From 227d7c5a7f4cc7734fde8a4ef4382d613486d3c8 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 17 Dec 2024 09:52:09 +0100 Subject: [PATCH 01/34] server : (UI) fix missing async generator on safari (#10857) * server : (UI) fix missing async generator on safari * fix --- examples/server/public/index.html.gz | Bin 1205104 -> 1205430 bytes examples/server/webui/package-lock.json | 7 +++++++ examples/server/webui/package.json | 1 + examples/server/webui/src/main.js | 7 +++++-- 4 files changed, 13 insertions(+), 2 deletions(-) diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz index 53d33feb344cd92d89ab2ca1f59e77722d7e6cfa..01dd41811d083cb3dfdf10860c00cc2b1f003832 100644 GIT binary patch delta 1178064 zcmV)AK*YcB#!0rxNq--U2mk;800062w7uJU+c>i*c;8PUae1_fc3F|)Zm<5Sz}j}= zj+2g^wCyCFR%pc) zN;HybEGMFbh7x0;8oHncgOB2}@%~lNC`F>PLN+o<>1Rg=FHVlyKkV%6|M0B&;_dT` zPw$QzQ$5S~e}8!9engz^|IlbWn+Y9b`>GV02k$R_-T5V`Gh{pyJh;xp&Ad=LXe2ad 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From 382bc7f2e8ffd0b89f23e840d097e21f301197ba Mon Sep 17 00:00:00 2001 From: Billel Mokeddem Date: Tue, 17 Dec 2024 19:24:56 +0400 Subject: [PATCH 03/34] llama : add Falcon3 support (#10864) --- convert_hf_to_gguf.py | 6 ++++++ convert_hf_to_gguf_update.py | 1 + src/llama.cpp | 18 ++++++++++++++++++ 3 files changed, 25 insertions(+) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 9dc1673bc2c06..66e268af61419 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -525,6 +525,9 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]: else: token: str = reverse_vocab[i] if token in added_vocab: + # We need to manually encode and decode the added tokens in case special characters + # used for `\n` / `\t` have been manually added in the added tokens + token = tokenizer.decode(tokenizer.encode(token)) if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token): toktypes.append(gguf.TokenType.CONTROL) else: @@ -571,6 +574,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": # ref: https://huggingface.co/tiiuae/falcon-7b res = "falcon" + if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e": + # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base + res = "falcon3" if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 res = "bert-bge" diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 88058442f6dc4..2ba346640b352 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -72,6 +72,7 @@ class TOKENIZER_TYPE(IntEnum): {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", }, {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, diff --git a/src/llama.cpp b/src/llama.cpp index 8b799e0ebeda7..1cc8a93323b4a 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1612,6 +1612,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN, LLM_CHAT_TEMPLATE_MISTRAL_V7, LLM_CHAT_TEMPLATE_PHI_3, + LLM_CHAT_TEMPLATE_FALCON_3, LLM_CHAT_TEMPLATE_ZEPHYR, LLM_CHAT_TEMPLATE_MONARCH, LLM_CHAT_TEMPLATE_GEMMA, @@ -1644,6 +1645,7 @@ static const std::map LLM_CHAT_TEMPLATES = { { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN }, { "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 }, { "phi3", LLM_CHAT_TEMPLATE_PHI_3 }, + { "falcon3", LLM_CHAT_TEMPLATE_FALCON_3 }, { "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR }, { "monarch", LLM_CHAT_TEMPLATE_MONARCH }, { "gemma", LLM_CHAT_TEMPLATE_GEMMA }, @@ -6473,6 +6475,11 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "falcon") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON; + } else if ( + tokenizer_pre == "falcon3") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3; + vocab.tokenizer_ignore_merges = true; + vocab.tokenizer_add_bos = true; } else if ( tokenizer_pre == "mpt") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT; @@ -22219,6 +22226,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) { } } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) { return LLM_CHAT_TEMPLATE_PHI_3; + } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) { + return LLM_CHAT_TEMPLATE_FALCON_3; } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) { return LLM_CHAT_TEMPLATE_ZEPHYR; } else if (tmpl_contains("bos_token + message['role']")) { @@ -22371,6 +22380,15 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) { + // Falcon 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>\n" << message->content << "\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) { // zephyr template for (auto message : chat) { From 05c3a444b8b017b01b366b725ba22c740aae6b38 Mon Sep 17 00:00:00 2001 From: krystiancha Date: Tue, 17 Dec 2024 16:00:24 +0000 Subject: [PATCH 04/34] server : fill usage info in embeddings and rerank responses (#10852) * server : fill usage info in embeddings response * server : fill usage info in reranking response --- examples/server/server.cpp | 16 ++++++++--- examples/server/tests/unit/test_embedding.py | 30 ++++++++++++++++++++ examples/server/tests/unit/test_rerank.py | 23 +++++++++++++++ examples/server/utils.hpp | 18 ++++++++---- 4 files changed, 77 insertions(+), 10 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index bc0d042ae9247..436170a034fde 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -719,14 +719,17 @@ struct server_task_result_embd : server_task_result { int index = 0; std::vector embedding; + int32_t n_tokens; + virtual int get_index() override { return index; } virtual json to_json() override { return json { - {"index", index}, - {"embedding", embedding}, + {"index", index}, + {"embedding", embedding}, + {"tokens_evaluated", n_tokens}, }; } }; @@ -735,14 +738,17 @@ struct server_task_result_rerank : server_task_result { int index = 0; float score = -1e6; + int32_t n_tokens; + virtual int get_index() override { return index; } virtual json to_json() override { return json { - {"index", index}, - {"score", score}, + {"index", index}, + {"score", score}, + {"tokens_evaluated", n_tokens}, }; } }; @@ -1995,6 +2001,7 @@ struct server_context { auto res = std::make_unique(); res->id = slot.id_task; res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; const int n_embd = llama_n_embd(model); @@ -2030,6 +2037,7 @@ struct server_context { auto res = std::make_unique(); res->id = slot.id_task; res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; for (int i = 0; i < batch.n_tokens; ++i) { if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py index fc7c20064ddfc..fea1d6510c89e 100644 --- a/examples/server/tests/unit/test_embedding.py +++ b/examples/server/tests/unit/test_embedding.py @@ -97,3 +97,33 @@ def test_same_prompt_give_same_result(): vi = res.body['data'][i]['embedding'] for x, y in zip(v0, vi): assert abs(x - y) < EPSILON + + +@pytest.mark.parametrize( + "content,n_tokens", + [ + ("I believe the meaning of life is", 7), + ("This is a test", 4), + ] +) +def test_embedding_usage_single(content, n_tokens): + global server + server.start() + res = server.make_request("POST", "/embeddings", data={"input": content}) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == n_tokens + + +def test_embedding_usage_multiple(): + global server + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "I believe the meaning of life is", + ], + }) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == 2 * 7 diff --git a/examples/server/tests/unit/test_rerank.py b/examples/server/tests/unit/test_rerank.py index 189bc4c962329..7203d79435702 100644 --- a/examples/server/tests/unit/test_rerank.py +++ b/examples/server/tests/unit/test_rerank.py @@ -53,3 +53,26 @@ def test_invalid_rerank_req(documents): }) assert res.status_code == 400 assert "error" in res.body + + +@pytest.mark.parametrize( + "query,doc1,doc2,n_tokens", + [ + ("Machine learning is", "A machine", "Learning is", 19), + ("Which city?", "Machine learning is ", "Paris, capitale de la", 26), + ] +) +def test_rerank_usage(query, doc1, doc2, n_tokens): + global server + server.start() + + res = server.make_request("POST", "/rerank", data={ + "query": query, + "documents": [ + doc1, + doc2, + ] + }) + assert res.status_code == 200 + assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] + assert res.body['usage']['prompt_tokens'] == n_tokens diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index c6f08bf21071a..8fffe484aec12 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -560,6 +560,7 @@ static json oaicompat_completion_params_parse( static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) { json data = json::array(); + int32_t n_tokens = 0; int i = 0; for (const auto & elem : embeddings) { data.push_back(json{ @@ -567,14 +568,16 @@ static json format_embeddings_response_oaicompat(const json & request, const jso {"index", i++}, {"object", "embedding"} }); + + n_tokens += json_value(elem, "tokens_evaluated", 0); } json res = json { {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, {"object", "list"}, - {"usage", json { // TODO: fill - {"prompt_tokens", 0}, - {"total_tokens", 0} + {"usage", json { + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} }}, {"data", data} }; @@ -584,20 +587,23 @@ static json format_embeddings_response_oaicompat(const json & request, const jso static json format_response_rerank(const json & request, const json & ranks) { json data = json::array(); + int32_t n_tokens = 0; int i = 0; for (const auto & rank : ranks) { data.push_back(json{ {"index", i++}, {"relevance_score", json_value(rank, "score", 0.0)}, }); + + n_tokens += json_value(rank, "tokens_evaluated", 0); } json res = json { {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, {"object", "list"}, - {"usage", json { // TODO: fill - {"prompt_tokens", 0}, - {"total_tokens", 0} + {"usage", json { + {"prompt_tokens", n_tokens}, + {"total_tokens", n_tokens} }}, {"results", data} }; From 0006f5a74a59945f22ff966e723c3d566b3ca8d0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Dec 2024 18:35:42 +0200 Subject: [PATCH 05/34] ggml : update ggml_backend_cpu_device_supports_op (#10867) * ggml : fix cpy op for IQ-quants to use reference impl ggml-ci * ggml : disable tests involving i-matrix quantization * ggml : update ggml_backend_cpu_device_supports_op ggml-ci --- ggml/src/ggml-cpu/ggml-cpu.cpp | 3 +++ tests/test-backend-ops.cpp | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index c390957afa8e3..0b6419f83f0c2 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -394,8 +394,11 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st switch (op->op) { case GGML_OP_CPY: return + op->type != GGML_TYPE_IQ3_XXS && + op->type != GGML_TYPE_IQ3_S && op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && + op->type != GGML_TYPE_IQ2_S && op->type != GGML_TYPE_IQ1_S && op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float case GGML_OP_MUL_MAT: diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b9454ba596c21..ccdd3fb57a504 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3549,8 +3549,8 @@ static std::vector> make_test_cases_eval() { for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) { for (ggml_type type_dst : all_types) { - test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4})); - test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows + test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4})); + test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows } } for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) { From 3919da8e335dbfd0741d239932a9b9e72061bf27 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 13 Dec 2024 08:19:38 +0100 Subject: [PATCH 06/34] ggml : add check for grad_accs (ggml/1046) * ggml : add check for grad_accs This commit adds a check for grad_accs in ggml_graph_get_grad and ggml_graph_get_grad_acc functions. This is necessary to avoid segfaults when grad_accs is not initialized. The motivation for this change is that I find it nice to be able to print out a computation graph using ggml_graph_print but this function segfaults when grad_accs is not initialized: ```console (gdb) p g1 $2 = (ggml_cgraph *) 0x7ffff66004b0 (gdb) p *g1 $3 = {size = 2048, n_nodes = 1, n_leafs = 2, nodes = 0x7ffff6600500, grads = 0x0, grad_accs = 0x0, leafs = 0x7ffff6604500, visited_hash_set = {size = 4099, used = 0x7ffff6610518, keys = 0x7ffff6608500}, order = GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT} (gdb) p ggml_graph_print(g1) === GRAPH === n_nodes = 1 Program received signal SIGSEGV, Segmentation fault. 0x0000555555579775 in ggml_graph_get_grad (cgraph=0x7ffff66004b0,node=0x7ffff6600340) at /ggml/ggml/src/ggml.c:5990 5990 return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL; ``` * squash! ggml : add check for grad_accs Fix the check in ggml_graph_get_grad. The check was incorrectly using cgraph->grad_accs instead of cgraph->grads. --- ggml/src/ggml.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 51cc856627989..030d93a5177c0 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -6037,12 +6037,12 @@ struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, co struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); - return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL; + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grads ? cgraph->grads[igrad] : NULL; } struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); - return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grad_accs[igrad] : NULL; + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grad_accs ? cgraph->grad_accs[igrad] : NULL; } void ggml_graph_print(const struct ggml_cgraph * cgraph) { From 130d0c90bd26b25e3a713b17a61a5d4eb0a66405 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sat, 14 Dec 2024 03:23:08 +0100 Subject: [PATCH 07/34] ggml : remove return from ggml_gallocr_allocate_node (ggml/1048) This commit removes the return statement from ggml_gallocr_allocate_node function. The motivation behind this change is to make the code more readable and consistent. --- ggml/src/ggml-alloc.c | 1 - 1 file changed, 1 deletion(-) diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index 2b2240be85dd3..8dc8226ac4932 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -534,7 +534,6 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node); hn->buffer_id = buffer_id; hn->offset = offset; - return; } } From 8dd19a48129d578972ea3d98896edbbf492891a9 Mon Sep 17 00:00:00 2001 From: gn64 Date: Mon, 16 Dec 2024 19:34:38 +0900 Subject: [PATCH 08/34] vulkan : fix soft_max.comp division by zero (whisper/2633) This change prevents a division by zero error when p.KY is 0. --- ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp index 6e20b6411cadc..a25808e16568a 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp @@ -32,7 +32,7 @@ shared FLOAT_TYPE vals[BLOCK_SIZE]; void soft_max(uint num_iters) { const uint tid = gl_LocalInvocationID.x; const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint rowy = rowx % p.KY; + const uint rowy = (p.KY > 0) ? (rowx % p.KY) : 0; if (rowx >= p.nrows_x) { return; From 78f766768d94e9c8b30ce10ca76f568e710d84f7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Dec 2024 18:34:32 +0200 Subject: [PATCH 09/34] cmake : fix "amd64" processor string (whisper/2638) --- ggml/src/ggml-cpu/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 0e0556703620f..5d47323370a66 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -179,7 +179,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$")) if (MSVC) # instruction set detection for MSVC only if (GGML_NATIVE) From 5437d4aaf5132c879acda0bb67f2f8f71da4c9fe Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 17 Dec 2024 18:36:02 +0200 Subject: [PATCH 10/34] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 47eae44f7d7b6..b4ac38bbf4669 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -74d66b63eaf207a24f3e93bb922aba131cbf2906 +e6d93f40dffe8733d5d72f1d8fa6b3ca27ae899f From 081b29bd2a3d91e7772e3910ce223dd63b8d7d26 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 17 Dec 2024 19:09:35 +0100 Subject: [PATCH 11/34] tests: add tests for GGUF (#10830) --- ggml/src/ggml-impl.h | 16 + ggml/src/ggml.c | 65 +-- tests/CMakeLists.txt | 1 + tests/test-gguf.cpp | 1303 ++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 1344 insertions(+), 41 deletions(-) create mode 100644 tests/test-gguf.cpp diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index f961134edd735..549772c57c90a 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -551,6 +551,22 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { #define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) #define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) +// expose GGUF internals for test code + +GGML_API size_t gguf_type_size(enum gguf_type type); + +GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params); + +struct gguf_buf { + void * data; + size_t size; + size_t offset; +}; +GGML_API struct gguf_buf gguf_buf_init(size_t size); +GGML_API void gguf_buf_free(struct gguf_buf buf); + +GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 030d93a5177c0..0efd2b2ebf780 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -6489,7 +6489,7 @@ struct gguf_context { void * data; }; -static size_t gguf_type_size(enum gguf_type type) { +size_t gguf_type_size(enum gguf_type type) { GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT); return GGUF_TYPE_SIZE[type]; } @@ -6617,13 +6617,7 @@ struct gguf_context * gguf_init_empty(void) { return ctx; } -struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) { - FILE * file = ggml_fopen(fname, "rb"); - if (!file) { - fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno)); - return NULL; - } - +struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) { // offset from start of file size_t offset = 0; @@ -6636,7 +6630,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p for (uint32_t i = 0; i < sizeof(magic); i++) { if (magic[i] != GGUF_MAGIC[i]) { fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]); - fclose(file); return NULL; } } @@ -6647,7 +6640,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p struct gguf_context * ctx = calloc(1, sizeof(struct gguf_context)); if (!ctx) { fprintf(stderr, "%s: failed to allocate memory for context\n", __func__); - fclose(file); return NULL; } @@ -6665,7 +6657,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (ctx->header.version == 1) { fprintf(stderr, "%s: GGUFv1 is no longer supported. please use a more up-to-date version\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6678,7 +6669,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6688,12 +6678,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p { const uint64_t n_kv = ctx->header.n_kv; - ctx->kv = calloc(n_kv, sizeof(struct gguf_kv)); - if (!ctx->kv) { - fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__); - fclose(file); - gguf_free(ctx); - return NULL; + if (n_kv > 0) { + ctx->kv = calloc(n_kv, sizeof(struct gguf_kv)); + if (!ctx->kv) { + fprintf(stderr, "%s: failed to allocate memory for kv pairs\n", __func__); + gguf_free(ctx); + return NULL; + } } for (uint64_t i = 0; i < n_kv; ++i) { @@ -6740,7 +6731,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // prevent from integer overflow in the malloc below if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) { fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); - fclose(file); gguf_free(ctx); return NULL; } @@ -6748,7 +6738,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p kv->value.arr.data = calloc(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); if (!kv->value.arr.data) { fprintf(stderr, "%s: failed to allocate memory for array\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6760,7 +6749,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // prevent from integer overflow in the malloc below if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) { fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); - fclose(file); gguf_free(ctx); return NULL; } @@ -6768,7 +6756,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p kv->value.arr.data = calloc(kv->value.arr.n, sizeof(struct gguf_str)); if (!kv->value.arr.data) { fprintf(stderr, "%s: failed to allocate memory for array\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6799,7 +6786,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (!ok) { fprintf(stderr, "%s: failed to read key-value pairs\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6810,7 +6796,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ctx->infos = calloc(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); if (!ctx->infos) { fprintf(stderr, "%s: failed to allocate memory for tensor infos\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6846,7 +6831,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (!ok) { fprintf(stderr, "%s: failed to read tensor info\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6889,7 +6873,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // this tensor type support have been removed: fprintf(stderr, "%s: tensor '%s' of type %d: %s\n", __func__, info->name.data, (int) info->type, ggml_type_name(info->type)); - fclose(file); gguf_free(ctx); return NULL; } @@ -6897,7 +6880,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (ne % ggml_blck_size(info->type) != 0) { fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n", __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type)); - fclose(file); gguf_free(ctx); return NULL; } @@ -6929,7 +6911,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p *params.ctx = ggml_init(pdata); if (*params.ctx == NULL) { fprintf(stderr, "%s: failed to initialize context\n", __func__); - fclose(file); gguf_free(ctx); return NULL; } @@ -6948,7 +6929,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (!ok) { fprintf(stderr, "%s: failed to read tensor data\n", __func__); - fclose(file); ggml_free(ctx_data); gguf_free(ctx); return NULL; @@ -6987,7 +6967,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (!ok) { fprintf(stderr, "%s: failed to read the tensor data\n", __func__); - fclose(file); ggml_free(ctx_data); gguf_free(ctx); return NULL; @@ -6996,11 +6975,21 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ggml_set_no_alloc(ctx_data, params.no_alloc); } - fclose(file); - return ctx; } +struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) { + FILE * file = ggml_fopen(fname, "rb"); + if (!file) { + fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno)); + return NULL; + } + + struct gguf_context * result = gguf_init_from_file_impl(file, params); + fclose(file); + return result; +} + void gguf_free(struct gguf_context * ctx) { if (ctx == NULL) { return; @@ -7460,13 +7449,7 @@ void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const vo // fwrite(val, sizeof(char), size, file); //} -struct gguf_buf { - void * data; - size_t size; - size_t offset; -}; - -static struct gguf_buf gguf_buf_init(size_t size) { +struct gguf_buf gguf_buf_init(size_t size) { struct gguf_buf buf = { /*buf.data =*/ size == 0 ? NULL : GGML_CALLOC(1, size), /*buf.size =*/ size, @@ -7476,7 +7459,7 @@ static struct gguf_buf gguf_buf_init(size_t size) { return buf; } -static void gguf_buf_free(struct gguf_buf buf) { +void gguf_buf_free(struct gguf_buf buf) { if (buf.data) { GGML_FREE(buf.data); } @@ -7514,7 +7497,7 @@ static void gguf_bwrite_el(struct gguf_buf * buf, const void * val, size_t el_si buf->offset += el_size; } -static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) { +void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * buf, bool only_meta) { // write header gguf_bwrite_el(buf, &ctx->header.magic, sizeof(ctx->header.magic)); gguf_bwrite_el(buf, &ctx->header.version, sizeof(ctx->header.version)); diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index daeed4564c1d1..2b5e5fd4abe95 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -129,6 +129,7 @@ llama_target_and_test(test-arg-parser.cpp) llama_target_and_test(test-chat-template.cpp) # llama_target_and_test(test-opt.cpp) # SLOW +llama_target_and_test(test-gguf.cpp) llama_target_and_test(test-backend-ops.cpp) llama_target_and_test(test-model-load-cancel.cpp LABEL "model") diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp new file mode 100644 index 0000000000000..e5b4cb7b8af1c --- /dev/null +++ b/tests/test-gguf.cpp @@ -0,0 +1,1303 @@ +#include "ggml.h" +#include "ggml-backend.h" +#include "../ggml/src/ggml-impl.h" + +#include +#include +#include +#include +#include +#include +#include + +constexpr int offset_has_kv = 1000; +constexpr int offset_has_tensors = 2000; +constexpr int offset_has_data = 3000; + +enum handcrafted_file_type { + HANDCRAFTED_HEADER_BAD_MAGIC = 10, + HANDCRAFTED_HEADER_BAD_VERSION_1 = 20, + HANDCRAFTED_HEADER_BAD_VERSION_FUTURE = 30, + HANDCRAFTED_HEADER_BAD_N_TENSORS = 40, + HANDCRAFTED_HEADER_BAD_N_KV = 50, + HANDCRAFTED_HEADER_EMPTY = 800, + + HANDCRAFTED_KV_BAD_KEY_SIZE = 10 + offset_has_kv, + HANDCRAFTED_KV_BAD_TYPE = 20 + offset_has_kv, + HANDCRAFTED_KV_BAD_VALUE_SIZE = 30 + offset_has_kv, + HANDCRAFTED_KV_DUPLICATE_KEY = 40 + offset_has_kv, + HANDCRAFTED_KV_SUCCESS = 800 + offset_has_kv, + + HANDCRAFTED_TENSORS_BAD_NAME_SIZE = 10 + offset_has_tensors, + HANDCRAFTED_TENSORS_BAD_N_DIMS = 20 + offset_has_tensors, + HANDCRAFTED_TENSORS_BAD_SHAPE = 30 + offset_has_tensors, + HANDCRAFTED_TENSORS_NE_TOO_BIG = 40 + offset_has_tensors, + HANDCRAFTED_TENSORS_BAD_TYPE = 50 + offset_has_tensors, + HANDCRAFTED_TENSORS_BAD_OFFSET = 60 + offset_has_tensors, + HANDCRAFTED_TENSORS_DUPLICATE_NAME = 70 + offset_has_tensors, + HANDCRAFTED_TENSORS_BAD_ALIGNMENT = 80 + offset_has_tensors, + HANDCRAFTED_TENSORS_SUCCESS = 800 + offset_has_tensors, + HANDCRAFTED_TENSORS_CUSTOM_ALIGN = 810 + offset_has_tensors, + + HANDCRAFTED_DATA_NOT_ENOUGH_DATA = 10 + offset_has_data, + HANDCRAFTED_DATA_BAD_ALIGNMENT = 20 + offset_has_data, + HANDCRAFTED_DATA_SUCCESS = 800 + offset_has_data, + HANDCRAFTED_DATA_CUSTOM_ALIGN = 810 + offset_has_data, +}; + +std::string handcrafted_file_type_name(const enum handcrafted_file_type hft) { + switch (hft) { + case HANDCRAFTED_HEADER_BAD_MAGIC: return "HEADER_BAD_MAGIC"; + case HANDCRAFTED_HEADER_BAD_VERSION_1: return "HEADER_BAD_VERSION_1"; + case HANDCRAFTED_HEADER_BAD_VERSION_FUTURE: return "HEADER_BAD_VERSION_FUTURE"; + case HANDCRAFTED_HEADER_BAD_N_KV: return "HEADER_BAD_N_KV"; + case HANDCRAFTED_HEADER_BAD_N_TENSORS: return "HEADER_BAD_N_TENSORS"; + case HANDCRAFTED_HEADER_EMPTY: return "HEADER_EMPTY"; + + case HANDCRAFTED_KV_BAD_KEY_SIZE: return "KV_BAD_KEY_SIZE"; + case HANDCRAFTED_KV_BAD_TYPE: return "KV_BAD_TYPE"; + case HANDCRAFTED_KV_BAD_VALUE_SIZE: return "KV_BAD_VALUE_SIZE"; + case HANDCRAFTED_KV_DUPLICATE_KEY: return "KV_DUPLICATE_KEY"; + case HANDCRAFTED_KV_SUCCESS: return "KV_RANDOM_KV"; + + case HANDCRAFTED_TENSORS_BAD_NAME_SIZE: return "TENSORS_BAD_NAME_SIZE"; + case HANDCRAFTED_TENSORS_BAD_N_DIMS: return "TENSORS_BAD_N_DIMS"; + case HANDCRAFTED_TENSORS_BAD_SHAPE: return "TENSORS_BAD_SHAPE"; + case HANDCRAFTED_TENSORS_NE_TOO_BIG: return "TENSORS_NE_TOO_BIG"; + case HANDCRAFTED_TENSORS_BAD_TYPE: return "TENSORS_BAD_TYPE"; + case HANDCRAFTED_TENSORS_BAD_OFFSET: return "TENSORS_BAD_OFFSET"; + case HANDCRAFTED_TENSORS_DUPLICATE_NAME: return "TENSORS_DUPLICATE_NAME"; + case HANDCRAFTED_TENSORS_BAD_ALIGNMENT: return "TENSORS_BAD_ALIGNMENT"; + case HANDCRAFTED_TENSORS_SUCCESS: return "TENSORS_SUCCESS"; + case HANDCRAFTED_TENSORS_CUSTOM_ALIGN: return "TENSORS_CUSTOM_ALIGN"; + + case HANDCRAFTED_DATA_NOT_ENOUGH_DATA: return "DATA_NOT_ENOUGH_DATA"; + case HANDCRAFTED_DATA_BAD_ALIGNMENT: return "DATA_BAD_ALIGNMENT"; + case HANDCRAFTED_DATA_SUCCESS: return "DATA_SUCCESS"; + case HANDCRAFTED_DATA_CUSTOM_ALIGN: return "DATA_CUSTOM_ALIGN"; + } + GGML_ABORT("fatal error"); +} + +static bool expect_context_not_null(const enum handcrafted_file_type hft) { + if (hft < offset_has_kv) { + return hft >= HANDCRAFTED_HEADER_EMPTY; + } + if (hft < offset_has_tensors) { + return hft >= HANDCRAFTED_KV_SUCCESS; + } + if (hft < offset_has_data) { + return hft >= HANDCRAFTED_TENSORS_SUCCESS; + } + return hft >= HANDCRAFTED_DATA_SUCCESS; +} + +typedef std::pair> tensor_config_t; + +std::vector get_tensor_configs(std::mt19937 & rng) { + std::vector tensor_configs; + tensor_configs.reserve(100); + + for (int i = 0; i < 100; ++i) { + const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT); + if (ggml_type_size(type) == 0) { + continue; + } + + std::array shape = {1, 1, 1, 1}; + shape[0] = (1 + rng() % 10) * ggml_blck_size(type); + const int n_dims = 1 + rng() % GGML_MAX_DIMS; + for (int i = 1; i < n_dims; ++i) { + shape[i] = 1 + rng() % 10; + } + + tensor_configs.push_back(std::make_pair(type, shape)); + } + + return tensor_configs; +} + +std::vector> get_kv_types(std::mt19937 rng) { + std::vector> kv_types; + kv_types.reserve(100); + + for (int i = 0; i < 100; ++i) { + const gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT); + + if (type == GGUF_TYPE_ARRAY) { + const gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT); + if (type_arr == GGUF_TYPE_ARRAY) { + continue; + } + kv_types.push_back(std::make_pair(type, type_arr)); + continue; + } + + kv_types.push_back(std::make_pair(type, gguf_type(-1))); + } + std::shuffle(kv_types.begin(), kv_types.end(), rng); + + return kv_types; +} + +static void helper_write(const void * data, const size_t nbytes, FILE * file) { + GGML_ASSERT(fwrite(data, 1, nbytes, file) == nbytes); +} + +static FILE * get_handcrafted_file(const unsigned int seed, const enum handcrafted_file_type hft, const int extra_bytes = 0) { + FILE * file = tmpfile(); + + std::mt19937 rng(seed); + + if (hft == HANDCRAFTED_HEADER_BAD_MAGIC) { + const char bad_magic[4] = {'F', 'U', 'G', 'G'}; + helper_write(bad_magic, sizeof(bad_magic), file); + } else { + helper_write(GGUF_MAGIC, 4, file); + } + + if (hft == HANDCRAFTED_HEADER_BAD_VERSION_1) { + const uint32_t version = 1; + helper_write(&version, sizeof(version), file); + } else if (hft == HANDCRAFTED_HEADER_BAD_VERSION_FUTURE) { + const uint32_t version = GGUF_VERSION + 1; + helper_write(&version, sizeof(version), file); + } else { + const uint32_t version = GGUF_VERSION; + helper_write(&version, sizeof(version), file); + } + + std::vector tensor_configs; + if (hft >= offset_has_tensors) { + tensor_configs = get_tensor_configs(rng); + } + + if (hft == HANDCRAFTED_HEADER_BAD_N_TENSORS) { + const uint64_t n_tensors = -1; + helper_write(&n_tensors, sizeof(n_tensors), file); + } else { + const uint64_t n_tensors = tensor_configs.size(); + helper_write(&n_tensors, sizeof(n_tensors), file); + } + + std::vector> kv_types; + if (hft >= offset_has_kv) { + kv_types = get_kv_types(rng); + } + { + uint64_t n_kv = kv_types.size(); + if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) { + n_kv += 1; + } else if (hft == HANDCRAFTED_HEADER_BAD_N_KV) { + n_kv = -1; + } + helper_write(&n_kv, sizeof(n_kv), file); + } + + if (hft < offset_has_kv) { + for (int i = 0; i < extra_bytes; ++i) { + const char tmp = 0; + helper_write(&tmp, sizeof(tmp), file); + } + rewind(file); + return file; + } + + for (int i = 0; i < int(kv_types.size()); ++i) { + const enum gguf_type type = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : kv_types[i].first); + const enum gguf_type type_arr = gguf_type(hft == HANDCRAFTED_KV_BAD_TYPE ? -1 : kv_types[i].second); + + const std::string key = "my_key_" + std::to_string((hft == HANDCRAFTED_KV_DUPLICATE_KEY ? i/2 : i)); + + if (hft == HANDCRAFTED_KV_BAD_KEY_SIZE) { + const uint64_t n = -1; + helper_write(&n, sizeof(n), file); + } else { + const uint64_t n = key.length(); + helper_write(&n, sizeof(n), file); + } + helper_write(key.data(), key.length(), file); + + { + const int32_t type32 = int32_t(type); + helper_write(&type32, sizeof(type32), file); + } + + uint32_t data[16]; + for (int j = 0; j < 16; ++j) { + data[j] = rng(); + if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) { + data[j] |= 0x01010101; // avoid random null-termination of string + } + } + + if (type == GGUF_TYPE_STRING) { + const uint64_t n = rng() % sizeof(data); + helper_write(&n, sizeof(n), file); + helper_write(data, n, file); + continue; + } + + if (type == GGUF_TYPE_ARRAY) { + { + const int32_t type32 = int32_t(type_arr); + helper_write(&type32, sizeof(type32), file); + } + if (type_arr == GGUF_TYPE_STRING) { + const uint64_t nstr = rng() % (16 + 1); + helper_write(&nstr, sizeof(nstr), file); + for (uint64_t istr = 0; istr < nstr; ++istr) { + const uint64_t n = rng() % (sizeof(uint32_t) + 1); + helper_write(&n, sizeof(n), file); + helper_write(&data[istr], n, file); + } + continue; + } + const size_t type_size = gguf_type_size(type_arr); + const uint64_t n = (rng() % sizeof(data)) / type_size; + helper_write(&n, sizeof(n), file); + helper_write(&data, n*type_size, file); + continue; + } + + size_t type_size = hft == HANDCRAFTED_KV_BAD_TYPE ? 1 : gguf_type_size(type); + if (hft == HANDCRAFTED_KV_BAD_VALUE_SIZE) { + type_size += rng() % 3; + } + helper_write(data, type_size, file); + } + + if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) { + const std::string key = "general.alignment"; + { + const uint64_t n = key.length(); + helper_write(&n, sizeof(n), file); + } + helper_write(key.data(), key.length(), file); + + const int32_t type = gguf_type(GGUF_TYPE_UINT32); + helper_write(&type, sizeof(type), file); + + const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT + 1; + helper_write(&alignment, sizeof(alignment), file); + } + + if (hft < offset_has_tensors) { + for (int i = 0; i < extra_bytes; ++i) { + const char tmp = 0; + helper_write(&tmp, sizeof(tmp), file); + } + rewind(file); + return file; + } + + uint32_t alignment = GGUF_DEFAULT_ALIGNMENT; + if (hft == HANDCRAFTED_TENSORS_BAD_ALIGNMENT || hft == HANDCRAFTED_DATA_BAD_ALIGNMENT) { + alignment -= 1; + } else if (hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN) { + alignment += 1; + } + + uint64_t offset = 0; + for (int i = 0; i < int(tensor_configs.size()); ++i) { + const ggml_type type = tensor_configs[i].first; + const std::array shape = tensor_configs[i].second; + + std::string name = "my_tensor"; + if (hft != HANDCRAFTED_TENSORS_DUPLICATE_NAME) { + name += "_" + std::to_string(i); + } + if (hft == HANDCRAFTED_TENSORS_BAD_NAME_SIZE) { + name += "_with_a_very_long_name_which_is_longer_than_what_is_allowed_for_ggml_tensors"; + GGML_ASSERT(name.length() >= GGML_MAX_NAME); + } + { + const uint64_t n = name.length(); + helper_write(&n, sizeof(n), file); + } + helper_write(name.data(), name.length(), file); + + uint32_t n_dims = hft == HANDCRAFTED_TENSORS_NE_TOO_BIG ? 