From 7c3f55c10051c634546247387c5c359c9d499360 Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Sat, 10 Aug 2024 11:43:26 +0200 Subject: [PATCH 01/28] Add support for encoder-only T5 models (#8900) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * gguf-py : add T5ENCODER model architecture * common : call llama_decode() during warmup only if the model has decoder * convert-hf : add T5EncoderModel * llama : add llama_model_has_decoder() API function * llama : split build_t5() into build_t5_encoder() and build_t5_decoder() * llama : add support for LLM_ARCH_T5ENCODER * llama-embedding : add support for LLAMA_POOLING_TYPE_NONE * llama-embedding : add support for encoder-only models --------- Co-authored-by: Stanisław Szymczyk --- common/common.cpp | 4 +- convert_hf_to_gguf.py | 139 +++++++ examples/embedding/embedding.cpp | 140 ++++--- gguf-py/gguf/constants.py | 17 + include/llama.h | 3 + src/llama.cpp | 628 +++++++++++++++++++------------ 6 files changed, 649 insertions(+), 282 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 560e20d080d0f..d3d896115ae36 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2156,7 +2156,9 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) { tmp.clear(); tmp.push_back(decoder_start_token_id); } - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + if (llama_model_has_decoder(model)) { + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + } llama_kv_cache_clear(lctx); llama_synchronize(lctx); llama_reset_timings(lctx); diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 7136db440644b..550dd5cfda99f 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -3324,6 +3324,145 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] +@Model.register("T5EncoderModel") +class T5EncoderModel(Model): + model_arch = gguf.MODEL_ARCH.T5ENCODER + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.shared_token_embeddings_found = False + + def set_vocab(self): + # to avoid TypeError: Descriptors cannot be created directly + # exception when importing sentencepiece_model_pb2 + os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + # many older models use spiece.model tokenizer model filename + if not tokenizer_path.is_file(): + tokenizer_path = self.dir_model / 'spiece.model' + + if not tokenizer_path.is_file(): + raise FileNotFoundError(f"File not found: {tokenizer_path}") + + sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue] + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + + # some models like Pile-T5 family use BPE tokenizer instead of Unigram + if sentencepiece_model.trainer_spec.model_type == 2: # BPE + # assure the tokenizer model file name is correct + assert tokenizer_path.name == 'tokenizer.model' + return self._set_vocab_sentencepiece() + else: + assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM + + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces + precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap + + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + for key in added_tokens_json: + token_id = added_tokens_json[key] + if token_id >= vocab_size: + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") + for i in range(1, pad_count + 1): + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + + self.gguf_writer.add_tokenizer_model("t5") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) + if precompiled_charsmap: + self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + self.gguf_writer.add_add_bos_token(False) + self.gguf_writer.add_add_eos_token(True) + + def set_gguf_parameters(self): + if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None: + logger.warning("Couldn't find context length in config.json, assuming default value of 512") + n_ctx = 512 + self.gguf_writer.add_context_length(n_ctx) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"]) + self.gguf_writer.add_block_count(self.hparams["num_layers"]) + self.gguf_writer.add_head_count(self.hparams["num_heads"]) + self.gguf_writer.add_key_length(self.hparams["d_kv"]) + self.gguf_writer.add_value_length(self.hparams["d_kv"]) + self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) + self.gguf_writer.add_file_type(self.ftype) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight", + # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored + # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder + # and decoder and ignore the remaining ones. + if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]: + if not self.shared_token_embeddings_found: + name = "shared.weight" + self.shared_token_embeddings_found = True + else: + logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.") + return [] + + return [(self.map_tensor_name(name), data_torch)] + + @Model.register("JAISLMHeadModel") class JaisModel(Model): model_arch = gguf.MODEL_ARCH.JAIS diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index cd7b448a619fa..b05aa006e7da5 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -31,13 +31,24 @@ static void batch_add_seq(llama_batch & batch, const std::vector & toke } static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) { + const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); + const struct llama_model * model = llama_get_model(ctx); + // clear previous kv_cache values (irrelevant for embeddings) llama_kv_cache_clear(ctx); // run model fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq); - if (llama_decode(ctx, batch) < 0) { - fprintf(stderr, "%s : failed to decode\n", __func__); + if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) { + // encoder-only model + if (llama_encode(ctx, batch) < 0) { + fprintf(stderr, "%s : failed to encode\n", __func__); + } + } else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) { + // decoder-only model + if (llama_decode(ctx, batch) < 0) { + fprintf(stderr, "%s : failed to decode\n", __func__); + } } for (int i = 0; i < batch.n_tokens; i++) { @@ -45,11 +56,22 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu continue; } - // try to get sequence embeddings - supported only when pooling_type is not NONE - const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); - GGML_ASSERT(embd != NULL && "failed to get sequence embeddings"); + const float * embd = nullptr; + int embd_pos = 0; + + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + // try to get token embeddings + embd = llama_get_embeddings_ith(ctx, i); + embd_pos = i; + GGML_ASSERT(embd != NULL && "failed to get token embeddings"); + } else { + // try to get sequence embeddings - supported only when pooling_type is not NONE + embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + embd_pos = batch.seq_id[i][0]; + GGML_ASSERT(embd != NULL && "failed to get sequence embeddings"); + } - float * out = output + batch.seq_id[i][0] * n_embd; + float * out = output + embd_pos * n_embd; llama_embd_normalize(embd, out, n_embd, embd_norm); } } @@ -93,8 +115,9 @@ int main(int argc, char ** argv) { const int n_ctx = llama_n_ctx(ctx); const enum llama_pooling_type pooling_type = llama_pooling_type(ctx); - if (pooling_type == LLAMA_POOLING_TYPE_NONE) { - fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__); + + if (llama_model_has_encoder(model) && llama_model_has_decoder(model)) { + fprintf(stderr, "%s: error: computing embeddings in encoder-decoder models is not supported\n", __func__); return 1; } @@ -153,13 +176,23 @@ int main(int argc, char ** argv) { const int n_prompts = prompts.size(); struct llama_batch batch = llama_batch_init(n_batch, 0, 1); + // count number of embeddings + int n_embd_count = 0; + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + for (int k = 0; k < n_prompts; k++) { + n_embd_count += inputs[k].size(); + } + } else { + n_embd_count = n_prompts; + } + // allocate output const int n_embd = llama_n_embd(model); - std::vector embeddings(n_prompts * n_embd, 0); + std::vector embeddings(n_embd_count * n_embd, 0); float * emb = embeddings.data(); // break into batches - int p = 0; // number of prompts processed already + int e = 0; // number of embeddings already stored int s = 0; // number of prompts in current batch for (int k = 0; k < n_prompts; k++) { // clamp to n_batch tokens @@ -169,11 +202,11 @@ int main(int argc, char ** argv) { // encode if at capacity if (batch.n_tokens + n_toks > n_batch) { - float * out = emb + p * n_embd; + float * out = emb + e * n_embd; batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); - llama_batch_clear(batch); - p += s; + e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s; s = 0; + llama_batch_clear(batch); } // add to batch @@ -182,39 +215,62 @@ int main(int argc, char ** argv) { } // final batch - float * out = emb + p * n_embd; + float * out = emb + e * n_embd; batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); if (params.embd_out.empty()) { - // print the first part of the embeddings or for a single prompt, the full embedding fprintf(stdout, "\n"); - for (int j = 0; j < n_prompts; j++) { - fprintf(stdout, "embedding %d: ", j); - for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) { - if (params.embd_normalize == 0) { - fprintf(stdout, "%6.0f ", emb[j * n_embd + i]); - } else { - fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); + + if (pooling_type == LLAMA_POOLING_TYPE_NONE) { + for (int j = 0; j < n_embd_count; j++) { + fprintf(stdout, "embedding %d: ", j); + for (int i = 0; i < std::min(3, n_embd); i++) { + if (params.embd_normalize == 0) { + fprintf(stdout, "%6.0f ", emb[j * n_embd + i]); + } else { + fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); + } + } + fprintf(stdout, " ... "); + for (int i = n_embd - 3; i < n_embd; i++) { + if (params.embd_normalize == 0) { + fprintf(stdout, "%6.0f ", emb[j * n_embd + i]); + } else { + fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); + } } + fprintf(stdout, "\n"); } - fprintf(stdout, "\n"); - } - - // print cosine similarity matrix - if (n_prompts > 1) { - fprintf(stdout, "\n"); - printf("cosine similarity matrix:\n\n"); - for (int i = 0; i < n_prompts; i++) { - fprintf(stdout, "%6.6s ", prompts[i].c_str()); + } else { + // print the first part of the embeddings or for a single prompt, the full embedding + for (int j = 0; j < n_prompts; j++) { + fprintf(stdout, "embedding %d: ", j); + for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) { + if (params.embd_normalize == 0) { + fprintf(stdout, "%6.0f ", emb[j * n_embd + i]); + } else { + fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); + } + } + fprintf(stdout, "\n"); } - fprintf(stdout, "\n"); - for (int i = 0; i < n_prompts; i++) { - for (int j = 0; j < n_prompts; j++) { - float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); - fprintf(stdout, "%6.2f ", sim); + + // print cosine similarity matrix + if (n_prompts > 1) { + fprintf(stdout, "\n"); + printf("cosine similarity matrix:\n\n"); + for (int i = 0; i < n_prompts; i++) { + fprintf(stdout, "%6.6s ", prompts[i].c_str()); } - fprintf(stdout, "%1.10s", prompts[i].c_str()); fprintf(stdout, "\n"); + for (int i = 0; i < n_prompts; i++) { + for (int j = 0; j < n_prompts; j++) { + float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + fprintf(stdout, "%6.2f ", sim); + } + fprintf(stdout, "%1.10s", prompts[i].c_str()); + fprintf(stdout, "\n"); + } } } } @@ -233,23 +289,23 @@ int main(int argc, char ** argv) { } fprintf(stdout, notArray ? "]\n }" : "]"); j++; - if (j < n_prompts) fprintf(stdout, notArray ? ",\n" : ","); else break; + if (j < n_embd_count) fprintf(stdout, notArray ? ",\n" : ","); else break; } fprintf(stdout, notArray ? "\n ]" : "]\n"); if (params.embd_out == "json+" && n_prompts > 1) { fprintf(stdout, ",\n \"cosineSimilarity\": [\n"); - for (int i = 0;;) { // at least two iteration (n_prompts > 1) + for (int i = 0;;) { // at least two iteration (n_embd_count > 1) fprintf(stdout, " ["); - for (int j = 0;;) { // at least two iteration (n_prompts > 1) + for (int j = 0;;) { // at least two iteration (n_embd_count > 1) float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); fprintf(stdout, "%6.2f", sim); j++; - if (j < n_prompts) fprintf(stdout, ", "); else break; + if (j < n_embd_count) fprintf(stdout, ", "); else break; } fprintf(stdout, " ]"); i++; - if (i < n_prompts) fprintf(stdout, ",\n"); else break; + if (i < n_embd_count) fprintf(stdout, ",\n"); else break; } fprintf(stdout, "\n ]"); } diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 89efe0c800964..f63ec450a4e09 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -217,6 +217,7 @@ class MODEL_ARCH(IntEnum): CHATGLM = auto() BITNET = auto() T5 = auto() + T5ENCODER = auto() JAIS = auto() @@ -344,6 +345,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.CHATGLM: "chatglm", MODEL_ARCH.BITNET: "bitnet", MODEL_ARCH.T5: "t5", + MODEL_ARCH.T5ENCODER: "t5encoder", MODEL_ARCH.JAIS: "jais", } @@ -1036,6 +1038,21 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ENC_FFN_UP, MODEL_TENSOR.ENC_OUTPUT_NORM, ], + MODEL_ARCH.T5ENCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ENC_ATTN_NORM, + MODEL_TENSOR.ENC_ATTN_Q, + MODEL_TENSOR.ENC_ATTN_K, + MODEL_TENSOR.ENC_ATTN_V, + MODEL_TENSOR.ENC_ATTN_OUT, + MODEL_TENSOR.ENC_ATTN_REL_B, + MODEL_TENSOR.ENC_FFN_NORM, + MODEL_TENSOR.ENC_FFN_GATE, + MODEL_TENSOR.ENC_FFN_DOWN, + MODEL_TENSOR.ENC_FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + ], MODEL_ARCH.JAIS: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/include/llama.h b/include/llama.h index 66c266298e86f..ce07f4fac8f10 100644 --- a/include/llama.h +++ b/include/llama.h @@ -504,6 +504,9 @@ extern "C" { // Returns true if the model contains an encoder that requires llama_encode() call LLAMA_API bool llama_model_has_encoder(const struct llama_model * model); + // Returns true if the model contains a decoder that requires llama_decode() call + LLAMA_API bool llama_model_has_decoder(const struct llama_model * model); + // For encoder-decoder models, this function returns id of the token that must be provided // to the decoder to start generating output sequence. For other models, it returns -1. LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model); diff --git a/src/llama.cpp b/src/llama.cpp index 97dd1b3fea4b9..9c4f2aa721640 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -208,6 +208,7 @@ enum llm_arch { LLM_ARCH_CHATGLM, LLM_ARCH_BITNET, LLM_ARCH_T5, + LLM_ARCH_T5ENCODER, LLM_ARCH_JAIS, LLM_ARCH_UNKNOWN, }; @@ -252,6 +253,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_CHATGLM, "chatglm" }, { LLM_ARCH_BITNET, "bitnet" }, { LLM_ARCH_T5, "t5" }, + { LLM_ARCH_T5ENCODER, "t5encoder" }, { LLM_ARCH_JAIS, "jais" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -1261,6 +1263,24 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_T5ENCODER, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" }, + { LLM_TENSOR_ENC_ATTN_NORM, "enc.blk.%d.attn_norm" }, + { LLM_TENSOR_ENC_ATTN_Q, "enc.blk.%d.attn_q" }, + { LLM_TENSOR_ENC_ATTN_K, "enc.blk.%d.attn_k" }, + { LLM_TENSOR_ENC_ATTN_V, "enc.blk.%d.attn_v" }, + { LLM_TENSOR_ENC_ATTN_OUT, "enc.blk.%d.attn_o" }, + { LLM_TENSOR_ENC_ATTN_REL_B, "enc.blk.%d.attn_rel_b" }, + { LLM_TENSOR_ENC_FFN_NORM, "enc.blk.%d.ffn_norm" }, + { LLM_TENSOR_ENC_FFN_GATE, "enc.blk.%d.ffn_gate" }, + { LLM_TENSOR_ENC_FFN_DOWN, "enc.blk.%d.ffn_down" }, + { LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_JAIS, { @@ -5187,6 +5207,12 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_T5ENCODER: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts); + model.type = e_model::MODEL_UNKNOWN; + } break; case LLM_ARCH_JAIS: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -7421,6 +7447,42 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_T5ENCODER: + { + const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts; + + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}); + layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}); + layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}); + + layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; case LLM_ARCH_JAIS: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -13135,7 +13197,7 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_t5() { + struct ggml_cgraph * build_t5_encoder() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); // mutable variable, needed during the last layer of the computation to skip unused tokens @@ -13150,303 +13212,323 @@ struct llm_build_context { inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - if (lctx.is_encoding) { - struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false); + GGML_ASSERT(lctx.is_encoding); + struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false); - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false); - // norm - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm_enc, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "attn_norm", il); + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; - // self-attention - { - struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_enc, cur); - cb(Qcur, "Qcur", il); + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm_enc, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); - struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_enc, cur); - cb(Kcur, "Kcur", il); + // self-attention + { + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_enc, cur); + cb(Qcur, "Qcur", il); - struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_enc, cur); - cb(Vcur, "Vcur", il); + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_enc, cur); + cb(Kcur, "Kcur", il); - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_enc, cur); + cb(Vcur, "Vcur", il); - struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); - cb(kq, "kq", il); + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); - struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc; - struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b); - struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias); - cb(kq_b, "kq_b", il); + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); - kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias); - cb(kq, "kq_soft_max_ext", il); + struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc; + struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b); + struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias); + cb(kq_b, "kq_b", il); - struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); - cb(v, "v", il); + kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); - struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq); - cb(kqv, "kqv", il); + struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); + cb(v, "v", il); - struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); - cb(kqv_merged, "kqv_merged", il); + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq); + cb(kqv, "kqv", il); - cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); - cb(cur, "kqv_merged_cont", il); + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); - ggml_build_forward_expand(gf, cur); + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); - cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_enc, cur); - cb(cur, "kqv_out", il); - } + ggml_build_forward_expand(gf, cur); - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - n_tokens = n_outputs; - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); - } + cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_enc, cur); + cb(cur, "kqv_out", il); + } - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm_enc, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "ffn_norm", il); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); - // T5 uses relu, flan-T5 uses gelu-gated - cur = llm_build_ffn(ctx0, lctx, cur, - model.layers[il].ffn_up_enc, NULL, NULL, - model.layers[il].ffn_gate_enc, NULL, NULL, - model.layers[il].ffn_down_enc, NULL, NULL, - NULL, - model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU, - model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ, - cb, il); - cb(cur, "ffn_out", il); - } + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm_enc, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); - cur = ggml_add(ctx0, cur, ffn_inp); + // T5 uses relu, flan-T5 uses gelu-gated + cur = llm_build_ffn(ctx0, lctx, cur, + model.layers[il].ffn_up_enc, NULL, NULL, + model.layers[il].ffn_gate_enc, NULL, NULL, + model.layers[il].ffn_down_enc, NULL, NULL, + NULL, + model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU, + model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ, + cb, il); cb(cur, "ffn_out", il); + } - ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); - if (layer_dir != nullptr) { - cur = ggml_add(ctx0, cur, layer_dir); - } - cb(cur, "l_out", il); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); - // input for next layer - inpL = cur; + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); } + cb(cur, "l_out", il); - cur = inpL; - cb(cur, "result_embd", -1); + // input for next layer + inpL = cur; + } - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm_enc, NULL, - LLM_NORM_RMS, cb, -1); - cb(cur, "result_norm", -1); - } else { - GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first"); + cur = inpL; + cb(cur, "result_embd", -1); - struct ggml_tensor * embd_enc = llm_build_inp_embd_enc(); - struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true); + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm_enc, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); - struct ggml_tensor * KQ_mask_dec = build_inp_KQ_mask(); - struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross(); + ggml_build_forward_expand(gf, cur); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; + return gf; + } - // norm - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "attn_norm", il); + struct ggml_cgraph * build_t5_decoder() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); - // self-attention - { - struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; - struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); + struct ggml_tensor * cur; + struct ggml_tensor * inpL; - llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - struct ggml_tensor * k = - ggml_view_3d(ctx0, kv_self.k_l[il], - n_embd_head_k, n_kv, n_head_kv, - ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), - 0); - cb(k, "k", il); + GGML_ASSERT(!lctx.is_encoding); + GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first"); - struct ggml_tensor * v = - ggml_view_3d(ctx0, kv_self.v_l[il], - n_kv, n_embd_head_v, n_head_kv, - ggml_element_size(kv_self.v_l[il])*n_ctx, - ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v, - 0); - cb(v, "v", il); + struct ggml_tensor * embd_enc = llm_build_inp_embd_enc(); + struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true); - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + struct ggml_tensor * KQ_mask_dec = build_inp_KQ_mask(); + struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross(); - struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; - struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); - cb(kq, "kq", il); + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); - struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b; - struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b); - struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias); - cb(kq_b, "kq_b", il); + // self-attention + { + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); - kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias); - cb(kq, "kq_soft_max_ext", il); + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); - struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); - cb(kqv, "kqv", il); + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); - struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); - cb(kqv_merged, "kqv_merged", il); + llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il); - cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); - cb(cur, "kqv_merged_cont", il); + struct ggml_tensor * k = + ggml_view_3d(ctx0, kv_self.k_l[il], + n_embd_head_k, n_kv, n_head_kv, + ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), + ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), + 0); + cb(k, "k", il); - ggml_build_forward_expand(gf, cur); + struct ggml_tensor * v = + ggml_view_3d(ctx0, kv_self.v_l[il], + n_kv, n_embd_head_v, n_head_kv, + ggml_element_size(kv_self.v_l[il])*n_ctx, + ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v, + 0); + cb(v, "v", il); - cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur); - cb(cur, "kqv_out", il); - } + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - cur = ggml_add(ctx0, cur, inpSA); - cb(cur, "cross_inp", il); + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - struct ggml_tensor * inpCA = cur; + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); - // norm - cur = llm_build_norm(ctx0, cur, hparams, - model.layers[il].attn_norm_cross, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "attn_norm_cross", il); + struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b; + struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b); + struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias); + cb(kq_b, "kq_b", il); - // cross-attention - { - struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_cross, cur); - cb(Qcur, "Qcur", il); + kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); - struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_cross, embd_enc); - cb(Kcur, "Kcur", il); + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); + cb(kqv, "kqv", il); - struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_cross, embd_enc); - cb(Vcur, "Vcur", il); + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); - Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc); + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); - struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); + ggml_build_forward_expand(gf, cur); - struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); - cb(kq, "kq", il); + cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur); + cb(cur, "kqv_out", il); + } - kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias); - cb(kq, "kq_soft_max_ext", il); + cur = ggml_add(ctx0, cur, inpSA); + cb(cur, "cross_inp", il); - struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc))); - cb(v, "v", il); + struct ggml_tensor * inpCA = cur; - struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq); - cb(kqv, "kqv", il); + // norm + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].attn_norm_cross, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm_cross", il); - struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); - cb(kqv_merged, "kqv_merged", il); + // cross-attention + { + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_cross, cur); + cb(Qcur, "Qcur", il); - cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); - cb(cur, "kqv_merged_cont", il); + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_cross, embd_enc); + cb(Kcur, "Kcur", il); - ggml_build_forward_expand(gf, cur); + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_cross, embd_enc); + cb(Vcur, "Vcur", il); - cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_cross, cur); - cb(cur, "kqv_out", il); - } + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc); - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - n_tokens = n_outputs; - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); - inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids); - } + struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); + struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3)); - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA); - cb(ffn_inp, "ffn_inp", il); + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + cb(kq, "kq", il); - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "ffn_norm", il); + kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); - // T5 uses relu, flan-T5 uses gelu-gated - cur = llm_build_ffn(ctx0, lctx, cur, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, - NULL, - model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU, - model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ, - cb, il); - cb(cur, "ffn_out", il); - } + struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc))); + cb(v, "v", il); - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "ffn_out", il); + struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq); + cb(kqv, "kqv", il); - ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); - if (layer_dir != nullptr) { - cur = ggml_add(ctx0, cur, layer_dir); - } - cb(cur, "l_out", il); + struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); - // input for next layer - inpL = cur; + cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens); + cb(cur, "kqv_merged_cont", il); + + ggml_build_forward_expand(gf, cur); + + cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_cross, cur); + cb(cur, "kqv_out", il); } - cur = inpL; - cb(cur, "result_embd", -1); + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids); + } - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, NULL, - LLM_NORM_RMS, cb, -1); - cb(cur, "result_norm", -1); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA); + cb(ffn_inp, "ffn_inp", il); - // lm_head - cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); - cb(cur, "result_output", -1); + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + // T5 uses relu, flan-T5 uses gelu-gated + cur = llm_build_ffn(ctx0, lctx, cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU, + model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ, + cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; } + cur = inpL; + cb(cur, "result_embd", -1); + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + cb(cur, "result_output", -1); + ggml_build_forward_expand(gf, cur); return gf; @@ -13898,7 +13980,15 @@ static struct ggml_cgraph * llama_build_graph( } break; case LLM_ARCH_T5: { - result = llm.build_t5(); + if (lctx.is_encoding) { + result = llm.build_t5_encoder(); + } else { + result = llm.build_t5_decoder(); + } + } break; + case LLM_ARCH_T5ENCODER: + { + result = llm.build_t5_encoder(); } break; case LLM_ARCH_JAIS: { @@ -14346,7 +14436,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { // TODO: use a per-batch flag for logits presence instead const bool has_logits = !cparams.embeddings; - const bool has_embd = lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE)); + const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE); const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0; const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0; @@ -14829,9 +14919,24 @@ static int llama_encode_internal( ggml_cgraph * gf = llama_build_graph(lctx, batch, false); // the output embeddings after the final encoder normalization - struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 1]; + struct ggml_tensor * embd = nullptr; - GGML_ASSERT(strcmp(embd->name, "result_norm") == 0); + // there are two cases here + if (llama_model_has_decoder(&lctx.model)) { + // first case is an encoder-decoder T5 model where embeddings are passed to decoder + embd = gf->nodes[gf->n_nodes - 1]; + GGML_ASSERT(strcmp(embd->name, "result_norm") == 0 && "missing result_output tensor"); + } else { + // second case is an encoder-only T5 model + if (cparams.embeddings) { + // only output embeddings if required + embd = gf->nodes[gf->n_nodes - 1]; + if (strcmp(embd->name, "result_embd_pooled") != 0) { + embd = gf->nodes[gf->n_nodes - 2]; + } + GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor"); + } + } ggml_backend_sched_alloc_graph(lctx.sched, gf); @@ -14844,20 +14949,54 @@ static int llama_encode_internal( ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); GGML_ASSERT(backend_embd != nullptr); - // extract token embeddings - GGML_ASSERT(lctx.embd != nullptr); + if (llama_model_has_decoder(&lctx.model)) { + lctx.embd_enc.resize(n_tokens*n_embd); + float * embd_out = lctx.embd_enc.data(); - lctx.embd_enc.resize(n_tokens*n_embd); - float * embd_out = lctx.embd_enc.data(); + ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float)); - ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float)); + // remember the sequence ids used during the encoding - needed for cross attention later + lctx.seq_ids_enc.resize(n_tokens); + for (uint32_t i = 0; i < n_tokens; i++) { + for (int s = 0; s < batch.n_seq_id[i]; s++) { + llama_seq_id seq_id = batch.seq_id[i][s]; + lctx.seq_ids_enc[i].insert(seq_id); + } + } + } else { + GGML_ASSERT(lctx.embd != nullptr); - // remember the sequence ids used during the encoding - needed for cross attention later - lctx.seq_ids_enc.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - for (int s = 0; s < batch.n_seq_id[i]; s++) { - llama_seq_id seq_id = batch.seq_id[i][s]; - lctx.seq_ids_enc[i].insert(seq_id); + switch (cparams.pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + // extract token embeddings + GGML_ASSERT(lctx.embd != nullptr); + float * embd_out = lctx.embd; + + GGML_ASSERT(n_tokens*n_embd <= (int64_t) lctx.embd_size); + ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float)); + } break; + case LLAMA_POOLING_TYPE_MEAN: + case LLAMA_POOLING_TYPE_CLS: + case LLAMA_POOLING_TYPE_LAST: + { + // extract sequence embeddings + auto & embd_seq_out = lctx.embd_seq; + embd_seq_out.clear(); + + for (uint32_t i = 0; i < n_tokens; i++) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { + continue; + } + embd_seq_out[seq_id].resize(n_embd); + ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float)); + } + } break; + case LLAMA_POOLING_TYPE_UNSPECIFIED: + { + GGML_ABORT("unknown pooling type"); + } } } } @@ -16567,6 +16706,8 @@ struct llama_context * llama_new_context_with_model( ctx->sampling.rng = std::mt19937(params.seed); ctx->logits_all = params.logits_all; + // build worst-case graph for encoder if a model contains encoder + ctx->is_encoding = llama_model_has_encoder(model); uint32_t kv_size = cparams.n_ctx; ggml_type type_k = params.type_k; @@ -16881,6 +17022,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_MAMBA: case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_T5: + case LLM_ARCH_T5ENCODER: case LLM_ARCH_JAIS: return LLAMA_ROPE_TYPE_NONE; @@ -17028,8 +17170,16 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch bool llama_model_has_encoder(const struct llama_model * model) { switch (model->arch) { - case LLM_ARCH_T5: return true; - default: return false; + case LLM_ARCH_T5: return true; + case LLM_ARCH_T5ENCODER: return true; + default: return false; + } +} + +bool llama_model_has_decoder(const struct llama_model * model) { + switch (model->arch) { + case LLM_ARCH_T5ENCODER: return false; + default: return true; } } From 7eb23840ed0f388e10c4bbc4d65802fdfb977b40 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 10 Aug 2024 13:04:40 +0200 Subject: [PATCH 02/28] llama : default n_swa for phi-3 (#8931) * default n_swa for phi-3 * fix * double check swa --- src/llama.cpp | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/src/llama.cpp b/src/llama.cpp index 9c4f2aa721640..e0fe8013b0ad2 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -4901,7 +4901,6 @@ static void llm_load_hparams( } break; case LLM_ARCH_PHI3: { - ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { @@ -4910,6 +4909,22 @@ static void llm_load_hparams( case 40: model.type = e_model::MODEL_14B; break; default: model.type = e_model::MODEL_UNKNOWN; } + + // for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931 + if ((hparams.n_layer == 32 || hparams.n_layer == 40) && hparams.n_ctx_train == 4096) { + // default value for Phi-3-mini-4k-instruct and Phi-3-medium-4k-instruct + hparams.n_swa = 2047; + } else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) { + // default value for Phi-3-mini-128k-instruct + hparams.n_swa = 262144; + } else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) { + // default value for Phi-3-medium-128k-instruct + hparams.n_swa = 131072; + } + bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); + if (!found_swa && hparams.n_swa == 0) { + throw std::runtime_error("invalid value for sliding_window"); + } } break; case LLM_ARCH_PLAMO: { From 6e02327e8b7837358e0406bf90a4632e18e27846 Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 10 Aug 2024 15:42:10 +0200 Subject: [PATCH 03/28] metal : fix uninitialized abort_callback (#8968) --- ggml/src/ggml-metal.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m index 9fc08ab3aa5cc..aad189430ab0b 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal.m @@ -310,7 +310,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]); // Configure context - struct ggml_backend_metal_context * ctx = malloc(sizeof(struct ggml_backend_metal_context)); + struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context)); ctx->device = device; ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); ctx->queue = [ctx->device newCommandQueue]; From 7c5bfd57f83fd3630934cfa70892aa4022d3faf7 Mon Sep 17 00:00:00 2001 From: Markus Tavenrath Date: Sun, 11 Aug 2024 10:09:09 +0200 Subject: [PATCH 04/28] Optimize Vulkan backend for better CPU performance and less GPU synchronization overhead. (#8943) * Optimize Vulkan backend for better CPU performance and less GPU synchronization overhead. - Allocation overhead for the temporary std::vectors was easily detectable with a sampling profiler and simple to remove. - ggml_vk_sync_buffer introduce a full pipeline sync which has a significant cost on the GPU side, sometimes larger than the actual kernel execution. Adding only barriers for shader read/writes and transfers seems to be sufficient looking at the code which either launches compute kernels or copies tensors. * Fix small typo --------- Co-authored-by: 0cc4m --- ggml/src/ggml-vulkan.cpp | 65 +++++++++++++++++++++------------------- 1 file changed, 34 insertions(+), 31 deletions(-) diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index b0f36a513f84b..86732837254f0 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -268,6 +268,10 @@ struct vk_subbuffer { vk_buffer buffer; uint64_t offset; uint64_t size; + + operator vk::DescriptorBufferInfo() const { + return { buffer->buffer, offset, size }; + } }; struct vk_semaphore { @@ -1063,13 +1067,14 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { static void ggml_vk_sync_buffers(vk_context& ctx) { VK_LOG_DEBUG("ggml_vk_sync_buffers()"); - const std::vector mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } }; - ctx->s->buffer.pipelineBarrier( ctx->q->stage_flags, ctx->q->stage_flags, {}, - mem_barriers, + { { + {vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite}, + {vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite} + } }, {}, {} ); @@ -2420,28 +2425,23 @@ static vk_submission ggml_vk_begin_submission(vk_device& device, vk_queue& q, bo return s; } -static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { + + +static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& subctx, vk_pipeline& pipeline, std::initializer_list const& descriptor_buffer_infos, size_t push_constant_size, const void* push_constants, std::array elements) { const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {"; - for (auto& buffer : buffers) { - std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.size << "), "; + for (auto& buffer : descriptor_buffer_infos) { + std::cerr << "(" << buffer << ", " << buffer.offset << ", " << buffer.size << "), "; } std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))"); - std::vector descriptor_buffer_infos; - std::vector write_descriptor_sets; GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size()); - GGML_ASSERT(buffers.size() == pipeline->parameter_count); - vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++]; - for (uint32_t i = 0; i < pipeline->parameter_count; i++) { - descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size}); - } - for (uint32_t i = 0; i < pipeline->parameter_count; i++) { - write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); - } + GGML_ASSERT(descriptor_buffer_infos.size() == pipeline->parameter_count); - ctx->device->device.updateDescriptorSets(write_descriptor_sets, {}); + vk::DescriptorSet& descriptor_set = pipeline->descriptor_sets[pipeline->descriptor_set_idx++]; + vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() }; + ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {}); subctx->s->buffer.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); @@ -3123,7 +3123,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub } else if (qx_needs_dequant) { const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } if (y_non_contig) { ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); @@ -3312,7 +3312,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, - { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, + { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z }); } @@ -3384,7 +3384,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); } static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3459,7 +3459,8 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, + { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); } static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3634,7 +3635,8 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& } else if (qx_needs_dequant) { const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, + { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, vk_subbuffer{ d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } if (y_non_contig) { ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); @@ -3834,7 +3836,8 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, - { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23}, { d_ids, ids_buf_offset, ids_sz } }, + { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, + vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23}, vk_subbuffer{ d_ids, ids_buf_offset, ids_sz } }, sizeof(vk_mat_vec_id_push_constants), &pc, { groups_x, (uint32_t)nei0, groups_z }); } @@ -4381,7 +4384,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co } ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (op == GGML_OP_ROPE) { // Empty src2 is possible in rope, but the shader needs a buffer vk_subbuffer subbuf_z; @@ -4392,20 +4395,20 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co } ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (op == GGML_OP_IM2COL) { // im2col uses only src1 and dst buffers ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src2) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); @@ -4442,10 +4445,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co if (use_src1) { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset + x_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset + y_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } else { ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset + x_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } } } From 33309f661a93c9c0ab65a79e5e7e30fa6162992e Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Sun, 11 Aug 2024 10:35:26 +0200 Subject: [PATCH 05/28] llama : check all graph nodes when searching for result_embd_pooled (#8956) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Stanisław Szymczyk --- src/llama.cpp | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index e0fe8013b0ad2..aaf8db496ecbd 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -14722,12 +14722,15 @@ static int llama_decode_internal( res = nullptr; embd = nullptr; } else if (cparams.embeddings) { - res = nullptr; // do not extract logits for embedding case - embd = gf->nodes[gf->n_nodes - 1]; - if (strcmp(embd->name, "result_embd_pooled") != 0) { - embd = gf->nodes[gf->n_nodes - 2]; + res = nullptr; // do not extract logits for embedding case + embd = nullptr; + for (int i = gf->n_nodes - 1; i >= 0; --i) { + if (strcmp(gf->nodes[i]->name, "result_embd_pooled") == 0) { + embd = gf->nodes[i]; + break; + } } - GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor"); + GGML_ASSERT(embd != nullptr && "missing embeddings tensor"); } else { embd = nullptr; // do not extract embeddings when not needed GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor"); From a21c6fd45032a20180e026773582d21294c85619 Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Sun, 11 Aug 2024 16:37:43 +0800 Subject: [PATCH 06/28] update guide (#8909) Co-authored-by: Neo Zhang <> --- docs/backend/SYCL.md | 145 +++++++++++++++++++++++++++++++------------ 1 file changed, 106 insertions(+), 39 deletions(-) diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index d36ac0a158dd4..59a39fbb67395 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -80,7 +80,14 @@ The following release is verified with good quality: ### Intel GPU -**Verified devices** +SYCL backend supports Intel GPU Family: + +- Intel Data Center Max Series +- Intel Flex Series, Arc Series +- Intel Built-in Arc GPU +- Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)). + +#### Verified devices | Intel GPU | Status | Verified Model | |-------------------------------|---------|---------------------------------------| @@ -88,7 +95,7 @@ The following release is verified with good quality: | Intel Data Center Flex Series | Support | Flex 170 | | Intel Arc Series | Support | Arc 770, 730M, Arc A750 | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | -| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 | +| Intel iGPU | Support | iGPU in 13700k, i5-1250P, i7-1260P, i7-1165G7 | *Notes:* @@ -237,6 +244,13 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic ### II. Build llama.cpp #### Intel GPU + +``` +./examples/sycl/build.sh +``` + +or + ```sh # Export relevant ENV variables source /opt/intel/oneapi/setvars.sh @@ -276,23 +290,26 @@ cmake --build build --config Release -j -v ### III. Run the inference -1. Retrieve and prepare model +#### Retrieve and prepare model You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example. -2. Enable oneAPI running environment +##### Check device + +1. Enable oneAPI running environment ```sh source /opt/intel/oneapi/setvars.sh ``` -3. List devices information +2. List devices information Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: ```sh ./build/bin/llama-ls-sycl-device ``` + This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following: ``` found 2 SYCL devices: @@ -304,12 +321,37 @@ found 2 SYCL devices: | 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| ``` +#### Choose level-zero devices + +|Chosen Device ID|Setting| +|-|-| +|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action| +|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`| +|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`| + +#### Execute + +Choose one of following methods to run. + +1. Script + +- Use device 0: + +```sh +./examples/sycl/run_llama2.sh 0 +``` +- Use multiple devices: + +```sh +./examples/sycl/run_llama2.sh +``` -4. Launch inference +2. Command line +Launch inference There are two device selection modes: -- Single device: Use one device target specified by the user. +- Single device: Use one device assigned by user. Default device id is 0. - Multiple devices: Automatically choose the devices with the same backend. In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR. @@ -326,11 +368,6 @@ Examples: ```sh ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 ``` -or run by script: - -```sh -./examples/sycl/run_llama2.sh 0 -``` - Use multiple devices: @@ -338,12 +375,6 @@ or run by script: ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer ``` -Otherwise, you can run the script: - -```sh -./examples/sycl/run_llama2.sh -``` - *Notes:* - Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: @@ -390,7 +421,7 @@ c. Verify installation In the oneAPI command line, run the following to print the available SYCL devices: ``` -sycl-ls +sycl-ls.exe ``` There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device: @@ -411,6 +442,18 @@ b. The new Visual Studio will install Ninja as default. (If not, please install ### II. Build llama.cpp +You could download the release package for Windows directly, which including binary files and depended oneAPI dll files. + +Choose one of following methods to build from source code. + +1. Script + +```sh +.\examples\sycl\win-build-sycl.bat +``` + +2. CMake + On the oneAPI command line window, step into the llama.cpp main directory and run the following: ``` @@ -425,12 +468,8 @@ cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPI cmake --build build --config Release -j ``` -Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions: -```sh -.\examples\sycl\win-build-sycl.bat -``` - Or, use CMake presets to build: + ```sh cmake --preset x64-windows-sycl-release cmake --build build-x64-windows-sycl-release -j --target llama-cli @@ -442,7 +481,9 @@ cmake --preset x64-windows-sycl-debug cmake --build build-x64-windows-sycl-debug -j --target llama-cli ``` -Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project. +3. Visual Studio + +You can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project. *Notes:* @@ -450,23 +491,25 @@ Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choos ### III. Run the inference -1. Retrieve and prepare model +#### Retrieve and prepare model -You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example. +You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example. -2. Enable oneAPI running environment +##### Check device + +1. Enable oneAPI running environment On the oneAPI command line window, run the following and step into the llama.cpp directory: ``` "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 ``` -3. List devices information +2. List devices information Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: ``` -build\bin\ls-sycl-device.exe +build\bin\llama-ls-sycl-device.exe ``` This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following: @@ -478,10 +521,28 @@ found 2 SYCL devices: | 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136| | 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216| +``` +#### Choose level-zero devices + +|Chosen Device ID|Setting| +|-|-| +|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action| +|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`| +|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`| + +#### Execute + +Choose one of following methods to run. + +1. Script + +``` +examples\sycl\win-run-llama2.bat ``` +2. Command line -4. Launch inference +Launch inference There are two device selection modes: @@ -508,11 +569,7 @@ build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website ca ``` build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer ``` -Otherwise, run the following wrapper script: -``` -.\examples\sycl\win-run-llama2.bat -``` Note: @@ -526,17 +583,18 @@ Or use 1 SYCL GPUs: [0] with Max compute units:512 ``` + ## Environment Variable #### Build | Name | Value | Function | |--------------------|-----------------------------------|---------------------------------------------| -| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. | +| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path.
