llama : add PLaMo model (#3557)

* add plamo mock

* add tensor loading

* plamo convert

* update norm

* able to compile

* fix norm_rms_eps hparam

* runnable

* use inp_pos

* seems ok

* update kqv code

* remove develop code

* update README

* shuffle attn_q.weight and attn_output.weight for broadcasting

* remove plamo_llm_build_kqv and use llm_build_kqv

* fix style

* update

* llama : remove obsolete KQ_scale

* plamo : fix tensor names for correct GPU offload

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

convert-hf-to-gguf.py

@@ -184,6 +184,8 @@ class Model:
             return MixtralModel
         if model_architecture == "PhiForCausalLM":
             return Phi2Model
+        if model_architecture == "PlamoForCausalLM":
+            return PlamoModel
         return Model
 
     def _is_model_safetensors(self) -> bool:
@@ -225,6 +227,8 @@ class Model:
             return gguf.MODEL_ARCH.LLAMA
         if arch == "PhiForCausalLM":
             return gguf.MODEL_ARCH.PHI2
+        if arch == "PlamoForCausalLM":
+            return gguf.MODEL_ARCH.PLAMO
 
         raise NotImplementedError(f'Architecture "{arch}" not supported!')
 
@@ -1002,11 +1006,91 @@ class Phi2Model(Model):
         self.gguf_writer.add_add_bos_token(False)
 
 
+class PlamoModel(Model):
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_name("PLaMo")
+        self.gguf_writer.add_context_length(4096)  # not in config.json
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(5)  # hparams["num_key_value_heads"]) is wrong
+        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
+
+    def shuffle_attn_q_weight(self, data_torch):
+        assert data_torch.size() == (5120, 5120)
+        data_torch = data_torch.reshape(8, 5, 128, 5120)
+        data_torch = torch.permute(data_torch, (1, 0, 2, 3))
+        data_torch = torch.reshape(data_torch, (5120, 5120))
+        return data_torch
+
+    def shuffle_attn_output_weight(self, data_torch):
+        assert data_torch.size() == (5120, 5120)
+        data_torch = data_torch.reshape(5120, 8, 5, 128)
+        data_torch = torch.permute(data_torch, (0, 2, 1, 3))
+        data_torch = torch.reshape(data_torch, (5120, 5120))
+        return data_torch
+
+    def write_tensors(self):
+        block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+
+        for name, data_torch in self.get_tensors():
+            if "self_attn.rotary_emb.inv_freq" in name:
+                continue
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            # shuffle for broadcasting of gqa in ggml_mul_mat
+            if new_name.endswith("attn_q.weight"):
+                data_torch = self.shuffle_attn_q_weight(data_torch)
+            elif new_name.endswith("attn_output.weight"):
+                data_torch = self.shuffle_attn_output_weight(data_torch)
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.squeeze().numpy()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
 ###### CONVERSION LOGIC ######
 
 
 def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file")
+    parser = argparse.ArgumentParser(
+        description="Convert a huggingface model to a GGML compatible file")
     parser.add_argument(
         "--vocab-only", action="store_true",
         help="extract only the vocab",