File size: 5,165 Bytes
76513ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | from __future__ import annotations
import sys
from typing import Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
class GrokModel(TextModel):
model_arch = gguf.MODEL_ARCH.GROK
def set_vocab(self):
if (self.dir_model / 'tokenizer.model').is_file():
self._set_vocab_sentencepiece()
return
if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
sys.exit(1)
self._set_vocab_gpt2()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
if (rope_dim := self.hparams.get("head_dim")) is None:
rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
# Treat "original" as "yarn", seems to have been a mistake
if self.hparams.get("rope_type") in ("yarn", "original"):
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])
if temp_len := self.hparams.get("attn_temperature_len"):
self.gguf_writer.add_attn_temperature_length(temp_len)
self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
_experts: list[dict[str, list[Tensor]]] | None = None
_cur_expert = ""
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
deferred: list[tuple[Tensor, str, int | None]] = []
is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
if not is_expert:
deferred.append((data_torch, name, bid))
# process the experts separately
if is_expert or self._cur_expert:
n_experts = self.hparams["num_local_experts"]
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
# concatenate split tensors
if name in self._experts[bid]:
self._cur_expert = name
self._experts[bid][name].append(data_torch)
return
elif is_expert:
self._cur_expert = name
self._experts[bid][name] = [data_torch]
return
else:
self._cur_expert = ""
for bid in range(self.block_count):
if len(self._experts[bid]) >= n_experts * 3:
# merge the experts into a single 3d tensor
for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
if ename not in self._experts[bid]:
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
tensor_list = self._experts[bid][ename]
datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid)
for t in deferred:
yield from super().modify_tensors(*t)
|