FP32 path - recommended for better perforemance than FP16 on quantized model| | GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. | | GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. | -| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. | -| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | +| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. | +| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | #### Runtime @@ -572,9 +630,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ``` Otherwise, please double-check the GPU driver installation steps. +- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend? + + No. We can't support Ollama issue directly, because we aren't familiar with Ollama. + + Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it. + + It's same for other projects including llama.cpp SYCL backend. + + ### **GitHub contribution**: Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay. ## TODO -- Support row layer split for multiple card runs. +- NA From 8cd1bcfd3fc9f2b5cbafd7fb7581b3278acec25f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 11 Aug 2024 16:58:58 +0300 Subject: [PATCH 07/28] flake.lock: Update (#8979) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index c54af88ea21c4..f9e1548a2aca5 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1722421184, - "narHash": "sha256-/DJBI6trCeVnasdjUo9pbnodCLZcFqnVZiLUfqLH4jA=", + "lastModified": 1723175592, + "narHash": "sha256-M0xJ3FbDUc4fRZ84dPGx5VvgFsOzds77KiBMW/mMTnI=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "9f918d616c5321ad374ae6cb5ea89c9e04bf3e58", + "rev": "5e0ca22929f3342b19569b21b2f3462f053e497b", "type": "github" }, "original": { From 4134999e01f31256b15342b41c4de9e2477c4a6c Mon Sep 17 00:00:00 2001 From: compilade Date: Sun, 11 Aug 2024 14:45:41 -0400 Subject: [PATCH 08/28] gguf-py : Numpy dequantization for most types (#8939) * gguf-py : Numpy dequantization for most types * gguf-py : Numpy dequantization for grid-based i-quants --- gguf-py/gguf/quants.py | 981 ++++++++++++++++++++++++++++++++++- gguf-py/tests/test_quants.py | 237 +++++++++ 2 files changed, 1215 insertions(+), 3 deletions(-) create mode 100755 gguf-py/tests/test_quants.py diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py index a443dd27e62b3..ff589b85245e5 100644 --- a/gguf-py/gguf/quants.py +++ b/gguf-py/gguf/quants.py @@ -1,10 +1,11 @@ from __future__ import annotations from abc import ABC, abstractmethod from typing import Any, Callable, Sequence +from math import log2, ceil from numpy.typing import DTypeLike -from .constants import GGML_QUANT_SIZES, GGMLQuantizationType +from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K from .lazy import LazyNumpyTensor import numpy as np @@ -64,8 +65,10 @@ def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: - if qtype == GGMLQuantizationType.F32 or qtype == GGMLQuantizationType.F16: - return data.astype(np.float32, copy=False) + if qtype == GGMLQuantizationType.F32: + return data.view(np.float32) + elif qtype == GGMLQuantizationType.F16: + return data.view(np.float16).astype(np.float32) elif (q := _type_traits.get(qtype)) is not None: return q.dequantize(data) else: @@ -77,6 +80,11 @@ class __Quant(ABC): block_size: int type_size: int + grid: np.ndarray[Any, np.dtype[np.float32]] | None = None + grid_shape: tuple[int, int] = (0, 0) + grid_map: tuple[int | float, ...] = () + grid_hex: bytes | None = None + def __init__(self): return TypeError("Quant conversion classes can't have instances") @@ -94,6 +102,27 @@ def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None: assert qtype not in _type_traits _type_traits[qtype] = cls + @classmethod + def init_grid(cls): + if cls.grid is not None or cls.grid_hex is None: + return + + bits_per_elem = ceil(log2(len(cls.grid_map))) + assert bits_per_elem != 0, cls.qtype.name + elems_per_byte = 8 // bits_per_elem + + grid = np.frombuffer(cls.grid_hex, dtype=np.uint8) + # decode hexadecimal chars from grid + grid = grid.reshape((-1, 2)) + grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2)) + grid = grid[..., 0] | grid[..., 1] + # unpack the grid values + grid = grid.reshape((-1, 1)) >> np.array([i for i in range(0, 8, 8 // elems_per_byte)], dtype=np.uint8).reshape((1, elems_per_byte)) + grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1)) + grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1)) + grid = np.take_along_axis(grid_map, grid, axis=-1) + cls.grid = grid.reshape((1, 1, *cls.grid_shape)) + @classmethod @abstractmethod def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: @@ -140,6 +169,7 @@ def __quantize_array(cls, array: np.ndarray) -> np.ndarray: @classmethod def __dequantize_array(cls, array: np.ndarray) -> np.ndarray: + cls.init_grid() return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape)) @classmethod @@ -187,6 +217,166 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32) +class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + imax = abs(blocks).argmax(axis=-1, keepdims=True) + max = np.take_along_axis(blocks, imax, axis=-1) + + d = max / -8 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + # FIXME: Q4_0's reference rounding is cursed and depends on FMA + qs = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15) + + qs = qs.reshape((n_blocks, 2, cls.block_size // 2)) + qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4)) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([d, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.int8) - np.int8(8) + + return (d * qs.astype(np.float32)) + + +class Q4_1(__Quant, qtype=GGMLQuantizationType.Q4_1): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + max = blocks.max(axis=-1, keepdims=True) + min = blocks.min(axis=-1, keepdims=True) + + d = (max - min) / 15 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 15) + + qs = qs.reshape((n_blocks, 2, cls.block_size // 2)) + qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4)) + + d = d.astype(np.float16).view(np.uint8) + m = min.astype(np.float16).view(np.uint8) + + return np.concatenate([d, m, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + m, qs = np.hsplit(rest, [2]) + + d = d.view(np.float16).astype(np.float32) + m = m.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.float32) + + return (d * qs) + m + + +class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + imax = abs(blocks).argmax(axis=-1, keepdims=True) + max = np.take_along_axis(blocks, imax, axis=-1) + + d = max / -16 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + # FIXME: Q5_0's reference rounding is cursed and depends on FMA + q = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31) + + qs = q.reshape((n_blocks, 2, cls.block_size // 2)) + qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4)) + + qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([d, qh, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qh, qs = np.hsplit(rest, [4]) + + d = d.view(np.float16).astype(np.float32) + qh = qh.view(np.uint32) + + qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32)) + ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = (qh & np.uint32(0x01)).astype(np.uint8) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1)) + + qs = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(16) + + return (d * qs.astype(np.float32)) + + +class Q5_1(__Quant, qtype=GGMLQuantizationType.Q5_1): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + max = blocks.max(axis=-1, keepdims=True) + min = blocks.min(axis=-1, keepdims=True) + + d = (max - min) / 31 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + q = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 31) + + qs = q.reshape((n_blocks, 2, cls.block_size // 2)) + qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4)) + + qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4) + + d = d.astype(np.float16).view(np.uint8) + m = min.astype(np.float16).view(np.uint8) + + return np.concatenate([d, m, qh, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + m, rest = np.hsplit(rest, [2]) + qh, qs = np.hsplit(rest, [4]) + + d = d.view(np.float16).astype(np.float32) + m = m.view(np.float16).astype(np.float32) + qh = qh.view(np.uint32) + + qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32)) + ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = (qh & np.uint32(0x01)).astype(np.uint8) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1)) + + qs = (ql | (qh << np.uint8(4))).astype(np.float32) + + return (d * qs) + m + + class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0): @classmethod # Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c @@ -211,3 +401,788 @@ def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: x = x.view(np.int8).astype(np.float32) return (x * d) + + +class Q2_K(__Quant, qtype=GGMLQuantizationType.Q2_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + scales, rest = np.hsplit(blocks, [QK_K // 16]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + d, dmin = np.hsplit(rest, [2]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + # (n_blocks, 16, 1) + dl = (d * (scales & 0xF).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1)) + ml = (dmin * (scales >> 4).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1)) + + shift = np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + + qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & np.uint8(3) + + qs = qs.reshape((n_blocks, QK_K // 16, 16)).astype(np.float32) + + qs = dl * qs - ml + + return qs.reshape((n_blocks, -1)) + + +class Q3_K(__Quant, qtype=GGMLQuantizationType.Q3_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + hmask, rest = np.hsplit(blocks, [QK_K // 8]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + scales, d = np.hsplit(rest, [12]) + + d = d.view(np.float16).astype(np.float32) + + # The scales are packed at 6-bit each in this pattern: + # 0: IIIIAAAA + # 1: JJJJBBBB + # 2: KKKKCCCC + # 3: LLLLDDDD + # 4: MMMMEEEE + # 5: NNNNFFFF + # 6: OOOOGGGG + # 7: PPPPHHHH + # 8: MMIIEEAA + # 9: NNJJFFBB + # 10: OOKKGGCC + # 11: PPLLHHDD + lscales, hscales = np.hsplit(scales, [8]) + lscales = lscales.reshape((n_blocks, 1, 8)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1)) + lscales = lscales.reshape((n_blocks, 16)) + hscales = hscales.reshape((n_blocks, 1, 4)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 4, 1)) + hscales = hscales.reshape((n_blocks, 16)) + scales = (lscales & np.uint8(0x0F)) | ((hscales & np.uint8(0x03)) << np.uint8(4)) + scales = (scales.astype(np.int8) - np.int8(32)).astype(np.float32) + + dl = (d * scales).reshape((n_blocks, 16, 1)) + + ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = hmask.reshape(n_blocks, -1, 1, 32) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1)) + ql = ql.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(3) + qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(1)) + qh = qh ^ np.uint8(1) # strangely, the offset is zero when the bitmask is 1 + q = (ql.astype(np.int8) - (qh << np.uint8(2)).astype(np.int8)).astype(np.float32) + + return (dl * q).reshape((n_blocks, QK_K)) + + +class Q4_K(__Quant, qtype=GGMLQuantizationType.Q4_K): + K_SCALE_SIZE = 12 + + @staticmethod + def get_scale_min(scales: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + n_blocks = scales.shape[0] + scales = scales.view(np.uint8) + ### Unpacking the following: ### + # 0 EEAAAAAA + # 1 FFBBBBBB + # 2 GGCCCCCC + # 3 HHDDDDDD + # 4 eeaaaaaa + # 5 ffbbbbbb + # 6 ggcccccc + # 7 hhdddddd + # 8 eeeeEEEE + # 9 ffffFFFF + # 10 ggggGGGG + # 11 hhhhHHHH + scales = scales.reshape((n_blocks, 3, 4)) + d, m, m_d = np.split(scales, 3, axis=-2) + + sc = np.concatenate([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], axis=-1) + min = np.concatenate([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], axis=-1) + + return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8))) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + dmin, rest = np.hsplit(rest, [2]) + scales, qs = np.hsplit(rest, [cls.K_SCALE_SIZE]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + sc, m = Q4_K.get_scale_min(scales) + + d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1)) + dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1)) + + qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 32)).astype(np.float32) + + return (d * qs - dm).reshape((n_blocks, QK_K)) + + +class Q5_K(__Quant, qtype=GGMLQuantizationType.Q5_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + dmin, rest = np.hsplit(rest, [2]) + scales, rest = np.hsplit(rest, [Q4_K.K_SCALE_SIZE]) + qh, qs = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + sc, m = Q4_K.get_scale_min(scales) + + d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1)) + dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1)) + + ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1)) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32)) + qh = (qh & np.uint8(0x01)).reshape((n_blocks, -1, 32)) + q = (ql | (qh << np.uint8(4))).astype(np.float32) + + return (d * q - dm).reshape((n_blocks, QK_K)) + + +class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + ql, rest = np.hsplit(blocks, [QK_K // 2]) + qh, rest = np.hsplit(rest, [QK_K // 4]) + scales, d = np.hsplit(rest, [QK_K // 16]) + + scales = scales.view(np.int8).astype(np.float32) + d = d.view(np.float16).astype(np.float32) + d = (d * scales).reshape((n_blocks, QK_K // 16, 1)) + + ql = ql.reshape((n_blocks, -1, 1, 64)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32)) + qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = (qh & np.uint8(0x03)).reshape((n_blocks, -1, 32)) + q = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(32) + q = q.reshape((n_blocks, QK_K // 16, -1)).astype(np.float32) + + return (d * q).reshape((n_blocks, QK_K)) + + +class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS): + ksigns: bytes = ( + b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f" + b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f" + b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf" + b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f" + b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf" + b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f" + b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f" + b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff" + ) + + # iq2xxs_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (256, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a00110014002000220028002a0041004400500058006100" + b"6400800082008a00a20001010401100115014001840198010002020222028202" + b"010404041004210424044004420448046004810484049004a404000502050805" + b"200546056905800591050906100640068406a406000805080808140828084108" + b"440850085208880804094009020a140a01100410101021104010601084109010" + b"951000110811201150115a118011241245120014081420142514491480141815" + b"6215001616160118041810184018811800190519a019511a002002200a204420" + b"6120802082202921482100220222012404241024402456240025412564259026" + b"082820289428442a014004401040184021402440404048405640604081408440" + b"9040004120416141804185410142104248425642684200440844204480449944" + b"124524450046014804481048404845480049584961498249454a904a00500850" + b"1150195020508050885004514251a4519152905492540a550156545600581158" + b"195864584059085a046010604060686000615561186260620064056410651265" + b"84654268008002800a8041808280048118814081118201840484108415844084" + b"608400854685948509864086608602880489118a0490109024904090a1901691" + b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.view(np.uint32).reshape(n_blocks, -1, 2) + + db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4)) + ksigns = np.frombuffer(cls.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128)) + signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1)) + signs = np.take_along_axis(ksigns, signs, axis=-1) + signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs[..., 0].copy().view(np.uint8).reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS): + # iq2xs_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (512, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a0011001400160019002000220025002800410044004600" + b"49005000520055005800610064008000820085008800910094009900a0000101" + b"04010601090110011201150118011a0121012401400142014501480151015401" + b"6001680181018401900100020202050208021102140220024102440250025502" + b"80028a0201040404060409041004120415041804210424044004420445044804" + b"5104540456046004810484049004000502050505080511051405200541054405" + b"500561058005010604061006260640064206840600080208050808080a081108" + b"14082008250841084408500858088008a008aa08010904091009400981098909" + b"000a200a280a960aa00a01100410061009101010121015101810211024104010" + b"4210451048105110541060106a10811084109010001102110511081111111411" + b"2011411144115011801194119611011204120612101240126012001402140514" + b"0814111414142014411444144914501464148014011504151015401500161416" + b"49160118041810181218401854188618001905196619511aa91a002002200520" + b"08200a201120142020204120442050208020a020012104211021402148216521" + b"002222228022a82201240424102429244024002541255225992501261a26a626" + b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440" + b"0640094010401240154018402140244040404240454048404a40514054406040" + b"6540814084409040004102410541084111411441204141414441504180418541" + b"a241014204421042124229424042004402440544084411441444194420444144" + b"4444504480449444014504451045244540459a4500460a464446504601480448" + b"1048404845485448624800491149444950496949044a00500250055008501150" + b"145020502850415044505050805001510451105115514051425100524452aa52" + b"0154045410542154405460548154a154005508558055885521566856a1560058" + b"14584158505899581a5940594259855a0160046010604060546062608660a960" + b"006124624a62926200641664106540654565a46501686a682569066a546a626a" + b"00800280058008801180148020802a8041804480508080808280a880aa800181" + b"0481068110814081518159810082208280828282a082a8820184048410841284" + b"158440846084898400854485a58518866a860088088825885a8880888288a888" + b"0689228a808a888a968aa88a0190049010904090569084900091229164915692" + b"89920094059444945094589429959095929541965198a6984999159a609a00a0" + b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4" + b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, scales = np.hsplit(rest, [2 * QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + qs = qs.view(np.uint16) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (np.float32(0.5) + scales) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128) + signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1) + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 2, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S): + # iq2s_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (1024, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a0011001400160019002000220025002800410044004600" + b"490050005200550058006100640066006900800082008500880091009400a000" + b"a500aa0001010401060109011001120115011801210124014001420145014801" + b"510154015601590160016501680181018401900192019501a101a40100020202" + b"050208021102140220022a02410244024602490250025502800285028a029402" + b"a202010404040604090410041204150418042104240426042904400442044504" + b"48044a0451045404560459046004620465048104840486048904900495049804" + b"a104a40400050205050508050a05110514051605190520052505280541054405" + b"46054905500552055505580561056405800582058505880591059405a0050106" + b"0406060609061006150640064506480651065406600681068406900600080208" + b"050808081108140816081908200825082a084108440846084908500852085508" + b"580861086408800885089408aa08010904091009120915091809210940094509" + b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410" + b"0610091010101210151018102110241026104010421045104810511054105610" + b"59106010621065106810811084108610901095109810a110a410001102110511" + b"08110a1111111411161119112011221125112811411144114611491150115211" + b"5511581161116411801182118511881191119411011204120912101215122112" + b"2412401245125112541281128412901200140214051408141114141416141914" + b"2014251428144114441446144914501452145514581461146414801482148514" + b"881491149414a014011504150615091510151215151518152115241540154215" + b"4515481551155415601581158415901500160516081611161416201641164416" + b"50168016aa160118041806180918101815181818211840184218451848185118" + b"541860188118841800190219051908191119141920194119441950196919a219" + b"041a101a401a561a00200220052008201120142016201920202025202a204120" + b"4420502052205520642080208a209420aa200121042110211221152121214021" + b"4221452151215421602181218421902100220a22222228222a22442250228822" + b"8a22a82201240424062409241024152418242124242440244224452448245124" + b"5424602481248424902400250525082511251425202541254425502566258025" + b"0126042610264026592600280528112814284128442850288a28aa2801290429" + b"102995290a2a222a642a882a8a2a014004400640094010401240154018401a40" + b"21402440264040404240454048404a4051405440564059406040624065408140" + b"8440904095409840a140a4400041024105410841114114411641194120412241" + b"2541414144414641494150415241554158416141644180418241854188419141" + b"9441a04101420442104212421542184224424042454248425142544260428142" + b"844200440244054408440a441144144416441944204422442544284441444444" + b"46444944504452445544584461446444804482448544884491449444a0440145" + b"0445064509451045124515451845214524454045424545454845514554456045" + b"6a4581458445904500460246054608461146144620464146444650468046a546" + b"0148044809481048124815481848214824484048424845484848514854486048" + b"84489048004902490549084911491449204941494449504980499649014a044a" + b"104a404a00500250055008501150145016501950205022502550285041504450" + b"4650495050505250555058506150645080508250855088509150945001510451" + b"0651095110511251155118512151245140514251455148515151545160518151" + b"8451905100520552085211521452205241524452505269528052015404540654" + b"0954105412541554185421542454405442544554485451545454605481548454" + b"9054005502550555085511551455205541554455505580550156045610562656" + b"405600580258055808581158145820584158445850585a588058015904591059" + b"4059005a195a855aa85a01600460066010601260156018602160246040604560" + b"4860516054606060846090600061026105610861116114612061416144615061" + b"806199610462106240625662a162006405640864116414642064416444645064" + b"806401650465106540654a656865926500669466016804681068656898680069" + b"2a69426aa16a0080028005800880118014801980208025804180448050805280" + b"5580588061808080858091809480018104810981108112811581188121812481" + b"408142814581488151815481818184819081a981008205820a82118214824182" + b"4482508201840484068409841084128415841884218440844284458448845184" + b"5484608481848484908400850285058508851185148520854185448550858085" + b"8a85018604861086298640860088058811881488418844885088a28801890489" + b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090" + b"4290459048905190549060908190849090900091059111911491419144915091" + b"5a910192049210924092a6920094029405940894119414942094419444945094" + b"8094969401950495109540959895a19500964696649601980498109826984098" + b"a998009949995299909a00a005a00aa014a022a02aa041a044a050a0a2a0aaa0" + b"40a165a102a20aa222a228a22aa282a288a28aa2a8a201a404a410a440a489a4" + b"a4a400a519a551a60aa828a8a2a854a986a908aa0aaa20aa22aa28aa88aaaaaa" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, rest = np.hsplit(rest, [QK_K // 8]) + signs, rest = np.hsplit(rest, [QK_K // 8]) + qh, scales = np.hsplit(rest, [QK_K // 32]) + + d = d.view(np.float16).astype(np.float32) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (np.float32(0.5) + scales) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # unpack the sign bits + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 2, 8)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4)) + qs = qs.astype(np.uint16) | ((qh & 0x03).astype(np.uint16) << 8).reshape((n_blocks, -1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ3_XXS(__Quant, qtype=GGMLQuantizationType.IQ3_XXS): + grid_shape = (256, 4) + grid_map = (0x04, 0x0c, 0x14, 0x1c, 0x24, 0x2c, 0x34, 0x3e) + grid_hex = ( + b"0000020004001100130017002000220031004200730075000101030110011201" + b"2101250130013201410154017001000202020402110220022202310233023702" + b"5102570275020103070310031203250370031304370444045704730475040105" + b"0705320552053506640610071407160743076107011003101010121021102310" + b"3010321034104710501000110211111120112211011203121012121221123012" + b"7212001302132013311346136613011405145014201524154615711505162217" + b"4017002002201120132020202220262031204220012103210521102112212121" + b"3021632167217021002202221122172220222222372240225522012310231423" + b"7023742335245324032527254125742501270327162745270130103012302130" + b"2330503065307230003102312031313144314631013203321032253252327232" + b"1133333330344734723400350635223555351436363663363337603704401740" + b"3540374053405740744120423742404260426642074345430444514464442545" + b"4345704505471047124730471250415070500051065126515551145232527252" + b"0253535310542354275472540255315550562457425724604460466064602161" + b"6161176264623063366344640565526533660367216703700570077010703270" + b"5270267140711272457252720073157333736073217441740075027524753076" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, scales = np.hsplit(rest, [QK_K // 4]) + + d = d.view(np.float16).astype(np.float32) + scales = scales.view(np.uint32) + + db = d * (np.float32(0.5) + (scales >> 28).astype(np.float32)) * np.float32(0.5) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4)) + ksigns = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128)) + signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1)) + signs = np.take_along_axis(ksigns, signs, axis=-1) + signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ3_S(__Quant, qtype=GGMLQuantizationType.IQ3_S): + grid_shape = (512, 4) + grid_map = (0x01, 0x03, 0x05, 0x07, 0x09, 0x0b, 0x0d, 0x0f) + grid_hex = ( + b"0000010002000500070010001100120014001600200021002500330040004200" + b"4500470051005300600062007100740077000001010102010401100111011501" + b"2001230127013101350144016101650172010002010205020702100213021602" + b"2102250230023402420245024702510253027002730203031103150320032203" + b"3103330336034403500352036703710375030004130417042104240432044004" + b"4304510470040205040520052205260533054105450547056605730506061106" + b"1306310652067106000702070407200722072607330750075407001001100210" + b"0410101011101310151017102010221031103410361054105610611072100011" + b"0111031106111011141121113011331141115011521170117611001212121512" + b"1712201224123212401243125512601272120113041307131013131321132713" + b"3013341341136213701303140514121414143114331442144614501454140115" + b"1015131521153015321551152016241627164416461601170317101712172117" + b"3517411762177017002001200320052007201020122014201620212023202720" + b"3020322041204320452050205220672070207320752000210221102113211721" + b"2221252131213421422151210122042207222122232230223722412253225722" + b"7122742200230223052311232223242331233323422350236623012407242024" + b"2324322435244124722475240425112522253725402553257025002602260726" + b"2126552661260527112726273027432750270230113013301530173022303130" + b"3330353042304430473051306330713001310331053114312131233140316031" + b"7231763100321232203232323432503201331033143321332333273330334133" + b"4333473355337333033411341634223431345234603464340135103512352535" + b"3235443556357335163641360137033720372237353700400440124020402440" + b"2740324041405040704002410741114113412241304135414341514155410142" + b"0342104215422142334240425742624270420443114313432043224331433543" + b"0044024424443744404471440545074521456245134634466046104715473047" + b"4347514702501050145022504050445047505250665074500151035105511251" + b"2151325172510052115223523052365253520253075310532753445351536553" + b"7353015404542054325446541255265551555355425602570457225711601360" + b"1560316033606060006120612761646112623462426255626262706200631463" + b"2163406325644364626400650365346560650566406611671367007004700770" + b"2070227036704070547062700271117124714371457101720472107216722172" + b"3072517202733273357353730174057413742074507422754275027631760077" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + qh, rest = np.hsplit(rest, [QK_K // 32]) + signs, scales = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (1 + 2 * scales) + db = db.reshape((n_blocks, -1, 1, 1)) + + # unpack the sign bits + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8) + qh = (qh & 0x01).astype(np.uint16).reshape((n_blocks, -1)) + qs = qs.astype(np.uint16) | (qh << 8) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ1_S(__Quant, qtype=GGMLQuantizationType.IQ1_S): + # iq1s_grid, with each byte packed into 2 bits + # -1, 0, 1 <=> 0, 1, 2 + grid_shape = (2048, 8) + grid_map = (-1, 0, 1) + grid_hex = ( + b"00000200050008000a00110015002000220028002a0045005100540056006500" + b"8000820088008a009500a000a200a800aa000401050111011401160119011a01" + b"2501410146014901520155015a0161016401660168018501910194019601a501" + b"0002020208020a0215022002220228022a024502510259026402690280028202" + b"88028a02910295029902a002a202a802aa021104140416042504410449045504" + b"5a046404650491049904a5040105040505050605150518051a05290540054505" + b"4a0550055105540555055605590560056205650568056a058105910595059805" + b"9a05a105a405a505a605a9051406190641064406500652065506580660066106" + b"6606690685069106940699060008020808080a0815082008220828082a084508" + b"5108560865088008820888088a089508a008a208a808aa080509110914091909" + b"2409250941095009510955096109640969099109940996099909a509000a020a" + b"080a0a0a150a200a220a280a2a0a450a510a590a610a650a800a820a850a880a" + b"8a0a950aa00aa20aa80aaa0a1010111014101910241025104110441050105510" + b"58106110641065106910911094109610a110a510011104110611091110111211" + b"1511181121112411291145114a11501151115211541155115611591160116511" + b"841192119511a111a41111121412161225124012461249125212551258125a12" + b"641266128512911294129612a512011406140914141415141814191421142614" + b"41144514461448144a1451145414551456145914621465146814841489149014" + b"94149514981499149a14a114a414a514a914021505150a151115141515151615" + b"191520152215251528152a154115441545154615511552155415551556155915" + b"5a1561156415651566156915801582158415851588158a159015911594159515" + b"961599159a15a015a215a51501160416051606161516161618161a1621162616" + b"401642164416451648164a165116551656165816591661166416651668166916" + b"6a1686168a1692169516a416a916111816182518411844184618491850185518" + b"58185a1860186118641866186918851891189418a5181019121915191a192119" + b"25194219441945194819511954195519561959195a19601965196a1989199119" + b"921995199819a119a619a919091a161a241a261a441a461a491a501a521a551a" + b"581a611a661a691a851a911a961a9a1a0020022008200a201520202022202520" + b"28202a20452051205920612065208020822088208a209520a020a220a520a820" + b"aa2005211121142119212521422144214921552158215a216121642165216621" + b"8521902196219921a521012208220a22112215222022222228222a2245225122" + b"562259226522812288228a2291229522a022a222a822aa220524142416241924" + b"252444244524462449245224552458245a2466248524912494249924a124a524" + b"0925152521252925402545254825512554255525592562256525682589259025" + b"9425952598259a25a125a425a625a92505261026122619262526412649265526" + b"6026612669268426862690269a260028022808280a2815282028222828282a28" + b"45285128542865288028822888288a28a028a228a828aa280929112914291929" + b"2529462949295229552961296429662969298529902996299929a429a529002a" + b"022a082a0a2a202a222a282a2a2a452a512a562a592a652a802a822a882a8a2a" + b"952aa02aa22aa82aaa2a054011401640254049405240554058405a4061406440" + b"664094409940a140a6400041014104410641094112411541164118411a412141" + b"26412941454148414a41514154415541564159415a41654168416a4181418441" + b"8641904192419541a041a141a241054211421442164225424142524255425a42" + b"6442694289429442a5420144154419442944454448444a445144544455445644" + b"61446244654468446a44814486448944904492449544a044a144a94401450245" + b"05450a4511451445154516451945204525452a45414544454545464549455045" + b"5145544555455645584559456145644565456645694582458445854588459145" + b"94459545964599459a45a545a845aa450146054609461446154618461a462146" + b"2446294640464246454648465046514652465546564659466246654668468146" + b"85468a4694469546a146a446a6460548114815481a4825484248494850485548" + b"5848614864486648694885489148944896489948a5480149054906490a491049" + b"144915491849214924492649404945494a495149524954495549564959496049" + b"6249654966496a49864989499249954996499849a149a449a649a949164a444a" + b"464a494a554a584a5a4a644a694a944aa54a0150045005500650095012501550" + b"1a50215024502950405045504850515054505550565059506550685086508950" + b"95509850a050a150a650a9500551085109510a51115114511551165118511951" + b"20512551265128512a5141514451455146514951505151515251545155515651" + b"585159515a51615164516551665169518251855191519451955196519951a051" + b"a551aa5101520652125215521a5221522452425245524a525152545255525652" + b"595262526552855290529252955299529a52a452045405541154145415541654" + b"185419542154255428542a54415444544554465449544a545054515454545554" + b"5654585459545a54615462546454655466546954805488548a54915494549554" + b"96549954a154a454a554aa540155025504550555065509551055115512551455" + b"1555165519551a55215524552555265529554055415542554455455546554855" + b"4955505551555255545555555655585559555a55605561556455655566556855" + b"69556a5581558455855589558a559055915594559555965598559955a155a455" + b"a555a655a9550056015602560456065608560956115614561556185619562056" + b"2156225624562556265628562956415645564656485649564a56505651565256" + b"545655565656585659565a566156645665566956825685568656885689568a56" + b"915695569a56a256a556a656a856a95604580558065809581058155818582158" + b"2a58455848584a58515854585558565858585958605862586458655882588958" + b"9058925895589858a158a9580159025905590a59115914591559165919592559" + b"41594459455946594959505951595259545955595659585959595a5961596459" + b"655966596959815985598959915994599559965998599959a559045a085a155a" + b"1a5a205a255a265a295a455a485a495a515a555a565a585a595a625a655a685a" + b"6a5a815a8a5a925a955a965a985a9a5aa15a0560146016601960256044605060" + b"5560566058605a60616064606660696081609660a56001610461066109611261" + b"15612161226126612961456149615161556156615961656166616a6184618a61" + b"92619561a161a661a96111621662196240624162466255625662586260628562" + b"91629662a56211641264156416641a6421642664296440644264456448644a64" + b"516454645564566459645a646064626465648464856489649064926494649564" + b"966498649a64a164a464a964056508650a651165156516651965446545654665" + b"496550655165546555655665596561656465656566656965866589658a659165" + b"9565966599659a65a265a565a665a86502660966156620662666286629664066" + b"456648664a66516654665566566658665a666066656668668066826685668a66" + b"9466966698669966a066a466a666aa661668196825684168526855685a686168" + b"6968856891689868a66801690469106915692169246926692969406941694569" + b"4669486951695469556956695969606965696a69826984698a699569a169a469" + b"a569a969116a166a186a416a446a496a506a556a586a5a6a646a656a696a866a" + b"946a986a9a6aa66a0080028008800a802080228028802a804580508051805480" + b"5680598065808080828088808a809580a080a280a880aa800581118114811681" + b"1981258141814481498150815281558156815881598164816681698185818981" + b"948196819981a5810082028208820a8215822082228228822a82518254825982" + b"65828082828288828a829582a082a282a882aa82148419844184448451845584" + b"5a846184648469849484998401850985128515851a8526852985408541854585" + b"4885518554855585568559855a856585668568856a8581858485868589859085" + b"928595859885a68511861686198625864186448649864a865086558659865a86" + b"618666866a86858691869a86a4860088028808880a8815882088228828882a88" + b"41884588518854885988658869888088828888888a889588a088a288a888aa88" + b"05890689118914891689258941894489468949895089528955895a8961896489" + b"858996899989a589008a028a088a0a8a158a208a228a288a2a8a458a518a548a" + b"568a808a828a888a8a8a958aa08aa28aa88aaa8a059011901690189019902590" + b"419046904990559058905a9069906a9085909190949096909990a59001910491" + b"069109911091159118911a912191249126912991409145915091519154915591" + b"569159916291659184918691929195919891a191a491a691a991059211921492" + b"19922592449246924992509252925592589266926992859294929692a9920194" + b"04940694109415941894269440944a9451945494559456945894599460946194" + b"62946594849486949294949495949894a194a9940095059508950a9510951195" + b"14951595169519952195259529952a9541954495459546954995509551955295" + b"549555955695589559955a956195649565956695699581958595889591959295" + b"94959595969599959a95a095a295a595a895aa95019604961096159619962096" + b"2696299645964896499651965296559656965996659668968296849689968a96" + b"929694969596a496a696a9960598169819982598419846985098529855985698" + b"5a98649865988598919896989998a59804990699099910991299159918991a99" + b"209921992499269940994299459948994a995199549955995699599962996599" + b"66996a99819984999099929995999a99a199a699059a159a259a449a469a499a" + b"509a559a589a619a859a919a949a959a969a00a002a008a00aa015a020a022a0" + b"28a02aa045a051a054a056a059a080a082a088a08aa095a0a0a0a2a0a8a0aaa0" + b"05a109a111a114a116a119a11aa146a149a151a155a158a15aa161a164a185a1" + b"90a192a196a199a102a208a20aa210a219a222a228a22aa245a251a256a259a2" + b"65a280a282a288a28aa295a2a0a2a2a2a8a2aaa219a425a441a444a450a454a4" + b"55a458a45aa461a465a466a468a469a485a406a509a510a512a515a518a526a5" + b"29a542a545a551a554a555a556a559a565a56aa581a584a585a586a589a592a5" + b"95a598a505a611a616a61aa621a625a644a646a64aa652a655a656a658a660a6" + b"62a686a690a695a696a699a6a1a6a4a6a6a600a802a808a80aa820a822a828a8" + b"2aa851a854a856a859a880a882a888a88aa895a8a0a8a2a8a8a8aaa805a914a9" + b"19a921a925a941a950a955a95aa961a966a969a990a996a900aa02aa08aa0aaa" + b"20aa22aa28aa2aaa51aa54aa56aa80aa82aa88aa8aaa95aaa0aaa2aaa8aaaaaa" + ) + + delta = np.float32(0.125) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, qh = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + qh = qh.view(np.uint16) + + dl = d * (2 * ((qh >> 12) & 7) + 1) + dl = dl.reshape((n_blocks, -1, 1, 1)) + delta = np.where((qh & np.uint16(0x8000)) == 0, cls.delta, -cls.delta) + delta = delta.reshape((n_blocks, -1, 1, 1)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4)) + qs = qs.astype(np.uint16) | ((qh & 7) << 8).reshape((n_blocks, -1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (dl * (grid + delta)).reshape((n_blocks, -1)) + + +class IQ1_M(__Quant, qtype=GGMLQuantizationType.IQ1_M): + grid_shape = IQ1_S.grid_shape + grid_map = IQ1_S.grid_map + grid_hex = IQ1_S.grid_hex + + delta = IQ1_S.delta + + # Okay *this* type is weird. It's the only one which stores the f16 scales in multiple parts. + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + qs, rest = np.hsplit(blocks, [QK_K // 8]) + qh, scales = np.hsplit(rest, [QK_K // 16]) + + # The f16 scale is packed across multiple bytes + scales = scales.view(np.uint16) + d = (scales.reshape((n_blocks, 4)) & np.uint16(0xF000)) >> np.array([12, 8, 4, 0], dtype=np.uint16).reshape((1, 4)) + d = d[..., 0] | d[..., 1] | d[..., 2] | d[..., 3] + d = d.view(np.float16).astype(np.float32).reshape((n_blocks, 1)) + + scales = scales.reshape(n_blocks, -1, 1) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4)) + scales = (scales & 0x07).reshape((n_blocks, -1)) + dl = d * (2 * scales + 1) + dl = dl.reshape((n_blocks, -1, 2, 1, 1)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + qs = qs.astype(np.uint16) | ((qh & 0x07).astype(np.uint16) << 8).reshape((n_blocks, -1)) + + delta = np.where(qh & 0x08 == 0, cls.delta, -cls.delta) + delta = delta.reshape((n_blocks, -1, 2, 2, 1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 2, 8)) + + return (dl * (grid + delta)).reshape((n_blocks, -1)) + + +class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL): + kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 1)) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16) + qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1)) + + return (d * qs) + + +class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + scales_h, rest = np.hsplit(rest, [2]) + scales_l, qs = np.hsplit(rest, [QK_K // 64]) + + d = d.view(np.float16).astype(np.float32) + scales_h = scales_h.view(np.uint16) + + scales_l = scales_l.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales_h = scales_h.reshape((n_blocks, 1, -1)) >> np.array([2 * i for i in range(QK_K // 32)], dtype=np.uint16).reshape((1, -1, 1)) + scales_l = scales_l.reshape((n_blocks, -1)) & np.uint8(0x0F) + scales_h = scales_h.reshape((n_blocks, -1)).astype(np.uint8) & np.uint8(0x03) + + scales = (scales_l | (scales_h << np.uint8(4))).astype(np.int8) - np.int8(32) + dl = (d * scales.astype(np.float32)).reshape((n_blocks, -1, 1)) + + qs = qs.reshape((n_blocks, -1, 1, 16)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = qs.reshape((n_blocks, -1, 32, 1)) & np.uint8(0x0F) + + kvalues = np.array(IQ4_NL.kvalues, dtype=np.int8).reshape((1, 1, 1, -1)) + qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1, 32)) + + return (dl * qs).reshape((n_blocks, -1)) diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py new file mode 100755 index 0000000000000..8b7a85c2c36d7 --- /dev/null +++ b/gguf-py/tests/test_quants.py @@ -0,0 +1,237 @@ +#!/usr/bin/env python3 + +# Test gguf.quants so that it exactly matches the C implementation of the (de)quantization + +# NOTE: this is kind of a mess, but at least it worked for initially testing the Python implementations. + +from __future__ import annotations + +import argparse +from math import prod +import os +import sys +from pathlib import Path +import ctypes +import logging +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf +from gguf.constants import GGMLQuantizationType + + +logger = logging.getLogger("test-quants") + + +c_float_p = ctypes.POINTER(ctypes.c_float) + + +class ggml_init_params(ctypes.Structure): + _fields_ = [ + ("mem_size", ctypes.c_size_t), + ("mem_buffer", ctypes.c_void_p), + ("no_alloc", ctypes.c_bool), + ] + + +class GGMLQuants: + libggml: ctypes.CDLL + + def __init__(self, libggml: Path): + self.libggml = ctypes.CDLL(str(libggml)) + self.libggml.ggml_quantize_chunk.restype = ctypes.c_size_t + # enum ggml_type type, + # const float * src, + # void * dst, + # int64_t start, + # int64_t nrows, + # int64_t n_per_row, + # const float * imatrix) { + self.libggml.ggml_quantize_chunk.argtypes = ( + ctypes.c_int, + ctypes.POINTER(ctypes.c_float), + ctypes.c_void_p, + ctypes.c_int64, + ctypes.c_int64, + ctypes.c_int64, + ctypes.POINTER(ctypes.c_float), + ) + + self.libggml.ggml_quantize_requires_imatrix.restype = ctypes.c_bool + self.libggml.ggml_quantize_requires_imatrix.argtypes = (ctypes.c_int,) + + for t in ( + "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", + "q2_K", "q3_K", "q4_K", "q5_K", "q6_K", + "iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m", + "iq4_nl", "iq4_xs", + ): + dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + t) + dequant_func.restype = None + dequant_func.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int64) + + self.libggml.ggml_fp16_to_fp32_row.restype = None + self.libggml.ggml_fp16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64) + self.libggml.ggml_bf16_to_fp32_row.restype = None + self.libggml.ggml_bf16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64) + + self.libggml.ggml_init.argtypes = (ggml_init_params,) + + self.libggml.ggml_init(ggml_init_params(1 * 1024 * 1024, 0, False)) + + def dequantize(self, tensor: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + result = np.zeros(gguf.quant_shape_from_byte_shape(tensor.shape, qtype), dtype=np.float32, order="C") + if qtype == GGMLQuantizationType.F32: + # no-op + result = tensor.view(np.float32) + elif qtype == GGMLQuantizationType.F16: + self.libggml.ggml_fp16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size) + elif qtype == GGMLQuantizationType.BF16: + self.libggml.ggml_bf16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size) + else: + lw_qname = qtype.name.lower() + if lw_qname[-1] == "k": + lw_qname = lw_qname[:-1] + "K" + dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + lw_qname) + dequant_func(tensor.ctypes.data_as(ctypes.c_void_p), result.ctypes.data_as(c_float_p), result.size) + return result + + def quantize(self, data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + result = np.zeros(gguf.quant_shape_to_byte_shape(data.shape, qtype), dtype=np.uint8, order="C") + if self.libggml.ggml_quantize_requires_imatrix(qtype.value): + # TODO: is a column-wise sum of squares appropriate? + qw = np.sum((data * data).reshape((-1, data.shape[-1])), axis=0).ctypes.data_as(c_float_p) + else: + qw = ctypes.cast(0, c_float_p) + result_size = self.libggml.ggml_quantize_chunk(qtype.value, data.ctypes.data_as(c_float_p), result.ctypes.data_as(ctypes.c_void_p), 0, prod(data.shape[:-1]), data.shape[-1], qw) + assert result.size == result_size + return result + + +def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType) -> bool: + same = np.array_equal(t1, t2) + if same: + return True + else: + block_size, type_size = gguf.GGML_QUANT_SIZES[qtype] + if t1.dtype == np.float32: + t1 = t1.reshape((-1, block_size)) + t2 = t2.reshape((-1, block_size)) + else: + t1 = t1.reshape((-1, type_size)) + t2 = t2.reshape((-1, type_size)) + x = t1.view(np.uint8) ^ t2.view(np.uint8) + diff_bits = np.count_nonzero(np.unpackbits(x, axis=-1), axis=-1) + num_bad_blocks = np.count_nonzero(diff_bits, axis=0) + if num_bad_blocks == 0 and t1.shape == t2.shape: + logger.debug("Bits are equal, but arrays don't match, likely contains NANs") + return True + logger.debug(f"{num_bad_blocks} bad blocks ({100 * num_bad_blocks / x.shape[0]:.6f}%)") + bad_block_id = np.argmax(diff_bits, axis=0) + logger.debug(f"Worst block id: {bad_block_id}") + logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}") + + sum_diff_bits = np.sum(diff_bits) + logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)") + return False + + +def do_test(libggml_path: Path, quick: bool = False): + ggml_quants = GGMLQuants(libggml_path) + + np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n}) + + r = np.random.randn(8, 1024, 1024).astype(np.float32, copy=False) + + for qtype in (GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()): + has_dequantize = False + has_quantize = False + + try: + gguf.dequantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][1]), dtype=np.uint8), qtype) + has_dequantize = True + except (NotImplementedError, AssertionError) as e: + if isinstance(e, AssertionError): + logger.error(f"Error with {qtype.name}: {e}") + raise e + try: + gguf.quantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][0]), dtype=np.float32), qtype) + has_quantize = True + except (NotImplementedError, AssertionError) as e: + if isinstance(e, AssertionError): + logger.error(f"Error with {qtype.name}: {e}") + raise e + + if not has_dequantize and not has_quantize: + continue + + logger.info(f"Testing {qtype.name}") + + rc = r.copy(order="C") + + pyq = None + ggq = None + + if has_quantize: + logger.debug(f"Quantizing to {qtype.name} with Python") + pyq = gguf.quants.quantize(rc, qtype) + + logger.debug(f"Quantizing to {qtype.name} with C") + ggq = ggml_quants.quantize(rc, qtype) + + if qtype == GGMLQuantizationType.F16: + pyq = pyq.view(np.uint8) + quant_equal = compare_tensors(pyq, ggq, qtype) + + if not quant_equal: + logger.error(f"Quantization to {qtype.name} does not match ❌") + else: + logger.info(f"Quantization to {qtype.name} matches exactly ✅") + + if has_dequantize: + if ggq is None and not quick: + logger.debug(f"Quantizing to {qtype.name} with C") + ggq = ggml_quants.quantize(rc, qtype) + + if ggq is not None: + logger.debug(f"Dequantizing from {qtype.name} with Python") + pydq = gguf.quants.dequantize(ggq, qtype) + logger.debug(f"Dequantizing from {qtype.name} with C") + ggdq = ggml_quants.dequantize(ggq, qtype) + + dequant_equal = compare_tensors(pydq, ggdq, qtype) + + if not dequant_equal: + logger.error(f"Dequantization from {qtype.name} does not match ❌") + else: + logger.info(f"Dequantization from {qtype.name} matches exactly ✅") + + rq_shape = gguf.quants.quant_shape_to_byte_shape((8, 1024, 1024 // 2), qtype) + rq = np.random.random(rq_shape).astype(np.float16).view(np.uint8) + + logger.debug(f"Dequantizing random f16 data as {qtype.name} with Python") + pydq = gguf.quants.dequantize(rq, qtype) + logger.debug(f"Dequantizing random f16 data as {qtype.name} with C") + ggdq = ggml_quants.dequantize(rq, qtype) + + dequant_equal = compare_tensors(pydq, ggdq, qtype) + + if not dequant_equal: + logger.error(f"Dequantization from random f16 data as {qtype.name} does not match ❌") + else: + logger.info(f"Dequantization from random f16 data as {qtype.name} matches exactly ✅") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation") + parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "ggml" / "src" / "libggml.so", help="The path to libggml.so") + parser.add_argument("--quick", action="store_true", help="Don't quantize with C when it's not strictly necessary") + + args = parser.parse_args() + + logging.basicConfig(level=logging.DEBUG) + + do_test(args.libggml, args.quick) From 5ef07e25ac39e62297a67208c5bcced50835a2dd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 12 Aug 2024 10:21:50 +0300 Subject: [PATCH 09/28] server : handle models with missing EOS token (#8997) ggml-ci --- examples/server/server.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 360f571e42867..1621c7c43961c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -631,6 +631,7 @@ struct server_context { bool clean_kv_cache = true; bool add_bos_token = true; + bool has_eos_token = false; int32_t n_ctx; // total context for all clients / slots @@ -693,7 +694,7 @@ struct server_context { n_ctx = llama_n_ctx(ctx); add_bos_token = llama_should_add_bos_token(model); - GGML_ASSERT(llama_add_eos_token(model) != 1); + has_eos_token = llama_add_eos_token(model) != 1; return true; } @@ -1031,7 +1032,7 @@ struct server_context { { slot.sparams.logit_bias.clear(); - if (json_value(data, "ignore_eos", false)) { + if (json_value(data, "ignore_eos", false) && has_eos_token) { slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; } From d3ae0ee8d75033921a076131d4d0fa1c6ec579a7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 12 Aug 2024 11:02:01 +0300 Subject: [PATCH 10/28] py : fix requirements check '==' -> '~=' (#8982) * py : fix requirements check '==' -> '~=' * cont : fix the fix * ci : run on all requirements.txt --- .github/workflows/python-check-requirements.yml | 6 ++---- examples/llava/requirements.txt | 2 +- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/.github/workflows/python-check-requirements.yml b/.github/workflows/python-check-requirements.yml index 4e0374fc63d95..46e80aecd0a0c 100644 --- a/.github/workflows/python-check-requirements.yml +++ b/.github/workflows/python-check-requirements.yml @@ -6,15 +6,13 @@ on: - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - - 'requirements.txt' - - 'requirements/*.txt' + - '**/requirements*.txt' pull_request: paths: - '.github/workflows/python-check-requirements.yml' - 'scripts/check-requirements.sh' - 'convert*.py' - - 'requirements.txt' - - 'requirements/*.txt' + - '**/requirements*.txt' concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt index dfe5fbe62cea6..cbcbf26c9b4e9 100644 --- a/examples/llava/requirements.txt +++ b/examples/llava/requirements.txt @@ -2,4 +2,4 @@ --extra-index-url https://download.pytorch.org/whl/cpu pillow~=10.2.0 torch~=2.2.1 -torchvision==0.17.1 +torchvision~=0.17.1 From 2589292cde038ba876c041bcd7b3f0c81f3f11fe Mon Sep 17 00:00:00 2001 From: Liu Jia Date: Mon, 12 Aug 2024 17:46:03 +0800 Subject: [PATCH 11/28] Fix a spelling mistake (#9001) --- src/llama-sampling.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 8910f6d6542e9..8f4841d9daf7b 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -85,14 +85,14 @@ void llama_sample_top_k_impl(struct llama_sampling * smpl, llama_token_data_arra constexpr float bucket_low = -10.0f; constexpr float bucket_high = 10.0f; constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low); - constexpr float bucker_inter = -bucket_low * bucket_scale; + constexpr float bucket_inter = -bucket_low * bucket_scale; std::vector bucket_idx(candidates->size); std::vector histo(nbuckets, 0); for (int i = 0; i < (int)candidates->size; ++i) { const float val = candidates->data[i].logit; - int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low); + int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low); ib = std::max(0, std::min(nbuckets-1, ib)); bucket_idx[i] = ib; ++histo[ib]; From df5478fbea7e652cfad4ee7974ac3b624fd6c7f6 Mon Sep 17 00:00:00 2001 From: DavidKorczynski Date: Mon, 12 Aug 2024 13:21:41 +0100 Subject: [PATCH 12/28] ggml: fix div-by-zero (#9003) Fixes: https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=70724 In order to access the above bug you need to login using one of the emails in https://github.com/google/oss-fuzz/blob/master/projects/llamacpp/project.yaml#L3-L5 Signed-off-by: David Korczynski --- ggml/src/ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 38990e3a05a3f..c9b0e81684903 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -21129,7 +21129,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p (int64_t) info->ne[2] * (int64_t) info->ne[3]; - if (ne % ggml_blck_size(info->type) != 0) { + if (ggml_blck_size(info->type) == 0 || ne % ggml_blck_size(info->type) != 0) { fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n", __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type)); fclose(file); From 1262e7ed13ac197c944f15e1ddb083cb4f36cf65 Mon Sep 17 00:00:00 2001 From: DavidKorczynski Date: Mon, 12 Aug 2024 13:36:41 +0100 Subject: [PATCH 13/28] grammar-parser : fix possible null-deref (#9004) Fixes: https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=70680 Signed-off-by: David Korczynski --- common/grammar-parser.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index a518b766dc33e..438452eab570f 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -369,6 +369,9 @@ namespace grammar_parser { } // Validate the state to ensure that all rules are defined for (const auto & rule : state.rules) { + if (rule.empty()) { + throw std::runtime_error("Undefined rule"); + } for (const auto & elem : rule) { if (elem.type == LLAMA_GRETYPE_RULE_REF) { // Ensure that the rule at that location exists From 84eb2f4fad28ceadd415a4e775320c983f4d9a7d Mon Sep 17 00:00:00 2001 From: Frank Mai Date: Mon, 12 Aug 2024 20:45:50 +0800 Subject: [PATCH 14/28] docs: introduce gpustack and gguf-parser (#8873) * readme: introduce gpustack GPUStack is an open-source GPU cluster manager for running large language models, which uses llama.cpp as the backend. Signed-off-by: thxCode * readme: introduce gguf-parser GGUF Parser is a tool to review/check the GGUF file and estimate the memory usage without downloading the whole model. Signed-off-by: thxCode --------- Signed-off-by: thxCode --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 1283f6805874e..7f48fde6e5f5a 100644 --- a/README.md +++ b/README.md @@ -186,10 +186,12 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption +- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage **Infrastructure:** - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp +- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs **Games:** - [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. From 0fd93cdef5e583aa980b3c0d693c0d207f0787a7 Mon Sep 17 00:00:00 2001 From: Nico Bosshard Date: Mon, 12 Aug 2024 17:13:59 +0200 Subject: [PATCH 15/28] llama : model-based max number of graph nodes calculation (#8970) * llama : model-based max number of graph nodes calculation * Update src/llama.cpp --------- Co-authored-by: slaren --- src/llama.cpp | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index aaf8db496ecbd..7f2f0003142a3 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -3575,13 +3575,8 @@ namespace GGUFMeta { using llama_buf_map = std::unordered_map; -// TODO: update when needed or think of some clever automatic way to do this -static size_t llama_model_max_nodes(const llama_model & /*model*/) { - //if (model.arch == LLM_ARCH_LLAMA && model.hparams.n_layer > ??) { // llama-3 405B - // return 32768; - //} - - return 8192; +static size_t llama_model_max_nodes(const llama_model & model) { + return std::max(8192, model.tensors_by_name.size()*5); } struct llama_model_loader { From 1f67436c5ee6f4c99e71a8518bdfc214c27ce934 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Mon, 12 Aug 2024 19:17:03 +0300 Subject: [PATCH 16/28] ci : enable RPC in all of the released builds (#9006) ref: #8912 --- .github/workflows/build.yml | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b9246659a6ef0..74b5d4f69d790 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -47,7 +47,7 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON -DBUILD_SHARED_LIBS=OFF .. + cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF .. cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -105,7 +105,7 @@ jobs: sysctl -a # Metal is disabled due to intermittent failures with Github runners not having a GPU: # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 - cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON -DBUILD_SHARED_LIBS=OFF + cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -222,7 +222,7 @@ jobs: run: | mkdir build cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DBUILD_SHARED_LIBS=OFF + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF cmake --build . --config Release -j $(nproc) - name: Test @@ -696,22 +696,20 @@ jobs: strategy: matrix: include: - - build: 'rpc-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON' - build: 'noavx-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON' - build: 'avx2-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON' - build: 'avx-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON' - build: 'avx512-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON' - build: 'openblas-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' - build: 'vulkan-x64' - defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON' + defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON' - build: 'llvm-arm64' defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' - build: 'msvc-arm64' From fc4ca27b25464a11b3b86c9dbb5b6ed6065965c2 Mon Sep 17 00:00:00 2001 From: Diogo Teles Sant'Anna Date: Mon, 12 Aug 2024 13:28:23 -0300 Subject: [PATCH 17/28] ci : fix github workflow vulnerable to script injection (#9008) Signed-off-by: Diogo Teles Sant'Anna --- .github/workflows/bench.