diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index df3f8a55d..0862099da 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -455,8 +455,12 @@ class ModelBase: class TextModel(ModelBase): + model_type = ModelType.TEXT + hf_arch: str + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) + self.hf_arch = get_model_architecture(self.hparams, self.model_type) if "text_config" in self.hparams: # move the text_config to the root level @@ -506,7 +510,7 @@ class TextModel(ModelBase): def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.block_count) - if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") @@ -1075,10 +1079,36 @@ class TextModel(ModelBase): if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None: self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0]) + def _try_set_pooling_type(self) -> None: + # get pooling path + pooling_path = None + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break + + # get pooling type + if pooling_path is not None: + with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: + pooling = json.load(f) + if pooling["pooling_mode_mean_tokens"]: + pooling_type = gguf.PoolingType.MEAN + elif pooling["pooling_mode_cls_token"]: + pooling_type = gguf.PoolingType.CLS + elif pooling["pooling_mode_lasttoken"]: + pooling_type = gguf.PoolingType.LAST + else: + raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported") + self.gguf_writer.add_pooling_type(pooling_type) + class VisionModel(ModelBase): + model_type = ModelType.VISION model_arch = gguf.MODEL_ARCH.CLIP_VISION - n_text_embd = 0 preprocessor_config: dict[str, Any] global_config: dict[str, Any] @@ -1089,6 +1119,8 @@ class VisionModel(ModelBase): raise TypeError("VisionModel must be subclassed with model_arch = gguf.MODEL_ARCH.CLIP_VISION") # get n_embd of the text model + if "text_config" not in self.hparams: + self.hparams["text_config"] = {} text_config = {**self.hparams, **self.hparams["text_config"]} self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0)) assert self.n_embd_text > 0, "n_embd not found in hparams" @@ -2540,7 +2572,7 @@ class QwenModel(TextModel): self.gguf_writer.add_file_type(self.ftype) -@ModelBase.register("Qwen2ForCausalLM") +@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM") class Qwen2Model(TextModel): model_arch = gguf.MODEL_ARCH.QWEN2 @@ -2552,12 +2584,18 @@ class Qwen2Model(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() + self._try_set_pooling_type() if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: if self.hparams["rope_scaling"].get("type") == "yarn": self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if self.hf_arch == "Qwen2Model": + name = f"model.{name}" # map to Qwen2ForCausalLM tensors + yield from super().modify_tensors(data_torch, name, bid) + @ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration") class Qwen2VLModel(TextModel): @@ -2583,6 +2621,82 @@ class Qwen2VLModel(TextModel): return [(self.map_tensor_name(name), data_torch)] +@ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration") +class Qwen2VLVisionModel(VisionModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.hparams["image_size"] = self.hparams.get("image_size", 560) + # rename config.json values + self.hparams["num_attention_heads"] = self.hparams.get("num_heads") + self.hparams["num_hidden_layers"] = self.hparams.get("depth") + if "embed_dim" in self.hparams: # qwen2vl + self.hparams["intermediate_size"] = self.hparams.get("hidden_size") + self.hparams["hidden_size"] = self.hparams.get("embed_dim") + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + if self.global_config['model_type'] == 'qwen2_vl': + self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN2VL) + elif self.global_config['model_type'] == 'qwen2_5_vl': + self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN25VL) + self.gguf_writer.add_vision_use_silu(True) + # find n_wa_pattern (window attention pattern) + fullatt_block_indexes = hparams.get("fullatt_block_indexes") + assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl" + n_wa_pattern = fullatt_block_indexes[0] + 1 + # validate n_wa_pattern + for i in range(1, len(fullatt_block_indexes)): + if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern: + raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}") + self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern) + else: + raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}") + # default values below are taken from HF tranformers code + self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6)) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + del bid, name, n_dims # unused + if ".patch_embd." in new_name: + return gguf.GGMLQuantizationType.F16 + if ".position_embd." in new_name: + return gguf.GGMLQuantizationType.F32 + return False + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + if name.startswith("visual."): + # process visual tensors + # split QKV tensors if needed + if ".qkv." in name: + if data_torch.ndim == 2: # weight + c3, _ = data_torch.shape + else: # bias + c3 = data_torch.shape[0] + assert c3 % 3 == 0 + c = c3 // 3 + wq = data_torch[:c] + wk = data_torch[c: c * 2] + wv = data_torch[c * 2:] + return [ + (self.map_tensor_name(name.replace("qkv", "q")), wq), + (self.map_tensor_name(name.replace("qkv", "k")), wk), + (self.map_tensor_name(name.replace("qkv", "v")), wv), + ] + elif 'patch_embed.proj.weight' in name: + # split Conv3D into Conv2Ds + c1, c2, kt, kh, kw = data_torch.shape + del c1, c2, kh, kw # unused + assert kt == 2, "Current implmentation only support temporal_patch_size of 2" + return [ + (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight" , data_torch[:, :, 0, ...]), + (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...]), + ] + else: + return [(self.map_tensor_name(name), data_torch)] + return [] # skip other tensors + + @ModelBase.register("WavTokenizerDec") class WavTokenizerDecModel(TextModel): model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC @@ -3318,29 +3432,7 @@ class BertModel(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_causal_attention(False) - - # get pooling path - pooling_path = None - module_path = self.dir_model / "modules.json" - if module_path.is_file(): - with open(module_path, encoding="utf-8") as f: - modules = json.load(f) - for mod in modules: - if mod["type"] == "sentence_transformers.models.Pooling": - pooling_path = mod["path"] - break - - # get pooling type - if pooling_path is not None: - with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: - pooling = json.load(f) - if pooling["pooling_mode_mean_tokens"]: - pooling_type = gguf.PoolingType.MEAN - elif pooling["pooling_mode_cls_token"]: - pooling_type = gguf.PoolingType.CLS - else: - raise NotImplementedError("Only MEAN and CLS pooling types supported") - self.gguf_writer.add_pooling_type(pooling_type) + self._try_set_pooling_type() def set_vocab(self): tokens, toktypes, tokpre = self.get_vocab_base() @@ -3549,8 +3641,13 @@ class NomicBertModel(BertModel): if self._tokenizer_is_xlmroberta: self._xlmroberta_tokenizer_init() - # the HF config claims n_ctx=8192, but it uses RoPE scaling - self.hparams["n_ctx"] = 2048 + npos, mtp = self.hparams["n_positions"], self.hparams.get("max_trained_positions", 2048) + if npos == 8192 and mtp == 2048: + self.hparams["n_positions"] = 2048 # nomic-embed-text v1 and v1.5 are trained for 2048 tokens. + elif npos == 2048 and mtp == 2048: + self.hparams["n_positions"] = 512 # nomic-embed-text-v2-moe is trained for 512 tokens. + else: + raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}") assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu" @@ -5879,8 +5976,7 @@ def split_str_to_n_bytes(split_str: str) -> int: return n -def get_model_architecture(dir_model: Path, model_type: ModelType, hparams: Any = None) -> str: - hparams = ModelBase.load_hparams(dir_model) if hparams is None else hparams +def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str: text_config = hparams.get("text_config", {}) vision_config = hparams.get("vision_config", {}) arch = hparams["architectures"][0] @@ -5951,7 +6047,8 @@ def main() -> None: with torch.inference_mode(): output_type = ftype_map[args.outtype] model_type = ModelType.VISION if args.mmproj else ModelType.TEXT - model_architecture = get_model_architecture(dir_model, model_type) + hparams = ModelBase.load_hparams(dir_model) + model_architecture = get_model_architecture(hparams, model_type) logger.info(f"Model architecture: {model_architecture}") try: model_class = ModelBase.from_model_architecture(model_architecture, model_type=model_type) diff --git a/examples/llava/qwen2_vl_surgery.py b/examples/llava/qwen2_vl_surgery.py deleted file mode 100644 index 7951a6fa8..000000000 --- a/examples/llava/qwen2_vl_surgery.py +++ /dev/null @@ -1,217 +0,0 @@ -import argparse -from typing import Dict, List, Optional - -import torch -import numpy as np -from gguf import * -from transformers import ( - AutoProcessor, - Qwen2VLConfig, - Qwen2VLProcessor, - Qwen2VLForConditionalGeneration, - Qwen2_5_VLConfig, # type: ignore[reportAttributeAccessIssue] - Qwen2_5_VLForConditionalGeneration, # type: ignore[reportAttributeAccessIssue] -) - - -VISION = "clip.vision" - - -def k(raw_key: str, arch: str) -> str: - return raw_key.format(arch=arch) - - -def get_n_wa_pattern(fullatt_block_indexes: Optional[List[int]]): - if fullatt_block_indexes is None: - return 0 - n_wa = fullatt_block_indexes[0] - for a, b in zip(fullatt_block_indexes, fullatt_block_indexes[1:]): - if b - a - 1 != n_wa: - raise ValueError( - f"window/full attention layer should have fix pattern of " - f"for each full-attention layer followed by {n_wa} window-attention layers" - ) - return n_wa + 1 - - -class VL2: - - @staticmethod - def to_gguf_name(name: str) -> str: - og = name - name = name.replace("text_model", "t").replace("vision_model", "v") - name = name.replace("blocks", "blk").replace("embeddings.", "") - name = name.replace("attn.", "attn_") - name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.") - # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln") - name = name.replace("norm1", "ln1").replace("norm2", "ln2") - name = name.replace("merger.mlp", 'mm') - print(f"[to_gguf_name] {og} --> {name}") - return name - - @classmethod - def find_vision_tensors(cls, qwen2vl, dtype) -> Dict[str, np.ndarray]: - vision_model = qwen2vl.visual - tensor_map = {} - for name, ten in vision_model.state_dict().items(): - ten = ten.numpy() - if 'qkv' in name: - if ten.ndim == 2: # weight - c3, _ = ten.shape - else: # bias - c3 = ten.shape[0] - assert c3 % 3 == 0 - c = c3 // 3 - wq = ten[:c] - wk = ten[c: c * 2] - wv = ten[c * 2:] - tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq - tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk - tensor_map[cls.to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv - elif 'merger' in name: - if name.endswith("ln_q.weight"): - tensor_map['v.post_ln.weight'] = ten - elif name.endswith("ln_q.bias"): - tensor_map['v.post_ln.bias'] = ten - else: - # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias" - tensor_map[cls.to_gguf_name(name)] = ten - elif 'patch_embed.proj.weight' in name: - # NOTE: split Conv3D into Conv2Ds - c1, c2, kt, kh, kw = ten.shape - assert kt == 2, "Current implmentation only support temporal_patch_size of 2" - tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...] - tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...] - else: - tensor_map[cls.to_gguf_name(f"vision_model.{name}")] = ten - - for new_name, ten in tensor_map.items(): - if ten.ndim <= 1 or new_name.endswith("_norm.weight"): - tensor_map[new_name] = ten.astype(np.float32) - else: - tensor_map[new_name] = ten.astype(dtype) - tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32) # dummy tensor, just here as a placeholder - return tensor_map - - -class VL25(VL2): - - @staticmethod - def to_gguf_name(name: str) -> str: - og = name - name = name.replace("text_model", "t").replace("vision_model", "v") - name = name.replace("blocks", "blk").replace("embeddings.", "") - name = name.replace("attn.", "attn_") - name = name.replace("mlp.down_proj", "ffn_down").replace("mlp.up_proj", "ffn_up") - name = name.replace("mlp.gate_proj", "ffn_gate").replace("proj.", "out.") - name = name.replace("norm1", "ln1").replace("norm2", "ln2") - name = name.replace("merger.mlp", 'mm') - print(f"[vl25][to_gguf_name] {og} --> {name}") - return name - - -def main(args): - if args.data_type == 'fp32': - dtype = torch.float32 - np_dtype = np.float32 - ftype = 0 - elif args.data_type == 'fp16': - dtype = torch.float16 - np_dtype = np.float16 - ftype = 1 - else: - raise ValueError() - - local_model = False - model_path = "" - model_name = args.model_name - print("model_name: ", model_name) - if args.model_type == "qwen2vl": - qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained( - model_name, torch_dtype=dtype, device_map="cpu" - ) - cfg: Qwen2VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] - vcfg = cfg.vision_config - else: - qwen2vl = Qwen2_5_VLForConditionalGeneration.from_pretrained( - model_name, torch_dtype=dtype, device_map="cpu" - ) - cfg: Qwen2_5_VLConfig = qwen2vl.config # type: ignore[reportAssignmentType] - vcfg = cfg.vision_config - - if os.path.isdir(model_name): - local_model = True - if model_name.endswith(os.sep): - model_name = model_name[:-1] - model_path = model_name - model_name = os.path.basename(model_name) - fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf" - - fout = GGUFWriter(path=fname_out, arch="clip") - fout.add_description("image encoder for Qwen2VL") - - fout.add_file_type(ftype) - fout.add_bool("clip.has_text_encoder", False) - fout.add_bool("clip.has_vision_encoder", True) - fout.add_bool("clip.has_qwen2vl_merger", True) - - print(cfg.vision_config) - if 'silu' in cfg.vision_config.hidden_act.lower(): - fout.add_bool("clip.use_silu", True) - fout.add_bool("clip.use_gelu", False) - elif 'gelu' in cfg.vision_config.hidden_act.lower(): - fout.add_bool("clip.use_silu", False) - fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower()) - else: - raise ValueError() - - if args.model_type == "qwen2.5vl": - fout.add_uint32("clip.vision.n_wa_pattern", get_n_wa_pattern(vcfg.fullatt_block_indexes)) - fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.hidden_size) - fout.add_uint32("clip.vision.projection_dim", vcfg.out_hidden_size) - fout.add_string("clip.projector_type", "qwen2.5vl_merger") - else: - fout.add_string("clip.projector_type", "qwen2vl_merger") - fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim) - fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size) - - if args.model_type == "qwen2.5vl": - tensor_map = VL25.find_vision_tensors(qwen2vl, np_dtype) - else: - tensor_map = VL2.find_vision_tensors(qwen2vl, np_dtype) - for name, data in tensor_map.items(): - fout.add_tensor(name, data) - - fout.add_uint32("clip.vision.patch_size", vcfg.patch_size) - fout.add_uint32("clip.vision.image_size", 14 * 40) # some reasonable size that is divable by (14*2) - fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads) - fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6) - fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth) - fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0) # not sure what this does, put 0 here as a placeholder - fout.add_name(model_name) - """ - HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig, - it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`. - """ - - if local_model: - processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path) - else: - processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name) - fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue] - fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue] - - fout.write_header_to_file() - fout.write_kv_data_to_file() - fout.write_tensors_to_file() - fout.close() - print("save model as: ", fname_out) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct") - parser.add_argument("--model_type", nargs='?', choices=['qwen2vl', 'qwen2.5vl'], default="qwen2vl") - parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32") - args = parser.parse_args() - main(args) diff --git a/examples/llava/tests.sh b/examples/llava/tests.sh index 4af370064..22c237497 100755 --- a/examples/llava/tests.sh +++ b/examples/llava/tests.sh @@ -36,12 +36,6 @@ add_test() { arr_tmpl+=("$tmpl") } -add_test_big() { - if [ "$RUN_BIG_TESTS" = true ]; then - add_test "$@" - fi -} - add_test "llama-mtmd-cli" "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0" add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0" @@ -58,8 +52,16 @@ add_test "llama-mtmd-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" # to test the big models, run: ./tests.sh big -add_test_big "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" -add_test_big "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7" +if [ "$RUN_BIG_TESTS" = true ]; then + add_test "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7" + add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M" + add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M" + # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra + # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M" # too big +fi # these models always give the wrong answer, not sure why # add_test "llama-mtmd-cli" "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M" diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index f6374f789..1d46158f9 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -1054,6 +1054,493 @@ class tinyBLAS_Q0_AVX { } \ } \ +template +class tinyBLAS_BF16_PPC { + public: + tinyBLAS_BF16_PPC(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n) { + mnpack(0, m, 0, n); + } + + private: + void vector_permute_store(vec_t *c, int numVec, unsigned char *vecOffset) { + vec_t t[8], s[8]; + vec_t swiz1 = {0, 1, 2, 3, 16, 17, 18, 19, 4, 5, 6, 7, 20, 21, 22, 23}; + vec_t swiz2 = {8, 9, 10, 11, 24, 25, 26, 27, 12, 13, 14, 15, 28, 29, 30, 