chore: import upstream snapshot with attribution

conversion/talkie.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import LazyTorchTensor, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("TalkieForCausalLM")
+class TalkieModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.TALKIE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        # Talkie used F.rms_norm without an explicit eps
+        self.gguf_writer.add_layer_norm_rms_eps(torch.finfo(torch.float32).eps)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        prefix = f"model.blocks.{bid}." if bid is not None else ""
+        suffix = name.removeprefix(prefix)
+
+        if suffix == "attn_gain.a_g":
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, bid, ".scale"), data_torch
+            return
+        elif suffix == "mlp_gain.a_g":
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid, ".scale"), data_torch
+            return
+        elif suffix == "lm_head_gain.w_g":
+            self.gguf_writer.add_logit_scale(LazyTorchTensor.to_eager(data_torch).item())
+            return
+        elif suffix in ("attn.attn_query.weight", "attn.attn_key.weight"):
+            # absorb inverse rope
+            head_dim = self.hparams["head_dim"]
+            shape = data_torch.shape
+            data_torch = torch.reshape(data_torch, (-1, head_dim, shape[-1]))
+            signs = torch.ones((1, head_dim, 1), dtype=data_torch.dtype)
+            signs[:, head_dim // 2 :, :] = -1
+            if self.lazy:
+                signs = LazyTorchTensor.from_eager(signs)
+            # (n_head, head_dim, n_in) -> (n_out, n_in)
+            data_torch = torch.reshape(data_torch * signs, shape)
+        elif suffix == "attn.head_gain.head_g":
+            # allow head gain to broadcast
+            data_torch = data_torch.unsqueeze(-1)
+
+        if not name.endswith(".weight"):
+            name += ".weight"
+
+        yield from super().modify_tensors(data_torch, name, bid)