diff --git a/examples/talk-llama/llama-vocab.cpp b/examples/talk-llama/llama-vocab.cpp index a771eccda30..d2f34ddd6b3 100644 --- a/examples/talk-llama/llama-vocab.cpp +++ b/examples/talk-llama/llama-vocab.cpp @@ -50,7 +50,7 @@ struct naive_trie { res.first->second.insert(key + 1, len - 1, value); } } - std::pair get_longest_prefix(const char * key, size_t len, size_t offset = 0) { + std::pair get_longest_prefix(const char * key, size_t len, size_t offset = 0) const { if (len == 0 || offset == len) { return std::make_pair(key, offset); } @@ -79,6 +79,15 @@ struct naive_trie { // impl // +struct llm_tokenizer { + llm_tokenizer() {} + virtual ~llm_tokenizer() = default; +}; + +llama_vocab::~llama_vocab() { + delete tokenizer; +} + int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const { GGML_ASSERT(token_left.find(' ') == std::string::npos); GGML_ASSERT(token_left.find('\n') == std::string::npos); @@ -187,10 +196,15 @@ struct llm_bigram_spm { size_t size; }; -struct llm_tokenizer_spm { - llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {} +struct llm_tokenizer_spm : llm_tokenizer { + llm_tokenizer_spm(const llama_vocab & /*vocab*/) : llm_tokenizer() {} +}; + +struct llm_tokenizer_spm_session { + llm_tokenizer_spm_session(const llama_vocab & vocab) : vocab(vocab) {} void tokenize(const std::string & text, std::vector & output) { + // split string into utf8 chars int index = 0; size_t offs = 0; @@ -271,7 +285,7 @@ struct llm_tokenizer_spm { return; } - resegment(symbols[p->second.first], output); + resegment(symbols[p->second.first], output); resegment(symbols[p->second.second], output); } @@ -279,7 +293,6 @@ struct llm_tokenizer_spm { if (left == -1 || right == -1) { return; } - const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n); auto token = vocab.token_to_id.find(text); @@ -306,10 +319,11 @@ struct llm_tokenizer_spm { } const llama_vocab & vocab; + // currently unused + // const llm_tokenizer_spm * spm_tokenizer; std::vector symbols; llm_bigram_spm::queue work_queue; - std::map> rev_merge; }; @@ -352,8 +366,8 @@ struct llm_bigram_bpe { size_t size; }; -struct llm_tokenizer_bpe { - llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) { +struct llm_tokenizer_bpe : llm_tokenizer { + llm_tokenizer_bpe(const llama_vocab & vocab) : llm_tokenizer() { GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE); switch (vocab.type_pre) { case LLAMA_VOCAB_PRE_TYPE_LLAMA3: @@ -450,6 +464,20 @@ struct llm_tokenizer_bpe { "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }; break; + case LLAMA_VOCAB_PRE_TYPE_CHAMELEON: + // Note: in theory, the special token (sentinel and image token) regex_exprs below + // are unnecessary, as they are split in `tokenizer_st_partition` anyway. + // However, since the upstream pre-tokenizer uses them, they are also + // included here (see https://huggingface.co/facebook/chameleon-7b). + regex_exprs = { + "", // Sentinel tokens + "(IMGIMG)((A|B|C|D|E|F|G|H|I){1,4})Z", // Image tokens + "([\\t\\n]| | )", // directly from tokenizer.json + "\\p{N}", // Individual digits + "[\\p{P}!-/:-@\\[-`{-~]", // Punctuation, Isolated + "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", + }; + break; default: // default regex for BPE tokenization pre-processing regex_exprs = { @@ -462,7 +490,14 @@ struct llm_tokenizer_bpe { } } - void append(const llama_vocab::id token_id, std::vector & output) const { + std::vector regex_exprs; +}; + +struct llm_tokenizer_bpe_session { + llm_tokenizer_bpe_session(const llama_vocab & vocab) : vocab(vocab), + bpe_tokenizer(static_cast(vocab.tokenizer)) {} + + static void append(const llama_vocab::id token_id, std::vector & output) { output.push_back(token_id); } @@ -501,12 +536,11 @@ struct llm_tokenizer_bpe { void tokenize(const std::string & text, std::vector & output) { int final_prev_index = -1; - - const auto word_collection = unicode_regex_split(text, regex_exprs); + const auto word_collection = unicode_regex_split(text, bpe_tokenizer->regex_exprs); symbols_final.clear(); - for (auto & word : word_collection) { + for (const auto & word : word_collection) { work_queue = llm_bigram_bpe::queue(); symbols.clear(); @@ -609,7 +643,6 @@ struct llm_tokenizer_bpe { if (left == -1 || right == -1) { return; } - std::string left_token = std::string(symbols[left].text, symbols[left].n); std::string right_token = std::string(symbols[right].text, symbols[right].n); @@ -633,12 +666,10 @@ struct llm_tokenizer_bpe { } const llama_vocab & vocab; - - std::vector regex_exprs; + const llm_tokenizer_bpe * bpe_tokenizer; std::vector symbols; std::vector symbols_final; - llm_bigram_bpe::queue work_queue; }; @@ -646,15 +677,17 @@ struct llm_tokenizer_bpe { // WPM tokenizer // -struct llm_tokenizer_wpm { - llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {} +struct llm_tokenizer_wpm : llm_tokenizer { + llm_tokenizer_wpm(const llama_vocab & /*vocab*/) : llm_tokenizer() {} +}; - void tokenize(const std::string & text, std::vector & output) const { - const auto & token_map = vocab.token_to_id; +struct llm_tokenizer_wpm_session { + llm_tokenizer_wpm_session(const llama_vocab & vocab) : vocab(vocab) {} + void tokenize(const std::string & text, std::vector & output) { + const auto & token_map = vocab.token_to_id; // normalize and split by whitespace std::vector words = preprocess(text); - // bos token prepended already // find the longest tokens that form the words @@ -699,7 +732,7 @@ struct llm_tokenizer_wpm { } // TODO: reduce string copies by using cpts_offs array - std::vector preprocess(const std::string & text) const { + static std::vector preprocess(const std::string & text) { const std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); std::vector words(1, ""); @@ -751,15 +784,18 @@ struct llm_tokenizer_wpm { //(cpt >= 0xFF00 && cpt <= 0xFFEF); } +private: const llama_vocab & vocab; + // currently unused + // const llm_tokenizer_wpm * wpm_tokenizer; }; // // UGM tokenizer // -struct llm_tokenizer_ugm { - llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) { +struct llm_tokenizer_ugm : llm_tokenizer { + llm_tokenizer_ugm(const llama_vocab & vocab) : llm_tokenizer() { if (vocab.precompiled_charsmap.size() > 0) { size_t charsmap_offset = 0; @@ -805,6 +841,30 @@ struct llm_tokenizer_ugm { unknown_token_score = min_score - unknown_token_score_penalty; } + // escaped space symbol - U+2581 (Lower One Eighth Block) + const std::string escaped_space = "\xE2\x96\x81"; + + const char * prefix_replacements = NULL; + size_t prefix_replacements_size = 0; + + const uint32_t * xcda_array = NULL; + size_t xcda_array_size = 0; + + struct naive_trie user_defined_token_matcher; + + float min_score = FLT_MAX; + float max_score = -FLT_MAX; + + float unknown_token_score_penalty = 10.0; + float unknown_token_score; + + struct naive_trie token_matcher; +}; + +struct llm_tokenizer_ugm_session { + llm_tokenizer_ugm_session(const llama_vocab & vocab) : vocab(vocab), + ugm_tokenizer(static_cast(vocab.tokenizer)) {} + /* This implementation is based on SentencePiece optimized Viterbi algorithm for * unigram language models. The general idea is to: * - move along the input sequence in steps of one UTF code point, @@ -843,7 +903,7 @@ struct llm_tokenizer_ugm { // traverse the token matcher trie to find a matching token bool single_codepoint_token_found = false; const struct best_tokenization & current_best = tokenization_results[input_offset]; - const struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]); + const struct naive_trie * node = ugm_tokenizer->token_matcher.traverse(normalized[prefix_offset++]); while (prefix_offset <= input_len && node != NULL) { // check if we found valid token in prefix @@ -873,7 +933,7 @@ struct llm_tokenizer_ugm { // if we didn't find a valid token corresponding to the whole UTF code point // then use unknown token as the tokenization of this UTF code point if (!single_codepoint_token_found) { - const double challenger_score = current_best.score_sum + unknown_token_score; + const double challenger_score = current_best.score_sum + ugm_tokenizer->unknown_token_score; prefix_offset = input_offset + n_utf8_code_units; struct best_tokenization & current_champ = tokenization_results[prefix_offset]; if (challenger_score > current_champ.score_sum) { @@ -905,7 +965,6 @@ struct llm_tokenizer_ugm { } private: - const llama_vocab & vocab; // helper structure for returning normalization results struct normalization_result { @@ -918,7 +977,7 @@ struct llm_tokenizer_ugm { normalized->clear(); normalized->reserve(input.size() * 3); - const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " "; + const std::string space = vocab.tokenizer_escape_whitespaces ? ugm_tokenizer->escaped_space : " "; bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix; bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix; @@ -1000,13 +1059,21 @@ struct llm_tokenizer_ugm { size_t xcda_array_size; }; + // this structure stores the best tokenization so far at input_offset + struct best_tokenization { + llama_token token_id; + size_t input_offset; + float score_sum; + }; + struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) { if (input_offset == input.size()) { return { &input[input_offset], 0, 0 }; } // if input prefix matches some user-defined token return this token as normalization result - auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset); + auto user_defined_token_match = + ugm_tokenizer->user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset); if (user_defined_token_match.second > 0) { return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second }; } @@ -1014,8 +1081,8 @@ struct llm_tokenizer_ugm { size_t longest_prefix_length = 0; size_t longest_prefix_offset = 0; - if (xcda_array_size > 0) { - struct xcda_array_view xcda_view(xcda_array, xcda_array_size); + if (ugm_tokenizer->xcda_array_size > 0) { + struct xcda_array_view xcda_view(ugm_tokenizer->xcda_array, ugm_tokenizer->xcda_array_size); // Find the longest normalized sequence matching the input prefix by walking // the XOR-compressed compact double array (XCDA) starting from the root node @@ -1051,50 +1118,27 @@ struct llm_tokenizer_ugm { if (longest_prefix_length > 0) { // we have a match, so return the replacement sequence - if (longest_prefix_offset >= prefix_replacements_size) { + if (longest_prefix_offset >= ugm_tokenizer->prefix_replacements_size) { throw std::runtime_error("Index out of array bounds in precompiled charsmap!"); } - const char * prefix_replacement = &prefix_replacements[longest_prefix_offset]; + const char * prefix_replacement = &(ugm_tokenizer->prefix_replacements)[longest_prefix_offset]; return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length }; - } else { - // check if the input prefix contains a valid sequence of UTF-8 code units - try { - // if yes, return this sequence unmodified - size_t prefix_offset = input_offset; - unicode_cpt_from_utf8(input, prefix_offset); - return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset }; - } catch (std::invalid_argument & /*ex*/) { - // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER - return { "\xEF\xBF\xBD", 3, 1 }; - } } - } - - // escaped space symbol - U+2581 (Lower One Eighth Block) - const std::string escaped_space = "\xE2\x96\x81"; - const char * prefix_replacements = NULL; - size_t prefix_replacements_size = 0; - - const uint32_t * xcda_array = NULL; - size_t xcda_array_size = 0; - - struct naive_trie user_defined_token_matcher; - - // this structure stores the best tokenization so far at input_offset - struct best_tokenization { - llama_token token_id; - size_t input_offset; - float score_sum; - }; - - float min_score = FLT_MAX; - float max_score = -FLT_MAX; - - float unknown_token_score_penalty = 10.0; - float unknown_token_score; + // check if the input prefix contains a valid sequence of UTF-8 code units + try { + // if yes, return this sequence unmodified + size_t prefix_offset = input_offset; + unicode_cpt_from_utf8(input, prefix_offset); + return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset }; + } catch (std::invalid_argument & /*ex*/) { + // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER + return { "\xEF\xBF\xBD", 3, 1 }; + } + } - struct naive_trie token_matcher; + const llama_vocab & vocab; + const llm_tokenizer_ugm * ugm_tokenizer; }; // @@ -1155,8 +1199,8 @@ static std::vector llama_unescape_rwkv_token(const std::string & escape return output; } -struct llm_tokenizer_rwkv { - llm_tokenizer_rwkv(const llama_vocab & vocab): vocab(vocab) { +struct llm_tokenizer_rwkv : llm_tokenizer { + llm_tokenizer_rwkv(const llama_vocab & vocab) : llm_tokenizer() { // RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens. // For now, we decode the vocab here into the lookup we'll use for tokenization. @@ -1168,11 +1212,17 @@ struct llm_tokenizer_rwkv { } } + struct naive_trie token_matcher; +}; + +struct llm_tokenizer_rwkv_session { + llm_tokenizer_rwkv_session(const llama_vocab & vocab) : vocab(vocab), + rwkv_tokenizer(static_cast(*vocab.tokenizer)) {} + void tokenize(const std::string & text, std::vector & output) { uint32_t position = 0; - while (position < text.size()) { - const struct naive_trie * node = token_matcher.traverse(text[position]); + const struct naive_trie * node = rwkv_tokenizer.token_matcher.traverse(text[position]); if (node == NULL) { // no matching token found, add unknown token output.push_back(vocab.special_unk_id); @@ -1197,11 +1247,33 @@ struct llm_tokenizer_rwkv { } } +private: const llama_vocab & vocab; - - struct naive_trie token_matcher; + const llm_tokenizer_rwkv & rwkv_tokenizer; }; +void llama_vocab::init_tokenizer() { + switch (type) { + case LLAMA_VOCAB_TYPE_SPM: + tokenizer = new llm_tokenizer_spm(*this); + break; + case LLAMA_VOCAB_TYPE_BPE: + tokenizer = new llm_tokenizer_bpe(*this); + break; + case LLAMA_VOCAB_TYPE_WPM: + tokenizer = new llm_tokenizer_wpm(*this); + break; + case LLAMA_VOCAB_TYPE_UGM: + tokenizer = new llm_tokenizer_ugm(*this); + break; + case LLAMA_VOCAB_TYPE_RWKV: + tokenizer = new llm_tokenizer_rwkv(*this); + break; + default: + GGML_ABORT("unsupported vocab type"); + } +} + // // (de-) tokenize // @@ -1263,7 +1335,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // if a fragment is text ( not yet processed ) if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { - auto & raw_text = fragment.raw_text; + const auto & raw_text = fragment.raw_text; auto raw_text_base_offset = fragment.offset; auto raw_text_base_length = fragment.length; @@ -1362,7 +1434,13 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< } } -std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) { +std::vector llama_tokenize_internal( + const llama_vocab & vocab, + std::string raw_text, + bool add_special, + bool parse_special) { + GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first."); + std::vector output; std::forward_list fragment_buffer; @@ -1399,9 +1477,9 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif - llm_tokenizer_spm tokenizer(vocab); llama_escape_whitespace(raw_text); - tokenizer.tokenize(raw_text, output); + llm_tokenizer_spm_session session(vocab); + session.tokenize(raw_text, output); is_prev_special = false; } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); @@ -1423,10 +1501,11 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, } break; case LLAMA_VOCAB_TYPE_BPE: { - llm_tokenizer_bpe tokenizer(vocab); - + llm_tokenizer_bpe_session session(vocab); + // it calls some other methods that are not exist in llm_tokenizer, + // here just cast it to bpe tokenizer object if (add_special) { - tokenizer.append_bos(output); + session.append_bos(output); } for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { @@ -1435,15 +1514,15 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif - tokenizer.tokenize(raw_text, output); + session.tokenize(raw_text, output); } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) - tokenizer.append(fragment.token, output); + session.append(fragment.token, output); } } if (add_special) { - tokenizer.append_eos(output); - tokenizer.check_double_bos_eos(output); + session.append_eos(output); + session.check_double_bos_eos(output); } } break; case LLAMA_VOCAB_TYPE_WPM: @@ -1453,7 +1532,7 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, output.push_back(vocab.special_cls_id); } - llm_tokenizer_wpm tokenizer(vocab); + llm_tokenizer_wpm_session session(vocab); for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { @@ -1462,7 +1541,7 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif - tokenizer.tokenize(raw_text, output); + session.tokenize(raw_text, output); } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); } @@ -1475,12 +1554,11 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, } break; case LLAMA_VOCAB_TYPE_UGM: { - llm_tokenizer_ugm tokenizer(vocab); - - if (add_special && vocab.tokenizer_add_bos != 0) { + if (add_special && vocab.tokenizer_add_bos) { GGML_ASSERT(vocab.special_bos_id != -1); output.push_back(vocab.special_bos_id); } + llm_tokenizer_ugm_session session(vocab); for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { @@ -1488,26 +1566,27 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif - tokenizer.tokenize(raw_text, output); + session.tokenize(raw_text, output); } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); } } - if (add_special && vocab.tokenizer_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) { LLAMA_LOG_WARN( "%s: Added a BOS token to the prompt as specified by the model but the prompt " "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " "Are you sure this is what you want?\n", __FUNCTION__); } - if (add_special && vocab.tokenizer_add_eos == 1) { + if (add_special && vocab.tokenizer_add_eos) { GGML_ASSERT(vocab.special_eos_id != -1); output.push_back(vocab.special_eos_id); } } break; case LLAMA_VOCAB_TYPE_RWKV: { + llm_tokenizer_rwkv_session session(vocab); for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); @@ -1516,8 +1595,7 @@ std::vector llama_tokenize_internal(const llama_vocab & vocab, LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); #endif - llm_tokenizer_rwkv tokenizer(vocab); - tokenizer.tokenize(raw_text, output); + session.tokenize(raw_text, output); } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); } @@ -1630,13 +1708,13 @@ llama_token llama_token_eom_impl(const struct llama_vocab & vocab) { } int32_t llama_tokenize_impl( - const struct llama_vocab & vocab, - const char * text, - int32_t text_len, - llama_token * tokens, - int32_t n_tokens_max, - bool add_special, - bool parse_special) { + const struct llama_vocab & vocab, + const char * text, + int32_t text_len, + llama_token * tokens, + int32_t n_tokens_max, + bool add_special, + bool parse_special) { auto res = llama_tokenize_internal(vocab, std::string(text, text_len), add_special, parse_special); if (n_tokens_max < (int) res.size()) { // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); @@ -1713,11 +1791,13 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token // suppressing them like CONTROL tokens. if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) { return _try_copy(token_text.data(), token_text.size()); - } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) { + } + if (attr & LLAMA_TOKEN_ATTR_NORMAL) { std::string result = token_text; llama_unescape_whitespace(result); return _try_copy(result.data(), result.size()); - } else if (attr & LLAMA_TOKEN_ATTR_BYTE) { + } + if (attr & LLAMA_TOKEN_ATTR_BYTE) { char byte = (char) llama_token_to_byte(vocab, token); return _try_copy((char*) &byte, 1); } @@ -1728,7 +1808,8 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token // suppressing them like CONTROL tokens. if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) { return _try_copy(token_text.data(), token_text.size()); - } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) { + } + if (attr & LLAMA_TOKEN_ATTR_NORMAL) { std::string result = llama_decode_text(token_text); return _try_copy(result.data(), result.size()); } @@ -1761,6 +1842,8 @@ int32_t llama_detokenize_impl( int32_t text_len_max, bool remove_special, bool unparse_special) { + GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first."); + int32_t avail = text_len_max; int32_t total = 0; diff --git a/examples/talk-llama/llama-vocab.h b/examples/talk-llama/llama-vocab.h index cc46f642bf1..069bdc423a6 100644 --- a/examples/talk-llama/llama-vocab.h +++ b/examples/talk-llama/llama-vocab.h @@ -8,6 +8,8 @@ #include #include +struct llm_tokenizer; + struct llama_vocab { using id = llama_token; using token = std::string; @@ -65,7 +67,14 @@ struct llama_vocab { std::vector precompiled_charsmap; + llm_tokenizer * tokenizer = nullptr; + + llama_vocab() = default; + ~llama_vocab(); + int find_bpe_rank(const std::string & token_left, const std::string & token_right) const; + + void init_tokenizer(); }; // diff --git a/examples/talk-llama/llama.cpp b/examples/talk-llama/llama.cpp index a718de054f9..4c0a1bb6182 100644 --- a/examples/talk-llama/llama.cpp +++ b/examples/talk-llama/llama.cpp @@ -215,6 +215,8 @@ enum llm_arch { LLM_ARCH_EXAONE, LLM_ARCH_RWKV6, LLM_ARCH_GRANITE, + LLM_ARCH_GRANITE_MOE, + LLM_ARCH_CHAMELEON, LLM_ARCH_UNKNOWN, }; @@ -266,6 +268,8 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_EXAONE, "exaone" }, { LLM_ARCH_RWKV6, "rwkv6" }, { LLM_ARCH_GRANITE, "granite" }, + { LLM_ARCH_GRANITE_MOE, "granitemoe" }, + { LLM_ARCH_CHAMELEON, "chameleon" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -302,6 +306,7 @@ enum llm_kv { LLM_KV_DECODER_START_TOKEN_ID, LLM_KV_ATTN_LOGIT_SOFTCAPPING, LLM_KV_FINAL_LOGIT_SOFTCAPPING, + LLM_KV_SWIN_NORM, LLM_KV_RESCALE_EVERY_N_LAYERS, LLM_KV_TIME_MIX_EXTRA_DIM, LLM_KV_TIME_DECAY_EXTRA_DIM, @@ -409,6 +414,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" }, { LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" }, { LLM_KV_FINAL_LOGIT_SOFTCAPPING, "%s.final_logit_softcapping" }, + { LLM_KV_SWIN_NORM, "%s.swin_norm" }, { LLM_KV_RESCALE_EVERY_N_LAYERS, "%s.rescale_every_n_layers" }, { LLM_KV_TIME_MIX_EXTRA_DIM, "%s.time_mix_extra_dim" }, { LLM_KV_TIME_DECAY_EXTRA_DIM, "%s.time_decay_extra_dim" }, @@ -600,6 +606,8 @@ enum llm_tensor { LLM_TENSOR_ENC_FFN_DOWN, LLM_TENSOR_ENC_FFN_UP, LLM_TENSOR_ENC_OUTPUT_NORM, + LLM_TENSOR_CLS, + LLM_TENSOR_CLS_OUT, }; static const std::map> LLM_TENSOR_NAMES = { @@ -787,6 +795,8 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_CLS, "cls" }, + { LLM_TENSOR_CLS_OUT, "cls.output" }, }, }, { @@ -822,6 +832,7 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_CLS, "cls" }, }, }, { @@ -1467,6 +1478,7 @@ static const std::map> LLM_TENSOR_NA { { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, @@ -1478,6 +1490,43 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_GRANITE_MOE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, + { + LLM_ARCH_CHAMELEON, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -2341,6 +2390,7 @@ struct llama_hparams { bool vocab_only; bool rope_finetuned; bool use_par_res; + bool swin_norm; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on @@ -2396,7 +2446,7 @@ struct llama_hparams { float f_max_alibi_bias = 0.0f; float f_logit_scale = 0.0f; - // Additional scale factors (Granite) + // Additional scale factors (Granite/Granite MoE) float f_residual_scale = 0.0f; float f_embedding_scale = 0.0f; float f_attention_scale = 0.0f; @@ -2849,6 +2899,7 @@ struct llama_model { llama_hparams hparams = {}; llama_vocab vocab; + // TODO: should init all tensors to nullptr struct ggml_tensor * tok_embd; struct ggml_tensor * type_embd; struct ggml_tensor * pos_embd; @@ -2861,6 +2912,12 @@ struct llama_model { struct ggml_tensor * output_b; struct ggml_tensor * output_norm_enc; + // classifier + struct ggml_tensor * cls; + struct ggml_tensor * cls_b; + struct ggml_tensor * cls_out = nullptr; + struct ggml_tensor * cls_out_b = nullptr; + std::vector layers; llama_split_mode split_mode; @@ -5445,8 +5502,10 @@ static void llm_load_hparams( } } else { switch (hparams.n_layer) { + case 16: model.type = e_model::MODEL_1B; break; // Llama 3.2 1B case 22: model.type = e_model::MODEL_1B; break; case 26: model.type = e_model::MODEL_3B; break; + case 28: model.type = e_model::MODEL_3B; break; // Llama 3.2 3B // granite uses a vocab with len 49152 case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break; case 36: model.type = e_model::MODEL_8B; break; // granite @@ -5559,11 +5618,11 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); - ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); hparams.f_max_alibi_bias = 8.0f; switch (hparams.n_layer) { - case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small + case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base } } break; @@ -6048,6 +6107,7 @@ static void llm_load_hparams( } } break; case LLM_ARCH_GRANITE: + case LLM_ARCH_GRANITE_MOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); @@ -6056,11 +6116,24 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale); switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_3B; break; case 40: model.type = e_model::MODEL_3B; break; // Add additional layer/vocab/etc checks here for other model sizes default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_CHAMELEON: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + hparams.f_norm_eps = 1e-5; // eps for qk-norm, torch default + ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm); + + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_7B; break; + case 48: model.type = e_model::MODEL_34B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -6254,6 +6327,7 @@ static void llm_load_vocab( tokenizer_pre == "phi-2" || tokenizer_pre == "jina-es" || tokenizer_pre == "jina-de" || + tokenizer_pre == "jina-v1-en" || tokenizer_pre == "jina-v2-es" || tokenizer_pre == "jina-v2-de" || tokenizer_pre == "jina-v2-code") { @@ -6318,6 +6392,11 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "exaone") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_EXAONE; + } else if ( + tokenizer_pre == "chameleon") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON; + vocab.tokenizer_add_bos = true; + vocab.tokenizer_clean_spaces = false; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -6375,7 +6454,12 @@ static void llm_load_vocab( for (uint32_t i = 0; i < n_vocab; i++) { std::string word = gguf_get_arr_str(ctx, token_idx, i); - GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0); + + //GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0); + if (word.empty()) { + LLAMA_LOG_WARN("%s: empty token at index %u\n", __func__, i); + word = "[EMPTY_" + std::to_string(i) + "]"; + } vocab.token_to_id[word] = i; vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size()); @@ -6400,6 +6484,8 @@ static void llm_load_vocab( } GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size()); + vocab.init_tokenizer(); + // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { // For Fill-In-the-Middle (FIM)/infill models which where converted @@ -6454,8 +6540,14 @@ static void llm_load_vocab( vocab.linefeed_id = ids[0]; } else { const std::vector ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A - GGML_ASSERT(!ids.empty() && "model vocab missing newline token"); - vocab.linefeed_id = ids[0]; + + //GGML_ASSERT(!ids.empty() && "model vocab missing newline token"); + if (ids.empty()) { + LLAMA_LOG_WARN("%s: model vocab missing newline token, using special_pad_id instead\n", __func__); + vocab.linefeed_id = vocab.special_pad_id; + } else { + vocab.linefeed_id = ids[0]; + } } // special tokens @@ -6810,7 +6902,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp); } - if (model.arch == LLM_ARCH_GRANITE) { + if (model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) { LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale); LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale); LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale); @@ -6984,6 +7076,7 @@ static bool llm_load_tensors( case LLM_ARCH_REFACT: case LLM_ARCH_MINICPM: case LLM_ARCH_GRANITE: + case LLM_ARCH_GRANITE_MOE: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -7327,6 +7420,12 @@ static bool llm_load_tensors( if (model.arch == LLM_ARCH_BERT) { model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}); + + model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + + model.cls_out = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_out_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); } model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); @@ -7379,6 +7478,8 @@ static bool llm_load_tensors( model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias + model.cls = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED); + model.cls_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_CLS, "bias"), {1}, llama_model_loader::TENSOR_NOT_REQUIRED); 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); @@ -8704,6 +8805,45 @@ static bool llm_load_tensors( } } break; + case LLM_ARCH_CHAMELEON: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_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 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -10173,6 +10313,10 @@ struct llm_build_context { struct ggml_tensor * cur; switch (pooling_type) { + case LLAMA_POOLING_TYPE_NONE: + { + cur = inp; + } break; case LLAMA_POOLING_TYPE_MEAN: { struct ggml_tensor * inp_mean = build_inp_mean(); @@ -10184,9 +10328,26 @@ struct llm_build_context { struct ggml_tensor * inp_cls = build_inp_cls(); cur = ggml_get_rows(ctx0, inp, inp_cls); } break; - case LLAMA_POOLING_TYPE_NONE: + case LLAMA_POOLING_TYPE_RANK: { - cur = inp; + struct ggml_tensor * inp_cls = build_inp_cls(); + inp = ggml_get_rows(ctx0, inp, inp_cls); + + // classification head + // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566 + GGML_ASSERT(model.cls != nullptr); + GGML_ASSERT(model.cls_b != nullptr); + + cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls, inp), model.cls_b); + cur = ggml_tanh(ctx0, cur); + + // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en + // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896 + if (model.cls_out) { + GGML_ASSERT(model.cls_out_b != nullptr); + + cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls_out, cur), model.cls_out_b); + } } break; default: { @@ -11415,8 +11576,8 @@ struct llm_build_context { inpL = cur; } - // final output cur = inpL; + cb(cur, "result_embd", -1); ggml_build_forward_expand(gf, cur); @@ -15848,6 +16009,184 @@ struct llm_build_context { return gf; } + + // ref: https://github.com/facebookresearch/chameleon + // based on the original build_llama() function, changes: + // * qk-norm + // * swin-norm + // * removed bias + // * removed MoE + struct ggml_cgraph * build_chameleon() { + 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 + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + if (hparams.swin_norm) { + cur = inpL; + } else { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + } + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + if (model.layers[il].attn_q_norm) { + Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens, + ggml_element_size(Qcur) * n_embd_head, + ggml_element_size(Qcur) * n_embd_head * n_head, + 0); + cb(Qcur, "Qcur", il); + + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, cb, il); + cb(Qcur, "Qcur", il); + } + + if (model.layers[il].attn_k_norm) { + Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens, + ggml_element_size(Kcur) * n_embd_head, + ggml_element_size(Kcur) * n_embd_head * n_head_kv, + 0); + cb(Kcur, "Kcur", il); + + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, cb, il); + cb(Kcur, "Kcur", il); + } + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, lctx, kv_self, gf, + model.layers[il].wo, nullptr, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + + if (hparams.swin_norm) { + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, 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); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + if (!hparams.swin_norm) { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + } + + 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, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + if (hparams.swin_norm) { + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = lctx.cvec.apply_to(ctx0, cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + 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_with_img_logits", -1); + + // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs. + // Needs to be removed once image outputs are supported. + int img_token_end_idx = 8196; + int img_token_start_idx = 4; + int num_img_tokens = img_token_end_idx - img_token_start_idx; + // creates 1d tensor of size num_img_tokens and values -FLT_MAX, + // which ensures that text token values are always at least larger than image token values + struct ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens); + img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX); + cb(img_logits, "img_logits", -1); + cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -15930,6 +16269,7 @@ static struct ggml_cgraph * llama_build_graph( switch (model.arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_GRANITE: + case LLM_ARCH_GRANITE_MOE: { result = llm.build_llama(); } break; @@ -16107,6 +16447,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_rwkv6(); } break; + case LLM_ARCH_CHAMELEON: + { + result = llm.build_chameleon(); + } break; default: GGML_ABORT("fatal error"); } @@ -16393,7 +16737,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { } } - if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { + if (cparams.embeddings && ( + cparams.pooling_type == LLAMA_POOLING_TYPE_CLS || + cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) { const int64_t n_tokens = batch.n_tokens; const int64_t n_seq_tokens = batch.n_seq_tokens; const int64_t n_seqs = batch.n_seqs; @@ -16408,7 +16754,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { const llama_seq_id seq_id = batch.seq_id[s][0]; // TODO: adapt limits to n_seqs when batch.equal_seqs is true - GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS"); + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK"); for (int i = 0; i < n_seq_tokens; ++i) { const llama_pos pos = batch.pos[s*n_seq_tokens + i]; @@ -16679,12 +17025,6 @@ static void llama_graph_compute( ggml_cgraph * gf, int n_threads, ggml_threadpool * threadpool) { -#ifdef GGML_USE_METAL - if (ggml_backend_is_metal(lctx.backend_metal)) { - ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); - } -#endif - if (lctx.backend_cpu != nullptr) { ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads); ggml_backend_cpu_set_threadpool(lctx.backend_cpu, threadpool); @@ -16948,6 +17288,20 @@ static int llama_decode_internal( 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_RANK: + { + // extract the rerank score - a single float per sequence + auto & embd_seq_out = lctx.embd_seq; + + for (uint32_t s = 0; s < ubatch.n_seqs; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[s][0]; + if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { + continue; + } + embd_seq_out[seq_id].resize(1); + ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float)); + } + } break; case LLAMA_POOLING_TYPE_UNSPECIFIED: { GGML_ABORT("unknown pooling type"); @@ -17154,6 +17508,13 @@ static int llama_encode_internal( 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_RANK: + { + // TODO: this likely should be the same logic as in llama_decoder_internal, but better to + // wait for an encoder model that requires this pooling type in order to test it + // https://github.com/ggerganov/llama.cpp/pull/9510 + GGML_ABORT("RANK pooling not implemented yet"); + } case LLAMA_POOLING_TYPE_UNSPECIFIED: { GGML_ABORT("unknown pooling type"); @@ -19231,6 +19592,8 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_DEEPSEEK2: case LLM_ARCH_CHATGLM: case LLM_ARCH_GRANITE: + case LLM_ARCH_GRANITE_MOE: + case LLM_ARCH_CHAMELEON: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 diff --git a/examples/talk-llama/llama.h b/examples/talk-llama/llama.h index 132937a0700..7cae1bbe2e5 100644 --- a/examples/talk-llama/llama.h +++ b/examples/talk-llama/llama.h @@ -102,6 +102,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_BLOOM = 23, LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, + LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26, }; enum llama_rope_type { @@ -192,6 +193,7 @@ extern "C" { LLAMA_POOLING_TYPE_MEAN = 1, LLAMA_POOLING_TYPE_CLS = 2, LLAMA_POOLING_TYPE_LAST = 3, + LLAMA_POOLING_TYPE_RANK = 4, // used by reranking models to attach the classification head to the graph }; enum llama_attention_type { @@ -201,9 +203,9 @@ extern "C" { }; enum llama_split_mode { - LLAMA_SPLIT_MODE_NONE = 0, // single GPU - LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs - LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs + LLAMA_SPLIT_MODE_NONE = 0, // single GPU + LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs + LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs }; // TODO: simplify (https://github.com/ggerganov/llama.cpp/pull/9294#pullrequestreview-2286561979) @@ -871,7 +873,8 @@ extern "C" { // Get the embeddings for a sequence id // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE - // shape: [n_embd] (1-dimensional) + // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[1] with the rank of the sequence + // otherwise: float[n_embd] (1-dimensional) LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id); // @@ -910,6 +913,8 @@ extern "C" { // // Tokenization // + // The API is thread-safe. + // /// @details Convert the provided text into tokens. /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. diff --git a/ggml/include/ggml-metal.h b/ggml/include/ggml-metal.h index d483cf1ac40..4d416532449 100644 --- a/ggml/include/ggml-metal.h +++ b/ggml/include/ggml-metal.h @@ -25,9 +25,6 @@ #include #include -// max memory buffers that can be mapped to the device -#define GGML_METAL_MAX_BUFFERS 64 - struct ggml_tensor; struct ggml_cgraph; @@ -48,8 +45,6 @@ GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size); -GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb); - GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data); GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index e24b8a319fc..ce3d92cb2e0 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -229,14 +229,16 @@ #define GGML_MAX_PARAMS 2048 #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 -#ifndef GGML_MAX_NAME -#define GGML_MAX_NAME 64 #define GGML_MAX_N_THREADS 512 +#define GGML_MAX_OP_PARAMS 64 +#ifndef GGML_MAX_NAME +# define GGML_MAX_NAME 64 #endif -#define GGML_MAX_OP_PARAMS 64 + #define GGML_DEFAULT_N_THREADS 4 #define GGML_DEFAULT_GRAPH_SIZE 2048 + #if UINTPTR_MAX == 0xFFFFFFFF #define GGML_MEM_ALIGN 4 #else @@ -259,21 +261,21 @@ #define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1)) #ifndef NDEBUG -#define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0) +# define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0) #elif defined(__GNUC__) -#define GGML_UNREACHABLE() __builtin_unreachable() +# define GGML_UNREACHABLE() __builtin_unreachable() #elif defined(_MSC_VER) -#define GGML_UNREACHABLE() __assume(0) +# define GGML_UNREACHABLE() __assume(0) #else -#define GGML_UNREACHABLE() ((void) 0) +# define GGML_UNREACHABLE() ((void) 0) #endif #ifdef __cplusplus -#define GGML_NORETURN [[noreturn]] +# define GGML_NORETURN [[noreturn]] #elif defined(_MSC_VER) -#define GGML_NORETURN __declspec(noreturn) +# define GGML_NORETURN __declspec(noreturn) #else -#define GGML_NORETURN _Noreturn +# define GGML_NORETURN _Noreturn #endif #define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__) @@ -575,10 +577,10 @@ extern "C" { // this tensor... enum ggml_tensor_flag { - GGML_TENSOR_FLAG_INPUT = 1, // ...is an input for the GGML compute graph - GGML_TENSOR_FLAG_OUTPUT = 2, // ...is an output for the GGML compute graph - GGML_TENSOR_FLAG_PARAM = 4, // ...contains trainable parameters - GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up) + GGML_TENSOR_FLAG_INPUT = 1, // ...is an input for the GGML compute graph + GGML_TENSOR_FLAG_OUTPUT = 2, // ...is an output for the GGML compute graph + GGML_TENSOR_FLAG_PARAM = 4, // ...contains trainable parameters + GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up) }; // n-dimensional tensor @@ -1408,14 +1410,14 @@ extern "C" { // supports 3D: a->ne[2] == b->ne[1] GGML_API struct ggml_tensor * ggml_get_rows( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * a, // data + struct ggml_tensor * b); // row indices GGML_API struct ggml_tensor * ggml_get_rows_back( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c); + struct ggml_tensor * a, // gradients of ggml_get_rows result + struct ggml_tensor * b, // row indices + struct ggml_tensor * c); // data for ggml_get_rows, only used for its shape GGML_API struct ggml_tensor * ggml_diag( struct ggml_context * ctx, @@ -1566,9 +1568,9 @@ extern "C" { // a - dy GGML_API struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c, + struct ggml_tensor * a, // gradients of ggml_rope result + struct ggml_tensor * b, // positions + struct ggml_tensor * c, // freq factors int n_dims, int mode, int n_ctx_orig, @@ -2034,15 +2036,15 @@ extern "C" { // loss function GGML_API struct ggml_tensor * ggml_cross_entropy_loss( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_context * ctx, + struct ggml_tensor * a, // logits + struct ggml_tensor * b); // labels GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c); + struct ggml_context * ctx, + struct ggml_tensor * a, // logits + struct ggml_tensor * b, // labels + struct ggml_tensor * c); // gradients of cross_entropy_loss result // AdamW optimizer step // Paper: https://arxiv.org/pdf/1711.05101v3.pdf @@ -2050,6 +2052,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_opt_step_adamw( struct ggml_context * ctx, struct ggml_tensor * a, + struct ggml_tensor * grad, float alpha, float beta1, float beta2, @@ -2064,7 +2067,7 @@ extern "C" { GGML_API void ggml_set_loss(struct ggml_tensor * tensor); GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); - GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate, bool keep); + GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate); GGML_API void ggml_build_opt_adamw( struct ggml_context * ctx, @@ -2507,6 +2510,9 @@ extern "C" { GGML_API int ggml_cpu_has_cann (void); GGML_API int ggml_cpu_has_llamafile (void); + // get the sve vector length in bytes + GGML_API int ggml_cpu_get_sve_cnt(void); + // // Internal types and functions exposed for tests and benchmarks // diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c index 2b01b4f9316..b27f411474f 100644 --- a/ggml/src/ggml-aarch64.c +++ b/ggml/src/ggml-aarch64.c @@ -1,4 +1,7 @@ -// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd. +// SPDX-FileCopyrightText: Copyright 2024 Arm Limited and/or its affiliates +// SPDX-License-Identifier: MIT +// + #define GGML_COMMON_IMPL_C #include "ggml-common.h" @@ -614,73 +617,67 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(ncols_interleaved); UNUSED(blocklen); -#if defined(__ARM_FEATURE_SVE) - if (ggml_sve_cnt_b == QK8_0) { - GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) && - "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance"); +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + if (ggml_cpu_has_neon()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "movi v31.16b, #0x4\n" + "movi v30.16b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x8\n" + "1:" // Column loop + "add x22, %x[a_ptr], #0x2\n" + "movi v29.16b, #0x0\n" + "mov x21, %x[nb]\n" + "2:" // Block loop + "ldr q28, [%x[b_ptr], #0x0]\n" + "ldr q27, [x22, #0x0]\n" + "movi v26.4s, #0x0\n" + "sub x20, x22, #0x2\n" + "ldr q25, [x22, #0x10]\n" + "ldr q24, [%x[b_ptr], #0x10]\n" + "sub x21, x21, #0x1\n" + "add x22, x22, #0x22\n" + "ldr q23, [%x[b_ptr], #0x20]\n" + "ldr q22, [%x[b_ptr], #0x30]\n" + "ld1r { v21.8h }, [x20]\n" + "ldr q20, [%x[b_ptr], #-0x8]\n" + "sshl v16.16b, v28.16b, v31.16b\n" + "and v28.16b, v28.16b, v30.16b\n" + "sshl v19.16b, v24.16b, v31.16b\n" + "and v24.16b, v24.16b, v30.16b\n" + "add %x[b_ptr], %x[b_ptr], #0x48\n" + "sshl v18.16b, v23.16b, v31.16b\n" + "and v23.16b, v23.16b, v30.16b\n" + ".inst 0x4f9be21a // sdot v26.4s, v16.16b, v27.4b[0]\n" + "sshl v17.16b, v22.16b, v31.16b\n" + "and v22.16b, v22.16b, v30.16b\n" + "fcvtl v21.4s, v21.4h\n" + "fcvtl v16.4s, v20.4h\n" + ".inst 0x4f99e39a // sdot v26.4s, v28.16b, v25.4b[0]\n" + "fmul v16.4s, v16.4s, v21.4s\n" + ".inst 0x4fbbe27a // sdot v26.4s, v19.16b, v27.4b[1]\n" + ".inst 0x4fb9e31a // sdot v26.4s, v24.16b, v25.4b[1]\n" + ".inst 0x4f9bea5a // sdot v26.4s, v18.16b, v27.4b[2]\n" + ".inst 0x4f99eafa // sdot v26.4s, v23.16b, v25.4b[2]\n" + ".inst 0x4fbbea3a // sdot v26.4s, v17.16b, v27.4b[3]\n" + ".inst 0x4fb9eada // sdot v26.4s, v22.16b, v25.4b[3]\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "fmla v29.4s, v26.4s, v16.4s\n" + "cbnz x21, 2b\n" + "sub %x[nc], %x[nc], #0x4\n" + "str q29, [%x[res_ptr], #0x0]\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22" + ); + return; } -#endif -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) && - "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance"); -#elif defined(__ARM_NEON) && defined(__aarch64__) && ! ((defined(_MSC_VER)) && ! defined(__clang__)) - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "movi v31.16b, #0x4\n" - "movi v30.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x22, %x[a_ptr], #0x2\n" - "movi v29.16b, #0x0\n" - "mov x21, %x[nb]\n" - "2:" // Block loop - "ldr q28, [%x[b_ptr], #0x0]\n" - "ldr q27, [x22, #0x0]\n" - "movi v26.4s, #0x0\n" - "sub x20, x22, #0x2\n" - "ldr q25, [x22, #0x10]\n" - "ldr q24, [%x[b_ptr], #0x10]\n" - "sub x21, x21, #0x1\n" - "add x22, x22, #0x22\n" - "ldr q23, [%x[b_ptr], #0x20]\n" - "ldr q22, [%x[b_ptr], #0x30]\n" - "ld1r { v21.8h }, [x20]\n" - "ldr q20, [%x[b_ptr], #-0x8]\n" - "sshl v16.16b, v28.16b, v31.16b\n" - "and v28.16b, v28.16b, v30.16b\n" - "sshl v19.16b, v24.16b, v31.16b\n" - "and v24.16b, v24.16b, v30.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "sshl v18.16b, v23.16b, v31.16b\n" - "and v23.16b, v23.16b, v30.16b\n" - ".inst 0x4f9be21a // sdot v26.4s, v16.16b, v27.4b[0]\n" - "sshl v17.16b, v22.16b, v31.16b\n" - "and v22.16b, v22.16b, v30.16b\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v16.4s, v20.4h\n" - ".inst 0x4f99e39a // sdot v26.4s, v28.16b, v25.4b[0]\n" - "fmul v16.4s, v16.4s, v21.4s\n" - ".inst 0x4fbbe27a // sdot v26.4s, v19.16b, v27.4b[1]\n" - ".inst 0x4fb9e31a // sdot v26.4s, v24.16b, v25.4b[1]\n" - ".inst 0x4f9bea5a // sdot v26.4s, v18.16b, v27.4b[2]\n" - ".inst 0x4f99eafa // sdot v26.4s, v23.16b, v25.4b[2]\n" - ".inst 0x4fbbea3a // sdot v26.4s, v17.16b, v27.4b[3]\n" - ".inst 0x4fb9eada // sdot v26.4s, v22.16b, v25.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v29.4s, v26.4s, v16.4s\n" - "cbnz x21, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q29, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22" - ); -#else +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) float sumf[4]; int sumi; @@ -704,7 +701,6 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * } for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; } -#endif } void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { @@ -726,79 +722,72 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(ncols_interleaved); UNUSED(blocklen); -#if defined(__ARM_FEATURE_SVE) - if (ggml_sve_cnt_b == QK8_0) { - GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) && - "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance"); +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "movi v2.16b, #0x4\n" + "movi v1.16b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x8\n" + "1:" // Column loop + "add x23, %x[a_ptr], #0x2\n" + "movi v0.16b, #0x0\n" + "mov x22, %x[nb]\n" + "2:" // Block loop + "ldr q31, [%x[b_ptr], #0x0]\n" + "ldr q30, [%x[b_ptr], #0x10]\n" + "mov x21, x23\n" + "movi v29.4s, #0x0\n" + "ldr q28, [%x[b_ptr], #0x20]\n" + "ldr q27, [%x[b_ptr], #0x30]\n" + "movi v26.4s, #0x0\n" + "sub x20, x23, #0x2\n" + "ld1r { v25.8h }, [x20]\n" + "ldr q24, [%x[b_ptr], #-0x8]\n" + "sub x22, x22, #0x1\n" + "add x23, x23, #0x22\n" + "ld1r { v23.2d }, [x21], #0x8\n" + "sshl v22.16b, v31.16b, v2.16b\n" + "sshl v16.16b, v30.16b, v2.16b\n" + "add %x[b_ptr], %x[b_ptr], #0x48\n" + "ld1r { v21.2d }, [x21], #0x8\n" + "sshl v20.16b, v28.16b, v2.16b\n" + "sshl v19.16b, v27.16b, v2.16b\n" + "ld1r { v18.2d }, [x21], #0x8\n" + "ld1r { v17.2d }, [x21], #0x8\n" + "and v31.16b, v31.16b, v1.16b\n" + "and v30.16b, v30.16b, v1.16b\n" + ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" + ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" + "and v28.16b, v28.16b, v1.16b\n" + "and v27.16b, v27.16b, v1.16b\n" + "fcvtl v25.4s, v25.4h\n" + "fcvtl v16.4s, v24.4h\n" + ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" + ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" + "fmul v16.4s, v16.4s, v25.4s\n" + ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" + ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" + ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" + ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" + "addp v29.4s, v29.4s, v26.4s\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v0.4s, v29.4s, v16.4s\n" + "cbnz x22, 2b\n" + "sub %x[nc], %x[nc], #0x4\n" + "str q0, [%x[res_ptr], #0x0]\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" + ); + return; } -#endif -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__)) - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "movi v2.16b, #0x4\n" - "movi v1.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x23, %x[a_ptr], #0x2\n" - "movi v0.16b, #0x0\n" - "mov x22, %x[nb]\n" - "2:" // Block loop - "ldr q31, [%x[b_ptr], #0x0]\n" - "ldr q30, [%x[b_ptr], #0x10]\n" - "mov x21, x23\n" - "movi v29.4s, #0x0\n" - "ldr q28, [%x[b_ptr], #0x20]\n" - "ldr q27, [%x[b_ptr], #0x30]\n" - "movi v26.4s, #0x0\n" - "sub x20, x23, #0x2\n" - "ld1r { v25.8h }, [x20]\n" - "ldr q24, [%x[b_ptr], #-0x8]\n" - "sub x22, x22, #0x1\n" - "add x23, x23, #0x22\n" - "ld1r { v23.2d }, [x21], #0x8\n" - "sshl v22.16b, v31.16b, v2.16b\n" - "sshl v16.16b, v30.16b, v2.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "ld1r { v21.2d }, [x21], #0x8\n" - "sshl v20.16b, v28.16b, v2.16b\n" - "sshl v19.16b, v27.16b, v2.16b\n" - "ld1r { v18.2d }, [x21], #0x8\n" - "ld1r { v17.2d }, [x21], #0x8\n" - "and v31.16b, v31.16b, v1.16b\n" - "and v30.16b, v30.16b, v1.16b\n" - ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" - ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" - "and v28.16b, v28.16b, v1.16b\n" - "and v27.16b, v27.16b, v1.16b\n" - "fcvtl v25.4s, v25.4h\n" - "fcvtl v16.4s, v24.4h\n" - ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" - ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" - "fmul v16.4s, v16.4s, v25.4s\n" - ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" - ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" - ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" - ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" - "addp v29.4s, v29.4s, v26.4s\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v0.4s, v29.4s, v16.4s\n" - "cbnz x22, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q0, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" - ); -#elif defined(__ARM_NEON) && defined(__aarch64__) - GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) && - "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal " - "performance"); -#else +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) float sumf[4]; int sumi; @@ -822,7 +811,6 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * } for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; } -#endif } void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { @@ -844,8 +832,9 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(ncols_interleaved); UNUSED(blocklen); -#if defined(__ARM_FEATURE_SVE) && ! ((defined(_MSC_VER)) && ! defined(__clang__)) - if (ggml_sve_cnt_b == QK8_0) { +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) +#if defined(__ARM_FEATURE_SVE) + if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) { const void * b_ptr = vx; const void * a_ptr = vy; float * res_ptr = s; @@ -910,24 +899,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * ); return; } - else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - GGML_ASSERT((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) && - "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal " - "performance"); - } - else if (ggml_cpu_has_neon()) { - GGML_ASSERT(((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) || ggml_cpu_has_matmul_int8()) && - "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 " - "quantization format for optimal performance"); - } -#endif -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - GGML_ASSERT(ggml_cpu_has_sve() && - "__ARM_FEATURE_SVE not defined, use the Q4_0_4_8 quantization format for optimal performance"); -#elif defined(__ARM_NEON) && defined(__aarch64__) - GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) && - "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal " - "performance"); +#endif // #if defined(__ARM_FEATURE_SVE) #elif defined(__AVX2__) // Lookup table to convert signed nibbles to signed bytes __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); @@ -1018,31 +990,33 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * _mm256_storeu_ps(s + (y * nr + x * 8), acc_row); } } -#else - float sumf[8]; - int sumi; + return; +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) + { + float sumf[8]; + int sumi; - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); } } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; } -#endif } void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { @@ -1065,505 +1039,500 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(ncols_interleaved); UNUSED(blocklen); -#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_sve_cnt_b == QK8_0) { - GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) && - "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance"); - } -#endif -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) && - "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance"); -#elif defined(__ARM_NEON) && defined(__aarch64__) && ! ((defined(_MSC_VER)) && ! defined(__clang__)) - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v23.16b, #0x0\n" - "movi v16.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v0.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v21.16b, #0x0\n" - "movi v8.16b, #0x0\n" - "movi v1.16b, #0x0\n" - "3:" // Block loop - "ldr q3, [x28, #0x0]\n" - "ldr q31, [x25, #0x0]\n" - "movi v28.16b, #0x4\n" - "movi v10.4s, #0x0\n" - "ldr q22, [x28, #0x10]\n" - "ldr q6, [x25, #0x10]\n" - "movi v29.4s, #0x0\n" - "movi v9.4s, #0x0\n" - "ldr q27, [x28, #0x20]\n" - "ldr q30, [x28, #0x30]\n" - "movi v20.4s, #0x0\n" - "movi v24.16b, #0xf0\n" - "ldr d2, [x25, #-0x8]\n" - "ldr d26, [x23, #-0x8]\n" - "sshl v12.16b, v3.16b, v28.16b\n" - "sub x20, x28, #0x8\n" - "ldr d17, [x20, #0x0]\n" - "and v3.16b, v3.16b, v24.16b\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" - ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" - ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" - ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" - "sshl v31.16b, v22.16b, v28.16b\n" - "and v22.16b, v22.16b, v24.16b\n" - "fcvtl v17.4s, v17.4h\n" - "fcvtl v2.4s, v2.4h\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" - ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" - ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" - ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" - "sshl v6.16b, v27.16b, v28.16b\n" - "sshl v28.16b, v30.16b, v28.16b\n" - "and v27.16b, v27.16b, v24.16b\n" - "and v30.16b, v30.16b, v24.16b\n" - "ldr q24, [x25, #0x20]\n" - ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x30]\n" - ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" - ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" - ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" - ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x40]\n" - ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x50]\n" - ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" - ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" - ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" - ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x60]\n" - ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" - ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" - ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" - ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" - "fmul v24.4s, v17.4s, v2.s[0]\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v15.4s, v10.4s, v24.4s\n" - "ldr q24, [x23, #0x0]\n" - "fmul v10.4s, v17.4s, v2.s[1]\n" - "fmla v19.4s, v29.4s, v10.4s\n" - "ldr q10, [x23, #0x10]\n" - "fmul v29.4s, v17.4s, v2.s[2]\n" - "fmul v2.4s, v17.4s, v2.s[3]\n" - "fmla v18.4s, v9.4s, v29.4s\n" - "movi v9.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" - "fmla v14.4s, v20.4s, v2.4s\n" - "movi v20.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x20]\n" - ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" - ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" - ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" - ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x30]\n" - ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x40]\n" - ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" - ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" - ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" - ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x50]\n" - ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x60]\n" - ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" - ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" - ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" - ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x0]\n" - ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" - ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" - ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" - ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" - "fmul v10.4s, v17.4s, v26.s[0]\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v11.4s, v9.4s, v10.4s\n" - "ldr q9, [x22, #0x10]\n" - "fmul v10.4s, v17.4s, v26.s[1]\n" - "fmla v13.4s, v29.4s, v10.4s\n" - "ldr d29, [x22, #-0x8]\n" - "fmul v10.4s, v17.4s, v26.s[2]\n" - "fmul v26.4s, v17.4s, v26.s[3]\n" - "fcvtl v29.4s, v29.4h\n" - "fmla v23.4s, v20.4s, v10.4s\n" - "movi v20.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v16.4s, v2.4s, v26.4s\n" - "movi v26.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x20]\n" - ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x30]\n" - ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x40]\n" - ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x50]\n" - ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x60]\n" - ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x21, #0x0]\n" - ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" - ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" - ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" - "fmul v9.4s, v17.4s, v29.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v25.4s, v20.4s, v9.4s\n" - "ldr q9, [x21, #0x10]\n" - "fmul v20.4s, v17.4s, v29.s[1]\n" - "fmla v7.4s, v10.4s, v20.4s\n" - "ldr d20, [x21, #-0x8]\n" - "fmul v10.4s, v17.4s, v29.s[2]\n" - "fmul v29.4s, v17.4s, v29.s[3]\n" - "fcvtl v20.4s, v20.4h\n" - "fmla v0.4s, v26.4s, v10.4s\n" - "movi v26.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v4.4s, v2.4s, v29.4s\n" - "movi v2.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" - "ldr q12, [x21, #0x20]\n" - "fmul v24.4s, v17.4s, v20.s[0]\n" - ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x30]\n" - "fmul v31.4s, v17.4s, v20.s[1]\n" - ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" - ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" - ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" - ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x40]\n" - "fmul v6.4s, v17.4s, v20.s[2]\n" - "fmul v20.4s, v17.4s, v20.s[3]\n" - ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x50]\n" - ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" - ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" - ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" - ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x60]\n" - ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" - "ldr q17, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" - ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" - ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" - ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" - ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" - ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" - ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" - ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "fmla v5.4s, v26.4s, v24.4s\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v21.4s, v10.4s, v31.4s\n" - "fmla v8.4s, v2.4s, v6.4s\n" - "fmla v1.4s, v29.4s, v20.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q16, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q0, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q21, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q8, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q1, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q7, [x24, #0x0]\n" - "ldr q5, [x25, #0x0]\n" - "movi v9.16b, #0x4\n" - "movi v4.4s, #0x0\n" - "ldr q3, [x24, #0x10]\n" - "ldr q2, [x25, #0x10]\n" - "movi v1.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q13, [x24, #0x20]\n" - "ldr q31, [x25, #0x20]\n" - "movi v30.4s, #0x0\n" - "movi v29.16b, #0xf0\n" - "ldr q28, [x24, #0x30]\n" - "ldr q27, [x25, #0x30]\n" - "sshl v20.16b, v7.16b, v9.16b\n" - "sub x20, x24, #0x8\n" - "ldr q26, [x25, #0x40]\n" - "ldr q25, [x25, #0x50]\n" - "sshl v17.16b, v3.16b, v9.16b\n" - "and v7.16b, v7.16b, v29.16b\n" - "ldr q24, [x25, #0x60]\n" - "ldr q16, [x25, #0x70]\n" - "sshl v22.16b, v13.16b, v9.16b\n" - "and v3.16b, v3.16b, v29.16b\n" - "ldr d21, [x20, #0x0]\n" - "ldr d12, [x25, #-0x8]\n" - ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" - ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" - ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" - ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" - "sshl v9.16b, v28.16b, v9.16b\n" - "subs x21, x21, #0x1\n" - "and v13.16b, v13.16b, v29.16b\n" - "and v28.16b, v28.16b, v29.16b\n" - "add x25, x25, #0x88\n" - "add x24, x24, #0x48\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v12.4s, v12.4h\n" - ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" - ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" - ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" - ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" - "fmul v11.4s, v21.4s, v12.s[0]\n" - "fmul v23.4s, v21.4s, v12.s[1]\n" - "fmul v17.4s, v21.4s, v12.s[2]\n" - ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" - "fmul v6.4s, v21.4s, v12.s[3]\n" - ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" - ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" - ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" - ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" - ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" - ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" - ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" - ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" - ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" - ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" - ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" - ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" - ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" - ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" - ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" - ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" - ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" - ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" - ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" - ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" - ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" - ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" - ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" - "scvtf v4.4s, v4.4s, #0x4\n" - "scvtf v1.4s, v1.4s, #0x4\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "fmla v15.4s, v4.4s, v11.4s\n" - "scvtf v30.4s, v30.4s, #0x4\n" - "fmla v19.4s, v1.4s, v23.4s\n" - "fmla v18.4s, v0.4s, v17.4s\n" - "fmla v14.4s, v30.4s, v6.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q14, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); -#else - float sumf[4][4]; - int sumi; +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + if (ggml_cpu_has_neon()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v23.16b, #0x0\n" + "movi v16.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v0.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v21.16b, #0x0\n" + "movi v8.16b, #0x0\n" + "movi v1.16b, #0x0\n" + "3:" // Block loop + "ldr q3, [x28, #0x0]\n" + "ldr q31, [x25, #0x0]\n" + "movi v28.16b, #0x4\n" + "movi v10.4s, #0x0\n" + "ldr q22, [x28, #0x10]\n" + "ldr q6, [x25, #0x10]\n" + "movi v29.4s, #0x0\n" + "movi v9.4s, #0x0\n" + "ldr q27, [x28, #0x20]\n" + "ldr q30, [x28, #0x30]\n" + "movi v20.4s, #0x0\n" + "movi v24.16b, #0xf0\n" + "ldr d2, [x25, #-0x8]\n" + "ldr d26, [x23, #-0x8]\n" + "sshl v12.16b, v3.16b, v28.16b\n" + "sub x20, x28, #0x8\n" + "ldr d17, [x20, #0x0]\n" + "and v3.16b, v3.16b, v24.16b\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" + ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" + ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" + ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" + "sshl v31.16b, v22.16b, v28.16b\n" + "and v22.16b, v22.16b, v24.16b\n" + "fcvtl v17.4s, v17.4h\n" + "fcvtl v2.4s, v2.4h\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" + ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" + ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" + ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" + "sshl v6.16b, v27.16b, v28.16b\n" + "sshl v28.16b, v30.16b, v28.16b\n" + "and v27.16b, v27.16b, v24.16b\n" + "and v30.16b, v30.16b, v24.16b\n" + "ldr q24, [x25, #0x20]\n" + ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x30]\n" + ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" + ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" + ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" + ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x40]\n" + ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x50]\n" + ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" + ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" + ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" + ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x60]\n" + ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" + ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" + ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" + ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" + "fmul v24.4s, v17.4s, v2.s[0]\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v15.4s, v10.4s, v24.4s\n" + "ldr q24, [x23, #0x0]\n" + "fmul v10.4s, v17.4s, v2.s[1]\n" + "fmla v19.4s, v29.4s, v10.4s\n" + "ldr q10, [x23, #0x10]\n" + "fmul v29.4s, v17.4s, v2.s[2]\n" + "fmul v2.4s, v17.4s, v2.s[3]\n" + "fmla v18.4s, v9.4s, v29.4s\n" + "movi v9.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" + "fmla v14.4s, v20.4s, v2.4s\n" + "movi v20.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x20]\n" + ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" + ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" + ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" + ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x30]\n" + ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x40]\n" + ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" + ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" + ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" + ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x50]\n" + ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x60]\n" + ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" + ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" + ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" + ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x0]\n" + ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" + ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" + ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" + ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" + "fmul v10.4s, v17.4s, v26.s[0]\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v11.4s, v9.4s, v10.4s\n" + "ldr q9, [x22, #0x10]\n" + "fmul v10.4s, v17.4s, v26.s[1]\n" + "fmla v13.4s, v29.4s, v10.4s\n" + "ldr d29, [x22, #-0x8]\n" + "fmul v10.4s, v17.4s, v26.s[2]\n" + "fmul v26.4s, v17.4s, v26.s[3]\n" + "fcvtl v29.4s, v29.4h\n" + "fmla v23.4s, v20.4s, v10.4s\n" + "movi v20.