31}; + vec_t swiz3 = {0, 1, 2, 3, 4, 5, 6, 7, 16, 17, 18, 19, 20, 21, 22, 23}; + vec_t swiz4 = {8, 9, 10, 11, 12, 13, 14, 15, 24, 25, 26, 27, 28, 29, 30, 31}; + + if (numVec == 2) { + t[0] = vec_perm(c[0], c[1], swiz1); + t[1] = vec_perm(c[2], c[3], swiz1); + s[0] = vec_perm(t[0], t[1], swiz3); + s[1] = vec_perm(t[0], t[1], swiz4); + vec_xst(s[0], 0, (vec_t*)vecOffset); + vec_xst(s[1], 0, (vec_t*)(vecOffset + 16)); + } else if (numVec == 4) { + t[0] = vec_perm(c[0], c[1], swiz1); + t[1] = vec_perm(c[0], c[1], swiz2); + t[2] = vec_perm(c[2], c[3], swiz1); + t[3] = vec_perm(c[2], c[3], swiz2); + s[0] = vec_perm(t[0], t[2], swiz3); + s[1] = vec_perm(t[0], t[2], swiz4); + s[2] = vec_perm(t[1], t[3], swiz3); + s[3] = vec_perm(t[1], t[3], swiz4); + for (int i = 0; i < 4; ++i) + vec_xst(s[i], 0, (vec_t*)(vecOffset + i * 16)); + } else if (numVec == 8) { + for (int i = 0; i < 4; i += 2) { + t[i+0] = vec_perm(c[i+0], c[i+1], swiz1); + t[i+1] = vec_perm(c[i+0], c[i+1], swiz2); + } + for (int i = 4; i < 8; i += 2) { + t[i+0] = vec_perm(c[i+0], c[i+1], swiz1); + t[i+1] = vec_perm(c[i+0], c[i+1], swiz2); + } + s[0] = vec_perm(t[0], t[2], swiz3); + s[1] = vec_perm(t[0], t[2], swiz4); + s[2] = vec_perm(t[1], t[3], swiz3); + s[3] = vec_perm(t[1], t[3], swiz4); + s[4] = vec_perm(t[4], t[6], swiz3); + s[5] = vec_perm(t[4], t[6], swiz4); + s[6] = vec_perm(t[5], t[7], swiz3); + s[7] = vec_perm(t[5], t[7], swiz4); + for (int i = 0; i < 8; ++i) + vec_xst(s[i], 0, (vec_t*)(vecOffset + i * 16)); + } + } + + void packNormal(const TA* a, int64_t lda, int rows, int cols, unsigned char* vec) { + int64_t i, j; + TA *aoffset = NULL; + unsigned char *vecOffset = NULL; + TA * aoffsets[8]; + vector unsigned char c_arr[8]; + aoffset = const_cast(a); + vecOffset = vec; + j = (rows >> 3); + if (j > 0) { + do { + if (cols == 4) { + aoffsets[0] = aoffset; + for (int it = 1; it < 4; ++it) + aoffsets[it] = aoffsets[it-1] + lda; + aoffset += 4 * lda; + for (int i = 0; i < 4; ++i) + c_arr[i] = vec_xl(0, (vector unsigned char*)aoffsets[i]); + vector_permute_store(c_arr, 4, vecOffset); + for (int i = 0; i<4; i++) + aoffsets[i] = aoffsets[i]+lda; + vecOffset +=64; + } + i = (cols >> 3); + if (i > 0) { + aoffsets[0] = aoffset; + for (int it = 1; it < 8; ++it) { + aoffsets[it] = aoffsets[it-1] + lda; + } + aoffset += 8 * lda; + do { + for (int it = 0; it < 8; ++it) + c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); + vector_permute_store(c_arr, 8, vecOffset); + for (int it = 0; it < 8; ++it) + aoffsets[it] = aoffsets[it] + 8*lda; + vecOffset += 128; + i--; + } while(i > 0); + } + j--; + } while(j > 0); + } + if (rows & 4) { + aoffsets[0] = aoffset; + for (int it = 1; it < 4; ++it) + aoffsets[it] = aoffsets[it-1] + lda; + aoffset += 4 * lda; + if (cols == 4) { + for (int it = 0; it < 4; ++it) + c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); + vector_permute_store(c_arr, 2, vecOffset); + for (int it = 0; it< 4; it++) + aoffsets[it] = aoffsets[it] + lda; + vecOffset += 32; + } + i = (cols >> 3); + if (i > 0) { + do { + for (int it = 0; it < 4; ++it) + c_arr[it] = vec_xl(0, (vector unsigned char*)aoffsets[it]); + vector_permute_store(c_arr, 4, vecOffset); + for (int it = 0; it< 4; it++) + aoffsets[it] = aoffsets[it] + 8*lda; + vecOffset += 64; + i--; + } while(i > 0); + } + } + if (rows & 3) { + aoffsets[0] = aoffset; + for (int it = 1; it < 4; ++it) + aoffsets[it] = aoffsets[it-1] + lda; + if (cols == 4) { + switch(rows) { + case 3: c_arr[2] = vec_xl(0, (vector unsigned char*)aoffsets[2]); + case 2: c_arr[1] = vec_xl(0, (vector unsigned char*)aoffsets[1]); + case 1: c_arr[0] = vec_xl(0, (vector unsigned char*)aoffsets[0]); + break; + } + vector_permute_store(c_arr, 2, vecOffset); + for (int it = 0; it< 4; it++) + aoffsets[it] = aoffsets[it] + lda; + vecOffset += 32; + } + i = (cols >> 3); + if (i > 0) { + do { + switch(rows) { + case 3: c_arr[2] = vec_xl(0, (vector unsigned char*)aoffsets[2]); + case 2: c_arr[1] = vec_xl(0, (vector unsigned char*)aoffsets[1]); + case 1: c_arr[0] = vec_xl(0, (vector unsigned char*)aoffsets[0]); + break; + } + vector_permute_store(c_arr, 4, vecOffset); + for (int it = 0; it <4; it++) + aoffsets[it] = aoffsets[it] + 8* lda; + vecOffset += 64; + i--; + } while(i > 0); + } + } + } + + void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + int m_rem = MIN(m - m0, 8); + int n_rem = MIN(n - n0, 8); + + if (m_rem >= 8 && n_rem >= 8) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if (m_rem >= 4 && n_rem >= 8) { + mc = 4; + nc = 8; + gemm<4,8>(m0, m, n0, n); + } else if (m_rem >=8 && n_rem >=4){ + mc = 8; + nc = 4; + gemm<8,4>(m0, m, n0, n); + } else if ((m_rem < 4) && (n_rem >= 8)) { + nc = 8; + switch(m_rem) { + case 1: + mc = 1; + gemm_Mx8<1>(m0, m, n0, n); + break; + case 2: + mc = 2; + gemm_Mx8<2>(m0, m, n0, n); + break; + case 3: + mc = 3; + gemm_Mx8<3>(m0, m, n0, n); + break; + default: + return; + } + } else if (m_rem >= 4 && n_rem >= 4) { + mc = 4; + nc = 4; + gemm_small<4, 4>(m0, m, n0, n); + } else if ((m_rem > 4) && (n_rem < 4)) { + mc = 4; + switch(n_rem) { + case 1: + nc = 1; + gemm_small<4, 1>(m0, m, n0, n); + break; + case 2: + nc = 2; + gemm_small<4, 2>(m0, m, n0, n); + break; + case 3: + nc = 3; + gemm_small<4, 3>(m0, m, n0, n); + break; + + default: + return; + } + } else { + switch((m_rem << 4) | n_rem) { + case 0x43: + mc = 4; + nc = 3; + gemm_small<4, 3>(m0, m, n0, n); + break; + case 0x42: + mc = 4; + nc = 2; + gemm_small<4, 2>(m0, m, n0, n); + break; + case 0x41: + mc = 4; + nc = 1; + gemm_small<4, 1>(m0, m, n0, n); + break; + case 0x34: + mc = 3; + nc = 4; + gemm_small<3, 4>(m0, m, n0, n); + break; + case 0x33: + mc = 3; + nc = 3; + gemm_small<3, 3>(m0, m, n0, n); + break; + case 0x32: + mc = 3; + nc = 2; + gemm_small<3, 2>(m0, m, n0, n); + break; + case 0x31: + mc = 3; + nc = 1; + gemm_small<3, 1>(m0, m, n0, n); + break; + case 0x24: + mc = 2; + nc = 4; + gemm_small<2,4>(m0, m, n0, n); + break; + case 0x23: + mc = 2; + nc = 3; + gemm_small<2, 3>(m0, m, n0, n); + break; + case 0x22: + mc = 2; + nc = 2; + gemm_small<2, 2>(m0, m, n0, n); + break; + case 0x21: + mc = 2; + nc = 1; + gemm_small<2, 1>(m0, m, n0, n); + break; + case 0x14: + mc = 1; + nc = 4; + gemm_small<1, 4>(m0, m, n0, n); + break; + case 0x13: + mc = 1; + nc = 3; + gemm_small<1, 3>(m0, m, n0, n); + break; + case 0x12: + mc = 1; + nc = 2; + gemm_small<1, 2>(m0, m, n0, n); + break; + case 0x11: + mc = 1; + nc = 1; + gemm_small<1, 1>(m0, m, n0, n); + break; + default: + return; + } + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + void KERNEL_4x8(int64_t ii, int64_t jj) { + vec_t vec_A[4], vec_B[8] , vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int l = 0; l < k; l+=8) { + packNormal((A+(ii*lda)+l), lda, 4, 8, (uint8_t*)vec_A); + packNormal((B+(jj*ldb)+l), ldb, 8, 8, (uint8_t*)vec_B); + for (int x = 0; x < 4; x++) { + __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x], vec_B[x+4]); + } + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + } + + void KERNEL_8x4(int64_t ii, int64_t jj) { + vec_t vec_A[8], vec_B[4] , vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int l = 0; l < k; l+=8) { + packNormal((A+(ii*lda)+l), lda, 8, 8, (uint8_t*)vec_A); + packNormal((B+(jj*ldb)+l), ldb, 8, 4, (uint8_t*)vec_B); + for (int x = 0; x < 4; x++) { + __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_1, vec_A[x+4], vec_B[x]); + } + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii+4, jj); + } + + + void KERNEL_8x8(int64_t ii, int64_t jj) { + vec_t vec_A[8], vec_B[8], vec_C[4]; + acc_t acc_0, acc_1, acc_2, acc_3; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + __builtin_mma_xxsetaccz(&acc_2); + __builtin_mma_xxsetaccz(&acc_3); + for (int l = 0; l < k; l+=8) { + packNormal(A+(ii*lda)+l, lda, 8, 8, (uint8_t*)vec_A); + packNormal(B+(jj*ldb)+l, ldb, 8, 8, (uint8_t*)vec_B); + for (int x = 0; x < 4; x++) { + __builtin_mma_xvbf16ger2pp(&acc_0, vec_A[x], vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_1, (vec_t)vec_A[x], (vec_t)vec_B[x+4]); + __builtin_mma_xvbf16ger2pp(&acc_2, (vec_t)vec_A[x+4], (vec_t)vec_B[x]); + __builtin_mma_xvbf16ger2pp(&acc_3, (vec_t)vec_A[x+4], (vec_t)vec_B[x+4]); + } + } + + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + SAVE_ACC(&acc_2, ii+4, jj); + SAVE_ACC(&acc_3, ii+4, jj+4); + } + + template + void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + vec_t vec_C[4]; + acc_t acc_0; + __builtin_mma_xxsetaccz(&acc_0); + vec_t vec_A[2], vec_B[2]; + for (int l=0; l + void gemm_Mx8(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int RN = 8; + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + vec_t vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + vec_t vec_A[4], vec_B[8]; + for (int l=0; l + inline void kernel(int64_t ii, int64_t jj) { + if constexpr(RM == 4 && RN == 8) { + KERNEL_4x8(ii,jj); + } else if constexpr(RM == 8 && RN == 8) { + KERNEL_8x8(ii,jj); + } else if constexpr(RM == 8 && RN == 4) { + KERNEL_8x4(ii,jj); + } else { + static_assert(false, "RN/RM values not supported"); + } + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + kernel(ii, jj); + } + } + + const TA *const A; + const TB *const B; + TC *C; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; + template class tinyBLAS_Q0_PPC { public: @@ -2202,6 +2689,7 @@ class tinyBLAS_PPC { boffset = vec; j = (rows >> 3); if (j > 0) { + do { aoffset1 = aoffset; aoffset2 = aoffset1 + lda; @@ -2875,9 +3363,22 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 (float *)C, ldc}; return tb.matmul(m, n); } +#elif defined(__MMA__) + if ((k % 8)) + return false; + if(Btype == GGML_TYPE_BF16) { + tinyBLAS_BF16_PPC tb{ k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc, + params->ith, params->nth}; + tb.matmul(m, n); + return true; + } #endif return false; } + case GGML_TYPE_F16: { #if defined(__AVX512F__) if (Btype == GGML_TYPE_F16) { diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index a675dfa4e..947b0d9e1 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -405,6 +405,8 @@ struct vk_device_struct { vk_pipeline pipeline_rwkv_wkv6_f32; vk_pipeline pipeline_rwkv_wkv7_f32; vk_pipeline pipeline_opt_step_adamw_f32; + vk_pipeline pipeline_conv2d_dw_whcn_f32; + vk_pipeline pipeline_conv2d_dw_cwhn_f32; // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned} vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2]; @@ -717,6 +719,24 @@ struct vk_op_rwkv_wkv7_push_constants { uint32_t H; }; +struct vk_op_conv2d_dw_push_constants { + uint32_t ne; + uint32_t batches; + uint32_t channels; + uint32_t dst_w; + uint32_t dst_h; + uint32_t src_w; + uint32_t src_h; + uint32_t knl_w; + uint32_t knl_h; + int32_t stride_x; + int32_t stride_y; + int32_t pad_x; + int32_t pad_y; + int32_t dilation_x; + int32_t dilation_y; +}; + struct vk_op_upscale_push_constants { uint32_t ne; uint32_t a_offset; uint32_t d_offset; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; @@ -2626,6 +2646,9 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f32, "conv2d_dw_cwhn_f32", conv2d_dw_cwhn_f32_len, conv2d_dw_cwhn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + for (auto &c : compiles) { c.wait(); } @@ -6161,6 +6184,15 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_leaky_relu_f32; } return nullptr; + case GGML_OP_CONV_2D_DW: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + if (ggml_is_contiguous(src1)) { + return ctx->device->pipeline_conv2d_dw_whcn_f32; + } else if (ggml_is_contiguous_channels(src1)) { + return ctx->device->pipeline_conv2d_dw_cwhn_f32; + } + } + return nullptr; default: return nullptr; } @@ -6187,6 +6219,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_REPEAT_BACK: case GGML_OP_ROPE: case GGML_OP_RMS_NORM: + case GGML_OP_CONV_2D_DW: return true; default: return false; @@ -6483,6 +6516,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co case GGML_OP_CONCAT: case GGML_OP_UPSCALE: case GGML_OP_UNARY: + case GGML_OP_CONV_2D_DW: { const uint32_t ne = ggml_nelements(dst); if (ne > 262144) { @@ -7269,6 +7303,30 @@ static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, c }, dryrun); } +static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { + vk_op_conv2d_dw_push_constants p{}; + p.ne = ggml_nelements(dst); + p.channels = dst->ne[2]; + p.batches = dst->ne[3]; + p.dst_w = dst->ne[0]; + p.dst_h = dst->ne[1]; + p.src_w = src1->ne[0]; + p.src_h = src1->ne[1]; + p.knl_w = src0->ne[0]; + p.knl_h = src0->ne[1]; + p.stride_x = dst->op_params[0]; + p.stride_y = dst->op_params[1]; + p.pad_x = dst->op_params[2]; + p.pad_y = dst->op_params[3]; + p.dilation_x = dst->op_params[4]; + p.dilation_y = dst->op_params[5]; + + GGML_ASSERT(src0->ne[3] == p.channels); + GGML_ASSERT(src1->ne[3] == p.batches); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONV_2D_DW, std::move(p), dryrun); +} + static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) { const float * op_params = (const float *)dst->op_params; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }, dryrun); @@ -8289,6 +8347,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: + case GGML_OP_CONV_2D_DW: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: case GGML_OP_LEAKY_RELU: @@ -8352,6 +8411,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: + case GGML_OP_CONV_2D_DW: case GGML_OP_LEAKY_RELU: { // These operations all go through ggml_vk_op_f32, so short-circuit and @@ -8525,6 +8585,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_POOL_2D: ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun); + break; + case GGML_OP_CONV_2D_DW: + ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node, dryrun); + break; case GGML_OP_LEAKY_RELU: ggml_vk_leaky_relu(ctx, compute_ctx, src0, node, dryrun); @@ -8646,6 +8710,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: + case GGML_OP_CONV_2D_DW: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: case GGML_OP_LEAKY_RELU: @@ -9623,6 +9688,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_COUNT_EQUAL: case GGML_OP_IM2COL: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_CONV_2D_DW: case GGML_OP_POOL_2D: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp new file mode 100644 index 000000000..938c74da5 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp @@ -0,0 +1,105 @@ +#version 450 + +#include "types.comp" + +layout (push_constant) uniform parameter +{ + uint ne; + uint batches; + uint channels; + uint dst_w; + uint dst_h; + uint src_w; + uint src_h; + uint knl_w; + uint knl_h; + int stride_x; + int stride_y; + int pad_x; + int pad_y; + int dilation_x; + int dilation_y; +} p; + +layout (binding = 0) readonly buffer A {A_TYPE knl_data[];}; +layout (binding = 1) readonly buffer B {B_TYPE src_data[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst_data[];}; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE conv_2d_dw_whcn(uint idx) { + uint i0 = idx / p.dst_w; + uint dst_x = idx - i0 * p.dst_w; + uint i1 = i0 / p.dst_h; + uint dst_y = i0 - i1 * p.dst_h; + uint n = i1 / p.channels; + uint c = i1 - n * p.channels; + + uint src_i = n * p.channels * p.src_h * p.src_w + c * p.src_h * p.src_w; + uint knl_i = c * p.knl_h * p.knl_w; + + FLOAT_TYPE sum = 0.0; + for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { + uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y >= p.src_h) { // src_y < 0 will wrap to a large unsigned int + continue; + } + for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { + uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x >= p.src_w) { // src_x < 0 will wrap to a large unsigned int + continue; + } + FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * p.src_w + src_x]); + FLOAT_TYPE k = FLOAT_TYPE(knl_data[knl_i + knl_y * p.knl_w + knl_x]); + sum = fma(v, k, sum); + } + } + return sum; +} + +FLOAT_TYPE conv_2d_dw_cwhn(uint idx) { + uint i0 = idx / p.channels; + uint c = idx - i0 * p.channels; + uint i1 = i0 / p.dst_w; + uint dst_x = i0 - i1 * p.dst_w; + uint n = i1 / p.dst_h; + uint dst_y = i1 - n * p.dst_h; + + uint src_i = n * p.channels * p.src_h * p.src_w; + uint src_row = p.src_w * p.channels; + uint knl_row = p.knl_w * p.channels; + + FLOAT_TYPE sum = 0.0; + for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { + uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y >= p.src_h) { // src_y < 0 will wrap to a large unsigned int + continue; + } + for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { + uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x >= p.src_w) { // src_x < 0 will wrap to a large unsigned int + continue; + } + FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * src_row + src_x * p.channels + c]); + FLOAT_TYPE k = FLOAT_TYPE(knl_data[ knl_y * knl_row + knl_x * p.channels + c]); + sum = fma(v, k, sum); + } + } + return sum; +} + +void main() { + uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + if (idx >= p.ne) { + return; + } + + FLOAT_TYPE result = +#ifdef WHCN + conv_2d_dw_whcn(idx); +#else + conv_2d_dw_cwhn(idx); +#endif + dst_data[idx] = D_TYPE(result); +} + diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 76e8a4511..7a4cd5d47 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -598,6 +598,9 @@ void process_shaders() { string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}})); + string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}})); + for (auto &c : compiles) { c.wait(); } diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a2540bd93..7dd7bb6d1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -234,6 +234,7 @@ class Keys: SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" USE_GELU = "clip.use_gelu" USE_SILU = "clip.use_silu" + N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl class Attention: HEAD_COUNT = "clip.vision.attention.head_count" @@ -2032,6 +2033,8 @@ class PoolingType(IntEnum): NONE = 0 MEAN = 1 CLS = 2 + LAST = 3 + RANK = 4 class GGMLQuantizationType(IntEnum): @@ -2162,6 +2165,8 @@ class VisionProjectorType: GEMMA3 = "gemma3" IDEFICS3 = "idefics3" PIXTRAL = "pixtral" + QWEN2VL = "qwen2vl_merger" + QWEN25VL = "qwen2.5vl_merger" # Items here are (block size, type size) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index a30c49e32..ff50d3de3 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -984,6 +984,9 @@ class GGUFWriter: def add_vision_projector_scale_factor(self, value: int) -> None: self.add_uint32(Keys.ClipVision.Projector.SCALE_FACTOR, value) + def add_vision_n_wa_pattern(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.N_WA_PATTERN, value) + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '' if not skip_pack_prefix: diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 2f6326104..2b089f84a 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -896,6 +896,7 @@ class TensorNameMap: MODEL_TENSOR.V_MMPROJ: ( "multi_modal_projector.linear_{bid}", + "visual.merger.mlp.