Felipe97/llama-cpp-compiled

1from __future__ import annotations2 3from typing import Iterable, Sequence, TYPE_CHECKING4 5import torch6 7if TYPE_CHECKING:8    from torch import Tensor9 10from .base import ModelBase, TextModel, MmprojModel, gguf, logger11 12 13@ModelBase.register("MiniMaxText01ForCausalLM")14@ModelBase.register("MiniMaxM1ForCausalLM")15@ModelBase.example("MiniMaxAI/MiniMax-Text-01", "MiniMaxAI/MiniMax-M1-40k")16class MiniMaxText01Model(TextModel):17    model_arch = gguf.MODEL_ARCH.MINIMAX0118 19    def _get_suppress_tokens(self) -> Sequence[int] | None:20        import json21        from transformers import AutoTokenizer22        from .base import LazyTorchTensor23 24        # check added tokens embeddings in embeddings tensor for zero-valued embeddings25        # they get in the way of the token sampling process and must be suppressed26 27        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)28        tokenizer_vocab_size = tokenizer.vocab_size  # ty: ignore[unresolved-attribute]29 30        with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:31            weight_map = json.load(f)["weight_map"]32 33        embeddings_tensor_name = "model.embed_tokens.weight"34        embeddings_shard_name = weight_map[embeddings_tensor_name]35        with gguf.utility.SafetensorsLocal(self.dir_model / embeddings_shard_name) as model_shard:36            embeddings_data = model_shard[embeddings_tensor_name]37 38        embeddings_weights_dtype = LazyTorchTensor._dtype_str_map[embeddings_data.dtype]39        embeddings_weights = torch.from_numpy(embeddings_data.mmap_bytes()).view(embeddings_weights_dtype).reshape(embeddings_data.shape)40        embeddings_vocab_size = embeddings_weights.shape[0]41 42        embeddings_added_tokens = embeddings_weights[tokenizer_vocab_size:embeddings_vocab_size]43        embeddings_zero_rows = torch.all(embeddings_added_tokens == 0, dim=1)44        tokens_zero_embeddings_ids = (torch.nonzero(embeddings_zero_rows, as_tuple=False).flatten() + tokenizer_vocab_size).tolist()45 46        return tokens_zero_embeddings_ids47 48    def set_vocab(self) -> None:49        from pathlib import Path50 51        self._set_vocab_gpt2()52 53        for tmpl_file in [54            self.dir_model / "chat_template.jinja",55            Path(__file__).parent.parent / "models" / "templates" / "MiniMax-M1.jinja"56        ]:57            if tmpl_file.is_file():58                self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))59                logger.info(f"Chat template overridden with {tmpl_file}.")60                break61 62    def set_gguf_parameters(self):63        super().set_gguf_parameters()64 65        suppress_tokens = self._get_suppress_tokens()66        if suppress_tokens:67            logger.info(f"Suppressing tokens with zero embeddings {suppress_tokens}")68            self.gguf_writer.add_suppress_tokens(suppress_tokens)69 70        layernorm_full_attention_alpha = self.hparams["layernorm_full_attention_alpha"]71        layernorm_full_attention_beta = self.hparams["layernorm_full_attention_beta"]72        layernorm_linear_attention_alpha = self.hparams["layernorm_linear_attention_alpha"]73        layernorm_linear_attention_beta = self.hparams["layernorm_linear_attention_beta"]74        layernorm_mlp_alpha = self.hparams["layernorm_mlp_alpha"]75        layernorm_mlp_beta = self.hparams["layernorm_mlp_beta"]76        assert layernorm_full_attention_alpha == layernorm_linear_attention_alpha == layernorm_mlp_alpha77        assert layernorm_full_attention_beta == layernorm_linear_attention_beta == layernorm_mlp_beta == 1.078        # we do not store the layernorm betas as they are all 1.079        # layernorm alphas are stored as single residual_scale hparam80        self.gguf_writer.add_residual_scale(layernorm_full_attention_alpha)81 82        self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])83 84    _experts: list[dict[str, Tensor]] | None = None85 86    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:87        # process the experts separately88        if name.find("block_sparse_moe.experts") != -1:89            