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v16.4s, v2.4s, v26.4s\n" + "movi v26.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x20]\n" + ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x30]\n" + ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x40]\n" + ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x50]\n" + ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x60]\n" + ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x21, #0x0]\n" + ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" + ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" + ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" + "fmul v9.4s, v17.4s, v29.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v25.4s, v20.4s, v9.4s\n" + "ldr q9, [x21, #0x10]\n" + "fmul v20.4s, v17.4s, v29.s[1]\n" + "fmla v7.4s, v10.4s, v20.4s\n" + "ldr d20, [x21, #-0x8]\n" + "fmul v10.4s, v17.4s, v29.s[2]\n" + "fmul v29.4s, v17.4s, v29.s[3]\n" + "fcvtl v20.4s, v20.4h\n" + "fmla v0.4s, v26.4s, v10.4s\n" + "movi v26.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v4.4s, v2.4s, v29.4s\n" + "movi v2.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" + "ldr q12, [x21, #0x20]\n" + "fmul v24.4s, v17.4s, v20.s[0]\n" + ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x30]\n" + "fmul v31.4s, v17.4s, v20.s[1]\n" + ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" + ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" + ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" + ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x40]\n" + "fmul v6.4s, v17.4s, v20.s[2]\n" + "fmul v20.4s, v17.4s, v20.s[3]\n" + ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x50]\n" + ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" + ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" + ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" + ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x60]\n" + ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" + "ldr q17, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" + ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" + ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" + ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" + ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" + ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" + ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" + ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "fmla v5.4s, v26.4s, v24.4s\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v21.4s, v10.4s, v31.4s\n" + "fmla v8.4s, v2.4s, v6.4s\n" + "fmla v1.4s, v29.4s, v20.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q16, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q0, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q21, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q8, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q1, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q7, [x24, #0x0]\n" + "ldr q5, [x25, #0x0]\n" + "movi v9.16b, #0x4\n" + "movi v4.4s, #0x0\n" + "ldr q3, [x24, #0x10]\n" + "ldr q2, [x25, #0x10]\n" + "movi v1.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q13, [x24, #0x20]\n" + "ldr q31, [x25, #0x20]\n" + "movi v30.4s, #0x0\n" + "movi v29.16b, #0xf0\n" + "ldr q28, [x24, #0x30]\n" + "ldr q27, [x25, #0x30]\n" + "sshl v20.16b, v7.16b, v9.16b\n" + "sub x20, x24, #0x8\n" + "ldr q26, [x25, #0x40]\n" + "ldr q25, [x25, #0x50]\n" + "sshl v17.16b, v3.16b, v9.16b\n" + "and v7.16b, v7.16b, v29.16b\n" + "ldr q24, [x25, #0x60]\n" + "ldr q16, [x25, #0x70]\n" + "sshl v22.16b, v13.16b, v9.16b\n" + "and v3.16b, v3.16b, v29.16b\n" + "ldr d21, [x20, #0x0]\n" + "ldr d12, [x25, #-0x8]\n" + ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" + ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" + ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" + ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" + "sshl v9.16b, v28.16b, v9.16b\n" + "subs x21, x21, #0x1\n" + "and v13.16b, v13.16b, v29.16b\n" + "and v28.16b, v28.16b, v29.16b\n" + "add x25, x25, #0x88\n" + "add x24, x24, #0x48\n" + "fcvtl v21.4s, v21.4h\n" + "fcvtl v12.4s, v12.4h\n" + ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" + ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" + ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" + ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" + "fmul v11.4s, v21.4s, v12.s[0]\n" + "fmul v23.4s, v21.4s, v12.s[1]\n" + "fmul v17.4s, v21.4s, v12.s[2]\n" + ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" + "fmul v6.4s, v21.4s, v12.s[3]\n" + ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" + ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" + ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" + ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" + ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" + ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" + ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" + ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" + ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" + ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" + ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" + ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" + ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" + ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" + ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" + ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" + ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" + ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" + ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" + ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" + ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" + ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" + ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" + "scvtf v4.4s, v4.4s, #0x4\n" + "scvtf v1.4s, v1.4s, #0x4\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "fmla v15.4s, v4.4s, v11.4s\n" + "scvtf v30.4s, v30.4s, #0x4\n" + "fmla v19.4s, v1.4s, v23.4s\n" + "fmla v18.4s, v0.4s, v17.4s\n" + "fmla v14.4s, v30.4s, v6.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q14, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); } } } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } } } } -#endif } void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { @@ -1586,413 +1555,406 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(ncols_interleaved); UNUSED(blocklen); -#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_sve_cnt_b == QK8_0) { - GGML_ASSERT(!(ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) && - "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance"); +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v22.16b, #0x0\n" + "movi v23.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v6.16b, #0x0\n" + "movi v30.16b, #0x0\n" + "movi v24.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "3:" // Block loop + "ldr q21, [x28, #0x0]\n" + "ldr q16, [x28, #0x10]\n" + "movi v1.16b, #0x4\n" + "movi v19.4s, #0x0\n" + "ldr q27, [x25, #0x0]\n" + "ldr q15, [x25, #0x10]\n" + "movi v26.4s, #0x0\n" + "movi v18.4s, #0x0\n" + "ldr q29, [x28, #0x20]\n" + "ldr q3, [x28, #0x30]\n" + "movi v17.4s, #0x0\n" + "movi v0.16b, #0xf0\n" + "ldr d20, [x25, #-0x8]\n" + "ldr d9, [x23, #-0x8]\n" + "sshl v8.16b, v21.16b, v1.16b\n" + "sshl v31.16b, v16.16b, v1.16b\n" + "and v21.16b, v21.16b, v0.16b\n" + "and v16.16b, v16.16b, v0.16b\n" + "sub x20, x28, #0x8\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" + ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" + "ldr q27, [x25, #0x20]\n" + ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" + ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" + "sshl v15.16b, v29.16b, v1.16b\n" + "sshl v1.16b, v3.16b, v1.16b\n" + "and v29.16b, v29.16b, v0.16b\n" + "and v3.16b, v3.16b, v0.16b\n" + "ldr q0, [x25, #0x30]\n" + "fcvtl v20.4s, v20.4h\n" + ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" + "fcvtl v9.4s, v9.4h\n" + ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" + "ldr q27, [x25, #0x40]\n" + ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" + ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" + "ldr q0, [x25, #0x50]\n" + ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" + ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" + "ldr q27, [x25, #0x60]\n" + ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" + ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" + "ldr q0, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" + ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" + "ldr d27, [x20, #0x0]\n" + ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" + ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" + "fcvtl v27.4s, v27.4h\n" + "uzp1 v0.2d, v19.2d, v26.2d\n" + "uzp2 v26.2d, v19.2d, v26.2d\n" + "fmul v19.4s, v27.4s, v20.s[0]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "fmla v2.4s, v0.4s, v19.4s\n" + "ldr q19, [x23, #0x0]\n" + "uzp1 v0.2d, v18.2d, v17.2d\n" + "uzp2 v18.2d, v18.2d, v17.2d\n" + "fmul v17.4s, v27.4s, v20.s[1]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v10.4s, v26.4s, v17.4s\n" + "ldr q17, [x23, #0x10]\n" + "fmul v26.4s, v27.4s, v20.s[2]\n" + "fmul v20.4s, v27.4s, v20.s[3]\n" + "fmla v12.4s, v0.4s, v26.4s\n" + "ldr d0, [x22, #-0x8]\n" + "ldr d26, [x21, #-0x8]\n" + "fcvtl v0.4s, v0.4h\n" + "fmla v28.4s, v18.4s, v20.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x23, #0x20]\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x23, #0x40]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q19, [x23, #0x60]\n" + ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" + ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" + "uzp1 v19.2d, v20.2d, v18.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp2 v20.2d, v20.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v9.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v11.4s, v19.4s, v18.4s\n" + "ldr q18, [x22, #0x0]\n" + "fmul v19.4s, v27.4s, v9.s[1]\n" + "fmla v13.4s, v20.4s, v19.4s\n" + "movi v19.4s, #0x0\n" + "movi v20.4s, #0x0\n" + ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" + ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" + "ldr q17, [x23, #0x30]\n" + ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" + "ldr q17, [x23, #0x50]\n" + ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" + "ldr q17, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v9.s[2]\n" + "fmul v9.4s, v27.4s, v9.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v22.4s, v17.4s, v19.4s\n" + "ldr q17, [x22, #0x10]\n" + "movi v19.4s, #0x0\n" + ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" + "fmla v23.4s, v20.4s, v9.4s\n" + "movi v20.4s, #0x0\n" + "movi v9.4s, #0x0\n" + ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" + "ldr q18, [x22, #0x20]\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" + ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" + "ldr q18, [x22, #0x40]\n" + ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" + ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" + "ldr q18, [x22, #0x60]\n" + ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" + ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" + "ldr q17, [x22, #0x30]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" + "ldr q17, [x22, #0x50]\n" + ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" + "ldr q17, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v0.s[0]\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v25.4s, v17.4s, v19.4s\n" + "ldr q19, [x21, #0x0]\n" + "fmul v17.4s, v27.4s, v0.s[1]\n" + "fmla v5.4s, v20.4s, v17.4s\n" + "ldr q17, [x21, #0x10]\n" + "uzp1 v20.2d, v9.2d, v18.2d\n" + "uzp2 v9.2d, v9.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v0.s[2]\n" + "fmul v0.4s, v27.4s, v0.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "fmla v7.4s, v20.4s, v18.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x21, #0x20]\n" + "fmla v4.4s, v9.4s, v0.4s\n" + "movi v9.4s, #0x0\n" + "movi v0.4s, #0x0\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + "fmul v8.4s, v27.4s, v26.s[0]\n" + ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" + "ldr q17, [x21, #0x30]\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + "fmul v31.4s, v27.4s, v26.s[1]\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x21, #0x40]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + "fmul v15.4s, v27.4s, v26.s[2]\n" + "fmul v27.4s, v27.4s, v26.s[3]\n" + ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" + "ldr q1, [x21, #0x50]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q26, [x21, #0x60]\n" + ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" + ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" + "ldr q21, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" + ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" + ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" + ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" + "uzp1 v29.2d, v20.2d, v18.2d\n" + "uzp2 v21.2d, v20.2d, v18.2d\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "uzp1 v18.2d, v9.2d, v0.2d\n" + "uzp2 v16.2d, v9.2d, v0.2d\n" + "scvtf v21.4s, v21.4s, #0x4\n" + "fmla v6.4s, v29.4s, v8.4s\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v30.4s, v21.4s, v31.4s\n" + "fmla v24.4s, v18.4s, v15.4s\n" + "fmla v14.4s, v16.4s, v27.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q28, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q22, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q6, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q30, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q24, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q6, [x24, #0x0]\n" + "ldr q5, [x24, #0x10]\n" + "movi v17.16b, #0x4\n" + "movi v8.4s, #0x0\n" + "ldr q4, [x25, #0x0]\n" + "ldr q13, [x25, #0x10]\n" + "movi v27.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q31, [x24, #0x20]\n" + "ldr q14, [x24, #0x30]\n" + "movi v29.4s, #0x0\n" + "movi v22.16b, #0xf0\n" + "ldr q11, [x25, #0x20]\n" + "ldr q23, [x25, #0x30]\n" + "sshl v21.16b, v6.16b, v17.16b\n" + "sshl v16.16b, v5.16b, v17.16b\n" + "ldr q20, [x25, #0x40]\n" + "ldr q26, [x25, #0x50]\n" + "and v6.16b, v6.16b, v22.16b\n" + "and v5.16b, v5.16b, v22.16b\n" + "ldr q25, [x25, #0x60]\n" + "ldr q3, [x25, #0x70]\n" + "sshl v19.16b, v31.16b, v17.16b\n" + "sshl v18.16b, v14.16b, v17.16b\n" + "ldr d17, [x25, #-0x8]\n" + ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" + ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" + "and v31.16b, v31.16b, v22.16b\n" + ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" + ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" + "and v14.16b, v14.16b, v22.16b\n" + "sub x20, x24, #0x8\n" + "ldr d16, [x20, #0x0]\n" + "subs x21, x21, #0x1\n" + "add x25, x25, #0x88\n" + "fcvtl v17.4s, v17.4h\n" + "add x24, x24, #0x48\n" + ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" + ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" + ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" + ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" + "fcvtl v16.4s, v16.4h\n" + ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" + ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" + "fmul v23.4s, v16.4s, v17.s[0]\n" + "fmul v21.4s, v16.4s, v17.s[1]\n" + "fmul v1.4s, v16.4s, v17.s[2]\n" + "fmul v20.4s, v16.4s, v17.s[3]\n" + ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" + ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" + ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" + ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" + ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" + ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" + "uzp1 v19.2d, v8.2d, v27.2d\n" + "uzp2 v18.2d, v8.2d, v27.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp1 v17.2d, v0.2d, v29.2d\n" + "uzp2 v16.2d, v0.2d, v29.2d\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v2.4s, v19.4s, v23.4s\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v10.4s, v18.4s, v21.4s\n" + "fmla v12.4s, v17.4s, v1.4s\n" + "fmla v28.4s, v16.4s, v20.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q28, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; } -#endif -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__)) - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v22.16b, #0x0\n" - "movi v23.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v6.16b, #0x0\n" - "movi v30.16b, #0x0\n" - "movi v24.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "3:" // Block loop - "ldr q21, [x28, #0x0]\n" - "ldr q16, [x28, #0x10]\n" - "movi v1.16b, #0x4\n" - "movi v19.4s, #0x0\n" - "ldr q27, [x25, #0x0]\n" - "ldr q15, [x25, #0x10]\n" - "movi v26.4s, #0x0\n" - "movi v18.4s, #0x0\n" - "ldr q29, [x28, #0x20]\n" - "ldr q3, [x28, #0x30]\n" - "movi v17.4s, #0x0\n" - "movi v0.16b, #0xf0\n" - "ldr d20, [x25, #-0x8]\n" - "ldr d9, [x23, #-0x8]\n" - "sshl v8.16b, v21.16b, v1.16b\n" - "sshl v31.16b, v16.16b, v1.16b\n" - "and v21.16b, v21.16b, v0.16b\n" - "and v16.16b, v16.16b, v0.16b\n" - "sub x20, x28, #0x8\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" - ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" - "ldr q27, [x25, #0x20]\n" - ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" - ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" - "sshl v15.16b, v29.16b, v1.16b\n" - "sshl v1.16b, v3.16b, v1.16b\n" - "and v29.16b, v29.16b, v0.16b\n" - "and v3.16b, v3.16b, v0.16b\n" - "ldr q0, [x25, #0x30]\n" - "fcvtl v20.4s, v20.4h\n" - ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" - "fcvtl v9.4s, v9.4h\n" - ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" - "ldr q27, [x25, #0x40]\n" - ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" - ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" - "ldr q0, [x25, #0x50]\n" - ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" - ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" - "ldr q27, [x25, #0x60]\n" - ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" - ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" - "ldr q0, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" - ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" - "ldr d27, [x20, #0x0]\n" - ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" - ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" - "fcvtl v27.4s, v27.4h\n" - "uzp1 v0.2d, v19.2d, v26.2d\n" - "uzp2 v26.2d, v19.2d, v26.2d\n" - "fmul v19.4s, v27.4s, v20.s[0]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v2.4s, v0.4s, v19.4s\n" - "ldr q19, [x23, #0x0]\n" - "uzp1 v0.2d, v18.2d, v17.2d\n" - "uzp2 v18.2d, v18.2d, v17.2d\n" - "fmul v17.4s, v27.4s, v20.s[1]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v10.4s, v26.4s, v17.4s\n" - "ldr q17, [x23, #0x10]\n" - "fmul v26.4s, v27.4s, v20.s[2]\n" - "fmul v20.4s, v27.4s, v20.s[3]\n" - "fmla v12.4s, v0.4s, v26.4s\n" - "ldr d0, [x22, #-0x8]\n" - "ldr d26, [x21, #-0x8]\n" - "fcvtl v0.4s, v0.4h\n" - "fmla v28.4s, v18.4s, v20.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x23, #0x20]\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x23, #0x40]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q19, [x23, #0x60]\n" - ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" - ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" - "uzp1 v19.2d, v20.2d, v18.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp2 v20.2d, v20.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v9.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v11.4s, v19.4s, v18.4s\n" - "ldr q18, [x22, #0x0]\n" - "fmul v19.4s, v27.4s, v9.s[1]\n" - "fmla v13.4s, v20.4s, v19.4s\n" - "movi v19.4s, #0x0\n" - "movi v20.4s, #0x0\n" - ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" - ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" - "ldr q17, [x23, #0x30]\n" - ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" - "ldr q17, [x23, #0x50]\n" - ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" - "ldr q17, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v9.s[2]\n" - "fmul v9.4s, v27.4s, v9.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v22.4s, v17.4s, v19.4s\n" - "ldr q17, [x22, #0x10]\n" - "movi v19.4s, #0x0\n" - ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" - "fmla v23.4s, v20.4s, v9.4s\n" - "movi v20.4s, #0x0\n" - "movi v9.4s, #0x0\n" - ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" - "ldr q18, [x22, #0x20]\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" - ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" - "ldr q18, [x22, #0x40]\n" - ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" - ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" - "ldr q18, [x22, #0x60]\n" - ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" - ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" - "ldr q17, [x22, #0x30]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" - "ldr q17, [x22, #0x50]\n" - ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" - "ldr q17, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v0.s[0]\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v25.4s, v17.4s, v19.4s\n" - "ldr q19, [x21, #0x0]\n" - "fmul v17.4s, v27.4s, v0.s[1]\n" - "fmla v5.4s, v20.4s, v17.4s\n" - "ldr q17, [x21, #0x10]\n" - "uzp1 v20.2d, v9.2d, v18.2d\n" - "uzp2 v9.2d, v9.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v0.s[2]\n" - "fmul v0.4s, v27.4s, v0.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "fmla v7.4s, v20.4s, v18.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x21, #0x20]\n" - "fmla v4.4s, v9.4s, v0.4s\n" - "movi v9.4s, #0x0\n" - "movi v0.4s, #0x0\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - "fmul v8.4s, v27.4s, v26.s[0]\n" - ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" - "ldr q17, [x21, #0x30]\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - "fmul v31.4s, v27.4s, v26.s[1]\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x21, #0x40]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - "fmul v15.4s, v27.4s, v26.s[2]\n" - "fmul v27.4s, v27.4s, v26.s[3]\n" - ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" - "ldr q1, [x21, #0x50]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q26, [x21, #0x60]\n" - ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" - ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" - "ldr q21, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" - ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" - ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" - ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" - "uzp1 v29.2d, v20.2d, v18.2d\n" - "uzp2 v21.2d, v20.2d, v18.2d\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "uzp1 v18.2d, v9.2d, v0.2d\n" - "uzp2 v16.2d, v9.2d, v0.2d\n" - "scvtf v21.4s, v21.4s, #0x4\n" - "fmla v6.4s, v29.4s, v8.4s\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v30.4s, v21.4s, v31.4s\n" - "fmla v24.4s, v18.4s, v15.4s\n" - "fmla v14.4s, v16.4s, v27.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q28, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q22, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q6, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q30, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q24, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q6, [x24, #0x0]\n" - "ldr q5, [x24, #0x10]\n" - "movi v17.16b, #0x4\n" - "movi v8.4s, #0x0\n" - "ldr q4, [x25, #0x0]\n" - "ldr q13, [x25, #0x10]\n" - "movi v27.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q31, [x24, #0x20]\n" - "ldr q14, [x24, #0x30]\n" - "movi v29.4s, #0x0\n" - "movi v22.16b, #0xf0\n" - "ldr q11, [x25, #0x20]\n" - "ldr q23, [x25, #0x30]\n" - "sshl v21.16b, v6.16b, v17.16b\n" - "sshl v16.16b, v5.16b, v17.16b\n" - "ldr q20, [x25, #0x40]\n" - "ldr q26, [x25, #0x50]\n" - "and v6.16b, v6.16b, v22.16b\n" - "and v5.16b, v5.16b, v22.16b\n" - "ldr q25, [x25, #0x60]\n" - "ldr q3, [x25, #0x70]\n" - "sshl v19.16b, v31.16b, v17.16b\n" - "sshl v18.16b, v14.16b, v17.16b\n" - "ldr d17, [x25, #-0x8]\n" - ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" - ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" - "and v31.16b, v31.16b, v22.16b\n" - ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" - ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" - "and v14.16b, v14.16b, v22.16b\n" - "sub x20, x24, #0x8\n" - "ldr d16, [x20, #0x0]\n" - "subs x21, x21, #0x1\n" - "add x25, x25, #0x88\n" - "fcvtl v17.4s, v17.4h\n" - "add x24, x24, #0x48\n" - ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" - ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" - ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" - ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" - "fcvtl v16.4s, v16.4h\n" - ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" - ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" - "fmul v23.4s, v16.4s, v17.s[0]\n" - "fmul v21.4s, v16.4s, v17.s[1]\n" - "fmul v1.4s, v16.4s, v17.s[2]\n" - "fmul v20.4s, v16.4s, v17.s[3]\n" - ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" - ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" - ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" - ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" - ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" - ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" - "uzp1 v19.2d, v8.2d, v27.2d\n" - "uzp2 v18.2d, v8.2d, v27.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp1 v17.2d, v0.2d, v29.2d\n" - "uzp2 v16.2d, v0.2d, v29.2d\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v2.4s, v19.4s, v23.4s\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v10.4s, v18.4s, v21.4s\n" - "fmla v12.4s, v17.4s, v1.4s\n" - "fmla v28.4s, v16.4s, v20.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q28, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); -#elif defined(__ARM_NEON) && defined(__aarch64__) - GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) && - "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal " - "performance"); -#else +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) float sumf[4][4]; int sumi; @@ -2012,7 +1974,7 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; } sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); } @@ -2025,7 +1987,6 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * } } } -#endif } void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { @@ -2048,8 +2009,9 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(ncols_interleaved); UNUSED(blocklen); -#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__)) - if (ggml_sve_cnt_b == QK8_0) { +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) +#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) { const void * b_ptr = vx; const void * a_ptr = vy; float * res_ptr = s; @@ -2459,154 +2421,329 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * ); return; } - else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - GGML_ASSERT((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) && - "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal " - "performance"); - } - else if (ggml_cpu_has_neon()) { - GGML_ASSERT(((ggml_cpu_has_sve() && (ggml_sve_cnt_b == QK8_0)) || ggml_cpu_has_matmul_int8()) && - "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 " - "quantization format for optimal performance"); - } -#endif -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - GGML_ASSERT(ggml_cpu_has_sve() && - "__ARM_FEATURE_SVE not defined, use the Q4_0_4_8 quantization format for optimal performance"); -#elif defined(__ARM_NEON) && defined(__aarch64__) - GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) && - "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal " - "performance"); +#endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) #elif defined(__AVX2__) || defined(__AVX512F__) - const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; - const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy; - int64_t b_nb = n / QK4_0; - int64_t y = 0; - // Mask to mask out nibbles from packed bytes - const __m256i m4b = _mm256_set1_epi8(0x0F); - const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3); - // Lookup table to convert signed nibbles to signed bytes - __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); - signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); - // Permute mask used for easier vector processing at later stages - __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4); - int64_t xstart = 0; - int anr = nr - nr%16; // Used to align nr with boundary of 16 -#ifdef __AVX512F__ - int anc = nc - nc%16; // Used to align nc with boundary of 16 - // Mask to mask out nibbles from packed bytes expanded to 512 bit length - const __m512i m4bexpanded = _mm512_set1_epi8(0x0F); - // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length - __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1); - - // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < anr / 4; y += 4) { - - const block_q8_0x4 * a_ptrs[4]; - - a_ptrs[0] = a_ptr_start + (y * nb); - for (int i = 0; i < 3; ++i) { - a_ptrs[i + 1] = a_ptrs[i] + nb; - } + { + const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; + const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy; + int64_t b_nb = n / QK4_0; + int64_t y = 0; + // Mask to mask out nibbles from packed bytes + const __m256i m4b = _mm256_set1_epi8(0x0F); + const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3); + // Lookup table to convert signed nibbles to signed bytes + __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + // Permute mask used for easier vector processing at later stages + __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4); + int64_t xstart = 0; + int anr = nr - nr%16; // Used to align nr with boundary of 16 + #ifdef __AVX512F__ + int anc = nc - nc%16; // Used to align nc with boundary of 16 + // Mask to mask out nibbles from packed bytes expanded to 512 bit length + const __m512i m4bexpanded = _mm512_set1_epi8(0x0F); + // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length + __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1); + + // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < anr / 4; y += 4) { + + const block_q8_0x4 * a_ptrs[4]; + + a_ptrs[0] = a_ptr_start + (y * nb); + for (int i = 0; i < 3; ++i) { + a_ptrs[i + 1] = a_ptrs[i] + nb; + } + + // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < anc / 8; x += 2) { - // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < anc / 8; x += 2) { + const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); + const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); - const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); - const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + // Master FP accumulators + __m512 acc_rows[16]; + for (int i = 0; i < 16; i++) { + acc_rows[i] = _mm512_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); + + const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); + const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); + const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); + const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); + const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); + + const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); + const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); + const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); + const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); - // Master FP accumulators - __m512 acc_rows[16]; - for (int i = 0; i < 16; i++) { - acc_rows[i] = _mm512_setzero_ps(); + // 4-bit -> 8-bit - Sign is maintained + const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) + const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + + const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) + const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) + + const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) + const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) + + const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) + const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) + + // Shuffle pattern one - right side input + const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) + const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) + + const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) + const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) + + const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) + const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) + + const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) + const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) + + // Shuffle pattern two - right side input + + const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) + const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) + + const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) + const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) + + const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) + const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + + const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) + const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + + // Scale values - Load the weight scale values of two block_q4_0x8 + const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + + // Process LHS in pairs of rows + for (int rp = 0; rp < 4; rp++) { + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector + __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); + __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); + __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); + __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); + __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); + __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); + __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); + __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); + + __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); + __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); + __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); + __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); + __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); + __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); + __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); + __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); + + // Shuffle