2 : 1; + for (int i = GGML_MAX_DIMS-1; i >= 1; --i) { + if (shape[i] != 1) { + n_dims = i + 1; + break; + } + } + if (hft == HANDCRAFTED_TENSORS_BAD_N_DIMS) { + const uint32_t n_dims_bad = GGML_MAX_DIMS + 1; + helper_write(&n_dims_bad, sizeof(n_dims_bad), file); + } else { + helper_write(&n_dims, sizeof(n_dims), file); + } + + if (hft == HANDCRAFTED_TENSORS_BAD_SHAPE) { + for (uint32_t j = 0; j < n_dims; ++j) { + const int64_t bad_dim = -1; + helper_write(&bad_dim, sizeof(bad_dim), file); + } + } else if (hft == HANDCRAFTED_TENSORS_NE_TOO_BIG){ + for (uint32_t j = 0; j < n_dims; ++j) { + const int64_t big_dim = 4*int64_t(INT32_MAX); + helper_write(&big_dim, sizeof(big_dim), file); + } + } else { + helper_write(shape.data(), n_dims*sizeof(int64_t), file); + } + + { + const int32_t type32 = hft == HANDCRAFTED_TENSORS_BAD_TYPE ? -1 : int32_t(type); + helper_write(&type32, sizeof(type32), file); + } + + if (hft == HANDCRAFTED_TENSORS_BAD_OFFSET) { + const uint64_t bad_offset = -1; + helper_write(&bad_offset, sizeof(bad_offset), file); + } else { + helper_write(&offset, sizeof(offset), file); + } + + int64_t ne = shape[0]; + for (uint32_t i = 1; i < n_dims; ++i) { + ne *= shape[i]; + } + offset += GGML_PAD(ggml_row_size(type, ne), alignment); + } + + const uint32_t alignment_overshoot = ftell(file) % alignment; + if (alignment_overshoot != 0) { + for (size_t i = alignment_overshoot; i < alignment; ++i) { + const char pad = 0; + helper_write(&pad, sizeof(pad), file); + } + } + + if (hft >= offset_has_data) { + rng.seed(seed + 1); + uint64_t nbytes = offset; + if (hft == HANDCRAFTED_DATA_NOT_ENOUGH_DATA) { + nbytes -= 1; + } + for (uint64_t i = 0; i < nbytes; ++i) { + const uint8_t random_byte = i % 256; + helper_write(&random_byte, sizeof(random_byte), file); + } + } + + for (int i = 0; i < extra_bytes; ++i) { + const char tmp = 0; + helper_write(&tmp, sizeof(tmp), file); + } + rewind(file); + return file; +} + +static bool handcrafted_check_header(const gguf_context * gguf_ctx, const unsigned int seed, const bool has_kv, const bool has_tensors, const bool alignment_defined) { + if (!gguf_ctx) { + return false; + } + + std::mt19937 rng(seed); + + std::vector tensor_configs; + if (has_tensors) { + tensor_configs = get_tensor_configs(rng); + } + std::vector> kv_types; + if (has_kv) { + kv_types = get_kv_types(rng); + } + + bool ok = true; + + if (gguf_get_version(gguf_ctx) != GGUF_VERSION) { + ok = false; + } + if (gguf_get_n_tensors(gguf_ctx) != int(tensor_configs.size())) { + ok = false; + } + if (gguf_get_n_kv(gguf_ctx) != int(alignment_defined ? kv_types.size() + 1 : kv_types.size())) { + ok = false; + } + + return ok; +} + +static bool handcrafted_check_kv(const gguf_context * gguf_ctx, const unsigned int seed, const bool has_tensors, const bool alignment_defined) { + if (!gguf_ctx) { + return false; + } + + std::mt19937 rng(seed); + + std::vector tensor_configs; + if (has_tensors) { + tensor_configs = get_tensor_configs(rng); + } + + std::vector> kv_types = get_kv_types(rng); + + bool ok = true; + + for (int i = 0; i < int(kv_types.size()); ++i) { + const enum gguf_type type = gguf_type(kv_types[i].first); + const enum gguf_type type_arr = gguf_type(kv_types[i].second); + + const std::string key = "my_key_" + std::to_string(i); + + uint32_t data[16]; + for (int j = 0; j < 16; ++j) { + data[j] = rng(); + if (type == GGUF_TYPE_STRING || type_arr == GGUF_TYPE_STRING) { + data[j] |= 0x01010101; // avoid random null-termination of string + } + } + + const char * data8 = reinterpret_cast(data); + const int id = gguf_find_key(gguf_ctx, key.c_str()); + + if (type == GGUF_TYPE_STRING) { + const char * str = gguf_get_val_str(gguf_ctx, id); + const uint64_t n = strlen(str); + const uint64_t n_expected = rng() % sizeof(data); + if (n != n_expected) { + ok = false; + continue; + } + if (!std::equal(str, str + n, data8)) { + ok = false; + } + continue; + } + + if (type == GGUF_TYPE_ARRAY) { + const size_t type_size = gguf_type_size(type_arr); + const uint64_t arr_n = gguf_get_arr_n(gguf_ctx, id); + + if (type_arr == GGUF_TYPE_STRING) { + const uint64_t nstr_expected = rng() % (16 + 1); + if (arr_n != nstr_expected) { + ok = false; + continue; + } + for (uint64_t istr = 0; istr < nstr_expected; ++istr) { + const char * str = gguf_get_arr_str(gguf_ctx, id, istr); + const uint64_t n = strlen(str); + const uint64_t n_expected = rng() % (sizeof(uint32_t) + 1); + + if (n != n_expected) { + ok = false; + continue; + } + const char * str_expected = reinterpret_cast(&data[istr]); + if (strncmp(str, str_expected, n) != 0) { + ok = false; + continue; + } + } + continue; + } + + const uint64_t arr_n_expected = (rng() % sizeof(data)) / type_size; + if (arr_n != arr_n_expected) { + ok = false; + continue; + } + + const char * data_gguf = reinterpret_cast(gguf_get_arr_data(gguf_ctx, id)); + if (!std::equal(data8, data8 + arr_n*type_size, data_gguf)) { + ok = false; + } + continue; + } + + const char * data_gguf = reinterpret_cast(gguf_get_val_data(gguf_ctx, id)); + if (!std::equal(data8, data8 + gguf_type_size(type), data_gguf)) { + ok = false; + } + } + + const uint32_t expected_alignment = alignment_defined ? GGUF_DEFAULT_ALIGNMENT + 1 : GGUF_DEFAULT_ALIGNMENT; + if (gguf_get_alignment(gguf_ctx) != expected_alignment) { + ok = false; + } + + return ok; +} + +static bool handcrafted_check_tensors(const gguf_context * gguf_ctx, const unsigned int seed) { + if (!gguf_ctx) { + return false; + } + + std::mt19937 rng(seed); + + std::vector tensor_configs = get_tensor_configs(rng); + + // Call get_kv_types to get the same RNG state: + get_kv_types(rng); + + bool ok = true; + + const int id_alignment = gguf_find_key(gguf_ctx, "general.alignment"); + const uint32_t alignment = id_alignment >= 0 ? gguf_get_val_u32(gguf_ctx, id_alignment) : GGUF_DEFAULT_ALIGNMENT; + + uint64_t expected_offset = 0; + for (int i = 0; i < int(tensor_configs.size()); ++i) { + const ggml_type type = tensor_configs[i].first; + const std::array shape = tensor_configs[i].second; + + const std::string name = "my_tensor_" + std::to_string(i); + const int id = gguf_find_tensor(gguf_ctx, name.c_str()); + + if (id >= 0) { + if (std::string(gguf_get_tensor_name(gguf_ctx, id)) != name) { + ok = false; + } + + if (gguf_get_tensor_type(gguf_ctx, id) != type) { + ok = false; + } + } else { + ok = false; + continue; + } + + const size_t offset = gguf_get_tensor_offset(gguf_ctx, id); + + if (offset != expected_offset) { + ok = false; + } + + int64_t ne = shape[0]; + for (size_t j = 1; j < GGML_MAX_DIMS; ++j) { + ne *= shape[j]; + } + expected_offset += GGML_PAD(ggml_row_size(type, ne), alignment); + } + + return ok; +} + +static bool handcrafted_check_tensor_data(const gguf_context * gguf_ctx, const unsigned int seed, FILE * file) { + if (!gguf_ctx) { + return false; + } + + std::mt19937 rng(seed); + + std::vector tensor_configs = get_tensor_configs(rng); + + bool ok = true; + + const uint32_t alignment = GGUF_DEFAULT_ALIGNMENT; + + for (int i = 0; i < int(tensor_configs.size()); ++i) { + const ggml_type type = tensor_configs[i].first; + const std::array shape = tensor_configs[i].second; + + int64_t ne = shape[0]; + for (size_t j = 1; j < GGML_MAX_DIMS; ++j) { + ne *= shape[j]; + } + const size_t size = ggml_row_size(type, ne); + + const std::string name = "my_tensor_" + std::to_string(i); + const size_t offset = gguf_get_tensor_offset(gguf_ctx, gguf_find_tensor(gguf_ctx, name.c_str())); + + std::vector data(size); + GGML_ASSERT(fseek(file, gguf_get_data_offset(gguf_ctx) + offset, SEEK_SET) == 0); + GGML_ASSERT(fread(data.data(), 1, size, file) == size); + + for (size_t j = 0; j < size; ++j) { + const uint8_t expected_byte = (j + offset) % 256; + if (data[j] != expected_byte) { + ok = false; + } + } + } + + return ok; +} + +static std::pair test_handcrafted_file(const unsigned int seed) { + int npass = 0; + int ntest = 0; + + const std::vector hfts = { + HANDCRAFTED_HEADER_BAD_MAGIC, + HANDCRAFTED_HEADER_BAD_VERSION_1, + // HANDCRAFTED_FILE_TYPE_BAD_VERSION_FUTURE, // FIXME + HANDCRAFTED_HEADER_BAD_N_KV, + HANDCRAFTED_HEADER_BAD_N_TENSORS, + HANDCRAFTED_HEADER_EMPTY, + + HANDCRAFTED_KV_BAD_KEY_SIZE, + HANDCRAFTED_KV_BAD_TYPE, + HANDCRAFTED_KV_BAD_VALUE_SIZE, + // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME + HANDCRAFTED_KV_SUCCESS, + + HANDCRAFTED_TENSORS_BAD_NAME_SIZE, + HANDCRAFTED_TENSORS_BAD_N_DIMS, + HANDCRAFTED_TENSORS_BAD_SHAPE, + HANDCRAFTED_TENSORS_NE_TOO_BIG, + HANDCRAFTED_TENSORS_BAD_TYPE, + // HANDCRAFTED_TENSORS_BAD_OFFSET, // FIXME + HANDCRAFTED_TENSORS_DUPLICATE_NAME, + // HANDCRAFTED_TENSORS_BAD_ALIGNMENT, // FIXME + HANDCRAFTED_TENSORS_SUCCESS, + HANDCRAFTED_TENSORS_CUSTOM_ALIGN, + + HANDCRAFTED_DATA_NOT_ENOUGH_DATA, + // HANDCRAFTED_DATA_BAD_ALIGNMENT, // FIXME + HANDCRAFTED_DATA_SUCCESS, + HANDCRAFTED_DATA_CUSTOM_ALIGN, + }; + + for (enum handcrafted_file_type hft : hfts) { + printf("%s: handcrafted_file_type=%s\n", __func__, handcrafted_file_type_name(hft).c_str()); + FILE * file = get_handcrafted_file(seed, hft); + +#ifdef _WIN32 + if (!file) { + printf("%s: failed to create tmpfile(), needs elevated privileges on Windows"); + printf("%s: skipping tests"); + continue; + } +#else + GGML_ASSERT(file); +#endif // _WIN32 + + struct ggml_context * ctx = nullptr; + struct gguf_init_params gguf_params = { + /*no_alloc =*/ false, + /*ctx =*/ hft >= offset_has_data ? &ctx : nullptr, + }; + struct gguf_context * gguf_ctx = gguf_init_from_file_impl(file, gguf_params); + + if (expect_context_not_null(hft)) { + printf("%s: - context_not_null: ", __func__); + } else { + printf("%s: - context_null: ", __func__); + } + if (bool(gguf_ctx) == expect_context_not_null(hft)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + if (false && hft >= offset_has_data && !expect_context_not_null(hft)) { // FIXME + printf("%s: - no_dangling_ggml_context_pointer: ", __func__); + if (ctx) { + printf("\033[1;31mFAIL\033[0m\n"); + } else { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } + ntest++; + } + + if (false && expect_context_not_null(hft)) { // FIXME + FILE * file_eb = get_handcrafted_file(seed, hft, /*extra_bytes =*/ 1); + struct gguf_context * gguf_ctx_eb = gguf_init_from_file_impl(file_eb, gguf_params); + + printf("%s: - context_null_with_extra_bytes: ", __func__); + if (gguf_ctx_eb) { + printf("\033[1;31mFAIL\033[0m\n"); + } else { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } + ntest++; + + gguf_free(gguf_ctx_eb); + fclose(file_eb); + } + + const bool alignment_defined = hft == HANDCRAFTED_TENSORS_CUSTOM_ALIGN || hft == HANDCRAFTED_DATA_CUSTOM_ALIGN; + + if (expect_context_not_null(hft)) { + printf("%s: - check_header: ", __func__); + if (handcrafted_check_header(gguf_ctx, seed, hft >= offset_has_kv, hft >= offset_has_tensors, alignment_defined)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + if (expect_context_not_null(hft) && hft >= offset_has_kv) { + printf("%s: - check_kv: ", __func__); + if (handcrafted_check_kv(gguf_ctx, seed, hft >= offset_has_tensors, alignment_defined)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + if (expect_context_not_null(hft) && hft >= offset_has_tensors) { + printf("%s: - check_tensors: ", __func__); + if (handcrafted_check_tensors(gguf_ctx, seed)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + if (expect_context_not_null(hft) && hft >= offset_has_data) { + printf("%s: - check_tensor_data: ", __func__); + if (handcrafted_check_tensor_data(gguf_ctx, seed, file)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + if (gguf_ctx) { + ggml_free(ctx); + gguf_free(gguf_ctx); + } + fclose(file); + printf("\n"); + } + + return std::make_pair(npass, ntest); +} + +struct random_gguf_context_result { + struct gguf_context * gguf_ctx; + struct ggml_context * ctx; + ggml_backend_buffer_t buffer; +}; + +static struct random_gguf_context_result get_random_gguf_context(ggml_backend_t backend, const unsigned int seed) { + std::mt19937 rng(seed); + + struct gguf_context * gguf_ctx = gguf_init_empty(); + + for (int i = 0; i < 256; ++i) { + const std::string key = "my_key_" + std::to_string(rng() % 1024); + const enum gguf_type type = gguf_type(rng() % GGUF_TYPE_COUNT); + + if (type == GGUF_TYPE_STRING || type == GGUF_TYPE_ARRAY) { + continue; // FIXME memory leak + } + + switch (type) { + case GGUF_TYPE_UINT8: gguf_set_val_u8 (gguf_ctx, key.c_str(), rng() % (1 << 7)); break; + case GGUF_TYPE_INT8: gguf_set_val_i8 (gguf_ctx, key.c_str(), rng() % (1 << 7) - (1 << 6)); break; + case GGUF_TYPE_UINT16: gguf_set_val_u16 (gguf_ctx, key.c_str(), rng() % (1 << 15)); break; + case GGUF_TYPE_INT16: gguf_set_val_i16 (gguf_ctx, key.c_str(), rng() % (1 << 15) - (1 << 14)); break; + case GGUF_TYPE_UINT32: gguf_set_val_u32 (gguf_ctx, key.c_str(), rng()); break; + case GGUF_TYPE_INT32: gguf_set_val_i32 (gguf_ctx, key.c_str(), rng() - (1 << 30)); break; + case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (gguf_ctx, key.c_str(), rng() % 1024 - 512); break; + case GGUF_TYPE_BOOL: gguf_set_val_bool(gguf_ctx, key.c_str(), rng() % 2 == 0); break; + case GGUF_TYPE_STRING: gguf_set_val_str (gguf_ctx, key.c_str(), std::to_string(rng()).c_str()); break; + case GGUF_TYPE_UINT64: gguf_set_val_u64 (gguf_ctx, key.c_str(), rng()); break; + case GGUF_TYPE_INT64: gguf_set_val_i64 (gguf_ctx, key.c_str(), rng() - (1 << 30)); break; + case GGUF_TYPE_FLOAT64: gguf_set_val_f32 (gguf_ctx, key.c_str(), rng() % 1024 - 512); break; + case GGUF_TYPE_ARRAY: { + const enum gguf_type type_arr = gguf_type(rng() % GGUF_TYPE_COUNT); + const uint64_t ne = rng() % 1024; + + switch (type_arr) { + case GGUF_TYPE_UINT8: + case GGUF_TYPE_INT8: + case GGUF_TYPE_UINT16: + case GGUF_TYPE_INT16: + case GGUF_TYPE_UINT32: + case GGUF_TYPE_INT32: + case GGUF_TYPE_FLOAT32: + case GGUF_TYPE_BOOL: + case GGUF_TYPE_UINT64: + case GGUF_TYPE_INT64: + case GGUF_TYPE_FLOAT64: { + const size_t nbytes = ne*gguf_type_size(type_arr); + std::vector random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t)); + for (size_t j = 0; j < random_data.size(); ++j) { + random_data[j] = rng(); + } + gguf_set_arr_data(gguf_ctx, key.c_str(), type_arr, random_data.data(), ne); + } break; + case GGUF_TYPE_STRING: { + std::vector data_cpp(ne); + std::vector data_c(ne); + for (size_t j = 0; j < data_cpp.size(); ++j) { + data_cpp[j] = std::to_string(rng()); + data_c[j] = data_cpp[j].c_str(); + } + gguf_set_arr_str(gguf_ctx, key.c_str(), data_c.data(), ne); + } break; + case GGUF_TYPE_ARRAY: { + break; // not supported + } + case GGUF_TYPE_COUNT: + default: { + GGML_ABORT("fatal error"); + } break; + } + } break; + case GGUF_TYPE_COUNT: + default: { + GGML_ABORT("fatal error"); + } break; + } + } + + struct ggml_init_params ggml_params = { + /*.mem_size =*/ 256*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + struct ggml_context * ctx = ggml_init(ggml_params); + + for (int i = 0; i < 256; ++i) { + const std::string name = "my_tensor_" + std::to_string(i); + const enum ggml_type type = ggml_type(rng() % GGML_TYPE_COUNT); + const size_t type_size = ggml_type_size(type); + + if (type_size == 0) { + continue; + } + + const int n_dims = 1 + rng() % GGML_MAX_DIMS; + int64_t ne[GGML_MAX_DIMS]; + ne[0] = (1 + rng() % 10) * ggml_blck_size(type); + for (int j = 1; j < n_dims; ++j) { + ne[j] = 1 + rng() % 10; + } + + struct ggml_tensor * tensor = ggml_new_tensor(ctx, type, n_dims, ne); + ggml_set_name(tensor, name.c_str()); + } + + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, backend); + for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + const size_t nbytes = ggml_nbytes(t); + std::vector random_data((nbytes + sizeof(uint32_t) - 1) / sizeof(uint32_t)); + for (size_t j = 0; j < random_data.size(); ++j) { + random_data[j] = rng(); + } + ggml_backend_tensor_set(t, random_data.data(), 0, nbytes); + + gguf_add_tensor(gguf_ctx, t); + } + + return {gguf_ctx, ctx, buf}; +} + +static bool all_kv_in_other(const gguf_context * ctx, const gguf_context * other) { + bool ok = true; + + const int n_kv = gguf_get_n_kv(ctx); + for (int id = 0; id < n_kv; ++id) { + const char * name = gguf_get_key(ctx, id); + + const int idx_other = gguf_find_key(other, name); + if (idx_other < 0) { + ok = false; + continue; + } + + const gguf_type type = gguf_get_kv_type(ctx, id); + if (type != gguf_get_kv_type(other, idx_other)) { + ok = false; + continue; + } + + if (type == GGUF_TYPE_ARRAY) { + const int arr_n = gguf_get_arr_n(ctx, id); + if (arr_n != gguf_get_arr_n(other, idx_other)) { + ok = false; + continue; + } + + const gguf_type type_arr = gguf_get_arr_type(ctx, id); + if (type_arr != gguf_get_arr_type(other, idx_other)) { + ok = false; + continue; + } + + if (type_arr == GGUF_TYPE_STRING) { + for (int arr_i = 0; arr_i < arr_n; ++arr_i) { + const std::string str = gguf_get_arr_str(ctx, id, arr_i); + const std::string str_other = gguf_get_arr_str(other, idx_other, arr_i); + if (str != str_other) { + ok = false; + } + } + continue; + } + + const char * data = reinterpret_cast(gguf_get_arr_data(ctx, id)); + const char * data_other = reinterpret_cast(gguf_get_arr_data(other, idx_other)); + if (!std::equal(data, data + arr_n*gguf_type_size(type_arr), data_other)) { + ok = false; + } + continue; + } + + if (type == GGUF_TYPE_STRING) { + const std::string str = gguf_get_val_str(ctx, id); + const std::string str_other = gguf_get_val_str(other, idx_other); + if (str != str_other) { + ok = false; + } + continue; + } + + const char * data = reinterpret_cast(gguf_get_val_data(ctx, id)); + const char * data_other = reinterpret_cast(gguf_get_val_data(other, idx_other)); + if (!std::equal(data, data + gguf_type_size(type), data_other)) { + ok = false; + } + } + + return ok; +} + +static bool all_tensors_in_other(const gguf_context * ctx, const gguf_context * other) { + bool ok = true; + + const int n_tensors = gguf_get_n_tensors(ctx); + for (int id = 0; id < n_tensors; ++id) { + const std::string name = gguf_get_tensor_name(ctx, id); + + const int idx_other = gguf_find_tensor(other, name.c_str()); + if (id != idx_other) { + ok = false; + if (idx_other < 0) { + continue; + } + } + + const ggml_type type = gguf_get_tensor_type(ctx, id); + if (type != gguf_get_tensor_type(other, id)) { + ok = false; + } + + const size_t offset = gguf_get_tensor_offset(ctx, id); + if (offset != gguf_get_tensor_offset(other, id)) { + ok = false; + } + } + + return ok; +} + +static bool same_tensor_data(const struct ggml_context * orig, const struct ggml_context * read) { + bool ok = true; + + struct ggml_tensor * t_orig = ggml_get_first_tensor(orig); + struct ggml_tensor * t_read = ggml_get_first_tensor(read); + while (t_orig) { + if (!t_read) { + ok = false; + break; + } + + const size_t nbytes = ggml_nbytes(t_orig); + if (ggml_nbytes(t_read) != nbytes) { + ok = false; + break; + } + std::vector data_orig(nbytes); + ggml_backend_tensor_get(t_orig, data_orig.data(), 0, nbytes); + if (!std::equal(data_orig.data(), data_orig.data() + nbytes, reinterpret_cast(t_read->data))) { + ok = false; + } + + t_orig = ggml_get_next_tensor(orig, t_orig); + t_read = ggml_get_next_tensor(orig, t_read); + } + if (t_read) { + ok = false; + } + + return true; +} + +static std::pair test_roundtrip(ggml_backend_dev_t dev, const unsigned int seed, const bool only_meta) { + FILE * file = tmpfile(); +#ifdef _WIN32 + if (!file) { + printf("%s: failed to create tmpfile(), needs elevated privileges on Windows"); + printf("%s: skipping tests"); + return std::make_pair(0, 0); + } +#else + GGML_ASSERT(file); +#endif // _WIN32 + + if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { + return std::make_pair(0, 0); // FIXME + } + + ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); + printf("%s: device=%s, backend=%s, only_meta=%s\n", + __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend), only_meta ? "yes" : "no"); + + int npass = 0; + int ntest = 0; + + struct gguf_context * gguf_ctx_0; + struct ggml_context * ctx_0; + ggml_backend_buffer_t bbuf; + { + struct random_gguf_context_result result = get_random_gguf_context(backend, seed); + gguf_ctx_0 = result.gguf_ctx; + ctx_0 = result.ctx; + bbuf = result.buffer; + } + + struct gguf_buf gbuf = gguf_buf_init(16 * 1024); + gguf_write_to_buf(gguf_ctx_0, &gbuf, only_meta); + helper_write(gbuf.data, gbuf.offset, file); + rewind(file); + + struct ggml_context * ctx_1 = nullptr; + struct gguf_init_params gguf_params = { + /*no_alloc =*/ false, + /*ctx =*/ only_meta ? nullptr : &ctx_1, + }; + struct gguf_context * gguf_ctx_1 = gguf_init_from_file_impl(file, gguf_params); + + printf("%s: same_version: ", __func__); + if (gguf_get_version(gguf_ctx_0) == gguf_get_version(gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: same_n_kv: ", __func__); + if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: same_n_tensors: ", __func__); + if (gguf_get_n_tensors(gguf_ctx_0) == gguf_get_n_tensors(gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_orig_kv_in_read: ", __func__); + if (all_kv_in_other(gguf_ctx_0, gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_read_kv_in_orig: ", __func__); + if (all_kv_in_other(gguf_ctx_1, gguf_ctx_0)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_orig_tensors_in_read: ", __func__); + if (all_tensors_in_other(gguf_ctx_0, gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_read_tensors_in_orig: ", __func__); + if (all_tensors_in_other(gguf_ctx_1, gguf_ctx_0)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + if (!only_meta) { + printf("%s: same_tensor_data: ", __func__); + if (same_tensor_data(ctx_0, ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + ggml_backend_buffer_free(bbuf); + ggml_free(ctx_0); + ggml_free(ctx_1); + gguf_free(gguf_ctx_0); + gguf_free(gguf_ctx_1); + gguf_buf_free(gbuf); + ggml_backend_free(backend); + GGML_ASSERT(fclose(file) == 0); + + printf("\n"); + return std::make_pair(npass, ntest); +} + +static std::pair test_gguf_set_kv(ggml_backend_dev_t dev, const unsigned int seed) { + ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr); + printf("%s: device=%s, backend=%s\n", __func__, ggml_backend_dev_description(dev), ggml_backend_name(backend)); + + int npass = 0; + int ntest = 0; + + struct gguf_context * gguf_ctx_0; + struct ggml_context * ctx_0; + ggml_backend_buffer_t bbuf_0; + { + struct random_gguf_context_result result = get_random_gguf_context(backend, seed); + gguf_ctx_0 = result.gguf_ctx; + ctx_0 = result.ctx; + bbuf_0 = result.buffer; + } + + struct gguf_context * gguf_ctx_1; + struct ggml_context * ctx_1; + ggml_backend_buffer_t bbuf_1; + { + struct random_gguf_context_result result = get_random_gguf_context(backend, seed + 1); + gguf_ctx_1 = result.gguf_ctx; + ctx_1 = result.ctx; + bbuf_1 = result.buffer; + } + + struct gguf_context * gguf_ctx_2 = gguf_init_empty(); + + gguf_set_kv(gguf_ctx_1, gguf_ctx_0); + gguf_set_kv(gguf_ctx_2, gguf_ctx_0); + + printf("%s: same_n_kv: ", __func__); + if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_2)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_kv_0_in_1: ", __func__); + if (all_kv_in_other(gguf_ctx_0, gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_kv_0_in_2: ", __func__); + if (all_kv_in_other(gguf_ctx_0, gguf_ctx_2)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + gguf_set_kv(gguf_ctx_0, gguf_ctx_1); + + printf("%s: same_n_kv_after_double_copy: ", __func__); + if (gguf_get_n_kv(gguf_ctx_0) == gguf_get_n_kv(gguf_ctx_1)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + printf("%s: all_kv_1_in_0_after_double_copy: ", __func__); + if (all_kv_in_other(gguf_ctx_1, gguf_ctx_0)) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + ggml_backend_buffer_free(bbuf_0); + ggml_backend_buffer_free(bbuf_1); + ggml_free(ctx_0); + ggml_free(ctx_1); + gguf_free(gguf_ctx_0); + gguf_free(gguf_ctx_1); + gguf_free(gguf_ctx_2); + ggml_backend_free(backend); + + printf("\n"); + return std::make_pair(npass, ntest); +} + +static void print_usage() { + printf("usage: test-gguf [seed]\n"); + printf(" if no seed is unspecified then a random seed is used\n"); +} + +int main(int argc, char ** argv) { + if (argc > 2) { + print_usage(); + return 1; + } + + std::random_device rd; + const unsigned int seed = argc < 2 ? rd() : std::stoi(argv[1]); + + // Initialize ggml backends early so the prints aren't interleaved with the test results: + ggml_backend_dev_count(); + fprintf(stderr, "\n"); + + int npass = 0; + int ntest = 0; + { + std::pair result = test_handcrafted_file(seed); + npass += result.first; + ntest += result.second; + } + + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + + for (bool only_meta : {true, false}) { + std::pair result = test_roundtrip(dev, seed, only_meta); + npass += result.first; + ntest += result.second; + } + + { + std::pair result = test_gguf_set_kv(dev, seed); + npass += result.first; + ntest += result.second; + } + } + + printf("%d/%d tests passed\n", npass, ntest); + if (npass != ntest) { + printf("\033[1;31mFAIL\033[0m\n"); + return 1; + } + printf("\033[1;32mOK\033[0m\n"); + return 0; +} From d62b532c52e0118323277eaa5f442e11ce6505ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Tue, 17 Dec 2024 17:24:22 -0500 Subject: [PATCH 12/34] Use model->gguf_kv for loading the template instead of using the C API. (#10868) * Bump model_template to 16384 bytes to support larger chat templates. * Use `model->gguf_kv` for efficiency. --- src/llama.cpp | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 1cc8a93323b4a..c7b180f20e73c 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -22651,15 +22651,15 @@ int32_t llama_chat_apply_template( std::string curr_tmpl(tmpl == nullptr ? "" : tmpl); if (tmpl == nullptr) { GGML_ASSERT(model != nullptr); - // load template from model - std::vector model_template(2048, 0); // longest known template is about 1200 bytes - std::string template_key = "tokenizer.chat_template"; - int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); - if (res < 0) { + + // load template from model, if available + const auto & it = model->gguf_kv.find("tokenizer.chat_template"); + if (it != model->gguf_kv.end() && it->second.size() > 0) { + curr_tmpl = it->second; + } + else { // worst case: there is no information about template, we will use chatml by default - curr_tmpl = "chatml"; // see llama_chat_apply_template_internal - } else { - curr_tmpl = std::string(model_template.data(), model_template.size()); + curr_tmpl = "chatml"; // see llama_chat_apply_template_internal } } From 4da69d1abd15263061aff94c10f205836a96a4bc Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Wed, 18 Dec 2024 01:36:46 +0100 Subject: [PATCH 13/34] Revert "llama : add Falcon3 support (#10864)" (#10876) This reverts commit 382bc7f2e8ffd0b89f23e840d097e21f301197ba. --- convert_hf_to_gguf.py | 6 ------ convert_hf_to_gguf_update.py | 1 - src/llama.cpp | 18 ------------------ 3 files changed, 25 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 66e268af61419..9dc1673bc2c06 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -525,9 +525,6 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]: else: token: str = reverse_vocab[i] if token in added_vocab: - # We need to manually encode and decode the added tokens in case special characters - # used for `\n` / `\t` have been manually added in the added tokens - token = tokenizer.decode(tokenizer.encode(token)) if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token): toktypes.append(gguf.TokenType.CONTROL) else: @@ -574,9 +571,6 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": # ref: https://huggingface.co/tiiuae/falcon-7b res = "falcon" - if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e": - # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base - res = "falcon3" if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 res = "bert-bge" diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 2ba346640b352..88058442f6dc4 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -72,7 +72,6 @@ class TOKENIZER_TYPE(IntEnum): {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, - {"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", }, {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", }, {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, diff --git a/src/llama.cpp b/src/llama.cpp index c7b180f20e73c..b7b04a41d99e6 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1612,7 +1612,6 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN, LLM_CHAT_TEMPLATE_MISTRAL_V7, LLM_CHAT_TEMPLATE_PHI_3, - LLM_CHAT_TEMPLATE_FALCON_3, LLM_CHAT_TEMPLATE_ZEPHYR, LLM_CHAT_TEMPLATE_MONARCH, LLM_CHAT_TEMPLATE_GEMMA, @@ -1645,7 +1644,6 @@ static const std::map LLM_CHAT_TEMPLATES = { { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN }, { "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 }, { "phi3", LLM_CHAT_TEMPLATE_PHI_3 }, - { "falcon3", LLM_CHAT_TEMPLATE_FALCON_3 }, { "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR }, { "monarch", LLM_CHAT_TEMPLATE_MONARCH }, { "gemma", LLM_CHAT_TEMPLATE_GEMMA }, @@ -6475,11 +6473,6 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "falcon") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON; - } else if ( - tokenizer_pre == "falcon3") { - vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3; - vocab.tokenizer_ignore_merges = true; - vocab.tokenizer_add_bos = true; } else if ( tokenizer_pre == "mpt") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT; @@ -22226,8 +22219,6 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) { } } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) { return LLM_CHAT_TEMPLATE_PHI_3; - } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) { - return LLM_CHAT_TEMPLATE_FALCON_3; } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) { return LLM_CHAT_TEMPLATE_ZEPHYR; } else if (tmpl_contains("bos_token + message['role']")) { @@ -22380,15 +22371,6 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } - } else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) { - // Falcon 3 - for (auto message : chat) { - std::string role(message->role); - ss << "<|" << role << "|>\n" << message->content << "\n"; - } - if (add_ass) { - ss << "<|assistant|>\n"; - } } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) { // zephyr template for (auto message : chat) { From 6b064c92b4c0228e333193c167f2c98d2ec8d9bc Mon Sep 17 00:00:00 2001 From: redbeard Date: Wed, 18 Dec 2024 00:35:00 -0800 Subject: [PATCH 14/34] =?UTF-8?q?docs:=20Fix=20HIP=20(n=C3=A9e=20hipBLAS)?= =?UTF-8?q?=20in=20README=20(#10880)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Related to #10524 / be0e350c references to hipBLAS have been removed across the repository. This fixes the link from the repositories `README.md`. Signed-off-by: Brian 'redbeard' Harrington --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index da8d9d1664383..3f3971d7eb263 100644 --- a/README.md +++ b/README.md @@ -221,7 +221,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU | | [MUSA](docs/build.md#musa) | Moore Threads MTT GPU | | [CUDA](docs/build.md#cuda) | Nvidia GPU | -| [hipBLAS](docs/build.md#hipblas) | AMD GPU | +| [HIP](docs/build.md#hip) | AMD GPU | | [Vulkan](docs/build.md#vulkan) | GPU | | [CANN](docs/build.md#cann) | Ascend NPU | From 46828872c31b25df16169cbbf5c2225fa9cb0675 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Wed, 18 Dec 2024 09:55:09 +0100 Subject: [PATCH 15/34] server : (embeddings) using same format for "input" and "content" (#10872) * server : (embeddings) using same format for "input" and "content" * fix test case * handle empty input case * fix test --- examples/server/server.cpp | 20 +++++++---- examples/server/tests/unit/test_embedding.py | 35 ++++++++++++++++++-- examples/server/utils.hpp | 1 + 3 files changed, 47 insertions(+), 9 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 436170a034fde..71566b94e61bb 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3651,25 +3651,33 @@ int main(int argc, char ** argv) { const json body = json::parse(req.body); bool oaicompat = false; - // an input prompt can be a string or a list of tokens (integer) + // for the shape of input/content, see tokenize_input_prompts() json prompt; - if (body.count("input") != 0) { + if (body.contains("input")) { oaicompat = true; prompt = body.at("input"); - } else if (body.count("content") != 0) { - // with "content", we only support single prompt - prompt = std::vector{body.at("content")}; + } else if (body.contains("content")) { + oaicompat = false; + prompt = body.at("content"); } else { res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); return; } + std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true); + for (const auto & tokens : tokenized_prompts) { + // this check is necessary for models that do not add BOS token to the input + if (tokens.empty()) { + res_error(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST)); + return; + } + } + // create and queue the task json responses = json::array(); bool error = false; { std::vector tasks; - std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, /* add_special */ false, true); for (size_t i = 0; i < tokenized_prompts.size(); i++) { server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); task.id = ctx_server.queue_tasks.get_new_id(); diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py index fea1d6510c89e..4f4e9dcf087fa 100644 --- a/examples/server/tests/unit/test_embedding.py +++ b/examples/server/tests/unit/test_embedding.py @@ -45,6 +45,35 @@ def test_embedding_multiple(): assert len(d['embedding']) > 1 +@pytest.mark.parametrize( + "content,is_multi_prompt", + [ + # single prompt + ("string", False), + ([12, 34, 56], False), + ([12, 34, "string", 56, 78], False), + # multiple prompts + (["string1", "string2"], True), + (["string1", [12, 34, 56]], True), + ([[12, 34, 56], [12, 34, 56]], True), + ([[12, 34, 56], [12, "string", 34, 56]], True), + ] +) +def test_embedding_mixed_input(content, is_multi_prompt: bool): + global server + server.start() + res = server.make_request("POST", "/embeddings", data={"content": content}) + assert res.status_code == 200 + if is_multi_prompt: + assert len(res.body) == len(content) + for d in res.body: + assert 'embedding' in d + assert len(d['embedding']) > 1 + else: + assert 'embedding' in res.body + assert len(res.body['embedding']) > 1 + + def test_embedding_openai_library_single(): global server server.start() @@ -102,8 +131,8 @@ def test_same_prompt_give_same_result(): @pytest.mark.parametrize( "content,n_tokens", [ - ("I believe the meaning of life is", 7), - ("This is a test", 4), + ("I believe the meaning of life is", 9), + ("This is a test", 6), ] ) def test_embedding_usage_single(content, n_tokens): @@ -126,4 +155,4 @@ def test_embedding_usage_multiple(): }) assert res.status_code == 200 assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] - assert res.body['usage']['prompt_tokens'] == 2 * 7 + assert res.body['usage']['prompt_tokens'] == 2 * 9 diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 8fffe484aec12..ffdffe904308f 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -138,6 +138,7 @@ static llama_tokens tokenize_mixed(const llama_context * ctx, const json & json_ * and multiple prompts (multi-tasks): * - "prompt": ["string1", "string2"] * - "prompt": ["string1", [12, 34, 56]] + * - "prompt": [[12, 34, 56], [78, 90, 12]] * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56]] */ static std::vector tokenize_input_prompts(llama_context * ctx, const json & json_prompt, bool add_special, bool parse_special) { From 0e70ba686e6c717a0aa41d88284e2a392c2bd0cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 18 Dec 2024 11:05:29 +0200 Subject: [PATCH 16/34] server : add "tokens" output (#10853) * server : add "tokens" output ggml-ci * server : update readme ggml-ci * server : return tokens ids only if requested ggml-ci * tests : improve "tokens" type check Co-authored-by: Xuan Son Nguyen * server : remove "tokens" from the OAI endpoint ggml-ci --------- Co-authored-by: Xuan Son Nguyen --- examples/server/README.md | 8 +++- examples/server/server.cpp | 38 ++++++++++++++----- examples/server/tests/unit/test_completion.py | 16 ++++++-- 3 files changed, 46 insertions(+), 16 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 63a7bf43a920d..ecd24c899fc86 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -438,19 +438,22 @@ These words will not be included in the completion, so make sure to add them to `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true` +`return_tokens`: Return the raw generated token ids in the `tokens` field. Otherwise `tokens` remains empty. Default: `false` + `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values. `timings_per_token`: Include prompt processing and text generation speed information in each response. Default: `false` **Response format** -- Note: In streaming mode (`stream`), only `content` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. +- Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: ```json { - "content": "", + "content": "", + "tokens": [ generated token ids if requested ], "probs": [ { "prob": float, @@ -468,6 +471,7 @@ These words will not be included in the completion, so make sure to add them to Notice that each `probs` is an array of length `n_probs`. - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. +- `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.). - `model`: The path to the model loaded with `-m` diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 71566b94e61bb..40aac33f0bf13 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -79,8 +79,9 @@ enum error_type { }; struct slot_params { - bool stream = true; - bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt + bool stream = true; + bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt + bool return_tokens = false; int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half @@ -199,6 +200,7 @@ struct server_task { params.stream = json_value(data, "stream", false); params.cache_prompt = json_value(data, "cache_prompt", true); + params.return_tokens = json_value(data, "return_tokens", false); params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); params.n_indent = json_value(data, "n_indent", defaults.n_indent); params.n_keep = json_value(data, "n_keep", defaults.n_keep); @@ -468,7 +470,10 @@ struct completion_token_output { struct server_task_result_cmpl_final : server_task_result { int index = 0; - std::string content; + + std::string content; + llama_tokens tokens; + bool stream; result_timings timings; std::string prompt; @@ -510,6 +515,7 @@ struct server_task_result_cmpl_final : server_task_result { json res = json { {"index", index}, {"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"tokens", stream ? llama_tokens {} : tokens}, {"id_slot", id_slot}, {"stop", true}, {"model", oaicompat_model}, @@ -539,9 +545,9 @@ struct server_task_result_cmpl_final : server_task_result { json choices = json::array({json{ {"finish_reason", finish_reason}, {"index", 0}, - {"message", json{ + {"message", json { {"content", content}, - {"role", "assistant"} + {"role", "assistant"} } }}}); @@ -605,7 +611,9 @@ struct server_task_result_cmpl_final : server_task_result { struct server_task_result_cmpl_partial : server_task_result { int index = 0; - std::string content; + + std::string content; + llama_tokens tokens; int32_t n_decoded; int32_t n_prompt_tokens; @@ -637,6 +645,7 @@ struct server_task_result_cmpl_partial : server_task_result { json res = json { {"index", index}, {"content", content}, + {"tokens", tokens}, {"stop", false}, {"id_slot", id_slot}, {"tokens_predicted", n_decoded}, @@ -678,7 +687,7 @@ struct server_task_result_cmpl_partial : server_task_result { json second_ret = json{ {"choices", json::array({json{{"finish_reason", nullptr}, {"index", 0}, - {"delta", json{ + {"delta", json { {"content", content}}} }})}, {"created", t}, @@ -693,7 +702,7 @@ struct server_task_result_cmpl_partial : server_task_result { {"finish_reason", nullptr}, {"index", 0}, {"delta", - json{ + json { {"content", content}, }}, }}); @@ -955,8 +964,11 @@ struct server_slot { size_t last_nl_pos = 0; - std::string generated_text; + std::string generated_text; + llama_tokens generated_tokens; + llama_tokens cache_tokens; + std::vector generated_token_probs; bool has_next_token = true; @@ -1000,6 +1012,7 @@ struct server_slot { n_sent_token_probs = 0; task_type = SERVER_TASK_TYPE_COMPLETION; + generated_tokens.clear(); generated_token_probs.clear(); } @@ -1740,8 +1753,10 @@ struct server_context { const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special); slot.sampled = result.tok; - // search stop word and delete it slot.generated_text += token_str; + if (slot.params.return_tokens) { + slot.generated_tokens.push_back(result.tok); + } slot.has_next_token = true; // check if there is incomplete UTF-8 character at the end @@ -1766,6 +1781,7 @@ struct server_context { break; } + // search stop word and delete it if (!incomplete) { size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); @@ -1918,6 +1934,7 @@ struct server_context { res->id = slot.id_task; res->index = slot.index; res->content = tkn.text_to_send; + res->tokens = { tkn.tok }; res->n_decoded = slot.n_decoded; res->n_prompt_tokens = slot.n_prompt_tokens; @@ -1958,6 +1975,7 @@ struct server_context { res->index = slot.index; res->content = slot.generated_text; + res->tokens = slot.generated_tokens; res->timings = slot.get_timings(); res->prompt = common_detokenize(ctx, slot.prompt_tokens, true); diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py index 062ebcd4a05cc..36aee57dd3638 100644 --- a/examples/server/tests/unit/test_completion.py +++ b/examples/server/tests/unit/test_completion.py @@ -10,16 +10,17 @@ def create_server(): global server server = ServerPreset.tinyllama2() -@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [ - ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False), - ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False), +@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated,return_tokens", [ + ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False, False), + ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False, True), ]) -def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool): +def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool, return_tokens: bool): global server server.start() res = server.make_request("POST", "/completion", data={ "n_predict": n_predict, "prompt": prompt, + "return_tokens": return_tokens, }) assert res.status_code == 200 assert res.body["timings"]["prompt_n"] == n_prompt @@ -27,6 +28,11 @@ def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, assert res.body["truncated"] == truncated assert type(res.body["has_new_line"]) == bool assert match_regex(re_content, res.body["content"]) + if return_tokens: + assert len(res.body["tokens"]) > 0 + assert all(type(tok) == int for tok in res.body["tokens"]) + else: + assert res.body["tokens"] == [] @pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [ @@ -56,6 +62,8 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp assert data["generation_settings"]["seed"] == server.seed assert match_regex(re_content, content) else: + assert len(data["tokens"]) > 