model : support LiquidAI LFM2 hybrid family (#14620)
**Important** LFM2 was [merged ](https://github.com/huggingface/transformers/pull/39340)into transformers, but has not yet been released. To convert into gguf, install transformers from source ```shell pip install "transformers @ git+https://github.com/huggingface/transformers.git@main" ```
This commit is contained in:
Tarek Dakhran
GitHub
parent
756aa1020a
commit
f5e96b368f
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@ -300,6 +300,7 @@ class ModelBase:
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gguf.MODEL_TENSOR.POS_EMBD,
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gguf.MODEL_TENSOR.TOKEN_TYPES,
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gguf.MODEL_TENSOR.SSM_CONV1D,
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gguf.MODEL_TENSOR.SHORTCONV_CONV,
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gguf.MODEL_TENSOR.TIME_MIX_FIRST,
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gguf.MODEL_TENSOR.TIME_MIX_W1,
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gguf.MODEL_TENSOR.TIME_MIX_W2,
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@ -836,6 +837,9 @@ class TextModel(ModelBase):
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if chkhsh == "f6791d196f87ce6b56a7d234be618e0d58f8cda3549416635b2bebcd22cd95c4":
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# ref: https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct
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res = "midm-2.0"
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if chkhsh == "169bf0296a13c4d9b7672313f749eb36501d931022de052aad6e36f2bf34dd51":
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# ref: https://huggingface.co/LiquidAI/LFM2-Tokenizer
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res = "lfm2"
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if res is None:
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logger.warning("\n")
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@ -7073,6 +7077,50 @@ class SmolLM3Model(LlamaModel):
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chat_template = tokenizer.chat_template.replace("[:]", "")
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self.gguf_writer.add_chat_template(chat_template)
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@ModelBase.register("Lfm2ForCausalLM")
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@ModelBase.register("LFM2ForCausalLM")
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class LFM2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.LFM2
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def _add_feed_forward_length(self):
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ff_dim = self.hparams["block_ff_dim"]
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auto_adjust_ff_dim = self.hparams["block_auto_adjust_ff_dim"]
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ff_dim = self.hparams["block_ff_dim"]
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ffn_dim_multiplier = self.hparams["block_ffn_dim_multiplier"]
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multiple_of = self.hparams["block_multiple_of"]
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if auto_adjust_ff_dim:
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ff_dim = int(2 * ff_dim / 3)
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# custom dim factor multiplier
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if ffn_dim_multiplier is not None:
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ff_dim = int(ffn_dim_multiplier * ff_dim)
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ff_dim = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
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self.gguf_writer.add_feed_forward_length(ff_dim)
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def set_gguf_parameters(self):
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# set num_key_value_heads only for attention layers
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self.hparams["num_key_value_heads"] = [
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self.hparams["num_key_value_heads"] if layer_type == "full_attention" else 0
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for layer_type in self.hparams["layer_types"]
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]
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super().set_gguf_parameters()
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self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
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self.gguf_writer.add_shortconv_l_cache(self.hparams["conv_L_cache"])
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["norm_eps"])
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self._add_feed_forward_length()
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# conv op requires 2d tensor
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if 'conv.conv' in name:
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data_torch = data_torch.squeeze(1)
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return [(self.map_tensor_name(name), data_torch)]
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###### CONVERSION LOGIC ######
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@ -130,6 +130,7 @@ models = [
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{"name": "seed-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
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{"name": "a.x-4.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/skt/A.X-4.0", },
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{"name": "midm-2.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", },
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{"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
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]
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# some models are known to be broken upstream, so we will skip them as exceptions
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@ -107,8 +107,11 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
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if (nc == 4) {
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ssm_conv_f32<threads, 4><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
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dst, dst_nb0, dst_nb1, dst_nb2, n_t);
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} else if (nc == 3) {