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml index eb69b82c47e64..56d22bc0cc394 100644 --- a/.github/workflows/bench.yml +++ b/.github/workflows/bench.yml @@ -129,6 +129,8 @@ jobs: - name: Server bench id: server_bench + env: + HEAD_REF: ${{ github.head_ref || github.ref_name }} run: | set -eux @@ -137,7 +139,7 @@ jobs: python bench.py \ --runner-label ${{ env.RUNNER_LABEL }} \ --name ${{ github.job }} \ - --branch ${{ github.head_ref || github.ref_name }} \ + --branch $HEAD_REF \ --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \ --scenario script.js \ --duration ${{ github.event.inputs.duration || env.DURATION }} \ From 828d6ff7d796f48b2c345f6be2805a3c531a089c Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 13 Aug 2024 11:41:14 +0200 Subject: [PATCH 18/28] export-lora : throw error if lora is quantized (#9002) --- examples/export-lora/README.md | 6 ++--- examples/export-lora/export-lora.cpp | 35 +++++++++++++++++++++------- 2 files changed, 29 insertions(+), 12 deletions(-) diff --git a/examples/export-lora/README.md b/examples/export-lora/README.md index 91c33c34acaa9..7dce99c9a9e61 100644 --- a/examples/export-lora/README.md +++ b/examples/export-lora/README.md @@ -17,9 +17,9 @@ For example: ```bash ./bin/llama-export-lora \ - -m open-llama-3b-v2-q8_0.gguf \ - -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \ - --lora lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.gguf + -m open-llama-3b-v2.gguf \ + -o open-llama-3b-v2-english2tokipona-chat.gguf \ + --lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf ``` Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters: diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index 3176d6e26ef8b..c7e5ca78845ee 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -10,6 +10,12 @@ static bool g_verbose = false; +struct tensor_transformation { + struct ggml_tensor * in; + struct ggml_tensor * out; + bool is_copy; +}; + static std::string get_kv_str(struct gguf_context * ctx_gguf, const std::string & key){ int id = gguf_find_key(ctx_gguf, key.c_str()); return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id)); @@ -198,8 +204,7 @@ struct lora_merge_ctx { } // mapping base tensor to out tensor (same shape with base, but different type) - // if out_tensor == nullptr, we only copy it - std::vector> base_to_out_tensors; + std::vector trans; for (auto & it : base_model.tensors) { bool t_a = true; bool t_b = true; @@ -212,14 +217,22 @@ struct lora_merge_ctx { // only copy struct ggml_tensor * cpy_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor); ggml_set_name(cpy_tensor, base_tensor->name); - base_to_out_tensors.push_back(std::make_pair(cpy_tensor, nullptr)); + trans.push_back({ + cpy_tensor, + cpy_tensor, + true, + }); gguf_add_tensor(ctx_out, cpy_tensor); } else if (t_a && t_b) { // need merging struct ggml_tensor * out_tensor = ggml_new_tensor( ctx_out_ggml, get_out_tensor_type(base_tensor), GGML_MAX_DIMS, base_tensor->ne); ggml_set_name(out_tensor, base_tensor->name); - base_to_out_tensors.push_back(std::make_pair(base_tensor, out_tensor)); + trans.push_back({ + base_tensor, + out_tensor, + false, + }); gguf_add_tensor(ctx_out, out_tensor); } else { throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b"); @@ -234,12 +247,12 @@ struct lora_merge_ctx { // process base model tensors size_t n_merged = 0; - for (auto & it : base_to_out_tensors) { - if (it.second != nullptr) { - merge_tensor(it.first, it.second); + for (auto & it : trans) { + if (!it.is_copy) { + merge_tensor(it.in, it.out); n_merged++; } else { - copy_tensor(it.first); + copy_tensor(it.in); } } @@ -252,7 +265,7 @@ struct lora_merge_ctx { } printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged); - printf("%s : wrote %ld tensors to output file\n", __func__, base_to_out_tensors.size()); + printf("%s : wrote %ld tensors to output file\n", __func__, trans.size()); } void copy_tensor(struct ggml_tensor * base) { @@ -285,6 +298,10 @@ struct lora_merge_ctx { for (size_t i = 0; i < adapters.size(); ++i) { auto t_a = adapters[i]->get_tensor(name_lora_a); auto t_b = adapters[i]->get_tensor(name_lora_b); + // TODO: add support for quantized lora + if (ggml_is_quantized(t_a->type) || ggml_is_quantized(t_b->type)) { + throw std::runtime_error("quantized LoRA adapters is not supported, please retry with f16 or f32"); + } inp_a[i] = ggml_dup_tensor(ctx, t_a); inp_b[i] = ggml_dup_tensor(ctx, t_b); } From 06943a69f678fb32829ff06d9c18367b17d4b361 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 13 Aug 2024 21:13:15 +0200 Subject: [PATCH 19/28] ggml : move rope type enum to ggml.h (#8949) * ggml : move rope type enum to ggml.h This commit moves the `llama_rope_type` enum from `llama.h` to `ggml.h` and changes its name to `ggml_rope_type`. The motivation for this change is to address the TODO in `llama.h` and use the enum in ggml. Note: This commit does not change the `mode` parameter to be of type `enum ggml_rope_type`. The name `mode` and its usage suggest that it might be more generic and possibly used as a bit field for multiple flags. Further investigation/discussion may be needed to determine if `mode` should be restricted to RoPE types. * squash! ggml : move rope type enum to ggml.h This commit removes GGML_ROPE_TYPE_NONE and GGML_ROPE_TYPE_GLM from ggml.h, and back the llama_rope_type enum. I've kept the assert for GGML_ROPE_TYPE_GLM as I'm not sure if it is safe to remove it yet. * squash! ggml : move rope type enum to ggml.h This commit removes the enum ggml_rope_type from ggml.h and replaces it with a define (GGML_ROPE_TYPE_NEOX). This define is used in the code to check if the mode is set to GPT-NeoX. Also the enum llama_rope_type has been updated to reflect this change. * squash! ggml : move rope type enum to ggml.h This commit contains a suggestion enable the GGML_ROPE_TYPE_NEOX macro/define to be passed to the shader compiler. * squash! ggml : move rope type enum to ggml.h This commit fixes the editorconfig-checker warnings. * squash! ggml : move rope type enum to ggml.h Update comment for ggml_rope function. * Revert "squash! ggml : move rope type enum to ggml.h" This reverts commit 6261222bd0dc0efd51f0fb0435ad3f16a5b52fd6. * squash! ggml : move rope type enum to ggml.h Add GGML_ROPE_TYPE_NEOX to rope_common.comp. * remove extra line --------- Co-authored-by: slaren --- ggml/include/ggml.h | 6 ++++-- ggml/src/ggml-cann/aclnn_ops.cpp | 2 +- ggml/src/ggml-cuda/rope.cu | 2 +- ggml/src/ggml-metal.m | 2 +- ggml/src/ggml-sycl/rope.cpp | 2 +- ggml/src/ggml-vulkan.cpp | 2 +- ggml/src/ggml.c | 4 ++-- ggml/src/kompute-shaders/op_rope_f16.comp | 2 +- ggml/src/kompute-shaders/op_rope_f32.comp | 2 +- ggml/src/kompute-shaders/rope_common.comp | 2 ++ include/llama.h | 7 ++----- 11 files changed, 17 insertions(+), 16 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 15602a96df7ad..1d2a354024675 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -244,6 +244,8 @@ #define GGML_EXIT_SUCCESS 0 #define GGML_EXIT_ABORTED 1 +#define GGML_ROPE_TYPE_NEOX 2 + #define GGUF_MAGIC "GGUF" #define GGUF_VERSION 3 @@ -1453,8 +1455,8 @@ extern "C" { struct ggml_tensor * b); // rotary position embedding - // if mode & 1 == 1, skip n_past elements (NOT SUPPORTED) - // if mode & 2 == 1, GPT-NeoX style + // if (mode & 1) - skip n_past elements (NOT SUPPORTED) + // if (mode & GGML_ROPE_TYPE_NEOX) - GPT-NeoX style // // b is an int32 vector with size a->ne[2], it contains the positions GGML_API struct ggml_tensor * ggml_rope( diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index 8c4132f5bb7ad..a4ec8418e2ab3 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -2881,7 +2881,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; // init cos/sin cache ggml_cann_pool_alloc sin_allocator( diff --git a/ggml/src/ggml-cuda/rope.cu b/ggml/src/ggml-cuda/rope.cu index 99ec1dd98ca9c..88f586d689cfd 100644 --- a/ggml/src/ggml-cuda/rope.cu +++ b/ggml/src/ggml-cuda/rope.cu @@ -226,7 +226,7 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const int32_t * pos = (const int32_t *) src1_d; diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m index aad189430ab0b..995f1934bc73b 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal.m @@ -2313,7 +2313,7 @@ static enum ggml_status ggml_metal_graph_compute( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; id pipeline = nil; diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp index c7545bcc1a8a9..1f06f78fa3d91 100644 --- a/ggml/src/ggml-sycl/rope.cpp +++ b/ggml/src/ggml-sycl/rope.cpp @@ -226,7 +226,7 @@ void ggml_sycl_op_rope( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const int32_t * pos = (const int32_t *) src1_dd; diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index 86732837254f0..c0504e43429be 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -4053,7 +4053,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const case GGML_OP_ROPE: { const int mode = ((const int32_t *) dst->op_params)[2]; - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index c9b0e81684903..88e4fb7325dd9 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -14094,7 +14094,7 @@ static void ggml_compute_forward_rope_f32( float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const float * freq_factors = NULL; if (src2 != NULL) { @@ -14219,7 +14219,7 @@ static void ggml_compute_forward_rope_f16( float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - const bool is_neox = mode & 2; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; const float * freq_factors = NULL; if (src2 != NULL) { diff --git a/ggml/src/kompute-shaders/op_rope_f16.comp b/ggml/src/kompute-shaders/op_rope_f16.comp index 1a4058b3f1f10..0ecfb2eab527c 100644 --- a/ggml/src/kompute-shaders/op_rope_f16.comp +++ b/ggml/src/kompute-shaders/op_rope_f16.comp @@ -11,7 +11,7 @@ void main() { const uint i2 = gl_WorkGroupID.y; const uint i1 = gl_WorkGroupID.x; - const bool is_neox = (pcs.mode & 2) != 0; + const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0; float corr_dims[2]; rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); diff --git a/ggml/src/kompute-shaders/op_rope_f32.comp b/ggml/src/kompute-shaders/op_rope_f32.comp index 65e03827a2660..cec0fd9a5d10c 100644 --- a/ggml/src/kompute-shaders/op_rope_f32.comp +++ b/ggml/src/kompute-shaders/op_rope_f32.comp @@ -11,7 +11,7 @@ void main() { const uint i2 = gl_WorkGroupID.y; const uint i1 = gl_WorkGroupID.x; - const bool is_neox = (pcs.mode & 2) != 0; + const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0; float corr_dims[2]; rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); diff --git a/ggml/src/kompute-shaders/rope_common.comp b/ggml/src/kompute-shaders/rope_common.comp index 7b9394cb2fffc..df4702896d46f 100644 --- a/ggml/src/kompute-shaders/rope_common.comp +++ b/ggml/src/kompute-shaders/rope_common.comp @@ -1,5 +1,7 @@ #include "common.comp" +#define GGML_ROPE_TYPE_NEOX 2 + // TODO: use a local size of 32 or more (Metal uses 1024) layout(local_size_x = 1) in; diff --git a/include/llama.h b/include/llama.h index ce07f4fac8f10..3c28cf0b509fb 100644 --- a/include/llama.h +++ b/include/llama.h @@ -95,13 +95,10 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22, }; - // note: these values should be synchronized with ggml_rope - // TODO: maybe move this enum to ggml.h (ggml_rope_type) enum llama_rope_type { LLAMA_ROPE_TYPE_NONE = -1, - LLAMA_ROPE_TYPE_NORM = 0, - LLAMA_ROPE_TYPE_NEOX = 2, - LLAMA_ROPE_TYPE_GLM = 4, + LLAMA_ROPE_TYPE_NORM = 0, + LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX, }; enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file From 43bdd3ce188cd247bda7c1bd1ad01fa64e566690 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 14 Aug 2024 09:14:49 +0300 Subject: [PATCH 20/28] cmake : remove unused option GGML_CURL (#9011) --- ggml/CMakeLists.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 7fe1661bb96b4..357e7e51e9152 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -129,7 +129,6 @@ option(GGML_CUDA_NO_VMM "ggml: do not try to use CUDA VMM" option(GGML_CUDA_FA_ALL_QUANTS "ggml: compile all quants for FlashAttention" OFF) option(GGML_CUDA_USE_GRAPHS "ggml: use CUDA graphs (llama.cpp only)" OFF) -option(GGML_CURL "ggml: use libcurl to download model from an URL" OFF) option(GGML_HIPBLAS "ggml: use hipBLAS" OFF) option(GGML_HIP_UMA "ggml: use HIP unified memory architecture" OFF) option(GGML_VULKAN "ggml: use Vulkan" OFF) From 98a532d474c73d3494a5353024cb6a4fbbabbb35 Mon Sep 17 00:00:00 2001 From: compilade Date: Wed, 14 Aug 2024 02:51:02 -0400 Subject: [PATCH 21/28] server : fix segfault on long system prompt (#8987) * server : fix segfault on long system prompt * server : fix parallel generation with very small batch sizes * server : fix typo in comment --- examples/server/server.cpp | 31 +++++++++++-------------------- 1 file changed, 11 insertions(+), 20 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1621c7c43961c..c25338f573767 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -754,13 +754,13 @@ struct server_context { default_generation_settings_for_props = get_formated_generation(slots.front()); default_generation_settings_for_props["seed"] = -1; - // the update_slots() logic will always submit a maximum of n_batch tokens + // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used) { const int32_t n_batch = llama_n_batch(ctx); // only a single seq_id per token is needed - batch = llama_batch_init(n_batch, 0, 1); + batch = llama_batch_init(std::max(n_batch, params.n_parallel), 0, 1); } metrics.init(); @@ -1137,28 +1137,19 @@ struct server_context { if (!system_prompt.empty()) { system_tokens = ::llama_tokenize(ctx, system_prompt, true); - llama_batch_clear(batch); + const int32_t n_batch = llama_n_batch(ctx); + const int32_t n_tokens_prompt = system_tokens.size(); - for (int i = 0; i < (int)system_tokens.size(); ++i) { - llama_batch_add(batch, system_tokens[i], i, { 0 }, false); - } + for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) { + const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i); - const int32_t n_batch = llama_n_batch(ctx); + llama_batch_clear(batch); - for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { - const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i); - llama_batch batch_view = { - n_tokens, - batch.token + i, - nullptr, - batch.pos + i, - batch.n_seq_id + i, - batch.seq_id + i, - batch.logits + i, - 0, 0, 0, // unused - }; + for (int32_t j = 0; j < n_tokens; ++j) { + llama_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false); + } - if (llama_decode(ctx, batch_view) != 0) { + if (llama_decode(ctx, batch) != 0) { LOG_ERROR("llama_decode() failed", {}); return; } From 5fd89a70ead34d1a17015ddecad05aaa2490ca46 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 14 Aug 2024 18:32:53 +0200 Subject: [PATCH 22/28] Vulkan Optimizations and Fixes (#8959) * Optimize Vulkan REPEAT performance * Use Vulkan GLSL fused multiply-add instruction where possible * Add GGML_VULKAN_PERF option to output performance data per operator * Rework and fix Vulkan descriptor set and descriptor pool handling * Fix float32 concat f16 shader validation error * Add Vulkan GROUP_NORM eps parameter * Fix validation error with transfer queue memory barrier flags * Remove trailing whitespaces --- Makefile | 4 + ggml/CMakeLists.txt | 1 + ggml/src/CMakeLists.txt | 4 + ggml/src/ggml-vulkan.cpp | 1374 ++++++++--------- ggml/src/vulkan-shaders/concat.comp | 6 +- ggml/src/vulkan-shaders/mul_mat_vec.comp | 3 +- ggml/src/vulkan-shaders/mul_mat_vec_nc.comp | 2 +- ggml/src/vulkan-shaders/mul_mat_vec_p021.comp | 2 +- ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp | 35 +- ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp | 19 +- ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp | 45 +- ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp | 56 +- ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp | 26 +- ggml/src/vulkan-shaders/mul_mm.comp | 15 +- ggml/src/vulkan-shaders/repeat.comp | 24 + .../src/vulkan-shaders/vulkan-shaders-gen.cpp | 4 + 16 files changed, 775 insertions(+), 845 deletions(-) create mode 100644 ggml/src/vulkan-shaders/repeat.comp diff --git a/Makefile b/Makefile index 649671ed6a72e..332496cfc39c1 100644 --- a/Makefile +++ b/Makefile @@ -763,6 +763,10 @@ ifdef GGML_VULKAN_MEMORY_DEBUG MK_CPPFLAGS += -DGGML_VULKAN_MEMORY_DEBUG endif +ifdef GGML_VULKAN_PERF + MK_CPPFLAGS += -DGGML_VULKAN_PERF +endif + ifdef GGML_VULKAN_VALIDATE MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE endif diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 357e7e51e9152..cc16858849783 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -135,6 +135,7 @@ option(GGML_VULKAN "ggml: use Vulkan" option(GGML_VULKAN_CHECK_RESULTS "ggml: run Vulkan op checks" OFF) option(GGML_VULKAN_DEBUG "ggml: enable Vulkan debug output" OFF) option(GGML_VULKAN_MEMORY_DEBUG "ggml: enable Vulkan memory debug output" OFF) +option(GGML_VULKAN_PERF "ggml: enable Vulkan perf output" OFF) option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation" OFF) option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF) option(GGML_KOMPUTE "ggml: use Kompute" OFF) diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 425a2589502eb..1775ef3cc9146 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -602,6 +602,10 @@ if (GGML_VULKAN) add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) endif() + if (GGML_VULKAN_PERF) + add_compile_definitions(GGML_VULKAN_PERF) + endif() + if (GGML_VULKAN_VALIDATE) add_compile_definitions(GGML_VULKAN_VALIDATE) endif() diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index c0504e43429be..7a0ec706f4c31 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -1,6 +1,6 @@ #include "ggml-vulkan.h" #include -#ifdef GGML_VULKAN_RUN_TESTS +#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_PERF) #include #endif @@ -17,6 +17,7 @@ #include #include #include +#include #include #include @@ -34,9 +35,7 @@ #define VK_VENDOR_ID_INTEL 0x8086 #define VK_VENDOR_ID_NVIDIA 0x10de -#define VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN 0 -#define VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI 1 -#define VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE 2 +#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 32 #define GGML_VK_MAX_NODES 8192 @@ -74,6 +73,8 @@ struct vk_queue { std::vector cmd_buffers; vk::PipelineStageFlags stage_flags; + + bool transfer_only; }; struct vk_pipeline_struct { @@ -133,6 +134,9 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { #ifdef GGML_VULKAN_MEMORY_DEBUG class vk_memory_logger; #endif +#ifdef GGML_VULKAN_PERF +class vk_perf_logger; +#endif static void ggml_vk_destroy_buffer(vk_buffer& buf); struct vk_device_struct { @@ -148,7 +152,6 @@ struct vk_device_struct { vk_queue compute_queue; vk_queue transfer_queue; bool single_queue; - uint32_t descriptor_set_mode; uint32_t subgroup_size; bool uma; @@ -186,6 +189,7 @@ struct vk_device_struct { vk_pipeline pipeline_sqr_f32; vk_pipeline pipeline_clamp_f32; vk_pipeline pipeline_pad_f32; + vk_pipeline pipeline_repeat_f32; vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; vk_pipeline pipeline_norm_f32; vk_pipeline pipeline_group_norm_f32; @@ -205,7 +209,8 @@ struct vk_device_struct { vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16; vk_pipeline pipeline_timestep_embedding_f32; - std::vector pipelines; + std::unordered_map pipelines; + std::unordered_map pipeline_descriptor_set_requirements; std::vector> pinned_memory; @@ -217,6 +222,9 @@ struct vk_device_struct { #ifdef GGML_VULKAN_MEMORY_DEBUG std::unique_ptr memory_logger; #endif +#ifdef GGML_VULKAN_PERF + std::unique_ptr perf_logger; +#endif ~vk_device_struct() { VK_LOG_DEBUG("destroy device " << name); @@ -231,11 +239,11 @@ struct vk_device_struct { } for (auto& pipeline : pipelines) { - if (pipeline.expired()) { + if (pipeline.second.expired()) { continue; } - vk_pipeline pl = pipeline.lock(); + vk_pipeline pl = pipeline.second.lock(); ggml_vk_destroy_pipeline(device, pl); } pipelines.clear(); @@ -479,6 +487,48 @@ class vk_memory_logger { #define VK_LOG_MEMORY(msg) ((void) 0) #endif // GGML_VULKAN_MEMORY_DEBUG +#if defined(GGML_VULKAN_PERF) + +class vk_perf_logger { +public: + void print_timings() { + std::cerr << "----------------\nVulkan Timings:" << std::endl; + for (const auto& t : timings) { + uint64_t total = 0; + for (const auto& time : t.second) { + total += time; + } + std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " ms" << std::endl; + } + + timings.clear(); + } + + void log_timing(const ggml_tensor * node, uint64_t time) { + if (node->op == GGML_OP_UNARY) { + timings[ggml_unary_op_name(ggml_get_unary_op(node))].push_back(time); + return; + } + if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { + const uint64_t m = node->src[0]->ne[1]; + const uint64_t n = node->src[1]->ne[1]; + const uint64_t k = node->src[1]->ne[0]; + std::string name = ggml_op_name(node->op); + if (n == 1) { + name += "_VEC m=" + std::to_string(m) + " k=" + std::to_string(k); + } else { + name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k); + } + timings[name].push_back(time); + return; + } + timings[ggml_op_name(node->op)].push_back(time); + } +private: + std::map> timings; +}; +#endif // GGML_VULKAN_PERF + struct ggml_backend_vk_context { std::string name; @@ -489,9 +539,6 @@ struct ggml_backend_vk_context { size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k; vk_buffer prealloc_x, prealloc_y, prealloc_split_k; vk::Fence fence; - vk_buffer staging; - size_t staging_size; - size_t staging_offset; vk_buffer buffer_pool[MAX_VK_BUFFERS]; @@ -595,35 +642,9 @@ static void ggml_vk_create_pipeline(vk_device& device, vk_pipeline& pipeline, co descriptor_set_layout_create_info.setPNext(&dslbfci); pipeline->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info); - // Check if device supports multiple descriptors per pool - if (device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { - const uint32_t alloc_count = 2; - - // Try allocating multiple sets from one pool - // This fails on AMD for some reason, so add a fall back to allocating one pool per set - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); - vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); - vk::DescriptorPool pool = device->device.createDescriptorPool(descriptor_pool_create_info); - - std::vector layouts(alloc_count); - for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline->dsl; - } - try { - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); - std::vector sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info); - } catch(vk::OutOfPoolMemoryError const&) { - device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; - } - - device->device.destroyDescriptorPool(pool); - } - - if (device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); - vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); - pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); - } + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE); + vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size); + pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); pipeline->descriptor_set_idx = 0; @@ -657,7 +678,7 @@ static void ggml_vk_create_pipeline(vk_device& device, vk_pipeline& pipeline, co pipeline->layout); pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - device->pipelines.push_back(pipeline); + device->pipelines.insert({ pipeline->name, pipeline }); } static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { @@ -678,34 +699,49 @@ static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) device.destroyPipeline(pipeline->pipeline); } -static void ggml_pipeline_allocate_descriptor_sets(vk_device& device, vk_pipeline& pipeline, uint32_t n) { - VK_LOG_DEBUG("ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")"); - if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) { - // Enough descriptors are available - return; - } +static void ggml_pipeline_request_descriptor_sets(vk_device& device, vk_pipeline& pipeline, uint32_t n) { + VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")"); + device->pipeline_descriptor_set_requirements[pipeline->name] += n; +} +static void ggml_pipeline_allocate_descriptor_sets(vk_device& device) { std::lock_guard guard(device->mutex); - if (device->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { - const uint32_t alloc_count = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size(); + for (auto& pair : device->pipeline_descriptor_set_requirements) { + vk_pipeline pipeline = device->pipelines.at(pair.first).lock(); + const uint64_t n = pair.second; + + VK_LOG_DEBUG("ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")"); - std::vector layouts(alloc_count); - for (uint32_t i = 0; i < alloc_count; i++) { - layouts[i] = pipeline->dsl; + if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) { + // Enough descriptors are available + continue; } - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[0], alloc_count, layouts.data()); - std::vector sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end()); - } else { - for (uint32_t i = pipeline->descriptor_sets.size(); i < pipeline->descriptor_set_idx + n; i++) { - vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count); - vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); - pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); - vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[i], 