diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 82b005e84..b8c7d97a7 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -3328,7 +3328,13 @@ class Qwen25OmniModel(Qwen2VLVisionModel): @ModelBase.register("InternVisionModel") class InternVisionModel(MmprojModel): def set_gguf_parameters(self): + assert self.hparams_vision is not None + if isinstance(self.hparams_vision['image_size'], list): + self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0] + if isinstance(self.hparams_vision['patch_size'], list): + self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0] super().set_gguf_parameters() + hparams = self.hparams self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL) self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"]) @@ -3352,14 +3358,30 @@ class InternVisionModel(MmprojModel): return gguf.GGMLQuantizationType.F32 return False + def _mapping_interns1_name(self, name): + names_map = { + "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias", + "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight", + "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias", + "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight", + "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias", + "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight", + } + if name in names_map: + name = names_map[name] + return name + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused - if name.startswith("vision_model") or name.startswith("mlp"): + vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector'] + # deal with intern-s1 special case + name = self._mapping_interns1_name(name) + if any([name.startswith(prefix) for prefix in vision_prefix]): # process visual tensors # correct name if name.startswith("vision_model"): name = "vision_tower." + name - if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"): + if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"): name += ".weight" # split QKV tensors if needed if ".qkv." in name: @@ -3445,6 +3467,10 @@ class Qwen2MoeModel(TextModel): def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: # process the experts separately + name = name.replace("language_model.", "") # InternVL + if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"): + # skip visual tensors + return [] if name.find("experts") != -1: n_experts = self.hparams["num_experts"] assert bid is not None @@ -3498,6 +3524,85 @@ class Qwen3Model(Qwen2Model): class Qwen3MoeModel(Qwen2MoeModel): model_arch = gguf.MODEL_ARCH.QWEN3MOE + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + hparams = ModelBase.load_hparams(self.dir_model) + self.origin_hf_arch = hparams.get('architectures', [None])[0] + + def set_vocab(self): + # deal with intern-s1 + if self.origin_hf_arch == 'InternS1ForConditionalGeneration': + self._set_vocab_interns1() + return + + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def _set_vocab_interns1(self): + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab()) + vocab_size = self.hparams.get("vocab_size", len(vocab)) + assert max(vocab.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + added_tokens_decoder = tokenizer.added_tokens_decoder + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token: str = reverse_vocab[i] + if token in added_vocab: + # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized. + # To avoid unexpected issues - we make sure to normalize non-normalized tokens + if not added_tokens_decoder[i].normalized: + previous_token = token + token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) + if previous_token != token: + logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer") + + if added_tokens_decoder[i].special or self.does_token_look_special(token): + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + toktypes.append(gguf.TokenType.NORMAL) + tokens.append(token) + + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_tokens_map_file = self.dir_model / 'special_tokens_map.json' + additional_special_tokens = [] + if special_tokens_map_file.is_file(): + with open(special_tokens_map_file, encoding = 'utf-8') as f: + additional_special_tokens = json.load(f).get('additional_special_tokens', []) + tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json' + if tokenizer_cfg_file.is_file(): + with open(tokenizer_cfg_file, encoding = 'utf-8') as f: + added_tokens_decoder = json.load(f).get('added_tokens_decoder', {}) + token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']} + for token in additional_special_tokens: + if token in token2ids_map: + special_vocab._set_special_token(token, token2ids_map[token]) + special_vocab._set_special_token('eos', 151645) + special_vocab._set_special_token("bos", 151643) + special_vocab.add_to_gguf(self.gguf_writer) + @ModelBase.register("GPT2LMHeadModel") class GPT2Model(TextModel): @@ -7997,7 +8102,6 @@ class GptOssModel(TextModel): def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: blocks0: Tensor = torch.zeros(1) blocks1: Tensor = torch.zeros(1) - found_mxfp4_tensors = False # we assume that tensors are loaded in the correct