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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.dockerignore
#	.github/workflows/build.yml
#	.github/workflows/docker.yml
#	Makefile
#	README.md
#	examples/infill/infill.cpp
#	examples/perplexity/perplexity.cpp
#	examples/server/README.md
#	examples/speculative/speculative.cpp
#	flake.lock
#	ggml/src/CMakeLists.txt
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
#	tests/test-sampling.cpp
This commit is contained in:
Concedo
2024-09-27 11:21:28 +08:00
parent 6342b414ea 95bc82fbc0
commit ea55f69dc1
39 changed files with 2587 additions and 1564 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-impl.h
+2
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@@ -28,6 +28,8 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void *
#define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...) llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define LLAMA_LOG_DEBUG(...) llama_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
#define LLAMA_LOG_CONT(...) llama_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__)
//
// helpers
src/llama-sampling.cpp
+4 -3
View File
@@ -3,13 +3,14 @@
#include "llama-vocab.h"
#include "llama-grammar.h"
#include <cassert>
#include <algorithm>
#include <cstring>
#include <ctime>
#include <cassert>
#include <cfloat>
#include <chrono>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <ctime>
#include <numeric>
#include <random>
#include <unordered_map>
src/llama-vocab.cpp
+1 -5
View File
@@ -1826,11 +1826,7 @@ llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, lla
}
bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
return token != -1 && (
token == llama_token_eos_impl(vocab) ||
token == llama_token_eot_impl(vocab) ||
token == llama_token_eom_impl(vocab)
);
return token != -1 && vocab.special_eog_ids.count(token) > 0;
}
bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
src/llama-vocab.h
+9 -5
View File
@@ -6,6 +6,7 @@
#include <vector>
#include <unordered_map>
#include <map>
#include <set>
struct llama_vocab {
using id = llama_token;
@@ -49,12 +50,15 @@ struct llama_vocab {
id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token
id special_eom_id = -1;
// set of all tokens that cause "end of generation"
std::set<id> special_eog_ids;
// tokenizer flags
bool tokenizer_add_space_prefix = false;
bool tokenizer_add_bos = false;
bool tokenizer_add_eos = false;
bool tokenizer_ignore_merges = false;
bool tokenizer_clean_spaces = false; // clean_up_tokenization_spaces
bool tokenizer_add_space_prefix = false;
bool tokenizer_add_bos = false;
bool tokenizer_add_eos = false;
bool tokenizer_ignore_merges = false;
bool tokenizer_clean_spaces = false; // clean_up_tokenization_spaces
bool tokenizer_remove_extra_whitespaces = false;
bool tokenizer_escape_whitespaces = true;
bool tokenizer_treat_whitespace_as_suffix = false;
src/llama.cpp
+111 -27
View File
@@ -225,6 +225,7 @@ enum llm_arch {
LLM_ARCH_EXAONE,
LLM_ARCH_RWKV6,
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_UNKNOWN,
};
@@ -276,6 +277,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_EXAONE, "exaone" },
{ LLM_ARCH_RWKV6, "rwkv6" },
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -1477,6 +1479,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
@@ -1488,6 +1491,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_GRANITE_MOE,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@@ -2410,7 +2431,7 @@ struct llama_hparams {
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;
// Additional scale factors (Granite)
// Additional scale factors (Granite/Granite MoE)
float f_residual_scale = 0.0f;
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
@@ -3070,18 +3091,14 @@ struct llama_sbatch {
} else {
// simple split
if (batch->n_seq_id) {
for (size_t i = 0; i < length; ++i) {
ubatch.n_seq_id = batch->n_seq_id + seq.offset;
}
ubatch.n_seq_id = batch->n_seq_id + seq.offset;
} else {
for (size_t i = 0; i < length; ++i) {
ubatch.n_seq_id[ubatch.n_seqs + i] = 1;
}
}
if (batch->seq_id) {
for (size_t i = 0; i < length; ++i) {
ubatch.seq_id = batch->seq_id + seq.offset;
}
ubatch.seq_id = batch->seq_id + seq.offset;
} else {
for (size_t i = 0; i < length; ++i) {
ubatch.seq_id[ubatch.n_seqs + i] = &seq.all_seq_id;
@@ -6084,6 +6101,7 @@ static void llm_load_hparams(
}
} break;
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
@@ -6092,6 +6110,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_3B; break;
case 40: model.type = e_model::MODEL_3B; break;
// Add additional layer/vocab/etc checks here for other model sizes
default: model.type = e_model::MODEL_UNKNOWN;
@@ -6563,21 +6582,21 @@ static void llm_load_vocab(