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ssm_conv_f32<threads, 3><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
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dst, dst_nb0, dst_nb1, dst_nb2, n_t);
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} else {
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GGML_ABORT("Only support kernel size = 4 now.");
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GGML_ABORT("Only support kernel size = 3 or size = 4 right now.");
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}
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} else {
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if (nc == 4) {
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@ -116,8 +119,13 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
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dim3 blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t);
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ssm_conv_long_token_f32<threads, 4, split_n_t><<<blocks, threads, 0, stream>>>(
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src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
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} else if (nc == 3) {
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const int64_t split_n_t = 32;
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dim3 blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t);
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ssm_conv_long_token_f32<threads, 3, split_n_t><<<blocks, threads, 0, stream>>>(
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src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
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} else {
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GGML_ABORT("Only support kernel size = 4 right now.");
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GGML_ABORT("Only support kernel size = 3 or size = 4 right now.");
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}
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}
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}
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@ -187,6 +187,9 @@ class Keys:
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class Classifier:
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OUTPUT_LABELS = "{arch}.classifier.output_labels"
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class ShortConv:
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L_CACHE = "{arch}.shortconv.l_cache"
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class Tokenizer:
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MODEL = "tokenizer.ggml.model"
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PRE = "tokenizer.ggml.pre"
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@ -362,6 +365,7 @@ class MODEL_ARCH(IntEnum):
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ERNIE4_5 = auto()
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HUNYUAN_MOE = auto()
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SMOLLM3 = auto()
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LFM2 = auto()
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class VISION_PROJECTOR_TYPE(IntEnum):
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@ -533,6 +537,9 @@ class MODEL_TENSOR(IntEnum):
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POSNET_ATTN_K = auto()
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POSNET_ATTN_V = auto()
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POSNET_ATTN_OUT = auto()
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SHORTCONV_CONV = auto()
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SHORTCONV_INPROJ = auto()
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SHORTCONV_OUTPROJ = auto()
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# vision
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V_MMPROJ = auto()
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V_MMPROJ_FC = auto()
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@ -673,6 +680,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.FALCON_H1: "falcon-h1",
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MODEL_ARCH.HUNYUAN_MOE: "hunyuan-moe",
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MODEL_ARCH.SMOLLM3: "smollm3",
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MODEL_ARCH.LFM2: "lfm2",
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}
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VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
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@ -844,6 +852,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.POSNET_ATTN_K: "posnet.{bid}.attn_k",
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MODEL_TENSOR.POSNET_ATTN_V: "posnet.{bid}.attn_v",
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MODEL_TENSOR.POSNET_ATTN_OUT: "posnet.{bid}.attn_output",
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MODEL_TENSOR.SHORTCONV_CONV: "blk.{bid}.shortconv.conv",
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MODEL_TENSOR.SHORTCONV_INPROJ: "blk.{bid}.shortconv.in_proj",
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MODEL_TENSOR.SHORTCONV_OUTPROJ: "blk.{bid}.shortconv.out_proj",
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# vision
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MODEL_TENSOR.V_MMPROJ: "mm.{bid}",
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MODEL_TENSOR.V_MMPROJ_FC: "mm.model.fc",
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@ -2356,6 +2367,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.LFM2: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.TOKEN_EMBD_NORM,
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MODEL_TENSOR.SHORTCONV_CONV,
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MODEL_TENSOR.SHORTCONV_INPROJ,
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MODEL_TENSOR.SHORTCONV_OUTPROJ,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.ATTN_NORM, # operator_norm
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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],
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# TODO
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}
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@ -648,6 +648,9 @@ class GGUFWriter:
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def add_convnext_block_count(self, length: int) -> None:
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self.add_uint32(Keys.ConvNext.BLOCK_COUNT.format(arch=self.arch), length)
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def add_shortconv_l_cache(self, length: int) -> None:
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self.add_uint32(Keys.ShortConv.L_CACHE.format(arch=self.arch), length)
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def add_block_count(self, length: int) -> None:
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self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
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@ -50,6 +50,7 @@ class TensorNameMap:
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"model.pre_ln", # rwkv7
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"model.layers.0.pre_norm", # rwkv7
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"backbone.norm", # wavtokenizer
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"model.embedding_norm", # lfm2
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),
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# Position embeddings
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@ -136,6 +137,7 @@ class TensorNameMap:
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"model.layers.{bid}.ln1", # rwkv7
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"model.layers.{bid}.input_layernorm", # llama4
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"transformer_encoder.{bid}.attention_norm", # neobert
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"model.layers.{bid}.operator_norm", # lfm2
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),
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# Attention norm 2
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@ -220,6 +222,7 @@ class TensorNameMap:
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"transformer.h.{bid}.self_attention.dense", # falcon
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"h.{bid}.self_attention.dense", # bloom
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"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2 phimoe
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"model.layers.{bid}.self_attn.out_proj", # lfm2
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"model.layers.{bid}.self_attn.linear_attn", # deci
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"layers.{bid}.attention.wo", # llama-pth
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"encoder.layer.{bid}.attention.output.dense", # bert
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@ -1015,6 +1018,18 @@ class TensorNameMap:
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"backbone.posnet.{bid}.proj_out", # wavtokenizer
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),
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MODEL_TENSOR.SHORTCONV_CONV: (
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"model.layers.{bid}.conv.conv",
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),
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MODEL_TENSOR.SHORTCONV_INPROJ: (
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"model.layers.{bid}.conv.in_proj",
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),
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MODEL_TENSOR.SHORTCONV_OUTPROJ: (
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"model.layers.{bid}.conv.out_proj",
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),
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#############################################################################
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## Vision encoder
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@ -83,6 +83,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
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{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
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{ LLM_ARCH_SMOLLM3, "smollm3" },
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{ LLM_ARCH_LFM2, "lfm2" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -188,6 +189,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
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{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
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{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
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{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
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{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
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@ -1830,6 +1833,27 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_LFM2,
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{
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_SHORTCONV_CONV, "blk.%d.shortconv.conv" },
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{ LLM_TENSOR_SHORTCONV_INPROJ, "blk.%d.shortconv.in_proj" },
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{ LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
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}
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -1997,6 +2021,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
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{LLM_TENSOR_CONVNEXT_PW1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_CONVNEXT_PW2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_CONVNEXT_GAMMA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
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{LLM_TENSOR_SHORTCONV_CONV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
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{LLM_TENSOR_SHORTCONV_INPROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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{LLM_TENSOR_SHORTCONV_OUTPROJ, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
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};
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LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
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@ -2068,6 +2095,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
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case LLM_ARCH_JAMBA:
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case LLM_ARCH_FALCON_H1:
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case LLM_ARCH_GRANITE_HYBRID:
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case LLM_ARCH_LFM2:
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return true;