{bid}", # qwen2vl ), MODEL_TENSOR.V_MMPROJ_FC: ( @@ -919,6 +920,7 @@ class TensorNameMap: "vpm.embeddings.patch_embedding", "model.vision_model.embeddings.patch_embedding", # SmolVLM "vision_tower.patch_conv", # pixtral + "visual.patch_embed.proj", # qwen2vl ), MODEL_TENSOR.V_ENC_EMBD_POS: ( @@ -932,6 +934,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.q_proj", "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral + "visual.blocks.{bid}.attn.q", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_ATTN_K: ( @@ -939,6 +942,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.k_proj", "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral + "visual.blocks.{bid}.attn.k", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_ATTN_V: ( @@ -946,6 +950,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.v_proj", "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral + "visual.blocks.{bid}.attn.v", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_INPUT_NORM: ( @@ -953,6 +958,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.layer_norm1", "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral + "visual.blocks.{bid}.norm1", # qwen2vl ), MODEL_TENSOR.V_ENC_OUTPUT: ( @@ -960,6 +966,7 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.self_attn.out_proj", "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral + "visual.blocks.{bid}.attn.proj", # qwen2vl ), MODEL_TENSOR.V_ENC_OUTPUT_NORM: ( @@ -967,17 +974,24 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.layer_norm2", "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral + "visual.blocks.{bid}.norm2", # qwen2vl ), + # some namings are messed up because the original llava code swapped fc1 and fc2 + # we have no better way to fix it, just be careful + # new models like pixtral use the correct naming MODEL_TENSOR.V_ENC_FFN_UP: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", "vpm.encoder.layers.{bid}.mlp.fc1", "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 (note: name is swapped) "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral + "visual.blocks.{bid}.mlp.fc2", # qwen2vl + "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl ), MODEL_TENSOR.V_ENC_FFN_GATE: ( "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral + "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl ), MODEL_TENSOR.V_ENC_FFN_DOWN: ( @@ -985,6 +999,8 @@ class TensorNameMap: "vpm.encoder.layers.{bid}.mlp.fc2", "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 (note: name is swapped) "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral + "visual.blocks.{bid}.mlp.fc1", # qwen2vl + "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl ), MODEL_TENSOR.V_PRE_NORM: ( @@ -995,6 +1011,7 @@ class TensorNameMap: MODEL_TENSOR.V_POST_NORM: ( "vision_tower.vision_model.post_layernorm", "model.vision_model.post_layernorm", # SmolVLM + "visual.merger.ln_q", # qwen2vl ), MODEL_TENSOR.V_MM_INP_PROJ: ( diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp index 01d5ca57f..a88b2fe30 100644 --- a/src/llama-batch.cpp +++ b/src/llama-batch.cpp @@ -189,7 +189,7 @@ llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) { return ubatch; } -void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) { +llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) { GGML_ASSERT(batch.n_tokens >= 0); this->batch = &batch; this->n_embd = n_embd; @@ -203,6 +203,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim for (size_t i = 0; i < n_tokens; ++i) { ids[i] = i; } + if (simple_split) { seq.resize(1); llama_sbatch_seq & s = seq[0]; @@ -212,6 +213,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim s.length = n_tokens; return; } + std::sort(ids.begin(), ids.end(), [&batch](size_t a, size_t b) { int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1; @@ -239,6 +241,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim return n_seq_a > n_seq_b; } ); + // init seq llama_sbatch_seq * last_seq = nullptr; @@ -262,6 +265,7 @@ void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool sim seq.push_back(new_seq); last_seq = &seq.back(); } + // keep shared prompts first at the end, then sort by length descending. std::sort(seq.begin(), seq.end(), [](llama_sbatch_seq & a, llama_sbatch_seq & b) { diff --git a/src/llama-batch.h b/src/llama-batch.h index f1df40d27..6305051b6 100644 --- a/src/llama-batch.h +++ b/src/llama-batch.h @@ -70,7 +70,8 @@ struct llama_sbatch { // sequence-wise split llama_ubatch split_seq(size_t n_ubatch); - void from_batch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false); + llama_sbatch() = default; + llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false); }; // temporary allocate memory for the input batch if needed diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 80865c5ce..2fb16ec9e 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -6,11 +6,9 @@ #include "llama-model.h" #include "llama-kv-cache.h" -#include #include #include #include -#include // // llama_context @@ -177,44 +175,13 @@ llama_context::llama_context( } // init the memory module - // TODO: for now, always create a unified KV cache if (!hparams.vocab_only) { - kv_self.reset(static_cast(model.create_memory())); + llama_memory_params params_mem = { + /*.type_k =*/ params.type_k, + /*.type_v =*/ params.type_v, + }; - LLAMA_LOG_DEBUG("%s: n_ctx = %u\n", __func__, cparams.n_ctx); - - cparams.n_ctx = GGML_PAD(cparams.n_ctx, kv_self->get_padding(cparams)); - - LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx); - - uint32_t kv_size = cparams.n_ctx; - ggml_type type_k = params.type_k; - ggml_type type_v = params.type_v; - - if (llama_model_is_recurrent(&model)) { - // Mamba needs at least as many KV cells as there are sequences kept at any time - kv_size = std::max((uint32_t) 1, params.n_seq_max); - // it's probably best to keep as much precision as possible for the states - type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states - type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states - } - - GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0); - GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0); - - if (!kv_self->init(model, cparams, type_k, type_v, kv_size, cparams.offload_kqv)) { - throw std::runtime_error("failed to initialize self-attention cache"); - } - - { - const size_t memory_size_k = kv_self->size_k_bytes(); - const size_t memory_size_v = kv_self->size_v_bytes(); - - LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, - (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), - ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), - ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); - } + memory.reset(model.create_memory(params_mem, cparams)); } // init backends @@ -305,7 +272,9 @@ llama_context::llama_context( int n_nodes_tg = -1; // simulate full KV cache - kv_self->n = kv_self->size; + llama_kv_cache * kv_self = static_cast(memory.get()); + + kv_self->set_full(); cross.v_embd.clear(); @@ -427,6 +396,18 @@ const llama_model & llama_context::get_model() const { return model; } +const llama_cparams & llama_context::get_cparams() const { + return cparams; +} + +ggml_backend_sched_t llama_context::get_sched() const { + return sched.get(); +} + +ggml_context * llama_context::get_ctx_compute() const { + return ctx_compute.get(); +} + uint32_t llama_context::n_ctx() const { return cparams.n_ctx; } @@ -456,338 +437,21 @@ uint32_t llama_context::n_threads_batch() const { } llama_kv_cache * llama_context::get_kv_self() { - return kv_self.get(); + llama_kv_cache * kv_self = static_cast(memory.get()); + return kv_self; } const llama_kv_cache * llama_context::get_kv_self() const { - return kv_self.get(); -} - -ggml_tensor * llama_context::build_rope_shift( - ggml_context * ctx0, - ggml_tensor * cur, - ggml_tensor * shift, - ggml_tensor * factors, - float freq_base, - float freq_scale) const { - const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; - - const auto & yarn_ext_factor = cparams.yarn_ext_factor; - const auto & yarn_beta_fast = cparams.yarn_beta_fast; - const auto & yarn_beta_slow = cparams.yarn_beta_slow; - - const auto & hparams = model.hparams; - - const auto & n_rot = hparams.n_rot; - const auto & rope_type = hparams.rope_type; - - // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly. - // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. - const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor; - - ggml_tensor * tmp; - - if (ggml_is_quantized(cur->type)) { - // dequantize to f32 -> RoPE -> quantize back - tmp = ggml_cast(ctx0, cur, GGML_TYPE_F32); - - tmp = ggml_rope_ext(ctx0, tmp, - shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); - - tmp = ggml_cpy(ctx0, tmp, cur); - } else { - // we rotate only the first n_rot dimensions - tmp = ggml_rope_ext_inplace(ctx0, cur, - shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); - } - - return tmp; -} - -class llm_graph_input_k_shift : public llm_graph_input_i { -public: - llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} - virtual ~llm_graph_input_k_shift() = default; - - void set_input(const llama_ubatch * ubatch) override; - - ggml_tensor * k_shift; // I32 [kv_size] - - const llama_kv_cache_unified * kv_self; -}; - -void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) { - GGML_UNUSED(ubatch); - - if (k_shift) { - assert(ggml_backend_buffer_is_host(k_shift->buffer)); - - int32_t * data = (int32_t *) k_shift->data; - - for (uint32_t i = 0; i < kv_self->size; ++i) { - data[i] = kv_self->cells[i].delta; - } - } -} - -llm_graph_result_ptr llama_context::build_kv_self_shift( - ggml_context * ctx0, - ggml_cgraph * gf) const { - auto res = std::make_unique(); - - const auto & hparams = model.hparams; - - const auto & n_layer = hparams.n_layer; - - const auto & n_embd_head_k = hparams.n_embd_head_k; - //const auto & n_embd_head_v = hparams.n_embd_head_v; - - //GGML_ASSERT(kv_self->size == n_ctx); - - auto inp = std::make_unique(kv_self.get()); - - inp->k_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, cparams.n_ctx); - ggml_set_input(inp->k_shift); - - for (uint32_t il = 0; il < n_layer; ++il) { - const int64_t n_head_kv = hparams.n_head_kv(il); - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); - - const bool is_swa = hparams.is_swa(il); - - // note: the swa rope params could become part of the cparams in the future - // if we decide to make them configurable, like the non-sliding ones - const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base; - const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale; - - ggml_tensor * rope_factors = kv_self->cbs.get_rope_factors(n_ctx_per_seq(), il); - - ggml_tensor * k = - ggml_view_3d(ctx0, kv_self->k_l[il], - n_embd_head_k, n_head_kv, kv_self->size, - ggml_row_size(kv_self->k_l[il]->type, n_embd_head_k), - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), - 0); - - ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); - - ggml_build_forward_expand(gf, cur); - } - - res->add_input(std::move(inp)); - - return res; -} - -llm_graph_result_ptr llama_context::build_kv_self_defrag( - ggml_context * ctx0, - ggml_cgraph * gf) const { - auto res = std::make_unique(); - - const auto & hparams = model.hparams; - - const auto & ids = kv_self->defrag_info.ids; - -#if 0 - // CPU defrag - // - // TODO: optimizations are possible: - // - multiple threads - // - avoid copying to the host memory when already there - // - // likely not worth the effort, as we have ggml_graph based defrag - // - - const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); - const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); - - const uint32_t kv_size = size; - - std::vector buf_k; - std::vector buf_v; - - for (uint32_t il = 0; il < n_layer; ++il) { - const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); - const size_t k_size = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size); - - const size_t v_size_el = ggml_type_size(v_l[il]->type); - const size_t v_size = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size); - - buf_k.resize(k_size); - buf_v.resize(v_size); - - ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size()); - ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size()); - - // batch move [i, i+nm) to [id, id+nm) - // note: cells can move only to a lower index - for (uint32_t i = 0; i < n_kv; ++i) { - const uint32_t id = ids[i]; - - if (i == id || id == n_kv) { - continue; - } - - uint32_t nm = 1; - - while (i + nm < n_kv && ids[i + nm] == id + nm) { - nm++; - } - - // move keys - { - const int64_t os = i*k_size_row; - const int64_t od = id*k_size_row; - - memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row); - } - - // move values (note: they are transposed) - { - const int64_t os = i; - const int64_t od = id; - - for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { - memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el); - } - } - - i += nm - 1; - } - - ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size()); - ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size()); - } -#else - for (uint32_t i = 0; i < ids.size(); ++i) { - const uint32_t id = ids[i]; - - if (i == id || id == ids.size()) { - continue; - } - - uint32_t nm = 1; - - while (i + nm < ids.size() && ids[i + nm] == id + nm) { - nm++; - } - - for (uint32_t il = 0; il < hparams.n_layer; ++il) { // NOLINT - const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); - const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); - - ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self->k_l[il], - n_embd_k_gqa, nm, - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa*i)); - - ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self->k_l[il], - n_embd_k_gqa, nm, - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa*id)); - - ggml_tensor * view_v_src; - ggml_tensor * view_v_dst; - - if (cparams.flash_attn) { - // NOTE: the V cache is not transposed when using flash attention - view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il], - n_embd_v_gqa, nm, - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa), - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa*i)); - - view_v_dst = ggml_view_2d(ctx0, kv_self->v_l[il], - n_embd_v_gqa, nm, - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa), - ggml_row_size(kv_self->v_l[il]->type, n_embd_v_gqa*id)); - } else { - view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il], - nm, n_embd_v_gqa, - ggml_row_size(kv_self->v_l[il]->type, kv_self->size), - ggml_row_size(kv_self->v_l[il]->type, i)); - - view_v_dst = ggml_view_2d(ctx0, kv_self->v_l[il], - nm, n_embd_v_gqa, - ggml_row_size(kv_self->v_l[il]->type, kv_self->size), - ggml_row_size(kv_self->v_l[il]->type, id)); - } - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst)); - } - - i += nm - 1; - } - - //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes); -#endif - - return res; + llama_kv_cache * kv_self = static_cast(memory.get()); + return kv_self; } void llama_context::kv_self_update() { - auto & kv = kv_self; - bool need_reserve = false; - if (kv->has_shift) { - if (!kv->get_can_shift()) { - printf("\nWARNING: The current context does not support K-shift!