n_experts = self.hparams["num_local_experts"]90 91            assert bid is not None92 93            if self._experts is None:94                self._experts = [{} for _ in range(self.block_count)]95 96            self._experts[bid][name] = data_torch97 98            if len(self._experts[bid]) >= n_experts * 3:99                # merge the experts into a single 3d tensor100                for wid in ["w1", "w2", "w3"]:101                    datas: list[Tensor] = []102 103                    for xid in range(n_experts):104                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"105                        datas.append(self._experts[bid][ename])106                        del self._experts[bid][ename]107 108                    data_torch = torch.stack(datas, dim=0)109 110                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"111 112                    new_name = self.map_tensor_name(merged_name)113 114                    yield from super().modify_tensors(data_torch, new_name, bid)115                return116            else:117                return118 119        yield from super().modify_tensors(data_torch, name, bid)120 121 122@ModelBase.register("MiniMaxM2ForCausalLM")123@ModelBase.example("MiniMaxAI/MiniMax-M2")124class MiniMaxM2Model(TextModel):125    model_arch = gguf.MODEL_ARCH.MINIMAXM2126    _experts_cache: dict[int, dict[str, Tensor]] = {}127 128    def set_gguf_parameters(self):129        super().set_gguf_parameters()130 131        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))132        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))133 134    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):135        # merge expert weights136        if "block_sparse_moe.experts." in name:137            n_experts = self.find_hparam(["num_local_experts", "num_experts"])138            assert bid is not None139 140            expert_cache = self._experts_cache.setdefault(bid, {})141            expert_cache[name] = data_torch142            expert_weights = ["w1", "w2", "w3"]143 144            # not enough expert weights to merge145            if len(expert_cache) < n_experts * len(expert_weights):146                return147 148            for w_name in expert_weights:149                datas: list[Tensor] = []150 151                for xid in range(n_experts):152                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"153                    datas.append(expert_cache[ename])154                    del expert_cache[ename]155 156                data_torch = torch.stack(datas, dim=0)157                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"158                new_name = self.map_tensor_name(merged_name)159                yield from super().modify_tensors(data_torch, new_name, bid)160 161            del self._experts_cache[bid]162            return163 164        yield from super().modify_tensors(data_torch, name, bid)165 166 167@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")168@ModelBase.example("MiniMaxAI/MiniMax-M3")169class MiniMaxM3Model(MiniMaxM2Model):170    model_arch = gguf.MODEL_ARCH.MINIMAXM3171 172    def tensor_force_quant(self, name, new_name, bid, n_dims):173        if ".indexer." in new_name:174            return gguf.GGMLQuantizationType.F32175        return super().tensor_force_quant(name, new_name, bid, n_dims)176 177    def set_gguf_parameters(self):178        super().set_gguf_parameters()179 180        self.gguf_writer.add_expert_shared_count(self.find_hparam(["n_shared_experts"]))181        self.gguf_writer.add_expert_weights_scale(self.find_hparam(["routed_scaling_factor"]))182        self.gguf_writer.add_expert_weights_norm(True)183 184        sac = self.find_hparam(["sparse_attention_config"])185        self.gguf_writer.add_indexer_head_count(sac["sparse_num_index_heads"])186        self.gguf_writer.add_indexer_key_length(sac["sparse_index_dim"])187        self.gguf_writer.add_indexer_top_k(sac["sparse_topk_blocks"])188        self.gguf_writer.add_indexer_block_size(sac["sparse_block_size"])189        self.gguf_writer.add_indexer_local_blocks(sac["sparse_local_block"])190 