model : add support for talkie-1930-13b (#22596)

* initial talkie support, coherent

* reorder to follow convention

* absorb inverse rope

* stop folding scalars to improve quantization

* use broadcasting instead of duplication

* style cleanup

* add scaling support to LoraTorchTensor; use that path in conversion

* use layer_out_scale instead of embd_skip_scale

conversion/__init__.py

@@ -215,6 +215,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "T5EncoderModel": "t5",
     "T5ForConditionalGeneration": "t5",
     "T5WithLMHeadModel": "t5",
+    "TalkieForCausalLM": "talkie",
     "UMT5ForConditionalGeneration": "t5",
     "UMT5Model": "t5",
     "UltravoxModel": "ultravox",

conversion/base.py

@@ -1622,6 +1622,9 @@ class TextModel(ModelBase):
         if chkhsh == "62f6fb0a6fd5098caeabb19b07a5c1099cafc8b9c40eab6ea89ece4ec02fbc57":
             # ref: https://huggingface.co/sarvamai/sarvam-30b
             res = "sarvam-moe"
+        if chkhsh == "f728162c1315c26e40249849799b4ba3fe584c32084b4795b03eb295e63cb5af":
+            # ref: https://huggingface.co/lewtun/talkie-1930-13b-it-hf
+            res = "talkie"
 
         if res is None:
             logger.warning("\n")

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)