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default:
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return false;
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@ -87,6 +87,7 @@ enum llm_arch {
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LLM_ARCH_ERNIE4_5,
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LLM_ARCH_HUNYUAN_MOE,
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LLM_ARCH_SMOLLM3,
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LLM_ARCH_LFM2,
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LLM_ARCH_UNKNOWN,
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};
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@ -227,6 +228,8 @@ enum llm_kv {
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LLM_KV_CLASSIFIER_OUTPUT_LABELS,
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LLM_KV_SHORTCONV_L_CACHE,
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// deprecated:
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LLM_KV_TOKENIZER_PREFIX_ID,
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LLM_KV_TOKENIZER_SUFFIX_ID,
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@ -396,6 +399,9 @@ enum llm_tensor {
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LLM_TENSOR_POS_NET_ATTN_K,
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LLM_TENSOR_POS_NET_ATTN_V,
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LLM_TENSOR_POS_NET_ATTN_OUT,
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LLM_TENSOR_SHORTCONV_CONV,
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LLM_TENSOR_SHORTCONV_INPROJ,
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LLM_TENSOR_SHORTCONV_OUTPROJ,
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};
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enum llm_tensor_layer {
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@ -71,6 +71,11 @@ uint32_t llama_hparams::n_embd_r() const {
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return token_shift_count * n_embd;
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}
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if (n_shortconv_l_cache != 0) {
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// for LFM2 models
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return n_embd * (n_shortconv_l_cache - 1);
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}
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// TODO: maybe support other convolution strides than 1
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// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
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// Corresponds to Mamba's conv_states size
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@ -55,6 +55,8 @@ struct llama_hparams {
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struct llama_hparams_posnet posnet;
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struct llama_hparams_convnext convnext;
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||||
|
||||
uint32_t n_shortconv_l_cache = 0;
|
||||
|
||||
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
|
||||
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
|
||||
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
|
||||
|
|
|
|||
|
|
@ -43,15 +43,18 @@ const char * llm_type_name(llm_type type) {
|
|||
case LLM_TYPE_256M: return "256M";
|
||||
case LLM_TYPE_270M: return "270M";
|
||||
case LLM_TYPE_335M: return "335M";
|
||||
case LLM_TYPE_350M: return "350M";
|
||||
case LLM_TYPE_410M: return "410M";
|
||||
case LLM_TYPE_450M: return "450M";
|
||||
case LLM_TYPE_475M: return "475M";
|
||||
case LLM_TYPE_700M: return "700M";
|
||||
case LLM_TYPE_770M: return "770M";
|
||||
case LLM_TYPE_780M: return "780M";
|
||||
case LLM_TYPE_0_3B: return "0.3B";
|
||||
case LLM_TYPE_0_5B: return "0.5B";
|
||||
case LLM_TYPE_0_6B: return "0.6B";
|
||||
case LLM_TYPE_1B: return "1B";
|
||||
case LLM_TYPE_1_2B: return "1.2B";
|
||||
case LLM_TYPE_1_3B: return "1.3B";
|
||||
case LLM_TYPE_1_4B: return "1.4B";
|
||||
case LLM_TYPE_1_5B: return "1.5B";
|
||||
|
|
@ -1663,6 +1666,20 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
|||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_LFM2:
|
||||
{
|
||||
ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
|
||||
hparams.recurrent_layer_arr[il] = hparams.n_head_kv(il) == 0;
|
||||
}
|
||||
switch (hparams.n_embd) {
|
||||
case 1024: type = LLM_TYPE_350M; break;
|
||||
case 1536: type = LLM_TYPE_700M; break;
|
||||
case 2048: type = LLM_TYPE_1_2B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
default: throw std::runtime_error("unsupported model architecture");
|
||||
}
|
||||
|
||||
|
|
@ -4906,6 +4923,39 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_LFM2:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
// ffn is same for transformer and conv layers
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
|
||||
// for operator_norm
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
if (!hparams.is_recurrent(i)) {
|
||||
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
GGML_ASSERT(n_embd_v_gqa == n_embd_k_gqa);
|
||||
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, hparams.n_embd_k_gqa(i)}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, hparams.n_embd_v_gqa(i)}, 0);
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
||||
} else {
|
||||
layer.shortconv.conv = create_tensor(tn(LLM_TENSOR_SHORTCONV_CONV, "weight", i), {hparams.n_shortconv_l_cache, n_embd}, 0);
|
||||
layer.shortconv.in_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_INPROJ, "weight", i), {n_embd, 3 * n_embd}, 0);
|
||||
layer.shortconv.out_proj = create_tensor(tn(LLM_TENSOR_SHORTCONV_OUTPROJ, "weight", i), {n_embd, n_embd}, 0);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
}
|
||||
|
|
@ -15859,6 +15909,163 @@ struct llm_build_smollm3 : public llm_graph_context {
|
|||
}
|
||||
};
|
||||
|
||||
struct llm_build_lfm2 : public llm_graph_context {
|
||||
const llama_model & model;
|
||||
|
||||
llm_build_lfm2(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params), model(model) {
|
||||
|
||||
ggml_tensor * cur = build_inp_embd(model.tok_embd);
|
||||
cb(cur, "model.embed_tokens", -1);
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
auto * inp_hybrid = build_inp_mem_hybrid();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
auto * prev_cur = cur;
|
||||
cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "model.layers.{}.operator_norm", il);
|
||||
|
||||
cur = hparams.is_recurrent(il) ?