order for name, data_torch in self.get_tensors(): if "mlp.experts.down_proj_blocks" in name: @@ -8005,7 +8109,6 @@ class GptOssModel(TextModel): elif "mlp.experts.down_proj_scales" in name: new_name = self.map_tensor_name(name.replace("_scales", ".weight")) self.repack_mxfp4(new_name, blocks0, data_torch) - found_mxfp4_tensors = True elif "mlp.experts.gate_up_proj_blocks" in name: blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :] elif "mlp.experts.gate_up_proj_scales" in name: @@ -8014,9 +8117,6 @@ class GptOssModel(TextModel): new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight")) self.repack_mxfp4(new_name_gate, blocks0, scales0) self.repack_mxfp4(new_name_up, blocks1, scales1) - found_mxfp4_tensors = True - if not found_mxfp4_tensors: - raise ValueError("No MXFP4 tensors found in the model. Please make sure you are using MXFP4 model.") return [] def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: @@ -8029,7 +8129,12 @@ class GptOssModel(TextModel): if "down_proj" in name: if name.endswith("_bias"): name = name.replace("down_proj_bias", "down_proj.bias") + elif "_blocks" not in name and "_scales" not in name: + logger.warning(f"{name} is not in MXFP4, performance may be degraded") + name = name.replace("down_proj", "down_proj.weight") + data_torch = data_torch.transpose(-1, -2) else: + # otherwise, it should already be repacked to ggml MXFP4 format return [] # split the gate_up into gate and up @@ -8042,7 +8147,18 @@ class GptOssModel(TextModel): (self.map_tensor_name(name_gate), gate_proj_bias), (self.map_tensor_name(name_up), up_proj_bias) ] + elif "_blocks" not in name and "_scales" not in name: + logger.warning(f"{name} is not in MXFP4, performance may be degraded") + name_up = name.replace("gate_up_proj", "up_proj.weight") + name_gate = name.replace("gate_up_proj", "gate_proj.weight") + data_torch = data_torch.transpose(-1, -2) + gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :] + return [ + (self.map_tensor_name(name_gate), gate_proj_weight), + (self.map_tensor_name(name_up), up_proj_weight) + ] else: + # otherwise, it should already be repacked to ggml MXFP4 format return [] return [(self.map_tensor_name(name), data_torch)] diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 8b0eb2a61..bf80f5dfb 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -550,6 +550,7 @@ struct vk_device_struct { ggml_backend_buffer_type buffer_type; bool disable_fusion; + bool disable_host_visible_vidmem; #ifdef GGML_VULKAN_MEMORY_DEBUG std::unique_ptr memory_logger; @@ -1820,6 +1821,8 @@ static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) { } else if (device->uma) { // Fall back to host memory type buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + } else if (device->disable_host_visible_vidmem) { + buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal, vk::MemoryPropertyFlagBits::eDeviceLocal); } else { // use rebar if available, otherwise fallback to device only visible memory buf = ggml_vk_create_buffer(device, size, vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, vk::MemoryPropertyFlagBits::eDeviceLocal); @@ -2299,14 +2302,14 @@ static void ggml_vk_load_shaders(vk_device& device) { }; #define CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, HSK, HSV, HEAD_SIZES) \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,false), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][0][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,false), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,false), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,false)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][0][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f16acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][0], "flash_attn_f32_f16_" #HEAD_SIZES "_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,true), 1, true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16 ## SUFFIX[TYPE][FA_HEAD_SIZE_##HEAD_SIZES][1][1][1], "flash_attn_f32_f16_" #HEAD_SIZES "_aligned_f32acc_smallrows" #NAMELC #SUFFIX, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,true), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,true), fa_rows_cols(FAPATH,HSK,HSV,0,TYPE,true)[1], true, FAPATH==FA_COOPMAT1, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ #define CREATE_FA(TYPE, NAMELC, FAPATH, SUFFIX) \ CREATE_FA2(TYPE, NAMELC, FAPATH, SUFFIX, 64, 64, 64) \ @@ -2923,7 +2926,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4], "get_rows_mxfp4_f32", get_rows_mxfp4_f32_len, get_rows_mxfp4_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 2, 4 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, 5 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1, "quantize_q8_1", quantize_q8_1_len, quantize_q8_1_data, "main", 2, 1 * sizeof(uint32_t), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1); for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) { @@ -3281,6 +3284,10 @@ static vk_device ggml_vk_get_device(size_t idx) { const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY"); device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr; + const char* GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM = getenv("GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM"); + //device->disable_host_visible_vidmem = GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM != nullptr; + device->disable_host_visible_vidmem = true; //kcpp requested fix for vulkan BSOD + bool fp16_storage = false; bool fp16_compute = false; bool maintenance4_support = false; @@ -6525,11 +6532,14 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co return supported; } -static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, ggml_tensor * dst, bool dryrun = false) { +static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3]; std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3]; std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + if (sinks) { + std::cerr << "), (" << sinks << ", name=" << sinks->name << ", type=" << sinks->type << ", ne0=" << sinks->ne[0] << ", ne1=" << sinks->ne[1] << ", ne2=" << sinks->ne[2] << ", ne3=" << sinks->ne[3] << ", nb0=" << sinks->nb[0] << ", nb1=" << sinks->nb[1] << ", nb2=" << sinks->nb[2] << ", nb3=" << sinks->nb[3]; + } std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); GGML_TENSOR_LOCALS(int64_t, neq, q, ne) @@ -6728,10 +6738,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr; - size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0; + vk_buffer d_Q = nullptr, d_K = nullptr, d_V = nullptr, d_D = nullptr, d_M = nullptr, d_S = nullptr; + size_t q_buf_offset = 0, k_buf_offset = 0, v_buf_offset = 0, d_buf_offset = 0, m_buf_offset = 0, s_buf_offset = 0; - bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false; + bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false, S_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx->device, q->data, d_Q, q_buf_offset); @@ -6746,6 +6756,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx ggml_vk_host_get(ctx->device, mask->data, d_M, m_buf_offset); M_uma = d_M != nullptr; } + if (sinks) { + ggml_vk_host_get(ctx->device, sinks->data, d_S, s_buf_offset); + S_uma = d_S != nullptr; + } } @@ -6781,7 +6795,17 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx } } - uint32_t mask_n_head_log2 = ((mask != nullptr) << 16) | n_head_log2; + if (!S_uma) { + d_S = d_Q; + s_buf_offset = q_buf_offset; + if (sinks) { + ggml_backend_vk_buffer_context * s_buf_ctx = (ggml_backend_vk_buffer_context*)sinks->buffer->context; + d_S = s_buf_ctx->dev_buffer; + s_buf_offset = vk_tensor_offset(sinks) + sinks->view_offs; + } + } + + uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | ((mask != nullptr) << 16) | n_head_log2; const vk_flash_attn_push_constants pc = { N, KV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, @@ -6805,6 +6829,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE}, + vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE}, }, // We only use split_k when group query attention is enabled, which means @@ -6814,10 +6839,11 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx pc, { workgroups_x * pipeline->wg_denoms[0], workgroups_y, workgroups_z }); ggml_vk_sync_buffers(subctx); - const std::array pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k }; + const std::array pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne3, split_k, (sinks != nullptr) }; ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce, { vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE}, + vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE}, }, pc2, { (uint32_t)ne1, HSV, (uint32_t)ne3 }); @@ -6828,6 +6854,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE}, + vk_subbuffer{d_S, s_buf_offset, VK_WHOLE_SIZE}, vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE}, }, pc, { workgroups_x, workgroups_y, workgroups_z }); @@ -9892,7 +9919,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; case GGML_OP_FLASH_ATTN_EXT: - ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun); + ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node->src[4], node, dryrun); break; @@ -10969,8 +10996,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm if (head_sizes == FA_HEAD_SIZE_UNSUPPORTED) { return false; } - // TODO: support attention sinks [TAG_ATTN_SINKS] - if (op->src[4]) { + if (op->src[4] && op->src[4]->type != GGML_TYPE_F32) { return false; } if (op->src[0]->type != GGML_TYPE_F32) { @@ -11565,6 +11591,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * if (tensor->op == GGML_OP_FLASH_ATTN_EXT) { const float * params = (const float *)tensor->op_params; tensor_clone = ggml_flash_attn_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], params[0], params[1], params[2]); + if (src_clone[4]) { + ggml_flash_attn_ext_add_sinks(tensor_clone, src_clone[4]); + } } else if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src_clone[0], src_clone[1]); } else if (tensor->op == GGML_OP_MUL_MAT_ID) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index 45c6e7736..d40848e15 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -305,6 +305,27 @@ void main() { return; } + if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2); + + float