pattern one - left side input + + const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m512i iacc_mat_00_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_01_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_10_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_11_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_00_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_01_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); + __m512i iacc_mat_10_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_11_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); + __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); + __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); + __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); + const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); + + // Multiply with appropiate scales and accumulate + acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); + acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); + acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); + acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + } + } + + // Store the accumulated values + for (int i = 0; i < 16; i++) { + _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } } + } + // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < nr / 4; y ++) { - for (int64_t b = 0; b < nb; b++) { - // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); - - const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); - const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); - const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); - const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); - const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - - const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); - const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); - const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); - const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); + const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - // 4-bit -> 8-bit - Sign is maintained - const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) - const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < anc / 8; x += 2) { + + const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); + const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + + // Master FP accumulators + __m512 acc_rows[4]; + for (int i = 0; i < 4; i++) { + acc_rows[i] = _mm512_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); + + const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); + const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); + const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); + const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) - const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) + const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); + const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) - const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) + const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); + const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); + const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); + const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); - const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) - const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) + // 4-bit -> 8-bit - Sign is maintained + const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) + const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) - // Shuffle pattern one - right side input - const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) - const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) + const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) + const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) - const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) - const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) + const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) + const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) - const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) - const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) + const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) + const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) - const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) - const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) + // Shuffle pattern one - right side input + const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) + const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) - // Shuffle pattern two - right side input + const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) + const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) - const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) - const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) + const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) + const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) - const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) - const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) + const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) + const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) - const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) - const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + // Shuffle pattern two - right side input - const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) - const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) + const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) - // Scale values - Load the weight scale values of two block_q4_0x8 - const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) + const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) - // Process LHS in pairs of rows - for (int rp = 0; rp < 4; rp++) { + const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) + const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + + const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) + const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + + + // Scale values - Load the weight scale values of two block_q4_0x8 + const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector - __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); - __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); - __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); - __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); @@ -2680,314 +2817,286 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); + const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); // Multiply with appropiate scales and accumulate - acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); - acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); - acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); - acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); + acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); + acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); + acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); } - } - // Store the accumulated values - for (int i = 0; i < 16; i++) { - _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + // Store the accumulated values + for (int i = 0; i < 4; i++) { + _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } } } - } - // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < nr / 4; y ++) { - - const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); + if (anc != nc) { + xstart = anc/8; + y = 0; + } + #endif // __AVX512F__ - // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < anc / 8; x += 2) { + // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation - const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); - const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + for (; y < anr / 4; y += 4) { + const block_q8_0x4 * a_ptrs[4]; - // Master FP accumulators - __m512 acc_rows[4]; - for (int i = 0; i < 4; i++) { - acc_rows[i] = _mm512_setzero_ps(); + a_ptrs[0] = a_ptr_start + (y * nb); + for (int i = 0; i < 3; ++i) { + a_ptrs[i + 1] = a_ptrs[i] + nb; } - for (int64_t b = 0; b < nb; b++) { - // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); - - const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); - const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); - const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); - const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); - const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - - const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); - const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); - const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); - const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); + // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = xstart; x < nc / 8; x++) { - // 4-bit -> 8-bit - Sign is maintained - const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) - const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) - const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) - - const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) - const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) - - const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) - const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) - - // Shuffle pattern one - right side input - const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) - const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) - - const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) - const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) - - const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) - const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) - - const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) - const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) - - // Shuffle pattern two - right side input + // Master FP accumulators + __m256 acc_rows[16]; + for (int i = 0; i < 16; i++) { + acc_rows[i] = _mm256_setzero_ps(); + } - const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) - const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) - - const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) - const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) - - const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) - const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) - - const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) - const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) - - - // Scale values - Load the weight scale values of two block_q4_0x8 - const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector - __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); - __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); - __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); - __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); - __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); - __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); - __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); - __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); - __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); - __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); - __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); - __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); - - __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); - __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); - __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); - __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); - __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); - __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); - __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); - __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); - - // Shuffle pattern one - left side input - - const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m512i iacc_mat_00_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_01_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_10_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_11_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_00_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_01_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); - __m512i iacc_mat_10_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_11_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); - __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); - __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); - __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); - const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - - // Multiply with appropiate scales and accumulate - acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); - acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); - acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); - acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); - } + for (int64_t b = 0; b < nb; b++) { + // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) + const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) + + const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) + const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) + + const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) + const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) + + const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) + const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) + + // Shuffle pattern one - right side input + const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) + const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) + + const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) + const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) + + const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) + const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) + + const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) + const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) + + // Shuffle pattern two - right side input + + const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) + const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) + + const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) + const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) + + const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) + const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) + + const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) + const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) + + // Scale values - Load the wight scale values of block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + + // Process LHS in groups of four + for (int rp = 0; rp < 4; rp++) { + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated into a 256 bit vector + __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); + __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); + __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); + __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); + __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); + __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); + __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); + __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); + + // Shuffle pattern one - left side input + const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m256i iacc_mat_00_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_01_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_10_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_11_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_00_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_01_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); + __m256i iacc_mat_10_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_11_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + // Straighten out to make 4 row vectors + __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); + __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); + __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); + __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); + + // Multiply with appropiate scales and accumulate + acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); + acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); + acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); + acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + } + } - // Store the accumulated values - for (int i = 0; i < 4; i++) { - _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + // Store the accumulated values + for (int i = 0; i < 16; i++) { + _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } } } - } - if (anc != nc) { - xstart = anc/8; - y = 0; - } -#endif // __AVX512F__ - // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation + // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < nr / 4; y ++) { - for (; y < anr / 4; y += 4) { - const block_q8_0x4 * a_ptrs[4]; + const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - a_ptrs[0] = a_ptr_start + (y * nb); - for (int i = 0; i < 3; ++i) { - a_ptrs[i + 1] = a_ptrs[i] + nb; - } + // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + for (int64_t x = xstart; x < nc / 8; x++) { - // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = xstart; x < nc / 8; x++) { + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + // Master FP accumulators + __m256 acc_rows[4]; + for (int i = 0; i < 4; i++) { + acc_rows[i] = _mm256_setzero_ps(); + } - // Master FP accumulators - __m256 acc_rows[16]; - for (int i = 0; i < 16; i++) { - acc_rows[i] = _mm256_setzero_ps(); - } + for (int64_t b = 0; b < nb; b++) { + // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - for (int64_t b = 0; b < nb; b++) { - // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) - const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) + const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) - const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) - const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) + const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) + const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) - const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) - const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) + const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) + const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) - const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) - const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) + const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) + const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) - // Shuffle pattern one - right side input - const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) - const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) + // Shuffle pattern one - right side input + const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) + const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) - const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) - const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) + const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) + const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) - const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) - const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) + const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) + const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) - const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) - const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) + const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) + const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) - // Shuffle pattern two - right side input + // Shuffle pattern two - right side input - const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) - const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) + const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) + const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) - const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) - const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) + const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) + const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) - const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) - const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) + const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) + const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) - const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) - const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) + const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) + const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) - // Scale values - Load the wight scale values of block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + // Scale values - Load the wight scale values of block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); - // Process LHS in groups of four - for (int rp = 0; rp < 4; rp++) { // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 // Loaded as set of 128 bit vectors and repeated into a 256 bit vector - __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); - __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); - __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); - __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); // Shuffle pattern one - left side input + const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) @@ -3001,6 +3110,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) // Shuffle pattern two - left side input + const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) @@ -3038,6 +3148,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + // Straighten out to make 4 row vectors __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); @@ -3045,187 +3156,24 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); + const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); // Multiply with appropiate scales and accumulate - acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); - acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); - acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); - acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); + acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); + acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); + acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); } - } - - // Store the accumulated values - for (int i = 0; i < 16; i++) { - _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - - // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < nr / 4; y ++) { - - const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - - // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - for (int64_t x = xstart; x < nc / 8; x++) { - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulators - __m256 acc_rows[4]; - for (int i = 0; i < 4; i++) { - acc_rows[i] = _mm256_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) - const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) - - const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) - const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) - - const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) - const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) - - const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) - const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) - - // Shuffle pattern one - right side input - const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) - const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) - - const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) - const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) - - const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) - const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) - - const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) - const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) - - // Shuffle pattern two - right side input - - const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) - const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) - - const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) - const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) - - const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) - const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) - - const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) - const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) - - // Scale values - Load the wight scale values of block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated into a 256 bit vector - __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); - __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); - __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); - __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); - __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); - __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); - __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); - __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); - __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); - __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); - __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); - __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); - - // Shuffle pattern one - left side input - - const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m256i iacc_mat_00_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_01_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_10_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_11_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_00_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_01_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); - __m256i iacc_mat_10_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_11_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); - __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); - __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); - __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); - - // Multiply with appropiate scales and accumulate - acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); - acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); - acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); - acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); - } - - // Store the accumulated values - for (int i = 0; i < 4; i++) { - _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + // Store the accumulated values + for (int i = 0; i < 4; i++) { + _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } } } + return; } -#else +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) float sumf[4][8]; int sumi; @@ -3258,5 +3206,4 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * } } } -#endif } diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h index e6a5701075f..edfa496148f 100644 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -227,6 +227,7 @@ struct ggml_backend_cann_context { * @brief Destructor for cleaning up resources. */ ~ggml_backend_cann_context() { + ggml_cann_set_device(device); if (copy_event != nullptr) { ACL_CHECK(aclrtDestroyEvent(copy_event)); } diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 0bb7f2d9975..6efdab14c36 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -187,7 +187,7 @@ static ggml_cuda_device_info ggml_cuda_init() { for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA) +#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) CUdevice device; CU_CHECK(cuDeviceGet(&device, id)); CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); @@ -199,7 +199,7 @@ static ggml_cuda_device_info ggml_cuda_init() { alloc_prop.location.id = id; CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA) +#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) info.devices[id].vmm = !!device_vmm; cudaDeviceProp prop; @@ -335,7 +335,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { }; // pool with virtual memory -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA) +#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) struct ggml_cuda_pool_vmm : public ggml_cuda_pool { static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB @@ -429,14 +429,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { GGML_ASSERT(ptr == (void *) (pool_addr + pool_used)); } }; -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA) +#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(int device) { -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA) +#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) if (ggml_cuda_info().devices[device].vmm) { return std::unique_ptr(new ggml_cuda_pool_vmm(device)); } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) && !defined(GGML_USE_MUSA) +#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) return std::unique_ptr(new ggml_cuda_pool_leg(device)); } diff --git a/ggml/src/ggml-cuda/im2col.cu b/ggml/src/ggml-cuda/im2col.cu index 3d0d8d4e6c6..16463ab0fb6 100644 --- a/ggml/src/ggml-cuda/im2col.cu +++ b/ggml/src/ggml-cuda/im2col.cu @@ -69,7 +69,6 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); - GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m index ef3b7f0e824..c1e3a66d931 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal.m @@ -12,6 +12,12 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +// max memory buffers that can be mapped to the device +#define GGML_METAL_MAX_BUFFERS 64 + +// max number of MTLCommandBuffer used to submit a graph for processing +#define GGML_METAL_MAX_COMMAND_BUFFERS 8 + #ifdef GGML_METAL_NDEBUG #define GGML_METAL_LOG(...) #define GGML_METAL_LOG_INFO(...) @@ -221,11 +227,11 @@ }; struct ggml_backend_metal_context { - int n_cb; - id device; id queue; + MTLComputePassDescriptor * edesc; + dispatch_queue_t d_queue; struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT]; @@ -233,7 +239,27 @@ bool support_simdgroup_reduction; bool support_simdgroup_mm; - bool should_capture_next_compute; + // capture state + bool capture_next_compute; + bool capture_started; + + id capture_scope; + + // command buffer state + int n_cb; // number of extra threads used to submit the command buffers + int n_nodes_0; // number of nodes submitted by the main thread + int n_nodes_1; // remaining number of nodes submitted by the n_cb threads + int n_nodes_per_cb; + + struct ggml_cgraph * gf; + + // the callback given to the thread pool + // TODO: ideally, this should be created once, utilizing the command buffer state above + // for some reason, doing it like this leads to a crash + void (^encode_async)(size_t ith); + + // n_cb command buffers + 1 used by the main thread + id command_buffers[GGML_METAL_MAX_COMMAND_BUFFERS + 1]; // abort ggml_metal_graph_compute if callback returns true ggml_abort_callback abort_callback; @@ -303,7 +329,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ return data; } -static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) { +static struct ggml_backend_metal_context * ggml_metal_init(void) { GGML_METAL_LOG_INFO("%s: allocating\n", __func__); #if TARGET_OS_OSX && !GGML_METAL_NDEBUG @@ -322,8 +348,9 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ // Configure 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]; + ctx->edesc = MTLComputePassDescriptor.computePassDescriptor; + ctx->edesc.dispatchType = MTLDispatchTypeSerial; ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); id metal_library; @@ -455,7 +482,15 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); - ctx->should_capture_next_compute = false; + ctx->capture_next_compute = false; + ctx->capture_started = false; + ctx->capture_scope = nil; + + ctx->gf = nil; + ctx->encode_async = nil; + for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) { + ctx->command_buffers[i] = nil; + } #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) if (@available(macOS 10.12, iOS 16.0, *)) { @@ -686,6 +721,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ } [metal_library release]; + return ctx; } @@ -874,874 +910,820 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_context * ctx } } -static enum ggml_status ggml_metal_graph_compute( - struct ggml_backend_metal_context * ctx, - struct ggml_cgraph * gf) { +static void ggml_metal_encode_node( + struct ggml_backend_metal_context * ctx, + int idx, + id encoder) { + struct ggml_cgraph * gf = ctx->gf; - @autoreleasepool { - MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor; - edesc.dispatchType = MTLDispatchTypeSerial; + struct ggml_tensor * node = ggml_graph_node(gf, idx); - // create multiple command buffers and enqueue them - // then, we encode the graph into the command buffers in parallel + //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, idx, ggml_op_name(node->op)); - const int n_nodes = gf->n_nodes; - const int n_cb = ctx->n_cb; - const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; + struct ggml_tensor * src0 = node->src[0]; + struct ggml_tensor * src1 = node->src[1]; + struct ggml_tensor * src2 = node->src[2]; + struct ggml_tensor * dst = node; - const bool should_capture = ctx->should_capture_next_compute; - if (should_capture) { - ctx->should_capture_next_compute = false; + if (ggml_is_empty(dst)) { + return; + } - MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; - descriptor.captureObject = ctx->queue; + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + { + // noop -> next node + } return; + default: + { + } break; + } - NSError * error = nil; - if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { - GGML_METAL_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]); - GGML_ABORT("capture failed"); - } + if (!ggml_metal_supports_op(ctx, dst)) { + GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); + GGML_ABORT("unsupported op"); } - id command_buffer_builder[n_cb]; - for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { - id command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; - command_buffer_builder[cb_idx] = command_buffer; + const int64_t ne00 = src0 ? src0->ne[0] : 0; + const int64_t ne01 = src0 ? src0->ne[1] : 0; + const int64_t ne02 = src0 ? src0->ne[2] : 0; + const int64_t ne03 = src0 ? src0->ne[3] : 0; + + const uint64_t nb00 = src0 ? src0->nb[0] : 0; + const uint64_t nb01 = src0 ? src0->nb[1] : 0; + const uint64_t nb02 = src0 ? src0->nb[2] : 0; + const uint64_t nb03 = src0 ? src0->nb[3] : 0; + + const int64_t ne10 = src1 ? src1->ne[0] : 0; + const int64_t ne11 = src1 ? src1->ne[1] : 0; + const int64_t ne12 = src1 ? src1->ne[2] : 0; + const int64_t ne13 = src1 ? src1->ne[3] : 0; + + const uint64_t nb10 = src1 ? src1->nb[0] : 0; + const uint64_t nb11 = src1 ? src1->nb[1] : 0; + const uint64_t nb12 = src1 ? src1->nb[2] : 0; + const uint64_t nb13 = src1 ? src1->nb[3] : 0; + + const int64_t ne20 = src2 ? src2->ne[0] : 0; + const int64_t ne21 = src2 ? src2->ne[1] : 0; + const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); + const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); + + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; + + const int64_t ne0 = dst ? dst->ne[0] : 0; + const int64_t ne1 = dst ? dst->ne[1] : 0; + const int64_t ne2 = dst ? dst->ne[2] : 0; + const int64_t ne3 = dst ? dst->ne[3] : 0; + + const uint64_t nb0 = dst ? dst->nb[0] : 0; + const uint64_t nb1 = dst ? dst->nb[1] : 0; + const uint64_t nb2 = dst ? dst->nb[2] : 0; + const uint64_t nb3 = dst ? dst->nb[3] : 0; + + const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; + const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; + const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; + + size_t offs_src0 = 0; + size_t offs_src1 = 0; + size_t offs_src2 = 0; + size_t offs_dst = 0; + + id id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil; + id id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil; + id id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; + id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; + + //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); + //if (src0) { + // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, + // ggml_is_contiguous(src0), src0->name); + //} + //if (src1) { + // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, + // ggml_is_contiguous(src1), src1->name); + //} + //if (dst) { + // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, + // dst->name); + //} + + switch (dst->op) { + case GGML_OP_CONCAT: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + + const int32_t dim = ((const int32_t *) dst->op_params)[0]; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ADD: + case GGML_OP_SUB: + case GGML_OP_MUL: + case GGML_OP_DIV: + { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); - // always enqueue the first two command buffers - // enqueue all of the command buffers if we don't need to abort - if (cb_idx < 2 || ctx->abort_callback == NULL) { - [command_buffer enqueue]; - } - } + const size_t offs = 0; - const id *command_buffers = command_buffer_builder; + bool bcast_row = false; - dispatch_apply(n_cb, ctx->d_queue, ^(size_t iter) { - const int cb_idx = iter; + int64_t nb = ne00; // used by the "row" kernels - size_t offs_src0 = 0; - size_t offs_src1 = 0; - size_t offs_src2 = 0; - size_t offs_dst = 0; + id pipeline = nil; - id command_buffer = command_buffers[cb_idx]; - id encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc]; + if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { + GGML_ASSERT(ggml_is_contiguous(src0)); - const int node_start = (cb_idx + 0) * n_nodes_per_cb; - const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); + // src1 is a row + GGML_ASSERT(ne11 == 1); - for (int i = node_start; i < node_end; ++i) { - if (i == -1) { - [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; - continue; - } + nb = ne00 / 4; + switch (dst->op) { + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; + case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; + default: GGML_ABORT("fatal error"); + } - //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); + bcast_row = true; + } else { + switch (dst->op) { + case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; + case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB].pipeline; break; + case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; + case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; + default: GGML_ABORT("fatal error"); + } + } - struct ggml_tensor * src0 = gf->nodes[i]->src[0]; - struct ggml_tensor * src1 = gf->nodes[i]->src[1]; - struct ggml_tensor * src2 = gf->nodes[i]->src[2]; - struct ggml_tensor * dst = gf->nodes[i]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; + + if (bcast_row) { + const int64_t n = ggml_nelements(dst)/4; + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } else { + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } + } break; + case GGML_OP_REPEAT: + { + id pipeline; + + switch (src0t) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; + case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; + default: GGML_ABORT("fatal error"); + } - if (ggml_is_empty(dst)) { - continue; - } + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ACC: + { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + GGML_ASSERT(dstt == GGML_TYPE_F32); + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + + const size_t pnb1 = ((const int32_t *) dst->op_params)[0]; + const size_t pnb2 = ((const int32_t *) dst->op_params)[1]; + const size_t pnb3 = ((const int32_t *) dst->op_params)[2]; + const size_t offs = ((const int32_t *) dst->op_params)[3]; + + const bool inplace = (bool) ((const int32_t *) dst->op_params)[4]; + + if (!inplace) { + // run a separete kernel to cpy src->dst + // not sure how to avoid this + // TODO: make a simpler cpy_bytes kernel + + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } - switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop -> next node - } continue; - default: - { - } break; - } + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; + [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; + [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; + [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; + [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; + [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; + [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); + + [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_SCALE: + { + GGML_ASSERT(ggml_is_contiguous(src0)); - if (!ggml_metal_supports_op(ctx, dst)) { - GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); - GGML_ABORT("unsupported op"); - } + float scale; + memcpy(&scale, dst->op_params, sizeof(scale)); - if (should_capture) { - [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; - } + int64_t n = ggml_nelements(dst); - const int64_t ne00 = src0 ? src0->ne[0] : 0; - const int64_t ne01 = src0 ? src0->ne[1] : 0; - const int64_t ne02 = src0 ? src0->ne[2] : 0; - const int64_t ne03 = src0 ? src0->ne[3] : 0; - - const uint64_t nb00 = src0 ? src0->nb[0] : 0; - const uint64_t nb01 = src0 ? src0->nb[1] : 0; - const uint64_t nb02 = src0 ? src0->nb[2] : 0; - const uint64_t nb03 = src0 ? src0->nb[3] : 0; - - const int64_t ne10 = src1 ? src1->ne[0] : 0; - const int64_t ne11 = src1 ? src1->ne[1] : 0; - const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; - - const uint64_t nb10 = src1 ? src1->nb[0] : 0; - const uint64_t nb11 = src1 ? src1->nb[1] : 0; - const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; - - const int64_t ne20 = src2 ? src2->ne[0] : 0; - const int64_t ne21 = src2 ? src2->ne[1] : 0; - const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); - const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); - const uint64_t nb21 = src2 ? src2->nb[1] : 0; - const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; - - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; - - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; - - const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; - const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; - const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; - - id id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil; - id id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil; - id id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; - id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; - - //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); - //if (src0) { - // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, - // ggml_is_contiguous(src0), src0->name); - //} - //if (src1) { - // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, - // ggml_is_contiguous(src1), src1->name); - //} - //if (dst) { - // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, - // dst->name); - //} - - switch (dst->op) { - case GGML_OP_CONCAT: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; - - const int32_t dim = ((int32_t *) dst->op_params)[0]; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; - - const int nth = MIN(1024, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ADD: - case GGML_OP_SUB: - case GGML_OP_MUL: - case GGML_OP_DIV: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - - const size_t offs = 0; - - bool bcast_row = false; - - int64_t nb = ne00; // used by the "row" kernels - - id pipeline = nil; - - if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { - GGML_ASSERT(ggml_is_contiguous(src0)); - - // src1 is a row - GGML_ASSERT(ne11 == 1); - - nb = ne00 / 4; - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; - case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; - default: GGML_ABORT("fatal error"); - } + id pipeline = nil; - bcast_row = true; - } else { - switch (dst->op) { - case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; - case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB].pipeline; break; - case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; - case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; - default: GGML_ABORT("fatal error"); - } - } + if (n % 4 == 0) { + n /= 4; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; + } - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; - - if (bcast_row) { - const int64_t n = ggml_nelements(dst)/4; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } else { - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } - } break; - case GGML_OP_REPEAT: - { - id pipeline; - - switch (src0t) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; - case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; - case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; - default: GGML_ABORT("fatal error"); - } + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_CLAMP: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ACC: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - GGML_ASSERT(dstt == GGML_TYPE_F32); - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - - const size_t pnb1 = ((int32_t *) dst->op_params)[0]; - const size_t pnb2 = ((int32_t *) dst->op_params)[1]; - const size_t pnb3 = ((int32_t *) dst->op_params)[2]; - const size_t offs = ((int32_t *) dst->op_params)[3]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - // run a separete kernel to cpy src->dst - // not sure how to avoid this - // TODO: make a simpler cpy_bytes kernel - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; + float min; + float max; + memcpy(&min, ((const int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&max, ((const int32_t *) dst->op_params) + 1, sizeof(float)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); - - [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_SCALE: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - float scale; - memcpy(&scale, dst->op_params, sizeof(scale)); - - int64_t n = ggml_nelements(dst); - - id pipeline = nil; - - if (n % 4 == 0) { - n /= 4; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; - } + const int64_t n = ggml_nelements(dst); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_CLAMP: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; - - float min; - float max; - memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); - memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&min length:sizeof(min) atIndex:2]; - [encoder setBytes:&max length:sizeof(max) atIndex:3]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(gf->nodes[i])) { - // we are not taking into account the strides, so for now require contiguous tensors - GGML_ASSERT(ggml_is_contiguous(src0)); - - case GGML_UNARY_OP_TANH: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + // we are not taking into account the strides, so for now require contiguous tensors + GGML_ASSERT(ggml_is_contiguous(src0)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + case GGML_UNARY_OP_TANH: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; - const int64_t n = ggml_nelements(dst); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_RELU: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; + const int64_t n = ggml_nelements(dst); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_RELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; - const int64_t n = ggml_nelements(dst); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_SIGMOID: - { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline; + const int64_t n = ggml_nelements(dst); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_SIGMOID: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline; - const int64_t n = ggml_nelements(dst); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU: - { - int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst); - id pipeline = nil; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_GELU: + { + int64_t n = ggml_nelements(dst); - if (n % 4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline; - n /= 4; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; - } + id pipeline = nil; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + } - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - int64_t n = ggml_nelements(dst); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - id pipeline = nil; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_GELU_QUICK: + { + int64_t n = ggml_nelements(dst); - if (n % 4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline; - n /= 4; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; - } + id pipeline = nil; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + } - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_UNARY_OP_SILU: - { - int64_t n = ggml_nelements(dst); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - id pipeline = nil; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_SILU: + { + int64_t n = ggml_nelements(dst); - if (n % 4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline; - n /= 4; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; - } + id pipeline = nil; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + } - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - default: - { - GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_SQR: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SQRT: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQRT].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SIN: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIN].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_COS: - { - GGML_ASSERT(ggml_is_contiguous(src0)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_COS].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - - const int64_t n = ggml_nelements(dst); - - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SUM_ROWS: - { - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SOFT_MAX: - { - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); - - int nth = 32; // SIMD width - - id pipeline = nil; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - - if (ne00%4 == 0) { - while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) { - nth *= 2; - } - if (use_f16) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; - } - } else { - while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { - nth *= 2; - } - if (use_f16) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline; - } - } + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - float scale; - float max_bias; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + default: + { + GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); + GGML_ABORT("fatal error"); + } + } break; + case GGML_OP_SQR: + { + GGML_ASSERT(ggml_is_contiguous(src0)); - memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); - memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; - const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src0->ne[1]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const uint32_t n_head = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + const int64_t n = ggml_nelements(dst); - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SQRT: + { + GGML_ASSERT(ggml_is_contiguous(src0)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - if (id_src1) { - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; - [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_DIAG_MASK_INF: - { - const int n_past = ((int32_t *)(dst->op_params))[0]; - - id pipeline = nil; - - if (ne00%8 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; - } + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQRT].pipeline; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - if (ne00%8 == 0) { - [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - else { - [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } - } break; - case GGML_OP_SSM_CONV: - { - GGML_ASSERT(src0t == GGML_TYPE_F32); - GGML_ASSERT(src1t == GGML_TYPE_F32); - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:11]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:12]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:15]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:16]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:17]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:18]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_SSM_SCAN: - { - struct ggml_tensor * src3 = gf->nodes[i]->src[3]; - struct ggml_tensor * src4 = gf->nodes[i]->src[4]; - struct ggml_tensor * src5 = gf->nodes[i]->src[5]; - - GGML_ASSERT(src3); - GGML_ASSERT(src4); - GGML_ASSERT(src5); - - size_t offs_src3 = 0; - size_t offs_src4 = 0; - size_t offs_src5 = 0; - - id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; - id id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil; - id id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil; - - const int64_t ne30 = src3->ne[0]; GGML_UNUSED(ne30); - const int64_t ne31 = src3->ne[1]; GGML_UNUSED(ne31); - - const uint64_t nb30 = src3->nb[0]; - const uint64_t nb31 = src3->nb[1]; - - const int64_t ne40 = src4->ne[0]; GGML_UNUSED(ne40); - const int64_t ne41 = src4->ne[1]; GGML_UNUSED(ne41); - const int64_t ne42 = src4->ne[2]; GGML_UNUSED(ne42); - - const uint64_t nb40 = src4->nb[0]; - const uint64_t nb41 = src4->nb[1]; - const uint64_t nb42 = src4->nb[2]; - - const int64_t ne50 = src5->ne[0]; GGML_UNUSED(ne50); - const int64_t ne51 = src5->ne[1]; GGML_UNUSED(ne51); - const int64_t ne52 = src5->ne[2]; GGML_UNUSED(ne52); - - const uint64_t nb50 = src5->nb[0]; - const uint64_t nb51 = src5->nb[1]; - const uint64_t nb52 = src5->nb[2]; - - const int64_t d_state = ne00; - const int64_t d_inner = ne01; - const int64_t n_seq_tokens = ne11; - const int64_t n_seqs = ne02; - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; - [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4]; - [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:6]; - - [encoder setBytes:&d_state length:sizeof(d_state) atIndex:7]; - [encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:8]; - [encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:9]; - [encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:10]; - - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:11]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:12]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; - [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:18]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:19]; - [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:20]; - [encoder setBytes:&nb30 length:sizeof(nb30) atIndex:21]; - [encoder setBytes:&nb31 length:sizeof(nb31) atIndex:22]; - [encoder setBytes:&nb40 length:sizeof(nb40) atIndex:23]; - [encoder setBytes:&nb41 length:sizeof(nb41) atIndex:24]; - [encoder setBytes:&nb42 length:sizeof(nb42) atIndex:25]; - [encoder setBytes:&nb50 length:sizeof(nb50) atIndex:26]; - [encoder setBytes:&nb51 length:sizeof(nb51) atIndex:27]; - [encoder setBytes:&nb52 length:sizeof(nb52) atIndex:28]; - - [encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_MUL_MAT: - { - GGML_ASSERT(ne00 == ne10); - - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); - - const uint r2 = ne12/ne02; - const uint r3 = ne13/ne03; - - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - int ne11_mm_min = 1; + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SIN: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIN].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_COS: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_COS].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SUM_ROWS: + { + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SOFT_MAX: + { + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); + + int nth = 32; // SIMD width + + id pipeline = nil; + + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); + + if (ne00%4 == 0) { + while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) { + nth *= 2; + } + if (use_f16) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; + } + } else { + while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { + nth *= 2; + } + if (use_f16) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline; + } + } + + float scale; + float max_bias; + + memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias)); + + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; + + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + if (id_src1) { + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; + [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_DIAG_MASK_INF: + { + const int n_past = ((const int32_t *)(dst->op_params))[0]; + + id pipeline = nil; + + if (ne00%8 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; + + if (ne00%8 == 0) { + [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } + else { + [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } + } break; + case GGML_OP_SSM_CONV: + { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:11]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:12]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:15]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:16]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:17]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:18]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_SSM_SCAN: + { + struct ggml_tensor * src3 = node->src[3]; + struct ggml_tensor * src4 = node->src[4]; + struct ggml_tensor * src5 = node->src[5]; + + GGML_ASSERT(src3); + GGML_ASSERT(src4); + GGML_ASSERT(src5); + + size_t offs_src3 = 0; + size_t offs_src4 = 0; + size_t offs_src5 = 0; + + id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; + id id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil; + id id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil; + + const int64_t ne30 = src3->ne[0]; GGML_UNUSED(ne30); + const int64_t ne31 = src3->ne[1]; GGML_UNUSED(ne31); + + const uint64_t nb30 = src3->nb[0]; + const uint64_t nb31 = src3->nb[1]; + + const int64_t ne40 = src4->ne[0]; GGML_UNUSED(ne40); + const int64_t ne41 = src4->ne[1]; GGML_UNUSED(ne41); + const int64_t ne42 = src4->ne[2]; GGML_UNUSED(ne42); + + const uint64_t nb40 = src4->nb[0]; + const uint64_t nb41 = src4->nb[1]; + const uint64_t nb42 = src4->nb[2]; + + const int64_t ne50 = src5->ne[0]; GGML_UNUSED(ne50); + const int64_t ne51 = src5->ne[1]; GGML_UNUSED(ne51); + const int64_t ne52 = src5->ne[2]; GGML_UNUSED(ne52); + + const uint64_t nb50 = src5->nb[0]; + const uint64_t nb51 = src5->nb[1]; + const uint64_t nb52 = src5->nb[2]; + + const int64_t d_state = ne00; + const int64_t d_inner = ne01; + const int64_t n_seq_tokens = ne11; + const int64_t n_seqs = ne02; + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4]; + [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:6]; + + [encoder setBytes:&d_state length:sizeof(d_state) atIndex:7]; + [encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:8]; + [encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:9]; + [encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:10]; + + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:11]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:12]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; + [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:18]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:19]; + [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:20]; + [encoder setBytes:&nb30 length:sizeof(nb30) atIndex:21]; + [encoder setBytes:&nb31 length:sizeof(nb31) atIndex:22]; + [encoder setBytes:&nb40 length:sizeof(nb40) atIndex:23]; + [encoder setBytes:&nb41 length:sizeof(nb41) atIndex:24]; + [encoder setBytes:&nb42 length:sizeof(nb42) atIndex:25]; + [encoder setBytes:&nb50 length:sizeof(nb50) atIndex:26]; + [encoder setBytes:&nb51 length:sizeof(nb51) atIndex:27]; + [encoder setBytes:&nb52 length:sizeof(nb52) atIndex:28]; + + [encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_MUL_MAT: + { + GGML_ASSERT(ne00 == ne10); + + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + const uint r2 = ne12/ne02; + const uint r3 = ne13/ne03; + + // find the break-even point where the matrix-matrix kernel becomes more efficient compared + // to the matrix-vector kernel + int ne11_mm_min = 1; #if 0 - // the numbers below are measured on M2 Ultra for 7B and 13B models - // these numbers do not translate to other devices or model sizes - // TODO: need to find a better approach + // the numbers below are measured on M2 Ultra for 7B and 13B models + // these numbers do not translate to other devices or model sizes + // TODO: need to find a better approach if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) { switch (src0t) { case GGML_TYPE_F16: ne11_mm_min = 2; break; @@ -1763,11 +1745,11 @@ static enum ggml_status ggml_metal_graph_compute( // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && - !ggml_is_transposed(src0) && - !ggml_is_transposed(src1) && - src1t == GGML_TYPE_F32 && - ne00 % 32 == 0 && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { + !ggml_is_transposed(src0) && + !ggml_is_transposed(src1) && + src1t == GGML_TYPE_F32 && + ne00 % 32 == 0 && ne00 >= 64 && + (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); // some Metal matrix data types require aligned pointers @@ -2001,8 +1983,8 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { + src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || + src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { @@ -2036,1041 +2018,1157 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } } - } break; - case GGML_OP_MUL_MAT_ID: - { - const int n_as = src0->ne[2]; + } break; + case GGML_OP_MUL_MAT_ID: + { + const int n_as = src0->ne[2]; + + // src2 = ids + const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); + + GGML_ASSERT(src2t == GGML_TYPE_I32); + + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); + + GGML_ASSERT(src1t == GGML_TYPE_F32); + + // find the break-even point where the matrix-matrix kernel becomes more efficient compared + // to the matrix-vector kernel + // ne20 = n_used_experts + // ne21 = n_rows + const int dst_rows = ne20*ne21; + const int dst_rows_min = n_as; + const int dst_rows_max = (ctx->device.maxThreadgroupMemoryLength - 32 - 8192)/4; + + // max size of the rowids array in the kernel shared buffer + GGML_ASSERT(dst_rows <= dst_rows_max); + + // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs + // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel + // !!! + // TODO: for now, always use mat-vec kernels until we figure out how to improve the + // indirect matrix multiplication + // !!! + if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && + ne00 % 32 == 0 && ne00 >= 64 && + dst_rows > dst_rows_min) { + + // some Metal matrix data types require aligned pointers + // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; + case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + default: break; + } - // src2 = ids - const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; + case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; + case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; + case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; + case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32 ].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break; + default: GGML_ABORT("MUL_MAT_ID not implemented"); + } - GGML_ASSERT(src2t == GGML_TYPE_I32); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; + + [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + } else { + int nth0 = 32; + int nth1 = 1; + int nrows = 1; + //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + // use custom matrix x vector kernel + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; + } break; + case GGML_TYPE_F16: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + nth0 = 32; + nth1 = 1; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; + } break; + case GGML_TYPE_Q4_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; + } break; + case GGML_TYPE_Q4_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; + } break; + case GGML_TYPE_Q5_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; + } break; + case GGML_TYPE_Q5_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; + } break; + case GGML_TYPE_Q8_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; + } break; + case GGML_TYPE_Q2_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; + } break; + case GGML_TYPE_Q3_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; + } break; + case GGML_TYPE_Q4_K: + { + nth0 = 4; //1; + nth1 = 8; //32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; + } break; + case GGML_TYPE_Q5_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; + } break; + case GGML_TYPE_Q6_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; + } break; + case GGML_TYPE_IQ3_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ3_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; + } break; + case GGML_TYPE_IQ2_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline; + } break; + case GGML_TYPE_IQ1_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; + } break; + case GGML_TYPE_IQ1_M: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline; + } break; + case GGML_TYPE_IQ4_NL: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; + } break; + case GGML_TYPE_IQ4_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; + } break; + default: + { + GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); + GGML_ABORT("not implemented"); + } + }; - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); + if (ggml_is_quantized(src0t)) { + GGML_ASSERT(ne00 >= nth0*nth1); + } - GGML_ASSERT(src1t == GGML_TYPE_F32); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; + + const int64_t _ne1 = 1; + const int tgz = dst_rows; + + if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || + src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || + src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { + const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { + const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { + const int mem_size = 32*sizeof(float); + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q3_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q5_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q6_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else { + const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1 + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + } + } break; + case GGML_OP_GET_ROWS: + { + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; + case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; + case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; + case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; + case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M ].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; + default: GGML_ABORT("not implemented"); + } - // find the break-even point where the matrix-matrix kernel becomes more efficient compared - // to the matrix-vector kernel - // ne20 = n_used_experts - // ne21 = n_rows - const int dst_rows = ne20*ne21; - const int dst_rows_min = n_as; - const int dst_rows_max = (ctx->device.maxThreadgroupMemoryLength - 32 - 8192)/4; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; + [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; + } break; + case GGML_OP_RMS_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous_1(src0)); - // max size of the rowids array in the kernel shared buffer - GGML_ASSERT(dst_rows <= dst_rows_max); + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - // !!! - // TODO: for now, always use mat-vec kernels until we figure out how to improve the - // indirect matrix multiplication - // !!! - if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && - ne00 % 32 == 0 && ne00 >= 64 && - dst_rows > dst_rows_min) { + int nth = 32; // SIMD width - // some Metal matrix data types require aligned pointers - // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; - case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; - default: break; - } + while (nth < ne00/4 && nth < 1024) { + nth *= 2; + } - id pipeline = nil; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; - case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; - case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; - case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32 ].