0 + assert all(type(tok) == int for tok in data["tokens"]) content += data["content"] From 152610eda91217ac409342cd976d05f5114ad39f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 18 Dec 2024 13:01:41 +0200 Subject: [PATCH 17/34] server : output embeddings for all tokens when pooling = none (#10861) * server : add "tokens" output ggml-ci * server : output embeddings for all tokens when pooling = none ggml-ci * server : update readme [no ci] * server : fix spacing [no ci] Co-authored-by: Xuan Son Nguyen * server : be explicit about the pooling type in the tests ggml-ci * server : update /embeddings and /v1/embeddings endpoints ggml-ci * server : do not normalize embeddings when there is no pooling ggml-ci * server : update readme ggml-ci * server : fixes * tests : update server tests ggml-ci * server : update readme [no ci] * server : remove rebase artifact --------- Co-authored-by: Xuan Son Nguyen --- common/common.cpp | 4 +- common/common.h | 3 +- examples/gritlm/gritlm.cpp | 2 +- examples/retrieval/retrieval.cpp | 2 +- examples/server/README.md | 42 +++++++++++ examples/server/server.cpp | 74 +++++++++++++++----- examples/server/tests/unit/test_embedding.py | 65 +++++++++++++---- examples/server/tests/utils.py | 3 + 8 files changed, 158 insertions(+), 37 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index c0c98232ed3bb..05d3ba766e38b 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1780,7 +1780,9 @@ void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) break; case 0: // max absolute for (int i = 0; i < n; i++) { - if (sum < std::abs(inp[i])) sum = std::abs(inp[i]); + if (sum < std::abs(inp[i])) { + sum = std::abs(inp[i]); + } } sum /= 32760.0; // make an int16 range break; diff --git a/common/common.h b/common/common.h index 5f556c24d933c..ec0e49f6f1806 100644 --- a/common/common.h +++ b/common/common.h @@ -596,7 +596,8 @@ void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_si // Embedding utils // -void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2); +// TODO: repace embd_norm with an enum +void common_embd_normalize(const float * inp, float * out, int n, int embd_norm); float common_embd_similarity_cos(const float * embd1, const float * embd2, int n); diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp index 6e42fa0734ecb..18a945b33905f 100644 --- a/examples/gritlm/gritlm.cpp +++ b/examples/gritlm/gritlm.cpp @@ -75,7 +75,7 @@ static std::vector> encode(llama_context * ctx, const std::ve } std::vector emb_norm(emb_unorm.size()); - common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd); + common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2); result.push_back(emb_norm); #ifdef GRIT_DEBUG diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 23ff4db27a420..a5c6fe7e58523 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -107,7 +107,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } float * out = output + batch.seq_id[i][0] * n_embd; - common_embd_normalize(embd, out, n_embd); + common_embd_normalize(embd, out, n_embd, 2); } } diff --git a/examples/server/README.md b/examples/server/README.md index ecd24c899fc86..d006a8d37cf6f 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -763,6 +763,8 @@ curl http://localhost:8080/v1/chat/completions \ ### POST `/v1/embeddings`: OpenAI-compatible embeddings API +This endpoint requires that the model uses a pooling different than type `none`. The embeddings are normalized using the Eucledian norm. + *Options:* See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). @@ -795,6 +797,46 @@ See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-r }' ``` +### POST `/embeddings`: non-OpenAI-compatible embeddings API + +This endpoint supports all poolings, including `--pooling none`. When the pooling is `none`, the responses will contain the *unnormalized* embeddings for *all* input tokens. For all other pooling types, only the pooled embeddings are returned, normalized using Euclidian norm. + +Note that the response format of this endpoint is different from `/v1/embeddings`. + +*Options:* + +Same as the `/v1/embeddings` endpoint. + +*Examples:* + +Same as the `/v1/embeddings` endpoint. + +**Response format** + +```json +[ + { + "index": 0, + "embedding": [ + [ ... embeddings for token 0 ... ], + [ ... embeddings for token 1 ... ], + [ ... ] + [ ... embeddings for token N-1 ... ], + ] + }, + ... + { + "index": P, + "embedding": [ + [ ... embeddings for token 0 ... ], + [ ... embeddings for token 1 ... ], + [ ... ] + [ ... embeddings for token N-1 ... ], + ] + } +] +``` + ### GET `/slots`: Returns the current slots processing state > [!WARNING] diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 40aac33f0bf13..5ed4e8d274428 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -726,18 +726,32 @@ struct server_task_result_cmpl_partial : server_task_result { struct server_task_result_embd : server_task_result { int index = 0; - std::vector embedding; + std::vector> embedding; int32_t n_tokens; + // OAI-compat fields + bool oaicompat = false; + virtual int get_index() override { return index; } virtual json to_json() override { + return oaicompat ? to_json_oaicompat() : to_json_non_oaicompat(); + } + + json to_json_non_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding}, + }; + } + + json to_json_oaicompat() { return json { {"index", index}, - {"embedding", embedding}, + {"embedding", embedding[0]}, {"tokens_evaluated", n_tokens}, }; } @@ -2017,9 +2031,10 @@ struct server_context { void send_embedding(const server_slot & slot, const llama_batch & batch) { auto res = std::make_unique(); - res->id = slot.id_task; - res->index = slot.index; - res->n_tokens = slot.n_prompt_tokens; + res->id = slot.id_task; + res->index = slot.index; + res->n_tokens = slot.n_prompt_tokens; + res->oaicompat = slot.params.oaicompat; const int n_embd = llama_n_embd(model); @@ -2038,12 +2053,18 @@ struct server_context { if (embd == NULL) { SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]); - res->embedding = std::vector(n_embd, 0.0f); + res->embedding.push_back(std::vector(n_embd, 0.0f)); continue; } - common_embd_normalize(embd, embd_res.data(), n_embd); - res->embedding = embd_res; + // normalize only when there is pooling + // TODO: configurable + if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) { + common_embd_normalize(embd, embd_res.data(), n_embd, 2); + res->embedding.push_back(embd_res); + } else { + res->embedding.push_back({ embd, embd + n_embd }); + } } SLT_DBG(slot, "%s", "sending embeddings\n"); @@ -2657,7 +2678,10 @@ struct server_context { // add prompt tokens for processing in the current batch while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { - common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false); + // without pooling, we want to output the embeddings for all the tokens in the batch + const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE; + + common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, need_embd); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); @@ -3665,14 +3689,17 @@ int main(int argc, char ** argv) { res_ok(res, data); }; - const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { + const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, bool oaicompat) { const json body = json::parse(req.body); - bool oaicompat = false; + + if (oaicompat && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { + res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST)); + return; + } // for the shape of input/content, see tokenize_input_prompts() json prompt; - if (body.contains("input")) { - oaicompat = true; + if (body.count("input") != 0) { prompt = body.at("input"); } else if (body.contains("content")) { oaicompat = false; @@ -3697,10 +3724,15 @@ int main(int argc, char ** argv) { { std::vector tasks; for (size_t i = 0; i < tokenized_prompts.size(); i++) { - server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); + server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); + task.id = ctx_server.queue_tasks.get_new_id(); task.index = i; task.prompt_tokens = std::move(tokenized_prompts[i]); + + // OAI-compat + task.params.oaicompat = oaicompat; + tasks.push_back(task); } @@ -3728,12 +3760,18 @@ int main(int argc, char ** argv) { } // write JSON response - json root = oaicompat - ? format_embeddings_response_oaicompat(body, responses) - : responses.size() == 1 ? responses[0] : json(responses); + json root = oaicompat ? format_embeddings_response_oaicompat(body, responses) : json(responses); res_ok(res, root); }; + const auto handle_embeddings = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { + handle_embeddings_impl(req, res, false); + }; + + const auto handle_embeddings_oai = [&handle_embeddings_impl](const httplib::Request & req, httplib::Response & res) { + handle_embeddings_impl(req, res, true); + }; + const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) { res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED)); @@ -3907,7 +3945,7 @@ int main(int argc, char ** argv) { svr->Post("/infill", handle_infill); svr->Post("/embedding", handle_embeddings); // legacy svr->Post("/embeddings", handle_embeddings); - svr->Post("/v1/embeddings", handle_embeddings); + svr->Post("/v1/embeddings", handle_embeddings_oai); svr->Post("/rerank", handle_rerank); svr->Post("/reranking", handle_rerank); svr->Post("/v1/rerank", handle_rerank); diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py index 4f4e9dcf087fa..e32d745829605 100644 --- a/examples/server/tests/unit/test_embedding.py +++ b/examples/server/tests/unit/test_embedding.py @@ -14,8 +14,9 @@ def create_server(): def test_embedding_single(): global server + server.pooling = 'last' server.start() - res = server.make_request("POST", "/embeddings", data={ + res = server.make_request("POST", "/v1/embeddings", data={ "input": "I believe the meaning of life is", }) assert res.status_code == 200 @@ -29,8 +30,9 @@ def test_embedding_single(): def test_embedding_multiple(): global server + server.pooling = 'last' server.start() - res = server.make_request("POST", "/embeddings", data={ + res = server.make_request("POST", "/v1/embeddings", data={ "input": [ "I believe the meaning of life is", "Write a joke about AI from a very long prompt which will not be truncated", @@ -46,7 +48,7 @@ def test_embedding_multiple(): @pytest.mark.parametrize( - "content,is_multi_prompt", + "input,is_multi_prompt", [ # single prompt ("string", False), @@ -59,25 +61,55 @@ def test_embedding_multiple(): ([[12, 34, 56], [12, "string", 34, 56]], True), ] ) -def test_embedding_mixed_input(content, is_multi_prompt: bool): +def test_embedding_mixed_input(input, is_multi_prompt: bool): global server server.start() - res = server.make_request("POST", "/embeddings", data={"content": content}) + res = server.make_request("POST", "/v1/embeddings", data={"input": input}) assert res.status_code == 200 + data = res.body['data'] if is_multi_prompt: - assert len(res.body) == len(content) - for d in res.body: + assert len(data) == len(input) + for d in data: assert 'embedding' in d assert len(d['embedding']) > 1 else: - assert 'embedding' in res.body - assert len(res.body['embedding']) > 1 + assert 'embedding' in data[0] + assert len(data[0]['embedding']) > 1 + + +def test_embedding_pooling_none(): + global server + server.pooling = 'none' + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": "hello hello hello", + }) + assert res.status_code == 200 + assert 'embedding' in res.body[0] + assert len(res.body[0]['embedding']) == 5 # 3 text tokens + 2 special + + # make sure embedding vector is not normalized + for x in res.body[0]['embedding']: + assert abs(sum([x ** 2 for x in x]) - 1) > EPSILON + + +def test_embedding_pooling_none_oai(): + global server + server.pooling = 'none' + server.start() + res = server.make_request("POST", "/v1/embeddings", data={ + "input": "hello hello hello", + }) + + # /v1/embeddings does not support pooling type 'none' + assert res.status_code == 400 def test_embedding_openai_library_single(): global server + server.pooling = 'last' server.start() - client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is") assert len(res.data) == 1 assert len(res.data[0].embedding) > 1 @@ -85,8 +117,9 @@ def test_embedding_openai_library_single(): def test_embedding_openai_library_multiple(): global server + server.pooling = 'last' server.start() - client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1") res = client.embeddings.create(model="text-embedding-3-small", input=[ "I believe the meaning of life is", "Write a joke about AI from a very long prompt which will not be truncated", @@ -100,8 +133,9 @@ def test_embedding_openai_library_multiple(): def test_embedding_error_prompt_too_long(): global server + server.pooling = 'last' server.start() - res = server.make_request("POST", "/embeddings", data={ + res = server.make_request("POST", "/v1/embeddings", data={ "input": "This is a test " * 512, }) assert res.status_code != 200 @@ -109,8 +143,9 @@ def test_embedding_error_prompt_too_long(): def test_same_prompt_give_same_result(): + server.pooling = 'last' server.start() - res = server.make_request("POST", "/embeddings", data={ + res = server.make_request("POST", "/v1/embeddings", data={ "input": [ "I believe the meaning of life is", "I believe the meaning of life is", @@ -138,7 +173,7 @@ def test_same_prompt_give_same_result(): def test_embedding_usage_single(content, n_tokens): global server server.start() - res = server.make_request("POST", "/embeddings", data={"input": content}) + res = server.make_request("POST", "/v1/embeddings", data={"input": content}) assert res.status_code == 200 assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens'] assert res.body['usage']['prompt_tokens'] == n_tokens @@ -147,7 +182,7 @@ def test_embedding_usage_single(content, n_tokens): def test_embedding_usage_multiple(): global server server.start() - res = server.make_request("POST", "/embeddings", data={ + res = server.make_request("POST", "/v1/embeddings", data={ "input": [ "I believe the meaning of life is", "I believe the meaning of life is", diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py index d988ccf5e3061..277125e88b534 100644 --- a/examples/server/tests/utils.py +++ b/examples/server/tests/utils.py @@ -65,6 +65,7 @@ class ServerProcess: server_reranking: bool | None = False server_metrics: bool | None = False server_slots: bool | None = False + pooling: str | None = None draft: int | None = None api_key: str | None = None response_format: str | None = None @@ -132,6 +133,8 @@ def start(self, timeout_seconds: int = 10) -> None: server_args.append("--metrics") if self.server_slots: server_args.append("--slots") + if self.pooling: + server_args.extend(["--pooling", self.pooling]) if self.model_alias: server_args.extend(["--alias", self.model_alias]) if self.n_ctx: From 7bbb5acf125d1d2840cac7d31b9aaa72210dd5ec Mon Sep 17 00:00:00 2001 From: Gaetan Bisson Date: Wed, 18 Dec 2024 04:00:07 -1000 Subject: [PATCH 18/34] server: avoid overwriting Authorization header (#10878) * server: avoid overwriting Authorization header If no API key is set, leave the Authorization header as is. It may be used by another part of the Web stack, such as an authenticating proxy. Fixes https://github.com/ggerganov/llama.cpp/issues/10854 * rebuild --------- Co-authored-by: Xuan Son Nguyen --- examples/server/public/index.html.gz | Bin 1205430 -> 1205430 bytes examples/server/webui/src/main.js | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/public/index.html.gz b/examples/server/public/index.html.gz index 01dd41811d083cb3dfdf10860c00cc2b1f003832..6a64915579c39cec4bfdc995b76f9994824cdddc 100644 GIT binary patch delta 976397 zcmV(~K+nIn$w{`!Nq~d_gaWh!=bL|O56znGJlhJnwim_Ht7!FZO$W1YQ17r_J)Xp}mL`LIP~ zi<;ZpPUmP`Fc&Q%+xM?wT;UszbfOI4pC>E=vuAF&pX{ae%e`Eo6&e z*$EO6b9CACn7veXx#o>wah|>F{Yw=bnGbmtMQNR0<7a zSoOcJ&30LP;M!>5l6!mGEU4S)=4rg>_GJ2JezmG0LFhSRJHcnT>p*}Sb-X17hFpStK@aVLt;GFK?T-Eexi>WZ2Yj> z)dz0KyU5yWi9mGOP4C&WR~N;FX`)B#%g+|8B+XCtA~E;z>N;*YfVw!i^JJnI#-+CW zxVgi2dOh=Rw|7*@6>=Z~H4U9LSE~lnBoA=XFCD4hvz(w3Jo?AuDz&(j-kJl7E*W1k5SbSQr`H1y9qp^QDJ$|cT4rEG zP-|S3RqfIw2R(oBn7z?gwKvRT)z}38f@)lUzA2wq(Gq`ZtB0HNAzqGW{U4KLQIuFs 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z&y!+yR^Io8vvbN+oE?U6^4q+n$?(m|@ZWm3}%-_wesBLw2ABdOwc z(kqTsmxS+Ru>wW=B!$RabDa7uswrJ}N3Lr|qK|DLkjEN+I?$RU@yhXS(+q8I)llXK zV>mf{I-T9`+kU_Z;JJT*=y-lt^;!zh4mAdE=DGrJY>paoasC3&8a?_g=X$UG70nWM zeRU{f!sz-`;_5B>D}Lld7oiZH(>NU-?8OBnAH7?NcYwf4LE!x)Z~iYLj_+aH Date: Wed, 18 Dec 2024 19:27:21 +0200 Subject: [PATCH 19/34] tts : add OuteTTS support (#10784) * server : add "tokens" output ggml-ci * server : output embeddings for all tokens when pooling = none ggml-ci * server : be explicit about the pooling type in the tests ggml-ci * server : do not normalize embeddings when there is no pooling ggml-ci * llama : add OuteTTS support (wip) * wip * extract features * first conv * group norm * resnet conv * resnet * attn * pos net * layer norm * convnext * head * hann window * fix n_embd + remove llama.cpp hacks * compute hann window * fft * spectrum processing * clean-up * tts : receive input text and generate codes * clip : fix new conv name * tts : minor fix * tts : add header + minor fixes ggml-ci * tts : add matchematical constant ggml-ci * tts : fix sampling + cut initial noise * tts : fixes * tts : update default samplers ggml-ci * tts : text pre-processing * tts : outetts-voc -> wavtokenizer-dec * tts : remove hardcoded constants ggml-ci * tts : fix tensor shapes * llama : refactor wavtokenizer tensors ggml-ci * cont ggml-ci * cont [no ci] * llama : update WavTokenizer to non-causal attn * llama : handle no-vocab detokenization * tts : add Python example for OuteTTS (wip) * tts : extend python example to generate spectrogram ggml-ci * server : fix rebase artifacts * tts : enable "return_tokens" in Python example ggml-ci * tts : minor fixes * common : support HF download for vocoder --- common/arg.cpp | 60 +- common/common.cpp | 7 +- common/common.h | 13 +- convert_hf_to_gguf.py | 59 +- examples/CMakeLists.txt | 1 + examples/llava/clip.cpp | 6 +- examples/tts/CMakeLists.txt | 5 + examples/tts/convert_pt_to_hf.py | 180 ++++++ examples/tts/tts-outetts.py | 175 ++++++ examples/tts/tts.cpp | 932 ++++++++++++++++++++++++++++++ ggml/include/ggml.h | 41 +- ggml/src/ggml.c | 220 ++++--- gguf-py/gguf/constants.py | 275 +++++---- gguf-py/gguf/gguf_writer.py | 21 + gguf-py/gguf/tensor_mapping.py | 70 +++ gguf-py/tests/test_quants.py | 2 +- include/llama.h | 3 - src/llama-vocab.cpp | 4 + src/llama.cpp | 963 ++++++++++++++++++++++--------- 19 files changed, 2507 insertions(+), 530 deletions(-) create mode 100644 examples/tts/CMakeLists.txt create mode 100644 examples/tts/convert_pt_to_hf.py create mode 100644 examples/tts/tts-outetts.py create mode 100644 examples/tts/tts.cpp diff --git a/common/arg.cpp b/common/arg.cpp index 3d55289c33192..e5ddd8318f787 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -119,29 +119,33 @@ std::string common_arg::to_string() { // utils // -static void common_params_handle_model_default(common_params & params) { - if (!params.hf_repo.empty()) { +static void common_params_handle_model_default( + std::string & model, + std::string & model_url, + std::string & hf_repo, + std::string & hf_file) { + if (!hf_repo.empty()) { // short-hand to avoid specifying --hf-file -> default it to --model - if (params.hf_file.empty()) { - if (params.model.empty()) { + if (hf_file.empty()) { + if (model.empty()) { throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); } - params.hf_file = params.model; - } else if (params.model.empty()) { + hf_file = model; + } else if (model.empty()) { // this is to avoid different repo having same file name, or same file name in different subdirs - std::string filename = params.hf_repo + "_" + params.hf_file; + std::string filename = hf_repo + "_" + hf_file; // to make sure we don't have any slashes in the filename string_replace_all(filename, "/", "_"); - params.model = fs_get_cache_file(filename); + model = fs_get_cache_file(filename); } - } else if (!params.model_url.empty()) { - if (params.model.empty()) { - auto f = string_split(params.model_url, '#').front(); + } else if (!model_url.empty()) { + if (model.empty()) { + auto f = string_split(model_url, '#').front(); f = string_split(f, '?').front(); - params.model = fs_get_cache_file(string_split(f, '/').back()); + model = fs_get_cache_file(string_split(f, '/').back()); } - } else if (params.model.empty()) { - params.model = DEFAULT_MODEL_PATH; + } else if (model.empty()) { + model = DEFAULT_MODEL_PATH; } } @@ -276,7 +280,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); } - common_params_handle_model_default(params); + // TODO: refactor model params in a common struct + common_params_handle_model_default(params.model, params.model_url, params.hf_repo, params.hf_file); + common_params_handle_model_default(params.vocoder.model, params.vocoder.model_url, params.vocoder.hf_repo, params.vocoder.hf_file); if (params.escape) { string_process_escapes(params.prompt); @@ -842,7 +848,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_sparam()); add_opt(common_arg( - {"--sampling-seq"}, "SEQUENCE", + {"--sampling-seq", "--sampler-seq"}, "SEQUENCE", string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()), [](common_params & params, const std::string & value) { params.sampling.samplers = common_sampler_types_from_chars(value); @@ -1581,6 +1587,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.hf_file = value; } ).set_env("LLAMA_ARG_HF_FILE")); + add_opt(common_arg( + {"-hfrv", "--hf-repo-v"}, "REPO", + "Hugging Face model repository for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO_V")); + add_opt(common_arg( + {"-hffv", "--hf-file-v"}, "FILE", + "Hugging Face model file for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE_V")); add_opt(common_arg( {"-hft", "--hf-token"}, "TOKEN", "Hugging Face access token (default: value from HF_TOKEN environment variable)", @@ -2178,5 +2198,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT")); + add_opt(common_arg( + {"-mv", "--model-vocoder"}, "FNAME", + "vocoder model for audio generation (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + return ctx_arg; } diff --git a/common/common.cpp b/common/common.cpp index 05d3ba766e38b..20be9291161ca 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1095,7 +1095,7 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p #define CURL_MAX_RETRY 3 #define CURL_RETRY_DELAY_SECONDS 2 -static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) { +static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) { int remaining_attempts = max_attempts; while (remaining_attempts > 0) { @@ -1119,7 +1119,6 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_ } static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) { - // Initialize libcurl std::unique_ptr curl(curl_easy_init(), &curl_easy_cleanup); if (!curl) { @@ -1192,11 +1191,13 @@ static bool common_download_file(const std::string & url, const std::string & pa std::string etag; std::string last_modified; }; + common_load_model_from_url_headers headers; + { typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *); auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { - common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata; + common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata; static std::regex header_regex("([^:]+): (.*)\r\n"); static std::regex etag_regex("ETag", std::regex_constants::icase); diff --git a/common/common.h b/common/common.h index ec0e49f6f1806..1d2bd932c211d 100644 --- a/common/common.h +++ b/common/common.h @@ -80,6 +80,7 @@ enum llama_example { LLAMA_EXAMPLE_LLAVA, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_PARALLEL, + LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_COUNT, }; @@ -159,6 +160,7 @@ struct common_params_sampling { struct common_params_speculative { std::vector devices; // devices to use for offloading + int32_t n_ctx = 0; // draft context size int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding int32_t n_min = 5; // minimum number of draft tokens to use for speculative decoding @@ -172,6 +174,14 @@ struct common_params_speculative { std::string model = ""; // draft model for speculative decoding // NOLINT }; +struct common_params_vocoder { + std::string hf_repo = ""; // HF repo // NOLINT + std::string hf_file = ""; // HF file // NOLINT + + std::string model = ""; // model path // NOLINT + std::string model_url = ""; // model url to download // NOLINT +}; + struct common_params { int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 4096; // context size @@ -214,8 +224,9 @@ struct common_params { enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings - struct common_params_sampling sampling; + struct common_params_sampling sampling; struct common_params_speculative speculative; + struct common_params_vocoder vocoder; std::string model = ""; // model path // NOLINT std::string model_alias = ""; // model alias // NOLINT diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 9dc1673bc2c06..4a0b00f69c699 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -221,17 +221,17 @@ def set_gguf_parameters(self): self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") - n_embd = self.find_hparam(["hidden_size", "n_embd"]) - self.gguf_writer.add_embedding_length(n_embd) - logger.info(f"gguf: embedding length = {n_embd}") + if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None: + self.gguf_writer.add_embedding_length(n_embd) + logger.info(f"gguf: embedding length = {n_embd}") if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) logger.info(f"gguf: feed forward length = {n_ff}") - n_head = self.find_hparam(["num_attention_heads", "n_head"]) - self.gguf_writer.add_head_count(n_head) - logger.info(f"gguf: head count = {n_head}") + if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None: + self.gguf_writer.add_head_count(n_head) + logger.info(f"gguf: head count = {n_head}") if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) @@ -296,7 +296,9 @@ def prepare_tensors(self): break for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)): - data = data_torch.squeeze().numpy() + # TODO: why do we squeeze here? + # data = data_torch.squeeze().numpy() + data = data_torch.numpy() # if data ends up empty, it means data_torch was a scalar tensor -> restore if len(data.shape) == 0: @@ -324,6 +326,8 @@ def prepare_tensors(self): gguf.MODEL_TENSOR.TIME_MIX_W2, gguf.MODEL_TENSOR.TIME_MIX_DECAY_W1, gguf.MODEL_TENSOR.TIME_MIX_DECAY_W2, + gguf.MODEL_TENSOR.POSNET_NORM1, + gguf.MODEL_TENSOR.POSNET_NORM2, ) ) or not new_name.endswith(".weight") @@ -689,6 +693,9 @@ def get_vocab_base_pre(self, tokenizer) -> str: return res # Marker: End get_vocab_base_pre + def _set_vocab_none(self) -> None: + self.gguf_writer.add_tokenizer_model("none") + def _set_vocab_gpt2(self) -> None: tokens, toktypes, tokpre = self.get_vocab_base() self.gguf_writer.add_tokenizer_model("gpt2") @@ -2027,6 +2034,44 @@ def get_tensors(self) -> Iterator[tuple[str, Tensor]]: yield name, data +@Model.register("WavTokenizerDec") +class WavTokenizerDecModel(Model): + model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if \ + name.endswith("codebook.cluster_size") or \ + name.endswith("codebook.embed_avg") or \ + name.endswith("codebook.inited"): + logger.debug(f"Skipping {name!r}") + return [] + + logger.info(f"{self.map_tensor_name(name)} -> {data_torch.shape}") + + return [(self.map_tensor_name(name), data_torch)] + + def set_vocab(self): + self._set_vocab_none() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_vocab_size (self.hparams["vocab_size"]) + self.gguf_writer.add_features_length (self.hparams["n_embd_features"]) + self.gguf_writer.add_feed_forward_length(self.hparams["n_ff"]) + self.gguf_writer.add_group_norm_eps (self.hparams["group_norm_epsilon"]) + self.gguf_writer.add_group_norm_groups (self.hparams["group_norm_groups"]) + + self.gguf_writer.add_posnet_embedding_length(self.hparams["posnet"]["n_embd"]) + self.gguf_writer.add_posnet_block_count (self.hparams["posnet"]["n_layer"]) + + self.gguf_writer.add_convnext_embedding_length(self.hparams["convnext"]["n_embd"]) + self.gguf_writer.add_convnext_block_count (self.hparams["convnext"]["n_layer"]) + + self.gguf_writer.add_causal_attention(False) + + @Model.register("Qwen2MoeForCausalLM") class Qwen2MoeModel(Model): model_arch = gguf.MODEL_ARCH.QWEN2MOE diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 21b31392e81d0..66cfab2c3b796 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -51,6 +51,7 @@ else() add_subdirectory(speculative) add_subdirectory(speculative-simple) add_subdirectory(tokenize) + add_subdirectory(tts) add_subdirectory(gen-docs) if (NOT GGML_BACKEND_DL) # these examples use the backends directly and cannot be built with dynamic loading diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index ba28c07c6aeec..463b7c865b90c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -896,7 +896,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3)); mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); // layer norm // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] @@ -944,7 +944,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // block_2 { // stride = 2 - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] // layer norm @@ -1005,7 +1005,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // mlp_2 ne [24, 24, 2048, 1] mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); // weight ne = [3, 3, 2048, 1] - struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + struct ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); diff --git a/examples/tts/CMakeLists.txt b/examples/tts/CMakeLists.txt new file mode 100644 index 0000000000000..c72bd814c3b31 --- /dev/null +++ b/examples/tts/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-tts) +add_executable(${TARGET} tts.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/tts/convert_pt_to_hf.py b/examples/tts/convert_pt_to_hf.py new file mode 100644 index 0000000000000..8909a65fd1e13 --- /dev/null +++ b/examples/tts/convert_pt_to_hf.py @@ -0,0 +1,180 @@ +# convert the https://huggingface.co/novateur/WavTokenizer-large-speech-75token to HF format +# the goal is to be able to reuse the convert_hf_to_gguf.py after that to create a GGUF file with the WavTokenizer decoder +# +# TODO: this script is LLM-generated and probably very inefficient and should be rewritten + +import torch +import json +import os +import sys +import re + +from safetensors.torch import save_file + +# default +model_path = './model.pt'; + +# read from CLI +if len(sys.argv) > 1: + model_path = sys.argv[1] + +# get the directory of the input model +path_dst = os.path.dirname(model_path) + +print(f"Loading model from {model_path}") + +model = torch.load(model_path, map_location='cpu') + +#print(model) + +# print all keys +for key in model.keys(): + print(key) + if key == 'hyper_parameters': + #print(model[key]) + # dump as json pretty + print(json.dumps(model[key], indent=4)) + #if key != 'state_dict' and key != 'optimizer_states': + # print(model[key]) + +# Check if the loaded model is a state_dict or a model instance +if isinstance(model, torch.nn.Module): + state_dict = model.state_dict() +else: + state_dict = model + +# Print the structure of the state_dict to understand its format +print("State dictionary keys:") +for key in state_dict.keys(): + print(key) + +# Ensure the state_dict is flat and contains only torch.Tensor objects +def flatten_state_dict(state_dict, parent_key='', sep='.'): + items = [] + items_new = [] + + for k, v in state_dict.items(): + new_key = f"{parent_key}{sep}{k}" if parent_key else k + if isinstance(v, torch.Tensor): + items.append((new_key, v)) + elif isinstance(v, dict): + items.extend(flatten_state_dict(v, new_key, sep=sep).items()) + return dict(items) + + size_total_mb = 0 + + for key, value in list(items): + # keep only what we need for inference + if not key.startswith('state_dict.feature_extractor.encodec.quantizer.') and \ + not key.startswith('state_dict.backbone.') and \ + not key.startswith('state_dict.head.out'): + print('Skipping key: ', key) + continue + + new_key = key + + new_key = new_key.replace('state_dict.', '') + new_key = new_key.replace('pos_net', 'posnet') + + # check if matches "backbone.posnet.%d.bias" or "backbone.posnet.%d.weight" + if new_key.startswith("backbone.posnet."): + match = re.match(r"backbone\.posnet\.(\d+)\.(bias|weight)", new_key) + if match: + new_key = f"backbone.posnet.{match.group(1)}.norm.{match.group(2)}" + + # "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed" -> "backbone.embedding.weight" + if new_key == "feature_extractor.encodec.quantizer.vq.layers.0._codebook.embed": + new_key = "backbone.embedding.weight" + + # these are the only rows used + # ref: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/wav_tokenizer/audio_codec.py#L100 + if new_key.endswith("norm.scale.weight"): + new_key = new_key.replace("norm.scale.weight", "norm.weight") + value = value[0] + + if new_key.endswith("norm.shift.weight"): + new_key = new_key.replace("norm.shift.weight", "norm.bias") + value = value[0] + + if new_key.endswith("gamma"): + new_key = new_key.replace("gamma", "gamma.weight") + + # convert from 1D [768] to 2D [768, 1] so that ggml_add can broadcast the bias + if (new_key.endswith("norm.weight") or new_key.endswith("norm1.weight") or new_key.endswith("norm2.weight") or new_key.endswith(".bias")) and (new_key.startswith("backbone.posnet") or new_key.startswith("backbone.embed.bias")): + value = value.unsqueeze(1) + + if new_key.endswith("dwconv.bias"): + value = value.unsqueeze(1) + + size_mb = value.element_size() * value.nelement() / (1024 * 1024) + print(f"{size_mb:8.2f} MB - {new_key}: {value.shape}") + + size_total_mb += size_mb + + #print(key, '->', new_key, ': ', value) + #print(key, '->', new_key) + + items_new.append((new_key, value)) + + print(f"Total size: {size_total_mb:8.2f} MB") + + return dict(items_new) + +flattened_state_dict = flatten_state_dict(state_dict) + + +# Convert the model to the safetensors format +output_path = path_dst + '/model.safetensors' +save_file(flattened_state_dict, output_path) + +print(f"Model has been successfully converted and saved to {output_path}") + +# Calculate the total size of the .safetensors file +total_size = os.path.getsize(output_path) + +# Create the weight map +weight_map = { + "model.safetensors": ["*"] # Assuming all weights are in one file +} + +# Create metadata for the index.json file +metadata = { + "total_size": total_size, + "weight_map": weight_map +} + +# Save the metadata to index.json +index_path = path_dst + '/index.json' +with open(index_path, 'w') as f: + json.dump(metadata, f, indent=4) + +print(f"Metadata has been saved to {index_path}") + +config = { + "architectures": [ + "WavTokenizerDec" + ], + "hidden_size": 1282, + "n_embd_features": 512, + "n_ff": 2304, + "vocab_size": 4096, + "n_head": 1, + "layer_norm_epsilon": 1e-6, + "group_norm_epsilon": 1e-6, + "group_norm_groups": 32, + "max_position_embeddings": 8192, # ? + "n_layer": 12, + "posnet": { + "n_embd": 768, + "n_layer": 6 + }, + "convnext": { + "n_embd": 768, + "n_layer": 12 + }, +} + +with open(path_dst + '/config.json', 'w') as f: + json.dump(config, f, indent=4) + +print(f"Config has been saved to {path_dst + 'config.json'}") diff --git a/examples/tts/tts-outetts.py b/examples/tts/tts-outetts.py new file mode 100644 index 0000000000000..0f81192fca52d --- /dev/null +++ b/examples/tts/tts-outetts.py @@ -0,0 +1,175 @@ +import sys +#import json +#import struct +import requests +import re + +def process_text(text: str): + text = re.sub(r'\d+(\.\d+)?', lambda x: x.group(), text.lower()) # TODO this needs to be fixed + text = re.sub(r'[-_/,\.