|
||||
build_shortconv_block(gf, cur, inp_hybrid->get_recr(), il) :
|
||||
build_attn_block(gf, cur, inp_pos, inp_hybrid->get_attn(), il) ;
|
||||
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
prev_cur = ggml_get_rows(ctx0, prev_cur, inp_out_ids);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, prev_cur, cur);
|
||||
cur = ggml_add(ctx0, cur, build_feed_forward(cur, il));
|
||||
}
|
||||
|
||||
cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
|
||||
cb(cur, "model.embedding_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// lm_head is tied with embeddings
|
||||
cur = build_lora_mm(model.tok_embd, cur);
|
||||
cb(cur, "lm_head", -1);
|
||||
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
ggml_tensor * build_feed_forward(ggml_tensor * cur,
|
||||
int il) const {
|
||||
cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "model.layers.{}.ffn_norm", il);
|
||||
|
||||
GGML_ASSERT(!model.layers[il].ffn_up_b);
|
||||
GGML_ASSERT(!model.layers[il].ffn_gate_b);
|
||||
GGML_ASSERT(!model.layers[il].ffn_down_b);
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, NULL,
|
||||
model.layers[il].ffn_gate, NULL, NULL,
|
||||
model.layers[il].ffn_down, NULL, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "model.layers.{}.feed_forward.w2", il);
|
||||
|
||||
return cur;
|
||||
}
|
||||
|
||||
ggml_tensor * build_attn_block(ggml_cgraph * gf,
|
||||
ggml_tensor * cur,
|
||||
ggml_tensor * inp_pos,
|
||||
llm_graph_input_attn_kv_unified * inp_attn,
|
||||
int il) const {
|
||||
GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il));
|
||||
auto const n_embd_head = hparams.n_embd_head_v;
|
||||
auto const n_head_kv = hparams.n_head_kv(il);
|
||||
|
||||
auto * q = build_lora_mm(model.layers[il].wq, cur);
|
||||
cb(q, "model.layers.{}.self_attn.q_proj", il);
|
||||
auto * k = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(k, "model.layers.{}.self_attn.k_proj", il);
|
||||
auto * v = build_lora_mm(model.layers[il].wv, cur);
|
||||
cb(v, "model.layers.{}.self_attn.v_proj", il);
|
||||
|
||||
q = ggml_reshape_3d(ctx0, q, n_embd_head, n_head, n_tokens);
|
||||
k = ggml_reshape_3d(ctx0, k, n_embd_head, n_head_kv, n_tokens);
|
||||
v = ggml_reshape_3d(ctx0, v, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
// qk norm
|
||||
q = build_norm(q, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(q, "model.layers.{}.self_attn.q_layernorm", il);
|
||||
k = build_norm(k, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(k, "model.layers.{}.self_attn.k_layernorm", il);
|
||||
|
||||
// RoPE
|
||||
q = ggml_rope_ext(
|
||||
ctx0, q, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
k = ggml_rope_ext(
|
||||
ctx0, k, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
cur = build_attn(inp_attn, gf, model.layers[il].wo, NULL,
|
||||
q, k, v, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
|
||||
|
||||
cb(cur, "model.layers.{}.self_attn.out_proj", il);
|
||||
|
||||
return cur;
|
||||
}
|
||||
|
||||
ggml_tensor * build_shortconv_block(ggml_cgraph * gf,
|
||||
ggml_tensor * cur,
|
||||
llm_graph_input_rs * inp_recr,
|
||||
int il) {
|
||||
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
|
||||
|
||||
auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur);
|
||||
cb(bcx, "model.layers.{}.conv.in_proj", il);
|
||||
|
||||
constexpr auto n_chunks = 3;
|
||||
GGML_ASSERT(bcx->ne[0] % n_chunks == 0);
|
||||
auto const chunk_size = bcx->ne[0] / n_chunks;
|
||||
auto * b = ggml_view_2d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->nb[1], 0 * chunk_size * ggml_element_size(bcx));
|
||||
auto * c = ggml_view_2d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->nb[1], 1 * chunk_size * ggml_element_size(bcx));
|
||||
auto * x = ggml_view_2d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->nb[1], 2 * chunk_size * ggml_element_size(bcx));
|
||||
|
||||
auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x));
|
||||
|
||||
// read conv state directly, with build_rs generation is slower