\n"); - } else { + llama_kv_cache * kv_self = static_cast(memory.get()); - // LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__); - - // apply K-shift if needed - if (model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { - ggml_backend_sched_reset(sched.get()); - - auto * gf = graph_init(); - - auto res = build_kv_self_shift(ctx_compute.get(), gf); - - ggml_backend_sched_alloc_graph(sched.get(), gf); - - res->set_inputs(nullptr); - - graph_compute(gf, false); - - need_reserve = true; - } - - { - kv->has_shift = false; - - for (uint32_t i = 0; i < kv->size; ++i) { - kv->cells[i].delta = 0; - } - } - } - } - - // defragment the KV cache if needed - if (kv->do_defrag) { - LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__); - - if (kv->defrag_prepare(graph_max_nodes())) { - ggml_backend_sched_reset(sched.get()); - - auto * gf = graph_init(); - - auto res = build_kv_self_defrag(ctx_compute.get(), gf); - - ggml_backend_sched_alloc_graph(sched.get(), gf); - - res->set_inputs(nullptr); - - graph_compute(gf, false); - - need_reserve = true; - } - - kv->do_defrag = false; - } + need_reserve = kv_self->update(*this); // reserve a worst case graph if needed if (need_reserve) { @@ -798,7 +462,7 @@ void llama_context::kv_self_update() { uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch); // simulate full KV cache - kv_self->n = kv_self->size; + kv_self->set_full(); llama_token token = model.vocab.token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; @@ -819,9 +483,6 @@ enum llama_pooling_type llama_context::pooling_type() const { } float * llama_context::get_logits() { - // reorder logits for backward compatibility - output_reorder(); - return logits; } @@ -864,9 +525,6 @@ float * llama_context::get_logits_ith(int32_t i) { } float * llama_context::get_embeddings() { - // reorder embeddings for backward compatibility - output_reorder(); - return embd; } @@ -1018,8 +676,8 @@ int llama_context::encode(llama_batch & inp_batch) { } // temporary allocate memory for the input batch if needed - // TODO: this is incorrect for multiple sequences because pos_max() is the maximum across all sequences - llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->pos_max() + 1); + // note: during encode, we always pass the full sequence starting from pos = 0 + llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : 0); const llama_batch & batch = batch_allocr.batch; const int32_t n_tokens = batch.n_tokens; @@ -1048,7 +706,7 @@ int llama_context::encode(llama_batch & inp_batch) { const int64_t n_embd = hparams.n_embd; - sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true); + llama_sbatch sbatch = llama_sbatch(batch, n_embd, /* simple_split */ true, /* logits_all */ true); const llama_ubatch ubatch = sbatch.split_simple(n_tokens); @@ -1182,9 +840,11 @@ int llama_context::decode(llama_batch & inp_batch) { return -1; } + llama_kv_cache * kv_self = static_cast(memory.get()); + // temporary allocate memory for the input batch if needed - // TODO: this is incorrect for multiple sequences because pos_max() is the maximum across all sequences - llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->pos_max() + 1); + // TODO: this is incorrect for multiple sequences because get_pos_max() is the maximum across all sequences + llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : kv_self->get_pos_max() + 1); const llama_batch & batch = batch_allocr.batch; @@ -1196,7 +856,7 @@ int llama_context::decode(llama_batch & inp_batch) { const int64_t n_tokens_all = batch.n_tokens; const int64_t n_embd = hparams.n_embd; - llama_kv_cache_guard kv_guard(kv_self.get()); + llama_kv_cache_guard kv_guard(kv_self); GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT @@ -1237,11 +897,7 @@ int llama_context::decode(llama_batch & inp_batch) { n_outputs_all = 1; } - const bool logits_all = n_outputs_all == n_tokens_all; - - sbatch.from_batch(batch, n_embd, - /* simple_split */ !kv_self->recurrent, - /* logits_all */ logits_all); + llama_sbatch sbatch = kv_self->sbatch_init(batch, /* logits_all */ n_outputs_all == n_tokens_all); // reserve output buffer if (output_reserve(n_outputs_all) < n_outputs_all) { @@ -1255,22 +911,7 @@ int llama_context::decode(llama_batch & inp_batch) { int64_t n_outputs_prev = 0; while (sbatch.n_tokens > 0) { - llama_ubatch ubatch = llama_ubatch(); - - const auto & n_ubatch = cparams.n_ubatch; - - if (kv_self->recurrent) { - if (embd_pooled) { - // Pooled embeddings cannot be split across ubatches (yet) - ubatch = sbatch.split_seq(cparams.n_ubatch); - } else { - // recurrent model architectures are easier to implement - // with equal-length sequences - ubatch = sbatch.split_equal(cparams.n_ubatch); - } - } else { - ubatch = sbatch.split_simple(n_ubatch); - } + llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled); // count the outputs in this u_batch { @@ -1290,24 +931,12 @@ int llama_context::decode(llama_batch & inp_batch) { } // find KV slot - { - if (!kv_self->find_slot(ubatch)) { - LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens); + if (!kv_self->find_slot(ubatch)) { + LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens); - return 1; - } - - if (!kv_self->recurrent) { - // a heuristic, to avoid attending the full cache if it is not yet utilized - // after enough generations, the benefit from this heuristic disappears - // if we start defragmenting the cache, the benefit from this will be more important - const uint32_t pad = kv_self->get_padding(cparams); - kv_self->n = std::min(kv_self->size, std::max(pad, GGML_PAD(kv_self->cell_max(), pad))); - } + return 1; } - //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self->n, kv_self->used, kv_self->head); - ggml_backend_sched_reset(sched.get()); ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data); @@ -1421,43 +1050,68 @@ int llama_context::decode(llama_batch & inp_batch) { // finalize the batch processing kv_guard.commit(); + // set to total number of outputs in the batch, for use in llama_get_logits_ith + n_outputs = n_outputs_all; + // set output mappings { bool sorted_output = true; - GGML_ASSERT(sbatch.out_ids.size() == (size_t) n_outputs_all); + auto & out_ids = sbatch.out_ids; + + GGML_ASSERT(out_ids.size() == (size_t) n_outputs_all); for (int64_t i = 0; i < n_outputs_all; ++i) { - int64_t out_id = sbatch.out_ids[i]; + int64_t out_id = out_ids[i]; output_ids[out_id] = i; if (out_id != i) { sorted_output = false; } } - if (sorted_output) { - sbatch.out_ids.clear(); + // make the outputs have the same order they had in the user-provided batch + // note: this is mostly relevant for recurrent models atm + if (!sorted_output) { + const uint32_t n_vocab = model.vocab.n_tokens(); + const uint32_t n_embd = model.hparams.n_embd; + + GGML_ASSERT((size_t) n_outputs == out_ids.size()); + + // TODO: is there something more efficient which also minimizes swaps? + // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort) + for (int32_t i = 0; i < n_outputs - 1; ++i) { + int32_t j_min = i; + for (int32_t j = i + 1; j < n_outputs; ++j) { + if (out_ids[j] < out_ids[j_min]) { + j_min = j; + } + } + if (j_min == i) { continue; } + std::swap(out_ids[i], out_ids[j_min]); + if (logits_size > 0) { + for (uint32_t k = 0; k < n_vocab; k++) { + std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]); + } + } + if (embd_size > 0) { + for (uint32_t k = 0; k < n_embd; k++) { + std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]); + } + } + } + std::fill(output_ids.begin(), output_ids.end(), -1); + for (int32_t i = 0; i < n_outputs; ++i) { + output_ids[out_ids[i]] = i; + } } } - // set to total number of outputs in the batch, for use in llama_get_logits_ith - n_outputs = n_outputs_all; - // wait for the computation to finish (automatically done when obtaining the model output) //synchronize(); // decide if we need to defrag the kv cache - if (cparams.causal_attn && cparams.defrag_thold > 0.0f) { - // - do not defrag small contexts (i.e. < 2048 tokens) - // - count the padding towards the number of used tokens - const float fragmentation = kv_self->n >= 2048 ? std::max(0.0f, 1.0f - float(kv_self->used + kv_self->get_padding(cparams))/float(kv_self->n)) : 0.0f; - - // queue defragmentation for next llama_kv_cache_update - if (fragmentation > cparams.defrag_thold) { - LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation); - - kv_self->defrag(); - } + if (cparams.defrag_thold > 0.0f) { + kv_self->defrag_sched(cparams.defrag_thold); } // Reset state for the next token before backend sync, to allow the CPU activities in the reset to @@ -1543,44 +1197,6 @@ int32_t llama_context::output_reserve(int32_t n_outputs) { return n_outputs_max; } -void llama_context::output_reorder() { - auto & out_ids = sbatch.out_ids; - if (!out_ids.empty()) { - const uint32_t n_vocab = model.vocab.n_tokens(); - const uint32_t n_embd = model.hparams.n_embd; - - GGML_ASSERT((size_t) n_outputs == out_ids.size()); - - // TODO: is there something more efficient which also minimizes swaps? - // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort) - for (int32_t i = 0; i < n_outputs - 1; ++i) { - int32_t j_min = i; - for (int32_t j = i + 1; j < n_outputs; ++j) { - if (out_ids[j] < out_ids[j_min]) { - j_min = j; - } - } - if (j_min == i) { continue; } - std::swap(out_ids[i], out_ids[j_min]); - if (logits_size > 0) { - for (uint32_t k = 0; k < n_vocab; k++) { - std::swap(logits[i*n_vocab + k], logits[j_min*n_vocab + k]); - } - } - if (embd_size > 0) { - for (uint32_t k = 0; k < n_embd; k++) { - std::swap(embd[i*n_embd + k], embd[j_min*n_embd + k]); - } - } - } - std::fill(output_ids.begin(), output_ids.end(), -1); - for (int32_t i = 0; i < n_outputs; ++i) { - output_ids[out_ids[i]] = i; - } - out_ids.clear(); - } -} - // // graph // @@ -1617,7 +1233,7 @@ llm_graph_result_ptr llama_context::graph_build( /*.backend_cpu =*/ backend_cpu, /*.cvec =*/ &cvec, /*.loras =*/ &loras, - /*.memory =*/ kv_self.get(), + /*.memory =*/ memory.get(), /*.cross =*/ &cross, /*.n_outputs =*/ n_outputs, /*.cb =*/ graph_get_cb(), @@ -2021,8 +1637,6 @@ size_t llama_context::state_write_data(llama_io_write_i & io) { { LLAMA_LOG_DEBUG("%s: - writing output ids\n", __func__); - output_reorder(); - const auto n_outputs = this->n_outputs; const auto & output_ids = this->output_ids; @@ -2076,6 +1690,8 @@ size_t llama_context::state_write_data(llama_io_write_i & io) { } LLAMA_LOG_DEBUG("%s: - writing KV self\n", __func__); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_write(io); return io.n_bytes(); @@ -2160,6 +1776,8 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { } LLAMA_LOG_DEBUG("%s: - reading KV self\n", __func__); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_read(io); return io.n_bytes(); @@ -2168,6 +1786,8 @@ size_t llama_context::state_read_data(llama_io_read_i & io) { size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id) { GGML_UNUSED(seq_id); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_write(io, seq_id); return io.n_bytes(); @@ -2176,6 +1796,8 @@ size_t llama_context::state_seq_write_data(llama_io_write_i & io, llama_seq_id s size_t llama_context::state_seq_read_data(llama_io_read_i & io, llama_seq_id seq_id) { GGML_UNUSED(seq_id); + llama_kv_cache * kv_self = static_cast(memory.get()); + kv_self->state_read(io, seq_id); return io.n_bytes(); @@ -2531,7 +2153,7 @@ void llama_kv_cache_seq_cp( llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { - return llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1); + llama_kv_self_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1); } void llama_kv_self_seq_cp( @@ -2545,14 +2167,14 @@ void llama_kv_self_seq_cp( return; } - return kv->seq_cp(seq_id_src, seq_id_dst, p0, p1); + kv->seq_cp(seq_id_src, seq_id_dst, p0, p1); } // deprecated void llama_kv_cache_seq_keep( llama_context * ctx, llama_seq_id seq_id) { - return llama_kv_self_seq_keep(ctx, seq_id); + llama_kv_self_seq_keep(ctx, seq_id); } void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) { @@ -2561,7 +2183,7 @@ void llama_kv_self_seq_keep(llama_context * ctx, llama_seq_id seq_id) { return; } - return kv->seq_keep(seq_id); + kv->seq_keep(seq_id); } // deprecated @@ -2571,7 +2193,7 @@ void llama_kv_cache_seq_add( llama_pos p0, llama_pos p1, llama_pos delta) { - return llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta); + llama_kv_self_seq_add(ctx, seq_id, p0, p1, delta); } void llama_kv_self_seq_add( @@ -2585,7 +2207,7 @@ void llama_kv_self_seq_add( return; } - return kv->seq_add(seq_id, p0, p1, delta); + kv->seq_add(seq_id, p0, p1, delta); } // deprecated @@ -2595,7 +2217,7 @@ void llama_kv_cache_seq_div( llama_pos p0, llama_pos p1, int d) { - return llama_kv_self_seq_div(ctx, seq_id, p0, p1, d); + llama_kv_self_seq_div(ctx, seq_id, p0, p1, d); } void llama_kv_self_seq_div( @@ -2609,7 +2231,7 @@ void llama_kv_self_seq_div( return; } - return kv->seq_div(seq_id, p0, p1, d); + kv->seq_div(seq_id, p0, p1, d); } // deprecated @@ -2628,7 +2250,7 @@ llama_pos llama_kv_self_seq_pos_max(llama_context * ctx, llama_seq_id seq_id) { // deprecated void llama_kv_cache_defrag(llama_context * ctx) { - return llama_kv_self_defrag(ctx); + llama_kv_self_defrag(ctx); } void llama_kv_self_defrag(llama_context * ctx) { @@ -2637,7 +2259,8 @@ void llama_kv_self_defrag(llama_context * ctx) { return; } - return kv->defrag(); + // force defrag + kv->defrag_sched(-1.0f); } // deprecated diff --git a/src/llama-context.h b/src/llama-context.h index 5457f077c..cf41ac57b 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -27,7 +27,12 @@ struct llama_context { void synchronize(); - const llama_model & get_model() const; + const llama_model & get_model() const; + const llama_cparams & get_cparams() const; + + ggml_backend_sched_t get_sched() const; + + ggml_context * get_ctx_compute() const; uint32_t n_ctx() const; uint32_t n_ctx_per_seq() const; @@ -137,49 +142,30 @@ private: // Returns max number of outputs for which space was reserved. int32_t output_reserve(int32_t n_outputs); - // make the outputs have the same order they had in the user-provided batch - // TODO: maybe remove this - void output_reorder(); - // // graph // +public: int32_t graph_max_nodes() const; // zero-out inputs and create the ctx_compute for the compute graph ggml_cgraph * graph_init(); - llm_graph_result_ptr graph_build( - ggml_context * ctx, - ggml_cgraph * gf, - const llama_ubatch & ubatch, - llm_graph_type gtype); - // returns the result of ggml_backend_sched_graph_compute_async execution ggml_status graph_compute( ggml_cgraph * gf, bool batched); +private: + llm_graph_result_ptr graph_build( + ggml_context * ctx, + ggml_cgraph * gf, + const llama_ubatch & ubatch, + llm_graph_type gtype); + llm_graph_cb graph_get_cb() const; - // used by kv_self_update() - ggml_tensor * build_rope_shift( - ggml_context * ctx0, - ggml_tensor * cur, - ggml_tensor * shift, - ggml_tensor * factors, - float freq_base, - float freq_scale) const; - - llm_graph_result_ptr build_kv_self_shift( - ggml_context * ctx0, - ggml_cgraph * gf) const; - - llm_graph_result_ptr build_kv_self_defrag( - ggml_context * ctx0, - ggml_cgraph * gf) const; - // TODO: read/write lora adapters and cvec size_t state_write_data(llama_io_write_i & io); size_t state_read_data (llama_io_read_i & io); @@ -196,11 +182,10 @@ private: llama_cparams cparams; llama_adapter_cvec cvec; llama_adapter_loras loras; - llama_sbatch sbatch; llama_cross cross; // TODO: tmp for handling cross-attention - need something better probably - std::unique_ptr kv_self; + std::unique_ptr memory; // TODO: remove bool logits_all = false; diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 7d01abf87..977d5848e 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -284,24 +284,7 @@ void llm_graph_input_s_copy::set_input(const llama_ubatch * ubatch) { // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n for (uint32_t i = 0; i < n_kv; ++i) { - const uint32_t cell_id = i + kv_self->head; - - ////////////////////////////////////////////// - // TODO: this should not mutate the KV cache ! - llama_kv_cell & kv_cell = const_cast(kv_self)->cells[i]; - - // prevent out-of-bound sources - if (kv_cell.src < 0 || (uint32_t) kv_cell.src >= kv_self->size) { - kv_cell.src = cell_id; - } - - data[i] = kv_cell.src; - - // TODO: do not mutate the KV cache - // ensure copy only happens once - if (kv_cell.src != (int32_t) cell_id) { - kv_cell.src = cell_id; - } + data[i] = kv_self->s_copy(i); } } } @@ -317,18 +300,7 @@ void llm_graph_input_s_mask::set_input(const llama_ubatch * ubatch) { // clear unused states for (int i = 0; i < n_kv; ++i) { - const uint32_t cell_id = i + kv_self->head; - - ////////////////////////////////////////////// - // TODO: this should not mutate the KV cache ! - llama_kv_cell & kv_cell = const_cast(kv_self)->cells[i]; - - data[i] = (float) (kv_cell.src >= 0); - - // only clear once - if (kv_cell.src < 0) { - kv_cell.src = cell_id; - } + data[i] = kv_self->s_mask(i); } } } @@ -1105,7 +1077,7 @@ ggml_tensor * llm_graph_context::build_inp_cls() const { } ggml_tensor * llm_graph_context::build_inp_s_copy() const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); auto inp = std::make_unique(kv_self); @@ -1122,7 +1094,7 @@ ggml_tensor * llm_graph_context::build_inp_s_copy() const { } ggml_tensor * llm_graph_context::build_inp_s_mask() const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); auto inp = std::make_unique(kv_self); @@ -1436,8 +1408,6 @@ ggml_tensor * llm_graph_context::build_attn( // store to KV cache { - GGML_ASSERT(!kv_self->recurrent); - const auto kv_head = kv_self->head; GGML_ASSERT(kv_self->size == n_ctx); @@ -1587,7 +1557,7 @@ ggml_tensor * llm_graph_context::build_copy_mask_state( ggml_tensor * state_mask, int32_t n_state, int32_t n_seqs) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto n_kv = kv_self->n; const auto kv_head = kv_self->head; @@ -1619,7 +1589,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load( ggml_tensor * state_mask, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto token_shift_count = hparams.token_shift_count; @@ -1640,7 +1610,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store( ggml_tensor * token_shift, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto token_shift_count = hparams.token_shift_count; const auto n_embd = hparams.n_embd; diff --git a/src/llama-graph.h b/src/llama-graph.h index d0c8d3219..5b404366d 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -19,6 +19,7 @@ struct llama_cparams; class llama_memory_i; class llama_kv_cache_unified; +class llama_kv_cache_recurrent; // certain models (typically multi-modal) can produce different types of graphs enum llm_graph_type { @@ -186,26 +187,26 @@ public: class llm_graph_input_s_copy : public llm_graph_input_i { public: - llm_graph_input_s_copy(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} + llm_graph_input_s_copy(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {} virtual ~llm_graph_input_s_copy() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * s_copy; // I32 [kv_size] - const llama_kv_cache_unified * kv_self; + const llama_kv_cache_recurrent * kv_self; }; class llm_graph_input_s_mask : public llm_graph_input_i { public: - llm_graph_input_s_mask(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} + llm_graph_input_s_mask(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {} virtual ~llm_graph_input_s_mask() = default; void set_input(const llama_ubatch * ubatch) override; ggml_tensor * s_mask; // F32 [1, n_kv] - const llama_kv_cache_unified * kv_self; + const llama_kv_cache_recurrent * kv_self; }; class llm_graph_input_cross_embd : public llm_graph_input_i { @@ -350,8 +351,8 @@ struct llm_graph_params { const llama_cparams & cparams; const llama_ubatch & ubatch; - ggml_backend_sched * sched; - ggml_backend * backend_cpu; + ggml_backend_sched_t sched; + ggml_backend_t backend_cpu; const llama_adapter_cvec * cvec; const llama_adapter_loras * loras; @@ -402,9 +403,9 @@ struct llm_graph_context { ggml_context * ctx0 = nullptr; - ggml_backend_sched * sched; + ggml_backend_sched_t sched; - ggml_backend * backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove? + ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove? const llama_adapter_cvec * cvec; const llama_adapter_loras * loras; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 5ff2726d1..80b0b6477 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -4,33 +4,41 @@ #include "llama-batch.h" #include "llama-cparams.h" #include "llama-model.h" +#include "llama-context.h" #include #include +#include #include #include #include -llama_kv_cache_unified::llama_kv_cache_unified(const llama_hparams & hparams, callbacks cbs) : hparams(hparams), cbs(std::move(cbs)) { +// +// llama_kv_cache_unified +// + +uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) { + // the FA kernels require padding to avoid extra runtime boundary checks + return cparams.flash_attn ? 