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; - case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break; - default: GGML_ABORT("MUL_MAT_ID not implemented"); - } + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; - - [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + const int64_t nrows = ggml_nrows(src0); - id pipeline = nil; + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_GROUP_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous(src0)); - // use custom matrix x vector kernel - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - nth0 = 32; - nth1 = 1; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; - } break; - case GGML_TYPE_IQ2_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_M: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline; - } break; - case GGML_TYPE_IQ4_NL: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; - } break; - case GGML_TYPE_IQ4_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; - } break; - default: - { - GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); - GGML_ABORT("not implemented"); - } - }; + float eps; + memcpy(&eps, dst->op_params + 1, sizeof(float)); - if (ggml_is_quantized(src0t)) { - GGML_ASSERT(ne00 >= nth0*nth1); - } + const int32_t n_groups = ((const int32_t *) dst->op_params)[0]; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; - - const int64_t _ne1 = 1; - const int tgz = dst_rows; + int nth = 32; // SIMD width - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { - const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { - const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { - const int mem_size = 32*sizeof(float); - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q3_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1 - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } - } break; - case GGML_OP_GET_ROWS: - { - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; - case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; - case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; - case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; - case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; - case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M ].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; - case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break; - case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; - default: GGML_ABORT("not implemented"); - } + //while (nth < ne00/4 && nth < 1024) { + // nth *= 2; + //} - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; - } break; - case GGML_OP_RMS_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ggml_is_contiguous_1(src0)); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - int nth = 32; // SIMD width - - while (nth < ne00/4 && nth < 1024) { - nth *= 2; - } + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_GROUP_NORM: - { - GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ggml_is_contiguous(src0)); - - float eps; - memcpy(&eps, dst->op_params + 1, sizeof(float)); - - const int32_t n_groups = ((int32_t *) dst->op_params)[0]; - - int nth = 32; // SIMD width - - //while (nth < ne00/4 && nth < 1024) { - // nth *= 2; - //} - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&eps length:sizeof( float) atIndex:9]; - [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - - [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_NORM: - { - GGML_ASSERT(ggml_is_contiguous_1(src0)); - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - const int nth = MIN(256, ne00); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; - - const int64_t nrows = ggml_nrows(src0); - - [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ROPE: - { - GGML_ASSERT(ne10 == ne02); - - const int nth = MIN(1024, ne00); - - const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - - float freq_base; - float freq_scale; - float ext_factor; - float attn_factor; - float beta_fast; - float beta_slow; - - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - 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 & GGML_ROPE_TYPE_NEOX; - - id pipeline = nil; - - if (!is_neox) { - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; - default: GGML_ABORT("fatal error"); - }; - } else { - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; - default: GGML_ABORT("fatal error"); - }; - } + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&eps length:sizeof( float) atIndex:9]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - if (id_src2 != nil) { - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_IM2COL: - { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int32_t N = src1->ne[is_2D ? 3 : 2]; - const int32_t IC = src1->ne[is_2D ? 2 : 1]; - const int32_t IH = is_2D ? src1->ne[1] : 1; - const int32_t IW = src1->ne[0]; - - const int32_t KH = is_2D ? src0->ne[1] : 1; - const int32_t KW = src0->ne[0]; - - const int32_t OH = is_2D ? dst->ne[2] : 1; - const int32_t OW = dst->ne[1]; - - const int32_t CHW = IC * KH * KW; - - const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; - const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - - id pipeline = nil; - - switch (dst->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; - default: GGML_ABORT("fatal error"); - }; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; - [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; - [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; - [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; - [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; - [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; - [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; - [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; - [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; - [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; - [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; - - [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; - } break; - case GGML_OP_UPSCALE: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const float sf0 = (float)ne0/src0->ne[0]; - const float sf1 = (float)ne1/src0->ne[1]; - const float sf2 = (float)ne2/src0->ne[2]; - const float sf3 = (float)ne3/src0->ne[3]; - - const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; - [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; - [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; - [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; - - const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_PAD: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - - const int nth = MIN(1024, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ARANGE: - { - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - float start; - float step; - - memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float)); - memcpy(&step, ((int32_t *) dst->op_params) + 2, sizeof(float)); - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; - [encoder setBytes:&start length:sizeof(start) atIndex:2]; - [encoder setBytes:&step length:sizeof(step) atIndex:3]; - - const int nth = MIN(1024, ne0); - - [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int dim = dst->op_params[0]; - const int max_period = dst->op_params[1]; - - const int half = dim / 2; - - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; - [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; - [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; - - const int nth = MIN(1024, half); - - [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - case GGML_OP_ARGSORT: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_I32); - - const int nrows = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - - // bitonic sort requires the number of elements to be power of 2 - int64_t ne00_padded = 1; - while (ne00_padded < ne00) { - ne00_padded *= 2; - } + [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_NORM: + { + GGML_ASSERT(ggml_is_contiguous_1(src0)); - // Metal kernels require the buffer size to be multiple of 16 bytes - // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength - const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16); + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); - id pipeline = nil; + const int nth = MIN(256, ne00); - switch (order) { - case GGML_SORT_ORDER_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; - case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; - default: GGML_ABORT("fatal error"); - }; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3]; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; + + const int64_t nrows = ggml_nrows(src0); + + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ROPE: + { + GGML_ASSERT(ne10 == ne02); + + const int nth = MIN(1024, ne00); + + const int n_past = ((const int32_t *) dst->op_params)[0]; + const int n_dims = ((const int32_t *) dst->op_params)[1]; + const int mode = ((const int32_t *) dst->op_params)[2]; + // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal + const int n_ctx_orig = ((const int32_t *) dst->op_params)[4]; + + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + + memcpy(&freq_base, (const int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (const int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (const int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (const int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (const int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (const int32_t *) dst->op_params + 10, sizeof(float)); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + + id pipeline = nil; + + if (!is_neox) { + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; + default: GGML_ABORT("fatal error"); + }; + } else { + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; + default: GGML_ABORT("fatal error"); + }; + } - [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)]; - } break; - case GGML_OP_LEAKY_RELU: - { - GGML_ASSERT(src0->type == GGML_TYPE_F32); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + if (id_src2 != nil) { + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; + [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_IM2COL: + { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); - float slope; - memcpy(&slope, dst->op_params, sizeof(float)); + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; + const int32_t N = src1->ne[is_2D ? 3 : 2]; + const int32_t IC = src1->ne[is_2D ? 2 : 1]; + const int32_t IH = is_2D ? src1->ne[1] : 1; + const int32_t IW = src1->ne[0]; - const int64_t n = ggml_nelements(dst); + const int32_t KH = is_2D ? src0->ne[1] : 1; + const int32_t KW = src0->ne[0]; - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - GGML_ASSERT(ne00 % 4 == 0); - GGML_ASSERT(ne11 % 32 == 0); + const int32_t OH = is_2D ? dst->ne[2] : 1; + const int32_t OW = dst->ne[1]; - GGML_ASSERT(src0->type == GGML_TYPE_F32); + const int32_t CHW = IC * KH * KW; - GGML_ASSERT(ggml_are_same_shape (src1, src2)); + const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; + const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; - struct ggml_tensor * src3 = gf->nodes[i]->src[3]; + id pipeline = nil; - size_t offs_src3 = 0; + switch (dst->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; + default: GGML_ABORT("fatal error"); + }; - id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } break; + case GGML_OP_UPSCALE: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; + + const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; + [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; + [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; + [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_PAD: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16); - GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && - "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); + float start; + float step; - const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); - //const int64_t ne31 = src3 ? src3->ne[1] : 0; - const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); - const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); + memcpy(&start, ((const int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&step, ((const int32_t *) dst->op_params) + 2, sizeof(float)); - const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30); - const uint64_t nb31 = src3 ? src3->nb[1] : 0; - const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32); - const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33); + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; - const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; + [encoder setBytes:&start length:sizeof(start) atIndex:2]; + [encoder setBytes:&step length:sizeof(step) atIndex:3]; - float scale; - float max_bias; - float logit_softcap; - memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); - memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); - memcpy(&logit_softcap, ((int32_t *) dst->op_params) + 2, sizeof(logit_softcap)); + const int nth = MIN(1024, ne0); - if (logit_softcap != 0.0f) { - scale /= logit_softcap; - } + [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); - const uint32_t n_head = src0->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + const int half = dim / 2; - id pipeline = nil; + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; - bool use_vec_kernel = false; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; + [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; - if (ne01 >= 4 || (ne00%128 != 0)) { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; - //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; - default: - { - GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_METAL_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } else { - use_vec_kernel = true; + const int nth = MIN(1024, half); - switch (ne00) { - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; - //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; - default: - { - GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_METAL_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } - } - } + [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_ARGSORT: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - if (id_src3) { - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; - [encoder setBytes:&scale length:sizeof( float) atIndex:23]; - [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; - [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28]; - - if (!use_vec_kernel) { - // half8x8 kernel - const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! - const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! - - GGML_ASSERT(nqptg <= 32); - GGML_ASSERT(nqptg % 8 == 0); - GGML_ASSERT(ncpsg % 32 == 0); - - int64_t nsgmax = 2; - - while (true) { - const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2); - if (smem > ctx->device.maxThreadgroupMemoryLength) { - break; - } - nsgmax *= 2; - } - nsgmax /= 2; + const int nrows = ggml_nrows(src0); - // simdgroups per threadgroup (a.k.a. warps) - const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; - const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2); + // bitonic sort requires the number of elements to be power of 2 + int64_t ne00_padded = 1; + while (ne00_padded < ne00) { + ne00_padded *= 2; + } - //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength); - GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength); + // Metal kernels require the buffer size to be multiple of 16 bytes + // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength + const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16); - [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; + id pipeline = nil; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; - } else { - // half1x4 kernel - const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! - const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! + switch (order) { + case GGML_SORT_ORDER_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; + case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; + default: GGML_ABORT("fatal error"); + }; - GGML_ASSERT(nqptg <= 32); - GGML_ASSERT(nqptg % 1 == 0); - GGML_ASSERT(ncpsg % 32 == 0); + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3]; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - // simdgroups per threadgroup (a.k.a. warps) - const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)); + [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)]; + } break; + case GGML_OP_LEAKY_RELU: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); - int64_t nsg = 1; - while (nsg <= nsgt) { - nsg *= 2; - } - nsg /= 2; + float slope; + memcpy(&slope, dst->op_params, sizeof(float)); - const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2); + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; - //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength); - GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength); - [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; - } - } break; - case GGML_OP_DUP: - case GGML_OP_CPY: - case GGML_OP_CONT: - { - GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); - - int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); - - id pipeline = nil; - - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); - - switch (dstt) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break; - default: GGML_ABORT("not implemented"); - }; - } break; - case GGML_TYPE_F16: - { - switch (dstt) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; - default: GGML_ABORT("not implemented"); - }; - } break; - default: GGML_ABORT("not implemented"); + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_OP_FLASH_ATTN_EXT: + { + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ne11 % 32 == 0); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + GGML_ASSERT(ggml_are_same_shape (src1, src2)); + + struct ggml_tensor * src3 = node->src[3]; + + size_t offs_src3 = 0; + + id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; + + GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16); + GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && + "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); + + const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); + //const int64_t ne31 = src3 ? src3->ne[1] : 0; + const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); + const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); + + const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30); + const uint64_t nb31 = src3 ? src3->nb[1] : 0; + const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32); + const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33); + + const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); + + float scale; + float max_bias; + float logit_softcap; + memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias)); + memcpy(&logit_softcap, ((const int32_t *) dst->op_params) + 2, sizeof(logit_softcap)); + + if (logit_softcap != 0.0f) { + scale /= logit_softcap; + } + + const uint32_t n_head = src0->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + id pipeline = nil; + + bool use_vec_kernel = false; + + if (ne01 >= 4 || (ne00%128 != 0)) { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; + //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + default: + { + GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_METAL_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } else { + use_vec_kernel = true; + + switch (ne00) { + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; + //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + default: + { + GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_METAL_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + if (id_src3) { + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; + [encoder setBytes:&scale length:sizeof( float) atIndex:23]; + [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; + [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28]; + + if (!use_vec_kernel) { + // half8x8 kernel + const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! + const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! + + GGML_ASSERT(nqptg <= 32); + GGML_ASSERT(nqptg % 8 == 0); + GGML_ASSERT(ncpsg % 32 == 0); + + int64_t nsgmax = 2; + + while (true) { + const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2); + if (smem > ctx->device.maxThreadgroupMemoryLength) { + break; } + nsgmax *= 2; + } + nsgmax /= 2; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; - default: - { - GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); - GGML_ABORT("fatal error"); + // simdgroups per threadgroup (a.k.a. warps) + const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; + + const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2); + + //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength); + GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength); + + [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; + } else { + // half1x4 kernel + const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! + const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! + + GGML_ASSERT(nqptg <= 32); + GGML_ASSERT(nqptg % 1 == 0); + GGML_ASSERT(ncpsg % 32 == 0); + + // simdgroups per threadgroup (a.k.a. warps) + const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)); + + int64_t nsg = 1; + while (nsg <= nsgt) { + nsg *= 2; } + nsg /= 2; + + const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2); + + //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength); + GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength); + [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; + } + } break; + case GGML_OP_DUP: + case GGML_OP_CPY: + case GGML_OP_CONT: + { + GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); + + int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); + + id pipeline = nil; + + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); + + switch (dstt) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break; + default: GGML_ABORT("not implemented"); + }; + } break; + case GGML_TYPE_F16: + { + switch (dstt) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; + default: GGML_ABORT("not implemented"); + }; + } break; + default: GGML_ABORT("not implemented"); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + default: + { + GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); + GGML_ABORT("fatal error"); } + } +} + +static enum ggml_status ggml_metal_graph_compute( + struct ggml_backend_metal_context * ctx, + struct ggml_cgraph * gf) { + // number of nodes encoded by the main thread (empirically determined) + const int n_main = 128; + + // number of threads in addition to the main thread + const int n_cb = ctx->n_cb; + + // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them + // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread + // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes + // each thread creates it's own command buffer and enqueues the ops in parallel + // + // tests on M1 Pro and M2 Ultra using LLaMA models, show that optimal values for n_cb are 1 or 2 + + @autoreleasepool { + ctx->gf = gf; - if (should_capture) { - [encoder popDebugGroup]; + ctx->n_nodes_0 = MIN(n_main, gf->n_nodes); + ctx->n_nodes_1 = gf->n_nodes - ctx->n_nodes_0; + + ctx->n_nodes_per_cb = (ctx->n_nodes_1 + ctx->n_cb - 1) / ctx->n_cb; + + const bool should_capture = ctx->capture_next_compute; + if (should_capture) { + ctx->capture_next_compute = false; + + if (!ctx->capture_started) { + // create capture scope + ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx->device]; + + MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; + descriptor.captureObject = ctx->capture_scope; + descriptor.destination = MTLCaptureDestinationGPUTraceDocument; + descriptor.outputURL = [NSURL fileURLWithPath:[NSString stringWithFormat:@"/tmp/perf-metal.gputrace"]]; + + NSError * error = nil; + if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { + GGML_METAL_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]); + } else { + [ctx->capture_scope beginScope]; + ctx->capture_started = true; + } } } - [encoder endEncoding]; + // TODO: how to avoid this allocation? I tried initializing it in ggml_backend_metal_set_n_cb but it crashes. + ctx->encode_async = ^(size_t iter) { + const int cb_idx = iter; + const int n_cb_l = ctx->n_cb; - if (cb_idx < 2 || ctx->abort_callback == NULL) { - [command_buffer commit]; - } - }); + const int n_nodes_0 = ctx->n_nodes_0; + const int n_nodes_1 = ctx->n_nodes_1; + + const int n_nodes_per_cb = ctx->n_nodes_per_cb; - // Wait for completion and check status of each command buffer - // needed to detect if the device ran out-of-memory for example (#1881) + id command_buffer = ctx->command_buffers[cb_idx]; + id encoder = [command_buffer computeCommandEncoderWithDescriptor: ctx->edesc]; - for (int i = 0; i < n_cb; ++i) { - id command_buffer = command_buffers[i]; - [command_buffer waitUntilCompleted]; + int node_start = 0; + int node_end = n_nodes_0; - MTLCommandBufferStatus status = [command_buffer status]; - if (status != MTLCommandBufferStatusCompleted) { - GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); - if (status == MTLCommandBufferStatusError) { - GGML_METAL_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]); + if (cb_idx < n_cb_l) { + node_start = n_nodes_0 + ( (cb_idx + 0) * n_nodes_per_cb); + node_end = n_nodes_0 + (MIN((cb_idx == n_cb_l - 1) ? n_nodes_1 : (cb_idx + 1) * n_nodes_per_cb, n_nodes_1)); } - return GGML_STATUS_FAILED; - } + for (int idx = node_start; idx < node_end; ++idx) { + if (should_capture) { + [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(ggml_graph_node(gf, idx)) encoding:NSUTF8StringEncoding]]; + } - id next_buffer = (i + 1 < n_cb ? command_buffers[i + 1] : nil); - if (!next_buffer) { - continue; + ggml_metal_encode_node(ctx, idx, encoder); + + if (should_capture) { + [encoder popDebugGroup]; + } + } + + [encoder endEncoding]; + + if (cb_idx < 2 || ctx->abort_callback == NULL) { + [command_buffer commit]; + } + }; + + // the main thread commits the first few commands immediately + // command_buffer[n_cb] + { + id command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; + ctx->command_buffers[n_cb] = command_buffer; + + [command_buffer enqueue]; + ctx->encode_async(n_cb); } - bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued); - if (next_queued) { - continue; + // prepare the rest of the command buffers asynchronously + // command_buffer[0.. n_cb) + for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { + id command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; + ctx->command_buffers[cb_idx] = command_buffer; + + // always enqueue the first two command buffers + // enqueue all of the command buffers if we don't need to abort + if (cb_idx < 2 || ctx->abort_callback == NULL) { + [command_buffer enqueue]; + } } - if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) { - GGML_METAL_LOG_INFO("%s: command buffer %d aborted", __func__, i); - return GGML_STATUS_ABORTED; + dispatch_apply(n_cb, ctx->d_queue, ctx->encode_async); + + // wait for completion and check status of each command buffer + // needed to detect if the device ran out-of-memory for example (#1881) + { + id command_buffer = ctx->command_buffers[n_cb]; + [command_buffer waitUntilCompleted]; + + MTLCommandBufferStatus status = [command_buffer status]; + if (status != MTLCommandBufferStatusCompleted) { + GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status); + if (status == MTLCommandBufferStatusError) { + GGML_METAL_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]); + } + + return GGML_STATUS_FAILED; + } } - [next_buffer commit]; - } + for (int i = 0; i < n_cb; ++i) { + id command_buffer = ctx->command_buffers[i]; + [command_buffer waitUntilCompleted]; - if (should_capture) { - [[MTLCaptureManager sharedCaptureManager] stopCapture]; - } + MTLCommandBufferStatus status = [command_buffer status]; + if (status != MTLCommandBufferStatusCompleted) { + GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); + if (status == MTLCommandBufferStatusError) { + GGML_METAL_LOG_INFO("error: %s\n", [[command_buffer error].localizedDescription UTF8String]); + } + + return GGML_STATUS_FAILED; + } + + id next_buffer = (i + 1 < n_cb ? ctx->command_buffers[i + 1] : nil); + if (!next_buffer) { + continue; + } + + const bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued); + if (next_queued) { + continue; + } + + if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) { + GGML_METAL_LOG_INFO("%s: command buffer %d aborted", __func__, i); + return GGML_STATUS_ABORTED; + } + + [next_buffer commit]; + } + if (!should_capture && ctx->capture_started) { + [ctx->capture_scope endScope]; + [[MTLCaptureManager sharedCaptureManager] stopCapture]; + } } + return GGML_STATUS_SUCCESS; } @@ -3405,6 +3503,25 @@ GGML_CALL static bool ggml_backend_metal_supports_buft(ggml_backend_t backend, g UNUSED(backend); } +static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { + GGML_ASSERT(ggml_backend_is_metal(backend)); + + struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; + + if (ctx->n_cb != n_cb) { + ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS); + + if (ctx->n_cb > 2) { + GGML_METAL_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb); + } + } + + // TODO: setting encode_async here causes crash during the next ggml_metal_graph_compute call. why? + //ctx->encode_async = ^(size_t iter) { + // ... + //}; +} + static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, @@ -3439,35 +3556,29 @@ static ggml_guid_t ggml_backend_metal_guid(void) { } ggml_backend_t ggml_backend_metal_init(void) { - struct ggml_backend_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); + struct ggml_backend_metal_context * ctx = ggml_metal_init(); if (ctx == NULL) { GGML_METAL_LOG_ERROR("%s: error: failed to allocate context\n", __func__); return NULL; } - ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend)); + ggml_backend_t backend = malloc(sizeof(struct ggml_backend)); - *metal_backend = (struct ggml_backend) { + *backend = (struct ggml_backend) { /* .guid = */ ggml_backend_metal_guid(), /* .interface = */ ggml_backend_metal_i, /* .context = */ ctx, }; - return metal_backend; + ggml_backend_metal_set_n_cb(backend, 1); + + return backend; } bool ggml_backend_is_metal(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid()); } -void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { - GGML_ASSERT(ggml_backend_is_metal(backend)); - - struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; - - ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); -} - void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data) { GGML_ASSERT(ggml_backend_is_metal(backend)); @@ -3489,7 +3600,7 @@ void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) { GGML_ASSERT(ggml_backend_is_metal(backend)); struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; - ctx->should_capture_next_compute = true; + ctx->capture_next_compute = true; } GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index 8bffce860a1..7aa6dce8907 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -4013,7 +4013,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r svfloat32_t sumv0 = svdup_n_f32(0.0f); svfloat32_t sumv1 = svdup_n_f32(0.0f); - const int vector_length = ggml_sve_cnt_b*8; + const int vector_length = ggml_cpu_get_sve_cnt()*8; // VLA Implementation using switch case switch (vector_length) { @@ -5597,7 +5597,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r svfloat32_t sumv0 = svdup_n_f32(0.0f); svfloat32_t sumv1 = svdup_n_f32(0.0f); - const int vector_length = ggml_sve_cnt_b*8; + const int vector_length = ggml_cpu_get_sve_cnt()*8; //VLA Implemenation for SVE switch (vector_length) { diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h index e96ce2b5e5c..df9c4b24ae7 100644 --- a/ggml/src/ggml-quants.h +++ b/ggml/src/ggml-quants.h @@ -142,10 +142,6 @@ void iq2xs_free_impl(enum ggml_type type); void iq3xs_init_impl(int grid_size); void iq3xs_free_impl(int grid_size); -#if defined(__ARM_FEATURE_SVE) -extern int ggml_sve_cnt_b; -#endif - #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp index f9da45881e9..00ad13bb956 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan.cpp @@ -20,6 +20,8 @@ #include #include #include +#include +#include #include "ggml-impl.h" #include "ggml-backend-impl.h" @@ -607,13 +609,16 @@ typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); -static void ggml_vk_create_pipeline(vk_device& device, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { +// variables to track number of compiles in progress +static uint32_t compile_count = 0; +static std::mutex compile_count_mutex; +static std::condition_variable compile_count_cond; + +static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector specialization_constants, uint32_t align) { VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")"); GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - std::lock_guard guard(device->mutex); - pipeline = std::make_shared(); pipeline->name = name; pipeline->parameter_count = parameter_count; @@ -681,7 +686,17 @@ 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.insert({ pipeline->name, pipeline }); + { + std::lock_guard guard(device->mutex); + device->pipelines.insert({ pipeline->name, pipeline }); + } + + { + std::lock_guard guard(compile_count_mutex); + assert(compile_count > 0); + compile_count--; + } + compile_count_cond.notify_all(); } static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { @@ -1079,7 +1094,8 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) { // Fall back to host memory type buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { - buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal); + // use rebar if available, otherwise fallback to device only visible memory + buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal); } } catch (const vk::SystemError& e) { std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; @@ -1148,11 +1164,11 @@ static void ggml_vk_load_shaders(vk_device& device) { // mulmat std::initializer_list warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; std::initializer_list warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_s = { device->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + std::initializer_list warptile_s = { std::max(device->subgroup_size, 16u), 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; std::initializer_list warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; std::initializer_list warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_mmq_s = { device->subgroup_size, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; + std::initializer_list warptile_mmq_s = { std::max(device->subgroup_size, 16u), 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -1193,6 +1209,20 @@ static void ggml_vk_load_shaders(vk_device& device) { device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared(); device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared(); + std::vector> compiles; + auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { + { + // wait until fewer than N compiles are in progress + uint32_t N = std::max(1u, std::thread::hardware_concurrency()); + std::unique_lock