\\]', ' ', text) + text = re.sub(r'[^a-z\s]', '', text) + text = re.sub(r'\s+', ' ', text).strip() + return text.split() + +# usage: +# python tts-outetts.py http://server-llm:port http://server-dec:port "text" + +if len(sys.argv) <= 3: + print("usage: python tts-outetts.py http://server-llm:port http://server-dec:port \"text\"") + exit(1) + +host_llm = sys.argv[1] +host_dec = sys.argv[2] +text = sys.argv[3] + +prefix = """<|im_start|> +<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>""" + +words = process_text(text) +words = "<|text_sep|>".join([i.strip() for i in words]) +words += "<|text_end|>\n" + +# voice data +# TODO: load from json +#suffix = """<|audio_start|> +#the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|> +#overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|> +#package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|> +#from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|> +#just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|> +#two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|> +#people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|> +#is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|> +#pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|> +#remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|> +#sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|> +#i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|> +#have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|> +#some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|> +#critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|> +#about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|> +#some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|> +#of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|> +#the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|> +#gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|> +#aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|> +#but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|> +#its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|> +#still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|> +#really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|> +#enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|> +#and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|> +#it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|> +#looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|> +#lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>""" + +# TODO: tokenization is slow for some reason - here is pre-tokenized input +suffix = [ 151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585, 152460, 153375, 151670, 198, 74455, + 155808, 151669, 151799, 151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470, 151970, 153413, + 152419, 153334, 153289, 153374, 153199, 152040, 153260, 152721, 152680, 153297, 152419, 153248, 152400, + 152691, 153368, 153437, 151670, 198, 1722, 155828, 151669, 152607, 152256, 152991, 152299, 152688, 153163, + 153016, 152789, 153198, 152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207, 152461, 153321, + 153309, 151750, 152137, 153340, 152573, 152267, 153347, 151789, 152681, 153339, 151992, 152512, 151751, + 152179, 153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904, 152311, 151670, 198, 1499, 155791, + 151669, 152276, 152454, 153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226, 153043, 152325, + 153267, 152622, 151670, 198, 4250, 155797, 151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271, + 152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213, 152112, 153204, 151722, 152542, 151670, 198, + 19789, 155796, 151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002, 152191, 151734, 152312, 152810, + 152237, 153224, 153169, 153224, 152244, 153387, 153404, 151670, 198, 16069, 155811, 151669, 152265, 151946, + 151808, 152412, 152363, 152305, 153156, 152733, 152810, 153157, 152016, 152100, 152069, 153234, 152317, + 152589, 152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504, 153376, 152272, 152433, 152325, + 151941, 151670, 198, 285, 155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381, 152474, 152680, + 152157, 153255, 152324, 151682, 151670, 198, 32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682, + 152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488, 153070, 151883, 152890, 152489, 153144, + 153375, 152358, 151685, 152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669, 151902, 152720, + 153377, 152027, 152378, 152821, 153207, 153459, 153028, 153068, 152507, 153255, 152158, 152921, 151958, + 152609, 152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470, 152606, 152162, 152186, 153071, + 152244, 153118, 153375, 153018, 152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736, 153380, + 153502, 152702, 152115, 153181, 152735, 153277, 153457, 152393, 153112, 152595, 151670, 198, 19098, 155808, + 151669, 152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239, 153163, 152922, 153402, 152034, + 152591, 153438, 152215, 151673, 152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482, 152718, + 152862, 153347, 151670, 198, 72, 155780, 151669, 151795, 152111, 152746, 152377, 153471, 152309, 151670, 198, + 19016, 155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701, 152939, 152536, 152091, 151815, 152733, + 151672, 151670, 198, 14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042, 153504, 152589, 153333, + 151839, 151941, 153038, 153180, 151670, 198, 36996, 8303, 155832, 151669, 152231, 152256, 152835, 152801, + 152985, 153400, 152393, 152818, 152765, 152249, 152600, 151699, 152302, 152752, 153018, 153009, 151992, + 153054, 152847, 153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458, 152048, 152757, 152428, + 153195, 151906, 153006, 153178, 153250, 152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418, + 152228, 152733, 151670, 198, 9096, 155801, 151669, 151698, 153321, 152217, 153039, 152935, 153400, 152122, + 152531, 153106, 152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851, 152901, 152885, 152594, + 153446, 153080, 151670, 198, 14689, 155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191, 151673, + 151690, 151698, 152714, 152846, 152981, 153171, 153384, 153364, 153188, 153246, 151670, 198, 1055, 155779, + 151669, 151869, 152388, 152711, 153334, 151736, 151670, 198, 1782, 155780, 151669, 153483, 153240, 152241, + 152558, 152697, 153046, 151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605, 153034, 153434, + 153372, 153347, 151887, 152453, 152758, 152133, 152510, 152694, 152431, 152321, 153088, 152676, 152223, + 152581, 152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032, 152903, 152859, 152989, 151748, + 152669, 152661, 152650, 152409, 151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469, 152988, + 152894, 151819, 152391, 153019, 152058, 153062, 153230, 151826, 152112, 152306, 152264, 152769, 153390, + 152384, 152435, 152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540, 151919, 151893, 152558, + 152817, 152946, 152956, 152129, 152715, 153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450, + 151670, 198, 8088, 155792, 151669, 152452, 153497, 153353, 152679, 152533, 152382, 152374, 152611, 153341, + 153163, 152285, 153411, 152495, 153141, 152320, 151670, 198, 1199, 155781, 151669, 151764, 152360, 153295, + 152634, 153342, 152199, 152271, 151670, 198, 43366, 155799, 151669, 152308, 151682, 152889, 152016, 152385, + 152629, 152495, 151826, 153321, 152958, 152180, 151886, 153432, 152922, 152128, 153024, 153040, 152593, + 152287, 151677, 151670, 198, 53660, 155808, 151669, 151727, 152092, 152680, 153331, 151699, 152316, 152938, + 152289, 152433, 153384, 151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691, 152489, 151941, + 152049, 152034, 153053, 152179, 153160, 151676, 153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350, + 152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234, 153135, 152291, 153235, 152143, 152583, + 152402, 153483, 152678, 152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825, 152548, 153442, + 152109, 152659, 153325, 152781, 152570, 152957, 151752, 152265, 153381, 152515, 151670, 198, 437, 155787, + 151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174, 151792, 153409, 153327, 152990, 151670, 198, + 275, 155781, 151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974, 151670, 198, 94273, 155799, + 151669, 152953, 152938, 153427, 152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331, 152257, + 152987, 152777, 153448, 152408, 151696, 152408, 152326, 152699, 151670, 198, 385, 16239, 155828, 151669, + 152306, 152268, 153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110, 152918, 152923, 152467, + 152331, 153053, 153330, 151889, 153444, 152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751, + 152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499, 152109, 152255, 151739, 152267, 152759, + 153318, 153165, 153349, 151670, ] + +response = requests.post( + host_llm + "/completion", + json={ + "prompt": [prefix + words, *suffix], + "n_predict": 1024, + "cache_prompt": True, + "return_tokens": True, + "samplers": ["top_k"], + "top_k": 16, + "seed": 1003, + } +) + +response_json = response.json() + +#print(json.dumps(response_json, indent=4)) +#print(json.dumps(response_json["prompt"], indent=4).replace("\\n", "\n")) +#print(json.dumps(response_json["timings"], indent=4)) +#print(json.dumps(response_json["tokens"], indent=4)) + +codes = response_json["tokens"] + +codes = [t - 151672 for t in codes if t >= 151672 and t <= 155772] + +response = requests.post( + host_dec + "/embeddings", + json={ + "input": [*codes], + } +) + +response_json = response.json() + +#print(json.dumps(response_json, indent=4)) + +# spectrogram +embd = response_json[0]["embedding"] + +n_codes = len(embd) +n_embd = len(embd[0]) + +print('spectrogram generated: n_codes: %d, n_embd: %d' % (n_codes, n_embd)) + +# post-process the spectrogram to convert to audio +# TODO: see the tts.cpp:embd_to_audio() and implement it in Python +print('converting to audio ...') +print('TODO: see the tts.cpp:embd_to_audio() and implement it in Python') diff --git a/examples/tts/tts.cpp b/examples/tts/tts.cpp new file mode 100644 index 0000000000000..7f36b80f0dee1 --- /dev/null +++ b/examples/tts/tts.cpp @@ -0,0 +1,932 @@ +#include "arg.h" +#include "common.h" +#include "sampling.h" +#include "log.h" +#include "llama.h" + +#define _USE_MATH_DEFINES // For M_PI on MSVC + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// +// Terminal utils +// + +#define SQR(X) ((X) * (X)) +#define UNCUBE(x) x < 48 ? 0 : x < 115 ? 1 : (x - 35) / 40 + +/** + * Quantizes 24-bit RGB to xterm256 code range [16,256). + */ +static int rgb2xterm256(int r, int g, int b) { + unsigned char cube[] = {0, 0137, 0207, 0257, 0327, 0377}; + int av, ir, ig, ib, il, qr, qg, qb, ql; + av = r * .299 + g * .587 + b * .114 + .5; + ql = (il = av > 238 ? 23 : (av - 3) / 10) * 10 + 8; + qr = cube[(ir = UNCUBE(r))]; + qg = cube[(ig = UNCUBE(g))]; + qb = cube[(ib = UNCUBE(b))]; + if (SQR(qr - r) + SQR(qg - g) + SQR(qb - b) <= + SQR(ql - r) + SQR(ql - g) + SQR(ql - b)) + return ir * 36 + ig * 6 + ib + 020; + return il + 0350; +} + +static std::string set_xterm256_foreground(int r, int g, int b) { + int x = rgb2xterm256(r, g, b); + std::ostringstream oss; + oss << "\033[38;5;" << x << "m"; + return oss.str(); +} + +const std::vector k_colors = { + set_xterm256_foreground(220, 5, 12), + set_xterm256_foreground(232, 96, 28), + set_xterm256_foreground(241, 147, 45), + set_xterm256_foreground(246, 193, 65), + set_xterm256_foreground(247, 240, 86), + set_xterm256_foreground(144, 201, 135), + set_xterm256_foreground( 78, 178, 101), +}; + +static void print_usage(int, char ** argv) { + LOG("\nexample usage:\n"); + LOG("\n %s -m model.gguf -p \"Hello!\"\n", argv[0]); + LOG("\n"); +} + +struct wav_header { + char riff[4] = {'R', 'I', 'F', 'F'}; + uint32_t chunk_size; + char wave[4] = {'W', 'A', 'V', 'E'}; + char fmt[4] = {'f', 'm', 't', ' '}; + uint32_t fmt_chunk_size = 16; + uint16_t audio_format = 1; // PCM + uint16_t num_channels = 1; // Mono + uint32_t sample_rate; + uint32_t byte_rate; + uint16_t block_align; + uint16_t bits_per_sample = 16; + char data[4] = {'d', 'a', 't', 'a'}; + uint32_t data_size; +}; + +static void save_wav16(const std::string & fname, const std::vector & data, int sample_rate) { + std::ofstream file(fname, std::ios::binary); + if (!file) { + LOG_ERR("%s: Failed to open file '%s' for writing", __func__, fname.c_str()); + return; + } + + wav_header header; + header.sample_rate = sample_rate; + header.byte_rate = header.sample_rate * header.num_channels * (header.bits_per_sample / 8); + header.block_align = header.num_channels * (header.bits_per_sample / 8); + header.data_size = data.size() * (header.bits_per_sample / 8); + header.chunk_size = 36 + header.data_size; + + file.write(reinterpret_cast(&header), sizeof(header)); + + for (const auto & sample : data) { + int16_t pcm_sample = static_cast(std::clamp(sample * 32767.0, -32768.0, 32767.0)); + file.write(reinterpret_cast(&pcm_sample), sizeof(pcm_sample)); + } + + file.close(); +} + +static void fill_hann_window(int length, bool periodic, float * output) { + int offset = -1; + if (periodic) { + offset = 0; + } + for (int i = 0; i < length; i++) { + output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset))); + } +} + +// very poor-man fft +static void twiddle(float * real, float * imag, int k, int N) { + float angle = 2 * M_PI * k / N; + *real = cos(angle); + *imag = sin(angle); +} + +static void irfft(int n, const float * inp_cplx, float * out_real) { + int N = n / 2 + 1; + + std::vector real_input(N); + std::vector imag_input(N); + for (int i = 0; i < N; ++i) { + real_input[i] = inp_cplx[2 * i]; + imag_input[i] = inp_cplx[2 * i + 1]; + } + + std::vector real_output(n); + std::vector imag_output(n); + + for (int k = 0; k < n; ++k) { + real_output[k] = 0.0f; + imag_output[k] = 0.0f; + for (int m = 0; m < N; ++m) { + float twiddle_real; + float twiddle_imag; + + twiddle(&twiddle_real, &twiddle_imag, k * m, n); + + real_output[k] += real_input[m] * twiddle_real - imag_input[m] * twiddle_imag; + imag_output[k] += real_input[m] * twiddle_imag + imag_input[m] * twiddle_real; + } + } + + for (int i = 0; i < n; ++i) { + out_real[i] = real_output[i] / N; + } +} + +// +// y = torch.nn.functional.fold( +// data, output_size=(1, output_size), kernel_size=(1, self.win_length), stride=(1, self.hop_length), +// )[:, 0, 0, pad:-pad] +// +// data.shape = torch.Size([1, 1280, 261]) +// output_size = 84480 +// win_length = 1280 +// hop_length = 320 +// pad = 480 +// +static void fold(const std::vector & data, int64_t n_out, int64_t n_win, int64_t n_hop, int64_t n_pad, std::vector & output) { + int64_t output_height = n_out; + int64_t kernel_w = n_win; + int64_t stride_w = n_hop; + int64_t width = n_out; + + output.resize(width, 0.0f); + + int64_t col_idx = 0; + for (int64_t w_col = 0; w_col < width; ++w_col) { + int64_t start = w_col * stride_w - n_pad; + int64_t end = start + kernel_w; + + for (int64_t w_im = start; w_im < end; ++w_im) { + if (w_im >= 0 && w_im < output_height && col_idx < (int64_t) data.size()) { + output[w_im] += data[col_idx]; + } + col_idx++; + } + } + + output.resize(n_out - 2 * n_pad); +} + +// TODO: not optimized at all +static std::vector embd_to_audio( + const float * embd, + const int n_codes, + const int n_embd, + const int n_thread) { + const int n_fft = 1280; + const int n_hop = 320; + const int n_win = 1280; + const int n_pad = (n_win - n_hop)/2; + const int n_out = (n_codes - 1)*n_hop + n_win; + + std::vector hann(n_fft); + + fill_hann_window(hann.size(), true, hann.data()); + + int n_spec = n_embd*n_codes; + + std::vector E (n_spec); + std::vector S (n_spec); + std::vector ST(n_spec); + + for (int l = 0; l < n_codes; ++l) { + for (int k = 0; k < n_embd; ++k) { + E[k*n_codes + l] = embd[l*n_embd + k]; + } + } + + for (int k = 0; k < n_embd/2; ++k) { + for (int l = 0; l < n_codes; ++l) { + float mag = E[(k )*n_codes + l]; + float phi = E[(k + n_embd/2)*n_codes + l]; + + mag = exp(mag); + + if (mag > 1e2) { + mag = 1e2; + } + S[2*(k*n_codes + l) + 0] = mag*cosf(phi); + S[2*(k*n_codes + l) + 1] = mag*sinf(phi); + } + } + + for (int l = 0; l < n_codes; ++l) { + for (int k = 0; k < n_embd/2; ++k) { + ST[l*n_embd + 2*k + 0] = S[2*(k*n_codes + l) + 0]; + ST[l*n_embd + 2*k + 1] = S[2*(k*n_codes + l) + 1]; + } + } + + std::vector res (n_codes*n_fft); + std::vector hann2(n_codes*n_fft); + + std::vector workers(n_thread); + for (int i = 0; i < n_thread; ++i) { + workers[i] = std::thread([&, i]() { + for (int l = i; l < n_codes; l += n_thread) { + irfft(n_fft, ST.data() + l*n_embd, res.data() + l*n_fft); + for (int j = 0; j < n_fft; ++j) { + res [l*n_fft + j] *= hann[j]; + hann2[l*n_fft + j] = hann[j] * hann[j]; + } + } + }); + } + for (int i = 0; i < n_thread; ++i) { + workers[i].join(); + } + + std::vector audio; + std::vector env; + + fold(res, n_out, n_win, n_hop, n_pad, audio); + fold(hann2, n_out, n_win, n_hop, n_pad, env); // TODO: can be done once + + for (size_t i = 0; i < audio.size(); ++i) { + audio[i] /= env[i]; + } + + return audio; +} + +static const std::map ones = { + {0, "zero"}, {1, "one"}, {2, "two"}, {3, "three"}, {4, "four"}, + {5, "five"}, {6, "six"}, {7, "seven"}, {8, "eight"}, {9, "nine"}, + {10, "ten"}, {11, "eleven"}, {12, "twelve"}, {13, "thirteen"}, {14, "fourteen"}, + {15, "fifteen"}, {16, "sixteen"}, {17, "seventeen"}, {18, "eighteen"}, {19, "nineteen"} +}; + +static const std::map tens = { + {2, "twenty"}, {3, "thirty"}, {4, "forty"}, {5, "fifty"}, + {6, "sixty"}, {7, "seventy"}, {8, "eighty"}, {9, "ninety"} +}; + +// Convert a number less than 1000 to words +static std::string convert_less_than_thousand(int num) { + std::string result; + + if (num >= 100) { + result += ones.at(num / 100) + " hundred "; + num %= 100; + } + + if (num >= 20) { + result += tens.at(num / 10); + if (num % 10 > 0) { + result += "-" + ones.at(num % 10); + } + } else if (num > 0) { + result += ones.at(num); + } + + return result; +} + +static std::string number_to_words(const std::string & number_str) { + try { + size_t decimal_pos = number_str.find('.'); + std::string integer_part = number_str.substr(0, decimal_pos); + + int int_number = std::stoi(integer_part); + std::string result; + + if (int_number == 0) { + result = "zero"; + } else { + if (int_number >= 1000000000) { + int billions = int_number / 1000000000; + result += convert_less_than_thousand(billions) + " billion "; + int_number %= 1000000000; + } + + if (int_number >= 1000000) { + int millions = int_number / 1000000; + result += convert_less_than_thousand(millions) + " million "; + int_number %= 1000000; + } + + if (int_number >= 1000) { + int thousands = int_number / 1000; + result += convert_less_than_thousand(thousands) + " thousand "; + int_number %= 1000; + } + + if (int_number > 0) { + result += convert_less_than_thousand(int_number); + } + } + + // Handle decimal part + if (decimal_pos != std::string::npos) { + result += " point"; + std::string decimal_part = number_str.substr(decimal_pos + 1); + for (char digit : decimal_part) { + result += " " + ones.at(digit - '0'); + } + } + + return result; + } catch (const std::exception& e) { + // Skip if fails + return " "; + } +} + +static std::string replace_numbers_with_words(const std::string & input_text) { + std::regex number_pattern(R"(\d+(\.\d+)?)"); + std::string result; + auto it = std::sregex_iterator(input_text.begin(), input_text.end(), number_pattern); + auto end = std::sregex_iterator(); + + size_t last_pos = 0; + for (std::sregex_iterator i = it; i != end; ++i) { + const std::smatch& match = *i; + result.append(input_text, last_pos, match.position() - last_pos); + result.append(number_to_words(match.str())); + last_pos = match.position() + match.length(); + } + result.append(input_text, last_pos); + + return result; +} + +// Based on: https://github.com/edwko/OuteTTS/blob/a613e79c489d8256dd657ea9168d78de75895d82/outetts/version/v1/prompt_processor.py#L39 +static std::string process_text(const std::string & text) { + + // For now I skipped text romanization as I am unsure how to handle + // uroman and MeCab implementations in C++ + // maybe something like https://github.com/anyascii/anyascii/ could work. + // currently only English would be supported in this function + + std::string processed_text = replace_numbers_with_words(text); + + std::transform(processed_text.begin(), processed_text.end(), + processed_text.begin(), ::tolower); + + std::regex special_chars(R"([-_/,\.\\])"); + processed_text = std::regex_replace(processed_text, special_chars, " "); + + std::regex non_alpha(R"([^a-z\s])"); + processed_text = std::regex_replace(processed_text, non_alpha, ""); + + std::regex multiple_spaces(R"(\s+)"); + processed_text = std::regex_replace(processed_text, multiple_spaces, " "); + + processed_text = std::regex_replace(processed_text, std::regex(R"(^\s+|\s+$)"), ""); + + /* + Replace spaces with the separator token same as in line 365 + + for (auto & c : prompt_user) { + if (c == ' ') { + prompt_clean += "<|text_sep|>"; + */ + processed_text = std::regex_replace(processed_text, std::regex(R"(\s)"), "<|text_sep|>"); + + return processed_text; +} + +static void prompt_add(llama_tokens & prompt, llama_token token) { + prompt.push_back(token); +} + +static void prompt_add(llama_tokens & prompt, const llama_tokens & tokens) { + prompt.insert(prompt.end(), tokens.begin(), tokens.end()); +} + +static void prompt_add(llama_tokens & prompt, const llama_model * model, const std::string & txt, bool add_special, bool parse_special) { + auto tmp = common_tokenize(model, txt, add_special, parse_special); + prompt_add(prompt, tmp); +} + +static void prompt_init(llama_tokens & prompt, const llama_model * model) { + prompt.clear(); + + prompt_add(prompt, model, "<|im_start|>\n", true, true); +} + +int main(int argc, char ** argv) { + common_params params; + + params.prompt = ""; + + params.n_predict = 4096; + params.n_batch = 8192; + params.n_ctx = 8192; + + params.sampling.top_k = 4; + params.sampling.samplers = { COMMON_SAMPLER_TYPE_TOP_K, }; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_TTS, print_usage)) { + return 1; + } + + const int n_parallel = params.n_parallel; + const int n_predict = params.n_predict; + + common_init(); + + // init LLM + + llama_backend_init(); + llama_numa_init(params.numa); + + llama_model * model_ttc = NULL; // text-to-codes + llama_model * model_cts = NULL; // codes-to-speech + + llama_context * ctx_ttc = NULL; + llama_context * ctx_cts = NULL; + + common_init_result llama_init_ttc = common_init_from_params(params); + model_ttc = llama_init_ttc.model; + ctx_ttc = llama_init_ttc.context; + + // TODO: refactor in a common struct + params.model = params.vocoder.model; + params.model_url = params.vocoder.model_url; + params.hf_repo = params.vocoder.hf_repo; + params.hf_file = params.vocoder.hf_file; + + params.embedding = true; + + common_init_result llama_init_cts = common_init_from_params(params); + model_cts = llama_init_cts.model; + ctx_cts = llama_init_cts.context; + + std::vector smpl(n_parallel); + for (int i = 0; i < n_parallel; ++i) { + params.sampling.no_perf = (i != 0); + params.sampling.seed = params.sampling.seed + 1; + + smpl[i] = common_sampler_init(model_ttc, params.sampling); + } + + LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl[0])); + LOG_INF("sampler params: \n%s\n", params.sampling.print().c_str()); + LOG_INF("sampler chain: %s\n", common_sampler_print(smpl[0]).c_str()); + + LOG_INF("%s: loading done\n", __func__); + + const auto t_main_start = ggml_time_us(); + + std::vector codes; + + // process prompt and generate voice codes + { + LOG_INF("%s: constructing prompt ..\n", __func__); + + std::vector prompt_inp; + + prompt_init(prompt_inp, model_ttc); + + prompt_add(prompt_inp, model_ttc, "<|text_start|>the<|text_sep|>overall<|text_sep|>package<|text_sep|>from<|text_sep|>just<|text_sep|>two<|text_sep|>people<|text_sep|>is<|text_sep|>pretty<|text_sep|>remarkable<|text_sep|>sure<|text_sep|>i<|text_sep|>have<|text_sep|>some<|text_sep|>critiques<|text_sep|>about<|text_sep|>some<|text_sep|>of<|text_sep|>the<|text_sep|>gameplay<|text_sep|>aspects<|text_sep|>but<|text_sep|>its<|text_sep|>still<|text_sep|>really<|text_sep|>enjoyable<|text_sep|>and<|text_sep|>it<|text_sep|>looks<|text_sep|>lovely<|text_sep|>", false, true); + + // convert the input text into the necessary format expected by OuteTTS + { + std::string prompt_clean = process_text(params.prompt); + + LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str()); + + prompt_add(prompt_inp, model_ttc, prompt_clean, false, true); + } + + prompt_add(prompt_inp, model_ttc, "<|text_end|>\n", false, true); + + // disabled to save time on tokenizing each time + // TODO: load voices from the json files +#if 0 + const std::string voice_data = R"(<|audio_start|> +the<|t_0.08|><|code_start|><|257|><|740|><|636|><|913|><|788|><|1703|><|code_end|> +overall<|t_0.36|><|code_start|><|127|><|201|><|191|><|774|><|700|><|532|><|1056|><|557|><|798|><|298|><|1741|><|747|><|1662|><|1617|><|1702|><|1527|><|368|><|1588|><|1049|><|1008|><|1625|><|747|><|1576|><|728|><|1019|><|1696|><|1765|><|code_end|> +package<|t_0.56|><|code_start|><|935|><|584|><|1319|><|627|><|1016|><|1491|><|1344|><|1117|><|1526|><|1040|><|239|><|1435|><|951|><|498|><|723|><|1180|><|535|><|789|><|1649|><|1637|><|78|><|465|><|1668|><|901|><|595|><|1675|><|117|><|1009|><|1667|><|320|><|840|><|79|><|507|><|1762|><|1508|><|1228|><|1768|><|802|><|1450|><|1457|><|232|><|639|><|code_end|> +from<|t_0.19|><|code_start|><|604|><|782|><|1682|><|872|><|1532|><|1600|><|1036|><|1761|><|647|><|1554|><|1371|><|653|><|1595|><|950|><|code_end|> +just<|t_0.25|><|code_start|><|1782|><|1670|><|317|><|786|><|1748|><|631|><|599|><|1155|><|1364|><|1524|><|36|><|1591|><|889|><|1535|><|541|><|440|><|1532|><|50|><|870|><|code_end|> +two<|t_0.24|><|code_start|><|1681|><|1510|><|673|><|799|><|805|><|1342|><|330|><|519|><|62|><|640|><|1138|><|565|><|1552|><|1497|><|1552|><|572|><|1715|><|1732|><|code_end|> +people<|t_0.39|><|code_start|><|593|><|274|><|136|><|740|><|691|><|633|><|1484|><|1061|><|1138|><|1485|><|344|><|428|><|397|><|1562|><|645|><|917|><|1035|><|1449|><|1669|><|487|><|442|><|1484|><|1329|><|1832|><|1704|><|600|><|761|><|653|><|269|><|code_end|> +is<|t_0.16|><|code_start|><|566|><|583|><|1755|><|646|><|1337|><|709|><|802|><|1008|><|485|><|1583|><|652|><|10|><|code_end|> +pretty<|t_0.32|><|code_start|><|1818|><|1747|><|692|><|733|><|1010|><|534|><|406|><|1697|><|1053|><|1521|><|1355|><|1274|><|816|><|1398|><|211|><|1218|><|817|><|1472|><|1703|><|686|><|13|><|822|><|445|><|1068|><|code_end|> +remarkable<|t_0.68|><|code_start|><|230|><|1048|><|1705|><|355|><|706|><|1149|><|1535|><|1787|><|1356|><|1396|><|835|><|1583|><|486|><|1249|><|286|><|937|><|1076|><|1150|><|614|><|42|><|1058|><|705|><|681|><|798|><|934|><|490|><|514|><|1399|><|572|><|1446|><|1703|><|1346|><|1040|><|1426|><|1304|><|664|><|171|><|1530|><|625|><|64|><|1708|><|1830|><|1030|><|443|><|1509|><|1063|><|1605|><|1785|><|721|><|1440|><|923|><|code_end|> +sure<|t_0.36|><|code_start|><|792|><|1780|><|923|><|1640|><|265|><|261|><|1525|><|567|><|1491|><|1250|><|1730|><|362|><|919|><|1766|><|543|><|1|><|333|><|113|><|970|><|252|><|1606|><|133|><|302|><|1810|><|1046|><|1190|><|1675|><|code_end|> +i<|t_0.08|><|code_start|><|123|><|439|><|1074|><|705|><|1799|><|637|><|code_end|> +have<|t_0.16|><|code_start|><|1509|><|599|><|518|><|1170|><|552|><|1029|><|1267|><|864|><|419|><|143|><|1061|><|0|><|code_end|> +some<|t_0.16|><|code_start|><|619|><|400|><|1270|><|62|><|1370|><|1832|><|917|><|1661|><|167|><|269|><|1366|><|1508|><|code_end|> +critiques<|t_0.60|><|code_start|><|559|><|584|><|1163|><|1129|><|1313|><|1728|><|721|><|1146|><|1093|><|577|><|928|><|27|><|630|><|1080|><|1346|><|1337|><|320|><|1382|><|1175|><|1682|><|1556|><|990|><|1683|><|860|><|1721|><|110|><|786|><|376|><|1085|><|756|><|1523|><|234|><|1334|><|1506|><|1578|><|659|><|612|><|1108|><|1466|><|1647|><|308|><|1470|><|746|><|556|><|1061|><|code_end|> +about<|t_0.29|><|code_start|><|26|><|1649|><|545|><|1367|><|1263|><|1728|><|450|><|859|><|1434|><|497|><|1220|><|1285|><|179|><|755|><|1154|><|779|><|179|><|1229|><|1213|><|922|><|1774|><|1408|><|code_end|> +some<|t_0.23|><|code_start|><|986|><|28|><|1649|><|778|><|858|><|1519|><|1|><|18|><|26|><|1042|><|1174|><|1309|><|1499|><|1712|><|1692|><|1516|><|1574|><|code_end|> +of<|t_0.07|><|code_start|><|197|><|716|><|1039|><|1662|><|64|><|code_end|> +the<|t_0.08|><|code_start|><|1811|><|1568|><|569|><|886|><|1025|><|1374|><|code_end|> +gameplay<|t_0.48|><|code_start|><|1269|><|1092|><|933|><|1362|><|1762|><|1700|><|1675|><|215|><|781|><|1086|><|461|><|838|><|1022|><|759|><|649|><|1416|><|1004|><|551|><|909|><|787|><|343|><|830|><|1391|><|1040|><|1622|><|1779|><|1360|><|1231|><|1187|><|1317|><|76|><|997|><|989|><|978|><|737|><|189|><|code_end|> +aspects<|t_0.56|><|code_start|><|1423|><|797|><|1316|><|1222|><|147|><|719|><|1347|><|386|><|1390|><|1558|><|154|><|440|><|634|><|592|><|1097|><|1718|><|712|><|763|><|1118|><|1721|><|1311|><|868|><|580|><|362|><|1435|><|868|><|247|><|221|><|886|><|1145|><|1274|><|1284|><|457|><|1043|><|1459|><|1818|><|62|><|599|><|1035|><|62|><|1649|><|778|><|code_end|> +but<|t_0.20|><|code_start|><|780|><|1825|><|1681|><|1007|><|861|><|710|><|702|><|939|><|1669|><|1491|><|613|><|1739|><|823|><|1469|><|648|><|code_end|> +its<|t_0.09|><|code_start|><|92|><|688|><|1623|><|962|><|1670|><|527|><|599|><|code_end|> +still<|t_0.27|><|code_start|><|636|><|10|><|1217|><|344|><|713|><|957|><|823|><|154|><|1649|><|1286|><|508|><|214|><|1760|><|1250|><|456|><|1352|><|1368|><|921|><|615|><|5|><|code_end|> +really<|t_0.36|><|code_start|><|55|><|420|><|1008|><|1659|><|27|><|644|><|1266|><|617|><|761|><|1712|><|109|><|1465|><|1587|><|503|><|1541|><|619|><|197|><|1019|><|817|><|269|><|377|><|362|><|1381|><|507|><|1488|><|4|><|1695|><|code_end|> +enjoyable<|t_0.49|><|code_start|><|678|><|501|><|864|><|319|><|288|><|1472|><|1341|><|686|><|562|><|1463|><|619|><|1563|><|471|><|911|><|730|><|1811|><|1006|><|520|><|861|><|1274|><|125|><|1431|><|638|><|621|><|153|><|876|><|1770|><|437|><|987|><|1653|><|1109|><|898|><|1285|><|80|><|593|><|1709|><|843|><|code_end|> +and<|t_0.15|><|code_start|><|1285|><|987|><|303|><|1037|><|730|><|1164|><|502|><|120|><|1737|><|1655|><|1318|><|code_end|> +it<|t_0.09|><|code_start|><|848|><|1366|><|395|><|1601|><|1513|><|593|><|1302|><|code_end|> +looks<|t_0.27|><|code_start|><|1281|><|1266|><|1755|><|572|><|248|><|1751|><|1257|><|695|><|1380|><|457|><|659|><|585|><|1315|><|1105|><|1776|><|736|><|24|><|736|><|654|><|1027|><|code_end|> +lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|1481|><|1721|><|1123|><|438|><|1246|><|1251|><|795|><|659|><|1381|><|1658|><|217|><|1772|><|562|><|952|><|107|><|1129|><|1112|><|467|><|550|><|1079|><|840|><|1615|><|1469|><|1380|><|168|><|917|><|836|><|1827|><|437|><|583|><|67|><|595|><|1087|><|1646|><|1493|><|1677|><|code_end|>)"; + + auto tmp = common_tokenize(model_ttc, voice_data, false, true); + printf("\n\n"); + for (int i = 0; i < tmp.size(); ++i) { + printf("%d, ", tmp[i]); + } + printf("\n\n"); +#else + prompt_add(prompt_inp, llama_tokens { + 151667, 198, 1782, 155780, 151669, 151929, 152412, 152308, 152585, + 152460, 153375, 151670, 198, 74455, 155808, 151669, 151799, + 151873, 151863, 152446, 152372, 152204, 152728, 152229, 152470, + 151970, 153413, 152419, 153334, 153289, 153374, 153199, 152040, + 153260, 152721, 152680, 153297, 152419, 153248, 152400, 152691, + 153368, 153437, 151670, 198, 1722, 155828, 151669, 152607, + 152256, 152991, 152299, 152688, 153163, 153016, 152789, 153198, + 152712, 151911, 153107, 152623, 152170, 152395, 152852, 152207, + 152461, 153321, 153309, 151750, 152137, 153340, 152573, 152267, + 153347, 151789, 152681, 153339, 151992, 152512, 151751, 152179, + 153434, 153180, 152900, 153440, 152474, 153122, 153129, 151904, + 152311, 151670, 198, 1499, 155791, 151669, 152276, 152454, + 153354, 152544, 153204, 153272, 152708, 153433, 152319, 153226, + 153043, 152325, 153267, 152622, 151670, 198, 4250, 155797, + 151669, 153454, 153342, 151989, 152458, 153420, 152303, 152271, + 152827, 153036, 153196, 151708, 153263, 152561, 153207, 152213, + 152112, 153204, 151722, 152542, 151670, 198, 19789, 155796, + 151669, 153353, 153182, 152345, 152471, 152477, 153014, 152002, + 152191, 151734, 152312, 152810, 152237, 153224, 153169, 153224, + 152244, 153387, 153404, 151670, 198, 16069, 155811, 151669, + 152265, 151946, 151808, 152412, 152363, 152305, 153156, 152733, + 152810, 153157, 152016, 152100, 152069, 153234, 152317, 152589, + 152707, 153121, 153341, 152159, 152114, 153156, 153001, 153504, + 153376, 152272, 152433, 152325, 151941, 151670, 198, 285, + 155788, 151669, 152238, 152255, 153427, 152318, 153009, 152381, + 152474, 152680, 152157, 153255, 152324, 151682, 151670, 198, + 32955, 155804, 151669, 153490, 153419, 152364, 152405, 152682, + 152206, 152078, 153369, 152725, 153193, 153027, 152946, 152488, + 153070, 151883, 152890, 152489, 153144, 153375, 152358, 151685, + 152494, 152117, 152740, 151670, 198, 37448, 480, 155840, 151669, + 151902, 152720, 153377, 152027, 152378, 152821, 153207, 153459, + 153028, 153068, 152507, 153255, 152158, 152921, 151958, 152609, + 152748, 152822, 152286, 151714, 152730, 152377, 152353, 152470, + 152606, 152162, 152186, 153071, 152244, 153118, 153375, 153018, + 152712, 153098, 152976, 152336, 151843, 153202, 152297, 151736, + 153380, 153502, 152702, 152115, 153181, 152735, 153277, 153457, + 152393, 153112, 152595, 151670, 198, 19098, 155808, 151669, + 152464, 153452, 152595, 153312, 151937, 151933, 153197, 152239, + 153163, 152922, 153402, 152034, 152591, 153438, 152215, 151673, + 152005, 151785, 152642, 151924, 153278, 151805, 151974, 153482, + 152718, 152862, 153347, 151670, 198, 72, 155780, 151669, 151795, + 152111, 152746, 152377, 153471, 152309, 151670, 198, 19016, + 155788, 151669, 153181, 152271, 152190, 152842, 152224, 152701, + 152939, 152536, 152091, 151815, 152733, 151672, 151670, 198, + 14689, 155788, 151669, 152291, 152072, 152942, 151734, 153042, + 153504, 152589, 153333, 151839, 151941, 153038, 153180, 151670, + 198, 36996, 8303, 155832, 151669, 152231, 152256, 152835, + 152801, 152985, 153400, 152393, 152818, 152765, 152249, 152600, + 151699, 152302, 152752, 153018, 153009, 151992, 153054, 152847, + 153354, 153228, 152662, 153355, 152532, 153393, 151782, 152458, + 152048, 152757, 152428, 153195, 151906, 153006, 153178, 153250, + 152331, 152284, 152780, 153138, 153319, 151980, 153142, 152418, + 152228, 152733, 151670, 198, 9096, 155801, 151669, 151698, + 153321, 152217, 153039, 152935, 153400, 152122, 152531, 153106, + 152169, 152892, 152957, 151851, 152427, 152826, 152451, 151851, + 152901, 152885, 152594, 153446, 153080, 151670, 198, 14689, + 155795, 151669, 152658, 151700, 153321, 152450, 152530, 153191, + 151673, 151690, 151698, 152714, 152846, 152981, 153171, 153384, + 153364, 153188, 153246, 151670, 198, 1055, 155779, 151669, + 151869, 152388, 152711, 153334, 151736, 151670, 198, 1782, + 155780, 151669, 153483, 153240, 152241, 152558, 152697, 153046, + 151670, 198, 5804, 1363, 155820, 151669, 152941, 152764, 152605, + 153034, 153434, 153372, 153347, 151887, 152453, 152758, 152133, + 152510, 152694, 152431, 152321, 153088, 152676, 152223, 152581, + 152459, 152015, 152502, 153063, 152712, 153294, 153451, 153032, + 152903, 152859, 152989, 151748, 152669, 152661, 152650, 152409, + 151861, 151670, 198, 300, 7973, 155828, 151669, 153095, 152469, + 152988, 152894, 151819, 152391, 153019, 152058, 153062, 153230, + 151826, 152112, 152306, 152264, 152769, 153390, 152384, 152435, + 152790, 153393, 152983, 152540, 152252, 152034, 153107, 152540, + 151919, 151893, 152558, 152817, 152946, 152956, 152129, 152715, + 153131, 153490, 151734, 152271, 152707, 151734, 153321, 152450, + 151670, 198, 8088, 155792, 151669, 152452, 153497, 153353, + 152679, 152533, 152382, 152374, 152611, 153341, 153163, 152285, + 153411, 152495, 153141, 152320, 151670, 198, 1199, 155781, + 151669, 151764, 152360, 153295, 152634, 153342, 152199, 152271, + 151670, 198, 43366, 155799, 151669, 152308, 151682, 152889, + 152016, 152385, 152629, 152495, 151826, 153321, 152958, 152180, + 151886, 153432, 152922, 152128, 153024, 153040, 152593, 152287, + 151677, 151670, 198, 53660, 155808, 151669, 151727, 152092, + 152680, 153331, 151699, 152316, 152938, 152289, 152433, 153384, + 151781, 153137, 153259, 152175, 153213, 152291, 151869, 152691, + 152489, 151941, 152049, 152034, 153053, 152179, 153160, 151676, + 153367, 151670, 198, 268, 4123, 480, 155821, 151669, 152350, + 152173, 152536, 151991, 151960, 153144, 153013, 152358, 152234, + 153135, 152291, 153235, 152143, 152583, 152402, 153483, 152678, + 152192, 152533, 152946, 151797, 153103, 152310, 152293, 151825, + 152548, 153442, 152109, 152659, 153325, 152781, 152570, 152957, + 151752, 152265, 153381, 152515, 151670, 198, 437, 155787, + 151669, 152957, 152659, 151975, 152709, 152402, 152836, 152174, + 151792, 153409, 153327, 152990, 151670, 198, 275, 155781, + 151669, 152520, 153038, 152067, 153273, 153185, 152265, 152974, + 151670, 198, 94273, 155799, 151669, 152953, 152938, 153427, + 152244, 151920, 153423, 152929, 152367, 153052, 152129, 152331, + 152257, 152987, 152777, 153448, 152408, 151696, 152408, 152326, + 152699, 151670, 198, 385, 16239, 155828, 151669, 152306, 152268, + 153438, 153228, 152978, 152957, 153153, 153393, 152795, 152110, + 152918, 152923, 152467, 152331, 153053, 153330, 151889, 153444, + 152234, 152624, 151779, 152801, 152784, 152139, 152222, 152751, + 152512, 153287, 153141, 153052, 151840, 152589, 152508, 153499, + 152109, 152255, 151739, 152267, 152759, 153318, 153165, 153349, + 151670,}); +#endif + + // print the prompt token-by-token + + LOG("\n"); + + for (auto id : prompt_inp) { + LOG("%s", common_token_to_piece(ctx_ttc, id).c_str()); + } + + LOG_INF("%s: prompt size: %d\n", __func__, (int) prompt_inp.size()); + + LOG("\n"); + + // create a llama_batch + // we use this object to submit token data for decoding + llama_batch batch = llama_batch_init(std::max(prompt_inp.size(), (size_t) n_parallel), 0, n_parallel); + + std::vector seq_ids(n_parallel, 0); + for (int32_t i = 0; i < n_parallel; ++i) { + seq_ids[i] = i; + } + + // evaluate the initial prompt + for (size_t i = 0; i < prompt_inp.size(); ++i) { + common_batch_add(batch, prompt_inp[i], i, seq_ids, false); + } + GGML_ASSERT(batch.n_tokens == (int) prompt_inp.size()); + + // llama_decode will output logits only for the last token of the prompt + batch.logits[batch.n_tokens - 1] = true; + + if (llama_decode(ctx_ttc, batch) != 0) { + LOG_ERR("%s: llama_decode() failed\n", __func__); + return 1; + } + + if (n_parallel > 1) { + LOG_INF("\n\n%s: generating %d sequences ...