256u : 32u; } -bool llama_kv_cache_unified::init( +llama_kv_cache_unified::llama_kv_cache_unified( const llama_model & model, - const llama_cparams & cparams, ggml_type type_k, ggml_type type_v, + bool v_trans, + bool offload, uint32_t kv_size, - bool offload) { + uint32_t padding) : model(model), hparams(model.hparams), v_trans(v_trans), padding(padding) { const int32_t n_layer = hparams.n_layer; has_shift = false; + can_shift = true; - recurrent = llama_model_is_recurrent(&model); - v_trans = !recurrent && !cparams.flash_attn; - can_shift = !recurrent; + LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d, padding = %d\n", + __func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, can_shift, padding); - LLAMA_LOG_INFO("%s: kv_size = %d, offload = %d, type_k = '%s', type_v = '%s', n_layer = %d, can_shift = %d\n", - __func__, kv_size, offload, ggml_type_name(type_k), ggml_type_name(type_v), n_layer, can_shift); + GGML_ASSERT(kv_size % padding == 0 && "kv_size must be a multiple of padding"); head = 0; size = kv_size; @@ -76,23 +84,20 @@ bool llama_kv_cache_unified::init( const char * dev_name = "CPU"; - ggml_backend_buffer_type_t buft; + ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type(); + if (offload) { auto * dev = model.dev_layer(i); buft = ggml_backend_dev_buffer_type(dev); dev_name = ggml_backend_dev_name(dev); - } else { - buft = ggml_backend_cpu_buffer_type(); } - // LLAMA_LOG_DEBUG("%s: layer %3d: n_embd_k_gqa = %d, n_embd_v_gqa = %d, dev = %s\n", __func__, - // i, n_embd_k_gqa, n_embd_v_gqa, dev_name); + //LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, i, dev_name); ggml_context * ctx = ctx_for_buft(buft); if (!ctx) { - LLAMA_LOG_ERROR("%s: failed to create ggml context for kv cache\n", __func__); - return false; + throw std::runtime_error("failed to create ggml context for kv cache"); } ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); @@ -110,55 +115,28 @@ bool llama_kv_cache_unified::init( ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); if (!buf) { - LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__); - return false; + throw std::runtime_error("failed to allocate buffer for kv cache"); } ggml_backend_buffer_clear(buf, 0); LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); bufs.emplace_back(buf); } - return true; -} + { + const size_t memory_size_k = size_k_bytes(); + const size_t memory_size_v = size_v_bytes(); -int32_t llama_kv_cache_unified::get_n_tokens() const { - int32_t result = 0; - - for (uint32_t i = 0; i < size; i++) { - result += cells[i].seq_id.size(); + LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, + (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), + ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), + ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); } - - return result; -} - -int32_t llama_kv_cache_unified::get_used_cells() const { - return used; -} - -size_t llama_kv_cache_unified::total_size() const { - size_t size = 0; - for (const auto & buf : bufs) { - size += ggml_backend_buffer_get_size(buf.get()); - } - - return size; -} - -llama_pos llama_kv_cache_unified::pos_max() const { - llama_pos pos_max = -1; - for (const auto & cell : cells) { - pos_max = std::max(pos_max, cell.pos); - } - - return pos_max; } void llama_kv_cache_unified::clear() { for (int32_t i = 0; i < (int32_t) size; ++i) { cells[i].pos = -1; cells[i].seq_id.clear(); - cells[i].src = -1; - cells[i].tail = -1; } head = 0; used = 0; @@ -179,35 +157,6 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1 = std::numeric_limits::max(); } - // models like Mamba or RWKV can't have a state partially erased - if (recurrent) { - if (seq_id >= (int64_t) size) { - // could be fatal - return false; - } - if (0 <= seq_id) { - int32_t & tail_id = cells[seq_id].tail; - if (tail_id >= 0) { - const llama_kv_cell & cell = cells[tail_id]; - // partial intersection is invalid - if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) { - return false; - } - // invalidate tails which will be cleared - if (p0 <= cell.pos && cell.pos < p1) { - tail_id = -1; - } - } - } else { - // seq_id is negative, then the range should include everything or nothing - if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits::max())) { - return false; - } - } - - // return true; - } - for (uint32_t i = 0; i < size; ++i) { if (cells[i].pos >= p0 && cells[i].pos < p1) { if (seq_id < 0) { @@ -224,7 +173,6 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos } cells[i].pos = -1; - cells[i].src = -1; if (new_head == size) { new_head = i; @@ -254,34 +202,6 @@ void llama_kv_cache_unified::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id p1 = std::numeric_limits::max(); } - if (recurrent) { - if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) { - llama_kv_cell & tail_src = cells[seq_id_src]; - llama_kv_cell & tail_dst = cells[seq_id_dst]; - if (tail_dst.tail >= 0) { - // clear destination seq_id if it wasn't empty - llama_kv_cell & cell_dst = cells[tail_dst.tail]; - - cell_dst.seq_id.erase(seq_id_dst); - tail_dst.tail = -1; - if (cell_dst.seq_id.empty()) { - cell_dst.pos = -1; - cell_dst.delta = -1; - cell_dst.src = -1; - used -= 1; - } - } - if (tail_src.tail >= 0) { - llama_kv_cell & cell_src = cells[tail_src.tail]; - - cell_src.seq_id.insert(seq_id_dst); - tail_dst.tail = tail_src.tail; - } - } - - return; - } - // otherwise, this is the KV of a Transformer-like model head = 0; @@ -296,17 +216,12 @@ void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) { uint32_t new_head = size; for (uint32_t i = 0; i < size; ++i) { - if (recurrent && (llama_seq_id) i != seq_id) { - cells[i].tail = -1; - } - if (!cells[i].has_seq_id(seq_id)) { if (cells[i].pos >= 0) { used--; } cells[i].pos = -1; - cells[i].src = -1; cells[i].seq_id.clear(); if (new_head == size){ @@ -344,20 +259,6 @@ void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_po return; } - if (recurrent) { - // for Mamba-like or RWKV models, only the pos needs to be shifted - if (0 <= seq_id && seq_id < (int64_t) size) { - const int32_t tail_id = cells[seq_id].tail; - if (tail_id >= 0) { - llama_kv_cell & cell = cells[tail_id]; - if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { - cell.pos += delta; - } - } - } - return; - } - for (uint32_t i = 0; i < size; ++i) { if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) { has_shift = true; @@ -400,21 +301,6 @@ void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_po return; } - if (recurrent) { - // for Mamba-like or RWKV models, only the pos needs to be changed - if (0 <= seq_id && seq_id < (int64_t) size) { - const int32_t tail_id = cells[seq_id].tail; - if (tail_id >= 0) { - llama_kv_cell & cell = cells[tail_id]; - if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { - cell.pos /= d; - } - } - } - - return; - } - for (uint32_t i = 0; i < size; ++i) { if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) { has_shift = true; @@ -440,23 +326,11 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const { return result; } -void llama_kv_cache_unified::defrag() { - if (!recurrent) { - do_defrag = true; - } -} - void llama_kv_cache_unified::restore() { if (pending.ranges.empty()) { return; } - // TODO: tmp - move to llama_kv_cache_recurrent - if (recurrent) { - seq_rm(-1, -1, -1); - return; - } - uint32_t new_head = size; for (auto & range : pending.ranges) { @@ -469,7 +343,6 @@ void llama_kv_cache_unified::restore() { } cells[i].pos = -1; - cells[i].src = -1; } new_head = std::min(new_head, range.c0); @@ -481,11 +354,6 @@ void llama_kv_cache_unified::restore() { } void llama_kv_cache_unified::commit() { - // TODO: tmp - move to llama_kv_cache_recurrent - if (recurrent) { - return; - } - if (pending.ranges.empty()) { if (!recurrent) { LLAMA_LOG_WARN("%s: no pending KV cache updates to commit - might indicate a bug (ref: %s)\n", @@ -497,8 +365,98 @@ void llama_kv_cache_unified::commit() { pending.ranges.clear(); } -bool llama_kv_cache_unified::get_can_shift() const { - return can_shift; +bool llama_kv_cache_unified::update(llama_context & lctx) { + bool need_reserve = false; + + auto * sched = lctx.get_sched(); + + if (has_shift) { + if (!get_can_shift()) { + printf("\nWARNING: The current KV cache / model configuration does not support K-shift"); + } else { + + LLAMA_LOG_DEBUG("%s: applying K-shift\n", __func__); + + // apply K-shift if needed + if (hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { + ggml_backend_sched_reset(sched); + + auto * gf = lctx.graph_init(); + + auto res = build_graph_shift(lctx.get_cparams(), lctx.get_ctx_compute(), gf); + + ggml_backend_sched_alloc_graph(sched, gf); + + res->set_inputs(nullptr); + + lctx.graph_compute(gf, false); + + need_reserve = true; + } + + { + has_shift = false; + + for (uint32_t i = 0; i < size; ++i) { + cells[i].delta = 0; + } + } + }} + + if (do_defrag) { + LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__); + + if (defrag_prepare(lctx.graph_max_nodes())) { + ggml_backend_sched_reset(sched); + + auto * gf = lctx.graph_init(); + + auto res = build_graph_defrag(lctx.get_cparams(), lctx.get_ctx_compute(), gf); + + ggml_backend_sched_alloc_graph(sched, gf); + + res->set_inputs(nullptr); + + lctx.graph_compute(gf, false); + + need_reserve = true; + } + + do_defrag = false; + } + + return need_reserve; +} + +void llama_kv_cache_unified::defrag_sched(float thold) { + // - do not defrag small contexts (i.e. < 2048 tokens) + // - count the padding towards the number of used tokens + const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + padding)/n)) : 0.0f; + + // queue defragmentation for next llama_kv_cache_update + if (fragmentation > thold) { + LLAMA_LOG_DEBUG("%s: fragmentation: %.2f - requesting defrag\n", __func__, fragmentation); + + do_defrag = true; + } +} + +void llama_kv_cache_unified::set_full() { + n = size; +} + +llama_sbatch llama_kv_cache_unified::sbatch_init( + const llama_batch & batch, + bool logits_all) { + return llama_sbatch(batch, hparams.n_embd, true, logits_all); +} + +llama_ubatch llama_kv_cache_unified::ubatch_next( + llama_sbatch & sbatch, + uint32_t n_ubatch, + bool embd_pooled) const { + GGML_UNUSED(embd_pooled); + return sbatch.split_simple(n_ubatch); } bool llama_kv_cache_unified::find_slot( @@ -513,169 +471,6 @@ bool llama_kv_cache_unified::find_slot( head = 0; } - if (recurrent) { - // For recurrent state architectures (like Mamba or RWKV), - // each cache cell can store the state for a whole sequence. - // A slot should be always be contiguous. - - // can only process batches with an equal number of new tokens in each sequence - GGML_ASSERT(ubatch.equal_seqs); - - int32_t min = size - 1; - int32_t max = 0; - - // everything should fit if all seq_ids are smaller than the max - for (uint32_t s = 0; s < n_seqs; ++s) { - const uint32_t n_seq_id = ubatch.n_seq_id[s]; - for (uint32_t j = 0; j < n_seq_id; ++j) { - const llama_seq_id seq_id = ubatch.seq_id[s][j]; - - if (seq_id < 0 || (uint32_t) seq_id >= size) { - // too big seq_id - // TODO: would it be possible to resize the cache instead? - LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size); - return false; - } - if (j > 0) { - llama_kv_cell & seq = cells[seq_id]; - if (seq.tail >= 0) { - llama_kv_cell & cell = cells[seq.tail]; - // clear cells from seq_ids that become shared - // (should not normally happen, but let's handle it anyway) - cell.seq_id.erase(seq_id); - seq.tail = -1; - if (cell.seq_id.empty()) { - cell.pos = -1; - cell.src = -1; - used -= 1; - } - } - } - } - } - -#ifndef NDEBUG - { - std::vector tails_verif; - tails_verif.assign(size, -1); - for (uint32_t i = 0; i < size; ++i) { - llama_kv_cell & cell = cells[i]; - for (llama_seq_id seq_id : cell.seq_id) { - if (tails_verif[seq_id] != -1) { - LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]); - } - tails_verif[seq_id] = i; - } - } - for (uint32_t i = 0; i < size; ++i) { - if (tails_verif[i] != cells[i].tail) { - LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]); - } - } - } -#endif - - // find next empty cell - uint32_t next_empty_cell = head; - - for (uint32_t i = 0; i < size; ++i) { - if (next_empty_cell >= size) { next_empty_cell -= size; } - llama_kv_cell & cell = cells[next_empty_cell]; - if (cell.is_empty()) { break; } - next_empty_cell += 1; - } - - // find usable cell range - for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_seq_id seq_id = ubatch.seq_id[s][0]; - llama_kv_cell & seq_meta = cells[seq_id]; - bool has_cell = false; - if (seq_meta.tail >= 0) { - llama_kv_cell & cell = cells[seq_meta.tail]; - GGML_ASSERT(cell.has_seq_id(seq_id)); - // does this seq_id "own" the cell? - if (cell.seq_id.size() == 1) { has_cell = true; } - } - if (!has_cell) { - llama_kv_cell & empty_cell = cells[next_empty_cell]; - GGML_ASSERT(empty_cell.is_empty()); - // copy old tail into the empty cell - if (seq_meta.tail >= 0) { - llama_kv_cell & orig_cell = cells[seq_meta.tail]; - empty_cell.pos = orig_cell.pos; - empty_cell.src = orig_cell.src; - orig_cell.seq_id.erase(seq_id); - empty_cell.seq_id.insert(seq_id); // will be overwritten - } - seq_meta.tail = next_empty_cell; - // find next empty cell - if (s + 1 < n_seqs) { - next_empty_cell += 1; - for (uint32_t i = 0; i < size; ++i) { - if (next_empty_cell >= size) { next_empty_cell -= size; } - llama_kv_cell & cell = cells[next_empty_cell]; - if (cell.is_empty()) { break; } - next_empty_cell += 1; - } - } - } - if (min > seq_meta.tail) { min = seq_meta.tail; } - if (max < seq_meta.tail) { max = seq_meta.tail; } - } - - // gather and re-order - for (uint32_t s = 0; s < n_seqs; ++s) { - int32_t dst_id = s + min; - int32_t src_id = cells[ubatch.seq_id[s][0]].tail; - if (dst_id != src_id) { - llama_kv_cell & dst_cell = cells[dst_id]; - llama_kv_cell & src_cell = cells[src_id]; - - std::swap(dst_cell.pos, src_cell.pos); - std::swap(dst_cell.src, src_cell.src); - std::swap(dst_cell.seq_id, src_cell.seq_id); - - // swap tails (assuming they NEVER overlap) - for (const llama_seq_id seq_id : src_cell.seq_id) { - cells[seq_id].tail = src_id; - } - for (const llama_seq_id seq_id : dst_cell.seq_id) { - cells[seq_id].tail = dst_id; - } - } - } - - // update the pos of the used seqs - for (uint32_t s = 0; s < n_seqs; ++s) { - const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1]; - int32_t cell_id = s + min; - llama_kv_cell & cell = cells[cell_id]; - - if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) { - // What should happen when the pos backtracks or skips a value? - // Clearing the state mid-batch would require special-casing which isn't done. - LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n", - __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens); - } - cell.pos = last_pos; - cell.seq_id.clear(); - for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) { - const llama_seq_id seq_id = ubatch.seq_id[s][j]; - cell.seq_id.insert(seq_id); - cells[seq_id].tail = cell_id; - } - } - - // allow getting the range of used cells, from head to head + n - head = min; - n = max - min + 1; - used = std::count_if(cells.begin(), cells.end(), - [](const llama_kv_cell& cell){ return !cell.is_empty(); }); - - // sanity check - return n >= n_seqs; - } - // otherwise, one cell per token. if (n_tokens > size) { @@ -727,24 +522,50 @@ bool llama_kv_cache_unified::find_slot( pending.ranges.push_back({head, head + n_tokens}); + // a heuristic, to avoid attending the full cache if it is not yet utilized + // after enough generations, the benefit from this heuristic disappears + // if we start defragmenting the cache, the benefit from this will be more important + n = std::min(size, std::max(padding, GGML_PAD(cell_max(), padding))); + + //printf("n = %5d, used = %5d, head = %5d\n", n, used, head); + return true; } -uint32_t llama_kv_cache_unified::get_padding(const llama_cparams & cparams) const { - // the FA kernels require padding to avoid extra runtime boundary checks - return cparams.flash_attn ? 