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https://github.com/ggerganov/llama.cpp.git
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Add test for MPT tokenization (#3728)
* Add test for MPT tokenization * Revert code motion * Remove unnecessary restriction in test case * Clarify logic in conversion
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@ -128,15 +128,22 @@ vocab_size = hparams["vocab_size"]
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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added_vocab = tokenizer.get_added_vocab()
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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for i in range(vocab_size):
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for i in range(vocab_size):
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tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
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if i not in reverse_vocab:
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scores.append(0.0) # dummy
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tokens.append(f"[PAD{i}]")
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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# NOTE: wouldn't we like to distinguish CONTROL tokens here?
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.USER_DEFINED)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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toktypes.append(gguf.TokenType.NORMAL)
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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gguf_writer.add_token_types(toktypes)
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gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
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17
llama.cpp
17
llama.cpp
@ -975,14 +975,15 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
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(void) tensor;
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(void) tensor;
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}
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}
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static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
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static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
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std::vector<char> result(8, 0);
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std::vector<char> result(8, 0);
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const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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if (n_tokens < 0) {
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if (n_tokens < 0) {
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result.resize(-n_tokens);
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result.resize(-n_tokens);
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int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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GGML_ASSERT(check == -n_tokens);
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GGML_ASSERT(check == -n_tokens);
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} else {
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}
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else {
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result.resize(n_tokens);
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result.resize(n_tokens);
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}
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}
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@ -1202,10 +1203,10 @@ struct llama_vocab {
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id special_eot_id = 32010;
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id special_eot_id = 32010;
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int find_bpe_rank(std::string token_left, std::string token_right) const {
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int find_bpe_rank(std::string token_left, std::string token_right) const {
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replace_all(token_left, " ", "\u0120");
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GGML_ASSERT(token_left.find(" ") == std::string::npos);
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replace_all(token_left, "\n", "\u010A");
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GGML_ASSERT(token_left.find("\n") == std::string::npos);
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replace_all(token_right, " ", "\u0120");
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GGML_ASSERT(token_right.find(" ") == std::string::npos);
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replace_all(token_right, "\n", "\u010A");
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GGML_ASSERT(token_right.find("\n") == std::string::npos);
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auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
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auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
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if (it == bpe_ranks.end()) {
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if (it == bpe_ranks.end()) {
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@ -7499,7 +7500,7 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
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for (size_t i = 0; i < candidates->size; ++i) {
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for (size_t i = 0; i < candidates->size; ++i) {
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const llama_token id = candidates->data[i].id;
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const llama_token id = candidates->data[i].id;
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const std::string piece = llama_token_to_str(ctx, id);
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const std::string piece = llama_token_to_piece(ctx, id);
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if (id == eos) {
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if (id == eos) {
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if (!allow_eos) {
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if (!allow_eos) {
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candidates->data[i].logit = -INFINITY;
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candidates->data[i].logit = -INFINITY;
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@ -7711,7 +7712,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
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GGML_ASSERT(false);
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GGML_ASSERT(false);
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}
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}
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const std::string piece = llama_token_to_str(ctx, token);
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const std::string piece = llama_token_to_piece(ctx, token);
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// Note terminating 0 in decoded string
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// Note terminating 0 in decoded string
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const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
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const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
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BIN
models/ggml-vocab-mpt.gguf
Normal file
BIN
models/ggml-vocab-mpt.gguf
Normal file
Binary file not shown.
@ -31,6 +31,7 @@ llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE
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llama_build_executable(test-tokenizer-1-bpe.cpp)
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llama_build_executable(test-tokenizer-1-bpe.cpp)
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llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
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llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
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llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
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llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
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llama_test_executable(test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
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llama_build_and_test_executable(test-grammar-parser.cpp)
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llama_build_and_test_executable(test-grammar-parser.cpp)
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llama_build_and_test_executable(test-llama-grammar.cpp)
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llama_build_and_test_executable(test-llama-grammar.cpp)
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llama_build_and_test_executable(test-grad0.cpp) # SLOW
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llama_build_and_test_executable(test-grad0.cpp) # SLOW
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