\n", __func__, n_parallel); + } + + llama_synchronize(ctx_ttc); + + LOG_INF("%s: time for prompt: %.3f ms\n\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f); + + const auto t_dec_start = ggml_time_us(); + + // main loop + + // remember the batch index of the last token for each parallel sequence + // we need this to determine which logits to sample from + std::vector i_batch(n_parallel, batch.n_tokens - 1); + + int n_past = batch.n_tokens; + int n_decode = 0; + + while (n_decode <= n_predict) { + // prepare the next batch + common_batch_clear(batch); + + // sample the next token for each parallel sequence / stream + for (int32_t i = 0; i < n_parallel; ++i) { + if (i_batch[i] < 0) { + // the stream has already finished + continue; + } + + const llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]); + + common_sampler_accept(smpl[i], new_token_id, true); + + codes.push_back(new_token_id); + + const auto * cands = common_sampler_get_candidates(smpl[i]); + + // is it an end of generation? -> mark the stream as finished + if (llama_token_is_eog(model_ttc, new_token_id) || n_decode == n_predict) { + std::string reason; + if (llama_token_is_eog(model_ttc, new_token_id)) { + reason = "eos"; + } else { + reason = "n_predict"; + } + + i_batch[i] = -1; + + LOG("\n"); + if (n_parallel > 1) { + LOG_CNT("\n"); + LOG_INF("%s: stream %d finished at n_past = %d, reason = '%s'\n", __func__, i, n_past, reason.c_str()); + } + + continue; + } + + { + const float p = cands->data[cands->selected].p; + + const int col = std::max(0, std::min((int) k_colors.size() - 1, (int) ((3*p)*float(k_colors.size())))); + + LOG_CNT("%s%d%s", k_colors[col].c_str(), i, "\033[0m"); + //LOG_CNT("%d", i); + } + + i_batch[i] = batch.n_tokens; + + // push this new token for next evaluation + common_batch_add(batch, new_token_id, n_past, { i }, true); + } + + // all streams are finished + if (batch.n_tokens == 0) { + break; + } + + n_decode += 1; + n_past += 1; + + // evaluate the current batch with the transformer model + if (llama_decode(ctx_ttc, batch)) { + LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1); + return 1; + } + } + + llama_batch_free(batch); + + LOG("\n"); + LOG_INF("%s: time for decoder: %.3f ms\n", __func__, (ggml_time_us() - t_dec_start) / 1000.0f); + } + + common_perf_print(ctx_ttc, smpl[0]); + + //std::vector codes = {198, 88225, 155856, 151669, 152205, + // 153064, 152537, 153421, 153209, 152524, 151689, 152993, 152438, 152695, + // 153091, 152945, 152829, 152534, 152934, 153020, 151997, 152263, 153010, + // 153146, 152399, 153208, 152496, 151793, 152848, 152263, 152571, 153286, + // 152227, 153300, 152934, 152263, 153208, 152263, 152965, 152430, 152296, + // 153146, 152920, 152376, 152556, 153363, 151775, 152044, 152972, 152690, + // 153379, 152368, 152233, 153422, 152490, 151996, 152022, 151694, 152061, + // 153238, 152539, 153356, 152640, 153021, 153123, 151962, 153094, 151670, + // 198, 20339, 13189, 155824, 151669, 152070, 152007, 152910, 151683, + // 152000, 152373, 152760, 152046, 151735, 152334, 152394, 153073, 152908, + // 151856, 151953, 153247, 153293, 151903, 153480, 153168, 152478, 153359, + // 153429, 151905, 151678, 152567, 152411, 152165, 152556, 153075, 153424, + // 151993, 152999, 153078, 152151, 152088, 153389, 152484, 151874, 151670, + // 198, 285, 155784, 151669, 152226, 152126, 152638, 153215, 151729, + // 152959, 153479, 153059, 151838, 151670, 198, 1782, 155783, 151669, + // 153288, 153055, 153314, 152497, 152962, 152741, 152076, 153253, 151670, + // 198, 471, 16488, 155825, 151669, 152060, 152916, 151893, 153469, 152501, + // 152080, 152743, 151932, 153161, 152096, 152761, 152698, 153401, 153242, + // 153336, 152441, 152838, 153467, 152706, 153496, 153310, 152422, 153360, + // 153115, 152763, 151998, 152373, 153450, 152554, 151968, 153323, 152055, + // 152468, 153111, 153358, 152813, 152010, 151770, 152823, 152960, 151670, + // 198, 22627, 155823, 151669, 152814, 152366, 153484, 152931, 153441, + // 152164, 152877, 152915, 153463, 151692, 152911, 152747, 152776, 151831, + // 153449, 151882, 152975, 152031, 152513, 153150, 152448, 152667, 153133, + // 153189, 152619, 153466, 152054, 152106, 153119, 152277, 152439, 153109, + // 152997, 152141, 153154, 153256, 153311, 151922, 151670, 198, 1055, + // 155781, 151669, 152633, 151850, 153060, 153270, 152560, 153348, 152729, + // 151670, 198, 25312, 155803, 151669, 152521, 153403, 152561, 153337, + // 153383, 152199, 153493, 153326, 151830, 152254, 152248, 152349, 152153, + // 153007, 151823, 153037, 152575, 152457, 152406, 152592, 153116, 153365, + // 153456, 151670, 198, 88225, 155817, 151669, 153271, 151925, 152218, + // 152418, 152253, 153140, 151903, 153151, 152626, 152338, 152647, 153464, + // 152785, 152768, 151711, 152037, 152033, 151804, 152216, 151701, 151855, + // 152348, 152995, 152955, 152905, 152342, 152340, 153391, 153453, 152418, + // 153415, 151990, 153083, 152884, 151670, 198, 151668, 198, 151645}; + + { + const std::string inp_txt = common_detokenize(ctx_ttc, codes, true); + + LOG("\n"); + LOG_INF("codes: '%s'\n", inp_txt.c_str()); + LOG_INF("%s: codes size: %d\n", __func__, (int) codes.size()); + } + + // remove all non-audio tokens (i.e. < 151672 || > 155772) + codes.erase(std::remove_if(codes.begin(), codes.end(), [](llama_token t) { return t < 151672 || t > 155772; }), codes.end()); + + { + const std::string inp_txt = common_detokenize(ctx_ttc, codes, true); + LOG_INF("codes audio: '%s'\n", inp_txt.c_str()); + LOG_INF("%s: codes audio size: %d\n", __func__, (int) codes.size()); + } + + for (auto & token : codes) { + token -= 151672; + } + + const auto t_voc_start = ggml_time_us(); + + const int n_codes = codes.size(); + + llama_batch batch = llama_batch_init(n_codes, 0, 1); + + for (size_t i = 0; i < codes.size(); ++i) { + common_batch_add(batch, codes[i], i, { 0 }, true); // TODO: all logits? + } + GGML_ASSERT(batch.n_tokens == n_codes); + + if (llama_decode(ctx_cts, batch) != 0) { + LOG_ERR("%s: llama_decode() failed\n", __func__); + return 1; + } + + llama_synchronize(ctx_cts); + + LOG_INF("%s: time for vocoder: %.3f ms\n", __func__, (ggml_time_us() - t_voc_start) / 1000.0f); + + const auto t_spec_start = ggml_time_us(); + +#if 1 + // spectral operations + const int n_embd = llama_n_embd(model_cts); + const float * embd = llama_get_embeddings(ctx_cts); + + auto audio = embd_to_audio(embd, n_codes, n_embd, params.cpuparams.n_threads); + +#else + // read the spectrogram from a file for debugging purposes + std::vector audio; + { + std::ifstream fin("out.bin", std::ios::binary); + if (!fin) { + LOG_ERR("%s: failed to open file '%s'\n", __func__, "out.bin"); + return 1; + } + + std::vector embd; + + int n_codes; + int n_embd; + + fin.read(reinterpret_cast(&n_codes), sizeof(int)); + fin.read(reinterpret_cast(&n_embd), sizeof(int)); + + embd.resize(n_codes * n_embd); + fin.read(reinterpret_cast(embd.data()), n_codes * n_embd * sizeof(float)); + fin.close(); + + LOG_INF("%s: n_codes: %d, n_embd: %d\n", __func__, n_codes, n_embd); + + audio = embd_to_audio(embd.data(), n_codes, n_embd, params.cpuparams.n_threads); + } +#endif + + const std::string fname = "output.wav"; + + const int n_sr = 24000; // sampling rate + + // zero out first 0.25 seconds + for (int i = 0; i < 24000/4; ++i) { + audio[i] = 0.0f; + } + + LOG_INF("%s: time for spectral ops: %.3f ms\n", __func__, (ggml_time_us() - t_spec_start) / 1000.0f); + LOG_INF("%s: total time: %.3f ms\n", __func__, (ggml_time_us() - t_main_start) / 1000.0f); + + save_wav16(fname, audio, n_sr); + + LOG_INF("%s: audio written to file '%s'\n", __func__, fname.c_str()); + + llama_free(ctx_ttc); + llama_free_model(model_ttc); + + llama_free(ctx_cts); + llama_free_model(model_cts); + + llama_backend_free(); + + return 0; +} diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index b0c1ac9ce2b89..c714fc8c837bb 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -1564,17 +1564,6 @@ extern "C" { int d1, // dilation dimension 1 bool is_2D); - GGML_API struct ggml_tensor * ggml_conv_depthwise_2d( - struct ggml_context * ctx, - struct ggml_tensor * a, // convolution kernel - struct ggml_tensor * b, // data - int s0, // stride dimension 0 - int s1, // stride dimension 1 - int p0, // padding dimension 0 - int p1, // padding dimension 1 - int d0, // dilation dimension 0 - int d1); // dilation dimension 1 - GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel @@ -1592,6 +1581,23 @@ extern "C" { int s, // stride int d); // dilation + // depthwise + // TODO: this is very likely wrong for some cases! - needs more testing + GGML_API struct ggml_tensor * ggml_conv_1d_dw( + struct ggml_context * ctx, + struct ggml_tensor * a, // convolution kernel + struct ggml_tensor * b, // data + int s0, // stride + int p0, // padding + int d0); // dilation + + GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, // convolution kernel + struct ggml_tensor * b, // data + int s0, // stride + int d0); // dilation + GGML_API struct ggml_tensor * ggml_conv_transpose_1d( struct ggml_context * ctx, struct ggml_tensor * a, // convolution kernel @@ -1611,7 +1617,6 @@ extern "C" { int d0, // dilation dimension 0 int d1); // dilation dimension 1 - // kernel size is a->ne[0] x a->ne[1] // stride is equal to kernel size // padding is zero @@ -1638,6 +1643,18 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); + // depthwise + GGML_API struct ggml_tensor * ggml_conv_2d_dw( + struct ggml_context * ctx, + struct ggml_tensor * a, // convolution kernel + struct ggml_tensor * b, // data + int s0, // stride dimension 0 + int s1, // stride dimension 1 + int p0, // padding dimension 0 + int p1, // padding dimension 1 + int d0, // dilation dimension 0 + int d1); // dilation dimension 1 + GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0( struct ggml_context * ctx, struct ggml_tensor * a, diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 0efd2b2ebf780..2bbe5f48257b2 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -3760,13 +3760,84 @@ struct ggml_tensor * ggml_clamp( return result; } -// ggml_conv_1d - static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) { return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; } -GGML_API struct ggml_tensor * ggml_conv_1d( +// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] +// a: [OC,IC, KH, KW] +// 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) { + if (is_2D) { + GGML_ASSERT(a->ne[2] == b->ne[2]); + } else { + //GGML_ASSERT(b->ne[1] % a->ne[1] == 0); + GGML_ASSERT(b->ne[1] == a->ne[1]); + GGML_ASSERT(b->ne[3] == 1); + } + + 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); + + GGML_ASSERT((!is_2D || OH > 0) && "b too small compared to a"); + GGML_ASSERT((OW > 0) && "b too small compared to a"); + + const int64_t ne[4] = { + is_2D ? (a->ne[2] * a->ne[1] * a->ne[0]) : a->ne[1] * a->ne[0], + OW, + is_2D ? OH : b->ne[2], + is_2D ? b->ne[3] : 1, + }; + + struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 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; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +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) { + 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->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_conv_1d + +struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -3796,137 +3867,75 @@ struct ggml_tensor* ggml_conv_1d_ph( return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d); } -// ggml_conv_transpose_1d - -static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) { - return (ins - 1) * s - 2 * p + d * (ks - 1) + 1; -} +// ggml_conv_1d_dw -GGML_API struct ggml_tensor * ggml_conv_transpose_1d( +struct ggml_tensor * ggml_conv_1d_dw( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int s0, int p0, int d0) { - GGML_ASSERT(ggml_is_matrix(b)); - GGML_ASSERT(a->ne[2] == b->ne[1]); - GGML_ASSERT(a->ne[3] == 1); + struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], 1, a->ne[1], a->ne[2]); + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]); - GGML_ASSERT(p0 == 0); - GGML_ASSERT(d0 == 1); + struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); - 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, - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a); - int32_t params[] = { s0, p0, d0 }; - ggml_set_op_params(result, params, sizeof(params)); - - result->op = GGML_OP_CONV_TRANSPOSE_1D; - result->src[0] = a; - result->src[1] = b; + result = ggml_reshape_3d(ctx, result, b->ne[0], b->ne[1], 1); return result; } -// ggml_conv_depthwise +// ggml_conv_1d_dw_ph -struct ggml_tensor * ggml_conv_depthwise_2d( +struct ggml_tensor * ggml_conv_1d_dw_ph( 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]), - s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] - struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + int d0) { + return ggml_conv_1d_dw(ctx, a, b, s0, a->ne[0] / 2, d0); +} - new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC,1, KH, KW] => [1, OC, 1, KH * KW] - struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); - result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] +// ggml_conv_transpose_1d - return result; +static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) { + return (ins - 1) * s - 2 * p + d * (ks - 1) + 1; } -// ggml_conv_2d -// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] -// a: [OC,IC, KH, KW] -// b: [N, IC, IH, IW] -// result: [N, OH, OW, IC*KH*KW] -struct ggml_tensor * ggml_im2col( +GGML_API struct ggml_tensor * ggml_conv_transpose_1d( 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); - } - - 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); + int d0) { + GGML_ASSERT(ggml_is_matrix(b)); + GGML_ASSERT(a->ne[2] == b->ne[1]); + GGML_ASSERT(a->ne[3] == 1); - GGML_ASSERT((!is_2D || OH > 0) && "b too small compared to a"); - GGML_ASSERT((OW > 0) && "b too small compared to a"); + GGML_ASSERT(p0 == 0); + GGML_ASSERT(d0 == 1); const int64_t ne[4] = { - is_2D ? (a->ne[2] * a->ne[1] * a->ne[0]) : a->ne[1] * a->ne[0], - OW, - is_2D ? OH : b->ne[2], - is_2D ? b->ne[3] : 1, + 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, }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); - int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; + int32_t params[] = { s0, p0, d0 }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_IM2COL; + result->op = GGML_OP_CONV_TRANSPOSE_1D; result->src[0] = a; result->src[1] = b; return result; } -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) { - 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->src[0] = a; - result->src[1] = b; - - return result; -} +// ggml_conv_2d // a: [OC,IC, KH, KW] // b: [N, IC, IH, IW] @@ -3973,6 +3982,31 @@ struct ggml_tensor * ggml_conv_2d_s1_ph( return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1); } +// ggml_conv_2d_dw + +struct ggml_tensor * ggml_conv_2d_dw( + 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]), + s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + + new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC,1, KH, KW] => [1, OC, 1, KH * KW] + struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] + + return result; +} + // ggml_conv_transpose_2d_p0 static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) { diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c2c7cad14e500..a40df974d1fca 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -90,6 +90,7 @@ class LLM: VOCAB_SIZE = "{arch}.vocab_size" CONTEXT_LENGTH = "{arch}.context_length" EMBEDDING_LENGTH = "{arch}.embedding_length" + FEATURES_LENGTH = "{arch}.features_length" BLOCK_COUNT = "{arch}.block_count" LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" @@ -122,6 +123,8 @@ class Attention: VALUE_LENGTH = "{arch}.attention.value_length" LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + GROUPNORM_EPS = "{arch}.attention.group_norm_epsilon" + GROUPNORM_GROUPS = "{arch}.attention.group_norm_groups" CAUSAL = "{arch}.attention.causal" Q_LORA_RANK = "{arch}.attention.q_lora_rank" KV_LORA_RANK = "{arch}.attention.kv_lora_rank" @@ -155,6 +158,14 @@ class SSM: class WKV: HEAD_SIZE = "{arch}.wkv.head_size" + class PosNet: + EMBEDDING_LENGTH = "{arch}.posnet.embedding_length" + BLOCK_COUNT = "{arch}.posnet.block_count" + + class ConvNext: + EMBEDDING_LENGTH = "{arch}.convnext.embedding_length" + BLOCK_COUNT = "{arch}.convnext.block_count" + class Tokenizer: MODEL = "tokenizer.ggml.model" PRE = "tokenizer.ggml.pre" @@ -209,58 +220,59 @@ class GGUFType: class MODEL_ARCH(IntEnum): - LLAMA = auto() - FALCON = auto() - BAICHUAN = auto() - GROK = auto() - GPT2 = auto() - GPTJ = auto() - GPTNEOX = auto() - MPT = auto() - STARCODER = auto() - REFACT = auto() - BERT = auto() - NOMIC_BERT = auto() - JINA_BERT_V2 = auto() - BLOOM = auto() - STABLELM = auto() - QWEN = auto() - QWEN2 = auto() - QWEN2MOE = auto() - QWEN2VL = auto() - PHI2 = auto() - PHI3 = auto() - PLAMO = auto() - CODESHELL = auto() - ORION = auto() - INTERNLM2 = auto() - MINICPM = auto() - MINICPM3 = auto() - GEMMA = auto() - GEMMA2 = auto() - STARCODER2 = auto() - RWKV6 = auto() - MAMBA = auto() - XVERSE = auto() - COMMAND_R = auto() - DBRX = auto() - OLMO = auto() - OLMO2 = auto() - OLMOE = auto() - OPENELM = auto() - ARCTIC = auto() - DEEPSEEK = auto() - DEEPSEEK2 = auto() - CHATGLM = auto() - BITNET = auto() - T5 = auto() - T5ENCODER = auto() - JAIS = auto() - NEMOTRON = auto() - EXAONE = auto() - GRANITE = auto() - GRANITE_MOE = auto() - CHAMELEON = auto() + LLAMA = auto() + FALCON = auto() + BAICHUAN = auto() + GROK = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + REFACT = auto() + BERT = auto() + NOMIC_BERT = auto() + JINA_BERT_V2 = auto() + BLOOM = auto() + STABLELM = auto() + QWEN = auto() + QWEN2 = auto() + QWEN2MOE = auto() + QWEN2VL = auto() + PHI2 = auto() + PHI3 = auto() + PLAMO = auto() + CODESHELL = auto() + ORION = auto() + INTERNLM2 = auto() + MINICPM = auto() + MINICPM3 = auto() + GEMMA = auto() + GEMMA2 = auto() + STARCODER2 = auto() + RWKV6 = auto() + MAMBA = auto() + XVERSE = auto() + COMMAND_R = auto() + DBRX = auto() + OLMO = auto() + OLMO2 = auto() + OLMOE = auto() + OPENELM = auto() + ARCTIC = auto() + DEEPSEEK = auto() + DEEPSEEK2 = auto() + CHATGLM = auto() + BITNET = auto() + T5 = auto() + T5ENCODER = auto() + JAIS = auto() + NEMOTRON = auto() + EXAONE = auto() + GRANITE = auto() + GRANITE_MOE = auto() + CHAMELEON = auto() + WAVTOKENIZER_DEC = auto() class MODEL_TENSOR(IntEnum): @@ -370,61 +382,78 @@ class MODEL_TENSOR(IntEnum): ENC_OUTPUT_NORM = auto() CLS = auto() # classifier CLS_OUT = auto() # classifier output projection + CONV1D = auto() + CONVNEXT_DW = auto() + CONVNEXT_NORM = auto() + CONVNEXT_PW1 = auto() + CONVNEXT_PW2 = auto() + CONVNEXT_GAMMA = auto() + POSNET_CONV1 = auto() + POSNET_CONV2 = auto() + POSNET_NORM = auto() + POSNET_NORM1 = auto() + POSNET_NORM2 = auto() + POSNET_ATTN_NORM = auto() + POSNET_ATTN_Q = auto() + POSNET_ATTN_K = auto() + POSNET_ATTN_V = auto() + POSNET_ATTN_OUT = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { - MODEL_ARCH.LLAMA: "llama", - MODEL_ARCH.FALCON: "falcon", - MODEL_ARCH.BAICHUAN: "baichuan", - MODEL_ARCH.GROK: "grok", - MODEL_ARCH.GPT2: "gpt2", - MODEL_ARCH.GPTJ: "gptj", - MODEL_ARCH.GPTNEOX: "gptneox", - MODEL_ARCH.MPT: "mpt", - MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.REFACT: "refact", - MODEL_ARCH.BERT: "bert", - MODEL_ARCH.NOMIC_BERT: "nomic-bert", - MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", - MODEL_ARCH.BLOOM: "bloom", - MODEL_ARCH.STABLELM: "stablelm", - MODEL_ARCH.QWEN: "qwen", - MODEL_ARCH.QWEN2: "qwen2", - MODEL_ARCH.QWEN2MOE: "qwen2moe", - MODEL_ARCH.QWEN2VL: "qwen2vl", - MODEL_ARCH.PHI2: "phi2", - MODEL_ARCH.PHI3: "phi3", - MODEL_ARCH.PLAMO: "plamo", - MODEL_ARCH.CODESHELL: "codeshell", - MODEL_ARCH.ORION: "orion", - MODEL_ARCH.INTERNLM2: "internlm2", - MODEL_ARCH.MINICPM: "minicpm", - MODEL_ARCH.MINICPM3: "minicpm3", - MODEL_ARCH.GEMMA: "gemma", - MODEL_ARCH.GEMMA2: "gemma2", - MODEL_ARCH.STARCODER2: "starcoder2", - MODEL_ARCH.RWKV6: "rwkv6", - MODEL_ARCH.MAMBA: "mamba", - MODEL_ARCH.XVERSE: "xverse", - MODEL_ARCH.COMMAND_R: "command-r", - MODEL_ARCH.DBRX: "dbrx", - MODEL_ARCH.OLMO: "olmo", - MODEL_ARCH.OLMO2: "olmo2", - MODEL_ARCH.OLMOE: "olmoe", - MODEL_ARCH.OPENELM: "openelm", - MODEL_ARCH.ARCTIC: "arctic", - MODEL_ARCH.DEEPSEEK: "deepseek", - MODEL_ARCH.DEEPSEEK2: "deepseek2", - MODEL_ARCH.CHATGLM: "chatglm", - MODEL_ARCH.BITNET: "bitnet", - MODEL_ARCH.T5: "t5", - MODEL_ARCH.T5ENCODER: "t5encoder", - MODEL_ARCH.JAIS: "jais", - MODEL_ARCH.NEMOTRON: "nemotron", - MODEL_ARCH.EXAONE: "exaone", - MODEL_ARCH.GRANITE: "granite", - MODEL_ARCH.GRANITE_MOE: "granitemoe", - MODEL_ARCH.CHAMELEON: "chameleon", + MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.FALCON: "falcon", + MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GROK: "grok", + MODEL_ARCH.GPT2: "gpt2", + MODEL_ARCH.GPTJ: "gptj", + MODEL_ARCH.GPTNEOX: "gptneox", + MODEL_ARCH.MPT: "mpt", + MODEL_ARCH.STARCODER: "starcoder", + MODEL_ARCH.REFACT: "refact", + MODEL_ARCH.BERT: "bert", + MODEL_ARCH.NOMIC_BERT: "nomic-bert", + MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", + MODEL_ARCH.BLOOM: "bloom", + MODEL_ARCH.STABLELM: "stablelm", + MODEL_ARCH.QWEN: "qwen", + MODEL_ARCH.QWEN2: "qwen2", + MODEL_ARCH.QWEN2MOE: "qwen2moe", + MODEL_ARCH.QWEN2VL: "qwen2vl", + MODEL_ARCH.PHI2: "phi2", + MODEL_ARCH.PHI3: "phi3", + MODEL_ARCH.PLAMO: "plamo", + MODEL_ARCH.CODESHELL: "codeshell", + MODEL_ARCH.ORION: "orion", + MODEL_ARCH.INTERNLM2: "internlm2", + MODEL_ARCH.MINICPM: "minicpm", + MODEL_ARCH.MINICPM3: "minicpm3", + MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.GEMMA2: "gemma2", + MODEL_ARCH.STARCODER2: "starcoder2", + MODEL_ARCH.RWKV6: "rwkv6", + MODEL_ARCH.MAMBA: "mamba", + MODEL_ARCH.XVERSE: "xverse", + MODEL_ARCH.COMMAND_R: "command-r", + MODEL_ARCH.DBRX: "dbrx", + MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.OLMO2: "olmo2", + MODEL_ARCH.OLMOE: "olmoe", + MODEL_ARCH.OPENELM: "openelm", + MODEL_ARCH.ARCTIC: "arctic", + MODEL_ARCH.DEEPSEEK: "deepseek", + MODEL_ARCH.DEEPSEEK2: "deepseek2", + MODEL_ARCH.CHATGLM: "chatglm", + MODEL_ARCH.BITNET: "bitnet", + MODEL_ARCH.T5: "t5", + MODEL_ARCH.T5ENCODER: "t5encoder", + MODEL_ARCH.JAIS: "jais", + MODEL_ARCH.NEMOTRON: "nemotron", + MODEL_ARCH.EXAONE: "exaone", + MODEL_ARCH.GRANITE: "granite", + MODEL_ARCH.GRANITE_MOE: "granitemoe", + MODEL_ARCH.CHAMELEON: "chameleon", + MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -534,6 +563,22 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm", MODEL_TENSOR.CLS: "cls", MODEL_TENSOR.CLS_OUT: "cls.output", + MODEL_TENSOR.CONV1D: "conv1d", + MODEL_TENSOR.CONVNEXT_DW: "convnext.{bid}.dw", + MODEL_TENSOR.CONVNEXT_NORM: "convnext.{bid}.norm", + MODEL_TENSOR.CONVNEXT_PW1: "convnext.{bid}.pw1", + MODEL_TENSOR.CONVNEXT_PW2: "convnext.{bid}.pw2", + MODEL_TENSOR.CONVNEXT_GAMMA: "convnext.{bid}.gamma", + MODEL_TENSOR.POSNET_CONV1: "posnet.{bid}.conv1", + MODEL_TENSOR.POSNET_CONV2: "posnet.{bid}.conv2", + MODEL_TENSOR.POSNET_NORM: "posnet.{bid}.norm", + MODEL_TENSOR.POSNET_NORM1: "posnet.{bid}.norm1", + MODEL_TENSOR.POSNET_NORM2: "posnet.{bid}.norm2", + MODEL_TENSOR.POSNET_ATTN_NORM: "posnet.{bid}.attn_norm", + MODEL_TENSOR.POSNET_ATTN_Q: "posnet.{bid}.attn_q", + MODEL_TENSOR.POSNET_ATTN_K: "posnet.{bid}.attn_k", + MODEL_TENSOR.POSNET_ATTN_V: "posnet.{bid}.attn_v", + MODEL_TENSOR.POSNET_ATTN_OUT: "posnet.{bid}.attn_output", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -1372,6 +1417,28 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.WAVTOKENIZER_DEC: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.CONV1D, + MODEL_TENSOR.CONVNEXT_DW, + MODEL_TENSOR.CONVNEXT_NORM, + MODEL_TENSOR.CONVNEXT_PW1, + MODEL_TENSOR.CONVNEXT_PW2, + MODEL_TENSOR.CONVNEXT_GAMMA, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.POSNET_CONV1, + MODEL_TENSOR.POSNET_CONV2, + MODEL_TENSOR.POSNET_NORM, + MODEL_TENSOR.POSNET_NORM1, + MODEL_TENSOR.POSNET_NORM2, + MODEL_TENSOR.POSNET_ATTN_NORM, + MODEL_TENSOR.POSNET_ATTN_Q, + MODEL_TENSOR.POSNET_ATTN_K, + MODEL_TENSOR.POSNET_ATTN_V, + MODEL_TENSOR.POSNET_ATTN_OUT, + ], # TODO } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 65a64e10dd33f..3023b539ae82b 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -631,6 +631,21 @@ def add_context_length(self, length: int) -> None: def add_embedding_length(self, length: int) -> None: self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) + def add_features_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.FEATURES_LENGTH.format(arch=self.arch), length) + + def add_posnet_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.PosNet.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_posnet_block_count(self, length: int) -> None: + self.add_uint32(Keys.PosNet.BLOCK_COUNT.format(arch=self.arch), length) + + def add_convnext_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.ConvNext.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_convnext_block_count(self, length: int) -> None: + self.add_uint32(Keys.ConvNext.BLOCK_COUNT.format(arch=self.arch), length) + def add_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) @@ -727,6 +742,12 @@ def add_layer_norm_eps(self, value: float) -> None: def add_layer_norm_rms_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + def add_group_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.GROUPNORM_EPS.format(arch=self.arch), value) + + def add_group_norm_groups(self, value: int) -> None: + self.add_uint32(Keys.Attention.GROUPNORM_GROUPS.format(arch=self.arch), value) + def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 573d0282ea599..82cdb121a1f26 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -42,6 +42,7 @@ class TensorNameMap: "emb_ln", # nomic-bert "transformer.norm", # openelm "rwkv.blocks.0.pre_ln", # rwkv + "backbone.norm", # wavtokenizer ), # Position embeddings @@ -60,6 +61,7 @@ class TensorNameMap: "lm_head.linear", # phi2 "output_layer", # chatglm "head", # rwkv + "head.out", # wavtokenizer ), # Output norm @@ -80,6 +82,7 @@ class TensorNameMap: "transformer.norm", # openelm "model.norm", # nemotron "rwkv.ln_out", # rwkv + "backbone.final_layer_norm", # wavtokenizer ), # Rope frequencies @@ -90,6 +93,10 @@ class TensorNameMap: MODEL_TENSOR.ROPE_FACTORS_LONG: (), MODEL_TENSOR.ROPE_FACTORS_SHORT: (), + + MODEL_TENSOR.CONV1D: ( + "backbone.embed", # roberta + ), } block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { @@ -681,6 +688,8 @@ class TensorNameMap: "encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5 ), + ############################################################################ + # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg MODEL_TENSOR.ENC_OUTPUT_NORM: ( "encoder.final_layer_norm", # t5 ), @@ -693,6 +702,67 @@ class TensorNameMap: MODEL_TENSOR.CLS_OUT: ( "classifier.out_proj", # roberta ), + ############################################################################# + + MODEL_TENSOR.CONVNEXT_DW: ( + "backbone.convnext.{bid}.dwconv", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_NORM: ( + "backbone.convnext.{bid}.norm", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_PW1: ( + "backbone.convnext.{bid}.pwconv1", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_PW2: ( + "backbone.convnext.{bid}.pwconv2", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_GAMMA: ( + "backbone.convnext.{bid}.gamma", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_CONV1: ( + "backbone.posnet.{bid}.conv1", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_CONV2: ( + "backbone.posnet.{bid}.conv2", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_NORM: ( + "backbone.posnet.{bid}.norm", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_NORM1: ( + "backbone.posnet.{bid}.norm1", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_NORM2: ( + "backbone.posnet.{bid}.norm2", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_NORM: ( + "backbone.posnet.{bid}.norm", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_Q: ( + "backbone.posnet.{bid}.q", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_K: ( + "backbone.posnet.{bid}.k", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_V: ( + "backbone.posnet.{bid}.v", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_OUT: ( + "backbone.posnet.{bid}.proj_out", # wavtokenizer + ), } # architecture-specific block mappings diff --git a/gguf-py/tests/test_quants.py b/gguf-py/tests/test_quants.py index 762067814224e..f04d5acce2793 100755 --- a/gguf-py/tests/test_quants.py +++ b/gguf-py/tests/test_quants.py @@ -136,7 +136,7 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType) logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}") sum_diff_bits = np.sum(diff_bits) - logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)") + logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits / (x.size * 8):.6f}%)") return False diff --git a/include/llama.h b/include/llama.h index efbb27d21523a..a4abf395bcd93 100644 --- a/include/llama.h +++ b/include/llama.h @@ -482,9 +482,6 @@ extern "C" { // Returns the total number of parameters in the model LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model); - // Get a llama model tensor - LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name); - // Returns true if the model contains an encoder that requires llama_encode() call LLAMA_API bool llama_model_has_encoder(const struct llama_model * model); diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index e38e598532345..7f2725f94be13 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1867,6 +1867,10 @@ int32_t llama_detokenize_impl( int32_t text_len_max, bool remove_special, bool unparse_special) { + if (vocab.type == LLAMA_VOCAB_TYPE_NONE) { + return 0; + } + GGML_ASSERT(vocab.tokenizer && "Tokenizer not initialized. Call llama_vocab::init_tokenizer() first."); int32_t avail = text_len_max; diff --git a/src/llama.cpp b/src/llama.cpp index b7b04a41d99e6..94160d534435f 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -197,63 +197,65 @@ enum llm_arch { LLM_ARCH_GRANITE, LLM_ARCH_GRANITE_MOE, LLM_ARCH_CHAMELEON, + LLM_ARCH_WAVTOKENIZER_DEC, LLM_ARCH_UNKNOWN, }; static const std::map LLM_ARCH_NAMES = { - { LLM_ARCH_LLAMA, "llama" }, - { LLM_ARCH_FALCON, "falcon" }, - { LLM_ARCH_GROK, "grok" }, - { LLM_ARCH_GPT2, "gpt2" }, - { LLM_ARCH_GPTJ, "gptj" }, - { LLM_ARCH_GPTNEOX, "gptneox" }, - { LLM_ARCH_MPT, "mpt" }, - { LLM_ARCH_BAICHUAN, "baichuan" }, - { LLM_ARCH_STARCODER, "starcoder" }, - { LLM_ARCH_REFACT, "refact" }, - { LLM_ARCH_BERT, "bert" }, - { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, - { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, - { LLM_ARCH_BLOOM, "bloom" }, - { LLM_ARCH_STABLELM, "stablelm" }, - { LLM_ARCH_QWEN, "qwen" }, - { LLM_ARCH_QWEN2, "qwen2" }, - { LLM_ARCH_QWEN2MOE, "qwen2moe" }, - { LLM_ARCH_QWEN2VL, "qwen2vl" }, - { LLM_ARCH_PHI2, "phi2" }, - { LLM_ARCH_PHI3, "phi3" }, - { LLM_ARCH_PLAMO, "plamo" }, - { LLM_ARCH_CODESHELL, "codeshell" }, - { LLM_ARCH_ORION, "orion" }, - { LLM_ARCH_INTERNLM2, "internlm2" }, - { LLM_ARCH_MINICPM, "minicpm" }, - { LLM_ARCH_MINICPM3, "minicpm3" }, - { LLM_ARCH_GEMMA, "gemma" }, - { LLM_ARCH_GEMMA2, "gemma2" }, - { LLM_ARCH_STARCODER2, "starcoder2" }, - { LLM_ARCH_MAMBA, "mamba" }, - { LLM_ARCH_XVERSE, "xverse" }, - { LLM_ARCH_COMMAND_R, "command-r" }, - { LLM_ARCH_DBRX, "dbrx" }, - { LLM_ARCH_OLMO, "olmo" }, - { LLM_ARCH_OLMO2, "olmo2" }, - { LLM_ARCH_OLMOE, "olmoe" }, - { LLM_ARCH_OPENELM, "openelm" }, - { LLM_ARCH_ARCTIC, "arctic" }, - { LLM_ARCH_DEEPSEEK, "deepseek" }, - { LLM_ARCH_DEEPSEEK2, "deepseek2" }, - { LLM_ARCH_CHATGLM, "chatglm" }, - { LLM_ARCH_BITNET, "bitnet" }, - { LLM_ARCH_T5, "t5" }, - { LLM_ARCH_T5ENCODER, "t5encoder" }, - { LLM_ARCH_JAIS, "jais" }, - { LLM_ARCH_NEMOTRON, "nemotron" }, - { 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)" }, + { LLM_ARCH_LLAMA, "llama" }, + { LLM_ARCH_FALCON, "falcon" }, + { LLM_ARCH_GROK, "grok" }, + { LLM_ARCH_GPT2, "gpt2" }, + { LLM_ARCH_GPTJ, "gptj" }, + { LLM_ARCH_GPTNEOX, "gptneox" }, + { LLM_ARCH_MPT, "mpt" }, + { LLM_ARCH_BAICHUAN, "baichuan" }, + { LLM_ARCH_STARCODER, "starcoder" }, + { LLM_ARCH_REFACT, "refact" }, + { LLM_ARCH_BERT, "bert" }, + { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, + { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, + { LLM_ARCH_BLOOM, "bloom" }, + { LLM_ARCH_STABLELM, "stablelm" }, + { LLM_ARCH_QWEN, "qwen" }, + { LLM_ARCH_QWEN2, "qwen2" }, + { LLM_ARCH_QWEN2MOE, "qwen2moe" }, + { LLM_ARCH_QWEN2VL, "qwen2vl" }, + { LLM_ARCH_PHI2, "phi2" }, + { LLM_ARCH_PHI3, "phi3" }, + { LLM_ARCH_PLAMO, "plamo" }, + { LLM_ARCH_CODESHELL, "codeshell" }, + { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, + { LLM_ARCH_MINICPM3, "minicpm3" }, + { LLM_ARCH_GEMMA, "gemma" }, + { LLM_ARCH_GEMMA2, "gemma2" }, + { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_MAMBA, "mamba" }, + { LLM_ARCH_XVERSE, "xverse" }, + { LLM_ARCH_COMMAND_R, "command-r" }, + { LLM_ARCH_DBRX, "dbrx" }, + { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_OLMO2, "olmo2" }, + { LLM_ARCH_OLMOE, "olmoe" }, + { LLM_ARCH_OPENELM, "openelm" }, + { LLM_ARCH_ARCTIC, "arctic" }, + { LLM_ARCH_DEEPSEEK, "deepseek" }, + { LLM_ARCH_DEEPSEEK2, "deepseek2" }, + { LLM_ARCH_CHATGLM, "chatglm" }, + { LLM_ARCH_BITNET, "bitnet" }, + { LLM_ARCH_T5, "t5" }, + { LLM_ARCH_T5ENCODER, "t5encoder" }, + { LLM_ARCH_JAIS, "jais" }, + { LLM_ARCH_NEMOTRON, "nemotron" }, + { LLM_ARCH_EXAONE, "exaone" }, + { LLM_ARCH_RWKV6, "rwkv6" }, + { LLM_ARCH_GRANITE, "granite" }, + { LLM_ARCH_GRANITE_MOE, "granitemoe" }, + { LLM_ARCH_CHAMELEON, "chameleon" }, + { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" }, + { LLM_ARCH_UNKNOWN, "(unknown)" }, }; enum llm_kv { @@ -273,6 +275,7 @@ enum llm_kv { LLM_KV_VOCAB_SIZE, LLM_KV_CONTEXT_LENGTH, LLM_KV_EMBEDDING_LENGTH, + LLM_KV_FEATURES_LENGTH, LLM_KV_BLOCK_COUNT, LLM_KV_LEADING_DENSE_BLOCK_COUNT, LLM_KV_FEED_FORWARD_LENGTH, @@ -304,6 +307,8 @@ enum llm_kv { LLM_KV_ATTENTION_VALUE_LENGTH, LLM_KV_ATTENTION_LAYERNORM_EPS, LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, + LLM_KV_ATTENTION_GROUPNORM_EPS, + LLM_KV_ATTENTION_GROUPNORM_GROUPS, LLM_KV_ATTENTION_CAUSAL, LLM_KV_ATTENTION_Q_LORA_RANK, LLM_KV_ATTENTION_KV_LORA_RANK, @@ -367,6 +372,12 @@ enum llm_kv { LLM_KV_ADAPTER_TYPE, LLM_KV_ADAPTER_LORA_ALPHA, + LLM_KV_POSNET_EMBEDDING_LENGTH, + LLM_KV_POSNET_BLOCK_COUNT, + + LLM_KV_CONVNEXT_EMBEDDING_LENGTH, + LLM_KV_CONVNEXT_BLOCK_COUNT, + // deprecated: LLM_KV_TOKENIZER_PREFIX_ID, LLM_KV_TOKENIZER_SUFFIX_ID, @@ -390,6 +401,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, + { LLM_KV_FEATURES_LENGTH, "%s.features_length" }, { LLM_KV_BLOCK_COUNT, "%s.block_count" }, { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" }, { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, @@ -421,6 +433,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" }, { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, + { LLM_KV_ATTENTION_GROUPNORM_EPS, "%s.attention.group_norm_epsilon" }, + { LLM_KV_ATTENTION_GROUPNORM_GROUPS, "%s.attention.group_norm_groups" }, { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" }, { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" }, { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" }, @@ -451,6 +465,12 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" }, + { LLM_KV_POSNET_EMBEDDING_LENGTH, "%s.posnet.embedding_length" }, + { LLM_KV_POSNET_BLOCK_COUNT, "%s.posnet.block_count" }, + + { LLM_KV_CONVNEXT_EMBEDDING_LENGTH, "%s.convnext.embedding_length" }, + { LLM_KV_CONVNEXT_BLOCK_COUNT, "%s.convnext.block_count" }, + { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, { LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, @@ -609,6 +629,22 @@ enum llm_tensor { LLM_TENSOR_ENC_OUTPUT_NORM, LLM_TENSOR_CLS, LLM_TENSOR_CLS_OUT, + LLM_TENSOR_CONV1D, + LLM_TENSOR_CONVNEXT_DW, + LLM_TENSOR_CONVNEXT_NORM, + LLM_TENSOR_CONVNEXT_PW1, + LLM_TENSOR_CONVNEXT_PW2, + LLM_TENSOR_CONVNEXT_GAMMA, + LLM_TENSOR_POS_NET_CONV1, + LLM_TENSOR_POS_NET_CONV2, + LLM_TENSOR_POS_NET_NORM, + LLM_TENSOR_POS_NET_NORM1, + LLM_TENSOR_POS_NET_NORM2, + LLM_TENSOR_POS_NET_ATTN_NORM, + LLM_TENSOR_POS_NET_ATTN_Q, + LLM_TENSOR_POS_NET_ATTN_K, + LLM_TENSOR_POS_NET_ATTN_V, + LLM_TENSOR_POS_NET_ATTN_OUT, }; static const std::map> LLM_TENSOR_NAMES = { @@ -1593,6 +1629,31 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, + { + LLM_ARCH_WAVTOKENIZER_DEC, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, + { LLM_TENSOR_CONV1D, "conv1d" }, + { LLM_TENSOR_CONVNEXT_DW, "convnext.%d.dw" }, + { LLM_TENSOR_CONVNEXT_NORM, "convnext.%d.norm" }, + { LLM_TENSOR_CONVNEXT_PW1, "convnext.%d.pw1" }, + { LLM_TENSOR_CONVNEXT_PW2, "convnext.%d.pw2" }, + { LLM_TENSOR_CONVNEXT_GAMMA, "convnext.%d.gamma" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_POS_NET_CONV1, "posnet.%d.conv1" }, + { LLM_TENSOR_POS_NET_CONV2, "posnet.%d.conv2" }, + { LLM_TENSOR_POS_NET_NORM, "posnet.%d.norm" }, + { LLM_TENSOR_POS_NET_NORM1, "posnet.%d.norm1" }, + { LLM_TENSOR_POS_NET_NORM2, "posnet.%d.norm2" }, + { LLM_TENSOR_POS_NET_ATTN_NORM, "posnet.%d.attn_norm" }, + { LLM_TENSOR_POS_NET_ATTN_Q, "posnet.%d.attn_q" }, + { LLM_TENSOR_POS_NET_ATTN_K, "posnet.%d.attn_k" }, + { LLM_TENSOR_POS_NET_ATTN_V, "posnet.%d.attn_v" }, + { LLM_TENSOR_POS_NET_ATTN_OUT, "posnet.%d.attn_output" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -2483,15 +2544,26 @@ static const size_t kiB = 1024; static const size_t MiB = 1024*kiB; static const size_t GiB = 1024*MiB; +struct llama_hparams_posnet { + uint32_t n_embd; + uint32_t n_layer; +}; + +struct llama_hparams_convnext { + uint32_t n_embd; + uint32_t n_layer; +}; + struct llama_hparams { bool vocab_only; bool rope_finetuned; bool use_par_res; bool swin_norm; - uint32_t n_vocab; + uint32_t n_vocab = 0; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; + uint32_t n_embd_features = 0; uint32_t n_layer; uint32_t n_rot; uint32_t n_swa = 0; // sliding window attention (SWA) @@ -2502,6 +2574,10 @@ struct llama_hparams { uint32_t n_vocab_type = 0; // for BERT-style token types uint32_t n_rel_attn_bkts = 0; + // for WavTokenizer + struct llama_hparams_posnet posnet; + struct llama_hparams_convnext convnext; + std::array n_head_arr; std::array n_head_kv_arr; std::array n_ff_arr; @@ -2516,6 +2592,9 @@ struct llama_hparams { float f_norm_eps; float f_norm_rms_eps; + float f_norm_group_eps; + + uint32_t n_norm_groups; float f_attn_logit_softcapping = 50.0f; float f_final_logit_softcapping = 30.0f; @@ -2561,66 +2640,6 @@ struct llama_hparams { enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE; - bool operator!