ms = 1.0f; + float vs = 1.0f; + + if (sink > Mf[r]) { + ms = exp(Mf[r] - sink); + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + Of[r][d] *= ms; + } + } else { + vs = exp(sink - Mf[r]); + } + + Lf[r] = Lf[r]*ms + vs; + } + } + float Lfrcp[Br]; [[unroll]] for (uint32_t r = 0; r < Br; ++r) { Lfrcp[r] = 1.0 / Lf[r]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp index 7defe72b4..b57c9dcfc 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.comp @@ -50,10 +50,13 @@ layout (push_constant) uniform parameter { uint32_t k_num; } p; +#define SINK_ENABLE_BIT (1<<24) #define MASK_ENABLE_BIT (1<<16) #define N_LOG2_MASK 0xFFFF -layout (binding = 4) writeonly buffer O {D_TYPE data_o[];}; +layout (binding = 4) readonly buffer S {float data_s[];}; + +layout (binding = 5) writeonly buffer O {D_TYPE data_o[];}; #if defined(A_TYPE_PACKED16) #define BINDING_IDX_K 0 @@ -111,6 +114,14 @@ ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const i return ACC_TYPE(pow(base, ACC_TYPE(exph))); } +// Load the sink value, indexed by Q's dimension 2. +ACC_TYPE perElemOpGetSink(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2) +{ + const uint32_t h = iq2 + (r % p.gqa_ratio); + + return ACC_TYPE(data_s[h]); +} + uint32_t i, N, KV, split_k_index, Tr, start_j, end_j, iq2, iq3, rk2, rk3, rv2, rv3, ik2, ik3, iv2, iv3, q_stride, k_stride, v_stride, m_stride; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp index 486735fe8..230e815f2 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -329,6 +329,27 @@ void main() { return; } + if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { + [[unroll]] for (uint32_t r = 0; r < Br; ++r) { + float sink = perElemOpGetSink(r, 0u, ACC_TYPE(0), iq2); + + float ms = 1.0f; + float vs = 1.0f; + + if (sink > Mf[r]) { + ms = exp(Mf[r] - sink); + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + Of[r][d] *= ACC_TYPE(ms); + } + } else { + vs = exp(sink - Mf[r]); + } + + Lf[r] = Lf[r]*ms + vs; + } + } + float Lfrcp[rows_per_thread]; [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { Lfrcp[r] = 1.0 / Lf[r]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp index 274f48fca..b0564ca0b 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -248,6 +248,34 @@ void main() { // resize L by using smear/reduce coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce); + if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { + coopmat S; + coopMatPerElementNV(S, S, perElemOpGetSink, iq2); + + coopmat Mr; + + // resize M by using smear/reduce + coopMatReduceNV(Mr, M, gl_CooperativeMatrixReduceRowNV, smearReduce); + + // O, Ldiag, Mr all have the same type so all element locations match + [[unroll]] for (uint32_t i = 0; i < Ldiag.length(); ++i) { + ACC_TYPE sink = S[i]; + + ACC_TYPE ms = ACC_TYPE(1.0f); + ACC_TYPE vs = ACC_TYPE(1.0f); + + if (sink > Mr[i]) { + ms = exp(Mr[i] - sink); + + O[i] *= ms; + } else { + vs = exp(sink - Mr[i]); + } + + Ldiag[i] = Ldiag[i]*ms + vs; + } + } + [[unroll]] for (int k = 0; k < Ldiag.length(); ++k) { Ldiag[k] = ACC_TYPE(1.0) / Ldiag[k]; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp index 0a17a9df2..76ef4b6df 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp @@ -7,13 +7,15 @@ layout(constant_id = 0) const uint BLOCK_SIZE = 32; layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {float data_d[];}; +layout (binding = 1) readonly buffer B {float data_s[];}; +layout (binding = 2) writeonly buffer D {float data_d[];}; layout (push_constant) uniform parameter { uint D; uint N; uint ne3; uint k_num; + uint sinks; } p; shared float tmpsh[BLOCK_SIZE]; @@ -73,6 +75,22 @@ void main() { } L = tmpsh[0]; + float sink; + if (p.sinks != 0) { + sink = data_s[n]; + + float ms = 1.0f; + float vs = 1.0f; + + if (sink > m_max) { + ms = exp(m_max - sink); + } else { + vs = exp(sink - m_max); + } + + L = L*ms + vs; + } + L = 1.0 / L; // D dimension is split across workgroups in the y dimension @@ -85,6 +103,13 @@ void main() { float m = data_a[m_offset + k * lm_stride]; O += exp(m - m_max) * data_a[o_offset]; } + if (p.sinks != 0) { + if (sink > m_max) { + float ms = 1.0f; + ms = exp(m_max - sink); + O *= ms; + } + } O *= L; data_d[iq3 * D * N + D * n + d] = O; } diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index a0f11563a..dc7c03b46 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1110,11 +1110,13 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_EMBD_CLS: ( "vision_tower.vision_model.embeddings.class_embedding", + "model.vision_tower.embeddings.cls_token", # Intern-S1 "vision_model.class_embedding", # llama 4 ), MODEL_TENSOR.V_ENC_EMBD_PATCH: ( "vision_tower.vision_model.embeddings.patch_embedding", + "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1 "vpm.embeddings.patch_embedding", "model.vision_model.embeddings.patch_embedding", # SmolVLM "vision_tower.patch_conv", # pixtral @@ -1124,6 +1126,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_EMBD_POS: ( "vision_tower.vision_model.embeddings.position_embedding", + "model.vision_tower.embeddings.position_embeddings", # Intern-S1 "vpm.embeddings.position_embedding", "model.vision_model.embeddings.position_embedding", # SmolVLM "vision_model.positional_embedding_vlm", # llama 4 @@ -1131,6 +1134,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_Q: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj", + "model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.q_proj", "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4 @@ -1140,10 +1144,12 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_Q_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1 ), MODEL_TENSOR.V_ENC_ATTN_K: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj", + "model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.k_proj", "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4 @@ -1153,10 +1159,12 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_K_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1 ), MODEL_TENSOR.V_ENC_ATTN_V: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj", + "model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.v_proj", "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4 @@ -1167,6 +1175,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_INPUT_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1", "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL + "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1 "vpm.encoder.layers.{bid}.layer_norm1", "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral @@ -1177,6 +1186,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_O: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj", "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.out_proj", "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4 @@ -1187,6 +1197,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_POST_ATTN_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL + "model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1 "vpm.encoder.layers.{bid}.layer_norm2", "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4 @@ -1196,6 +1207,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_FFN_UP: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", + "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1 "vpm.encoder.layers.{bid}.mlp.fc1", "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral @@ -1211,6 +1223,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_FFN_DOWN: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", + "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1 "vpm.encoder.layers.{bid}.mlp.fc2", "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral @@ -1221,10 +1234,12 @@ class TensorNameMap: MODEL_TENSOR.V_LAYER_SCALE_1: ( "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL + "model.vision_tower.encoder.layer.{bid}.lambda_1", # Intern-S1 ), MODEL_TENSOR.V_LAYER_SCALE_2: ( "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL + "model.vision_tower.encoder.layer.{bid}.lambda_2", # Intern-S1 ), MODEL_TENSOR.V_PRE_NORM: ( diff --git a/klite.embd b/klite.embd index 55b95dc5f..14752ec75 100644 --- a/klite.embd +++ b/klite.embd @@ -14522,7 +14522,7 @@ Current version indicated by LITEVER below. inputtxt = inputtxt.replace(/%ExpandBtn%(?:\n{0,2})?/g, function (m) { let curr = matches[matchiter]; let expandedhtml = `
`; - if(!curr || curr.trim()=="") + if(!curr || curr.trim()=="" || curr.trim()=="/nothink") { expandedhtml = `
`; } @@ -16234,7 +16234,7 @@ Current version indicated by LITEVER below. } else if(localsettings.think_injected==2 && localsettings.start_thinking_tag!="" && localsettings.stop_thinking_tag!="") { - pending_context_preinjection = pending_context_preinjection + localsettings.start_thinking_tag.replaceAll("\\n", "\n") + "\n\n" + localsettings.stop_thinking_tag.replaceAll("\\n", "\n"); + pending_context_preinjection = pending_context_preinjection + localsettings.start_thinking_tag.replaceAll("\\n", "\n") + "\n"+"/nothink"+"\n" + localsettings.stop_thinking_tag.replaceAll("\\n", "\n"); } if(localsettings.inject_jailbreak_instruct) { @@ -16967,7 +16967,8 @@ Current version indicated by LITEVER below. let is_using_o1 = custom_oai_model.toLowerCase().startsWith("o1-") || custom_oai_model.toLowerCase()=="o1" || custom_oai_model.toLowerCase().startsWith("o3-") || custom_oai_model.toLowerCase()=="o3" || custom_oai_model.toLowerCase().startsWith("o4-") || custom_oai_model.toLowerCase()=="o4"; let is_using_4o_search = custom_oai_model.toLowerCase().includes("-search-preview"); - if(is_using_o1 || is_using_4o_search) + let is_using_gpt5 = custom_oai_model.toLowerCase().includes("gpt-5"); + if(is_using_o1 || is_using_4o_search || is_using_gpt5) { //o1 does not support ANY customization oai_payload = @@ -26123,6 +26124,9 @@ Current version indicated by LITEVER below. + + + diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 4ad7165d7..7711e7d86 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -1002,7 +1002,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use); // TODO: temporary sanity check that the F16 -> MXFP4 is lossless -#if 1 +#if 0 if (new_type == GGML_TYPE_MXFP4) { auto * x = f32_data_03;