chore: import upstream snapshot with attribution

conversion/grok.py

@@ -0,0 +1,116 @@
+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)