=(const llama_hparams & other) const { - if (this->vocab_only != other.vocab_only) return true; - if (this->n_vocab != other.n_vocab) return true; - if (this->n_ctx_train != other.n_ctx_train) return true; - if (this->n_embd != other.n_embd) return true; - if (this->n_layer != other.n_layer) return true; - if (this->n_rot != other.n_rot) return true; - if (this->n_swa != other.n_swa) return true; - if (this->n_embd_head_k != other.n_embd_head_k) return true; - if (this->n_embd_head_v != other.n_embd_head_v) return true; - if (this->n_expert != other.n_expert) return true; - if (this->n_expert_used != other.n_expert_used) return true; - - if (this->n_head_arr != other.n_head_arr) return true; - if (this->n_head_kv_arr != other.n_head_kv_arr) return true; - if (this->n_ff_arr != other.n_ff_arr) return true; - - if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true; - if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true; - if (this->n_lora_q != other.n_lora_q) return true; - if (this->n_lora_kv != other.n_lora_kv) return true; - if (this->n_ff_exp != other.n_ff_exp) return true; - if (this->n_ff_shexp != other.n_ff_shexp) return true; - if (this->n_expert_shared != other.n_expert_shared) return true; - - if (this->rope_finetuned != other.rope_finetuned) return true; - if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true; - if (std::equal(std::begin(this->rope_sections), - std::end(this->rope_sections), - std::begin(other.rope_sections))) return true; - - if (this->ssm_d_conv != other.ssm_d_conv) return true; - if (this->ssm_d_inner != other.ssm_d_inner) return true; - if (this->ssm_d_state != other.ssm_d_state) return true; - if (this->ssm_dt_rank != other.ssm_dt_rank) return true; - if (this->ssm_dt_b_c_rms != other.ssm_dt_b_c_rms) return true; - - if (this->rescale_every_n_layers != other.rescale_every_n_layers) return true; - if (this->time_mix_extra_dim != other.time_mix_extra_dim) return true; - if (this->time_decay_extra_dim != other.time_decay_extra_dim) return true; - if (this->wkv_head_size != other.wkv_head_size) return true; - - if (this->dec_start_token_id != other.dec_start_token_id) return true; - - const float EPSILON = 1e-9f; - - if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; - if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true; - if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true; - if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; - if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; - if (!is_float_close(this->expert_weights_scale, other.expert_weights_scale, EPSILON)) return true; - if (!is_float_close(this->rope_yarn_log_mul, other.rope_yarn_log_mul, EPSILON)) return true; - if (!is_float_close(this->f_residual_scale, other.f_residual_scale, EPSILON)) return true; - if (!is_float_close(this->f_embedding_scale, other.f_embedding_scale, EPSILON)) return true; - if (!is_float_close(this->f_attention_scale, other.f_attention_scale, EPSILON)) return true; - - return false; - } - uint32_t n_head(uint32_t il = 0) const { if (il < n_layer) { return n_head_arr[il]; @@ -2673,21 +2692,21 @@ struct llama_hparams { if (wkv_head_size != 0) { // for RWKV models return 2 * n_embd; - } else { - // TODO: maybe support other convolution strides than 1 - // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed - return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner; } + + // TODO: maybe support other convolution strides than 1 + // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed + return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner; } uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings if (wkv_head_size != 0) { // corresponds to RWKV's wkv_states size return n_embd * wkv_head_size; - } else { - // corresponds to Mamba's ssm_states size - return ssm_d_state * ssm_d_inner; } + + // corresponds to Mamba's ssm_states size + return ssm_d_state * ssm_d_inner; } }; @@ -2725,142 +2744,187 @@ struct llama_cparams { void * cb_eval_user_data; }; -// TODO: separate into "llama_layer_enc" and "llama_layer_dec" -struct llama_layer { - llama_layer() { - // initialize all pointers to NULL - std::memset(this, 0, sizeof(*this)); - } +struct llama_layer_posnet { + // resnet + struct ggml_tensor * norm1 = nullptr; + struct ggml_tensor * norm1_b = nullptr; + + struct ggml_tensor * conv1 = nullptr; + struct ggml_tensor * conv1_b = nullptr; + struct ggml_tensor * norm2 = nullptr; + struct ggml_tensor * norm2_b = nullptr; + + struct ggml_tensor * conv2 = nullptr; + struct ggml_tensor * conv2_b = nullptr; + + // attention + struct ggml_tensor * attn_norm = nullptr; + struct ggml_tensor * attn_norm_b = nullptr; + + struct ggml_tensor * attn_q = nullptr; + struct ggml_tensor * attn_q_b = nullptr; + + struct ggml_tensor * attn_k = nullptr; + struct ggml_tensor * attn_k_b = nullptr; + + struct ggml_tensor * attn_v = nullptr; + struct ggml_tensor * attn_v_b = nullptr; + + struct ggml_tensor * attn_o = nullptr; + struct ggml_tensor * attn_o_b = nullptr; + + // normalize + struct ggml_tensor * norm = nullptr; + struct ggml_tensor * norm_b = nullptr; +}; + +struct llama_layer_convnext { + struct ggml_tensor * dw = nullptr; + struct ggml_tensor * dw_b = nullptr; + + struct ggml_tensor * norm = nullptr; + struct ggml_tensor * norm_b = nullptr; + + struct ggml_tensor * pw1 = nullptr; + struct ggml_tensor * pw1_b = nullptr; + + struct ggml_tensor * pw2 = nullptr; + struct ggml_tensor * pw2_b = nullptr; + + struct ggml_tensor * gamma = nullptr; +}; + +struct llama_layer { // normalization - struct ggml_tensor * attn_norm; - struct ggml_tensor * attn_norm_b; - struct ggml_tensor * attn_norm_2; - struct ggml_tensor * attn_norm_2_b; - struct ggml_tensor * attn_q_norm; - struct ggml_tensor * attn_q_norm_b; - struct ggml_tensor * attn_k_norm; - struct ggml_tensor * attn_k_norm_b; - struct ggml_tensor * attn_out_norm; - struct ggml_tensor * attn_out_norm_b; - struct ggml_tensor * attn_q_a_norm; - struct ggml_tensor * attn_kv_a_norm; - struct ggml_tensor * attn_sub_norm; - struct ggml_tensor * attn_post_norm; - struct ggml_tensor * ffn_sub_norm; - struct ggml_tensor * attn_norm_cross; - struct ggml_tensor * attn_norm_enc; + struct ggml_tensor * attn_norm = nullptr; + struct ggml_tensor * attn_norm_b = nullptr; + struct ggml_tensor * attn_norm_2 = nullptr; + struct ggml_tensor * attn_norm_2_b = nullptr; + struct ggml_tensor * attn_q_norm = nullptr; + struct ggml_tensor * attn_q_norm_b = nullptr; + struct ggml_tensor * attn_k_norm = nullptr; + struct ggml_tensor * attn_k_norm_b = nullptr; + struct ggml_tensor * attn_out_norm = nullptr; + struct ggml_tensor * attn_out_norm_b = nullptr; + struct ggml_tensor * attn_q_a_norm = nullptr; + struct ggml_tensor * attn_kv_a_norm = nullptr; + struct ggml_tensor * attn_sub_norm = nullptr; + struct ggml_tensor * attn_post_norm = nullptr; + struct ggml_tensor * ffn_sub_norm = nullptr; + struct ggml_tensor * attn_norm_cross = nullptr; + struct ggml_tensor * attn_norm_enc = nullptr; // attention - struct ggml_tensor * wq; - struct ggml_tensor * wk; - struct ggml_tensor * wv; - struct ggml_tensor * wo; - struct ggml_tensor * wqkv; - struct ggml_tensor * wq_a; - struct ggml_tensor * wq_b; - struct ggml_tensor * wkv_a_mqa; - struct ggml_tensor * wkv_b; - struct ggml_tensor * wq_cross; - struct ggml_tensor * wk_cross; - struct ggml_tensor * wv_cross; - struct ggml_tensor * wo_cross; - struct ggml_tensor * wq_enc; - struct ggml_tensor * wk_enc; - struct ggml_tensor * wv_enc; - struct ggml_tensor * wo_enc; + struct ggml_tensor * wq = nullptr; + struct ggml_tensor * wk = nullptr; + struct ggml_tensor * wv = nullptr; + struct ggml_tensor * wo = nullptr; + struct ggml_tensor * wqkv = nullptr; + struct ggml_tensor * wq_a = nullptr; + struct ggml_tensor * wq_b = nullptr; + struct ggml_tensor * wkv_a_mqa = nullptr; + struct ggml_tensor * wkv_b = nullptr; + struct ggml_tensor * wq_cross = nullptr; + struct ggml_tensor * wk_cross = nullptr; + struct ggml_tensor * wv_cross = nullptr; + struct ggml_tensor * wo_cross = nullptr; + struct ggml_tensor * wq_enc = nullptr; + struct ggml_tensor * wk_enc = nullptr; + struct ggml_tensor * wv_enc = nullptr; + struct ggml_tensor * wo_enc = nullptr; // attention bias - struct ggml_tensor * bq; - struct ggml_tensor * bk; - struct ggml_tensor * bv; - struct ggml_tensor * bo; - struct ggml_tensor * bqkv; + struct ggml_tensor * bq = nullptr; + struct ggml_tensor * bk = nullptr; + struct ggml_tensor * bv = nullptr; + struct ggml_tensor * bo = nullptr; + struct ggml_tensor * bqkv = nullptr; // relative position bias - struct ggml_tensor * attn_rel_b; - struct ggml_tensor * attn_rel_b_enc; - struct ggml_tensor * attn_rel_b_cross; + struct ggml_tensor * attn_rel_b = nullptr; + struct ggml_tensor * attn_rel_b_enc = nullptr; + struct ggml_tensor * attn_rel_b_cross = nullptr; // normalization - struct ggml_tensor * ffn_norm; - struct ggml_tensor * ffn_norm_b; - struct ggml_tensor * ffn_post_norm; - struct ggml_tensor * layer_out_norm; - struct ggml_tensor * layer_out_norm_b; - struct ggml_tensor * ffn_norm_exps; - struct ggml_tensor * ffn_norm_enc; + struct ggml_tensor * ffn_norm = nullptr; + struct ggml_tensor * ffn_norm_b = nullptr; + struct ggml_tensor * ffn_post_norm = nullptr; + struct ggml_tensor * layer_out_norm = nullptr; + struct ggml_tensor * layer_out_norm_b = nullptr; + struct ggml_tensor * ffn_norm_exps = nullptr; + struct ggml_tensor * ffn_norm_enc = nullptr; // ff - struct ggml_tensor * ffn_gate; // w1 - struct ggml_tensor * ffn_down; // w2 - struct ggml_tensor * ffn_up; // w3 - struct ggml_tensor * ffn_gate_enc; - struct ggml_tensor * ffn_down_enc; - struct ggml_tensor * ffn_up_enc; + struct ggml_tensor * ffn_gate = nullptr; // w1 + struct ggml_tensor * ffn_down = nullptr; // w2 + struct ggml_tensor * ffn_up = nullptr; // w3 + struct ggml_tensor * ffn_gate_enc = nullptr; + struct ggml_tensor * ffn_down_enc = nullptr; + struct ggml_tensor * ffn_up_enc = nullptr; // ff MoE - struct ggml_tensor * ffn_gate_inp; - struct ggml_tensor * ffn_gate_exps; - struct ggml_tensor * ffn_down_exps; - struct ggml_tensor * ffn_up_exps ; + struct ggml_tensor * ffn_gate_inp = nullptr; + struct ggml_tensor * ffn_gate_exps = nullptr; + struct ggml_tensor * ffn_down_exps = nullptr; + struct ggml_tensor * ffn_up_exps = nullptr; // ff shared expert (shexp) - struct ggml_tensor * ffn_gate_inp_shexp; - struct ggml_tensor * ffn_gate_shexp; - struct ggml_tensor * ffn_down_shexp; - struct ggml_tensor * ffn_up_shexp; + struct ggml_tensor * ffn_gate_inp_shexp = nullptr; + struct ggml_tensor * ffn_gate_shexp = nullptr; + struct ggml_tensor * ffn_down_shexp = nullptr; + struct ggml_tensor * ffn_up_shexp = nullptr; // ff bias - struct ggml_tensor * ffn_gate_b; - struct ggml_tensor * ffn_down_b; // b2 - struct ggml_tensor * ffn_up_b; // b3 - struct ggml_tensor * ffn_act; + struct ggml_tensor * ffn_gate_b = nullptr; + struct ggml_tensor * ffn_down_b = nullptr; // b2 + struct ggml_tensor * ffn_up_b = nullptr; // b3 + struct ggml_tensor * ffn_act = nullptr; // mamba proj - struct ggml_tensor * ssm_in; - struct ggml_tensor * ssm_x; - struct ggml_tensor * ssm_dt; - struct ggml_tensor * ssm_out; + struct ggml_tensor * ssm_in = nullptr; + struct ggml_tensor * ssm_x = nullptr; + struct ggml_tensor * ssm_dt = nullptr; + struct ggml_tensor * ssm_out = nullptr; // mamba - struct ggml_tensor * ssm_conv1d; - struct ggml_tensor * ssm_a; - struct ggml_tensor * ssm_d; + struct ggml_tensor * ssm_conv1d = nullptr; + struct ggml_tensor * ssm_a = nullptr; + struct ggml_tensor * ssm_d = nullptr; // mamba bias - struct ggml_tensor * ssm_conv1d_b; - struct ggml_tensor * ssm_dt_b; + struct ggml_tensor * ssm_conv1d_b = nullptr; + struct ggml_tensor * ssm_dt_b = nullptr; // rwkv - struct ggml_tensor * time_mix_w1; - struct ggml_tensor * time_mix_w2; - struct ggml_tensor * time_mix_lerp_x; - struct ggml_tensor * time_mix_lerp_w; - struct ggml_tensor * time_mix_lerp_k; - struct ggml_tensor * time_mix_lerp_v; - struct ggml_tensor * time_mix_lerp_r; - struct ggml_tensor * time_mix_lerp_g; - - struct ggml_tensor * time_mix_first; - struct ggml_tensor * time_mix_decay; - struct ggml_tensor * time_mix_decay_w1; - struct ggml_tensor * time_mix_decay_w2; - struct ggml_tensor * time_mix_key; - struct ggml_tensor * time_mix_value; - struct ggml_tensor * time_mix_receptance; - struct ggml_tensor * time_mix_gate; - - struct ggml_tensor * time_mix_ln; - struct ggml_tensor * time_mix_ln_b; - struct ggml_tensor * time_mix_output; - - struct ggml_tensor * channel_mix_lerp_k; - struct ggml_tensor * channel_mix_lerp_r; - - struct ggml_tensor * channel_mix_key; - struct ggml_tensor * channel_mix_receptance; - struct ggml_tensor * channel_mix_value; + struct ggml_tensor * time_mix_w1 = nullptr; + struct ggml_tensor * time_mix_w2 = nullptr; + struct ggml_tensor * time_mix_lerp_x = nullptr; + struct ggml_tensor * time_mix_lerp_w = nullptr; + struct ggml_tensor * time_mix_lerp_k = nullptr; + struct ggml_tensor * time_mix_lerp_v = nullptr; + struct ggml_tensor * time_mix_lerp_r = nullptr; + struct ggml_tensor * time_mix_lerp_g = nullptr; + + struct ggml_tensor * time_mix_first = nullptr; + struct ggml_tensor * time_mix_decay = nullptr; + struct ggml_tensor * time_mix_decay_w1 = nullptr; + struct ggml_tensor * time_mix_decay_w2 = nullptr; + struct ggml_tensor * time_mix_key = nullptr; + struct ggml_tensor * time_mix_value = nullptr; + struct ggml_tensor * time_mix_receptance = nullptr; + struct ggml_tensor * time_mix_gate = nullptr; + + struct ggml_tensor * time_mix_ln = nullptr; + struct ggml_tensor * time_mix_ln_b = nullptr; + struct ggml_tensor * time_mix_output = nullptr; + + struct ggml_tensor * channel_mix_lerp_k = nullptr; + struct ggml_tensor * channel_mix_lerp_r = nullptr; + + struct ggml_tensor * channel_mix_key = nullptr; + struct ggml_tensor * channel_mix_receptance = nullptr; + struct ggml_tensor * channel_mix_value = nullptr; // long rope factors struct ggml_tensor * rope_long = nullptr; @@ -2868,13 +2932,17 @@ struct llama_layer { struct ggml_tensor * rope_freqs = nullptr; // bitnet scale - struct ggml_tensor * wq_scale; - struct ggml_tensor * wk_scale; - struct ggml_tensor * wv_scale; - struct ggml_tensor * wo_scale; - struct ggml_tensor * ffn_gate_scale; - struct ggml_tensor * ffn_up_scale; - struct ggml_tensor * ffn_down_scale; + struct ggml_tensor * wq_scale = nullptr; + struct ggml_tensor * wk_scale = nullptr; + struct ggml_tensor * wv_scale = nullptr; + struct ggml_tensor * wo_scale = nullptr; + struct ggml_tensor * ffn_gate_scale = nullptr; + struct ggml_tensor * ffn_up_scale = nullptr; + struct ggml_tensor * ffn_down_scale = nullptr; + + struct llama_layer_posnet posnet; + + struct llama_layer_convnext convnext; }; // very similar to llama_batch, @@ -3005,6 +3073,9 @@ struct llama_model { struct ggml_tensor * cls_out = nullptr; struct ggml_tensor * cls_out_b = nullptr; + struct ggml_tensor * conv1d = nullptr; + struct ggml_tensor * conv1d_b = nullptr; + std::vector layers; // gguf metadata @@ -3089,6 +3160,7 @@ struct llama_sbatch { // batch indices of the output std::vector out_ids; std::vector seq; + const llama_batch * batch = nullptr; // buffers for the ubatch @@ -3509,6 +3581,17 @@ static int llama_get_device_count(const llama_model & model) { return (int) model.devices.size(); } +static struct ggml_tensor * llama_get_model_tensor(const struct llama_model * model, const char * name) { + auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(), + [name](const std::pair & it) { + return it.first == name; + }); + if (it == model->tensors_by_name.end()) { + return nullptr; + } + return it->second; +} + template static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) { ggml_init_params params = { @@ -3562,7 +3645,9 @@ static bool llama_kv_cache_init( const struct llama_hparams & hparams = model.hparams; - const int64_t n_layer = hparams.n_layer; + const int32_t n_layer = hparams.n_layer; + + LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d\n", __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer); cache.has_shift = false; @@ -3603,10 +3688,12 @@ static bool llama_kv_cache_init( cache.k_l.reserve(n_layer); cache.v_l.reserve(n_layer); - for (int i = 0; i < (int) n_layer; i++) { + for (int i = 0; i < n_layer; i++) { const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); + LLAMA_LOG_DEBUG("%s: layer %d: n_embd_k_gqa = %d, n_embd_v_gqa = %d\n", __func__, i, n_embd_k_gqa, n_embd_v_gqa); + ggml_backend_buffer_type_t buft; if (offload) { auto * dev = model.dev_layer.at(i).dev; @@ -5519,7 +5606,7 @@ static void llm_load_hparams( ml.get_key(LLM_KV_GENERAL_NAME, model.name, false); // get hparams kv - ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); + ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, false); // everything past this point is not vocab-related if (hparams.vocab_only) { @@ -5532,6 +5619,16 @@ static void llm_load_hparams( ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false); ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false); + if (model.arch == LLM_ARCH_WAVTOKENIZER_DEC) { + ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features); + + ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd); + ml.get_key(LLM_KV_POSNET_BLOCK_COUNT, hparams.posnet.n_layer); + + ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd); + ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT, hparams.convnext.n_layer); + } + GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS); GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert); if (hparams.n_expert > 0) { @@ -5540,13 +5637,13 @@ static void llm_load_hparams( GGML_ASSERT(hparams.n_expert_used == 0); } - // zero-out the per-layer hparams + // zero-out the array hparams std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0); std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0); std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0); - ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer); - ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer); + ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false); + ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false); // n_head_kv is optional, default to n_head hparams.n_head_kv_arr = hparams.n_head_arr; @@ -6291,6 +6388,13 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_WAVTOKENIZER_DEC: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS, hparams.f_norm_group_eps); + ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + } break; default: (void)0; } @@ -6320,7 +6424,7 @@ static void llm_load_vocab( ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model); ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false); - if (tokenizer_model == "no_vocab") { + if (tokenizer_model == "no_vocab" || tokenizer_model == "none") { vocab.type = LLAMA_VOCAB_TYPE_NONE; // default special tokens @@ -7299,6 +7403,22 @@ static const std::map llm_tensor_info_mapping = { {LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, // this tensor is loaded for T5, but never used {LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}}, + {LLM_TENSOR_CONV1D, {LLM_TENSOR_LAYER_INPUT, GGML_OP_IM2COL}}, + {LLM_TENSOR_POS_NET_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_POS_NET_NORM1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_POS_NET_NORM2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_POS_NET_CONV1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}}, + {LLM_TENSOR_POS_NET_CONV2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}}, + {LLM_TENSOR_POS_NET_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_POS_NET_ATTN_Q, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_POS_NET_ATTN_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_POS_NET_ATTN_V, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_POS_NET_ATTN_OUT, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CONVNEXT_DW, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}}, + {LLM_TENSOR_CONVNEXT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, + {LLM_TENSOR_CONVNEXT_PW1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CONVNEXT_PW2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}}, + {LLM_TENSOR_CONVNEXT_GAMMA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, }; // checks if the weight tensor can be used with the specified buffer type and device @@ -7403,6 +7523,12 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state); } break; + case GGML_OP_IM2COL: + { + const int n_embd = hparams.n_embd; + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1); + op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16); + } break; default: GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); } @@ -7533,7 +7659,8 @@ static bool llm_load_tensors( model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; - const int n_layer = hparams.n_layer; + const int n_layer = hparams.n_layer; + bool use_mmap_buffer = true; // build a list of buffer types for the CPU and GPU devices @@ -9336,9 +9463,9 @@ static bool llm_load_tensors( } break; case LLM_ARCH_CHAMELEON: { - model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); - // output + // output model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); model.output = create_tensor(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 @@ -9367,6 +9494,109 @@ static bool llm_load_tensors( layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; + case LLM_ARCH_WAVTOKENIZER_DEC: + { + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0); + + model.conv1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0); + model.conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"), {1, hparams.posnet.n_embd}, 0); + + // posnet + { + const int64_t n_embd = hparams.posnet.n_embd; + + for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) { + auto & layer = model.layers[i].posnet; + + // posnet: + // + // - resnet + // - resnet + // - attn + // - resnet + // - resnet + // - norm + // + switch (i) { + case 0: + case 1: + case 3: + case 4: + { + layer.norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0); + layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", i), {1, n_embd}, 0); + + layer.conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0); + layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", i), {1, n_embd}, 0); + + layer.norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0); + layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", i), {1, n_embd}, 0); + + layer.conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0); + layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", i), {1, n_embd}, 0); + } break; + case 2: + { + layer.attn_norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0); + layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", i), {1, n_embd}, 0); + + layer.attn_q = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "weight", i), {1, n_embd, n_embd}, 0); + layer.attn_q_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "bias", i), {1, n_embd}, 0); + + layer.attn_k = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "weight", i), {1, n_embd, n_embd}, 0); + layer.attn_k_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "bias", i), {1, n_embd}, 0); + + layer.attn_v = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "weight", i), {1, n_embd, n_embd}, 0); + layer.attn_v_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "bias", i), {1, n_embd}, 0); + + layer.attn_o = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "weight", i), {1, n_embd, n_embd}, 0); + layer.attn_o_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "bias", i), {1, n_embd}, 0); + } break; + case 5: + { + layer.norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0); + layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", i), {1, n_embd}, 0); + } break; + default: GGML_ABORT("unknown posnet layer"); + }; + } + } + + GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd); + + model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0); + model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {hparams.posnet.n_embd}, 0); + + // convnext + { + const int64_t n_embd = hparams.convnext.n_embd; + + for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) { + auto & layer = model.layers[i].convnext; + + layer.dw = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW, "weight", i), {7, 1, n_embd}, 0); + layer.dw_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW, "bias", i), {1, n_embd}, 0); + + layer.norm = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM, "weight", i), {n_embd}, 0); + layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM, "bias", i), {n_embd}, 0); + + layer.pw1 = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1, "weight", i), {n_embd, n_ff}, 0); + layer.pw1_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1, "bias", i), {n_ff}, 0); + + layer.pw2 = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2, "weight", i), {n_ff, n_embd}, 0); + layer.pw2_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2, "bias", i), {n_embd}, 0); + + layer.gamma = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0); + } + + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0); + } + + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0); + model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_embd}, 0); + } break; default: throw std::runtime_error("unknown architecture"); } @@ -9586,6 +9816,7 @@ enum llm_ffn_gate_type { enum llm_norm_type { LLM_NORM, LLM_NORM_RMS, + LLM_NORM_GROUP, }; static struct ggml_tensor * llm_build_inp_embd( @@ -9606,7 +9837,7 @@ static struct ggml_tensor * llm_build_inp_embd( inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { - lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); inpL = lctx.inp_embd; ggml_set_input(lctx.inp_embd); } @@ -9727,8 +9958,14 @@ static struct ggml_tensor * llm_build_norm( const llm_build_cb & cb, int il) { switch (type) { - case LLM_NORM: cur = ggml_norm (ctx, cur, hparams.f_norm_eps); break; - case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break; + case LLM_NORM: cur = ggml_norm (ctx, cur, hparams.f_norm_eps); break; + case LLM_NORM_RMS: cur = ggml_rms_norm (ctx, cur, hparams.f_norm_rms_eps); break; + case LLM_NORM_GROUP: + { + cur = ggml_reshape_3d(ctx, cur, cur->ne[0], 1, cur->ne[1]); + cur = ggml_group_norm(ctx, cur, hparams.n_norm_groups, hparams.f_norm_group_eps); + cur = ggml_reshape_2d(ctx, cur, cur->ne[0], cur->ne[2]); + } break; } if (mw || mb) { @@ -15854,7 +16091,7 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_t5_encoder() { + struct ggml_cgraph * build_t5_enc() { 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 @@ -15986,7 +16223,7 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_t5_decoder() { + struct ggml_cgraph * build_t5_dec() { 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 @@ -16935,6 +17172,158 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_wavtokenizer_dec() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL)); + + cur = ggml_conv_1d_ph(ctx0, model.conv1d, cur, 1, 1); + cur = ggml_add(ctx0, cur, model.conv1d_b); + + // posnet + for (uint32_t il = 0; il < hparams.posnet.n_layer; ++il) { + const auto & layer = model.layers[il].posnet; + + inpL = cur; + + switch (il) { + case 0: + case 1: + case 3: + case 4: + { + cur = llm_build_norm(ctx0, cur, hparams, + layer.norm1, + layer.norm1_b, + LLM_NORM_GROUP, cb, 0); + + cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur); + + cur = ggml_conv_1d_ph(ctx0, layer.conv1, cur, 1, 1); + cur = ggml_add(ctx0, cur, layer.conv1_b); + + cur = llm_build_norm(ctx0, cur, hparams, + layer.norm2, + layer.norm2_b, + LLM_NORM_GROUP, cb, 0); + + cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur); + + cur = ggml_conv_1d_ph(ctx0, layer.conv2, cur, 1, 1); + cur = ggml_add(ctx0, cur, layer.conv2_b); + + cur = ggml_add(ctx0, cur, inpL); + } break; + case 2: + { + cur = llm_build_norm(ctx0, cur, hparams, + layer.attn_norm, + layer.attn_norm_b, + LLM_NORM_GROUP, cb, 0); + + struct ggml_tensor * q; + struct ggml_tensor * k; + struct ggml_tensor * v; + + q = ggml_conv_1d_ph(ctx0, layer.attn_q, cur, 1, 1); + k = ggml_conv_1d_ph(ctx0, layer.attn_k, cur, 1, 1); + v = ggml_conv_1d_ph(ctx0, layer.attn_v, cur, 1, 1); + + q = ggml_add(ctx0, q, layer.attn_q_b); + k = ggml_add(ctx0, k, layer.attn_k_b); + v = ggml_add(ctx0, v, layer.attn_v_b); + + q = ggml_cont(ctx0, ggml_transpose(ctx0, q)); + k = ggml_cont(ctx0, ggml_transpose(ctx0, k)); + + struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + + kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(hparams.posnet.n_embd)), 0.0f); + + cur = ggml_mul_mat(ctx0, kq, v); + + cur = ggml_conv_1d_ph(ctx0, layer.attn_o, cur, 1, 1); + cur = ggml_add(ctx0, cur, layer.attn_o_b); + + cur = ggml_add(ctx0, cur, inpL); + } break; + case 5: + { + cur = llm_build_norm(ctx0, cur, hparams, + layer.norm, + layer.norm_b, + LLM_NORM_GROUP, cb, 0); + } break; + default: GGML_ABORT("unknown posnet layer"); + }; + } + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + cur = llm_build_norm(ctx0, cur, hparams, + model.tok_norm, + model.tok_norm_b, + LLM_NORM, cb, -1); + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + inpL = cur; + + // convnext + for (uint32_t il = 0; il < hparams.convnext.n_layer; ++il) { + const auto & layer = model.layers[il].convnext; + + cur = inpL; + + cur = ggml_conv_1d_dw_ph(ctx0, layer.dw, cur, 1, 1); + cur = ggml_add(ctx0, cur, layer.dw_b); + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + cur = llm_build_norm(ctx0, cur, hparams, + layer.norm, + layer.norm_b, + LLM_NORM, cb, -1); + + cur = llm_build_ffn(ctx0, lctx, cur, + layer.pw1, layer.pw1_b, NULL, + NULL, NULL, NULL, + layer.pw2, layer.pw2_b, NULL, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + + cur = ggml_mul(ctx0, cur, layer.gamma); + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + inpL = ggml_add(ctx0, cur, inpL); + } + + cur = inpL; + + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + + // lm_head + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + + cur = ggml_add(ctx0, cur, model.output_b); + cb(cur, "result_embd", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -17181,14 +17570,14 @@ static struct ggml_cgraph * llama_build_graph( case LLM_ARCH_T5: { if (lctx.is_encoding) { - result = llm.build_t5_encoder(); + result = llm.build_t5_enc(); } else { - result = llm.build_t5_decoder(); + result = llm.build_t5_dec(); } } break; case LLM_ARCH_T5ENCODER: { - result = llm.build_t5_encoder(); + result = llm.build_t5_enc(); } break; case LLM_ARCH_JAIS: { @@ -17210,6 +17599,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_chameleon(); } break; + case LLM_ARCH_WAVTOKENIZER_DEC: + { + result = llm.build_wavtokenizer_dec(); + } break; default: GGML_ABORT("fatal error"); } @@ -17301,30 +17694,35 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) } if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) { - GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs"); - const int64_t n_tokens = ubatch.n_tokens; + //GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs"); - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer)); - int32_t * data = (int32_t *) lctx.inp_out_ids->data; + if (!lctx.inp_out_ids) { + LLAMA_LOG_WARN("%s: 'lctx.inp_out_ids' is not created\n", __func__); + } else { + const int64_t n_tokens = ubatch.n_tokens; - if (lctx.n_outputs == n_tokens) { - for (int i = 0; i < n_tokens; ++i) { - data[i] = i; - } - } else if (ubatch.output) { - int32_t n_outputs = 0; - for (int i = 0; i < n_tokens; ++i) { - if (ubatch.output[i]) { - data[n_outputs++] = i; + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer)); + int32_t * data = (int32_t *) lctx.inp_out_ids->data; + + if (lctx.n_outputs == n_tokens) { + for (int i = 0; i < n_tokens; ++i) { + data[i] = i; } + } else if (ubatch.output) { + int32_t n_outputs = 0; + for (int i = 0; i < n_tokens; ++i) { + if (ubatch.output[i]) { + data[n_outputs++] = i; + } + } + // the graph needs to have been passed the correct number of outputs + GGML_ASSERT(lctx.n_outputs == n_outputs); + } else if (lctx.n_outputs == 1) { + // only keep last output + data[0] = n_tokens - 1; + } else { + GGML_ASSERT(lctx.n_outputs == 0); } - // the graph needs to have been passed the correct number of outputs - GGML_ASSERT(lctx.n_outputs == n_outputs); - } else if (lctx.n_outputs == 1) { - // only keep last output - data[0] = n_tokens - 1; - } else { - GGML_ASSERT(lctx.n_outputs == 0); } } @@ -17995,6 +18393,7 @@ static int llama_decode_internal( embd = nullptr; // do not extract embeddings when not needed GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor"); } + // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); @@ -20383,6 +20782,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_T5ENCODER: case LLM_ARCH_JAIS: case LLM_ARCH_RWKV6: + case LLM_ARCH_WAVTOKENIZER_DEC: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values @@ -20500,17 +20900,6 @@ uint64_t llama_model_n_params(const struct llama_model * model) { return model->n_elements; } -struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) { - auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(), - [name](const std::pair & it) { - return it.first == name; - }); - if (it == model->tensors_by_name.end()) { - return nullptr; - } - return it->second; -} - bool llama_model_has_encoder(const struct llama_model * model) { switch (model->arch) { case LLM_ARCH_T5: return true; From 9177484f589d770ffc4e655b9819124d6a22c1d9 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Wed, 18 Dec 2024 23:21:42 +0100 Subject: [PATCH 20/34] ggml : fix arm build (#10890) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * ggml: GGML_NATIVE uses -mcpu=native on ARM Signed-off-by: Adrien Gallouët * ggml: Show detected features with GGML_NATIVE Signed-off-by: Adrien Gallouët * remove msvc support, add GGML_CPU_ARM_ARCH option * disable llamafile in android example * march -> mcpu, skip adding feature macros ggml-ci --------- Signed-off-by: Adrien Gallouët Co-authored-by: Adrien Gallouët --- examples/llama.android/llama/build.gradle.kts | 1 + ggml/CMakeLists.txt | 11 +- ggml/src/ggml-cpu/CMakeLists.txt | 137 +++++++----------- ggml/src/ggml-cpu/ggml-cpu.cpp | 6 + ggml/src/ggml-cpu/llamafile/sgemm.cpp | 1 + 5 files changed, 65 insertions(+), 91 deletions(-) diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts index 2d1dfba2040da..28dbc1904888b 100644 --- a/examples/llama.android/llama/build.gradle.kts +++ b/examples/llama.android/llama/build.gradle.kts @@ -19,6 +19,7 @@ android { externalNativeBuild { cmake { arguments += "-DLLAMA_BUILD_COMMON=ON" + arguments += "-DGGML_LLAMAFILE=OFF" arguments += "-DCMAKE_BUILD_TYPE=Release" cppFlags += listOf() arguments += listOf() diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 3442142adbb43..e33d974827cbe 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -74,10 +74,10 @@ if (NOT GGML_CUDA_GRAPHS_DEFAULT) endif() # general -option(GGML_STATIC "ggml: static link libraries" OFF) -option(GGML_NATIVE "ggml: enable -march=native flag" ${GGML_NATIVE_DEFAULT}) -option(GGML_LTO "ggml: enable link time optimization" OFF) -option(GGML_CCACHE "ggml: use ccache if available" ON) +option(GGML_STATIC "ggml: static link libraries" OFF) +option(GGML_NATIVE "ggml: optimize the build for the current system" ${GGML_NATIVE_DEFAULT}) +option(GGML_LTO "ggml: enable link time optimization" OFF) +option(GGML_CCACHE "ggml: use ccache if available" ON) # debug option(GGML_ALL_WARNINGS "ggml: enable all compiler warnings" ON) @@ -120,8 +120,9 @@ endif() option(GGML_LASX "ggml: enable lasx" ON) option(GGML_LSX "ggml: enable lsx" ON) option(GGML_RVV "ggml: enable rvv" ON) -option(GGML_SVE "ggml: enable SVE" OFF) + option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF) +set(GGML_CPU_ARM_ARCH "" CACHE STRING "ggml: CPU architecture for ARM") if (WIN32) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 5d47323370a66..ea4ef5a006d3c 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -74,112 +74,77 @@ function(ggml_add_cpu_backend_variant_impl tag_name) if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR - (NOT CMAKE_OSX_ARCHITECTURES AND - NOT CMAKE_GENERATOR_PLATFORM_LWR AND + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) message(STATUS "ARM detected") - if (MSVC) - list(APPEND ARCH_DEFINITIONS __aarch64__) # MSVC defines _M_ARM64 instead - list(APPEND ARCH_DEFINITIONS __ARM_NEON) - list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FMA) - - set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) - string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) - if (GGML_COMPILER_SUPPORT_DOTPROD) - list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD) - - message(STATUS "ARM feature DOTPROD enabled") - endif () - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) - - if (GGML_COMPILER_SUPPORT_MATMUL_INT8) - list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8) - - message(STATUS "ARM feature MATMUL_INT8 enabled") - endif () - - check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - - message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled") - endif () + if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang") + message(FATAL_ERROR "MSVC is not supported for ARM, use clang") + else() + check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) + if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") + list(APPEND ARCH_FLAGS -mfp16-format=ieee) + endif() - set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) - elseif (APPLE) if (GGML_NATIVE) - set(USER_PROVIDED_MARCH FALSE) - foreach(flag_var IN ITEMS CMAKE_C_FLAGS CMAKE_CXX_FLAGS CMAKE_REQUIRED_FLAGS) - if ("${${flag_var}}" MATCHES "-march=[a-zA-Z0-9+._-]+") - set(USER_PROVIDED_MARCH TRUE) - break() - endif() - endforeach() - - if (NOT USER_PROVIDED_MARCH) - set(MARCH_FLAGS "-march=armv8.2a") - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) - if (GGML_COMPILER_SUPPORT_DOTPROD) - set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod") - list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD) + list(APPEND ARCH_FLAGS -mcpu=native) - message(STATUS "ARM feature DOTPROD enabled") - endif () + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) - set(TEST_I8MM_FLAGS "-march=armv8.2a+i8mm") + # -mcpu=native does not always enable all the features in some compilers, + # so we check for them manually and enable them if available - set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) - set(CMAKE_REQUIRED_FLAGS "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}") + include(CheckCXXSourceRuns) - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) - if (GGML_COMPILER_SUPPORT_MATMUL_INT8) - set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm") - list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8) + set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod") + check_cxx_source_runs( + "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" + GGML_COMPILER_SUPPORT_DOTPROD) + if (GGML_COMPILER_SUPPORT_DOTPROD) + set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod") + endif() - message(STATUS "ARM feature MATMUL_INT8 enabled") - endif () + set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm") + check_cxx_source_runs( + "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" + GGML_COMPILER_SUPPORT_I8MM) + if (GGML_COMPILER_SUPPORT_I8MM) + set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm") + endif() - set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) - list(APPEND ARCH_FLAGS "${MARCH_FLAGS}") - endif () - endif () - else() - check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) - if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") - list(APPEND ARCH_FLAGS -mfp16-format=ieee) - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") - # Raspberry Pi 1, Zero - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") - if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android") - # Android armeabi-v7a - list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations) - else() - # Raspberry Pi 2 - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) + else() + if (GGML_CPU_ARM_ARCH) + list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH}) endif() endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") - # Android arm64-v8a - # Raspberry Pi 3, 4, Zero 2 (32-bit) - list(APPEND ARCH_FLAGS -mno-unaligned-access) - endif() - if (GGML_SVE) - list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) + + # show enabled features + execute_process( + COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E - + INPUT_FILE "/dev/null" + OUTPUT_VARIABLE ARM_FEATURE + RESULT_VARIABLE ARM_FEATURE_RESULT + ) + if (ARM_FEATURE_RESULT) + message(FATAL_ERROR "Failed to get ARM features") + else() + foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC) + string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos) + if (NOT ${feature_pos} EQUAL -1) + message(STATUS "ARM feature ${feature} enabled") + endif() + endforeach() endif() endif() elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$")) + + message(STATUS "x86 detected") + if (MSVC) # instruction set detection for MSVC only if (GGML_NATIVE) diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 0b6419f83f0c2..f11399cc628ca 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -522,6 +522,12 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r if (ggml_cpu_has_sve()) { features.push_back({ "SVE", "1" }); } + if (ggml_cpu_has_dotprod()) { + features.push_back({ "DOTPROD", "1" }); + } + if (ggml_cpu_has_matmul_int8()) { + features.push_back({ "MATMUL_INT8", "1" }); + } if (ggml_cpu_get_sve_cnt() > 0) { static std::string sve_cnt = std::to_string(ggml_cpu_get_sve_cnt()); features.push_back({ "SVE_CNT", sve_cnt.c_str() }); diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index da4146ec4f688..f80a727819283 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -204,6 +204,7 @@ template <> inline float32x4_t load(const float *p) { return vld1q_f32(p); } #if !defined(_MSC_VER) +// FIXME: this should check for __ARM_FEATURE_FP16_VECTOR_ARITHMETIC template <> inline float16x8_t load(const ggml_fp16_t *p) { return vld1q_f16((const float16_t *)p); } From 7909e8588ddf70820adf1f325490eb3f67b32875 Mon Sep 17 00:00:00 2001 From: Eric Curtin Date: Thu, 19 Dec 2024 02:58:00 +0000 Subject: [PATCH 21/34] llama-run : improve progress bar (#10821) Set default width to whatever the terminal is. Also fixed a small bug around default n_gpu_layers value. Signed-off-by: Eric Curtin --- README.md | 2 +- examples/run/README.md | 10 +- examples/run/run.cpp | 418 +++++++++++++++++++++++++++++------------ 3 files changed, 304 insertions(+), 126 deletions(-) diff --git a/README.md b/README.md index 3f3971d7eb263..d6d1958c8fc03 100644 --- a/README.md +++ b/README.md @@ -448,7 +448,7 @@ To learn more about model quantization, [read this documentation](examples/quant -[^3]: [https://github.com/containers/ramalama](RamaLama) +[^3]: [RamaLama](https://github.com/containers/ramalama) ## [`llama-simple`](examples/simple) diff --git a/examples/run/README.md b/examples/run/README.md index 6162658e947d4..874293516f4b6 100644 --- a/examples/run/README.md +++ b/examples/run/README.md @@ -4,7 +4,7 @@ The purpose of this example is to demonstrate a minimal usage of llama.cpp for r ```bash llama-run granite-code -... +``` ```bash llama-run -h @@ -19,6 +19,8 @@ Options: Context size (default: 2048) -n, --ngl Number of GPU layers (default: 0) + -v, --verbose, --log-verbose + Set verbosity level to infinity (i.e. log all messages, useful for debugging) -h, --help Show help message @@ -42,6 +44,6 @@ Examples: llama-run https://example.com/some-file1.gguf llama-run some-file2.gguf llama-run file://some-file3.gguf - llama-run --ngl 99 some-file4.gguf - llama-run --ngl 99 some-file5.gguf Hello World -... + llama-run --ngl 999 some-file4.gguf + llama-run --ngl 999 some-file5.gguf Hello World +``` diff --git a/examples/run/run.cpp b/examples/run/run.cpp index 834ea8f7b4aeb..03da54ca3b2ef 100644 --- a/examples/run/run.cpp +++ b/examples/run/run.cpp @@ -1,6 +1,8 @@ #if defined(_WIN32) # include #else +# include +# include # include #endif @@ -8,6 +10,7 @@ # include #endif +#include #include #include #include @@ -21,15 +24,37 @@ #include "json.hpp" #include "llama-cpp.h" -#define printe(...) \ - do { \ - fprintf(stderr, __VA_ARGS__); \ - } while (0) +GGML_ATTRIBUTE_FORMAT(1, 2) +static std::string fmt(const char * fmt, ...) { + va_list ap; + va_list ap2; + va_start(ap, fmt); + va_copy(ap2, ap); + const int size = vsnprintf(NULL, 0, fmt, ap); + GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT + std::string buf; + buf.resize(size); + const int size2 = vsnprintf(const_cast(buf.data()), buf.size() + 1, fmt, ap2); + GGML_ASSERT(size2 == size); + va_end(ap2); + va_end(ap); + + return buf; +} + +GGML_ATTRIBUTE_FORMAT(1, 2) +static int printe(const char * fmt, ...) { + va_list args; + va_start(args, fmt); + const int ret = vfprintf(stderr, fmt, args); + va_end(args); + + return ret; +} class Opt { public: int init(int argc, const char ** argv) { - construct_help_str_(); // Parse arguments if (parse(argc, argv)) { printe("Error: Failed to parse arguments.