vbatts/llama.cpp
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llm : add Falcon support (#2717)

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* llama : refactor GGUF constants into static maps

* llama : check if model architecture is known

* llama : refactor llama_model_load_internal()

* gguf : add KV constant maps

* llm : read arch-specific KVs

* convert : add dummy scores + types

* falcon : load tensor data (CPU only)

* llama : fix loading progress bar

* llama : add arch member to llama_model

* falcon : CPU inference working

* falcon : support non-40B models

* falcon : minor

* llama : minor updates

ggml-ci

* convert-falcon-hf-to-gguf.py : fix special token mapping

* llama.cpp : llama default UNK token = id 0

* llama.cpp : fix bpe tokenizer

* llama.cpp : fix the fix of bpe tokenizer

* ggml : pass eps to ggml_norm

* metal : implement RoPE (mode = 2) + avoid ggml_repeat

* ggml : ggml_repeat always creates new tensor

* falcon : copy-paste self-attention from LLaMA

* metal : print extra compute pipeline info

* falcon : minor changes (still chasing the Metal problem)

* llama.cpp : fix linefeed token

* metal : fix GELU kernel numerical stability by using precise::tanh

* metal : temporary workaround for the concurrency optimization bug

* falcon : add CUDA offloading (#2739)

* llama : better model naming and size reporting

* llama : prep new tokenizer support

* llama : advanced BPE tokenizer based on ggllm.cpp imlpementation

* llama : remove oboslete comment

ggml-ci

* common : remove obsolete BPE API + disable test-tokenizer-1

* llama : revert BPE special-case in llama_byte_to_token()

* cuda : add TODOs for RoPE NeoX implementation

* llama : default special tokens based on vocab type

* perplexity : add log for start of tokenization

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov 2023-08-23 23:08:04 +03:00 • committed by GitHub
parent a192860cfe
commit cf658adc83
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18 changed files with 1596 additions and 668 deletions
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31
examples/perplexity/perplexity.cpp
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@ -28,7 +28,6 @@ std::vector<float> softmax(const std::vector<float>& logits) {
}
void perplexity_v2(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
@ -38,7 +37,13 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
return;
}
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
const bool add_bos = is_spm;
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
const int calc_chunk = params.n_ctx;
@ -86,7 +91,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
const auto token_org = tokens[batch_start];
// add BOS token for the first batch of each chunk
if (j == 0) {
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(ctx);
}
@ -136,7 +141,6 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
}
void perplexity(llama_context * ctx, const gpt_params & params) {
if (params.ppl_stride > 0) {
perplexity_v2(ctx, params);
return;
@ -146,7 +150,13 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
// BOS tokens will be added for each chunk before eval
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
const bool add_bos = is_spm;
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
const int n_chunk_max = tokens.size() / params.n_ctx;
@ -177,7 +187,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
const auto token_org = tokens[batch_start];
// add BOS token for the first batch of each chunk
if (j == 0) {
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(ctx);
}
@ -295,8 +305,10 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t hs_task_count = prompt_lines.size()/6;
fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
// This is needed as usual for LLaMA models
bool prepend_bos = true;
const bool add_bos = is_spm;
// Number of tasks to use when computing the score
if ( params.hellaswag_tasks < hs_task_count ) {
@ -352,14 +364,13 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
std::vector<float> tok_logits(n_vocab);
for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
// Tokenize the context to count tokens
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
size_t context_size = context_embd.size();
// Do the 1st ending
// In this case we include the context when evaluating
auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], prepend_bos);
auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
auto query_size = query_embd.size();
//printf("First query: %d\n",(int)query_size);