guard(compile_count_mutex); + while (compile_count >= N) { + compile_count_cond.wait(guard); + } + compile_count++; + } + compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align)); + }; + if (device->fp16) { ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); @@ -1742,6 +1772,10 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16_len, im2col_f32_f16_data, "main", 2, sizeof(vk_op_im2col_push_constants), {256, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1); + + for (auto &c : compiles) { + c.wait(); + } } static vk_device ggml_vk_get_device(size_t idx) { @@ -2806,7 +2840,11 @@ static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) { VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")"); - if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + + // If the device is not an UMA device the memory is host-accessible through rebar. While writing + // through PCIe is sufficient fast reading back data from PCIe is slower than going through + // the HW device to host copy path. + if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) { GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); memcpy(dst, (uint8_t *) src->ptr + offset, size); @@ -5008,6 +5046,8 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t } } + ggml_pipeline_allocate_descriptor_sets(ctx->device); + vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); @@ -5124,7 +5164,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t avg_err /= m * n; - std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl; + double tflops = 2.0*m*n*k*batch*num_it / (time / 1000.0) / (1000.0*1000.0*1000.0*1000.0); + + std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl; if (avg_err > 0.1) { std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; @@ -5246,12 +5288,14 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_ ggml_pipeline_request_descriptor_sets(ctx->device, p, 1); + ggml_pipeline_allocate_descriptor_sets(ctx->device); + ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); ggml_vk_ctx_begin(ctx->device, subctx); const std::vector pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne }; - ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_dispatch_pipeline(ctx, subctx, p, { vk_subbuffer{ qx_buf, 0, qx_sz }, vk_subbuffer{ x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); @@ -5378,6 +5422,8 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, } } + ggml_pipeline_allocate_descriptor_sets(ctx->device); + ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); ggml_vk_buffer_write(y_buf, 0, y, y_sz); @@ -5445,7 +5491,9 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, avg_err /= m * n; - std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms avg_err=" << avg_err << std::endl; + double tflops = 2.0*m*n*k*batch*num_it / (time_ms / 1000.0) / (1000.0*1000.0*1000.0*1000.0); + + std::cerr << "TEST MMQ " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl; if (avg_err > 0.01 || std::isnan(avg_err)) { std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; @@ -5497,9 +5545,6 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) 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, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, - vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32); ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0); ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 4b782b0c135..bcbc32d913d 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -39,9 +39,6 @@ #include #endif -#if defined(__ARM_FEATURE_SVE) -int ggml_sve_cnt_b = 0; -#endif #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) #undef GGML_USE_LLAMAFILE #endif @@ -455,6 +452,15 @@ static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; +#if defined(__ARM_ARCH) +struct ggml_arm_arch_features_type { + int has_neon; + int has_i8mm; + int has_sve; + int sve_cnt; +} ggml_arm_arch_features = {-1, -1, -1, 0}; +#endif + GGML_CALL const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; @@ -3673,6 +3679,70 @@ static inline int ggml_up(int n, int m) { //////////////////////////////////////////////////////////////////////////////// +#if defined(__ARM_ARCH) + +#if defined(__linux__) && defined(__aarch64__) +#include +#elif defined(__APPLE__) +#include +#endif + +#if !defined(HWCAP2_I8MM) +#define HWCAP2_I8MM 0 +#endif + +static void ggml_init_arm_arch_features(void) { +#if defined(__linux__) && defined(__aarch64__) + uint32_t hwcap = getauxval(AT_HWCAP); + uint32_t hwcap2 = getauxval(AT_HWCAP2); + + ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); + ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); + ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); + +#if defined(__ARM_FEATURE_SVE) + ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); +#endif +#elif defined(__APPLE__) + int oldp = 0; + size_t size = sizeof(oldp); + if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_neon = oldp; + + if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_i8mm = oldp; + + ggml_arm_arch_features.has_sve = 0; + ggml_arm_arch_features.sve_cnt = 0; +#else +// Run-time CPU feature detection not implemented for this platform, fallback to compile time +#if defined(__ARM_NEON) + ggml_arm_arch_features.has_neon = 1; +#else + ggml_arm_arch_features.has_neon = 0; +#endif + +#if defined(__ARM_FEATURE_MATMUL_INT8) + ggml_arm_arch_features.has_i8mm = 1; +#else + ggml_arm_arch_features.has_i8mm = 0; +#endif + +#if defined(__ARM_FEATURE_SVE) + ggml_arm_arch_features.has_sve = 1; + ggml_arm_arch_features.sve_cnt = 16; +#else + ggml_arm_arch_features.has_sve = 0; + ggml_arm_arch_features.sve_cnt = 0; +#endif +#endif +} +#endif + struct ggml_context * ggml_init(struct ggml_init_params params) { // make this function thread safe ggml_critical_section_start(); @@ -3723,6 +3793,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } +#if defined(__ARM_ARCH) + ggml_init_arm_arch_features(); +#endif + is_first_call = false; } @@ -3771,12 +3845,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_ASSERT_ALIGNED(ctx->mem_buffer); -#if defined(__ARM_FEATURE_SVE) - if (!ggml_sve_cnt_b) { - ggml_sve_cnt_b = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); - } -#endif - GGML_PRINT_DEBUG("%s: context initialized\n", __func__); ggml_critical_section_end(); @@ -4657,18 +4725,11 @@ struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * nam static struct ggml_tensor * ggml_dup_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + struct ggml_tensor * a, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_DUP; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_DUP; result->src[0] = a; return result; @@ -4676,13 +4737,13 @@ static struct ggml_tensor * ggml_dup_impl( struct ggml_tensor * ggml_dup( struct ggml_context * ctx, - struct ggml_tensor * a) { + struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, false); } struct ggml_tensor * ggml_dup_inplace( struct ggml_context * ctx, - struct ggml_tensor * a) { + struct ggml_tensor * a) { return ggml_dup_impl(ctx, a, true); } @@ -4690,21 +4751,14 @@ struct ggml_tensor * ggml_dup_inplace( static struct ggml_tensor * ggml_add_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - bool inplace) { + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_ADD; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ADD; result->src[0] = a; result->src[1] = b; @@ -4713,15 +4767,15 @@ static struct ggml_tensor * ggml_add_impl( struct ggml_tensor * ggml_add( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { return ggml_add_impl(ctx, a, b, true); } @@ -4729,9 +4783,9 @@ struct ggml_tensor * ggml_add_inplace( static struct ggml_tensor * ggml_add_cast_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - enum ggml_type type) { + struct ggml_tensor * a, + struct ggml_tensor * b, + enum ggml_type type) { // TODO: support less-strict constraint // GGML_ASSERT(ggml_can_repeat(b, a)); GGML_ASSERT(ggml_can_repeat_rows(b, a)); @@ -4741,18 +4795,9 @@ static struct ggml_tensor * ggml_add_cast_impl( a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16); - bool is_node = false; - - if (a->grad || b->grad) { - // TODO: support backward pass for broadcasting - GGML_ASSERT(ggml_are_same_shape(a, b)); - is_node = true; - } - struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); - result->op = GGML_OP_ADD; - result->grad = is_node ? ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne) : NULL; + result->op = GGML_OP_ADD; result->src[0] = a; result->src[1] = b; @@ -4761,9 +4806,9 @@ static struct ggml_tensor * ggml_add_cast_impl( struct ggml_tensor * ggml_add_cast( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - enum ggml_type type) { + struct ggml_tensor * a, + struct ggml_tensor * b, + enum ggml_type type) { return ggml_add_cast_impl(ctx, a, b, type); } @@ -4771,22 +4816,15 @@ struct ggml_tensor * ggml_add_cast( static struct ggml_tensor * ggml_add1_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - bool inplace) { + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { GGML_ASSERT(ggml_is_scalar(b)); GGML_ASSERT(ggml_is_padded_1d(a)); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_ADD1; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ADD1; result->src[0] = a; result->src[1] = b; @@ -4795,15 +4833,15 @@ static struct ggml_tensor * ggml_add1_impl( struct ggml_tensor * ggml_add1( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { return ggml_add1_impl(ctx, a, b, false); } struct ggml_tensor * ggml_add1_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { return ggml_add1_impl(ctx, a, b, true); } @@ -4811,31 +4849,24 @@ struct ggml_tensor * ggml_add1_inplace( static struct ggml_tensor * ggml_acc_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - size_t nb1, - size_t nb2, - size_t nb3, - size_t offset, - bool inplace) { + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset, + bool inplace) { GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a)); GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(a->type == GGML_TYPE_F32); GGML_ASSERT(b->type == GGML_TYPE_F32); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_ACC; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ACC; result->src[0] = a; result->src[1] = b; @@ -4844,23 +4875,23 @@ static struct ggml_tensor * ggml_acc_impl( struct ggml_tensor * ggml_acc( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - size_t nb1, - size_t nb2, - size_t nb3, - size_t offset) { + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); } struct ggml_tensor * ggml_acc_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - size_t nb1, - size_t nb2, - size_t nb3, - size_t offset) { + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true); } @@ -4868,23 +4899,14 @@ struct ggml_tensor * ggml_acc_inplace( static struct ggml_tensor * ggml_sub_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - bool inplace) { + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - // TODO: support backward pass for broadcasting - GGML_ASSERT(ggml_are_same_shape(a, b)); - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_SUB; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SUB; result->src[0] = a; result->src[1] = b; @@ -4893,15 +4915,15 @@ static struct ggml_tensor * ggml_sub_impl( struct ggml_tensor * ggml_sub( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, false); } struct ggml_tensor * ggml_sub_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { return ggml_sub_impl(ctx, a, b, true); } @@ -4909,27 +4931,14 @@ struct ggml_tensor * ggml_sub_inplace( static struct ggml_tensor * ggml_mul_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - bool inplace) { + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - // TODO: support backward pass for broadcasting - GGML_ASSERT(ggml_are_same_shape(a, b)); - is_node = true; - } - - if (inplace) { - GGML_ASSERT(!is_node); - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_MUL; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MUL; result->src[0] = a; result->src[1] = b; @@ -4954,25 +4963,14 @@ struct ggml_tensor * ggml_mul_inplace( static struct ggml_tensor * ggml_div_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - bool inplace) { + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { GGML_ASSERT(ggml_can_repeat(b, a)); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - is_node = true; - } - - if (inplace) { - GGML_ASSERT(!is_node); - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_DIV; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_DIV; result->src[0] = a; result->src[1] = b; @@ -4997,18 +4995,11 @@ struct ggml_tensor * ggml_div_inplace( static struct ggml_tensor * ggml_sqr_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + struct ggml_tensor * a, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_SQR; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SQR; result->src[0] = a; return result; @@ -5030,18 +5021,11 @@ struct ggml_tensor * ggml_sqr_inplace( static struct ggml_tensor * ggml_sqrt_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + struct ggml_tensor * a, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_SQRT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SQRT; result->src[0] = a; return result; @@ -5064,17 +5048,10 @@ struct ggml_tensor * ggml_sqrt_inplace( static struct ggml_tensor * ggml_log_impl( struct ggml_context * ctx, struct ggml_tensor * a, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_LOG; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_LOG; result->src[0] = a; return result; @@ -5097,17 +5074,10 @@ struct ggml_tensor * ggml_log_inplace( static struct ggml_tensor * ggml_sin_impl( struct ggml_context * ctx, struct ggml_tensor * a, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_SIN; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SIN; result->src[0] = a; return result; @@ -5130,17 +5100,10 @@ struct ggml_tensor * ggml_sin_inplace( static struct ggml_tensor * ggml_cos_impl( struct ggml_context * ctx, struct ggml_tensor * a, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_COS; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_COS; result->src[0] = a; return result; @@ -5162,17 +5125,10 @@ struct ggml_tensor * ggml_cos_inplace( struct ggml_tensor * ggml_sum( struct ggml_context * ctx, - struct ggml_tensor * a) { - bool is_node = false; - - if (a->grad) { - is_node = true; - } - + struct ggml_tensor * a) { struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); - result->op = GGML_OP_SUM; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SUM; result->src[0] = a; return result; @@ -5182,13 +5138,7 @@ struct ggml_tensor * ggml_sum( struct ggml_tensor * ggml_sum_rows( struct ggml_context * ctx, - struct ggml_tensor * a) { - bool is_node = false; - - if (a->grad) { - is_node = true; - } - + struct ggml_tensor * a) { int64_t ne[GGML_MAX_DIMS] = { 1 }; for (int i = 1; i < GGML_MAX_DIMS; ++i) { ne[i] = a->ne[i]; @@ -5196,8 +5146,7 @@ struct ggml_tensor * ggml_sum_rows( struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); - result->op = GGML_OP_SUM_ROWS; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SUM_ROWS; result->src[0] = a; return result; @@ -5207,19 +5156,11 @@ struct ggml_tensor * ggml_sum_rows( struct ggml_tensor * ggml_mean( struct ggml_context * ctx, - struct ggml_tensor * a) { - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement - is_node = true; - } - + struct ggml_tensor * a) { int64_t ne[4] = { 1, a->ne[1], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_MEAN; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MEAN; result->src[0] = a; return result; @@ -5229,19 +5170,12 @@ struct ggml_tensor * ggml_mean( struct ggml_tensor * ggml_argmax( struct ggml_context * ctx, - struct ggml_tensor * a) { + struct ggml_tensor * a) { GGML_ASSERT(ggml_is_matrix(a)); - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); - is_node = true; - } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]); - result->op = GGML_OP_ARGMAX; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ARGMAX; result->src[0] = a; return result; @@ -5251,20 +5185,13 @@ struct ggml_tensor * ggml_argmax( struct ggml_tensor * ggml_repeat( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(a, b)); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne); - result->op = GGML_OP_REPEAT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_REPEAT; result->src[0] = a; return result; @@ -5274,24 +5201,13 @@ struct ggml_tensor * ggml_repeat( struct ggml_tensor * ggml_repeat_back( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * a, + struct ggml_tensor * b) { GGML_ASSERT(ggml_can_repeat(b, a)); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - - if (ggml_are_same_shape(a, b) && !is_node) { - return a; - } - struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne); - result->op = GGML_OP_REPEAT_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_REPEAT_BACK; result->src[0] = a; return result; @@ -5301,9 +5217,9 @@ struct ggml_tensor * ggml_repeat_back( struct ggml_tensor * ggml_concat( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int dim) { + struct ggml_tensor * a, + struct ggml_tensor * b, + int dim) { GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS); int64_t ne[GGML_MAX_DIMS]; @@ -5316,19 +5232,11 @@ struct ggml_tensor * ggml_concat( ne[d] = a->ne[d]; } - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ABORT("fatal error"); // TODO: implement - is_node = true; - } - struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); ggml_set_op_params_i32(result, 0, dim); - result->op = GGML_OP_CONCAT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CONCAT; result->src[0] = a; result->src[1] = b; @@ -5437,20 +5345,14 @@ struct ggml_tensor * ggml_relu_inplace( struct ggml_tensor * ggml_leaky_relu( struct ggml_context * ctx, - struct ggml_tensor * a, float negative_slope, bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - GGML_ABORT("fatal error"); // TODO: not implemented - is_node = true; - } - + struct ggml_tensor * a, + float negative_slope, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); - result->op = GGML_OP_LEAKY_RELU; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_LEAKY_RELU; result->src[0] = a; return result; @@ -5518,17 +5420,9 @@ struct ggml_tensor * ggml_silu_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { - bool is_node = false; - - if (a->grad || b->grad) { - // TODO: implement backward - is_node = true; - } - struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - result->op = GGML_OP_SILU_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SILU_BACK; result->src[0] = a; result->src[1] = b; @@ -5536,6 +5430,7 @@ struct ggml_tensor * ggml_silu_back( } // ggml hardswish + struct ggml_tensor * ggml_hardswish( struct ggml_context * ctx, struct ggml_tensor * a) { @@ -5543,6 +5438,7 @@ struct ggml_tensor * ggml_hardswish( } // ggml hardsigmoid + struct ggml_tensor * ggml_hardsigmoid( struct ggml_context * ctx, struct ggml_tensor * a) { @@ -5550,6 +5446,7 @@ struct ggml_tensor * ggml_hardsigmoid( } // ggml exp + struct ggml_tensor * ggml_exp( struct ggml_context * ctx, struct ggml_tensor * a) { @@ -5567,21 +5464,13 @@ struct ggml_tensor * ggml_exp_inplace( static struct ggml_tensor * ggml_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, - float eps, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - + float eps, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); - result->op = GGML_OP_NORM; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_NORM; result->src[0] = a; return result; @@ -5590,14 +5479,14 @@ static struct ggml_tensor * ggml_norm_impl( struct ggml_tensor * ggml_norm( struct ggml_context * ctx, struct ggml_tensor * a, - float eps) { + float eps) { return ggml_norm_impl(ctx, a, eps, false); } struct ggml_tensor * ggml_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - float eps) { + float eps) { return ggml_norm_impl(ctx, a, eps, true); } @@ -5606,20 +5495,13 @@ struct ggml_tensor * ggml_norm_inplace( static struct ggml_tensor * ggml_rms_norm_impl( struct ggml_context * ctx, struct ggml_tensor * a, - float eps, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - + float eps, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); - result->op = GGML_OP_RMS_NORM; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RMS_NORM; result->src[0] = a; return result; @@ -5628,14 +5510,14 @@ static struct ggml_tensor * ggml_rms_norm_impl( struct ggml_tensor * ggml_rms_norm( struct ggml_context * ctx, struct ggml_tensor * a, - float eps) { + float eps) { return ggml_rms_norm_impl(ctx, a, eps, false); } struct ggml_tensor * ggml_rms_norm_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - float eps) { + float eps) { return ggml_rms_norm_impl(ctx, a, eps, true); } @@ -5645,20 +5527,12 @@ struct ggml_tensor * ggml_rms_norm_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - float eps) { - bool is_node = false; - - if (a->grad) { - // TODO: implement backward - is_node = true; - } - + float eps) { struct ggml_tensor * result = ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &eps, sizeof(eps)); - result->op = GGML_OP_RMS_NORM_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RMS_NORM_BACK; result->src[0] = a; result->src[1] = b; @@ -5668,43 +5542,35 @@ struct ggml_tensor * ggml_rms_norm_back( // ggml_group_norm static struct ggml_tensor * ggml_group_norm_impl( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_groups, - float eps, - bool inplace) { - - bool is_node = false; - if (!inplace && (a->grad)) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_groups, + float eps, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params_i32(result, 0, n_groups); ggml_set_op_params_f32(result, 1, eps); - result->op = GGML_OP_GROUP_NORM; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_GROUP_NORM; result->src[0] = a; return result; } struct ggml_tensor * ggml_group_norm( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_groups, - float eps) { + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_groups, + float eps) { return ggml_group_norm_impl(ctx, a, n_groups, eps, false); } struct ggml_tensor * ggml_group_norm_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_groups, - float eps) { + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_groups, + float eps) { return ggml_group_norm_impl(ctx, a, n_groups, eps, true); } @@ -5717,17 +5583,10 @@ struct ggml_tensor * ggml_mul_mat( GGML_ASSERT(ggml_can_mul_mat(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } - const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_MUL_MAT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MUL_MAT; result->src[0] = a; result->src[1] = b; @@ -5773,17 +5632,10 @@ struct ggml_tensor * ggml_mul_mat_id( GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast - bool is_node = false; - - if (as->grad || b->grad) { - is_node = true; - } - const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_MUL_MAT_ID; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MUL_MAT_ID; result->src[0] = as; result->src[1] = b; result->src[2] = ids; @@ -5800,18 +5652,11 @@ struct ggml_tensor * ggml_out_prod( GGML_ASSERT(ggml_can_out_prod(a, b)); GGML_ASSERT(!ggml_is_transposed(a)); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } - // a is broadcastable to b for ne[2] and ne[3] -> use b->ne[2] and b->ne[3] const int64_t ne[4] = { a->ne[0], b->ne[0], b->ne[2], b->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_OUT_PROD; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_OUT_PROD; result->src[0] = a; result->src[1] = b; @@ -5824,21 +5669,14 @@ static struct ggml_tensor * ggml_scale_impl( struct ggml_context * ctx, struct ggml_tensor * a, float s, - bool inplace) { + bool inplace) { GGML_ASSERT(ggml_is_padded_1d(a)); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, &s, sizeof(s)); - result->op = GGML_OP_SCALE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SCALE; result->src[0] = a; return result; @@ -5846,15 +5684,15 @@ static struct ggml_tensor * ggml_scale_impl( struct ggml_tensor * ggml_scale( struct ggml_context * ctx, - struct ggml_tensor * a, - float s) { + struct ggml_tensor * a, + float s) { return ggml_scale_impl(ctx, a, s, false); } struct ggml_tensor * ggml_scale_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - float s) { + struct ggml_tensor * a, + float s) { return ggml_scale_impl(ctx, a, s, true); } @@ -5868,15 +5706,9 @@ static struct ggml_tensor * ggml_set_impl( size_t nb2, size_t nb3, size_t offset, - bool inplace) { + bool inplace) { GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b)); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } - // make a view of the destination struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); @@ -5884,8 +5716,7 @@ static struct ggml_tensor * ggml_set_impl( int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_SET; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SET; result->src[0] = a; result->src[1] = b; @@ -5894,8 +5725,8 @@ static struct ggml_tensor * ggml_set_impl( struct ggml_tensor * ggml_set( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, + struct ggml_tensor * a, + struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, @@ -5905,8 +5736,8 @@ struct ggml_tensor * ggml_set( struct ggml_tensor * ggml_set_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, + struct ggml_tensor * a, + struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, @@ -5916,24 +5747,24 @@ struct ggml_tensor * ggml_set_inplace( struct ggml_tensor * ggml_set_1d( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, + struct ggml_tensor * a, + struct ggml_tensor * b, size_t offset) { return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false); } struct ggml_tensor * ggml_set_1d_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, + struct ggml_tensor * a, + struct ggml_tensor * b, size_t offset) { return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true); } struct ggml_tensor * ggml_set_2d( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, + struct ggml_tensor * a, + struct ggml_tensor * b, size_t nb1, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false); @@ -5941,8 +5772,8 @@ struct ggml_tensor * ggml_set_2d( struct ggml_tensor * ggml_set_2d_inplace( struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, + struct ggml_tensor * a, + struct ggml_tensor * b, size_t nb1, size_t offset) { return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, true); @@ -5956,13 +5787,6 @@ static struct ggml_tensor * ggml_cpy_impl( struct ggml_tensor * b) { GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); - bool is_node = false; - - if (a->grad || b->grad) { - // inplace is false and either one have a grad - is_node = true; - } - // make a view of the destination struct ggml_tensor * result = ggml_view_tensor(ctx, b); if (strlen(b->name) > 0) { @@ -5971,8 +5795,7 @@ static struct ggml_tensor * ggml_cpy_impl( ggml_format_name(result, "%s (copy)", a->name); } - result->op = GGML_OP_CPY; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CPY; result->src[0] = a; result->src[1] = b; @@ -5990,13 +5813,10 @@ struct ggml_tensor * ggml_cast( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_type type) { - bool is_node = false; - struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); ggml_format_name(result, "%s (copy)", a->name); - result->op = GGML_OP_CPY; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CPY; result->src[0] = a; result->src[1] = result; @@ -6008,17 +5828,10 @@ struct ggml_tensor * ggml_cast( static struct ggml_tensor * ggml_cont_impl( struct ggml_context * ctx, struct ggml_tensor * a) { - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = ggml_dup_tensor(ctx, a); ggml_format_name(result, "%s (cont)", a->name); - result->op = GGML_OP_CONT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CONT; result->src[0] = a; return result; @@ -6064,13 +5877,10 @@ struct ggml_tensor * ggml_cont_4d( int64_t ne3) { GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3)); - bool is_node = false; - struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3); ggml_format_name(result, "%s (cont)", a->name); - result->op = GGML_OP_CONT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CONT; result->src[0] = a; return result; @@ -6086,22 +5896,10 @@ struct ggml_tensor * ggml_reshape( // as only the shape of b is relevant, and not its memory layout, b is allowed to be non contiguous. GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - - if (b->grad) { - // gradient propagation is not supported - //GGML_ABORT("fatal error"); - } - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b->ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); - result->op = GGML_OP_RESHAPE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RESHAPE; result->src[0] = a; return result; @@ -6114,18 +5912,11 @@ struct ggml_tensor * ggml_reshape_1d( GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - const int64_t ne[1] = { ne0 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); - result->op = GGML_OP_RESHAPE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RESHAPE; result->src[0] = a; return result; @@ -6139,18 +5930,11 @@ struct ggml_tensor * ggml_reshape_2d( GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); - result->op = GGML_OP_RESHAPE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RESHAPE; result->src[0] = a; return result; @@ -6165,18 +5949,11 @@ struct ggml_tensor * ggml_reshape_3d( GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); - result->op = GGML_OP_RESHAPE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RESHAPE; result->src[0] = a; return result; @@ -6192,18 +5969,11 @@ struct ggml_tensor * ggml_reshape_4d( GGML_ASSERT(ggml_is_contiguous(a)); GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2*ne3); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); - result->op = GGML_OP_RESHAPE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RESHAPE; result->src[0] = a; return result; @@ -6215,20 +5985,12 @@ static struct ggml_tensor * ggml_view_impl( int n_dims, const int64_t * ne, size_t offset) { - - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset); ggml_format_name(result, "%s (view)", a->name); ggml_set_op_params(result, &offset, sizeof(offset)); - result->op = GGML_OP_VIEW; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_VIEW; result->src[0] = a; return result; @@ -6241,7 +6003,6 @@ struct ggml_tensor * ggml_view_1d( struct ggml_tensor * a, int64_t ne0, size_t offset) { - struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset); return result; @@ -6256,7 +6017,6 @@ struct ggml_tensor * ggml_view_2d( int64_t ne1, size_t nb1, size_t offset) { - const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset); @@ -6279,7 +6039,6 @@ struct ggml_tensor * ggml_view_3d( size_t nb1, size_t nb2, size_t offset) { - const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset); @@ -6304,7 +6063,6 @@ struct ggml_tensor * ggml_view_4d( size_t nb2, size_t nb3, size_t offset) { - const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset); @@ -6337,12 +6095,6 @@ struct ggml_tensor * ggml_permute( GGML_ASSERT(axis1 != axis3); GGML_ASSERT(axis2 != axis3); - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = ggml_view_tensor(ctx, a); ggml_format_name(result, "%s (permuted)", a->name); @@ -6369,8 +6121,7 @@ struct ggml_tensor * ggml_permute( result->nb[2] = nb[2]; result->nb[3] = nb[3]; - result->op = GGML_OP_PERMUTE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_PERMUTE; result->src[0] = a; int32_t params[] = { axis0, axis1, axis2, axis3 }; @@ -6384,12 +6135,6 @@ struct ggml_tensor * ggml_permute( struct ggml_tensor * ggml_transpose( struct ggml_context * ctx, struct ggml_tensor * a) { - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = ggml_view_tensor(ctx, a); ggml_format_name(result, "%s (transposed)", a->name); @@ -6399,8 +6144,7 @@ struct ggml_tensor * ggml_transpose( result->nb[0] = a->nb[1]; result->nb[1] = a->nb[0]; - result->op = GGML_OP_TRANSPOSE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_TRANSPOSE; result->src[0] = a; return result; @@ -6416,12 +6160,6 @@ struct ggml_tensor * ggml_get_rows( GGML_ASSERT(b->ne[3] == 1); GGML_ASSERT(b->type == GGML_TYPE_I32); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } - // TODO: implement non F32 return enum ggml_type type = GGML_TYPE_F32; if (a->type == GGML_TYPE_I32) { @@ -6429,8 +6167,7 @@ struct ggml_tensor * ggml_get_rows( } struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]); - result->op = GGML_OP_GET_ROWS; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_GET_ROWS; result->src[0] = a; result->src[1] = b; @@ -6447,18 +6184,11 @@ struct ggml_tensor * ggml_get_rows_back( GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0])); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } - // TODO: implement non F32 return //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]); - result->op = GGML_OP_GET_ROWS_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_GET_ROWS_BACK; result->src[0] = a; result->src[1] = b; @@ -6471,17 +6201,11 @@ struct ggml_tensor * ggml_diag( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(a->ne[1] == 1); - bool is_node = false; - - if (a->grad) { - is_node = true; - } const int64_t ne[4] = { a->ne[0], a->ne[0], a->ne[2], a->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, 4, ne); - result->op = GGML_OP_DIAG; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_DIAG; result->src[0] = a; return result; @@ -6494,19 +6218,12 @@ static struct ggml_tensor * ggml_diag_mask_inf_impl( struct ggml_tensor * a, int n_past, bool inplace) { - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { n_past }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_DIAG_MASK_INF; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_DIAG_MASK_INF; result->src[0] = a; return result; @@ -6533,19 +6250,12 @@ static struct ggml_tensor * ggml_diag_mask_zero_impl( struct ggml_tensor * a, int n_past, bool inplace) { - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[] = { n_past }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_DIAG_MASK_ZERO; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_DIAG_MASK_ZERO; result->src[0] = a; return result; @@ -6588,19 +6298,12 @@ static struct ggml_tensor * ggml_soft_max_impl( GGML_ASSERT(mask); } - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); float params[] = { scale, max_bias }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_SOFT_MAX; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SOFT_MAX; result->src[0] = a; result->src[1] = mask; @@ -6635,16 +6338,9 @@ static struct ggml_tensor * ggml_soft_max_back_impl( struct ggml_tensor * a, struct ggml_tensor * b, bool inplace) { - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; // TODO : implement backward pass - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - result->op = GGML_OP_SOFT_MAX_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SOFT_MAX_BACK; result->src[0] = a; result->src[1] = b; @@ -6693,12 +6389,6 @@ static struct ggml_tensor * ggml_rope_impl( GGML_ASSERT(c->ne[0] >= n_dims / 2); } - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig }; @@ -6710,8 +6400,7 @@ static struct ggml_tensor * ggml_rope_impl( memcpy(params + 10, &beta_slow, sizeof(float)); ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_ROPE; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ROPE; result->src[0] = a; result->src[1] = b; result->src[2] = c; @@ -6839,13 +6528,6 @@ struct ggml_tensor * ggml_rope_back( GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); - bool is_node = false; - - if (a->grad) { - GGML_ASSERT(false && "backwards pass not implemented"); - is_node = false; - } - struct ggml_tensor * result = ggml_dup_tensor(ctx, a); int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig }; @@ -6857,8 +6539,7 @@ struct ggml_tensor * ggml_rope_back( memcpy(params + 10, &beta_slow, sizeof(float)); ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_ROPE_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ROPE_BACK; result->src[0] = a; result->src[1] = b; result->src[2] = c; @@ -6873,21 +6554,13 @@ struct ggml_tensor * ggml_clamp( struct ggml_tensor * a, float min, float max) { - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - // TODO: when implement backward, fix this: struct ggml_tensor * result = ggml_view_tensor(ctx, a); float params[] = { min, max }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_CLAMP; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CLAMP; result->src[0] = a; return result; @@ -6949,13 +6622,6 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d( GGML_ASSERT(p0 == 0); GGML_ASSERT(d0 == 1); - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - const int64_t ne[4] = { ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/), a->ne[1], b->ne[2], 1, @@ -6965,8 +6631,7 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d( int32_t params[] = { s0, p0, d0 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_CONV_TRANSPOSE_1D; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CONV_TRANSPOSE_1D; result->src[0] = a; result->src[1] = b; @@ -6974,17 +6639,17 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d( } // ggml_conv_depthwise -struct ggml_tensor * ggml_conv_depthwise_2d( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int s0, - int s1, - int p0, - int p1, - int d0, - int d1) { +struct ggml_tensor * ggml_conv_depthwise_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), @@ -7004,29 +6669,23 @@ struct ggml_tensor * ggml_conv_depthwise_2d( // b: [N, IC, IH, IW] // result: [N, OH, OW, IC*KH*KW] struct ggml_tensor * ggml_im2col( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int s0, - int s1, - int p0, - int p1, - int d0, - int d1, - bool is_2D, - enum ggml_type dst_type) { - + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1, + bool is_2D, + enum ggml_type dst_type) { if(is_2D) { GGML_ASSERT(a->ne[2] == b->ne[2]); } else { GGML_ASSERT(a->ne[1] == b->ne[1]); GGML_ASSERT(b->ne[3] == 1); } - bool is_node = false; - - if (/*a->grad ||*/ b->grad) { // a is only used for its shape, not its data - is_node = true; - } const int64_t OH = is_2D ? ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1) : 0; const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); @@ -7045,8 +6704,7 @@ struct ggml_tensor * ggml_im2col( int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_IM2COL; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_IM2COL; result->src[0] = a; result->src[1] = b; @@ -7054,30 +6712,22 @@ struct ggml_tensor * ggml_im2col( } struct ggml_tensor * ggml_im2col_back( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int64_t * ne, - int s0, - int s1, - int p0, - int p1, - int d0, - int d1, - bool is_2D) { - - bool is_node = false; - - if (/*a->grad ||*/ b->grad) { // a is only used for its shape, not its data - is_node = true; - } - + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int64_t * ne, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1, + bool is_2D) { struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_IM2COL_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_IM2COL_BACK; result->src[0] = a; result->src[1] = b; @@ -7091,12 +6741,12 @@ struct ggml_tensor * ggml_conv_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - int s0, - int s1, - int p0, - int p1, - int d0, - int d1) { + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, a->type); // [N, OH, OW, IC * KH * KW] struct ggml_tensor * result = @@ -7112,6 +6762,7 @@ struct ggml_tensor * ggml_conv_2d( } // ggml_conv_2d_sk_p0 + struct ggml_tensor * ggml_conv_2d_sk_p0( struct ggml_context * ctx, struct ggml_tensor * a, @@ -7141,13 +6792,6 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0( int stride) { GGML_ASSERT(a->ne[3] == b->ne[2]); - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - const int64_t ne[4] = { ggml_calc_conv_transpose_output_size(b->ne[0], a->ne[0], stride, 0 /*p0*/), ggml_calc_conv_transpose_output_size(b->ne[1], a->ne[1], stride, 0 /*p1*/), @@ -7158,8 +6802,7 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0( ggml_set_op_params_i32(result, 0, stride); - result->op = GGML_OP_CONV_TRANSPOSE_2D; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CONV_TRANSPOSE_2D; result->src[0] = a; result->src[1] = b; @@ -7181,14 +6824,6 @@ struct ggml_tensor * ggml_pool_1d( int k0, int s0, int p0) { - - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - const int64_t ne[4] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), a->ne[1], @@ -7200,8 +6835,7 @@ struct ggml_tensor * ggml_pool_1d( int32_t params[] = { op, k0, s0, p0 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_POOL_1D; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_POOL_1D; result->src[0] = a; return result; @@ -7219,13 +6853,6 @@ struct ggml_tensor * ggml_pool_2d( int s1, float p0, float p1) { - - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result; const int64_t ne[4] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), @@ -7238,9 +6865,9 @@ struct ggml_tensor * ggml_pool_2d( int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_POOL_2D; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_POOL_2D; result->src[0] = a; + return result; } @@ -7255,100 +6882,74 @@ struct ggml_tensor * ggml_pool_2d_back( int s1, float p0, float p1) { - - bool is_node = false; - - if (a->grad) { - is_node = true; - } - struct ggml_tensor * result; result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, af->ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_POOL_2D_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_POOL_2D_BACK; result->src[0] = a; result->src[1] = af; + return result; } // ggml_upscale static struct ggml_tensor * ggml_upscale_impl( - struct ggml_context * ctx, - struct ggml_tensor * a, - int ne0, - int ne1, - int ne2, - int ne3) { - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3) { GGML_ASSERT(a->ne[0] <= ne0); GGML_ASSERT(a->ne[1] <= ne1); GGML_ASSERT(a->ne[2] <= ne2); GGML_ASSERT(a->ne[3] <= ne3); - struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, - ne0, - ne1, - ne2, - ne3 - ); - - result->op = GGML_OP_UPSCALE; + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3); - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_UPSCALE; result->src[0] = a; return result; } struct ggml_tensor * ggml_upscale( - struct ggml_context * ctx, - struct ggml_tensor * a, - int scale_factor) { + struct ggml_context * ctx, + struct ggml_tensor * a, + int scale_factor) { return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]); } struct ggml_tensor * ggml_upscale_ext( - struct ggml_context * ctx, - struct ggml_tensor * a, - int ne0, - int ne1, - int ne2, - int ne3) { + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3) { return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3); } // ggml_pad struct ggml_tensor * ggml_pad( - struct ggml_context * ctx, - struct ggml_tensor * a, - int p0, int p1, int p2, int p3) { - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1, + int p2, + int p3) { struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0] + p0, a->ne[1] + p1, a->ne[2] + p2, a->ne[3] + p3); - result->op = GGML_OP_PAD; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_PAD; result->src[0] = a; return result; @@ -7357,39 +6958,32 @@ struct ggml_tensor * ggml_pad( // ggml_arange struct ggml_tensor * ggml_arange( - struct ggml_context * ctx, - float start, - float stop, - float step) { - + struct ggml_context * ctx, + float start, + float stop, + float step) { GGML_ASSERT(stop > start); const int64_t steps = (int64_t) ceilf((stop - start) / step); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps); - result->op = GGML_OP_ARANGE; ggml_set_op_params_f32(result, 0, start); ggml_set_op_params_f32(result, 1, stop); ggml_set_op_params_f32(result, 2, step); + result->op = GGML_OP_ARANGE; + return result; } // ggml_timestep_embedding struct ggml_tensor * ggml_timestep_embedding( - struct ggml_context * ctx, - struct ggml_tensor * timesteps, - int dim, - int max_period) { - bool is_node = false; - - if (timesteps->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period) { int actual_dim = dim; if (dim % 2 != 0) { actual_dim = dim + 1; @@ -7397,11 +6991,10 @@ struct ggml_tensor * ggml_timestep_embedding( struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]); - result->op = GGML_OP_TIMESTEP_EMBEDDING; ggml_set_op_params_i32(result, 0, dim); ggml_set_op_params_i32(result, 1, max_period); - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_TIMESTEP_EMBEDDING; result->src[0] = timesteps; return result; @@ -7410,22 +7003,14 @@ struct ggml_tensor * ggml_timestep_embedding( // ggml_argsort struct ggml_tensor * ggml_argsort( - struct ggml_context * ctx, - struct ggml_tensor * a, - enum ggml_sort_order order) { - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: not implemented - is_node = true; - } - + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_sort_order order) { struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne); ggml_set_op_params_i32(result, 0, (int32_t) order); - result->op = GGML_OP_ARGSORT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ARGSORT; result->src[0] = a; return result; @@ -7478,10 +7063,6 @@ struct ggml_tensor * ggml_flash_attn_ext( bool is_node = false; - if (q->grad || k->grad || v->grad) { - is_node = true; - } - // permute(0, 2, 1, 3) int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); @@ -7608,17 +7189,9 @@ struct ggml_tensor * ggml_ssm_conv( GGML_ASSERT(sx->ne[1] == d_inner); GGML_ASSERT(n_t >= 0); - bool is_node = false; - - if (sx->grad || c->grad) { - GGML_ABORT("fatal error"); // TODO: implement - is_node = true; - } - struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_t, n_s); - result->op = GGML_OP_SSM_CONV; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_SSM_CONV; result->src[0] = sx; result->src[1] = c; @@ -7662,18 +7235,10 @@ struct ggml_tensor * ggml_ssm_scan( GGML_ASSERT(B->ne[2] == n_seqs); } - bool is_node = false; - - if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad) { - GGML_ABORT("fatal error"); // TODO: implement - is_node = true; - } - // concatenated y + ssm_states struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s)); result->op = GGML_OP_SSM_SCAN; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = s; result->src[1] = x; result->src[2] = dt; @@ -7693,13 +7258,6 @@ struct ggml_tensor * ggml_win_part( GGML_ASSERT(a->ne[3] == 1); GGML_ASSERT(a->type == GGML_TYPE_F32); - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - // padding const int px = (w - a->ne[1]%w)%w; const int py = (w - a->ne[2]%w)%w; @@ -7714,8 +7272,7 @@ struct ggml_tensor * ggml_win_part( int32_t params[] = { npx, npy, w }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_WIN_PART; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_WIN_PART; result->src[0] = a; return result; @@ -7731,21 +7288,13 @@ struct ggml_tensor * ggml_win_unpart( int w) { GGML_ASSERT(a->type == GGML_TYPE_F32); - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - const int64_t ne[4] = { a->ne[0], w0, h0, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { w }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_WIN_UNPART; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_WIN_UNPART; result->src[0] = a; return result; @@ -7761,18 +7310,10 @@ struct ggml_tensor * ggml_get_rel_pos( GGML_ASSERT(qh == kh); GGML_ASSERT(2*MAX(qh, kh) - 1 == a->ne[1]); - bool is_node = false; - - if (a->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - const int64_t ne[4] = { a->ne[0], kh, qh, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 3, ne); - result->op = GGML_OP_GET_REL_POS; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_GET_REL_POS; result->src[0] = a; return result; @@ -7796,17 +7337,10 @@ static struct ggml_tensor * ggml_add_rel_pos_impl( GGML_ASSERT(pw->ne[0]*pw->ne[0] == a->ne[0]); GGML_ASSERT(pw->ne[1]*pw->ne[2] == a->ne[1]); - bool is_node = false; - - if (!inplace && (a->grad || pw->grad || ph->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params_i32(result, 0, inplace ? 1 : 0); - result->op = GGML_OP_ADD_REL_POS; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_ADD_REL_POS; result->src[0] = a; result->src[1] = pw; result->src[2] = ph; @@ -7834,12 +7368,12 @@ struct ggml_tensor * ggml_add_rel_pos_inplace( struct ggml_tensor * ggml_rwkv_wkv( struct ggml_context * ctx, - struct ggml_tensor * k, - struct ggml_tensor * v, - struct ggml_tensor * r, - struct ggml_tensor * tf, - struct ggml_tensor * td, - struct ggml_tensor * state) { + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * r, + struct ggml_tensor * tf, + struct ggml_tensor * td, + struct ggml_tensor * state) { GGML_ASSERT(ggml_is_contiguous(k)); GGML_ASSERT(ggml_is_contiguous(v)); GGML_ASSERT(ggml_is_contiguous(r)); @@ -7860,19 +7394,11 @@ struct ggml_tensor * ggml_rwkv_wkv( GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs); } - bool is_node = false; - - if (k->grad || v->grad || r->grad || tf->grad || td->grad || state->grad) { - GGML_ABORT("fatal error"); // TODO: implement backward - is_node = true; - } - // concat output and new_state const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_RWKV_WKV; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_RWKV_WKV; result->src[0] = k; result->src[1] = v; result->src[2] = r; @@ -7887,23 +7413,16 @@ struct ggml_tensor * ggml_rwkv_wkv( static struct ggml_tensor * ggml_unary_impl( struct ggml_context * ctx, - struct ggml_tensor * a, - enum ggml_unary_op op, - bool inplace) { + struct ggml_tensor * a, + enum ggml_unary_op op, + bool inplace) { GGML_ASSERT(ggml_is_contiguous_1(a)); - bool is_node = false; - - if (!inplace && (a->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params_i32(result, 0, (int32_t) op); - result->op = GGML_OP_UNARY; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_UNARY; result->src[0] = a; return result; @@ -7912,14 +7431,14 @@ static struct ggml_tensor * ggml_unary_impl( struct ggml_tensor * ggml_unary( struct ggml_context * ctx, struct ggml_tensor * a, - enum ggml_unary_op op) { + enum ggml_unary_op op) { return ggml_unary_impl(ctx, a, op, false); } struct ggml_tensor * ggml_unary_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - enum ggml_unary_op op) { + enum ggml_unary_op op) { return ggml_unary_impl(ctx, a, op, true); } @@ -7928,20 +7447,13 @@ struct ggml_tensor * ggml_unary_inplace( static struct ggml_tensor * ggml_map_unary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, - const ggml_unary_op_f32_t fun, - bool inplace) { - bool is_node = false; - - if (!inplace && a->grad) { - is_node = true; - } - + const ggml_unary_op_f32_t fun, + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_UNARY; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_UNARY; result->src[0] = a; return result; @@ -7950,14 +7462,14 @@ static struct ggml_tensor * ggml_map_unary_impl_f32( struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, - const ggml_unary_op_f32_t fun) { + const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, false); } struct ggml_tensor * ggml_map_unary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, - const ggml_unary_op_f32_t fun) { + const ggml_unary_op_f32_t fun) { return ggml_map_unary_impl_f32(ctx, a, fun, true); } @@ -7967,22 +7479,15 @@ static struct ggml_tensor * ggml_map_binary_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - const ggml_binary_op_f32_t fun, - bool inplace) { + const ggml_binary_op_f32_t fun, + bool inplace) { GGML_ASSERT(ggml_are_same_shape(a, b)); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_BINARY; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_BINARY; result->src[0] = a; result->src[1] = b; @@ -7993,7 +7498,7 @@ struct ggml_tensor * ggml_map_binary_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - const ggml_binary_op_f32_t fun) { + const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, false); } @@ -8001,7 +7506,7 @@ struct ggml_tensor * ggml_map_binary_inplace_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - const ggml_binary_op_f32_t fun) { + const ggml_binary_op_f32_t fun) { return ggml_map_binary_impl_f32(ctx, a, b, fun, true); } @@ -8011,19 +7516,12 @@ static struct ggml_tensor * ggml_map_custom1_impl_f32( struct ggml_context * ctx, struct ggml_tensor * a, const ggml_custom1_op_f32_t fun, - bool inplace) { - bool is_node = false; - - if (!inplace && a->grad) { - is_node = true; - } - + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_CUSTOM1_F32; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_CUSTOM1_F32; result->src[0] = a; return result; @@ -8050,19 +7548,12 @@ static struct ggml_tensor * ggml_map_custom2_impl_f32( struct ggml_tensor * a, struct ggml_tensor * b, const ggml_custom2_op_f32_t fun, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - is_node = true; - } - + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_CUSTOM2_F32; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_CUSTOM2_F32; result->src[0] = a; result->src[1] = b; @@ -8093,19 +7584,12 @@ static struct ggml_tensor * ggml_map_custom3_impl_f32( struct ggml_tensor * b, struct ggml_tensor * c, const ggml_custom3_op_f32_t fun, - bool inplace) { - bool is_node = false; - - if (!inplace && (a->grad || b->grad || c->grad)) { - is_node = true; - } - + bool inplace) { struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); - result->op = GGML_OP_MAP_CUSTOM3_F32; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_CUSTOM3_F32; result->src[0] = a; result->src[1] = b; result->src[2] = c; @@ -8133,26 +7617,20 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32( // ggml_map_custom1 struct ggml_map_custom1_op_params { - ggml_custom1_op_t fun; - int n_tasks; - void * userdata; + ggml_custom1_op_t fun; + int n_tasks; + void * userdata; }; static struct ggml_tensor * ggml_map_custom1_impl( - struct ggml_context * ctx, - struct ggml_tensor * a, - const ggml_custom1_op_t fun, - int n_tasks, - void * userdata, - bool inplace) { + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_t fun, + int n_tasks, + void * userdata, + bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); - bool is_node = false; - - if (!inplace && a->grad) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom1_op_params params = { @@ -8162,55 +7640,48 @@ static struct ggml_tensor * ggml_map_custom1_impl( }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); - result->op = GGML_OP_MAP_CUSTOM1; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_CUSTOM1; result->src[0] = a; return result; } struct ggml_tensor * ggml_map_custom1( - struct ggml_context * ctx, - struct ggml_tensor * a, - const ggml_custom1_op_t fun, - int n_tasks, - void * userdata) { + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_t fun, + int n_tasks, + void * userdata) { return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom1_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - const ggml_custom1_op_t fun, - int n_tasks, - void * userdata) { + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_t fun, + int n_tasks, + void * userdata) { return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true); } // ggml_map_custom2 struct ggml_map_custom2_op_params { - ggml_custom2_op_t fun; - int n_tasks; - void * userdata; + ggml_custom2_op_t fun; + int n_tasks; + void * userdata; }; static struct ggml_tensor * ggml_map_custom2_impl( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - const ggml_custom2_op_t fun, - int n_tasks, - void * userdata, - bool inplace) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_t fun, + int n_tasks, + void * userdata, + bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); - bool is_node = false; - - if (!inplace && (a->grad || b->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom2_op_params params = { @@ -8220,8 +7691,7 @@ static struct ggml_tensor * ggml_map_custom2_impl( }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); - result->op = GGML_OP_MAP_CUSTOM2; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_CUSTOM2; result->src[0] = a; result->src[1] = b; @@ -8229,22 +7699,22 @@ static struct ggml_tensor * ggml_map_custom2_impl( } struct ggml_tensor * ggml_map_custom2( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - const ggml_custom2_op_t fun, - int n_tasks, - void * userdata) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_t fun, + int n_tasks, + void * userdata) { return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom2_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - const ggml_custom2_op_t fun, - int n_tasks, - void * userdata) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_t fun, + int n_tasks, + void * userdata) { return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true); } @@ -8257,22 +7727,16 @@ struct ggml_map_custom3_op_params { }; static struct ggml_tensor * ggml_map_custom3_impl( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c, - const ggml_custom3_op_t fun, - int n_tasks, - void * userdata, - bool inplace) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_t fun, + int n_tasks, + void * userdata, + bool inplace) { GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); - bool is_node = false; - - if (!inplace && (a->grad || b->grad || c->grad)) { - is_node = true; - } - struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_map_custom3_op_params params = { @@ -8282,8 +7746,7 @@ static struct ggml_tensor * ggml_map_custom3_impl( }; ggml_set_op_params(result, (const void *) ¶ms, sizeof(params)); - result->op = GGML_OP_MAP_CUSTOM3; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_MAP_CUSTOM3; result->src[0] = a; result->src[1] = b; result->src[2] = c; @@ -8292,44 +7755,38 @@ static struct ggml_tensor * ggml_map_custom3_impl( } struct ggml_tensor * ggml_map_custom3( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c, - const ggml_custom3_op_t fun, - int n_tasks, - void * userdata) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_t fun, + int n_tasks, + void * userdata) { return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false); } struct ggml_tensor * ggml_map_custom3_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c, - const ggml_custom3_op_t fun, - int n_tasks, - void * userdata) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_t fun, + int n_tasks, + void * userdata) { return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true); } // ggml_cross_entropy_loss struct ggml_tensor * ggml_cross_entropy_loss( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { GGML_ASSERT(ggml_are_same_shape(a, b)); - bool is_node = false; - - if (a->grad || b->grad) { - is_node = true; - } struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); - result->op = GGML_OP_CROSS_ENTROPY_LOSS; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_CROSS_ENTROPY_LOSS; result->src[0] = a; result->src[1] = b; @@ -8339,17 +7796,16 @@ struct ggml_tensor * ggml_cross_entropy_loss( // ggml_cross_entropy_loss_back struct ggml_tensor * ggml_cross_entropy_loss_back( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_tensor * c) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c) { GGML_ASSERT(ggml_are_same_shape(a, b)); GGML_ASSERT(ggml_is_scalar(c)); struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK; - result->grad = NULL; + result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK; result->src[0] = a; result->src[1] = b; result->src[2] = c; @@ -8362,12 +7818,14 @@ struct ggml_tensor * ggml_cross_entropy_loss_back( struct ggml_tensor * ggml_opt_step_adamw( struct ggml_context * ctx, struct ggml_tensor * a, + struct ggml_tensor * grad, float alpha, float beta1, float beta2, float eps, float wd) { - GGML_ASSERT(a->grad); + GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM); + GGML_ASSERT(ggml_are_same_shape(a, grad)); GGML_ASSERT(alpha > 0.0f); GGML_ASSERT(beta1 >= 0.0f && beta1 <= 1.0f); GGML_ASSERT(beta2 >= 0.0f && beta2 <= 1.0f); @@ -8376,13 +7834,6 @@ struct ggml_tensor * ggml_opt_step_adamw( struct ggml_tensor * result = ggml_view_tensor(ctx, a); - result->op = GGML_OP_OPT_STEP_ADAMW; - result->grad = NULL; - result->src[0] = a; - result->src[1] = a->grad; - result->src[2] = ggml_dup_tensor(ctx, a->grad); - result->src[3] = ggml_dup_tensor(ctx, a->grad); - const int64_t iter = 1; memcpy(&result->op_params[0], &iter, sizeof(int64_t)); ggml_set_op_params_f32(result, 2, alpha); @@ -8391,26 +7842,17 @@ struct ggml_tensor * ggml_opt_step_adamw( ggml_set_op_params_f32(result, 5, eps); ggml_set_op_params_f32(result, 6, wd); + result->op = GGML_OP_OPT_STEP_ADAMW; + result->src[0] = a; + result->src[1] = grad; + result->src[2] = ggml_dup_tensor(ctx, grad); + result->src[3] = ggml_dup_tensor(ctx, grad); + return result; } //////////////////////////////////////////////////////////////////////////////// -void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) { - tensor->flags |= GGML_TENSOR_FLAG_PARAM; - - GGML_ASSERT(tensor->grad == NULL); - tensor->grad = ggml_dup_tensor(ctx, tensor); - ggml_format_name(tensor->grad, "%s (grad)", tensor->name); -} - -void ggml_set_loss(struct ggml_tensor * tensor) { - GGML_ASSERT(ggml_is_scalar(tensor)); - GGML_ASSERT(tensor->type == GGML_TYPE_F32); - GGML_ASSERT(tensor->grad); - tensor->flags |= GGML_TENSOR_FLAG_LOSS; -} - // ggml_compute_forward_dup static void ggml_compute_forward_dup_same_cont( @@ -18130,7 +17572,7 @@ void ggml_build_backward_gradient_checkpointing( struct ggml_tensor * * checkpoints, int n_checkpoints) { ggml_graph_cpy(gf, gb_tmp); - ggml_build_backward_expand(ctx, gf, gb_tmp, false, true); + ggml_build_backward_expand(ctx, gf, gb_tmp, false); if (n_checkpoints <= 0) { ggml_graph_cpy(gb_tmp, gb); @@ -18782,7 +18224,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_soft_max_back(ctx, tensor->grad, tensor), zero_table, acc_table); } - + GGML_ASSERT((!src1 || !src1->grad) && "backward pass for softmax mask not implemented"); } break; case GGML_OP_SOFT_MAX_BACK: { @@ -18823,6 +18265,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor beta_slow), zero_table, acc_table); } + GGML_ASSERT((!src2 || !src2->grad) && "gradients for freq factors not implemented"); } break; case GGML_OP_ROPE_BACK: { @@ -18944,6 +18387,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor } case GGML_OP_FLASH_ATTN_EXT: { + GGML_ABORT("FA backward pass not adapted after rework"); struct ggml_tensor * flash_grad = NULL; if (src0->grad || src1->grad || tensor->src[2]->grad) { int32_t t = ggml_get_op_params_i32(tensor, 0); @@ -19118,6 +18562,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor tensor->grad), zero_table, acc_table); } + GGML_ASSERT(!src1->grad && "backward pass for labels not implemented"); } break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { @@ -19168,7 +18613,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * } } - if (node->op == GGML_OP_NONE && node->grad == NULL) { + if (node->op == GGML_OP_NONE && !(node->flags & GGML_TENSOR_FLAG_PARAM)) { // reached a leaf node, not part of the gradient graph (e.g. a constant) GGML_ASSERT(cgraph->n_leafs < cgraph->size); @@ -19186,9 +18631,6 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * } cgraph->nodes[cgraph->n_nodes] = node; - if (cgraph->grads) { - cgraph->grads[cgraph->n_nodes] = node->grad; - } cgraph->n_nodes++; } } @@ -19216,20 +18658,58 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * ggml_build_forward_impl(cgraph, tensor, true); } -void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate, bool keep) { +void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate) { GGML_ASSERT(gf->n_nodes > 0); GGML_ASSERT(gf->grads); - // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph - if (keep) { - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; + for (int i = 0; i < gf->n_nodes; ++i) { + struct ggml_tensor * node = gf->nodes[i]; + + bool needs_grad = node->flags & GGML_TENSOR_FLAG_PARAM; + bool ignore_src[GGML_MAX_SRC] = {false}; + switch (node->op) { + // gradients in node->src[0] for one reason or another have no effect on output gradients + case GGML_OP_IM2COL: // only used for its shape + case GGML_OP_IM2COL_BACK: // same as IM2COL + ignore_src[0] = true; + break; + case GGML_OP_UNARY: { + const enum ggml_unary_op uop = ggml_get_unary_op(node); + // SGN and STEP unary ops are piecewise constant + if (uop == GGML_UNARY_OP_SGN || uop == GGML_UNARY_OP_STEP) { + ignore_src[0] = true; + } + } break; + + // gradients in node->src[1] for one reason or another have no effect on output gradients + case GGML_OP_CPY: // gradients in CPY target are irrelevant + case GGML_OP_GET_ROWS: // row indices not differentiable + case GGML_OP_GET_ROWS_BACK: // same as for GET_ROWS + case GGML_OP_ROPE: // positions not differentiable + ignore_src[1] = true; + break; - if (node->grad) { - node->grad = ggml_dup_tensor(ctx, node); - gf->grads[i] = node->grad; + default: + break; + } + for (int j = 0; j < GGML_MAX_SRC; ++j) { + if (!node->src[j] || !node->src[j]->grad || ignore_src[j]) { + continue; } + GGML_ASSERT(node->src[j]->type == GGML_TYPE_F32 || node->src[j]->type == GGML_TYPE_F16); + needs_grad = true; + break; + } + if (!needs_grad) { + continue; } + + // inplace operations are currently not supported + GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW || + node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE); + + // create a new tensor with the same type and shape as the node and set it as grad + node->grad = ggml_dup_tensor(ctx, node); } // keep tables of original gradients for replacement/accumulation logic @@ -19291,7 +18771,7 @@ void ggml_build_opt_adamw( if (node->flags & GGML_TENSOR_FLAG_PARAM) { GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); - struct ggml_tensor * opt_step = ggml_opt_step_adamw(ctx, node, alpha, beta1, beta2, eps, wd); + struct ggml_tensor * opt_step = ggml_opt_step_adamw(ctx, node, node->grad, alpha, beta1, beta2, eps, wd); ggml_build_forward_expand(gb, opt_step); } } @@ -22094,8 +21574,6 @@ enum ggml_opt_result ggml_opt( struct ggml_context * ctx, struct ggml_opt_params params, struct ggml_tensor * f) { - GGML_ASSERT(f->grad && "ggml_set_param called for at least one parent tensor."); - bool free_ctx = false; if (ctx == NULL) { struct ggml_init_params params_ctx = { @@ -22136,7 +21614,7 @@ enum ggml_opt_result ggml_opt_resume( ggml_build_forward_expand(gf, f); struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf); - ggml_build_backward_expand(ctx, gf, gb, false, true); + ggml_build_backward_expand(ctx, gf, gb, false); return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); } @@ -22189,6 +21667,17 @@ void ggml_set_output(struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_OUTPUT; } +void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) { + GGML_UNUSED(ctx); // TODO: remove this parameter + tensor->flags |= GGML_TENSOR_FLAG_PARAM; +} + +void ggml_set_loss(struct ggml_tensor * tensor) { + GGML_ASSERT(ggml_is_scalar(tensor)); + GGML_ASSERT(tensor->type == GGML_TYPE_F32); + tensor->flags |= GGML_TENSOR_FLAG_LOSS; +} + //////////////////////////////////////////////////////////////////////////////// void ggml_quantize_init(enum ggml_type type) { @@ -23578,16 +23067,16 @@ int ggml_cpu_has_fma(void) { } int ggml_cpu_has_neon(void) { -#if defined(__ARM_NEON) - return 1; +#if defined(__ARM_ARCH) + return ggml_arm_arch_features.has_neon; #else return 0; #endif } int ggml_cpu_has_sve(void) { -#if defined(__ARM_FEATURE_SVE) - return 1; +#if defined(__ARM_ARCH) + return ggml_arm_arch_features.has_sve; #else return 0; #endif @@ -23734,11 +23223,18 @@ int ggml_cpu_has_vsx(void) { } int ggml_cpu_has_matmul_int8(void) { -#if defined(__ARM_FEATURE_MATMUL_INT8) - return 1; +#if defined(__ARM_ARCH) + return ggml_arm_arch_features.has_i8mm; #else return 0; #endif } +int ggml_cpu_get_sve_cnt(void) { +#if defined(__ARM_ARCH) + return ggml_arm_arch_features.sve_cnt; +#else + return 0; +#endif +} //////////////////////////////////////////////////////////////////////////////// diff --git a/ggml/src/vulkan-shaders/argsort.comp b/ggml/src/vulkan-shaders/argsort.comp index e55414b03c5..d4fa45b1e10 100644 --- a/ggml/src/vulkan-shaders/argsort.comp +++ b/ggml/src/vulkan-shaders/argsort.comp @@ -29,20 +29,18 @@ void main() { const int col = int(gl_LocalInvocationID.x); const uint row = gl_WorkGroupID.y; - if (col >= p.ncols_pad) { - return; - } - const uint row_offset = row * p.ncols; // initialize indices - dst_row[col] = col; + if (col < p.ncols_pad) { + dst_row[col] = col; + } barrier(); for (uint k = 2; k <= p.ncols_pad; k *= 2) { for (uint j = k / 2; j > 0; j /= 2) { const uint ixj = col ^ j; - if (ixj > col) { + if (col < p.ncols_pad && ixj > col) { if ((col & k) == 0) { if (dst_row[col] >= p.ncols || (dst_row[ixj] < p.ncols && (p.order == ASC ? diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 801d1bdbdf8..c989ee82192 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -6cb634ac423cc251317dd34a821b91a8dd400fbb +6ebf0cf75db1739b6c8b26ccca3f5029ab35fe4a diff --git a/src/whisper.cpp b/src/whisper.cpp index 585a6fc02e8..9c7c66b01a3 100644 --- a/src/whisper.cpp +++ b/src/whisper.cpp @@ -204,11 +204,6 @@ static bool ggml_graph_compute_helper( if (ggml_backend_is_blas(backend)) { ggml_backend_blas_set_n_threads(backend, n_threads); } -#endif -#ifdef GGML_USE_METAL - if (ggml_backend_is_metal(backend)) { - ggml_backend_metal_set_n_cb(backend, n_threads); - } #endif }