191        moe_layer_freq = self.find_hparam(["moe_layer_freq"])192        n_dense = 0193        for v in moe_layer_freq:194            if v == 0:195                n_dense += 1196            else:197                break198        self.gguf_writer.add_leading_dense_block_count(n_dense)199 200    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):201        # Gemma-style (1 + w) RMSNorm: bake the +1 in so llama.cpp can use plain RMSNorm202        if name.endswith("norm.weight"):203            data_torch = data_torch + 1.0204 205        yield from super().modify_tensors(data_torch, name, bid)206 207 208@ModelBase.register("MiniMaxM3SparseForConditionalGeneration", "MiniMaxM3VLForConditionalGeneration")209@ModelBase.example("MiniMaxAI/MiniMax-M3")210class MiniMaxM3VisionModel(MmprojModel):211    @classmethod212    def filter_tensors(cls, item):213        name, gen = item214        # keep only the vision-side tensors; text / mtp / sparse-index are dropped215        if not name.startswith(("vision_tower.", "multi_modal_projector.", "patch_merge_mlp.")):216            return None217        return super().filter_tensors((name, gen))218 219    def set_gguf_parameters(self):220        super().set_gguf_parameters()221        assert self.hparams_vision is not None222 223        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINIMAXM3)224        self.gguf_writer.add_vision_use_gelu(True)225 226        # the ViT carries its own LayerNorm eps (text tower uses a different one)227        self.gguf_writer.add_vision_attention_layernorm_eps(228            self.hparams_vision.get("layer_norm_eps", 1e-5)229        )230 231        comp = self.hparams_vision.get("img_token_compression_config", {})232        merge_size = comp.get("spatial_merge_size", 2)233        self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))234 235    def modify_tensors(self, data_torch, name, bid):236        assert self.hparams_vision is not None237 238        # Conv3d patch embed -> Conv2d slices239        if name == "vision_tower.vision_model.embeddings.patch_embedding.weight":240            if data_torch.ndim != 5:241                raise ValueError(f"unexpected patch_embedding rank {data_torch.ndim} for {name}")242            kt = data_torch.shape[2]243            base = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH]244            for t in range(kt):245                suffix = ".weight" if t == 0 else f".weight.{t}"246                yield (base + suffix, data_torch[:, :, t, ...])247            return248 249        # Permute ViT q/k. HF [Ta Ha Wa | Tb Hb Wb | pad] reorder to [Ta Tb | Ha Hb | Wa Wb | pad].250        for new_name, tensor in super().modify_tensors(data_torch, name, bid):251            if ".attn_q." in new_name or ".attn_k." in new_name:252                tensor = self._permute_vit_qk(tensor, new_name)253            yield new_name, tensor254 255    def _permute_vit_qk(self, t: "Tensor", new_name: str) -> "Tensor":256        assert self.hparams_vision is not None257        n_head = self.hparams_vision["num_attention_heads"]258        d_head = t.shape[0] // n_head259        axis_dim = 2 * ((2 * (d_head // 2) // 3) // 2)260        ah = axis_dim // 2261        half = 3 * ah262        perm = []263        perm += list(range(0, ah))264        perm += list(range(half, half + ah))265        perm += list(range(ah, 2 * ah))266        perm += list(range(half + ah, half + 2 * ah))267        perm += list(range(2 * ah, 3 * ah))268        perm += list(range(half + 2 * ah, half + 3 * ah))269        perm += list(range(2 * half, d_head))270 271        assert axis_dim % 2 == 0272        assert 3 * axis_dim <= d_head273        assert len(perm) == d_head274        assert sorted(perm) == list(range(d_head)), "perm is not a bijection of d_head"275        assert t.shape[0] == n_head * d_head, f"{new_name}: {t.shape[0]} != {n_head}*{d_head}"276        assert d_head == 80277 278        idx = torch.tensor(perm, dtype=torch.long)279        if t.ndim == 2:280            return t.reshape(n_head, d_head, t.shape[1])[:, idx, :].reshape(t.shape)281        return t.reshape(n_head, d_head)[:, idx].reshape(t.shape)282