256u : 32u; -} +int32_t llama_kv_cache_unified::get_n_tokens() const { + int32_t result = 0; -uint32_t llama_kv_cache_unified::cell_max() const { - for (uint32_t i = size; i > 0; --i) { - const llama_kv_cell & cell = cells[i - 1]; - - if (cell.pos >= 0 && !cell.is_empty()) { - return i; - } + for (uint32_t i = 0; i < size; i++) { + result += cells[i].seq_id.size(); } - return 0; + return result; +} + +int32_t llama_kv_cache_unified::get_used_cells() const { + return used; +} + +bool llama_kv_cache_unified::get_can_shift() const { + return can_shift; +} + +llama_pos llama_kv_cache_unified::get_pos_max() const { + llama_pos pos_max = -1; + for (const auto & cell : cells) { + pos_max = std::max(pos_max, cell.pos); + } + + return pos_max; +} + +size_t llama_kv_cache_unified::total_size() const { + size_t size = 0; + for (const auto & buf : bufs) { + size += ggml_backend_buffer_get_size(buf.get()); + } + + return size; } size_t llama_kv_cache_unified::size_k_bytes() const { @@ -767,6 +588,269 @@ size_t llama_kv_cache_unified::size_v_bytes() const { return size_v_bytes; } +ggml_tensor * llama_kv_cache_unified::build_rope_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_tensor * cur, + ggml_tensor * shift, + ggml_tensor * factors, + float freq_base, + float freq_scale) const { + const auto & n_ctx_orig = cparams.n_ctx_orig_yarn; + + const auto & yarn_ext_factor = cparams.yarn_ext_factor; + const auto & yarn_beta_fast = cparams.yarn_beta_fast; + const auto & yarn_beta_slow = cparams.yarn_beta_slow; + + const auto & n_rot = hparams.n_rot; + const auto & rope_type = hparams.rope_type; + + // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly. + // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. + const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor; + + ggml_tensor * tmp; + + if (ggml_is_quantized(cur->type)) { + // dequantize to f32 -> RoPE -> quantize back + tmp = ggml_cast(ctx, cur, GGML_TYPE_F32); + + tmp = ggml_rope_ext(ctx, tmp, + shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); + + tmp = ggml_cpy(ctx, tmp, cur); + } else { + // we rotate only the first n_rot dimensions + tmp = ggml_rope_ext_inplace(ctx, cur, + shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow); + } + + return tmp; +} + +class llm_graph_input_k_shift : public llm_graph_input_i { +public: + llm_graph_input_k_shift(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {} + virtual ~llm_graph_input_k_shift() = default; + + void set_input(const llama_ubatch * ubatch) override; + + ggml_tensor * k_shift; // I32 [kv_size] + + const llama_kv_cache_unified * kv_self; +}; + +void llm_graph_input_k_shift::set_input(const llama_ubatch * ubatch) { + GGML_UNUSED(ubatch); + + if (k_shift) { + assert(ggml_backend_buffer_is_host(k_shift->buffer)); + + int32_t * data = (int32_t *) k_shift->data; + + for (uint32_t i = 0; i < kv_self->size; ++i) { + data[i] = kv_self->cells[i].delta; + } + } +} + +llm_graph_result_ptr llama_kv_cache_unified::build_graph_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const { + auto res = std::make_unique(); + + const auto & n_layer = hparams.n_layer; + + const auto & n_embd_head_k = hparams.n_embd_head_k; + //const auto & n_embd_head_v = hparams.n_embd_head_v; + + const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max; + + //GGML_ASSERT(kv_self->size == n_ctx); + + auto inp = std::make_unique(this); + + inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, cparams.n_ctx); + ggml_set_input(inp->k_shift); + + for (uint32_t il = 0; il < n_layer; ++il) { + const int64_t n_head_kv = hparams.n_head_kv(il); + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + + const bool is_swa = hparams.is_swa(il); + + // note: the swa rope params could become part of the cparams in the future + // if we decide to make them configurable, like the non-sliding ones + const float freq_base_l = is_swa ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base; + const float freq_scale_l = is_swa ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale; + + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); + + ggml_tensor * k = + ggml_view_3d(ctx, k_l[il], + n_embd_head_k, n_head_kv, size, + ggml_row_size(k_l[il]->type, n_embd_head_k), + ggml_row_size(k_l[il]->type, n_embd_k_gqa), + 0); + + ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l); + + ggml_build_forward_expand(gf, cur); + } + + res->add_input(std::move(inp)); + + return res; +} + +llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const { + auto res = std::make_unique(); + + const auto & ids = defrag_info.ids; + +#if 0 + // CPU defrag + // + // TODO: optimizations are possible: + // - multiple threads + // - avoid copying to the host memory when already there + // + // likely not worth the effort, as we have ggml_graph based defrag + // + + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + + const uint32_t kv_size = size; + + std::vector buf_k; + std::vector buf_v; + + for (uint32_t il = 0; il < n_layer; ++il) { + const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); + const size_t k_size = ggml_row_size(k_l[il]->type, n_embd_k_gqa*kv_size); + + const size_t v_size_el = ggml_type_size(v_l[il]->type); + const size_t v_size = ggml_row_size (v_l[il]->type, n_embd_v_gqa*kv_size); + + buf_k.resize(k_size); + buf_v.resize(v_size); + + ggml_backend_tensor_get(k_l[il], buf_k.data(), 0, buf_k.size()); + ggml_backend_tensor_get(v_l[il], buf_v.data(), 0, buf_v.size()); + + // batch move [i, i+nm) to [id, id+nm) + // note: cells can move only to a lower index + for (uint32_t i = 0; i < n_kv; ++i) { + const uint32_t id = ids[i]; + + if (i == id || id == n_kv) { + continue; + } + + uint32_t nm = 1; + + while (i + nm < n_kv && ids[i + nm] == id + nm) { + nm++; + } + + // move keys + { + const int64_t os = i*k_size_row; + const int64_t od = id*k_size_row; + + memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row); + } + + // move values (note: they are transposed) + { + const int64_t os = i; + const int64_t od = id; + + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el); + } + } + + i += nm - 1; + } + + ggml_backend_tensor_set(k_l[il], buf_k.data(), 0, buf_k.size()); + ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size()); + } +#else + for (uint32_t i = 0; i < ids.size(); ++i) { + const uint32_t id = ids[i]; + + if (i == id || id == ids.size()) { + continue; + } + + uint32_t nm = 1; + + while (i + nm < ids.size() && ids[i + nm] == id + nm) { + nm++; + } + + for (uint32_t il = 0; il < hparams.n_layer; ++il) { // NOLINT + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il); + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il); + + ggml_tensor * view_k_src = ggml_view_2d(ctx, k_l[il], + n_embd_k_gqa, nm, + ggml_row_size(k_l[il]->type, n_embd_k_gqa), + ggml_row_size(k_l[il]->type, n_embd_k_gqa*i)); + + ggml_tensor * view_k_dst = ggml_view_2d(ctx, k_l[il], + n_embd_k_gqa, nm, + ggml_row_size(k_l[il]->type, n_embd_k_gqa), + ggml_row_size(k_l[il]->type, n_embd_k_gqa*id)); + + ggml_tensor * view_v_src; + ggml_tensor * view_v_dst; + + if (cparams.flash_attn) { + // NOTE: the V cache is not transposed when using flash attention + view_v_src = ggml_view_2d(ctx, v_l[il], + n_embd_v_gqa, nm, + ggml_row_size(v_l[il]->type, n_embd_v_gqa), + ggml_row_size(v_l[il]->type, n_embd_v_gqa*i)); + + view_v_dst = ggml_view_2d(ctx, v_l[il], + n_embd_v_gqa, nm, + ggml_row_size(v_l[il]->type, n_embd_v_gqa), + ggml_row_size(v_l[il]->type, n_embd_v_gqa*id)); + } else { + view_v_src = ggml_view_2d(ctx, v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(v_l[il]->type, size), + ggml_row_size(v_l[il]->type, i)); + + view_v_dst = ggml_view_2d(ctx, v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(v_l[il]->type, size), + ggml_row_size(v_l[il]->type, id)); + } + + ggml_build_forward_expand(gf, ggml_cpy(ctx, view_k_src, view_k_dst)); + ggml_build_forward_expand(gf, ggml_cpy(ctx, view_v_src, view_v_dst)); + } + + i += nm - 1; + } + + //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes); +#endif + + return res; +} + bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) { const uint32_t n_layer = hparams.n_layer; @@ -869,7 +953,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) { cells[i0 + nf] = cell1; // clear the old cell and move the head there - cell1 = llama_kv_cell(); + cell1 = kv_cell(); head = n_used; if (!cont) { @@ -897,13 +981,25 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) { return false; } - LLAMA_LOG_DEBUG("(tmp log) KV defrag cell moves: %u\n", n_moves); + LLAMA_LOG_DEBUG("%s: (tmp log) KV defrag cell moves: %u\n", __func__, n_moves); - LLAMA_LOG_DEBUG("expected gf nodes: %u\n", 6*n_moves*n_layer); + LLAMA_LOG_DEBUG("%s: expected gf nodes: %u\n", __func__, 6*n_moves*n_layer); return true; } +uint32_t llama_kv_cache_unified::cell_max() const { + for (uint32_t i = size; i > 0; --i) { + const kv_cell & cell = cells[i - 1]; + + if (cell.pos >= 0 && !cell.is_empty()) { + return i; + } + } + + return 0; +} + void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq_id) const { std::vector> cell_ranges; // ranges, from inclusive, to exclusive uint32_t cell_count = 0; @@ -1112,7 +1208,7 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell clear(); for (uint32_t i = 0; i < cell_count; ++i) { - llama_kv_cell & cell = cells[i]; + kv_cell & cell = cells[i]; llama_pos pos; uint32_t n_seq_id; @@ -1135,15 +1231,6 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell } cell.seq_id.insert(seq_id); - - if (recurrent) { - int32_t & tail = cells[seq_id].tail; - if (tail != -1) { - LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail); - return false; - } - tail = i; - } } } @@ -1151,14 +1238,6 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell used = cell_count; } - if (recurrent) { - for (uint32_t i = 0; i < cell_count; ++i) { - uint32_t cell_id = head + i; - // make sure the recurrent states will keep their restored state - cells[cell_id].src = cell_id; - } - } - return true; } @@ -1176,7 +1255,1034 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size); return false; } - if (v_trans != (bool) v_trans) { + if (this->v_trans != (bool) v_trans) { + LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__); + return false; + } + + // For each layer, read the keys for each cell, one row is one cell, read as one contiguous block + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + + // Read type of key + int32_t k_type_i_ref; + io.read_to(&k_type_i_ref, sizeof(k_type_i_ref)); + const int32_t k_type_i = (int32_t) k_l[il]->type; + if (k_type_i != k_type_i_ref) { + LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il); + return false; + } + + // Read row size of key + uint64_t k_size_row_ref; + io.read_to(&k_size_row_ref, sizeof(k_size_row_ref)); + const size_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); + if (k_size_row != k_size_row_ref) { + LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, (size_t) k_size_row_ref, il); + return false; + } + + if (cell_count) { + // Read and set the keys for the whole cell range + ggml_backend_tensor_set(k_l[il], io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row); + } + } + + if (!this->v_trans) { + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Read type of value + int32_t v_type_i_ref; + io.read_to(&v_type_i_ref, sizeof(v_type_i_ref)); + const int32_t v_type_i = (int32_t)v_l[il]->type; + if (v_type_i != v_type_i_ref) { + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return false; + } + + // Read row size of value + uint64_t v_size_row_ref; + io.read_to(&v_size_row_ref, sizeof(v_size_row_ref)); + const size_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa); + if (v_size_row != v_size_row_ref) { + LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, (size_t) v_size_row_ref, il); + return false; + } + + if (cell_count) { + // Read and set the values for the whole cell range + ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row); + } + } + } else { + // For each layer, read the values for each cell (transposed) + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Read type of value + int32_t v_type_i_ref; + io.read_to(&v_type_i_ref, sizeof(v_type_i_ref)); + const int32_t v_type_i = (int32_t)v_l[il]->type; + if (v_type_i != v_type_i_ref) { + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return false; + } + + // Read element size of value + uint32_t v_size_el_ref; + io.read_to(&v_size_el_ref, sizeof(v_size_el_ref)); + const size_t v_size_el = ggml_type_size(v_l[il]->type); + if (v_size_el != v_size_el_ref) { + LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, (size_t) v_size_el_ref, il); + return false; + } + + // Read GQA embedding size + uint32_t n_embd_v_gqa_ref; + io.read_to(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref)); + if (n_embd_v_gqa != n_embd_v_gqa_ref) { + LLAMA_LOG_ERROR("%s: mismatched GQA embedding size (%u != %u, layer %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref, il); + return false; + } + + if (cell_count) { + // For each row in the transposed matrix, read the values for the whole cell range + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + const size_t dst_offset = (head + j * size) * v_size_el; + ggml_backend_tensor_set(v_l[il], io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el); + } + } + } + } + + return true; +} + +// +// llama_kv_cache_recurrent +// + +llama_kv_cache_recurrent::llama_kv_cache_recurrent( + const llama_model & model, + ggml_type type_k, + ggml_type type_v, + bool offload, + uint32_t kv_size) : hparams(model.hparams) { + const int32_t n_layer = hparams.n_layer; + + LLAMA_LOG_INFO("%s: kv_size = %d, type_k = '%s', type_v = '%s', n_layer = %d\n", + __func__, kv_size, ggml_type_name(type_k), ggml_type_name(type_v), n_layer); + + head = 0; + size = kv_size; + used = 0; + + this->type_k = type_k; + this->type_v = type_v; + + cells.clear(); + cells.resize(kv_size); + + // create a context for each buffer type + std::map ctx_map; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { + ggml_init_params params = { + /*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()), + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ctx = ggml_init(params); + if (!ctx) { + return nullptr; + } + + ctx_map[buft] = ctx; + ctxs.emplace_back(ctx); + + return ctx; + } + + return it->second; + }; + + k_l.reserve(n_layer); + v_l.reserve(n_layer); + + 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(); + + const char * dev_name = "CPU"; + + ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type(); + + if (offload) { + auto * dev = model.dev_layer(i); + buft = ggml_backend_dev_buffer_type(dev); + + dev_name = ggml_backend_dev_name(dev); + } + + LLAMA_LOG_DEBUG("%s, layer %3d: dev = %s\n", __func__, i, dev_name); + + ggml_context * ctx = ctx_for_buft(buft); + if (!ctx) { + throw std::runtime_error("failed to create ggml context for kv cache"); + } + + ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); + ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size); + ggml_format_name(k, "cache_k_l%d", i); + ggml_format_name(v, "cache_v_l%d", i); + k_l.push_back(k); + v_l.push_back(v); + } + + // allocate tensors and initialize the buffers to avoid NaNs in the padding + for (auto it : ctx_map) { + auto * buft = it.first; + auto * ctx = it.second; + + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + if (!buf) { + throw std::runtime_error("failed to allocate buffer for kv cache"); + } + ggml_backend_buffer_clear(buf, 0); + LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); + bufs.emplace_back(buf); + } + + { + const size_t memory_size_k = size_k_bytes(); + const size_t memory_size_v = size_v_bytes(); + + LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__, + (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f), + ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f), + ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f)); + } +} + +void llama_kv_cache_recurrent::clear() { + for (int32_t i = 0; i < (int32_t) size; ++i) { + cells[i].pos = -1; + cells[i].seq_id.clear(); + cells[i].src = -1; + cells[i].tail = -1; + } + head = 0; + used = 0; + + for (auto & buf : bufs) { + ggml_backend_buffer_clear(buf.get(), 0); + } +} + +bool llama_kv_cache_recurrent::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { + uint32_t new_head = size; + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + // models like Mamba or RWKV can't have a state partially erased + if (seq_id >= (int64_t) size) { + // could be fatal + return false; + } + if (0 <= seq_id) { + int32_t & tail_id = cells[seq_id].tail; + if (tail_id >= 0) { + const kv_cell & cell = cells[tail_id]; + // partial intersection is invalid + if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) { + return false; + } + // invalidate tails which will be cleared + if (p0 <= cell.pos && cell.pos < p1) { + tail_id = -1; + } + } + } else { + // seq_id is negative, then the range should include everything or nothing + if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits::max())) { + return false; + } + } + + for (uint32_t i = 0; i < size; ++i) { + if (cells[i].pos >= p0 && cells[i].pos < p1) { + if (seq_id < 0) { + cells[i].seq_id.clear(); + } else if (cells[i].has_seq_id(seq_id)) { + cells[i].seq_id.erase(seq_id); + } else { + continue; + } + if (cells[i].is_empty()) { + // keep count of the number of used cells + if (cells[i].pos >= 0) { + used--; + } + cells[i].pos = -1; + cells[i].src = -1; + if (new_head == size) { + new_head = i; + } + } + } + } + + // If we freed up a slot, set head to it so searching can start there. + if (new_head != size && new_head < head) { + head = new_head; + } + + return true; +} + +void llama_kv_cache_recurrent::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { + if (seq_id_src == seq_id_dst) { + return; + } + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + if ((uint32_t) seq_id_dst < size && (uint32_t) seq_id_src < size) { + kv_cell & tail_src = cells[seq_id_src]; + kv_cell & tail_dst = cells[seq_id_dst]; + if (tail_dst.tail >= 0) { + // clear destination seq_id if it wasn't empty + kv_cell & cell_dst = cells[tail_dst.tail]; + + cell_dst.seq_id.erase(seq_id_dst); + tail_dst.tail = -1; + if (cell_dst.seq_id.empty()) { + cell_dst.pos = -1; + cell_dst.src = -1; + used -= 1; + } + } + if (tail_src.tail >= 0) { + kv_cell & cell_src = cells[tail_src.tail]; + + cell_src.seq_id.insert(seq_id_dst); + tail_dst.tail = tail_src.tail; + } + } +} + +void llama_kv_cache_recurrent::seq_keep(llama_seq_id seq_id) { + uint32_t new_head = size; + + for (uint32_t i = 0; i < size; ++i) { + if ((llama_seq_id) i != seq_id) { + cells[i].tail = -1; + } + + if (!cells[i].has_seq_id(seq_id)) { + if (cells[i].pos >= 0) { + used--; + } + + cells[i].pos = -1; + cells[i].src = -1; + cells[i].seq_id.clear(); + + if (new_head == size){ + new_head = i; + } + } else { + cells[i].seq_id.clear(); + cells[i].seq_id.insert(seq_id); + } + } + + // If we freed up a slot, set head to it so searching can start there. + if (new_head != size && new_head < head) { + head = new_head; + } +} + +void llama_kv_cache_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { + if (delta == 0) { + return; + } + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + // If there is no range then return early to avoid looping over the + if (p0 == p1) { + return; + } + + // for