|
||||
ggml_tensor * conv_state = mctx_cur->get_r_l(il);
|
||||
const int64_t n_seqs = ubatch.n_seqs;
|
||||
ggml_tensor * conv = build_rs(inp_recr, gf, conv_state, hparams.n_embd_r(), n_seqs);
|
||||
conv = ggml_reshape_3d(ctx0, conv_state, hparams.n_shortconv_l_cache - 1, hparams.n_embd, n_seqs);
|
||||
|
||||
bx = ggml_concat(ctx0, conv, bx, 0);
|
||||
GGML_ASSERT(bx->ne[0] > conv->ne[0]);
|
||||
|
||||
auto * new_conv = ggml_view_2d(ctx0, bx, conv->ne[0], bx->ne[1], bx->nb[1], (bx->ne[0] - conv->ne[0]) * ggml_element_size(bx));
|
||||
GGML_ASSERT(ggml_are_same_shape(conv, new_conv));
|
||||
|
||||
// write conv state
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_conv, conv_state));
|
||||
|
||||
auto * conv_kernel = model.layers[il].shortconv.conv;
|
||||
GGML_ASSERT(hparams.n_shortconv_l_cache > 0);
|
||||
|
||||
// construct ssm_conv op
|
||||
ggml_tensor * conv_out = ggml_ssm_conv(ctx0, bx, conv_kernel);
|
||||
cb(conv_out, "model.layers.{}.conv.conv", il);
|
||||
|
||||
auto * y = ggml_mul(ctx0, c, conv_out);
|
||||
|
||||
y = build_lora_mm(model.layers[il].shortconv.out_proj, y);
|
||||
cb(y, "model.layers.{}.conv.out_proj", il);
|
||||
|
||||
return y;
|
||||
}
|
||||
};
|
||||
|
||||
llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
|
||||
llama_memory_i * res;
|
||||
|
||||
|
|
@ -16261,6 +16468,10 @@ llm_graph_result_ptr llama_model::build_graph(
|
|||
{
|
||||
llm = std::make_unique<llm_build_falcon_h1>(*this, params, gf);
|
||||
} break;
|
||||
case LLM_ARCH_LFM2:
|
||||
{
|
||||
llm = std::make_unique<llm_build_lfm2>(*this, params, gf);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
|
|
@ -16454,6 +16665,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_MINICPM3:
|
||||
case LLM_ARCH_DOTS1:
|
||||
case LLM_ARCH_HUNYUAN_MOE:
|
||||
case LLM_ARCH_LFM2:
|
||||
return LLAMA_ROPE_TYPE_NEOX;
|
||||
|
||||
case LLM_ARCH_QWEN2VL:
|
||||
|
|
|
|||
|
|
@ -35,15 +35,18 @@ enum llm_type {
|
|||
LLM_TYPE_256M,
|
||||
LLM_TYPE_270M,
|
||||
LLM_TYPE_335M,
|
||||
LLM_TYPE_350M,
|
||||
LLM_TYPE_410M,
|
||||
LLM_TYPE_450M,
|
||||
LLM_TYPE_475M,
|
||||
LLM_TYPE_700M,
|
||||
LLM_TYPE_770M,
|
||||
LLM_TYPE_780M,
|
||||
LLM_TYPE_0_3B,
|
||||
LLM_TYPE_0_5B,
|
||||
LLM_TYPE_0_6B,
|
||||
LLM_TYPE_1B,
|
||||
LLM_TYPE_1_2B,
|
||||
LLM_TYPE_1_3B,
|
||||
LLM_TYPE_1_4B,
|
||||
LLM_TYPE_1_5B,
|
||||
|
|
@ -155,6 +158,12 @@ struct llama_layer_convnext {
|
|||
struct ggml_tensor * gamma = nullptr;
|
||||
};
|
||||
|
||||
struct llama_layer_shortconv {
|
||||
struct ggml_tensor * in_proj = nullptr;
|
||||
struct ggml_tensor * conv = nullptr;
|
||||
struct ggml_tensor * out_proj = nullptr;
|
||||
};
|
||||
|
||||
struct llama_layer {
|
||||
// normalization
|
||||
struct ggml_tensor * attn_norm = nullptr;
|
||||
|
|
@ -341,6 +350,8 @@ struct llama_layer {
|
|||
struct llama_layer_posnet posnet;
|
||||
|
||||
struct llama_layer_convnext convnext;
|
||||
|
||||
struct llama_layer_shortconv shortconv;
|
||||
};
|
||||
|
||||
struct llama_model {
|
||||
|
|
|
|||
|
|
@ -844,6 +844,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
|
|||
// do not quantize Mamba's small yet 2D weights
|
||||
// NOTE: can't use LLM_TN here because the layer number is not known
|
||||
quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
|
||||
quantize &= name.find("shortconv.conv.weight") == std::string::npos;
|
||||
|
||||
// do not quantize RWKV's small yet 2D weights
|
||||
quantize &= name.find("time_mix_first.weight") == std::string::npos;
|
||||
|
|
|
|||
|
|
@ -1525,7 +1525,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
tokenizer_pre == "falcon3" ||
|
||||
tokenizer_pre == "falcon-h1" ||
|
||||
tokenizer_pre == "pixtral" ||
|
||||
tokenizer_pre == "midm-2.0") {
|
||||
tokenizer_pre == "midm-2.0" ||
|
||||
tokenizer_pre == "lfm2") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
|
||||
ignore_merges = true;
|
||||
add_bos = true;
|
||||
|
|
|
|||
Loading…
Reference in New Issue