1, &pipeline->dsl); + uint32_t to_alloc = pipeline->descriptor_set_idx + n - pipeline->descriptor_sets.size(); + uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - pipeline->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE; + uint32_t pool_idx = pipeline->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE; + + while (to_alloc > 0) { + const uint32_t alloc_count = std::min(pool_remaining, to_alloc); + to_alloc -= alloc_count; + pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE; + + if (pool_idx >= pipeline->descriptor_pools.size()) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline->parameter_count * VK_DEVICE_DESCRIPTOR_POOL_SIZE); + vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size); + pipeline->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); + } + + std::vector layouts(alloc_count); + for (uint32_t i = 0; i < alloc_count; i++) { + layouts[i] = pipeline->dsl; + } + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline->descriptor_pools[pool_idx], alloc_count, layouts.data()); std::vector sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info); - pipeline->descriptor_sets.push_back(sets[0]); + pipeline->descriptor_sets.insert(pipeline->descriptor_sets.end(), sets.begin(), sets.end()); + + pool_idx++; } } } @@ -866,11 +902,12 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector guard(device->mutex); q.queue_family_index = queue_family_index; + q.transfer_only = transfer_only; vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index); q.pool = device->device.createCommandPool(command_pool_create_info_compute); @@ -1067,13 +1104,16 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { static void ggml_vk_sync_buffers(vk_context& ctx) { VK_LOG_DEBUG("ggml_vk_sync_buffers()"); + + const bool transfer_queue = ctx->q->transfer_only; + ctx->s->buffer.pipelineBarrier( ctx->q->stage_flags, ctx->q->stage_flags, {}, { { - {vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite}, - {vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite} + { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }, + { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) } } }, {}, {} @@ -1664,6 +1704,8 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_repeat_f32, "repeat_f32", repeat_f32_len, repeat_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_gelu_quick_f32, "gelu_quick_f32", gelu_quick_f32_len, gelu_quick_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); @@ -1703,6 +1745,9 @@ static vk_device ggml_vk_get_device(size_t idx) { #ifdef GGML_VULKAN_MEMORY_DEBUG device->memory_logger = std::unique_ptr(new vk_memory_logger()); #endif +#ifdef GGML_VULKAN_PERF + device->perf_logger = std::unique_ptr(new vk_perf_logger()); +#endif size_t dev_num = vk_instance.device_indices[idx]; @@ -1833,17 +1878,15 @@ static vk_device ggml_vk_get_device(size_t idx) { device_create_info.setPNext(&device_features2); device->device = device->physical_device.createDevice(device_create_info); - device->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; - // Queues - ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); + ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); // Shaders ggml_vk_load_shaders(device); if (!device->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); } else { // TODO: Use pointer or reference to avoid copy device->transfer_queue = device->compute_queue; @@ -2130,9 +2173,6 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->fence = ctx->device->device.createFence({}); - ctx->staging_size = 0; - ctx->staging_offset = 0; - #ifdef GGML_VULKAN_CHECK_RESULTS const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks)); @@ -2565,23 +2605,15 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont return; } - // Staging buffer required - vk_buffer staging = ctx->staging; - size_t staging_offset = ctx->staging_offset; - const size_t copy_size = ts*ne/bs; - if (ctx->staging->size < ctx->staging_offset + copy_size) { - if (sync_staging) { - // Create temporary larger buffer - ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size); - - staging = ctx->device->sync_staging; - staging_offset = 0; - } else { - GGML_ABORT("fatal error"); - } + if (!sync_staging) { + GGML_ABORT("Asynchronous write to non-pinned memory not supported"); } - VkBufferCopy buf_copy{ staging_offset, offset, copy_size }; + // Staging buffer required + vk_buffer& staging = ctx->device->sync_staging; + const uint64_t copy_size = ts*ne/bs; + ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size); + VkBufferCopy buf_copy{ 0, offset, copy_size }; ggml_vk_sync_buffers(subctx); vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); @@ -2590,14 +2622,14 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont for (uint64_t i2 = 0; i2 < ne2; i2++) { // Find longest contiguous slice if (ne1*nb1 == dstnb2) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); } else { for (uint64_t i1 = 0; i1 < ne1; i1++) { if (ne0*nb0/bs == dstnb1) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); } else { const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; - const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; + const uint64_t d_off = i3*dstnb3 + i2*dstnb2 + i1*dstnb1; for (uint64_t i0 = 0; i0 < ne0; i0++) { deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys); } @@ -2608,7 +2640,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont } } -static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, vk_buffer staging_buffer, size_t staging_offset, bool sync_staging = false) { +static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")"); // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { @@ -2643,21 +2675,18 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz } VK_LOG_DEBUG("STAGING"); + if (!sync_staging) { + GGML_ABORT("Asynchronous write to non-pinned memory not supported"); + } + // Staging buffer required const size_t copy_size = width*height; - if (staging_buffer == nullptr || staging_buffer->size < staging_offset + copy_size) { - if (sync_staging) { - ggml_vk_ensure_sync_staging_buffer(dst->device, copy_size); + ggml_vk_ensure_sync_staging_buffer(dst->device, copy_size); - staging_buffer = dst->device->sync_staging; - staging_offset = 0; - } else { - GGML_ABORT("fatal error"); - } - } + vk_buffer& staging_buffer = dst->device->sync_staging; VkBufferCopy buf_copy = { - staging_offset, + 0, offset, copy_size}; @@ -2665,17 +2694,17 @@ static void ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, siz vkCmdCopyBuffer(subctx->s->buffer, staging_buffer->buffer, dst->buffer, 1, &buf_copy); if (width == spitch) { - deferred_memcpy((uint8_t *)staging_buffer->ptr + staging_offset, src, width * height, &subctx->in_memcpys); + deferred_memcpy((uint8_t *)staging_buffer->ptr, src, width * height, &subctx->in_memcpys); } else { for (size_t i = 0; i < height; i++) { - deferred_memcpy((uint8_t *)staging_buffer->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); + deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); } } } -static void ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, vk_buffer staging_buffer, size_t staging_offset, bool sync_staging = false) { +static void ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")"); - return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, 1, staging_buffer, staging_offset, sync_staging); + return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, 1, sync_staging); } static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { @@ -2690,7 +2719,7 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * } else { vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue); ggml_vk_ctx_begin(dst->device, subctx); - ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, width, height, nullptr, 0, true); + ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, width, height, true); ggml_vk_ctx_end(subctx); for (auto& cpy : subctx->in_memcpys) { @@ -2708,7 +2737,7 @@ static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); } -static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, vk_buffer staging_buffer, size_t staging_offset, bool sync_staging = false) { +static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")"); GGML_ASSERT(width > 0); GGML_ASSERT(height > 0); @@ -2745,18 +2774,15 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size } VK_LOG_DEBUG("STAGING"); + if (!sync_staging) { + GGML_ABORT("Asynchronous read from non-pinned memory not supported"); + } + // Fall back to staging buffer const size_t copy_size = dpitch * height; - if (staging_buffer == nullptr || staging_buffer->size < staging_offset + copy_size) { - if (sync_staging) { - // Create temporary larger buffer - ggml_vk_ensure_sync_staging_buffer(src->device, copy_size); + ggml_vk_ensure_sync_staging_buffer(src->device, copy_size); - staging_buffer = src->device->sync_staging; - } else { - GGML_ABORT("fatal error"); - } - } + vk_buffer& staging_buffer = src->device->sync_staging; ggml_vk_sync_buffers(subctx); subctx->s->buffer.copyBuffer(src->buffer, staging_buffer->buffer, slices); @@ -2764,8 +2790,8 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys); } -static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, vk_buffer staging_buffer, size_t staging_offset, bool sync_staging = false) { - return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, staging_buffer, staging_offset, sync_staging); +static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) { + return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging); } static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) { @@ -2777,7 +2803,7 @@ static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_ } else { vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue); ggml_vk_ctx_begin(src->device, subctx); - ggml_vk_buffer_read_async(subctx, src, offset, dst, size, nullptr, 0, true); + ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true); ggml_vk_ctx_end(subctx); ggml_vk_submit(subctx, src->device->fence); @@ -2978,10 +3004,11 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 }); } -static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -3055,6 +3082,56 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + } else { + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + + if (dryrun) { + const uint64_t x_sz_upd = x_sz * ne02 * ne03; + const uint64_t y_sz_upd = y_sz * ne12 * ne13; + const uint64_t split_k_size = split_k > 1 ? d_sz * ne12 * ne13 * 4 : 0; + if ( + (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || + (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size) || + (split_k > 1 && split_k_size > ctx->device->max_memory_allocation_size)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { + ctx->prealloc_size_x = x_sz_upd; + } + if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { + ctx->prealloc_size_y = y_sz_upd; + } + if (split_k > 1 && ctx->prealloc_size_split_k < split_k_size) { + ctx->prealloc_size_split_k = split_k_size; + } + + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); + } + if (split_k > 1) { + ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, 1); + } + return; + } + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); @@ -3090,34 +3167,6 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); - } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - - // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx->device, pipeline, 1); - if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_1, 1); - } - if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, 1); - } - if (x_non_contig) { ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); } else if (qx_needs_dequant) { @@ -3151,10 +3200,11 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub ); // NOLINT } -static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << (dryrun ? "dryrun" : "") << "),)"); GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -3218,6 +3268,47 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + GGML_ASSERT(dmmv != nullptr); + + if (dryrun) { + const uint64_t x_sz_upd = x_sz * ne02 * ne03; + const uint64_t y_sz_upd = y_sz * ne12 * ne13; + if ( + (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || + (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { + ctx->prealloc_size_x = x_sz_upd; + } + if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { + ctx->prealloc_size_y = y_sz_upd; + } + + // Request descriptor sets + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); + } + ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1); + return; + } + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); @@ -3250,30 +3341,6 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - GGML_ASSERT(dmmv != nullptr); - - // Allocate descriptor sets - if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); - } - ggml_pipeline_allocate_descriptor_sets(ctx->device, dmmv, ne12 * ne13); - if (x_non_contig) { GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); @@ -3316,10 +3383,11 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& sizeof(vk_mat_vec_push_constants), &pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z }); } -static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32(" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT @@ -3360,6 +3428,12 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; + if (dryrun) { + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1); + return; + } + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); @@ -3372,9 +3446,6 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c GGML_ASSERT(d_Qx != nullptr); } - // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; @@ -3387,10 +3458,11 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); } -static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); @@ -3435,6 +3507,12 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const uint64_t qy_sz = ggml_nbytes(src1); const uint64_t d_sz = sizeof(float) * d_ne; + if (dryrun) { + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1); + return; + } + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); @@ -3447,9 +3525,6 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con GGML_ASSERT(d_Qx != nullptr); } - // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_mul_mat_vec_nc_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; @@ -3463,20 +3538,20 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); } -static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")"); if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1) { - ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst); + ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst, dryrun); } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1) { - ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst); + ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun); } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst); + ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun); } else { - ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst); + ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, dryrun); } } -static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { +static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; @@ -3566,6 +3641,48 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& const uint64_t ids_sz = nbi2; const uint64_t d_sz = sizeof(float) * d_ne; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + } else { + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + + if (dryrun) { + const uint64_t x_sz_upd = x_sz * ne02 * ne03; + const uint64_t y_sz_upd = y_sz * ne12 * ne13; + if ( + (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || + (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { + ctx->prealloc_size_x = x_sz_upd; + } + if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { + ctx->prealloc_size_y = y_sz_upd; + } + + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); + } + return; + } + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); @@ -3605,31 +3722,6 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); - } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - - // Allocate descriptor sets - ggml_pipeline_allocate_descriptor_sets(ctx->device, pipeline, 1); - if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_1, 1); - } - if (x_non_contig) { ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); } else if (qx_needs_dequant) { @@ -3664,11 +3756,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& ); // NOLINT } -static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ids->type == GGML_TYPE_I32); @@ -3742,6 +3835,47 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte const uint64_t ids_sz = nbi2; const uint64_t d_sz = sizeof(float) * d_ne; + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type); + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + GGML_ASSERT(dmmv != nullptr); + + if (dryrun) { + const uint64_t x_sz_upd = x_sz * ne02 * ne03; + const uint64_t y_sz_upd = y_sz * ne12 * ne13; + if ( + (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || + (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz_upd) { + ctx->prealloc_size_x = x_sz_upd; + } + if (qy_needs_dequant && ctx->prealloc_size_y < y_sz_upd) { + ctx->prealloc_size_y = y_sz_upd; + } + + // Request descriptor sets + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx->device, to_fp16_vk_1, 1); + } + ggml_pipeline_request_descriptor_sets(ctx->device, dmmv, 1); + return; + } + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); @@ -3779,30 +3913,6 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte GGML_ASSERT(qy_sz == y_sz); } - vk_pipeline to_fp16_vk_0 = nullptr; - vk_pipeline to_fp16_vk_1 = nullptr; - if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); - } - if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); - } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); - } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type); - GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT - GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT - GGML_ASSERT(dmmv != nullptr); - - // Allocate descriptor sets - if (qx_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_0, 1); - } - if (qy_needs_dequant) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); - } - ggml_pipeline_allocate_descriptor_sets(ctx->device, dmmv, ne12 * ne13); - if (x_non_contig) { GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); @@ -3841,85 +3951,15 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte sizeof(vk_mat_vec_id_push_constants), &pc, { groups_x, (uint32_t)nei0, groups_z }); } -static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { +static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")"); if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst); + ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun); } else { - ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst); - } -} - -static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - VK_LOG_DEBUG("ggml_vk_op_repeat(" << src0 << ", " << src1 << ", " << dst << ")"); - const uint64_t ne0 = dst->ne[0]; - const uint64_t ne1 = dst->ne[1]; - const uint64_t ne2 = dst->ne[2]; - const uint64_t ne3 = dst->ne[3]; - - const uint64_t ne00 = src0->ne[0]; - const uint64_t ne01 = src0->ne[1]; - const uint64_t ne02 = src0->ne[2]; - const uint64_t ne03 = src0->ne[3]; - - const uint64_t nb0 = dst->nb[0]; - const uint64_t nb1 = dst->nb[1]; - const uint64_t nb2 = dst->nb[2]; - const uint64_t nb3 = dst->nb[3]; - - const uint64_t nb00 = src0->nb[0]; - const uint64_t nb01 = src0->nb[1]; - const uint64_t nb02 = src0->nb[2]; - const uint64_t nb03 = src0->nb[3]; - - // guaranteed to be an integer due to the check in ggml_can_repeat - const uint64_t nr0 = ne0/ne00; - const uint64_t nr1 = ne1/ne01; - const uint64_t nr2 = ne2/ne02; - const uint64_t nr3 = ne3/ne03; - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - - const vk_buffer src_buf = extra_src0->buffer_gpu.lock(); - const uint64_t src_offset = extra_src0->offset + src0->view_offs; - vk_buffer dst_buf = extra->buffer_gpu.lock(); - const uint64_t dst_offset = extra->offset + dst->view_offs; - - std::vector copies; - - for (uint64_t i3 = 0; i3 < nr3; i3++) { - for (uint64_t k3 = 0; k3 < ne03; k3++) { - for (uint64_t i2 = 0; i2 < nr2; i2++) { - for (uint64_t k2 = 0; k2 < ne02; k2++) { - for (uint64_t i1 = 0; i1 < nr1; i1++) { - for (uint64_t k1 = 0; k1 < ne01; k1++) { - for (uint64_t i0 = 0; i0 < nr0; i0++) { - copies.push_back({ - src_offset + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01, - dst_offset + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0, - ne00*nb0, - }); - } - } - } - } - } - } + ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun); } - - ggml_vk_sync_buffers(subctx); - subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); - - GGML_UNUSED(ctx); - GGML_UNUSED(src1); } - static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_GET_ROWS: @@ -3985,6 +4025,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_pad_f32; } return nullptr; + case GGML_OP_REPEAT: + if (ggml_type_size(src0->type) == sizeof(float) && ggml_type_size(dst->type) == sizeof(float)) { + return ctx->device->pipeline_repeat_f32; + } + return nullptr; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: @@ -4107,15 +4152,6 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const GGML_UNUSED(src2); } -static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { - switch(op) { - case GGML_OP_REPEAT: - return ggml_vk_op_repeat; - default: - return nullptr; - } -} - static bool ggml_vk_op_supports_incontiguous(ggml_op op) { switch (op) { case GGML_OP_CPY: @@ -4129,6 +4165,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_SQR: case GGML_OP_CLAMP: case GGML_OP_PAD: + case GGML_OP_REPEAT: return true; default: return false; @@ -4136,7 +4173,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -4144,7 +4181,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co if (src2 != nullptr) { std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; } - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")"); + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << ggml_op_name(op) << ", " << (dryrun ? "dryrun" : "") << ")"); GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT GGML_ASSERT(ggml_vk_op_supports_incontiguous(op) || ggml_vk_dim01_contiguous(src0)); // NOLINT GGML_ASSERT(dst->extra != nullptr); @@ -4176,20 +4214,18 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co const uint64_t ned = ned0 * ned1; vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); - ggml_vk_func_t op_func; if (pipeline == nullptr) { - op_func = ggml_vk_op_get_func(op); - if (op_func == nullptr) { - std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type); - if (src1 != nullptr) { - std::cerr << " and " << ggml_type_name(src1->type); - } - std::cerr << " to " << ggml_type_name(dst->type) << std::endl; - GGML_ABORT("fatal error"); + std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type); + if (src1 != nullptr) { + std::cerr << " and " << ggml_type_name(src1->type); } + std::cerr << " to " << ggml_type_name(dst->type) << std::endl; + GGML_ABORT("fatal error"); + } - op_func(ctx, subctx, src0, src1, dst); + if (dryrun) { + ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); return; } @@ -4278,188 +4314,141 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co std::array elements; // Single call if dimension 2 is contiguous - if (op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, pipeline, 1); - - switch (op) { - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_SOFT_MAX: - case GGML_OP_SUM_ROWS: - { - const uint32_t nr = ggml_nrows(src0); - if (nr > 262144) { - elements = { 512, 512, CEIL_DIV(nr, 262144) }; - } else if (nr > 512) { - elements = { 512, CEIL_DIV(nr, 512), 1 }; - } else { - elements = { nr, 1, 1 }; - } - } break; - case GGML_OP_GROUP_NORM: - { - const uint32_t num_groups = dst->op_params[0]; - elements = { num_groups * (uint32_t)src0->ne[3], 1, 1 }; - } break; - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_ROPE: - elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; - break; - case GGML_OP_GET_ROWS: - elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; - break; - case GGML_OP_ARGSORT: - elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 }; - break; - case GGML_OP_IM2COL: - { - const bool is_2D = dst->op_params[6] == 1; + GGML_ASSERT(op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))); - const uint32_t IC = src1->ne[is_2D ? 2 : 1]; - - const uint32_t KH = is_2D ? src0->ne[1] : 1; - const uint32_t KW = src0->ne[0]; + switch (op) { + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_SOFT_MAX: + case GGML_OP_SUM_ROWS: + { + const uint32_t nr = ggml_nrows(src0); + if (nr > 262144) { + elements = { 512, 512, CEIL_DIV(nr, 262144) }; + } else if (nr > 512) { + elements = { 512, CEIL_DIV(nr, 512), 1 }; + } else { + elements = { nr, 1, 1 }; + } + } break; + case GGML_OP_GROUP_NORM: + { + const uint32_t num_groups = dst->op_params[0]; + elements = { num_groups * (uint32_t)src0->ne[3], 1, 1 }; + } break; + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_ROPE: + elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; + break; + case GGML_OP_GET_ROWS: + elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + break; + case GGML_OP_ARGSORT: + elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 }; + break; + case GGML_OP_IM2COL: + { + const bool is_2D = dst->op_params[6] == 1; - const uint32_t OH = is_2D ? dst->ne[2] : 1; - const uint32_t OW = dst->ne[1]; + const uint32_t IC = src1->ne[is_2D ? 