\n"); @@ -48,14 +73,64 @@ class Opt { std::string model_; std::string user_; - int context_size_ = 2048, ngl_ = -1; + int context_size_ = -1, ngl_ = -1; + bool verbose_ = false; private: - std::string help_str_; bool help_ = false; - void construct_help_str_() { - help_str_ = + bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) { + return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0; + } + + int handle_option_with_value(int argc, const char ** argv, int & i, int & option_value) { + if (i + 1 >= argc) { + return 1; + } + + option_value = std::atoi(argv[++i]); + return 0; + } + + int parse(int argc, const char ** argv) { + bool options_parsing = true; + for (int i = 1, positional_args_i = 0; i < argc; ++i) { + if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) { + if (handle_option_with_value(argc, argv, i, context_size_) == 1) { + return 1; + } + } else if (options_parsing && (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0)) { + if (handle_option_with_value(argc, argv, i, ngl_) == 1) { + return 1; + } + } else if (options_parsing && + (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) { + verbose_ = true; + } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) { + help_ = true; + return 0; + } else if (options_parsing && strcmp(argv[i], "--") == 0) { + options_parsing = false; + } else if (positional_args_i == 0) { + if (!argv[i][0] || argv[i][0] == '-') { + return 1; + } + + ++positional_args_i; + model_ = argv[i]; + } else if (positional_args_i == 1) { + ++positional_args_i; + user_ = argv[i]; + } else { + user_ += " " + std::string(argv[i]); + } + } + + return 0; + } + + void help() const { + printf( "Description:\n" " Runs a llm\n" "\n" @@ -64,15 +139,11 @@ class Opt { "\n" "Options:\n" " -c, --context-size \n" - " Context size (default: " + - std::to_string(context_size_); - help_str_ += - ")\n" + " Context size (default: %d)\n" " -n, --ngl \n" - " Number of GPU layers (default: " + - std::to_string(ngl_); - help_str_ += - ")\n" + " Number of GPU layers (default: %d)\n" + " -v, --verbose, --log-verbose\n" + " Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n" " -h, --help\n" " Show help message\n" "\n" @@ -92,67 +163,102 @@ class Opt { " llama-run ollama://granite-code\n" " llama-run ollama://smollm:135m\n" " llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n" - " llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n" + " llama-run " + "huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n" " llama-run https://example.com/some-file1.gguf\n" " llama-run some-file2.gguf\n" " llama-run file://some-file3.gguf\n" - " llama-run --ngl 99 some-file4.gguf\n" - " llama-run --ngl 99 some-file5.gguf Hello World\n"; + " llama-run --ngl 999 some-file4.gguf\n" + " llama-run --ngl 999 some-file5.gguf Hello World\n", + llama_context_default_params().n_batch, llama_model_default_params().n_gpu_layers); } +}; - int parse(int argc, const char ** argv) { - int positional_args_i = 0; - for (int i = 1; i < argc; ++i) { - if (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0) { - if (i + 1 >= argc) { - return 1; - } +struct progress_data { + size_t file_size = 0; + std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now(); + bool printed = false; +}; - context_size_ = std::atoi(argv[++i]); - } else if (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0) { - if (i + 1 >= argc) { - return 1; - } +static int get_terminal_width() { +#if defined(_WIN32) + CONSOLE_SCREEN_BUFFER_INFO csbi; + GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi); + return csbi.srWindow.Right - csbi.srWindow.Left + 1; +#else + struct winsize w; + ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); + return w.ws_col; +#endif +} - ngl_ = std::atoi(argv[++i]); - } else if (strcmp(argv[i], "-h") == 0 || strcmp(argv[i], "--help") == 0) { - help_ = true; - return 0; - } else if (!positional_args_i) { - ++positional_args_i; - model_ = argv[i]; - } else if (positional_args_i == 1) { - ++positional_args_i; - user_ = argv[i]; - } else { - user_ += " " + std::string(argv[i]); +#ifdef LLAMA_USE_CURL +class File { + public: + FILE * file = nullptr; + + FILE * open(const std::string & filename, const char * mode) { + file = fopen(filename.c_str(), mode); + + return file; + } + + int lock() { + if (file) { +# ifdef _WIN32 + fd = _fileno(file); + hFile = (HANDLE) _get_osfhandle(fd); + if (hFile == INVALID_HANDLE_VALUE) { + fd = -1; + + return 1; } + + OVERLAPPED overlapped = { 0 }; + if (!LockFileEx(hFile, LOCKFILE_EXCLUSIVE_LOCK | LOCKFILE_FAIL_IMMEDIATELY, 0, MAXDWORD, MAXDWORD, + &overlapped)) { + fd = -1; + + return 1; + } +# else + fd = fileno(file); + if (flock(fd, LOCK_EX | LOCK_NB) != 0) { + fd = -1; + + return 1; + } +# endif } - return model_.empty(); // model_ is the only required value + return 0; } - void help() const { printf("%s", help_str_.c_str()); } -}; - -struct progress_data { - size_t file_size = 0; - std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now(); - bool printed = false; -}; + ~File() { + if (fd >= 0) { +# ifdef _WIN32 + if (hFile != INVALID_HANDLE_VALUE) { + OVERLAPPED overlapped = { 0 }; + UnlockFileEx(hFile, 0, MAXDWORD, MAXDWORD, &overlapped); + } +# else + flock(fd, LOCK_UN); +# endif + } -struct FileDeleter { - void operator()(FILE * file) const { if (file) { fclose(file); } } -}; -typedef std::unique_ptr FILE_ptr; + private: + int fd = -1; +# ifdef _WIN32 + HANDLE hFile; +# endif +}; -#ifdef LLAMA_USE_CURL -class CurlWrapper { +class HttpClient { public: int init(const std::string & url, const std::vector & headers, const std::string & output_file, const bool progress, std::string * response_str = nullptr) { @@ -163,10 +269,20 @@ class CurlWrapper { } progress_data data; - FILE_ptr out; + File out; if (!output_file.empty()) { output_file_partial = output_file + ".partial"; - out.reset(fopen(output_file_partial.c_str(), "ab")); + if (!out.open(output_file_partial, "ab")) { + printe("Failed to open file\n"); + + return 1; + } + + if (out.lock()) { + printe("Failed to exclusively lock file\n"); + + return 1; + } } set_write_options(response_str, out); @@ -181,7 +297,7 @@ class CurlWrapper { return 0; } - ~CurlWrapper() { + ~HttpClient() { if (chunk) { curl_slist_free_all(chunk); } @@ -195,13 +311,13 @@ class CurlWrapper { CURL * curl = nullptr; struct curl_slist * chunk = nullptr; - void set_write_options(std::string * response_str, const FILE_ptr & out) { + void set_write_options(std::string * response_str, const File & out) { if (response_str) { curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data); curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str); } else { curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data); - curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.get()); + curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.file); } } @@ -219,7 +335,7 @@ class CurlWrapper { if (progress) { curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L); curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data); - curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, progress_callback); + curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, update_progress); } } @@ -255,37 +371,31 @@ class CurlWrapper { int mins = (static_cast(seconds) % 3600) / 60; int secs = static_cast(seconds) % 60; - std::ostringstream out; if (hrs > 0) { - out << hrs << "h " << std::setw(2) << std::setfill('0') << mins << "m " << std::setw(2) << std::setfill('0') - << secs << "s"; + return fmt("%dh %02dm %02ds", hrs, mins, secs); } else if (mins > 0) { - out << mins << "m " << std::setw(2) << std::setfill('0') << secs << "s"; + return fmt("%dm %02ds", mins, secs); } else { - out << secs << "s"; + return fmt("%ds", secs); } - - return out.str(); } static std::string human_readable_size(curl_off_t size) { static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" }; - char length = sizeof(suffix) / sizeof(suffix[0]); - int i = 0; - double dbl_size = size; + char length = sizeof(suffix) / sizeof(suffix[0]); + int i = 0; + double dbl_size = size; if (size > 1024) { for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) { dbl_size = size / 1024.0; } } - std::ostringstream out; - out << std::fixed << std::setprecision(2) << dbl_size << " " << suffix[i]; - return out.str(); + return fmt("%.2f %s", dbl_size, suffix[i]); } - static int progress_callback(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t, - curl_off_t) { + static int update_progress(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t, + curl_off_t) { progress_data * data = static_cast(ptr); if (total_to_download <= 0) { return 0; @@ -293,27 +403,68 @@ class CurlWrapper { total_to_download += data->file_size; const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size; - const curl_off_t percentage = (now_downloaded_plus_file_size * 100) / total_to_download; - const curl_off_t pos = (percentage / 5); + const curl_off_t percentage = calculate_percentage(now_downloaded_plus_file_size, total_to_download); + std::string progress_prefix = generate_progress_prefix(percentage); + + const double speed = calculate_speed(now_downloaded, data->start_time); + const double tim = (total_to_download - now_downloaded) / speed; + std::string progress_suffix = + generate_progress_suffix(now_downloaded_plus_file_size, total_to_download, speed, tim); + + int progress_bar_width = calculate_progress_bar_width(progress_prefix, progress_suffix); std::string progress_bar; - for (int i = 0; i < 20; ++i) { - progress_bar.append((i < pos) ? "█" : " "); - } + generate_progress_bar(progress_bar_width, percentage, progress_bar); - // Calculate download speed and estimated time to completion - const auto now = std::chrono::steady_clock::now(); - const std::chrono::duration elapsed_seconds = now - data->start_time; - const double speed = now_downloaded / elapsed_seconds.count(); - const double estimated_time = (total_to_download - now_downloaded) / speed; - printe("\r%ld%% |%s| %s/%s %.2f MB/s %s ", percentage, progress_bar.c_str(), - human_readable_size(now_downloaded).c_str(), human_readable_size(total_to_download).c_str(), - speed / (1024 * 1024), human_readable_time(estimated_time).c_str()); - fflush(stderr); + print_progress(progress_prefix, progress_bar, progress_suffix); data->printed = true; return 0; } + static curl_off_t calculate_percentage(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download) { + return (now_downloaded_plus_file_size * 100) / total_to_download; + } + + static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", percentage); } + + static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) { + const auto now = std::chrono::steady_clock::now(); + const std::chrono::duration elapsed_seconds = now - start_time; + return now_downloaded / elapsed_seconds.count(); + } + + static std::string generate_progress_suffix(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download, + double speed, double estimated_time) { + const int width = 10; + return fmt("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(), width, + human_readable_size(total_to_download).c_str(), width, human_readable_size(speed).c_str(), width, + human_readable_time(estimated_time).c_str()); + } + + static int calculate_progress_bar_width(const std::string & progress_prefix, const std::string & progress_suffix) { + int progress_bar_width = get_terminal_width() - progress_prefix.size() - progress_suffix.size() - 3; + if (progress_bar_width < 1) { + progress_bar_width = 1; + } + + return progress_bar_width; + } + + static std::string generate_progress_bar(int progress_bar_width, curl_off_t percentage, + std::string & progress_bar) { + const curl_off_t pos = (percentage * progress_bar_width) / 100; + for (int i = 0; i < progress_bar_width; ++i) { + progress_bar.append((i < pos) ? "█" : " "); + } + + return progress_bar; + } + + static void print_progress(const std::string & progress_prefix, const std::string & progress_bar, + const std::string & progress_suffix) { + printe("\r%*s\r%s%s| %s", get_terminal_width(), " ", progress_prefix.c_str(), progress_bar.c_str(), + progress_suffix.c_str()); + } // Function to write data to a file static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) { FILE * out = static_cast(stream); @@ -357,8 +508,8 @@ class LlamaData { #ifdef LLAMA_USE_CURL int download(const std::string & url, const std::vector & headers, const std::string & output_file, const bool progress, std::string * response_str = nullptr) { - CurlWrapper curl; - if (curl.init(url, headers, output_file, progress, response_str)) { + HttpClient http; + if (http.init(url, headers, output_file, progress, response_str)) { return 1; } @@ -438,13 +589,17 @@ class LlamaData { } int resolve_model(std::string & model_) { + int ret = 0; + if (string_starts_with(model_, "file://") || std::filesystem::exists(model_)) { + remove_proto(model_); + + return ret; + } + const std::string bn = basename(model_); const std::vector headers = { "--header", "Accept: application/vnd.docker.distribution.manifest.v2+json" }; - int ret = 0; - if (string_starts_with(model_, "file://") || std::filesystem::exists(bn)) { - remove_proto(model_); - } else if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) { + if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) { remove_proto(model_); ret = huggingface_dl(model_, headers, bn); } else if (string_starts_with(model_, "ollama://")) { @@ -467,19 +622,23 @@ class LlamaData { llama_model_params model_params = llama_model_default_params(); model_params.n_gpu_layers = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers; resolve_model(opt.model_); + printe( + "\r%*s" + "\rLoading model", + get_terminal_width(), " "); llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params)); if (!model) { printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str()); } + printe("\r%*s\r", static_cast(sizeof("Loading model")), " "); return model; } // Initializes the context with the specified parameters llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) { llama_context_params ctx_params = llama_context_default_params(); - ctx_params.n_ctx = n_ctx; - ctx_params.n_batch = n_ctx; + ctx_params.n_ctx = ctx_params.n_batch = n_ctx >= 0 ? n_ctx : ctx_params.n_batch; llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params)); if (!context) { printe("%s: error: failed to create the llama_context\n", __func__); @@ -609,16 +768,20 @@ static int read_user_input(std::string & user) { } // Function to generate a response based on the prompt -static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response) { +static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response, + const bool stdout_a_terminal) { // Set response color - printf("\033[33m"); + if (stdout_a_terminal) { + printf("\033[33m"); + } + if (generate(llama_data, prompt, response)) { printe("failed to generate response\n"); return 1; } // End response with color reset and newline - printf("\n\033[0m"); + printf("\n%s", stdout_a_terminal ? "\033[0m" : ""); return 0; } @@ -642,15 +805,37 @@ static int handle_user_input(std::string & user_input, const std::string & user_ } printf( - "\r " - "\r\033[32m> \033[0m"); + "\r%*s" + "\r\033[32m> \033[0m", + get_terminal_width(), " "); return read_user_input(user_input); // Returns true if input ends the loop } +static bool is_stdin_a_terminal() { +#if defined(_WIN32) + HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE); + DWORD mode; + return GetConsoleMode(hStdin, &mode); +#else + return isatty(STDIN_FILENO); +#endif +} + +static bool is_stdout_a_terminal() { +#if defined(_WIN32) + HANDLE hStdout = GetStdHandle(STD_OUTPUT_HANDLE); + DWORD mode; + return GetConsoleMode(hStdout, &mode); +#else + return isatty(STDOUT_FILENO); +#endif +} + // Function to tokenize the prompt static int chat_loop(LlamaData & llama_data, const std::string & user_) { int prev_len = 0; llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get())); + static const bool stdout_a_terminal = is_stdout_a_terminal(); while (true) { // Get user input std::string user_input; @@ -665,7 +850,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) { std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len); std::string response; - if (generate_response(llama_data, prompt, response)) { + if (generate_response(llama_data, prompt, response, stdout_a_terminal)) { return 1; } @@ -682,22 +867,13 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) { return 0; } -static void log_callback(const enum ggml_log_level level, const char * text, void *) { - if (level == GGML_LOG_LEVEL_ERROR) { +static void log_callback(const enum ggml_log_level level, const char * text, void * p) { + const Opt * opt = static_cast(p); + if (opt->verbose_ || level == GGML_LOG_LEVEL_ERROR) { printe("%s", text); } } -static bool is_stdin_a_terminal() { -#if defined(_WIN32) - HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE); - DWORD mode; - return GetConsoleMode(hStdin, &mode); -#else - return isatty(STDIN_FILENO); -#endif -} - static std::string read_pipe_data() { std::ostringstream result; result << std::cin.rdbuf(); // Read all data from std::cin @@ -721,7 +897,7 @@ int main(int argc, const char ** argv) { opt.user_ += read_pipe_data(); } - llama_log_set(log_callback, nullptr); + llama_log_set(log_callback, &opt); LlamaData llama_data; if (llama_data.init(opt)) { return 1; From cd920d0ac38ec243605a5a57c50941140a193f9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 19 Dec 2024 08:53:58 +0100 Subject: [PATCH 22/34] tests: disable GGUF test for bad value size (#10886) --- tests/test-gguf.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test-gguf.cpp b/tests/test-gguf.cpp index e5b4cb7b8af1c..1bb5fb47c4317 100644 --- a/tests/test-gguf.cpp +++ b/tests/test-gguf.cpp @@ -634,7 +634,7 @@ static std::pair test_handcrafted_file(const unsigned int seed) { HANDCRAFTED_KV_BAD_KEY_SIZE, HANDCRAFTED_KV_BAD_TYPE, - HANDCRAFTED_KV_BAD_VALUE_SIZE, + // HANDCRAFTED_KV_BAD_VALUE_SIZE, // FIXME sanitizer limit // HANDCRAFTED_FILE_TYPE_DUPLICATE_KEY, // FIXME HANDCRAFTED_KV_SUCCESS, From 7585edbdebd02861e0994dae67c9338731fb3fc5 Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Thu, 19 Dec 2024 10:37:12 +0100 Subject: [PATCH 23/34] convert : Add support for Microsoft Phi-4 model (#10817) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * convert : use GPT2 vocab for Phi-4 model * convert : use null value of sliding_window to distinguish Phi-4 from other PHI3-based models * llama : do not use sliding window attention mask for Phi-4 model --------- Co-authored-by: Stanisław Szymczyk --- convert_hf_to_gguf.py | 15 ++++++++++++++- src/llama.cpp | 10 ++++++++-- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 4a0b00f69c699..7b433ee6dc375 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2200,6 +2200,15 @@ class Phi3MiniModel(Model): model_arch = gguf.MODEL_ARCH.PHI3 def set_vocab(self): + # Phi-4 model uses GPT2Tokenizer + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + tokenizer_class = tokenizer_config_json['tokenizer_class'] + if tokenizer_class == 'GPT2Tokenizer': + return self._set_vocab_gpt2() + from sentencepiece import SentencePieceProcessor tokenizer_path = self.dir_model / 'tokenizer.model' @@ -2316,7 +2325,11 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_dimension_count(rope_dims) self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"])) + sliding_window = self.hparams.get("sliding_window") + # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models + if sliding_window is None: + sliding_window = 0 + self.gguf_writer.add_sliding_window(sliding_window) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: n_embd = self.find_hparam(["hidden_size", "n_embd"]) diff --git a/src/llama.cpp b/src/llama.cpp index 94160d534435f..cec15a00589d4 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -13333,7 +13333,13 @@ struct llm_build_context { 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_swa = build_inp_KQ_mask_swa(); + struct ggml_tensor * KQ_mask = nullptr; + if (hparams.n_swa == 0) { + // Phi-4 doesn't use sliding window attention + KQ_mask = build_inp_KQ_mask(); + } else { + KQ_mask = build_inp_KQ_mask_swa(); + } for (int il = 0; il < n_layer; ++il) { auto residual = inpL; @@ -13391,7 +13397,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { From 2fffc52b50992ac5bc64db19d33c39cbc06f52cf Mon Sep 17 00:00:00 2001 From: Sukriti Sharma Date: Thu, 19 Dec 2024 06:04:51 -0700 Subject: [PATCH 24/34] llama : fix Roberta embeddings (#10856) * fix: Use gpt2 tokenizer for roberta and add eos/bos tokens Branch: RobertaTokenizer Signed-off-by: Gabe Goodhart * fixes to position embeddings Signed-off-by: Sukriti-Sharma4 * map roberta-bpe to gpt-2 Signed-off-by: Sukriti-Sharma4 * fix linting Signed-off-by: Sukriti-Sharma4 --------- Signed-off-by: Gabe Goodhart Signed-off-by: Sukriti-Sharma4 Co-authored-by: Gabe Goodhart --- convert_hf_to_gguf.py | 47 ++++++++++++++++++++++++++++++++++++++++++- src/llama.cpp | 3 ++- 2 files changed, 48 insertions(+), 2 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 7b433ee6dc375..ecd69be6bbb07 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2628,7 +2628,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] -@Model.register("BertModel", "CamembertModel", "RobertaModel") +@Model.register("BertModel", "CamembertModel") class BertModel(Model): model_arch = gguf.MODEL_ARCH.BERT @@ -2701,6 +2701,51 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter return [(self.map_tensor_name(name), data_torch)] +@Model.register("RobertaModel") +class RobertaModel(BertModel): + model_arch = gguf.MODEL_ARCH.BERT + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # we need the pad_token_id to know how to chop down position_embd matrix + if (pad_token_id := self.hparams.get("pad_token_id")) is not None: + self._position_offset = 1 + pad_token_id + if "max_position_embeddings" in self.hparams: + self.hparams["max_position_embeddings"] -= self._position_offset + else: + self._position_offset = None + + def set_vocab(self): + """Support BPE tokenizers for roberta models""" + bpe_tok_path = self.dir_model / "tokenizer.json" + if bpe_tok_path.exists(): + self._set_vocab_gpt2() + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + # we need this to validate the size of the token_type embeddings + # though currently we are passing all zeros to the token_type embeddings + # "Sequence A" or "Sequence B" + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) + + else: + return super().set_vocab() + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # if name starts with "roberta.", remove the prefix + # e.g. https://huggingface.co/BAAI/bge-reranker-v2-m3/tree/main + if name.startswith("roberta."): + name = name[8:] + + # position embeddings start at pad_token_id + 1, so just chop down the weight tensor + if name == "embeddings.position_embeddings.weight": + if self._position_offset is not None: + data_torch = data_torch[self._position_offset:,:] + + return super().modify_tensors(data_torch, name, bid) + + @Model.register("NomicBertModel") class NomicBertModel(BertModel): model_arch = gguf.MODEL_ARCH.NOMIC_BERT diff --git a/src/llama.cpp b/src/llama.cpp index cec15a00589d4..b442781a062eb 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -6592,7 +6592,8 @@ static void llm_load_vocab( tokenizer_pre == "jina-v1-en" || tokenizer_pre == "jina-v2-es" || tokenizer_pre == "jina-v2-de" || - tokenizer_pre == "jina-v2-code") { + tokenizer_pre == "jina-v2-code" || + tokenizer_pre == "roberta-bpe") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "refact") { From a3c33b1dce2d4f25040b75f66629104bd1e40128 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= Date: Thu, 19 Dec 2024 14:20:41 +0100 Subject: [PATCH 25/34] ggml: fix arm build with gcc (#10895) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Adrien Gallouët --- ggml/src/ggml-cpu/CMakeLists.txt | 29 +++++++++++++++++++++-------- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index ea4ef5a006d3c..683b90aff8b55 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -88,32 +88,45 @@ function(ggml_add_cpu_backend_variant_impl tag_name) endif() if (GGML_NATIVE) - list(APPEND ARCH_FLAGS -mcpu=native) - - set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) - # -mcpu=native does not always enable all the features in some compilers, # so we check for them manually and enable them if available + execute_process( + COMMAND ${CMAKE_C_COMPILER} -mcpu=native -E -v - + INPUT_FILE "/dev/null" + OUTPUT_QUIET + ERROR_VARIABLE ARM_MCPU + RESULT_VARIABLE ARM_MCPU_RESULT + ) + if (NOT ARM_MCPU_RESULT) + string(REGEX MATCH "-mcpu=[^ ']+" ARM_MCPU_FLAG "${ARM_MCPU}") + endif() + if ("${ARM_MCPU_FLAG}" STREQUAL "") + set(ARM_MCPU_FLAG -mcpu=native) + message(STATUS "ARM -mcpu not found, -mcpu=native will be used") + endif() + + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) include(CheckCXXSourceRuns) - set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+dotprod") + set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+dotprod") check_cxx_source_runs( "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) if (GGML_COMPILER_SUPPORT_DOTPROD) - set(ARCH_FLAGS "${ARCH_FLAGS}+dotprod") + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+dotprod") endif() - set(CMAKE_REQUIRED_FLAGS "${ARCH_FLAGS}+i8mm") + set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+i8mm") check_cxx_source_runs( "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_I8MM) if (GGML_COMPILER_SUPPORT_I8MM) - set(ARCH_FLAGS "${ARCH_FLAGS}+i8mm") + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+i8mm") endif() set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}") else() if (GGML_CPU_ARM_ARCH) From 57bb2c40cd94c5a09f5210ed8264cc93b21c4b7e Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 19 Dec 2024 15:40:08 +0100 Subject: [PATCH 26/34] server : fix logprobs, make it OAI-compatible (#10783) * server : fix logprobs, make it openai-compatible * update docs * add std::log * return pre-sampling p * sort before apply softmax * add comment * fix test * set p for sampled token * update docs * add --multi-token-probs * update docs * add `post_sampling_probs` option * update docs [no ci] * remove --multi-token-probs * "top_probs" with "post_sampling_probs" * resolve review comments * rename struct token_prob to prob_info * correct comment placement * fix setting prob for sampled token --- examples/server/README.md | 78 ++++-- examples/server/server.cpp | 229 ++++++++++++------ .../server/tests/unit/test_chat_completion.py | 62 ++++- examples/server/tests/unit/test_completion.py | 71 +++++- examples/server/tests/unit/test_embedding.py | 3 + examples/server/utils.hpp | 60 +++++ 6 files changed, 396 insertions(+), 107 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index d006a8d37cf6f..6d64656926250 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -343,6 +343,10 @@ node index.js ### POST `/completion`: Given a `prompt`, it returns the predicted completion. +> [!IMPORTANT] +> +> This endpoint is **not** OAI-compatible + *Options:* `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: @@ -444,38 +448,68 @@ These words will not be included in the completion, so make sure to add them to `timings_per_token`: Include prompt processing and text generation speed information in each response. Default: `false` +`post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain. + **Response format** - Note: In streaming mode (`stream`), only `content`, `tokens` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support. -- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: - -```json -{ - "content": "", - "tokens": [ generated token ids if requested ], - "probs": [ - { - "prob": float, - "tok_str": "" - }, - { - "prob": float, - "tok_str": "" - }, +- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has a nested array `top_logprobs`. It contains at **maximum** `n_probs` elements: + ```json + { + "content": "", + "tokens": [ generated token ids if requested ], ... - ] -}, -``` - -Notice that each `probs` is an array of length `n_probs`. + "probs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + }, + ... + ] + }, + { + "id": , + "logprob": float, + "token": "", + "bytes": [int, int, ...], + "top_logprobs": [ + ... + ] + }, + ... + ] + }, + ``` + Please note that if `post_sampling_probs` is set to `true`: + - `logprob` will be replaced with `prob`, with the value between 0.0 and 1.0 + - `top_logprobs` will be replaced with `top_probs`. Each element contains: + - `id`: token ID + - `token`: token in string + - `bytes`: token in bytes + - `prob`: token probability, with the value between 0.0 and 1.0 + - Number of elements in `top_probs` may be less than `n_probs` - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. - `tokens`: Same as `content` but represented as raw token ids. Only populated if `"return_tokens": true` or `"stream": true` in the request. - `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options) - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.). -- `model`: The path to the model loaded with `-m` -- `prompt`: The provided `prompt` +- `model`: The model alias (for model path, please use `/props` endpoint) +- `prompt`: The processed `prompt` (special tokens may be added) - `stop_type`: Indicating whether the completion has stopped. Possible values are: - `none`: Generating (not stopped) - `eos`: Stopped because it encountered the EOS token diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 5ed4e8d274428..fa3682a920649 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -93,6 +93,7 @@ struct slot_params { std::vector antiprompt; bool timings_per_token = false; + bool post_sampling_probs = false; bool ignore_eos = false; struct common_params_sampling sampling; @@ -151,6 +152,7 @@ struct slot_params { {"speculative.n_min", speculative.n_min}, {"speculative.p_min", speculative.p_min}, {"timings_per_token", timings_per_token}, + {"post_sampling_probs", post_sampling_probs}, }; } }; @@ -231,6 +233,7 @@ struct server_task { params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); @@ -436,36 +439,67 @@ inline std::string stop_type_to_str(stop_type type) { struct completion_token_output { llama_token tok; + float prob; std::string text_to_send; - struct token_prob { + struct prob_info { llama_token tok; - std::string tok_str; + std::string txt; float prob; }; - std::vector probs; + std::vector probs; - json to_json() const { + json to_json(bool post_sampling_probs) const { json probs_for_token = json::array(); for (const auto & p : probs) { + std::string txt(p.txt); + txt.resize(validate_utf8(txt)); probs_for_token.push_back(json { - {"tok_str", p.tok_str}, - {"prob", p.prob}, + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.txt)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, }); } return probs_for_token; } - static json probs_vector_to_json(const std::vector & probs) { + static json probs_vector_to_json(const std::vector & probs, bool post_sampling_probs) { json out = json::array(); - for (const auto & prob : probs) { - const std::string tok_str = prob.text_to_send; + for (const auto & p : probs) { + std::string txt(p.text_to_send); + txt.resize(validate_utf8(txt)); out.push_back(json { - {"content", tok_str}, - {"probs", prob.to_json()}, + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.text_to_send)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + { + post_sampling_probs ? "top_probs" : "top_logprobs", + p.to_json(post_sampling_probs) + }, }); } return out; } + + static float logarithm(float x) { + // nlohmann::json converts -inf to null, so we need to prevent that + return x == 0.0f ? std::numeric_limits::lowest() : std::log(x); + } + + static std::vector str_to_bytes(const std::string & str) { + std::vector bytes; + for (unsigned char c : str) { + bytes.push_back(c); + } + return bytes; + } }; struct server_task_result_cmpl_final : server_task_result { @@ -486,6 +520,7 @@ struct server_task_result_cmpl_final : server_task_result { std::string stopping_word; stop_type stop = STOP_TYPE_NONE; + bool post_sampling_probs; std::vector probs_output; slot_params generation_params; @@ -530,8 +565,8 @@ struct server_task_result_cmpl_final : server_task_result { {"tokens_cached", n_tokens_cached}, {"timings", timings.to_json()}, }; - if (!probs_output.empty()) { - res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output); + if (!stream && !probs_output.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); } return res; } @@ -542,19 +577,25 @@ struct server_task_result_cmpl_final : server_task_result { finish_reason = "stop"; } - json choices = json::array({json{ + json choice = json{ {"finish_reason", finish_reason}, {"index", 0}, {"message", json { {"content", content}, {"role", "assistant"} } - }}}); + }}; + + if (!stream && probs_output.size() > 0) { + choice["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } std::time_t t = std::time(0); json res = json { - {"choices", choices}, + {"choices", json::array({choice})}, {"created", t}, {"model", oaicompat_model}, {"object", "chat.completion"}, @@ -584,12 +625,14 @@ struct server_task_result_cmpl_final : server_task_result { finish_reason = "stop"; } - json choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); + json choice = json{ + {"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()} + }; json ret = json { - {"choices", choices}, + {"choices", json::array({choice})}, {"created", t}, {"id", oaicompat_cmpl_id}, {"model", oaicompat_model}, @@ -618,7 +661,8 @@ struct server_task_result_cmpl_partial : server_task_result { int32_t n_decoded; int32_t n_prompt_tokens; - std::vector probs_output; + bool post_sampling_probs; + completion_token_output prob_output; result_timings timings; // OAI-compat fields @@ -655,8 +699,8 @@ struct server_task_result_cmpl_partial : server_task_result { if (timings.prompt_n > 0) { res.push_back({"timings", timings.to_json()}); } - if (!probs_output.empty()) { - res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output); + if (!prob_output.probs.