.typ_p},
{"repeat_last_n", slot.sparams.penalty_last_n},
{"repeat_penalty", slot.sparams.penalty_repeat},
{"presence_penalty", slot.sparams.penalty_present},
{"frequency_penalty", slot.sparams.penalty_freq},
{"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
{"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
{"mirostat", slot.sparams.mirostat},
{"mirostat_tau", slot.sparams.mirostat_tau},
{"mirostat_eta", slot.sparams.mirostat_eta},
@@ -1100,13 +1054,13 @@ struct llama_server_context
{"stop", slot.params.antiprompt},
{"n_predict", slot.params.n_predict},
{"n_keep", params.n_keep},
{"ignore_eos", ignore_eos},
{"ignore_eos", slot.sparams.ignore_eos},
{"stream", slot.params.stream},
{"logit_bias", slot.sparams.logit_bias},
//{"logit_bias", slot.sparams.logit_bias},
{"n_probs", slot.sparams.n_probs},
{"min_keep", slot.sparams.min_keep},
{"grammar", slot.sparams.grammar},
{"samplers", samplers_sequence}
{"samplers", samplers}
};
}
@@ -1302,7 +1256,7 @@ struct llama_server_context
};
if (llama_decode(ctx, batch_view))
{
LOG_TEE("%s : failed to eval\n", __func__);
LOG_WRN("%s : failed to eval\n", __func__);
return false;
}
}
@@ -1330,7 +1284,7 @@ struct llama_server_context
};
if (llama_decode(ctx, batch_img))
{
LOG_TEE("%s : failed to eval image\n", __func__);
LOG_WRN("%s : failed to eval image\n", __func__);
return false;
}
slot.n_past += n_eval;
@@ -1735,7 +1689,7 @@ struct llama_server_context
if (!slot.params.cache_prompt)
{
llama_sampling_reset(slot.ctx_sampling);
gpt_sampler_reset(slot.smpl);
slot.n_past = 0;
slot.n_past_se = 0;
@@ -1747,7 +1701,7 @@ struct llama_server_context
// push the prompt into the sampling context (do not apply grammar)
for (auto &token : prompt_tokens)
{
llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
gpt_sampler_accept(slot.smpl, token, false);
}
slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
@@ -1890,10 +1844,10 @@ struct llama_server_context
const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
LOG_TEE("\n");
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
LOG_DBG("\n");
LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
LOG_DBG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
@@ -1903,7 +1857,7 @@ struct llama_server_context
slot.ga_i += slot.ga_w / slot.ga_n;
LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
LOG_DBG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
}
slot.n_past_se += n_tokens;
}
@@ -1928,11 +1882,11 @@ struct llama_server_context
if (n_batch == 1 || ret < 0)
{
// if you get here, it means the KV cache is full - try increasing it via the context size
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
LOG_WRN("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
return false;
}
LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
LOG_WRN("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
// retry with half the batch size to try to find a free slot in the KV cache
n_batch /= 2;
@@ -1957,9 +1911,9 @@ struct llama_server_context
}
completion_token_output result;
const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);
const llama_token id = gpt_sampler_sample(slot.smpl, ctx, slot.i_batch - i);
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
gpt_sampler_accept(slot.smpl, id, true);
slot.n_decoded += 1;
if (slot.n_decoded == 1)
@@ -1969,20 +1923,15 @@ struct llama_server_context
metrics.on_prompt_eval(slot);
}
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
result.tok = id;
const auto * cur_p = gpt_sampler_get_candidates(slot.smpl);
const int32_t n_probs = slot.sparams.n_probs;
if (slot.sparams.temp <= 0 && n_probs > 0)
{
// for llama_sample_token_greedy we need to sort candidates
llama_sample_softmax(ctx, &cur_p);
}
for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
{
result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
}
for (size_t i = 0; i < (size_t) slot.sparams.n_probs; ++i) {
result.probs.push_back({
cur_p->data[i].id,
i >= cur_p->size ? 0.0f : cur_p->data[i].p,
});
}
if (!process_token(result, slot))
{
@@ -2552,8 +2501,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
}
else if (arg == "--log-disable")
{
log_set_target(stdout);
LOG_DEBUG("logging to file is disabled.", {});
LOG_WARNING("DEPRECATED: --log-disable does nothing anymore", {});
}
else if (arg == "--slots-endpoint-disable")
{
@@ -2763,7 +2711,7 @@ int main(int argc, char **argv) {
#endif
#if SERVER_VERBOSE != 1
log_disable();
gpt_log_set_verbosity_thold(-1);
#endif
// own arguments required by this example
gpt_params params;