2 : 1]; - const uint32_t batch = src1->ne[3]; + const uint32_t KH = is_2D ? src0->ne[1] : 1; + const uint32_t KW = src0->ne[0]; - elements = { OW * KW * KH, OH, batch * IC }; - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - const uint32_t dim = dst->op_params[0]; - uint32_t half_ceil = (dim + 1) / 2; - elements = { half_ceil, (uint32_t)src0->ne[0], 1 }; - } break; - case GGML_OP_ADD: - case GGML_OP_DIV: - case GGML_OP_MUL: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_CPY: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_UNARY: - { - const uint32_t ne = ggml_nelements(dst); - if (ne > 262144) { - elements = { 512, 512, CEIL_DIV(ne, 262144) }; - } else if (ne > 512) { - elements = { 512, CEIL_DIV(ne, 512), 1 }; - } else { - elements = { ne, 1, 1 }; - } - } break; - default: - elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; - break; - } + const uint32_t OH = is_2D ? dst->ne[2] : 1; + const uint32_t OW = dst->ne[1]; - if (!op_supports_incontiguous) { - if (x_sz != VK_WHOLE_SIZE) { - x_sz *= ne02 * ne03; - } - if (use_src1 && y_sz != VK_WHOLE_SIZE) { - y_sz *= ne12 * ne13; - } - if (use_src2 && z_sz != VK_WHOLE_SIZE) { - z_sz *= ne22 * ne23; - } - if (d_sz != VK_WHOLE_SIZE) { - d_sz *= ned2 * ned3; - } - } + const uint32_t batch = src1->ne[3]; - if (op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible in soft_max, but the shader needs a buffer - vk_subbuffer subbuf_y; - if (use_src1) { - subbuf_y = { d_Y, y_buf_offset, y_sz }; + elements = { OW * KW * KH, OH, batch * IC }; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + const uint32_t dim = dst->op_params[0]; + uint32_t half_ceil = (dim + 1) / 2; + elements = { half_ceil, (uint32_t)src0->ne[0], 1 }; + } break; + case GGML_OP_ADD: + case GGML_OP_DIV: + case GGML_OP_MUL: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_PAD: + case GGML_OP_REPEAT: + case GGML_OP_CPY: + case GGML_OP_CONCAT: + case GGML_OP_UPSCALE: + case GGML_OP_UNARY: + { + const uint32_t ne = ggml_nelements(dst); + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; } else { - subbuf_y = { d_X, 0, x_sz }; + elements = { ne, 1, 1 }; } + } break; + default: + elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; + break; + } - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (op == GGML_OP_ROPE) { - // Empty src2 is possible in rope, but the shader needs a buffer - vk_subbuffer subbuf_z; - if (use_src2) { - subbuf_z = { d_Z, z_buf_offset, z_sz }; - } else { - subbuf_z = { d_X, 0, x_sz }; - } + if (!op_supports_incontiguous) { + if (x_sz != VK_WHOLE_SIZE) { + x_sz *= ne02 * ne03; + } + if (use_src1 && y_sz != VK_WHOLE_SIZE) { + y_sz *= ne12 * ne13; + } + if (use_src2 && z_sz != VK_WHOLE_SIZE) { + z_sz *= ne22 * ne23; + } + if (d_sz != VK_WHOLE_SIZE) { + d_sz *= ned2 * ned3; + } + } - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (op == GGML_OP_IM2COL) { - // im2col uses only src1 and dst buffers - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (use_src2) { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); - } else if (use_src1) { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + if (op == GGML_OP_SOFT_MAX) { + // Empty src1 is possible in soft_max, but the shader needs a buffer + vk_subbuffer subbuf_y; + if (use_src1) { + subbuf_y = { d_Y, y_buf_offset, y_sz }; } else { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + subbuf_y = { d_X, 0, x_sz }; } - } else { - GGML_ASSERT(op != GGML_OP_SOFT_MAX); - GGML_ASSERT(op != GGML_OP_ARGSORT); - GGML_ASSERT(!use_src2); - ggml_pipeline_allocate_descriptor_sets(ctx->device, pipeline, ne02 * ne03); - - switch (op) { - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - elements = { (uint32_t)ne01, 1, 1 }; - break; - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_ROPE: - elements = { (uint32_t)ne01, (uint32_t)ne00, 1 }; - break; - case GGML_OP_GET_ROWS: - elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; - break; - default: - elements = { (uint32_t)ne0, 1, 1 }; - break; + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (op == GGML_OP_ROPE) { + // Empty src2 is possible in rope, but the shader needs a buffer + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; + } else { + subbuf_z = { d_X, 0, x_sz }; } - for (uint64_t i03 = 0; i03 < ne03; i03++) { - for (uint64_t i02 = 0; i02 < ne02; i02++) { - const uint32_t it_idx0 = (i03 * ne02 + i02); - const uint32_t it_idx1 = use_src1 ? ((i03 % ne13) * ne12 + (i02 % ne12)) : 0; - const uint32_t x_offset = x_sz * it_idx0; - const uint32_t y_offset = y_sz * it_idx1; - const uint32_t d_offset = d_sz * it_idx0; - - if (use_src1) { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset + x_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset + y_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); - } else { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset + x_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); - } - } - } + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (op == GGML_OP_IM2COL) { + // im2col uses only src1 and dst buffers + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (use_src2) { + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_Z, z_buf_offset, z_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (use_src1) { + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_Y, y_buf_offset, y_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else { + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } } -static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_REPEAT, {}); -} - -static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4471,10 +4460,10 @@ static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, 0, - }); + }, dryrun); } -static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4486,10 +4475,10 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, 0, - }); + }, dryrun); } -static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4501,10 +4490,10 @@ static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, 0, - }); + }, dryrun); } -static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4516,10 +4505,10 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, 0, - }); + }, dryrun); } -static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { int * op_params = (int *)dst->op_params; const uint32_t src0_type_size = ggml_type_size(src0->type); @@ -4533,10 +4522,10 @@ static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, co (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, op_params[0], - }); + }, dryrun); } -static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const float sf0 = (float)dst->ne[0] / src0->ne[0]; @@ -4549,10 +4538,10 @@ static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, c (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], (uint32_t)dst->ne[2],(uint32_t)dst->ne[3], sf0, sf1, sf2, sf3, - }); + }, dryrun); } -static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { float * op_params = (float *)dst->op_params; const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4563,10 +4552,10 @@ static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, con (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, op_params[0], 0.0f - }); + }, dryrun); } -static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4576,10 +4565,10 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, - }); + }, dryrun); } -static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { float * op_params = (float *)dst->op_params; const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4590,10 +4579,10 @@ static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, con (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, op_params[0], op_params[1], - }); + }, dryrun); } -static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4603,10 +4592,23 @@ static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, 0, 0.0f, 0.0f, - }); + }, dryrun); +} + +static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_REPEAT, { + (uint32_t)ggml_nelements(dst), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }, dryrun); } -static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); @@ -4618,40 +4620,41 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, d_offset, 0.0f, 0.0f, - }); + }, dryrun); } -static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun); } -static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { - int * op_params = (int *)dst->op_params; +static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { + const int * int_op_params = (const int *)dst->op_params; + const float * float_op_params = (const float *)dst->op_params; - uint32_t num_groups = op_params[0]; - uint32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - static const float eps = 1e-6f; + const uint32_t num_groups = int_op_params[0]; + const float eps = float_op_params[1]; + const uint32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f }, dryrun); } -static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }, dryrun); } -static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }, dryrun); } -static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }, dryrun); } -static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { float * op_params = (float *)dst->op_params; float scale = op_params[0]; @@ -4673,10 +4676,10 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, scale, max_bias, m0, m1, n_head_log2, - }); + }, dryrun); } -static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) { const int n_dims = ((int32_t *) dst->op_params)[1]; // const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -4697,10 +4700,10 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, cons (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale, src2 != nullptr, - }); + }, dryrun); } -static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { int32_t * op_params = (int32_t *)dst->op_params; uint32_t ncols = src0->ne[0]; @@ -4716,14 +4719,14 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, c ncols, ncols_pad, op_params[0], - }); + }, dryrun); } -static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }); +static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun); } -static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { const int32_t s0 = dst->op_params[0]; const int32_t s1 = dst->op_params[1]; const int32_t p0 = dst->op_params[2]; @@ -4754,22 +4757,22 @@ static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, co pelements, IC * KH * KW, s0, s1, p0, p1, d0, d1, - }); + }, dryrun); } -static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const uint32_t dim = dst->op_params[0]; const uint32_t max_period = dst->op_params[1]; const uint32_t nb1 = dst->nb[1] / ggml_type_size(dst->type); ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_TIMESTEP_EMBEDDING, { nb1, dim, max_period, - }); + }, dryrun); } -static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const float * op_params = (const float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun); } #ifdef GGML_VULKAN_RUN_TESTS @@ -4915,9 +4918,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t } } - ggml_pipeline_allocate_descriptor_sets(ctx->device, p, num_it); + ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it); if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it); if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer @@ -5164,7 +5167,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_ ggml_vk_quantize_data(x, qx, ne, quant); ggml_vk_dequantize_data(qx, x_ref, ne, quant); - ggml_pipeline_allocate_descriptor_sets(ctx->device, p, 1); + ggml_pipeline_request_descriptor_sets(ctx->device, p, 1); ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); @@ -5285,9 +5288,9 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, y[i] = (i % k == i / k) ? 1.0f : 0.0f; } - ggml_pipeline_allocate_descriptor_sets(ctx->device, p, num_it); + ggml_pipeline_request_descriptor_sets(ctx->device, p, num_it); if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_request_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it); if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer @@ -5415,135 +5418,6 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) return extra; } -static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ - VK_LOG_DEBUG("ggml_vk_preallocate_buffers_graph(" << node << ")"); - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; - - if (extra == nullptr) { - return; - } - - ggml_tensor * src0 = node->src[0]; - ggml_tensor * src1 = node->src[1]; - - const bool use_src0 = src0 != nullptr; - const int64_t ne00 = use_src0 ? src0->ne[0] : 0; - const int64_t ne01 = use_src0 ? src0->ne[1] : 0; - const int64_t ne02 = use_src0 ? src0->ne[2] : 0; - const int64_t ne03 = use_src0 ? src0->ne[3] : 0; - const bool use_src1 = src1 != nullptr && node->op != GGML_OP_CPY && node->op != GGML_OP_CONT && node->op != GGML_OP_DUP; - const int64_t ne10 = use_src1 ? src1->ne[0] : 0; - const int64_t ne11 = use_src1 ? src1->ne[1] : 0; - const int64_t ne12 = use_src1 ? src1->ne[2] : 0; - const int64_t ne13 = use_src1 ? src1->ne[3] : 0; - const int64_t ne20 = node->ne[0]; - const int64_t ne21 = node->ne[1]; - const int64_t ne22 = node->ne[2]; - const int64_t ne23 = node->ne[3]; - - const ggml_type src0_type = (use_src0 && src0->type == GGML_TYPE_F32) ? src0->type : GGML_TYPE_F16; - const ggml_type src1_type = (use_src1 && src1->type == GGML_TYPE_F32) ? src1->type : GGML_TYPE_F16; - - const bool x_non_contig = use_src0 && !ggml_vk_dim01_contiguous(src0); - const bool y_non_contig = use_src1 && !ggml_vk_dim01_contiguous(src1); - - const bool y_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32 && !y_non_contig; - - bool mmp = (use_src0 && use_src1 && (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID)) ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type) != nullptr : false; - - const bool qx_needs_dequant = use_src0 && (!mmp || x_non_contig); - const bool qy_needs_dequant = use_src1 && ((src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig); - - int split_k; - if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { - split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - } else { - split_k = 1; - } - const uint32_t x_ne = ne00 * ne01; - const uint32_t y_ne = ne10 * ne11; - const uint32_t d_ne = ne20 * ne21; - - const uint64_t x_sz = (use_src0 && qx_needs_dequant) ? ggml_vk_align_size(sizeof(src0_type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t y_sz = (use_src1 && qy_needs_dequant) ? ggml_vk_align_size(sizeof(src1_type) * y_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; - const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; - - if (extra->buffer_gpu.expired()) { - // Workaround for CPU backend BLAS matmul calls - extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz); - } - - switch (node->op) { - case GGML_OP_REPEAT: - case GGML_OP_GET_ROWS: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_ADD: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_CPY: - case GGML_OP_CONT: - case GGML_OP_DUP: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - case GGML_OP_ARGSORT: - case GGML_OP_SUM_ROWS: - case GGML_OP_IM2COL: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_LEAKY_RELU: - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_TANH: - break; - default: - return; - } - break; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - if ( - x_sz > ctx->device->max_memory_allocation_size || - y_sz > ctx->device->max_memory_allocation_size || - d_sz > ctx->device->max_memory_allocation_size || - split_k_size > ctx->device->max_memory_allocation_size) { - GGML_ABORT("Requested preallocation size is too large"); - } - if (ctx->prealloc_size_x < x_sz) { - ctx->prealloc_size_x = x_sz; - } - if (ctx->prealloc_size_y < y_sz) { - ctx->prealloc_size_y = y_sz; - } - if (ctx->prealloc_size_split_k < split_k_size) { - ctx->prealloc_size_split_k = split_k_size; - } - if (ctx->staging_size < x_sz + y_sz) { - ctx->staging_size = x_sz + y_sz; - } - break; - default: - return; - } -} - static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { #if defined(GGML_VULKAN_RUN_TESTS) ctx->staging = ggml_vk_create_buffer_check(ctx->device, 100ul * 1024ul * 1024ul, @@ -5708,19 +5582,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_split_k); } - if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) { - VK_LOG_MEMORY("ggml_vk_preallocate_buffers(staging_size: " << ctx->staging_size << ")"); - // Resize buffer - if (ctx->staging != nullptr) { - ggml_vk_destroy_buffer(ctx->staging); - } - ctx->staging = ggml_vk_create_buffer_check(ctx->device, ctx->staging_size, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); - } } -static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, bool last_node){ +static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, bool last_node, bool dryrun){ ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; if (ggml_is_empty(node) || extra == nullptr) { @@ -5729,7 +5593,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")"); ctx->semaphore_idx = 0; - ctx->staging_offset = 0; const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; @@ -5791,75 +5654,77 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod vk_context compute_ctx; - if (ctx->compute_ctx.expired()) { - compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); - ctx->compute_ctx = compute_ctx; - ggml_vk_ctx_begin(ctx->device, compute_ctx); - } else { - compute_ctx = ctx->compute_ctx.lock(); + if (!dryrun) { + if (ctx->compute_ctx.expired()) { + compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); + ctx->compute_ctx = compute_ctx; + ggml_vk_ctx_begin(ctx->device, compute_ctx); + } else { + compute_ctx = ctx->compute_ctx.lock(); + } } switch (node->op) { case GGML_OP_REPEAT: - ggml_vk_repeat(ctx, compute_ctx, src0, node); + ggml_vk_repeat(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_GET_ROWS: - ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node); + ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_ADD: - ggml_vk_add(ctx, compute_ctx, src0, src1, node); + ggml_vk_add(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_MUL: - ggml_vk_mul(ctx, compute_ctx, src0, src1, node); + ggml_vk_mul(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_DIV: - ggml_vk_div(ctx, compute_ctx, src0, src1, node); + ggml_vk_div(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_CONCAT: - ggml_vk_concat(ctx, compute_ctx, src0, src1, node); + ggml_vk_concat(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_UPSCALE: - ggml_vk_upscale(ctx, compute_ctx, src0, node); + ggml_vk_upscale(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_SCALE: - ggml_vk_scale(ctx, compute_ctx, src0, node); + ggml_vk_scale(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_SQR: - ggml_vk_sqr(ctx, compute_ctx, src0, node); + ggml_vk_sqr(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_CLAMP: - ggml_vk_clamp(ctx, compute_ctx, src0, node); + ggml_vk_clamp(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_PAD: - ggml_vk_pad(ctx, compute_ctx, src0, node); + ggml_vk_pad(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - ggml_vk_cpy(ctx, compute_ctx, src0, node); + ggml_vk_cpy(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_NORM: - ggml_vk_norm(ctx, compute_ctx, src0, node); + ggml_vk_norm(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_GROUP_NORM: - ggml_vk_group_norm(ctx, compute_ctx, src0, node); + ggml_vk_group_norm(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_RMS_NORM: - ggml_vk_rms_norm(ctx, compute_ctx, src0, node); + ggml_vk_rms_norm(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_UNARY: @@ -5869,59 +5734,63 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_TANH: - ggml_vk_unary(ctx, compute_ctx, src0, node); + ggml_vk_unary(ctx, compute_ctx, src0, node, dryrun); break; default: return; } break; case GGML_OP_DIAG_MASK_INF: - ggml_vk_diag_mask_inf(ctx, compute_ctx, src0, node); + ggml_vk_diag_mask_inf(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, compute_ctx, src0, src1, node); + ggml_vk_soft_max(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_ROPE: - ggml_vk_rope(ctx, compute_ctx, src0, src1, src2, node); + ggml_vk_rope(ctx, compute_ctx, src0, src1, src2, node, dryrun); break; case GGML_OP_ARGSORT: - ggml_vk_argsort(ctx, compute_ctx, src0, node); + ggml_vk_argsort(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_SUM_ROWS: - ggml_vk_sum_rows(ctx, compute_ctx, src0, node); + ggml_vk_sum_rows(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_IM2COL: - ggml_vk_im2col(ctx, compute_ctx, src0, src1, node); + ggml_vk_im2col(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_TIMESTEP_EMBEDDING: - ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node); + ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_LEAKY_RELU: - ggml_vk_leaky_relu(ctx, compute_ctx, src0, node); + ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun); break; case GGML_OP_MUL_MAT: - ggml_vk_mul_mat(ctx, compute_ctx, src0, src1, node); + ggml_vk_mul_mat(ctx, compute_ctx, src0, src1, node, dryrun); break; case GGML_OP_MUL_MAT_ID: - ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node); + ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node, dryrun); break; default: return; } + if (dryrun) { + return; + } + ctx->tensor_ctxs[node_idx] = compute_ctx; -#ifdef GGML_VULKAN_CHECK_RESULTS +#if defined(GGML_VULKAN_CHECK_RESULTS) || defined(GGML_VULKAN_PERF) // Force context reset on each node so that each tensor ends up in its own context // and can be run and compared to its CPU equivalent separately last_node = true; @@ -6005,6 +5874,10 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock(); +#ifdef GGML_VULKAN_PERF + std::chrono::steady_clock::time_point start; +#endif // GGML_VULKAN_PERF + // Only run if ctx hasn't been submitted yet if (!subctx->seqs.empty()) { // Do staging buffer copies @@ -6012,11 +5885,21 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * memcpy(cpy.dst, cpy.src, cpy.n); } +#ifdef GGML_VULKAN_PERF + start = std::chrono::steady_clock::now(); +#endif // GGML_VULKAN_PERF + ggml_vk_submit(subctx, ctx->fence); } if (tensor_idx == subctx->exit_tensor_idx) { VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + +#ifdef GGML_VULKAN_PERF + auto duration = std::chrono::duration_cast(std::chrono::steady_clock::now() - start); + ctx->device->perf_logger->log_timing(tensor, duration.count()); +#endif // GGML_VULKAN_PERF + ctx->device->device.resetFences({ ctx->fence }); // Do staging buffer copies @@ -6038,12 +5921,14 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { } ctx->gc.temp_buffers.clear(); - for (auto& pipeline : ctx->device->pipelines) { - if (pipeline.expired()) { + for (auto& dsr : ctx->device->pipeline_descriptor_set_requirements) { + vk_pipeline_ref plr = ctx->device->pipelines[dsr.first]; + + if (plr.expired()) { continue; } - vk_pipeline pl = pipeline.lock(); + vk_pipeline pl = plr.lock(); ggml_pipeline_cleanup(pl); } @@ -6067,10 +5952,9 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { ctx->device->device.resetEvent(event); } - ctx->staging_offset = 0; - ctx->tensor_ctxs.clear(); ctx->gc.contexts.clear(); + ctx->device->pipeline_descriptor_set_requirements.clear(); } // Clean up on backend free @@ -6081,7 +5965,6 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { ggml_vk_destroy_buffer(ctx->prealloc_x); ggml_vk_destroy_buffer(ctx->prealloc_y); ggml_vk_destroy_buffer(ctx->prealloc_split_k); - ggml_vk_destroy_buffer(ctx->staging); for (auto& buffer : ctx->buffer_pool) { ggml_vk_destroy_buffer(buffer); @@ -6090,7 +5973,6 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { ctx->prealloc_size_x = 0; ctx->prealloc_size_y = 0; ctx->prealloc_size_split_k = 0; - ctx->staging_size = 0; for (auto& event : ctx->gc.events) { ctx->device->device.destroyEvent(event); @@ -6419,7 +6301,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g vk_buffer buf = extra->buffer_gpu.lock(); - ggml_vk_buffer_write_async(transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size, ctx->staging, ctx->staging_offset); + ggml_vk_buffer_write_async(transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { @@ -6442,7 +6324,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c vk_buffer buf = extra->buffer_gpu.lock(); - ggml_vk_buffer_read_async(transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size, ctx->staging, ctx->staging_offset); + ggml_vk_buffer_read_async(transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { @@ -6508,9 +6390,10 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]); + ggml_vk_build_graph(ctx, cgraph->nodes[i], i, 0, true); } ggml_vk_preallocate_buffers(ctx); + ggml_pipeline_allocate_descriptor_sets(ctx->device); int last_node = cgraph->n_nodes - 1; @@ -6523,7 +6406,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen ctx->tensor_ctxs.resize(cgraph->n_nodes); for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(ctx, cgraph->nodes[i], i, i == last_node); + ggml_vk_build_graph(ctx, cgraph->nodes[i], i, i == last_node, false); } for (int i = 0; i < cgraph->n_nodes; i++) { @@ -6549,6 +6432,10 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen GGML_ASSERT(ok); } +#ifdef GGML_VULKAN_PERF + ctx->device->perf_logger->print_timings(); +#endif + ggml_vk_graph_cleanup(ctx); return GGML_STATUS_SUCCESS; @@ -6640,10 +6527,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const return false; } break; case GGML_OP_REPEAT: - { - ggml_type src0_type = op->src[0]->type; - return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; - } break; + return ggml_type_size(op->type) == sizeof(float) && ggml_type_size(op->src[0]->type) == sizeof(float); case GGML_OP_ROPE: return ggml_is_contiguous(op->src[0]); case GGML_OP_NONE: @@ -7107,12 +6991,14 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else if (tensor->op == GGML_OP_PAD) { tensor_clone = ggml_pad(ggml_ctx, src0_clone, tensor->ne[0] - src0_clone->ne[0], tensor->ne[1] - src0_clone->ne[1], tensor->ne[2] - src0_clone->ne[2], tensor->ne[3] - src0_clone->ne[3]); + } else if (tensor->op == GGML_OP_REPEAT) { + tensor_clone = ggml_repeat(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_ADD) { tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_NORM) { tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_GROUP_NORM) { - tensor_clone = ggml_group_norm(ggml_ctx, src0_clone, *(int *)tensor->op_params); + tensor_clone = ggml_group_norm(ggml_ctx, src0_clone, *(int *)tensor->op_params, ((float *)tensor->op_params)[1]); } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { diff --git a/ggml/src/vulkan-shaders/concat.comp b/ggml/src/vulkan-shaders/concat.comp index 08ab5514bfb49..c23b6eb1b0cd5 100644 --- a/ggml/src/vulkan-shaders/concat.comp +++ b/ggml/src/vulkan-shaders/concat.comp @@ -30,6 +30,10 @@ void main() { #ifndef OPTIMIZATION_ERROR_WORKAROUND data_d[p.d_offset + dst_idx] = D_TYPE(is_src0 ? data_a[src0_idx] : data_b[src1_idx]); #else - data_d[p.d_offset + dst_idx] = is_src0 ? data_a[src0_idx] : data_b[src1_idx]; + if (is_src0) { + data_d[p.d_offset + dst_idx] = data_a[src0_idx]; + } else { + data_d[p.d_offset + dst_idx] = data_b[src1_idx]; + } #endif } diff --git a/ggml/src/vulkan-shaders/mul_mat_vec.comp b/ggml/src/vulkan-shaders/mul_mat_vec.comp index 46a6369bcfd20..d3ccba7fcb3fd 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec.comp @@ -39,8 +39,7 @@ void main() { vec2 v = dequantize(ib, iqs, a_offset / QUANT_K); // matrix multiplication - tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[b_offset + iybs + iqs]) + - FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); + tmp[tid] = fma(FLOAT_TYPE(v.x), FLOAT_TYPE(data_b[b_offset + iybs + iqs]), fma(FLOAT_TYPE(v.y), FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]), tmp[tid])); } // sum up partial sums and write back result diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp index cb3f3c0df0801..1cc4996d393a2 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp @@ -53,7 +53,7 @@ void main() { const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); - tmp[tid] += xi * FLOAT_TYPE(data_b[iy]); + tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]); } // sum up partial sums and write back result diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp b/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp index 4b1871caaf4c2..9b443807d8781 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp @@ -52,7 +52,7 @@ void main() { // y is not transposed but permuted const uint iy = channel*nrows_y + row_y; - tmp[tid] += xi * FLOAT_TYPE(data_b[iy]); + tmp[tid] = fma(xi, FLOAT_TYPE(data_b[iy]), tmp[tid]); } // dst is not transposed and not permuted diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp index 4cd97799df196..ec8eadcd5828a 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp @@ -39,24 +39,25 @@ void main() { FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3); - sum2 += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF); + sum1 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3), sum1)))))))); + sum2 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF), sum2)))))))); } - tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; + const uint tmp_idx = 16 * ix + tid; + tmp[tmp_idx] = fma(dall, sum1, fma(-dmin, sum2, tmp[tmp_idx])); } // sum up partial sums and write back result diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp index a6e430ea08c5d..3ca4ad85a5ca0 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp @@ -40,16 +40,17 @@ void main() { FLOAT_TYPE sum = FLOAT_TYPE(0.0); for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)); + sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); } - tmp[16 * ix + tid] += d * sum; + const uint tmp_idx = 16 * ix + tid; + tmp[tmp_idx] = fma(d, sum, tmp[tmp_idx]); } // sum up partial sums and write back result diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp index 75569363c64a9..d91e00e10061a 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp @@ -67,17 +67,17 @@ void main() { const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); - const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1 + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3); - const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_5 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7); - const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx]) * q4_8 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_9 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * q4_10 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11); - const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_12 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_13 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * q4_14 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15); - const FLOAT_TYPE smin = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx ]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx ]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7 - ); - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); + const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx]), q4_0, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), q4_1, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3))); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_4, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), q4_5, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), q4_6, FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7))); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx]), q4_8, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), q4_9, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), q4_10, FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11))); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_12, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), q4_13, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), q4_14, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), sc7, + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), sc7, + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 3]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 35]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 3]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7))))))))))))))); + const uint tmp_idx = 16 * ix + tid; + tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); #else const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); @@ -88,16 +88,19 @@ void main() { const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx ]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); - const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); - const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx ]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); - const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); - const FLOAT_TYPE smin = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7 - ); - - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); + const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), q4_0, FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), q4_4, FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, + + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7))))))); + + tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); + const uint tmp_idx = 16 * ix + tid; + tmp[tmp_idx] = fma(dall, (fma(sx, FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f), fma(sy, FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f), + fma(sz, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)), fma(sw, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))))))), fma(-dmin, smin, tmp[tmp_idx])); #endif } diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp index 9be3645bdea0e..2306785af4226 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp @@ -66,35 +66,33 @@ void main() { const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4); const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); - const FLOAT_TYPE sx = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx ]) * (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE sy = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE sz = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y2_idx ]) * (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE sw = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE smin = FLOAT_TYPE( - (FLOAT_TYPE(data_b[b_offset + y1_idx]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17])) * sc2 + (FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49])) * sc3 - + (FLOAT_TYPE(data_b[b_offset + y2_idx]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17])) * sc6 + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7 - ); - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); + const FLOAT_TYPE sx = + fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 16]), (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)), + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))))); + const FLOAT_TYPE sy = + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 48]), (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)), + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))))); + const FLOAT_TYPE sz = + fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 16]), (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)), + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))))); + const FLOAT_TYPE sw = + fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)), + fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 48]), (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)), + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]), sc2, + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]), sc3, + fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]), sc6, + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7))); + const uint tmp_idx = 16 * ix + tid; + tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); } // sum up partial sums and write back result diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp index d610cf0306b0a..95c286eeb17e1 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp +++ b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -44,22 +44,22 @@ void main() { const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); #if K_QUANTS_PER_ITERATION == 1 - FLOAT_TYPE sum = FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32); - tmp[16 * ix + tid] += sum; + const uint tmp_idx = 16 * ix + tid; + tmp[tmp_idx] = fma(FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32), tmp[tmp_idx])))))))); #else FLOAT_TYPE sum = FLOAT_TYPE(0.0); [[unroll]] for (int l = 0; l < 4; ++l) { - sum += FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32); + sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32), + fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32), sum)))); } tmp[16 * ix + tid] += sum; #endif diff --git a/ggml/src/vulkan-shaders/mul_mm.comp b/ggml/src/vulkan-shaders/mul_mm.comp index 5fe9d5241381e..fffdd18189d55 100644 --- a/ggml/src/vulkan-shaders/mul_mm.comp +++ b/ggml/src/vulkan-shaders/mul_mm.comp @@ -326,10 +326,10 @@ void main() { mbyte = uint8_t((data_a[ib].scales[is + 4] >> 4) | ((data_a[ib].scales[is ] >> 6) << 4)); } const float d = loadd.x * sc; - const float m = loadd.y * mbyte; + const float m = -loadd.y * mbyte; - buf_a[buf_idx ] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF) - m); - buf_a[buf_idx + 1] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) - m); + buf_a[buf_idx ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF), m)); + buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF), m)); #elif defined(DATA_A_Q5_K) const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; @@ -357,10 +357,10 @@ void main() { mbyte = uint8_t((data_a[ib].scales[is + 4] >> 4) | ((data_a[ib].scales[is ] >> 6) << 4)); } const float d = loadd.x * sc; - const float m = loadd.y * mbyte; + const float m = -loadd.y * mbyte; - buf_a[buf_idx ] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi ] & hm) != 0 ? 16 : 0)) - m); - buf_a[buf_idx + 1] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0)) - m); + buf_a[buf_idx ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi ] & hm) != 0 ? 16 : 0), m)); + buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0), m)); #elif defined(DATA_A_Q6_K) const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; @@ -463,7 +463,8 @@ void main() { [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { [[unroll]] for (uint cc = 0; cc < TN; cc++) { [[unroll]] for (uint cr = 0; cr < TM; cr++) { - sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr] += float(cache_a[wsir * TM + cr]) * float(cache_b[wsic * TN + cc]); + const uint sums_idx = (wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr; + sums[sums_idx] = fma(float(cache_a[wsir * TM + cr]), float(cache_b[wsic * TN + cc]), sums[sums_idx]); } } } diff --git a/ggml/src/vulkan-shaders/repeat.comp b/ggml/src/vulkan-shaders/repeat.comp new file mode 100644 index 0000000000000..a86af87e7b7f9 --- /dev/null +++ b/ggml/src/vulkan-shaders/repeat.comp @@ -0,0 +1,24 @@ +#version 450 + +#include "types.comp" +#include "generic_unary_head.comp" + +uint src0_idx_mod(uint idx) { + const uint i13 = idx / (p.ne12*p.ne11*p.ne10); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + return (i13 % p.ne03)*p.nb03 + (i12 % p.ne02)*p.nb02 + (i11 % p.ne01)*p.nb01 + (i10 % p.ne00)*p.nb00; +} + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + data_d[p.d_offset + dst_idx(idx)] = D_TYPE(data_a[src0_idx_mod(idx)]); +} diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp index a792e203b273a..53ceb13d30fde 100644 --- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp @@ -380,6 +380,10 @@ void process_shaders(std::vector>& tasks) { string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); })); + tasks.push_back(std::async(std::launch::async, [] { + string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + })); + tasks.push_back(std::async(std::launch::async, [] { string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); })); From 234b30676a97ce227b604c38beb9dcaca406dea9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ji=C5=99=C3=AD=20Podiv=C3=ADn?= <66251151+jpodivin@users.noreply.github.com> Date: Thu, 15 Aug 2024 08:21:57 +0200 Subject: [PATCH 23/28] server : init stop and error fields of the result struct (#9026) Signed-off-by: Jiri Podivin --- examples/server/server.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c25338f573767..ace17a12f7931 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1852,6 +1852,8 @@ struct server_context { llama_lora_adapters_apply(ctx, lora_adapters); server_task_result result; result.id = task.id; + result.stop = true; + result.error = false; result.data = json{{ "success", true }}; queue_results.send(result); } break; From d5492f0525fa533817a67e93a4bde9d71d81cf58 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 15 Aug 2024 10:11:11 +0300 Subject: [PATCH 24/28] ci : disable bench workflow (#9010) --- .github/workflows/{bench.yml => bench.yml.disabled} | 3 +++ 1 file changed, 3 insertions(+) rename .github/workflows/{bench.yml => bench.yml.disabled} (98%) diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml.disabled similarity index 98% rename from .github/workflows/bench.yml rename to .github/workflows/bench.yml.disabled index 56d22bc0cc394..bfdbb4ef5e385 100644 --- a/.github/workflows/bench.yml +++ b/.github/workflows/bench.yml.disabled @@ -1,3 +1,6 @@ +# TODO: there have been some issues with the workflow, so disabling for now +# https://github.com/ggerganov/llama.cpp/issues/7893 +# # Benchmark name: Benchmark From 6bda7ce6c3a9284fcbb70c1ace4107db8eb63e5c Mon Sep 17 00:00:00 2001 From: Esko Toivonen Date: Thu, 15 Aug 2024 10:17:12 +0300 Subject: [PATCH 25/28] llama : add pre-tokenizer regexes for BLOOM and gpt3-finnish (#8850) --- convert_hf_to_gguf.py | 8 +++++++- convert_hf_to_gguf_update.py | 2 ++ include/llama.h | 2 ++ src/llama-vocab.cpp | 2 ++ src/llama.cpp | 6 ++++++ 5 files changed, 19 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 550dd5cfda99f..41063d94b684e 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -590,6 +590,12 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249": # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M res = "smollm" + if chkhsh == "3c30d3ad1d6b64202cd222813e7736c2db6e1bd6d67197090fc1211fbc612ae7": + # ref: https://huggingface.co/bigscience/bloom + res = "bloom" + if chkhsh == "bc01ce58980e1db43859146dc51b1758b3b88729b217a74792e9f8d43e479d21": + # ref: https://huggingface.co/TurkuNLP/gpt3-finnish-small + res = "gpt3-finnish" if res is None: logger.warning("\n") @@ -893,7 +899,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return tensors -@Model.register("BloomForCausalLM") +@Model.register("BloomForCausalLM", "BloomModel") class BloomModel(Model): model_arch = gguf.MODEL_ARCH.BLOOM diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index d5a2d925eaef5..ba98f5c88990e 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -94,6 +94,8 @@ class TOKENIZER_TYPE(IntEnum): {"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", }, {"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", }, {"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", }, + {'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", }, + {'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", }, ] diff --git a/include/llama.h b/include/llama.h index 3c28cf0b509fb..fda68da851408 100644 --- a/include/llama.h +++ b/include/llama.h @@ -93,6 +93,8 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_TEKKEN = 20, LLAMA_VOCAB_PRE_TYPE_SMOLLM = 21, LLAMA_VOCAB_PRE_TYPE_CODESHELL = 22, + LLAMA_VOCAB_PRE_TYPE_BLOOM = 23, + LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, }; enum llama_rope_type { diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 749f8571829df..063af648eb357 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -410,6 +410,8 @@ struct llm_tokenizer_bpe { }; break; case LLAMA_VOCAB_PRE_TYPE_PORO: + case LLAMA_VOCAB_PRE_TYPE_BLOOM: + case LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH: regex_exprs = { " ?[^(\\s|.,!?…。，、।۔،)]+", }; diff --git a/src/llama.cpp b/src/llama.cpp index 7f2f0003142a3..bf7a57c79905d 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -5467,6 +5467,12 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "codeshell") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL; + } else if ( + tokenizer_pre == "bloom") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_BLOOM; + } else if ( + tokenizer_pre == "gpt3-finnish") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } From 4af8420afba39de4968adf2695ce12fd42422a13 Mon Sep 17 00:00:00 2001 From: Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> Date: Thu, 15 Aug 2024 15:23:23 +0800 Subject: [PATCH 26/28] common : remove duplicate function llama_should_add_bos_token (#8778) --- common/common.cpp | 6 ------ common/common.h | 4 ---- examples/cvector-generator/cvector-generator.cpp | 2 +- examples/eval-callback/eval-callback.cpp | 2 +- examples/imatrix/imatrix.cpp | 4 ++-- examples/infill/infill.cpp | 4 ++-- examples/main/main.cpp | 4 ++-- examples/perplexity/perplexity.cpp | 12 ++++++------ examples/server/server.cpp | 7 +++---- examples/tokenize/tokenize.cpp | 2 +- include/llama.h | 7 ++----- src/llama-vocab.cpp | 4 ++-- src/llama-vocab.h | 4 ++-- src/llama.cpp | 4 ++-- 14 files changed, 26 insertions(+), 40 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index d3d896115ae36..ce46e65ae2834 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2702,12 +2702,6 @@ std::string llama_detokenize(llama_context * ctx, const std::vector return text; } -bool llama_should_add_bos_token(const llama_model * model) { - const int add_bos = llama_add_bos_token(model); - - return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); -} - // // Chat template utils // diff --git a/common/common.h b/common/common.h index bbc33a499afcd..df23460a50fe0 100644 --- a/common/common.h +++ b/common/common.h @@ -380,10 +380,6 @@ std::string llama_detokenize( const std::vector & tokens, bool special = true); -// Uses the value from the model metadata if possible, otherwise -// defaults to true when model type is SPM, otherwise false. -bool llama_should_add_bos_token(const llama_model * model); - // // Chat template utils // diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp index a12e90d828275..8fa492571aa44 100644 --- a/examples/cvector-generator/cvector-generator.cpp +++ b/examples/cvector-generator/cvector-generator.cpp @@ -271,7 +271,7 @@ struct tokenized_prompt { size_t max_seq_len; tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) { - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true); tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true); max_seq_len = std::max(tokens_pos.size(), tokens_neg.size()); diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index ef35ba2c03942..5e89988e2beda 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -127,7 +127,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { } static bool run(llama_context * ctx, const gpt_params & params) { - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 58814b96e7d49..83b85d72b043a 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -433,8 +433,8 @@ static void process_logits( } static bool compute_imatrix(llama_context * ctx, const gpt_params & params) { - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); + const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); const int n_ctx = llama_n_ctx(ctx); auto tim1 = std::chrono::high_resolution_clock::now(); diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 92d630b15fdf1..05700c1d591d9 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -203,8 +203,8 @@ int main(int argc, char ** argv) { LOG_TEE("\n"); LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } - const bool add_bos = llama_should_add_bos_token(model); - GGML_ASSERT(llama_add_eos_token(model) != 1); + const bool add_bos = llama_add_bos_token(model); + GGML_ASSERT(!llama_add_eos_token(model)); LOG("add_bos: %d\n", add_bos); std::vector embd_inp; diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 6e0635a66cd06..4a342ad031663 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -267,9 +267,9 @@ int main(int argc, char ** argv) { } } - const bool add_bos = llama_should_add_bos_token(model); + const bool add_bos = llama_add_bos_token(model); if (!llama_model_has_encoder(model)) { - GGML_ASSERT(llama_add_eos_token(model) != 1); + GGML_ASSERT(!llama_add_eos_token(model)); } LOG("add_bos: %d\n", add_bos); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 372684f092de2..484dd589109c7 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -340,8 +340,8 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); + const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); @@ -480,8 +480,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); + const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); std::ofstream logits_stream; if (!params.logits_file.empty()) { @@ -1733,8 +1733,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { const int n_batch = params.n_batch; const int num_batches = (n_ctx + n_batch - 1)/n_batch; const int nv = 2*((n_vocab + 1)/2) + 4; - const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); - GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); + const bool add_bos = llama_add_bos_token(llama_get_model(ctx)); + GGML_ASSERT(!llama_add_eos_token(llama_get_model(ctx))); std::vector log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ace17a12f7931..3fe0e6558b087 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -693,9 +693,8 @@ struct server_context { n_ctx = llama_n_ctx(ctx); - add_bos_token = llama_should_add_bos_token(model); - has_eos_token = llama_add_eos_token(model) != 1; - + add_bos_token = llama_add_bos_token(model); + has_eos_token = !llama_add_eos_token(model); return true; } @@ -2038,7 +2037,7 @@ struct server_context { slot.t_start_generation = 0; if (slot.infill) { - const bool add_bos = llama_should_add_bos_token(model); + const bool add_bos = llama_add_bos_token(model); bool suff_rm_leading_spc = true; if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { params.input_suffix.erase(0, 1); diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index 17f5e496153a7..c817be566cf54 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -362,7 +362,7 @@ int main(int raw_argc, char ** raw_argv) { prompt = stdin_buffer.str(); } - const bool model_wants_add_bos = llama_should_add_bos_token(model); + const bool model_wants_add_bos = llama_add_bos_token(model); const bool add_bos = model_wants_add_bos && !no_bos; const bool parse_special = !no_parse_special; diff --git a/include/llama.h b/include/llama.h index fda68da851408..ed81aa4697d17 100644 --- a/include/llama.h +++ b/include/llama.h @@ -914,11 +914,8 @@ extern "C" { LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding - // Returns -1 if unknown, 1 for true or 0 for false. - LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model); - - // Returns -1 if unknown, 1 for true or 0 for false. - LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model); + LLAMA_API bool llama_add_bos_token(const struct llama_model * model); + LLAMA_API bool llama_add_eos_token(const struct llama_model * model); // Codellama infill tokens LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 063af648eb357..11fffce9386d7 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1468,11 +1468,11 @@ llama_token llama_token_pad_impl(const struct llama_vocab & vocab) { return vocab.special_pad_id; } -int32_t llama_add_bos_token_impl(const struct llama_vocab & vocab) { +bool llama_add_bos_token_impl(const struct llama_vocab & vocab) { return vocab.tokenizer_add_bos; } -int32_t llama_add_eos_token_impl(const struct llama_vocab & vocab) { +bool llama_add_eos_token_impl(const struct llama_vocab & vocab) { return vocab.tokenizer_add_eos; } diff --git a/src/llama-vocab.h b/src/llama-vocab.h index 7adfc16da3af3..6e8f30be43ba1 100644 --- a/src/llama-vocab.h +++ b/src/llama-vocab.h @@ -95,8 +95,8 @@ llama_token llama_token_sep_impl(const struct llama_vocab & vocab); llama_token llama_token_nl_impl (const struct llama_vocab & vocab); llama_token llama_token_pad_impl(const struct llama_vocab & vocab); -int32_t llama_add_bos_token_impl(const struct llama_vocab & vocab); -int32_t llama_add_eos_token_impl(const struct llama_vocab & vocab); +bool llama_add_bos_token_impl(const struct llama_vocab & vocab); +bool llama_add_eos_token_impl(const struct llama_vocab & vocab); llama_token llama_token_prefix_impl(const struct llama_vocab & vocab); llama_token llama_token_middle_impl(const struct llama_vocab & vocab); diff --git a/src/llama.cpp b/src/llama.cpp index bf7a57c79905d..ee36de977cdc2 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -18705,11 +18705,11 @@ llama_token llama_token_pad(const struct llama_model * model) { return llama_token_pad_impl(model->vocab); } -int32_t llama_add_bos_token(const struct llama_model * model) { +bool llama_add_bos_token(const struct llama_model * model) { return llama_add_bos_token_impl(model->vocab); } -int32_t llama_add_eos_token(const struct llama_model * model) { +bool llama_add_eos_token(const struct llama_model * model) { return llama_add_eos_token_impl(model->vocab); } From 37501d9c79ed5897db4b73ea7502211d25b3f763 Mon Sep 17 00:00:00 2001 From: Riceball LEE Date: Thu, 15 Aug 2024 15:28:05 +0800 Subject: [PATCH 27/28] server : fix duplicated n_predict key in the generation_settings (#8994) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3fe0e6558b087..e073f5813d459 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1321,7 +1321,7 @@ struct server_context { return json { {"n_ctx", slot.n_ctx}, - {"n_predict", slot.n_predict}, + {"n_predict", slot.n_predict}, // Server configured n_predict {"model", params.model_alias}, {"seed", slot.sparams.seed}, {"temperature", slot.sparams.temp}, @@ -1343,7 +1343,7 @@ struct server_context { {"mirostat_eta", slot.sparams.mirostat_eta}, {"penalize_nl", slot.sparams.penalize_nl}, {"stop", slot.params.antiprompt}, - {"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict + {"max_tokens", slot.params.n_predict}, // User configured n_predict {"n_keep", slot.params.n_keep}, {"n_discard", slot.params.n_discard}, {"ignore_eos", ignore_eos}, From 4b9afbbe9037f8a2d659097c0c7d9fce32c6494c Mon Sep 17 00:00:00 2001 From: gtygo Date: Thu, 15 Aug 2024 15:40:12 +0800 Subject: [PATCH 28/28] retrieval : fix memory leak in retrieval query handling (#8955) * retrieval * Reuse querybatch to reduce frequent memory allocation * delete unused white space --- examples/retrieval/retrieval.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 65b19ce71cbe3..aab9d81058af9 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -253,6 +253,8 @@ int main(int argc, char ** argv) { chunks[i].tokens.clear(); } + struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1); + // start loop, receive query and return top k similar chunks based on cosine similarity std::string query; while (true) { @@ -260,7 +262,6 @@ int main(int argc, char ** argv) { std::getline(std::cin, query); std::vector query_tokens = llama_tokenize(ctx, query, true); - struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1); batch_add_seq(query_batch, query_tokens, 0); std::vector query_emb(n_embd, 0); @@ -293,6 +294,7 @@ int main(int argc, char ** argv) { } // clean up + llama_batch_free(query_batch); llama_print_timings(ctx); llama_free(ctx); llama_free_model(model);