Mamba-like or RWKV models, only the pos needs to be shifted + if (0 <= seq_id && seq_id < (int64_t) size) { + const int32_t tail_id = cells[seq_id].tail; + if (tail_id >= 0) { + kv_cell & cell = cells[tail_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos += delta; + } + } + } +} + +void llama_kv_cache_recurrent::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { + if (d == 1) { + return; + } + + if (p0 < 0) { + p0 = 0; + } + + if (p1 < 0) { + p1 = std::numeric_limits::max(); + } + + // If there is no range then return early to avoid looping over the cache. + if (p0 == p1) { + return; + } + + // for Mamba-like or RWKV models, only the pos needs to be changed + if (0 <= seq_id && seq_id < (int64_t) size) { + const int32_t tail_id = cells[seq_id].tail; + if (tail_id >= 0) { + kv_cell & cell = cells[tail_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos /= d; + } + } + } +} + +llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const { + llama_pos result = 0; + + for (uint32_t i = 0; i < size; ++i) { + if (cells[i].has_seq_id(seq_id)) { + result = std::max(result, cells[i].pos); + } + } + + return result; +} + +void llama_kv_cache_recurrent::restore() { + if (pending.ranges.empty()) { + return; + } + + seq_rm(-1, -1, -1); +} + +void llama_kv_cache_recurrent::commit() { + pending.ranges.clear(); +} + +bool llama_kv_cache_recurrent::update(llama_context & lctx) { + GGML_UNUSED(lctx); + return false; +} + +void llama_kv_cache_recurrent::defrag_sched(float thold) { + GGML_UNUSED(thold); + // noop +} + +void llama_kv_cache_recurrent::set_full() { + n = size; +} + +llama_sbatch llama_kv_cache_recurrent::sbatch_init( + const llama_batch & batch, + bool logits_all) { + return llama_sbatch(batch, hparams.n_embd, false, logits_all); +} + +llama_ubatch llama_kv_cache_recurrent::ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const { + if (embd_pooled) { + // Pooled embeddings cannot be split across ubatches (yet) + return sbatch.split_seq(n_ubatch); + } + + return sbatch.split_equal(n_ubatch); +} + +bool llama_kv_cache_recurrent::find_slot( + const llama_ubatch & ubatch) { + const uint32_t n_tokens = ubatch.n_tokens; + const uint32_t n_seqs = ubatch.n_seqs; + + const uint32_t n_seq_tokens = ubatch.n_seq_tokens; + + // if we have enough unused cells before the current head -> + // better to start searching from the beginning of the cache, hoping to fill it + if (head > used + 2*n_tokens) { + head = 0; + } + + // For recurrent state architectures (like Mamba or RWKV), + // each cache cell can store the state for a whole sequence. + // A slot should be always be contiguous. + + // can only process batches with an equal number of new tokens in each sequence + GGML_ASSERT(ubatch.equal_seqs); + + int32_t min = size - 1; + int32_t max = 0; + + // everything should fit if all seq_ids are smaller than the max + for (uint32_t s = 0; s < n_seqs; ++s) { + const uint32_t n_seq_id = ubatch.n_seq_id[s]; + for (uint32_t j = 0; j < n_seq_id; ++j) { + const llama_seq_id seq_id = ubatch.seq_id[s][j]; + + if (seq_id < 0 || (uint32_t) seq_id >= size) { + // too big seq_id + // TODO: would it be possible to resize the cache instead? + LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, size); + return false; + } + if (j > 0) { + kv_cell & seq = cells[seq_id]; + if (seq.tail >= 0) { + kv_cell & cell = cells[seq.tail]; + // clear cells from seq_ids that become shared + // (should not normally happen, but let's handle it anyway) + cell.seq_id.erase(seq_id); + seq.tail = -1; + if (cell.seq_id.empty()) { + cell.pos = -1; + cell.src = -1; + used -= 1; + } + } + } + } + } + +#ifndef NDEBUG + { + std::vector tails_verif; + tails_verif.assign(size, -1); + for (uint32_t i = 0; i < size; ++i) { + kv_cell & cell = cells[i]; + for (llama_seq_id seq_id : cell.seq_id) { + if (tails_verif[seq_id] != -1) { + LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]); + } + tails_verif[seq_id] = i; + } + } + for (uint32_t i = 0; i < size; ++i) { + if (tails_verif[i] != cells[i].tail) { + LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cells[i].tail, tails_verif[i]); + } + } + } +#endif + + // find next empty cell + uint32_t next_empty_cell = head; + + for (uint32_t i = 0; i < size; ++i) { + if (next_empty_cell >= size) { next_empty_cell -= size; } + kv_cell & cell = cells[next_empty_cell]; + if (cell.is_empty()) { break; } + next_empty_cell += 1; + } + + // find usable cell range + for (uint32_t s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[s][0]; + kv_cell & seq_meta = cells[seq_id]; + bool has_cell = false; + if (seq_meta.tail >= 0) { + kv_cell & cell = cells[seq_meta.tail]; + GGML_ASSERT(cell.has_seq_id(seq_id)); + // does this seq_id "own" the cell? + if (cell.seq_id.size() == 1) { has_cell = true; } + } + if (!has_cell) { + kv_cell & empty_cell = cells[next_empty_cell]; + GGML_ASSERT(empty_cell.is_empty()); + // copy old tail into the empty cell + if (seq_meta.tail >= 0) { + kv_cell & orig_cell = cells[seq_meta.tail]; + empty_cell.pos = orig_cell.pos; + empty_cell.src = orig_cell.src; + orig_cell.seq_id.erase(seq_id); + empty_cell.seq_id.insert(seq_id); // will be overwritten + } + seq_meta.tail = next_empty_cell; + // find next empty cell + if (s + 1 < n_seqs) { + next_empty_cell += 1; + for (uint32_t i = 0; i < size; ++i) { + if (next_empty_cell >= size) { next_empty_cell -= size; } + kv_cell & cell = cells[next_empty_cell]; + if (cell.is_empty()) { break; } + next_empty_cell += 1; + } + } + } + if (min > seq_meta.tail) { min = seq_meta.tail; } + if (max < seq_meta.tail) { max = seq_meta.tail; } + } + + // gather and re-order + for (uint32_t s = 0; s < n_seqs; ++s) { + int32_t dst_id = s + min; + int32_t src_id = cells[ubatch.seq_id[s][0]].tail; + if (dst_id != src_id) { + kv_cell & dst_cell = cells[dst_id]; + kv_cell & src_cell = cells[src_id]; + + std::swap(dst_cell.pos, src_cell.pos); + std::swap(dst_cell.src, src_cell.src); + std::swap(dst_cell.seq_id, src_cell.seq_id); + + // swap tails (assuming they NEVER overlap) + for (const llama_seq_id seq_id : src_cell.seq_id) { + cells[seq_id].tail = src_id; + } + for (const llama_seq_id seq_id : dst_cell.seq_id) { + cells[seq_id].tail = dst_id; + } + } + } + + // update the pos of the used seqs + for (uint32_t s = 0; s < n_seqs; ++s) { + const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1]; + int32_t cell_id = s + min; + kv_cell & cell = cells[cell_id]; + + if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) { + // What should happen when the pos backtracks or skips a value? + // Clearing the state mid-batch would require special-casing which isn't done. + LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n", + __func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens); + } + cell.pos = last_pos; + cell.seq_id.clear(); + for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) { + const llama_seq_id seq_id = ubatch.seq_id[s][j]; + cell.seq_id.insert(seq_id); + cells[seq_id].tail = cell_id; + } + } + + // allow getting the range of used cells, from head to head + n + head = min; + n = max - min + 1; + used = std::count_if(cells.begin(), cells.end(), + [](const kv_cell & cell){ return !cell.is_empty(); }); + + // sanity check + return n >= n_seqs; +} + +int32_t llama_kv_cache_recurrent::get_n_tokens() const { + int32_t result = 0; + + for (uint32_t i = 0; i < size; i++) { + result += cells[i].seq_id.size(); + } + + return result; +} + +int32_t llama_kv_cache_recurrent::get_used_cells() const { + return used; +} + +llama_pos llama_kv_cache_recurrent::get_pos_max() const { + llama_pos pos_max = -1; + for (const auto & cell : cells) { + pos_max = std::max(pos_max, cell.pos); + } + + return pos_max; +} + +bool llama_kv_cache_recurrent::get_can_shift() const { + return false; +} + +int32_t llama_kv_cache_recurrent::s_copy(int i) const { + const uint32_t cell_id = i + head; + + ////////////////////////////////////////////// + // TODO: this should not mutate the KV cache ! + kv_cell & cell = const_cast(cells[cell_id]); + + // prevent out-of-bound sources + if (cell.src < 0 || (uint32_t) cell.src >= size) { + cell.src = cell_id; + } + + int32_t res = cell.src; + + // TODO: do not mutate the KV cache + // ensure copy only happens once + if (cell.src != (int32_t) cell_id) { + cell.src = cell_id; + } + + return res; +} + +float llama_kv_cache_recurrent::s_mask(int i) const { + const uint32_t cell_id = i + head; + + ////////////////////////////////////////////// + // TODO: this should not mutate the KV cache ! + kv_cell & cell = const_cast(cells[cell_id]); + + float res = (float) (cell.src >= 0); + + // only clear once + if (cell.src < 0) { + cell.src = cell_id; + } + + return res; +} + +uint32_t llama_kv_cache_recurrent::cell_max() const { + for (uint32_t i = size; i > 0; --i) { + const kv_cell & cell = cells[i - 1]; + + if (cell.pos >= 0 && !cell.is_empty()) { + return i; + } + } + + return 0; +} + +size_t llama_kv_cache_recurrent::total_size() const { + size_t size = 0; + for (const auto & buf : bufs) { + size += ggml_backend_buffer_get_size(buf.get()); + } + + return size; +} + +size_t llama_kv_cache_recurrent::size_k_bytes() const { + size_t size_k_bytes = 0; + + for (const auto & k : k_l) { + size_k_bytes += ggml_nbytes(k); + } + + return size_k_bytes; +} + +size_t llama_kv_cache_recurrent::size_v_bytes() const { + size_t size_v_bytes = 0; + + for (const auto & v : v_l) { + size_v_bytes += ggml_nbytes(v); + } + + return size_v_bytes; +} + +void llama_kv_cache_recurrent::state_write(llama_io_write_i & io, llama_seq_id seq_id) const { + std::vector> cell_ranges; // ranges, from inclusive, to exclusive + uint32_t cell_count = 0; + + // Count the number of cells with the specified seq_id + // Find all the ranges of cells with this seq id (or all, when -1) + uint32_t cell_range_begin = size; + for (uint32_t i = 0; i < size; ++i) { + const auto & cell = cells[i]; + if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) { + ++cell_count; + if (cell_range_begin == size) { + cell_range_begin = i; + } + } else { + if (cell_range_begin != size) { + cell_ranges.emplace_back(cell_range_begin, i); + cell_range_begin = size; + } + } + } + if (cell_range_begin != size) { + cell_ranges.emplace_back(cell_range_begin, size); + } + + // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count + uint32_t cell_count_check = 0; + for (const auto & range : cell_ranges) { + cell_count_check += range.second - range.first; + } + GGML_ASSERT(cell_count == cell_count_check); + + io.write(&cell_count, sizeof(cell_count)); + + state_write_meta(io, cell_ranges, seq_id); + state_write_data(io, cell_ranges); +} + +void llama_kv_cache_recurrent::state_read(llama_io_read_i & io, llama_seq_id seq_id) { + uint32_t cell_count; + io.read_to(&cell_count, sizeof(cell_count)); + + bool res = true; + res = res && state_read_meta(io, cell_count, seq_id); + res = res && state_read_data(io, cell_count); + + if (!res) { + if (seq_id == -1) { + clear(); + } else { + seq_rm(seq_id, -1, -1); + } + throw std::runtime_error("failed to restore kv cache"); + } +} + +void llama_kv_cache_recurrent::state_write_meta(llama_io_write_i & io, const std::vector> & cell_ranges, llama_seq_id seq_id) const { + for (const auto & range : cell_ranges) { + for (uint32_t i = range.first; i < range.second; ++i) { + const auto & cell = cells[i]; + const llama_pos pos = cell.pos; + const uint32_t n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0; + + io.write(&pos, sizeof(pos)); + io.write(&n_seq_id, sizeof(n_seq_id)); + + if (n_seq_id) { + for (auto seq_id : cell.seq_id) { + io.write(&seq_id, sizeof(seq_id)); + } + } + } + } +} + +void llama_kv_cache_recurrent::state_write_data(llama_io_write_i & io, const std::vector> & cell_ranges) const { + const uint32_t v_trans = 0; + const uint32_t n_layer = hparams.n_layer; + + io.write(&v_trans, sizeof(v_trans)); + io.write(&n_layer, sizeof(n_layer)); + + std::vector tmp_buf; + + // Iterate and write all the keys first, each row is a cell + // Get whole range at a time + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s(); + + // Write key type + const int32_t k_type_i = (int32_t)k_l[il]->type; + io.write(&k_type_i, sizeof(k_type_i)); + + // Write row size of key + const uint64_t k_size_row = ggml_row_size(k_l[il]->type, n_embd_k_gqa); + io.write(&k_size_row, sizeof(k_size_row)); + + // Read each range of cells of k_size length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t buf_size = range_size * k_size_row; + io.write_tensor(k_l[il], range.first * k_size_row, buf_size); + } + } + + if (!v_trans) { + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Write value type + const int32_t v_type_i = (int32_t)v_l[il]->type; + io.write(&v_type_i, sizeof(v_type_i)); + + // Write row size of value + const uint64_t v_size_row = ggml_row_size(v_l[il]->type, n_embd_v_gqa); + io.write(&v_size_row, sizeof(v_size_row)); + + // Read each range of cells of v_size length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t buf_size = range_size * v_size_row; + io.write_tensor(v_l[il], range.first * v_size_row, buf_size); + } + } + } else { + // When v is transposed, we also need the element size and get the element ranges from each row + const uint32_t kv_size = size; + for (uint32_t il = 0; il < n_layer; ++il) { + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s(); + + // Write value type + const int32_t v_type_i = (int32_t)v_l[il]->type; + io.write(&v_type_i, sizeof(v_type_i)); + + // Write element size + const uint32_t v_size_el = ggml_type_size(v_l[il]->type); + io.write(&v_size_el, sizeof(v_size_el)); + + // Write GQA embedding size + io.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa)); + + // For each row, we get the element values of each cell + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + // Read each range of cells of v_size_el length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t src_offset = (range.first + j * kv_size) * v_size_el; + const size_t buf_size = range_size * v_size_el; + io.write_tensor(v_l[il], src_offset, buf_size); + } + } + } + } +} + +bool llama_kv_cache_recurrent::state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id) { + if (dest_seq_id != -1) { + // single sequence + + seq_rm(dest_seq_id, -1, -1); + + llama_sbatch sbatch; + llama_ubatch batch = sbatch.reserve_ubatch(cell_count, /* has_embd */ false); + + batch.n_tokens = cell_count; + batch.n_seq_tokens = cell_count; + batch.n_seqs = 1; + + for (uint32_t i = 0; i < cell_count; ++i) { + llama_pos pos; + uint32_t n_seq_id; + + io.read_to(&pos, sizeof(pos)); + io.read_to(&n_seq_id, sizeof(n_seq_id)); + + if (n_seq_id != 0) { + LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__); + return false; + } + + batch.pos[i] = pos; + } + batch.n_seq_id[0] = 1; + batch.seq_id[0] = &dest_seq_id; + if (!find_slot(batch)) { + LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__); + return false; + } + commit(); + + // DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values) + // Assume that this is one contiguous block of cells + GGML_ASSERT(head + cell_count <= size); + GGML_ASSERT(cells[head].pos == batch.pos[0]); + GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]); + GGML_ASSERT(cells[head].has_seq_id(dest_seq_id)); + GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id)); + } else { + // whole KV cache restore + + if (cell_count > size) { + LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__); + return false; + } + + clear(); + + for (uint32_t i = 0; i < cell_count; ++i) { + kv_cell & cell = cells[i]; + + llama_pos pos; + uint32_t n_seq_id; + + io.read_to(&pos, sizeof(pos)); + io.read_to(&n_seq_id, sizeof(n_seq_id)); + + cell.pos = pos; + + for (uint32_t j = 0; j < n_seq_id; ++j) { + llama_seq_id seq_id; + io.read_to(&seq_id, sizeof(seq_id)); + + // TODO: llama_kv_cache_recurrent should have a notion of max sequences + //if (seq_id < 0 || (uint32_t) seq_id >= llama_n_seq_max(ctx)) { + if (seq_id < 0) { + //LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, %u)\n", __func__, seq_id, llama_n_seq_max(ctx)); + LLAMA_LOG_ERROR("%s: invalid seq_id, %d is out of range [0, inf)\n", __func__, seq_id); + return false; + } + + cell.seq_id.insert(seq_id); + + int32_t & tail = cells[seq_id].tail; + if (tail != -1) { + LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail); + return false; + } + tail = i; + } + } + + head = 0; + used = cell_count; + } + + for (uint32_t i = 0; i < cell_count; ++i) { + uint32_t cell_id = head + i; + // make sure the recurrent states will keep their restored state + cells[cell_id].src = cell_id; + } + + return true; +} + +bool llama_kv_cache_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell_count) { + uint32_t v_trans; + uint32_t n_layer; + io.read_to(&v_trans, sizeof(v_trans)); + io.read_to(&n_layer, sizeof(n_layer)); + + if (n_layer != hparams.n_layer) { + LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, hparams.n_layer); + return false; + } + if (cell_count > size) { + LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size); + return false; + } + if (false != (bool) v_trans) { LLAMA_LOG_ERROR("%s: incompatible V transposition\n", __func__); return false; } @@ -1328,7 +2434,7 @@ void llama_kv_cache_view_update(llama_kv_cache_view * view, const llama_kv_cache view->cells_sequences = (llama_seq_id *)p; } - const std::vector & kv_cells = kvu->cells; + const std::vector & kv_cells = kvu->cells; llama_kv_cache_view_cell * c_curr = view->cells; llama_seq_id * cs_curr = view->cells_sequences; int32_t used_cells = 0; diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h index 56c74035a..bf3b4b6a4 100644 --- a/src/llama-kv-cache.h +++ b/src/llama-kv-cache.h @@ -2,32 +2,72 @@ #include "llama.h" #include "llama-io.h" +#include "llama-graph.h" #include "llama-memory.h" #include "ggml-cpp.h" -#include #include #include struct llama_cparams; struct llama_hparams; struct llama_ubatch; +struct llama_sbatch; +struct llama_model; +struct llama_context; struct llama_kv_cache : public