// for now, we apply this workaround to find the EOT token based on its text
if (vocab.special_eot_id == -1) {
for (const auto & t : vocab.token_to_id) {
if (
if (false
// TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
// need to fix convert script
//vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
(t.first == "<|eot_id|>" ||
t.first == "<|im_end|>" ||
t.first == "<|end|>" ||
t.first == "<end_of_turn>" ||
t.first == "<|endoftext|>"
)
|| t.first == "<|eot_id|>"
|| t.first == "<|im_end|>"
|| t.first == "<|end|>"
|| t.first == "<end_of_turn>"
|| t.first == "<|endoftext|>"
|| t.first == "<EOT>"
) {
vocab.special_eot_id = t.second;
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
break;
@@ -6600,6 +6619,44 @@ static void llm_load_vocab(
}
}
}
// maintain a list of tokens that cause end-of-generation
// this is currently determined based on the token text, which is obviously not ideal
// ref: https://github.com/ggerganov/llama.cpp/issues/9606
vocab.special_eog_ids.clear();
for (const auto & t : vocab.token_to_id) {
if (false
|| t.first == "<|eot_id|>"
|| t.first == "<|im_end|>"
|| t.first == "<|end|>"
|| t.first == "<end_of_turn>"
|| t.first == "<|endoftext|>"
|| t.first == "<|eom_id|>"
|| t.first == "<EOT>"
) {
vocab.special_eog_ids.insert(t.second);
if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
__func__, t.first.c_str());
vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
}
}
}
if (vocab.special_eos_id != -1 && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
vocab.special_eog_ids.insert(vocab.special_eos_id);
LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
}
if (vocab.special_eot_id != -1 && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
vocab.special_eog_ids.insert(vocab.special_eot_id);
LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
}
if (vocab.special_eom_id != -1 && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
vocab.special_eog_ids.insert(vocab.special_eom_id);
LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
}
}
// build special tokens cache
@@ -6803,6 +6860,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
if (vocab.special_eom_id != -1) { LLAMA_LOG_INFO( "%s: EOM token = %d '%s'\n", __func__, vocab.special_eom_id, vocab.id_to_token[vocab.special_eom_id].text.c_str() ); }
for (const auto & id : vocab.special_eog_ids) {
LLAMA_LOG_INFO( "%s: EOG token = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
}
LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
@@ -6821,7 +6883,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
}
if (model.arch == LLM_ARCH_GRANITE) {
if (model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@@ -7004,6 +7066,7 @@ static bool llm_load_tensors(
case LLM_ARCH_REFACT:
case LLM_ARCH_MINICPM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@@ -9993,17 +10056,36 @@ struct llm_build_context {
const int64_t n_head_kv = hparams.n_head_kv(il);
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
struct ggml_tensor * rope_factors = build_rope_factors(il);
struct ggml_tensor * tmp =
// we rotate only the first n_rot dimensions
ggml_rope_ext_inplace(ctx0,
ggml_view_3d(ctx0, kv_self.k_l[il],
n_embd_head_k, n_head_kv, n_ctx,
ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
0),
struct ggml_tensor * k =
ggml_view_3d(ctx0, kv_self.k_l[il],
n_embd_head_k, n_head_kv, n_ctx,
ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
0);
struct ggml_tensor * tmp;
if (ggml_is_quantized(k->type)) {
// dequantize to f32 -> RoPE -> quantize back
tmp = ggml_cast(ctx0, k, GGML_TYPE_F32);
cb(tmp, "K_f32", il);
for (auto * backend : lctx.backends) {
// Figure out which backend KV cache belongs to
if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft)) {
ggml_backend_sched_set_tensor_backend(lctx.sched, tmp, backend);
break;
}
}
tmp = ggml_rope_ext_inplace(ctx0, tmp,
lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(tmp, "K_shifted_f32", il);
tmp = ggml_cpy(ctx0, tmp, k);
} else {
// we rotate only the first n_rot dimensions
tmp = ggml_rope_ext_inplace(ctx0, k,
lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
}
cb(tmp, "K_shifted", il);
ggml_build_forward_expand(gf, tmp);
}
@@ -15949,6 +16031,7 @@ static struct ggml_cgraph * llama_build_graph(
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
result = llm.build_llama();
} break;
@@ -18719,9 +18802,9 @@ struct llama_model * llama_load_model_from_file(
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
*cur_percentage_p = percentage;
LLAMA_LOG(".");
LLAMA_LOG_CONT(".");
if (percentage >= 100) {
LLAMA_LOG("\n");
LLAMA_LOG_CONT("\n");
}
}
return true;
@@ -19236,6 +19319,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2