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs); } return res; } @@ -708,6 +752,14 @@ struct server_task_result_cmpl_partial : server_task_result { }}); } + GGML_ASSERT(choices.size() >= 1); + + if (prob_output.probs.size() > 0) { + choices[0]["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + json ret = json { {"choices", choices}, {"created", t}, @@ -1001,7 +1053,6 @@ struct server_slot { // stats size_t n_sent_text = 0; // number of sent text character - size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_generation; @@ -1023,7 +1074,6 @@ struct server_slot { stopping_word = ""; n_past = 0; n_sent_text = 0; - n_sent_token_probs = 0; task_type = SERVER_TASK_TYPE_COMPLETION; generated_tokens.clear(); @@ -1764,7 +1814,7 @@ struct server_context { bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling - const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special); + const std::string token_str = result.text_to_send; slot.sampled = result.tok; slot.generated_text += token_str; @@ -1774,26 +1824,7 @@ struct server_context { slot.has_next_token = true; // check if there is incomplete UTF-8 character at the end - bool incomplete = false; - for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) { - unsigned char c = slot.generated_text[slot.generated_text.size() - i]; - if ((c & 0xC0) == 0x80) { - // continuation byte: 10xxxxxx - continue; - } - if ((c & 0xE0) == 0xC0) { - // 2-byte character: 110xxxxx ... - incomplete = i < 2; - } else if ((c & 0xF0) == 0xE0) { - // 3-byte character: 1110xxxx ... - incomplete = i < 3; - } else if ((c & 0xF8) == 0xF0) { - // 4-byte character: 11110xxx ... - incomplete = i < 4; - } - // else 1-byte character or invalid byte - break; - } + bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size(); // search stop word and delete it if (!incomplete) { @@ -1923,6 +1954,55 @@ struct server_context { return slot.has_next_token; // continue } + void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) { + size_t n_probs = slot.params.sampling.n_probs; + size_t n_vocab = llama_n_vocab(llama_get_model(ctx)); + if (post_sampling) { + const auto * cur_p = common_sampler_get_candidates(slot.smpl); + const size_t max_probs = cur_p->size; + + // set probability for sampled token + for (size_t i = 0; i < max_probs; i++) { + if (cur_p->data[i].id == result.tok) { + result.prob = cur_p->data[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(max_probs); + for (size_t i = 0; i < std::min(max_probs, n_probs); i++) { + result.probs.push_back({ + cur_p->data[i].id, + common_detokenize(ctx, {cur_p->data[i].id}, special), + cur_p->data[i].p + }); + } + } else { + // TODO: optimize this with min-p optimization + std::vector cur = get_token_probabilities(ctx, idx); + + // set probability for sampled token + for (size_t i = 0; i < n_vocab; i++) { + // set probability for sampled token + if (cur[i].id == result.tok) { + result.prob = cur[i].p; + break; + } + } + + // set probability for top n_probs tokens + result.probs.reserve(n_probs); + for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) { + result.probs.push_back({ + cur[i].id, + common_detokenize(ctx, {cur[i].id}, special), + cur[i].p + }); + } + } + } + void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { send_error(task.id, error, type); } @@ -1950,8 +2030,9 @@ struct server_context { res->content = tkn.text_to_send; res->tokens = { tkn.tok }; - res->n_decoded = slot.n_decoded; - res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->post_sampling_probs = slot.params.post_sampling_probs; res->verbose = slot.params.verbose; res->oaicompat = slot.params.oaicompat; @@ -1961,17 +2042,7 @@ struct server_context { // populate res.probs_output if (slot.params.sampling.n_probs > 0) { - const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); - - const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); - const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); - - std::vector probs_output; - if (probs_pos < probs_stop_pos) { - res->probs_output = std::vector( - slot.generated_token_probs.begin() + probs_pos, - slot.generated_token_probs.begin() + probs_stop_pos); - } + res->prob_output = tkn; // copy the token probs } // populate timings if this is final response or timings_per_token is enabled @@ -1993,13 +2064,14 @@ struct server_context { res->timings = slot.get_timings(); res->prompt = common_detokenize(ctx, slot.prompt_tokens, true); - res->truncated = slot.truncated; - res->n_decoded = slot.n_decoded; - res->n_prompt_tokens = slot.n_prompt_tokens; - res->n_tokens_cached = slot.n_past; - res->has_new_line = slot.has_new_line; - res->stopping_word = slot.stopping_word; - res->stop = slot.stop; + res->truncated = slot.truncated; + res->n_decoded = slot.n_decoded; + res->n_prompt_tokens = slot.n_prompt_tokens; + res->n_tokens_cached = slot.n_past; + res->has_new_line = slot.has_new_line; + res->stopping_word = slot.stopping_word; + res->stop = slot.stop; + res->post_sampling_probs = slot.params.post_sampling_probs; res->verbose = slot.params.verbose; res->stream = slot.params.stream; @@ -2796,7 +2868,9 @@ struct server_context { continue; // continue loop of slots } - llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i); + const int tok_idx = slot.i_batch - i; + + llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx); slot.i_batch = -1; @@ -2815,17 +2889,12 @@ struct server_context { slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3; completion_token_output result; - result.tok = id; + result.tok = id; + result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special); + result.prob = 1.0f; // TODO: set it here instead of doing inside populate_token_probs - const auto * cur_p = common_sampler_get_candidates(slot.smpl); - - for (size_t i = 0; i < (size_t) slot.params.sampling.n_probs; ++i) { - auto tok_id = cur_p->data[i].id; - result.probs.push_back({ - tok_id, - tokens_to_output_formatted_string(ctx, tok_id), - i >= cur_p->size ? 0.0f : cur_p->data[i].p, - }); + if (slot.params.sampling.n_probs > 0) { + populate_token_probs(slot, result, slot.params.post_sampling_probs, params_base.special, tok_idx); } if (!process_token(result, slot)) { @@ -2909,7 +2978,11 @@ struct server_context { for (size_t i = 0; i < ids.size(); ++i) { completion_token_output result; - result.tok = ids[i]; + result.tok = ids[i]; + result.text_to_send = common_token_to_piece(ctx, result.tok, params_base.special); + result.prob = 1.0f; // set later + + // TODO: set result.probs if (!process_token(result, slot)) { // release slot because of stop condition diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py index 6573cc17f7b87..0fa1a17c1f50a 100644 --- a/examples/server/tests/unit/test_chat_completion.py +++ b/examples/server/tests/unit/test_chat_completion.py @@ -92,7 +92,6 @@ def test_chat_completion_with_openai_library(): seed=42, temperature=0.8, ) - print(res) assert res.choices[0].finish_reason == "length" assert res.choices[0].message.content is not None assert match_regex("(Suddenly)+", res.choices[0].message.content) @@ -163,3 +162,64 @@ def test_chat_completion_with_timings_per_token(): assert "predicted_per_second" in data["timings"] assert "predicted_n" in data["timings"] assert data["timings"]["predicted_n"] <= 10 + + +def test_logprobs(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + ) + output_text = res.choices[0].message.content + aggregated_text = '' + assert res.choices[0].logprobs is not None + assert res.choices[0].logprobs.content is not None + for token in res.choices[0].logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text + + +def test_logprobs_stream(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + temperature=0.0, + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=5, + logprobs=True, + top_logprobs=10, + stream=True, + ) + output_text = '' + aggregated_text = '' + for data in res: + choice = data.choices[0] + if choice.finish_reason is None: + if choice.delta.content: + output_text += choice.delta.content + assert choice.logprobs is not None + assert choice.logprobs.content is not None + for token in choice.logprobs.content: + aggregated_text += token.token + assert token.logprob <= 0.0 + assert token.bytes is not None + assert token.top_logprobs is not None + assert len(token.top_logprobs) > 0 + assert aggregated_text == output_text diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py index 36aee57dd3638..b88d45f18547f 100644 --- a/examples/server/tests/unit/test_completion.py +++ b/examples/server/tests/unit/test_completion.py @@ -270,9 +270,68 @@ def test_n_probs(): assert "completion_probabilities" in res.body assert len(res.body["completion_probabilities"]) == 5 for tok in res.body["completion_probabilities"]: - assert "probs" in tok - assert len(tok["probs"]) == 10 - for prob in tok["probs"]: - assert "prob" in prob - assert "tok_str" in prob - assert 0.0 <= prob["prob"] <= 1.0 + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_stream(): + global server + server.start() + res = server.make_stream_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "stream": True, + }) + for data in res: + if data["stop"] == False: + assert "completion_probabilities" in data + assert len(data["completion_probabilities"]) == 1 + for tok in data["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "logprob" in tok and tok["logprob"] <= 0.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_logprobs"]) == 10 + for prob in tok["top_logprobs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "logprob" in prob and prob["logprob"] <= 0.0 + assert "bytes" in prob and type(prob["bytes"]) == list + + +def test_n_probs_post_sampling(): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "n_probs": 10, + "temperature": 0.0, + "n_predict": 5, + "post_sampling_probs": True, + }) + assert res.status_code == 200 + assert "completion_probabilities" in res.body + assert len(res.body["completion_probabilities"]) == 5 + for tok in res.body["completion_probabilities"]: + assert "id" in tok and tok["id"] > 0 + assert "token" in tok and type(tok["token"]) == str + assert "prob" in tok and 0.0 < tok["prob"] <= 1.0 + assert "bytes" in tok and type(tok["bytes"]) == list + assert len(tok["top_probs"]) == 10 + for prob in tok["top_probs"]: + assert "id" in prob and prob["id"] > 0 + assert "token" in prob and type(prob["token"]) == str + assert "prob" in prob and 0.0 <= prob["prob"] <= 1.0 + assert "bytes" in prob and type(prob["bytes"]) == list + # because the test model usually output token with either 100% or 0% probability, we need to check all the top_probs + assert any(prob["prob"] == 1.0 for prob in tok["top_probs"]) diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py index e32d745829605..43e372fc70d71 100644 --- a/examples/server/tests/unit/test_embedding.py +++ b/examples/server/tests/unit/test_embedding.py @@ -50,6 +50,8 @@ def test_embedding_multiple(): @pytest.mark.parametrize( "input,is_multi_prompt", [ + # do not crash on empty input + ("", False), # single prompt ("string", False), ([12, 34, 56], False), @@ -103,6 +105,7 @@ def test_embedding_pooling_none_oai(): # /v1/embeddings does not support pooling type 'none' assert res.status_code == 400 + assert "error" in res.body def test_embedding_openai_library_single(): diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index ffdffe904308f..94bb285b6f2d1 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -171,6 +171,36 @@ static std::vector tokenize_input_prompts(llama_context * ctx, con return result; } +// return the last index of character that can form a valid string +// if the last character is potentially cut in half, return the index before the cut +// if validate_utf8(text) == text.size(), then the whole text is valid utf8 +static size_t validate_utf8(const std::string& text) { + size_t len = text.size(); + if (len == 0) return 0; + + // Check the last few bytes to see if a multi-byte character is cut off + for (size_t i = 1; i <= 4 && i <= len; ++i) { + unsigned char c = text[len - i]; + // Check for start of a multi-byte sequence from the end + if ((c & 0xE0) == 0xC0) { + // 2-byte character start: 110xxxxx + // Needs at least 2 bytes + if (i < 2) return len - i; + } else if ((c & 0xF0) == 0xE0) { + // 3-byte character start: 1110xxxx + // Needs at least 3 bytes + if (i < 3) return len - i; + } else if ((c & 0xF8) == 0xF0) { + // 4-byte character start: 11110xxx + // Needs at least 4 bytes + if (i < 4) return len - i; + } + } + + // If no cut-off multi-byte character is found, return full length + return len; +} + // // template utils // @@ -671,3 +701,33 @@ static json format_logit_bias(const std::vector & logit_bias) static std::string safe_json_to_str(json data) { return data.dump(-1, ' ', false, json::error_handler_t::replace); } + +static std::vector get_token_probabilities(llama_context * ctx, int idx) { + std::vector cur; + const auto * logits = llama_get_logits_ith(ctx, idx); + const int n_vocab = llama_n_vocab(llama_get_model(ctx)); + + cur.resize(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f}; + } + + // sort tokens by logits + std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) { + return a.logit > b.logit; + }); + + // apply softmax + float max_l = cur[0].logit; + float cum_sum = 0.0f; + for (size_t i = 0; i < cur.size(); ++i) { + float p = expf(cur[i].logit - max_l); + cur[i].p = p; + cum_sum += p; + } + for (size_t i = 0; i < cur.size(); ++i) { + cur[i].p /= cum_sum; + } + + return cur; +} From 36319dec5d75a7dfe3e3de37b9ca2a76cd52b7b2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Dec 2024 17:35:15 +0200 Subject: [PATCH 27/34] tts : small QoL for easy model fetch (#10903) --- common/arg.cpp | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/common/arg.cpp b/common/arg.cpp index e5ddd8318f787..c3d66efb5a701 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2206,5 +2206,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + // model-specific + add_opt(common_arg( + {"--tts-oute-default"}, + string_format("use default OuteTTS models (note: can download weights from the internet)"), + [](common_params & params) { + params.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF"; + params.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf"; + params.vocoder.hf_repo = "ggml-org/WavTokenizer"; + params.vocoder.hf_file = "WavTokenizer-Large-75-F16.gguf"; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + return ctx_arg; } From 5cab3e4aaa892df4620b720f20a503a1bbbe7a52 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Dec 2024 17:42:13 +0200 Subject: [PATCH 28/34] llama : minor grammar refactor (#10897) ggml-ci --- examples/gbnf-validator/gbnf-validator.cpp | 11 +++----- src/llama-grammar.cpp | 30 +++++++++++----------- src/llama-grammar.h | 7 ++--- tests/test-grammar-integration.cpp | 9 +++---- tests/test-llama-grammar.cpp | 6 ++--- 5 files changed, 26 insertions(+), 37 deletions(-) diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp index 7493af9d3aec3..17a0e27c444e8 100644 --- a/examples/gbnf-validator/gbnf-validator.cpp +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -11,19 +11,15 @@ static bool llama_grammar_validate(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) { const auto cpts = unicode_cpts_from_utf8(input_str); - const llama_grammar_rules & rules = llama_grammar_get_rules (grammar); - llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); + auto & stacks_cur = llama_grammar_get_stacks(grammar); size_t pos = 0; for (const auto & cpt : cpts) { - const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy - - llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur); + llama_grammar_accept(grammar, cpt); if (stacks_cur.empty()) { error_pos = pos; error_msg = "Unexpected character '" + unicode_cpt_to_utf8(cpt) + "'"; - stacks_cur = stacks_prev; return false; } ++pos; @@ -82,7 +78,8 @@ int main(int argc, char** argv) { llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_str.c_str(), "root"); if (grammar == nullptr) { - throw std::runtime_error("Failed to initialize llama_grammar"); + fprintf(stdout, "Failed to initialize llama_grammar\n"); + return 1; } // Read the input file std::string input_str; diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp index 74e9f64b393b2..76d0cb3a2ff78 100644 --- a/src/llama-grammar.cpp +++ b/src/llama-grammar.cpp @@ -822,15 +822,11 @@ llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar) return grammar->stacks; } -void llama_grammar_accept( - const llama_grammar_rules & rules, - const llama_grammar_stacks & stacks, - const uint32_t chr, - llama_grammar_stacks & stacks_new) { - stacks_new.clear(); - stacks_new.reserve(stacks.size()); +void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr) { + llama_grammar_stacks stacks_new; + stacks_new.reserve(grammar->stacks.size()); - for (const auto & stack : stacks) { + for (const auto & stack : grammar->stacks) { if (stack.empty()) { continue; } @@ -844,9 +840,11 @@ void llama_grammar_accept( if (!llama_grammar_is_end_of_sequence(pos)) { new_stack.push_back(pos); } - llama_grammar_advance_stack(rules, new_stack, stacks_new); + llama_grammar_advance_stack(grammar->rules, new_stack, stacks_new); } } + + grammar->stacks = std::move(stacks_new); } llama_grammar_candidates llama_grammar_reject_candidates_for_stack( @@ -1051,7 +1049,12 @@ void llama_grammar_free_impl(struct llama_grammar * grammar) { } struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & grammar) { - llama_grammar * result = new llama_grammar { grammar.vocab, grammar.rules, grammar.stacks, grammar.partial_utf8, }; + llama_grammar * result = new llama_grammar { + grammar.vocab, + grammar.rules, + grammar.stacks, + grammar.partial_utf8, + }; // redirect elements in stacks to point to new rules for (size_t is = 0; is < result->stacks.size(); is++) { @@ -1059,7 +1062,7 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra for (size_t ir0 = 0; ir0 < grammar.rules.size(); ir0++) { for (size_t ir1 = 0; ir1 < grammar.rules[ir0].size(); ir1++) { if (grammar.stacks[is][ie] == &grammar.rules[ir0][ir1]) { - result->stacks[is][ie] = &result->rules[ir0][ir1]; + result->stacks[is][ie] = &result->rules[ir0][ir1]; } } } @@ -1126,11 +1129,8 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token const auto decoded = decode_utf8(piece, grammar.partial_utf8); const auto & code_points = decoded.first; - llama_grammar_stacks stacks_new; - for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { - llama_grammar_accept(grammar.rules, grammar.stacks, *it, stacks_new); - grammar.stacks = std::move(stacks_new); + llama_grammar_accept(&grammar, *it); } grammar.partial_utf8 = decoded.second; diff --git a/src/llama-grammar.h b/src/llama-grammar.h index f529ce351e416..13e940fb52e24 100644 --- a/src/llama-grammar.h +++ b/src/llama-grammar.h @@ -58,6 +58,7 @@ using llama_grammar_rules = std::vector; using llama_grammar_stacks = std::vector; using llama_grammar_candidates = std::vector; +// TODO: remove, needed for tests atm const llama_grammar_rules & llama_grammar_get_rules (const struct llama_grammar * grammar); llama_grammar_stacks & llama_grammar_get_stacks( struct llama_grammar * grammar); @@ -65,11 +66,7 @@ const llama_grammar_rules & llama_grammar_get_rules (const struct llama_grammar // be positioned at a character range (see `llama_grammar_advance_stack`), and // produces the N possible stacks if the given char is accepted at those // positions -void llama_grammar_accept( - const llama_grammar_rules & rules, - const llama_grammar_stacks & stacks, - uint32_t chr, - llama_grammar_stacks & stacks_new); +void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr); std::vector llama_grammar_reject_candidates_for_stack( const llama_grammar_rules & rules, diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 5cc0cdb04751f..e1bdbb9250fca 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -32,13 +32,10 @@ static bool test_build_grammar_fails(const std::string & grammar_str) { static bool match_string(const std::string & input, llama_grammar * grammar) { const auto cpts = unicode_cpts_from_utf8(input); - const llama_grammar_rules & rules = llama_grammar_get_rules (grammar); - llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); + auto & stacks_cur = llama_grammar_get_stacks(grammar); for (const auto & cpt : cpts) { - const llama_grammar_stacks stacks_prev = llama_grammar_get_stacks(grammar); // copy - - llama_grammar_accept(rules, stacks_prev, cpt, stacks_cur); + llama_grammar_accept(grammar, cpt); if (stacks_cur.empty()) { // no stacks means that the grammar failed to match at this point @@ -63,7 +60,7 @@ static void test(const std::string & test_desc, const std::string & grammar_str, auto * grammar = build_grammar(grammar_str); // Save the original grammar stacks so that we can reset after every new string we want to test - const llama_grammar_stacks stacks_org = llama_grammar_get_stacks(grammar); + const llama_grammar_stacks stacks_org = llama_grammar_get_stacks(grammar); // copy llama_grammar_stacks & stacks_cur = llama_grammar_get_stacks(grammar); diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 6f1374ca8ed58..e2129206be156 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -113,12 +113,10 @@ int main() } } - llama_grammar * grammar = NULL; std::vector grammar_rules(parsed_grammar.c_rules()); - grammar = llama_grammar_init_impl(nullptr, grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - if (grammar == nullptr) - { + llama_grammar * grammar = llama_grammar_init_impl(nullptr, grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + if (grammar == nullptr) { throw std::runtime_error("Failed to initialize llama_grammar"); } From d408bb9268a988c5a60a5746d3a6430386e7604d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 19 Dec 2024 18:47:15 +0200 Subject: [PATCH 29/34] clip : disable GPU support (#10896) ggml-ci --- examples/llava/clip.cpp | 86 ++++++++++++++++++++--------------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 463b7c865b90c..3cd0d2fa8590c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -8,25 +8,25 @@ #include "ggml-alloc.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_SYCL -#include "ggml-sycl.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif - -#ifdef GGML_USE_VULKAN -#include "ggml-vulkan.h" -#endif +//#ifdef GGML_USE_CUDA +//#include "ggml-cuda.h" +//#endif +// +//#ifdef GGML_USE_SYCL +//#include "ggml-sycl.h" +//#endif +// +//#ifdef GGML_USE_METAL +//#include "ggml-metal.h" +//#endif +// +//#ifdef GGML_USE_CANN +//#include "ggml-cann.h" +//#endif +// +//#ifdef GGML_USE_VULKAN +//#include "ggml-vulkan.h" +//#endif #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" @@ -1222,30 +1222,30 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } } -#ifdef GGML_USE_CUDA - new_clip->backend = ggml_backend_cuda_init(0); - LOG_INF("%s: CLIP using CUDA backend\n", __func__); -#endif - -#ifdef GGML_USE_METAL - new_clip->backend = ggml_backend_metal_init(); - LOG_INF("%s: CLIP using Metal backend\n", __func__); -#endif - -#ifdef GGML_USE_CANN - new_clip->backend = ggml_backend_cann_init(0); - LOG_INF("%s: CLIP using CANN backend\n", __func__); -#endif - -#ifdef GGML_USE_VULKAN - new_clip->backend = ggml_backend_vk_init(0); - LOG_INF("%s: CLIP using Vulkan backend\n", __func__); -#endif - -#ifdef GGML_USE_SYCL - new_clip->backend = ggml_backend_sycl_init(0); - LOG_INF("%s: CLIP using SYCL backend\n", __func__); -#endif +//#ifdef GGML_USE_CUDA +// new_clip->backend = ggml_backend_cuda_init(0); +// LOG_INF("%s: CLIP using CUDA backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_METAL +// new_clip->backend = ggml_backend_metal_init(); +// LOG_INF("%s: CLIP using Metal backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_CANN +// new_clip->backend = ggml_backend_cann_init(0); +// LOG_INF("%s: CLIP using CANN backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_VULKAN +// new_clip->backend = ggml_backend_vk_init(0); +// LOG_INF("%s: CLIP using Vulkan backend\n", __func__); +//#endif +// +//#ifdef GGML_USE_SYCL +// new_clip->backend = ggml_backend_sycl_init(0); +// LOG_INF("%s: CLIP using SYCL backend\n", __func__); +//#endif if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); From 0a11f8b7b5c39fdf6e91ef9674bc68ff08681af7 Mon Sep 17 00:00:00 2001 From: Molly Sophia Date: Fri, 20 Dec 2024 17:44:58 +0800 Subject: [PATCH 30/34] convert : fix RWKV v6 model conversion (#10913) * Enable --no-context-shift for llama-perplexity example Signed-off-by: Molly Sophia * RWKV 6: Fix error in ggml_cuda_op_bin_bcast Signed-off-by: Molly Sophia --------- Signed-off-by: Molly Sophia --- common/arg.cpp | 2 +- convert_hf_to_gguf.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index c3d66efb5a701..deb11378657f4 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -626,7 +626,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params) { params.ctx_shift = false; } - ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); + ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); add_opt(common_arg( {"--chunks"}, "N", string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index ecd69be6bbb07..76ab11ebe8e2c 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -3065,6 +3065,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter if new_name.endswith("time_mix_w2.weight"): data_torch = data_torch.permute(0, 2, 1) + if new_name.endswith("time_mix_decay.weight") or "lerp" in new_name: + data_torch = data_torch.squeeze() + rescale_every_n_layers = self.hparams["rescale_every"] if rescale_every_n_layers > 0: if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"): From 21ae3b9be83820565d1a720999b7f63ce95b4920 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 20 Dec 2024 13:31:28 +0100 Subject: [PATCH 31/34] ggml : add test for SVE and disable when it fails (#10906) --- ggml/src/ggml-cpu/CMakeLists.txt | 38 ++++++++++++++++---------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 683b90aff8b55..12d7908255483 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -82,8 +82,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name) if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang") message(FATAL_ERROR "MSVC is not supported for ARM, use clang") else() - check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) - if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") + check_cxx_compiler_flag(-mfp16-format=ieee GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E) + if (NOT "${GGML_COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") list(APPEND ARCH_FLAGS -mfp16-format=ieee) endif() @@ -106,28 +106,28 @@ function(ggml_add_cpu_backend_variant_impl tag_name) message(STATUS "ARM -mcpu not found, -mcpu=native will be used") endif() - set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) include(CheckCXXSourceRuns) - set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+dotprod") - check_cxx_source_runs( - "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" - GGML_COMPILER_SUPPORT_DOTPROD) - if (GGML_COMPILER_SUPPORT_DOTPROD) - set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+dotprod") - endif() + function(check_arm_feature tag code) + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) + set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+${tag}") + check_cxx_source_runs( + "${code}" + GGML_MACHINE_SUPPORTS_${tag} + ) + if (GGML_MACHINE_SUPPORTS_${tag}) + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+${tag}" PARENT_SCOPE) + else() + set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+no${tag}" PARENT_SCOPE) + endif() + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + endfunction() - set(CMAKE_REQUIRED_FLAGS "${ARM_MCPU_FLAG}+i8mm") - check_cxx_source_runs( - "#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" - GGML_COMPILER_SUPPORT_I8MM) - if (GGML_COMPILER_SUPPORT_I8MM) - set(ARM_MCPU_FLAG_FIX "${ARM_MCPU_FLAG_FIX}+i8mm") - endif() + check_arm_feature(dotprod "#include \nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }") + check_arm_feature(i8mm "#include \nint main() { int8x16_t _a, _b; volatile int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }") + check_arm_feature(sve "#include \nint main() { svfloat32_t _a, _b; volatile svfloat32_t _c = svadd_f32_z(svptrue_b8(), _a, _b); return 0; }") - set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) list(APPEND ARCH_FLAGS "${ARM_MCPU_FLAG}${ARM_MCPU_FLAG_FIX}") - else() if (GGML_CPU_ARM_ARCH) list(APPEND ARCH_FLAGS -march=${GGML_CPU_ARM_ARCH}) From 0ca416c91aea4549d9c77e3efeca403e15aa6c75 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 20 Dec 2024 14:12:06 +0100 Subject: [PATCH 32/34] server : (UI) fix copy to clipboard function (#10916) --- examples/server/public/index.html.gz | Bin 1205430 -> 1205858 bytes examples/server/webui/src/main.js | 23 +++++++++++++++++++---- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git 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const BASE_URL = isDev @@ -130,9 +145,9 @@ const VueMarkdown = defineComponent( }; window.copyStr = copyStr; const content = computed(() => md.value.render(props.source)); - return () => h("div", { innerHTML: content.value }); + return () => h('div', { innerHTML: content.value }); }, - { props: ["source"] } + { props: ['source'] } ); // input field to be used by settings modal From eb5c3dc64bd967f2e23c87d9dec195f45468de60 Mon Sep 17 00:00:00 2001 From: Akarshan Biswas Date: Fri, 20 Dec 2024 21:01:28 +0530 Subject: [PATCH 33/34] SYCL: Migrate away from deprecated ggml_tensor->backend (#10840) * Migrate to tensor->buffer for checking backend buffer type: 1 * SYCL: common.cpp try to migrate away from tensor->backend * SYCL: fix assertions and add proper comments * SYCL: remove extra space * SYCL: Add back static to ggml_backend_buffer_is_sycl_split function * SYCL: Add pragma directive to suppress warning spam * SYCL: Integrate debug logs with GGML_LOG and other fixes * Revert "SYCL: Integrate debug logs with GGML_LOG and other fixes" This reverts commit 2607b7de0f0d2f4f1f690226f86fa861aa39cb97. Let's keep the current SYCL specific logging mechanism for now * SYCL: Use GGML_SYCL_DEBUG after reverting * SYCL: reg_get_proc_address func, update to the current func signature * SYCL: Refactor SYCL buffer checks in ggml_sycl_cpy_tensor_2d --- ggml/src/ggml-sycl/common.cpp | 8 +++--- ggml/src/ggml-sycl/common.hpp | 4 +++ ggml/src/ggml-sycl/ggml-sycl.cpp | 46 ++++++++++++++++++-------------- 3 files changed, 35 insertions(+), 23 deletions(-) diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp index a9ee404911460..88314a5cd73af 100644 --- a/ggml/src/ggml-sycl/common.cpp +++ b/ggml/src/ggml-sycl/common.cpp @@ -11,6 +11,8 @@ // #include "common.hpp" + +#include "ggml-backend-impl.h" #include "ggml-impl.h" int get_current_device_id() { @@ -65,9 +67,9 @@ void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *sr const ggml_sycl_op_flatten_t op) try { const bool use_src1 = src1 != nullptr; - - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + if(use_src1) + GGML_ASSERT(strcmp(src1->buffer->buft->iface.get_name(src1->buffer->buft), GGML_SYCL_NAME "_Split") != 0); + GGML_ASSERT(strcmp(dst->buffer->buft->iface.get_name(dst->buffer->buft), GGML_SYCL_NAME "_Split") != 0); // dd = data device float * src0_ddf = (float *) src0->data; diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index c1582f610e5f4..62b4cea3ada85 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -26,7 +26,11 @@ #define GGML_COMMON_DECL_SYCL #define GGML_COMMON_IMPL_SYCL +/* suppress warning spam */ +#pragma clang diagnostic push +#pragma clang diagnostic ignored "-Wnested-anon-types" #include "ggml-common.h" +#pragma clang diagnostic pop void* ggml_sycl_host_malloc(size_t size); void ggml_sycl_host_free(void* ptr); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 84f1328e7cf1c..312ccfeb85359 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -288,10 +288,8 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor *tensor) try { ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; - if (tensor->view_src != NULL && tensor->view_offs == 0) { + if (tensor->view_src != NULL) { assert(tensor->view_src->buffer->buft == buffer->buft); - tensor->backend = tensor->view_src->backend; - tensor->extra = tensor->view_src->extra; return; } @@ -539,7 +537,7 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { auto dev_count = ggml_backend_sycl_get_device_count(); if (device>=dev_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + GGML_LOG_ERROR("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", device, dev_count-1); GGML_ASSERT(devicedevice; if (device>=ggml_sycl_info().device_count or device<0) { - printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + GGML_LOG_ERROR("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", device, ggml_sycl_info().device_count-1); GGML_ASSERT(devicetype, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); } - // FIXME: do not crash if cudaMalloc fails + // FIXME: do not crash if SYCL Buffer alloc fails // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first ggml_sycl_set_device(i); const queue_ptr stream = ctx->streams[i]; @@ -788,7 +786,6 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); } } - tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; tensor->extra = extra; } catch (sycl::exception const &exc) { @@ -2349,12 +2346,22 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, dpct::memcpy_direction kind; char * src_ptr; - if (src->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src->buffer)) { kind = dpct::host_to_device; + //GGML_SYCL_DEBUG("%s: Host buffer type src tensor\n", __func__); src_ptr = (char *) src->data; // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr); - } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); + } else if (ggml_backend_buffer_is_sycl(src->buffer)) { + // If buffer is a SYCL buffer + //GGML_SYCL_DEBUG("%s: SYCL buffer type src tensor\n", __func__); + kind = dpct::device_to_device; + src_ptr = (char *) src->data; + } else if (ggml_backend_buffer_is_sycl_split(src->buffer)) { + /* + If buffer is a SYCL split buffer + */ + //GGML_SYCL_DEBUG("%s: Split buffer type src tensor\n", __func__); + GGML_ASSERT(i1_low == 0 && i1_high == src->ne[1]); kind = dpct::device_to_device; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; @@ -2857,8 +2864,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten const int nb2 = dst->nb[2]; const int nb3 = dst->nb[3]; - GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(dst->buffer)); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src1->buffer)); GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -2878,7 +2885,7 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; + const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer); GGML_ASSERT(!(split && ne02 > 1)); GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); @@ -3198,7 +3205,7 @@ static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const gg const ggml_tensor *src1, ggml_tensor *dst) try { GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer)); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation GGML_ASSERT(src0->type == GGML_TYPE_F16); @@ -3231,7 +3238,7 @@ static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer)); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -3293,7 +3300,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer)); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_TENSOR_BINARY_OP_LOCALS @@ -4638,10 +4645,9 @@ static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t re static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) { GGML_UNUSED(reg); - // TODO: update to the current function signature - //if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { - // return (void *)ggml_backend_sycl_split_buffer_type; - //} + if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { + return (void *)ggml_backend_sycl_split_buffer_type; + } // SYCL doesn't support registering host memory, left here for reference // "ggml_backend_register_host_buffer" From e34c5af43f941f0ddb92466776339897295aca11 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= Date: Sat, 21 Dec 2024 00:33:37 +0100 Subject: [PATCH 34/34] ggml-cpu: replace NEON asm with intrinsics in ggml_gemv_q4_0_4x8_q8_0() (#10874) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * ggml-cpu: replace NEON asm with intrinsics in ggml_gemv_q4_0_4x8_q8_0() Signed-off-by: Adrien Gallouët * ggml-cpu: format code Signed-off-by: Adrien Gallouët --------- Signed-off-by: Adrien Gallouët --- ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp | 122 +++++++++++-------------- 1 file changed, 51 insertions(+), 71 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp index a51d1a6c59517..2d79b8b611de3 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp @@ -564,21 +564,21 @@ static void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *)vx; + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx; for (int c = 0; c < nc; c += ncols_interleaved) { - const block_q8_0 * a_ptr = (const block_q8_0 *)vy; + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; float32x4_t acc = vdupq_n_f32(0); for (int b = 0; b < nb; b++) { - int8x16_t b0 = vld1q_s8((const int8_t *)b_ptr->qs); - int8x16_t b1 = vld1q_s8((const int8_t *)b_ptr->qs + 16); - int8x16_t b2 = vld1q_s8((const int8_t *)b_ptr->qs + 32); - int8x16_t b3 = vld1q_s8((const int8_t *)b_ptr->qs + 48); - float16x4_t bd = vld1_f16((const __fp16 *)b_ptr->d); + int8x16_t b0 = vld1q_s8((const int8_t *) b_ptr->qs); + int8x16_t b1 = vld1q_s8((const int8_t *) b_ptr->qs + 16); + int8x16_t b2 = vld1q_s8((const int8_t *) b_ptr->qs + 32); + int8x16_t b3 = vld1q_s8((const int8_t *) b_ptr->qs + 48); + float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d); int8x16_t a0 = vld1q_s8(a_ptr->qs); int8x16_t a1 = vld1q_s8(a_ptr->qs + qk/2); - float16x4_t ad = vld1_dup_f16((const __fp16 *)&a_ptr->d); + float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d); int32x4_t ret = vdupq_n_s32(0); @@ -647,72 +647,52 @@ static void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c UNUSED(ncols_interleaved); UNUSED(blocklen); -#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; +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx; - __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" - ); + for (int c = 0; c < nc; c += ncols_interleaved) { + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + float32x4_t acc = vdupq_n_f32(0); + for (int b = 0; b < nb; b++) { + int8x16_t b0 = vld1q_s8((const int8_t *) b_ptr->qs); + int8x16_t b1 = vld1q_s8((const int8_t *) b_ptr->qs + 16); + int8x16_t b2 = vld1q_s8((const int8_t *) b_ptr->qs + 32); + int8x16_t b3 = vld1q_s8((const int8_t *) b_ptr->qs + 48); + float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d); + + int8x16_t a0 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs); + int8x16_t a1 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 1); + int8x16_t a2 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 2); + int8x16_t a3 = (int8x16_t) vld1q_dup_s64((const int64_t *) a_ptr->qs + 3); + float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d); + + int32x4_t ret0 = vdupq_n_s32(0); + int32x4_t ret1 = vdupq_n_s32(0); + + ret0 = vdotq_s32(ret0, b0 << 4, a0); + ret1 = vdotq_s32(ret1, b1 << 4, a0); + ret0 = vdotq_s32(ret0, b2 << 4, a1); + ret1 = vdotq_s32(ret1, b3 << 4, a1); + + ret0 = vdotq_s32(ret0, b0 & 0xf0U, a2); + ret1 = vdotq_s32(ret1, b1 & 0xf0U, a2); + ret0 = vdotq_s32(ret0, b2 & 0xf0U, a3); + ret1 = vdotq_s32(ret1, b3 & 0xf0U, a3); + + int32x4_t ret = vpaddq_s32(ret0, ret1); + + acc = vfmaq_f32(acc, vcvtq_n_f32_s32(ret, 4), + vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd))); + a_ptr++; + b_ptr++; + } + vst1q_f32(s, acc); + s += ncols_interleaved; + } return; } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) float sumf[4]; int sumi;

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