llama_memory_i { - using llama_memory_i::llama_memory_i; + virtual ~llama_kv_cache() = default; - virtual void restore() = 0; // call if batch processing fails - restores the cache state - virtual void commit() = 0; // call after successful batch processing - clears any pending state + // call if batch processing fails - restores the cache state + virtual void restore() = 0; - virtual int32_t get_n_tokens() const = 0; - virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache + // call after successful batch processing - clears any pending state + virtual void commit() = 0; - virtual bool get_can_shift() const = 0; + // process any pending defrag/shift/etc. operations + // optionally call once before processing a new batch + virtual bool update(llama_context & lctx) = 0; + + // schedule a defrag if the fragmentation threshold is exceeded. otherwise, do nothing + virtual void defrag_sched(float thold) = 0; + + // simulate full cache, used for allocating worst-case compute buffers + virtual void set_full() = 0; + + // + // batch processing + // + + virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0; + + // different KV caches require different batch splitting strategies + virtual llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const = 0; + + // find an empty slot of size "n_tokens" in the cache + virtual bool find_slot(const llama_ubatch & batch) = 0; + + // getters + virtual int32_t get_n_tokens() const = 0; + virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache + virtual llama_pos get_pos_max() const = 0; + virtual bool get_can_shift() const = 0; bool get_can_edit() const override { return get_can_shift(); } + + // + // state write/read + // + + virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0; + virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0; }; +// +// llama_kv_cache_guard +// + struct llama_kv_cache_guard { llama_kv_cache_guard(llama_kv_cache * kv) : kv(kv) {} @@ -43,65 +83,50 @@ private: llama_kv_cache * kv; }; -struct llama_kv_cell { - llama_pos pos = -1; - llama_pos delta = 0; - int32_t src = -1; // used by recurrent state models to copy states - int32_t tail = -1; +// +// llama_kv_cache_unified +// - std::set seq_id; - - bool has_seq_id(const llama_seq_id & id) const { - return seq_id.find(id) != seq_id.end(); - } - - bool is_empty() const { - return seq_id.empty(); - } - - bool is_same_seq(const llama_kv_cell & other) const { - return seq_id == other.seq_id; - } -}; - -// ring-buffer of cached KV data -// TODO: pimpl // TODO: add notion of max sequences class llama_kv_cache_unified : public llama_kv_cache { public: - // can be used to query data from the model if needed - struct callbacks { - std::function get_rope_factors; + struct kv_cell { + llama_pos pos = -1; + llama_pos delta = 0; + + std::set seq_id; + + bool has_seq_id(const llama_seq_id & id) const { + return seq_id.find(id) != seq_id.end(); + } + + bool is_empty() const { + return seq_id.empty(); + } + + bool is_same_seq(const kv_cell & other) const { + return seq_id == other.seq_id; + } }; + static uint32_t get_padding(const llama_cparams & cparams); + llama_kv_cache_unified( - const llama_hparams & hparams, - callbacks cbs); - - virtual ~llama_kv_cache_unified() = default; - - // TODO: become constructor - bool init( - const llama_model & model, // TODO: do not reference the model - const llama_cparams & cparams, + const llama_model & model, ggml_type type_k, ggml_type type_v, + bool v_trans, + bool offload, uint32_t kv_size, - bool offload); + uint32_t padding); - int32_t get_n_tokens() const override; - int32_t get_used_cells() const override; + ~llama_kv_cache_unified() = default; - size_t total_size() const; - - // TODO: better data structures to reduce the cost of this operation - llama_pos pos_max() const; + // + // llama_memory_i + // void clear() override; - void defrag() override; - - virtual void restore() override; - virtual void commit() override; bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override; void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override; @@ -111,63 +136,40 @@ public: llama_pos seq_pos_max(llama_seq_id seq_id) const override; - bool get_can_shift() const override; + // + // llama_kv_cache + // + + void restore() override; + void commit() override; + + bool update(llama_context & ctx) override; + + void defrag_sched(float thold) override; + + void set_full() override; + + llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override; + + llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override; - // find an empty slot of size "n_tokens" in the cache // updates the cache head // Note: On success, it's important that cache.head points // to the first cell of the slot. - bool find_slot(const llama_ubatch & batch); + bool find_slot(const llama_ubatch & batch) override; - // TODO: maybe not needed - uint32_t get_padding(const llama_cparams & cparams) const; + int32_t get_n_tokens() const override; + int32_t get_used_cells() const override; - // find how many cells are currently in use - uint32_t cell_max() const; + // TODO: better data structures to reduce the cost of this operation + llama_pos get_pos_max() const override; - size_t size_k_bytes() const; - size_t size_v_bytes() const; - - // defrag - - struct { - std::vector ids; - } defrag_info; - - // return true if cells have been moved - bool defrag_prepare(int32_t n_max_nodes); - - // commit/restore cache - - struct slot_range { - uint32_t c0 = 0; // note: these are cell indices, not sequence positions - uint32_t c1 = 0; - }; - - // pending cell updates that are not yet committed - struct { - std::vector ranges; - } pending; + bool get_can_shift() const override; // state write/load - void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const; - void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1); - - // members - - const llama_hparams & hparams; - - callbacks cbs; - - bool has_shift = false; - bool do_defrag = false; - - // TODO: remove this and implement llama_kv_cache_recurrent instead - bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token - - bool v_trans = true; // the value tensor is transposed - bool can_shift = false; + void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override; + void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override; // Note: The value of head isn't only used to optimize searching // for a free KV slot. llama_decode_impl also uses it, so it @@ -179,18 +181,213 @@ public: // computed before each graph build uint32_t n = 0; - std::vector cells; + std::vector cells; std::vector k_l; // per layer std::vector v_l; private: + const llama_model & model; + const llama_hparams & hparams; + + bool has_shift = false; + bool do_defrag = false; + + bool v_trans = true; // the value tensor is transposed + bool can_shift = false; + + // required padding + uint32_t padding = 1; + ggml_type type_k = GGML_TYPE_F16; ggml_type type_v = GGML_TYPE_F16; std::vector ctxs; std::vector bufs; + // defrag + struct { + std::vector ids; + } defrag_info; + + // return true if cells have been moved + bool defrag_prepare(int32_t n_max_nodes); + + // commit/restore cache + struct slot_range { + uint32_t c0 = 0; // note: these are cell indices, not sequence positions + uint32_t c1 = 0; + }; + + // pending cell updates that are not yet committed + struct { + std::vector ranges; + } pending; + + // find how many cells are currently in use + uint32_t cell_max() const; + + size_t total_size() const; + + size_t size_k_bytes() const; + size_t size_v_bytes() const; + + ggml_tensor * build_rope_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_tensor * cur, + ggml_tensor * shift, + ggml_tensor * factors, + float freq_base, + float freq_scale) const; + + llm_graph_result_ptr build_graph_shift( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const; + + llm_graph_result_ptr build_graph_defrag( + const llama_cparams & cparams, + ggml_context * ctx, + ggml_cgraph * gf) const; + + void state_write_meta(llama_io_write_i & io, const std::vector> & cell_ranges, llama_seq_id seq_id = -1) const; + void state_write_data(llama_io_write_i & io, const std::vector> & cell_ranges) const; + + bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1); + bool state_read_data(llama_io_read_i & io, uint32_t cell_count); +}; + +// +// llama_kv_cache_recurrent +// + +class llama_kv_cache_recurrent : public llama_kv_cache { +public: + struct kv_cell { + llama_pos pos = -1; + int32_t src = -1; // used to copy states + int32_t tail = -1; + + std::set seq_id; + + bool has_seq_id(const llama_seq_id & id) const { + return seq_id.find(id) != seq_id.end(); + } + + bool is_empty() const { + return seq_id.empty(); + } + + bool is_same_seq(const kv_cell & other) const { + return seq_id == other.seq_id; + } + }; + + llama_kv_cache_recurrent( + const llama_model & model, + ggml_type type_k, + ggml_type type_v, + bool offload, + uint32_t kv_size); + + ~llama_kv_cache_recurrent() = default; + + // + // llama_memory_i + // + + void clear() override; + + bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) override; + void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override; + void seq_keep(llama_seq_id seq_id) override; + void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) override; + void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) override; + + llama_pos seq_pos_max(llama_seq_id seq_id) const override; + + // + // llama_kv_cache + // + + void restore() override; + void commit() override; + + bool update(llama_context & lctx) override; + + void defrag_sched(float thold) override; + + void set_full() override; + + llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override; + + llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override; + + bool find_slot(const llama_ubatch & batch) override; + + int32_t get_n_tokens() const override; + int32_t get_used_cells() const override; + + // TODO: better data structures to reduce the cost of this operation + llama_pos get_pos_max() const override; + + bool get_can_shift() const override; + + // TODO: temporary methods - they are not really const as they do const_cast<>, fix this + int32_t s_copy(int i) const; + float s_mask(int i) const; + + // state write/load + + void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override; + void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) override; + + // Note: The value of head isn't only used to optimize searching + // for a free KV slot. llama_decode_impl also uses it, so it + // cannot be freely changed after a slot has been allocated. + uint32_t head = 0; + uint32_t size = 0; + uint32_t used = 0; // used cells (i.e. at least one seq_id) + + // computed before each graph build + uint32_t n = 0; + + std::vector cells; + + std::vector k_l; // per layer + std::vector v_l; + +private: + //const llama_model & model; + const llama_hparams & hparams; + + // commit/restore cache + // TODO: rework for recurrent cache + struct slot_range { + uint32_t c0 = 0; // note: these are cell indices, not sequence positions + uint32_t c1 = 0; + }; + + // pending cell updates that are not yet committed + struct { + std::vector ranges; + } pending; + + ggml_type type_k = GGML_TYPE_F16; + ggml_type type_v = GGML_TYPE_F16; + + std::vector ctxs; + std::vector bufs; + + // find how many cells are currently in use + uint32_t cell_max() const; + + size_t total_size() const; + + size_t size_k_bytes() const; + size_t size_v_bytes() const; + void state_write_meta(llama_io_write_i & io, const std::vector> & cell_ranges, llama_seq_id seq_id = -1) const; void state_write_data(llama_io_write_i & io, const std::vector> & cell_ranges) const; @@ -198,11 +395,6 @@ private: bool state_read_data(llama_io_read_i & io, uint32_t cell_count); }; -// TODO: temporary reusing llama_kv_cache_unified -- implement recurrent cache and simplify llama_kv_cache_unified -//class llama_kv_cache_recurrent : public llama_kv_cache_unified { -//public: -// using llama_kv_cache_unified::llama_kv_cache_unified; -//}; // // kv cache view diff --git a/src/llama-memory.h b/src/llama-memory.h index dfa8c4e90..c7412d591 100644 --- a/src/llama-memory.h +++ b/src/llama-memory.h @@ -2,12 +2,22 @@ #include "llama.h" +struct llama_memory_params { + // kv cache + ggml_type type_k; + ggml_type type_v; + + // parameters for other types of memory + // ... +}; + // general concept of LLM memory // the KV cache is a type of LLM memory, but there can be other types class llama_memory_i { public: + virtual ~llama_memory_i() = default; + virtual void clear() = 0; - virtual void defrag() = 0; virtual bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) = 0; virtual void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 0388bfa39..4c8b59740 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -778,6 +778,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { // fall through case LLM_ARCH_QWEN2: { + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break; @@ -4544,6 +4545,19 @@ const ggml_tensor * llama_model::get_tensor(const char * name) const { return it->second; } +ggml_tensor * llama_model::get_rope_factors(uint32_t n_ctx_per_seq, int il) const { + // choose long/short freq factors based on the context size + if (layers[il].rope_freqs != nullptr) { + return layers[il].rope_freqs; + } + + if (n_ctx_per_seq > hparams.n_ctx_orig_yarn) { + return layers[il].rope_long; + } + + return layers[il].rope_short; +} + struct llm_build_llama : public llm_graph_context { llm_build_llama(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -4587,7 +4601,7 @@ struct llm_build_llama : public llm_graph_context { #if defined(GGML_USE_CLBLAST) struct ggml_tensor * rope_factors = nullptr; //clblast does not work with rope_factors #else - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); #endif // compute Q and K and RoPE them @@ -4813,7 +4827,7 @@ struct llm_build_deci : public llm_graph_context { } else if (n_head > 0) { // self-attention // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -7295,7 +7309,7 @@ struct llm_build_phi3 : public llm_graph_context { // self-attention { // rope freq factors for 128k context - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); ggml_tensor* attn_norm_output = build_norm(inpL, model.layers[il].attn_norm, @@ -8047,7 +8061,7 @@ struct llm_build_minicpm3 : public llm_graph_context { for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // norm cur = build_norm(inpL, @@ -8814,7 +8828,7 @@ struct llm_build_mamba : public llm_graph_context { ggml_tensor * state_mask, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto kv_head = kv_self->head; @@ -9115,7 +9129,7 @@ struct llm_build_cohere2 : public llm_graph_context { // self-attention { // rope freq factors for 128k context - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -10053,7 +10067,7 @@ struct llm_build_deepseek : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -11417,7 +11431,7 @@ struct llm_build_exaone : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -11562,7 +11576,7 @@ struct llm_build_rwkv6_base : public llm_graph_context { ggml_tensor * state_mask, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto n_tokens = ubatch.n_tokens; const auto n_seqs = ubatch.n_seqs; @@ -11958,7 +11972,7 @@ struct llm_build_rwkv7_base : public llm_graph_context { ggml_tensor *& first_layer_value, const llama_ubatch & ubatch, int il) const { - const llama_kv_cache_unified * kv_self = static_cast(memory); + const llama_kv_cache_recurrent * kv_self = static_cast(memory); const auto n_tokens = ubatch.n_tokens; const auto n_seqs = ubatch.n_seqs; @@ -12798,7 +12812,7 @@ struct llm_build_bailingmoe : public llm_graph_context { // self-attention { // rope freq factors for llama3; may return nullptr for llama2 and other models - ggml_tensor * rope_factors = static_cast(memory)->cbs.get_rope_factors(n_ctx_per_seq, il); + ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il); // compute Q and K and RoPE them ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); @@ -12918,7 +12932,7 @@ struct llm_build_bailingmoe : public llm_graph_context { } }; -llama_memory_i * llama_model::create_memory() const { +llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const { llama_memory_i * res; switch (arch) { @@ -12928,26 +12942,29 @@ llama_memory_i * llama_model::create_memory() const { case LLM_ARCH_RWKV7: case LLM_ARCH_ARWKV7: { - res = new llama_kv_cache_unified(hparams, { - /*.get_rope_factors =*/ nullptr - }); + res = new llama_kv_cache_recurrent( + *this, + GGML_TYPE_F32, + GGML_TYPE_F32, + cparams.offload_kqv, + std::max((uint32_t) 1, cparams.n_seq_max)); } break; default: { - res = new llama_kv_cache_unified(hparams, { - /*.get_rope_factors =*/ [this](uint32_t n_ctx_per_seq, int il) { - // choose long/short freq factors based on the context size - if (layers[il].rope_freqs != nullptr) { - return layers[il].rope_freqs; - } + const auto padding = llama_kv_cache_unified::get_padding(cparams); - if (n_ctx_per_seq > hparams.n_ctx_orig_yarn) { - return layers[il].rope_long; - } + cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding); - return layers[il].rope_short; - } - }); + LLAMA_LOG_DEBUG("%s: n_ctx = %u (padded)\n", __func__, cparams.n_ctx); + + res = new llama_kv_cache_unified( + *this, + params.type_k, + params.type_v, + !cparams.flash_attn, + cparams.offload_kqv, + cparams.n_ctx, + padding); } } diff --git a/src/llama-model.h b/src/llama-model.h index 34aac337c..4c7e7a335 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -395,8 +395,11 @@ struct llama_model { const struct ggml_tensor * get_tensor(const char * name) const; + ggml_tensor * get_rope_factors(uint32_t n_ctx_per_seq, int il) const; + + // note: can mutate `cparams` // TODO: move this to new llm_arch_model_i interface - llama_memory_i * create_memory() const; // TODO: params + llama_memory_i * create_memory(const llama_memory_params & params, llama_cparams & cparams) const; // TODO: move this to new llm_arch_model_i interface llm_graph_result_ptr build_graph(