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 <[email protected]>

Author: okdshin

README.md

@@ -102,6 +102,7 @@ as the main playground for developing new features for the [ggml](https://github
 - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
 - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
 - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
+- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
 
 **Multimodal models:**
 

convert-hf-to-gguf.py

@@ -184,6 +184,8 @@ def from_model_architecture(model_architecture):
             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 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH:
             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 @@ def set_gguf_parameters(self):
         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",

gguf-py/gguf/constants.py

@@ -96,6 +96,7 @@ class MODEL_ARCH(IntEnum):
     STABLELM  = auto()
     QWEN      = auto()
     PHI2      = auto()
+    PLAMO     = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -142,6 +143,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.STABLELM:       "stablelm",
     MODEL_ARCH.QWEN:           "qwen",
     MODEL_ARCH.PHI2:           "phi2",
+    MODEL_ARCH.PLAMO:          "plamo",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -349,6 +351,21 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.PLAMO: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.GPT2: [
         # TODO
     ],

gguf-py/gguf/tensor_mapping.py

@@ -79,6 +79,7 @@ class TensorNameMap:
             "language_model.encoder.layers.{bid}.input_layernorm",  # persimmon
             "model.layers.{bid}.ln1",                               # yi
             "transformer.h.{bid}.ln",                               # phi2
+            "model.layers.layers.{bid}.norm",                       # plamo
         ),
 
         # Attention norm 2
@@ -99,26 +100,29 @@ class TensorNameMap:
 
         # Attention query
         MODEL_TENSOR.ATTN_Q: (
-            "model.layers.{bid}.self_attn.q_proj",       # llama-hf
-            "layers.{bid}.attention.wq",                 # llama-pth
-            "encoder.layer.{bid}.attention.self.query",  # bert
-            "transformer.h.{bid}.attn.q_proj",           # gpt-j
+            "model.layers.{bid}.self_attn.q_proj",         # llama-hf
+            "layers.{bid}.attention.wq",                   # llama-pth
+            "encoder.layer.{bid}.attention.self.query",    # bert
+            "transformer.h.{bid}.attn.q_proj",             # gpt-j
+            "model.layers.layers.{bid}.self_attn.q_proj",  # plamo
         ),
 
         # Attention key
         MODEL_TENSOR.ATTN_K: (
-            "model.layers.{bid}.self_attn.k_proj",     # llama-hf
-            "layers.{bid}.attention.wk",               # llama-pth
-            "encoder.layer.{bid}.attention.self.key",  # bert
-            "transformer.h.{bid}.attn.k_proj",         # gpt-j
+            "model.layers.{bid}.self_attn.k_proj",         # llama-hf
+            "layers.{bid}.attention.wk",                   # llama-pth
+            "encoder.layer.{bid}.attention.self.key",      # bert
+            "transformer.h.{bid}.attn.k_proj",             # gpt-j
+            "model.layers.layers.{bid}.self_attn.k_proj",  # plamo
         ),
 
         # Attention value
         MODEL_TENSOR.ATTN_V: (
-            "model.layers.{bid}.self_attn.v_proj",       # llama-hf
-            "layers.{bid}.attention.wv",                 # llama-pth
-            "encoder.layer.{bid}.attention.self.value",  # bert
-            "transformer.h.{bid}.attn.v_proj",           # gpt-j
+            "model.layers.{bid}.self_attn.v_proj",         # llama-hf
+            "layers.{bid}.attention.wv",                   # llama-pth
+            "encoder.layer.{bid}.attention.self.value",    # bert
+            "transformer.h.{bid}.attn.v_proj",             # gpt-j
+            "model.layers.layers.{bid}.self_attn.v_proj",  # plamo
         ),
 
         # Attention output
@@ -134,12 +138,14 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.out_proj",                         # gpt-j
             "language_model.encoder.layers.{bid}.self_attention.dense",  # persimmon
             "transformer.h.{bid}.mixer.out_proj",                        # phi2
+            "model.layers.layers.{bid}.self_attn.o_proj",                # plamo
         ),
 
         # Rotary embeddings
         MODEL_TENSOR.ATTN_ROT_EMBD: (
-            "model.layers.{bid}.self_attn.rotary_emb.inv_freq",   # llama-hf
-            "layers.{bid}.attention.inner_attention.rope.freqs",  # llama-pth
+            "model.layers.{bid}.self_attn.rotary_emb.inv_freq",        # llama-hf
+            "layers.{bid}.attention.inner_attention.rope.freqs",       # llama-pth
+            "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
         ),
 
         # Feed-forward norm
@@ -174,6 +180,7 @@ class TensorNameMap:
             "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h",  # persimmon
             "transformer.h.{bid}.mlp.w1",                             # qwen
             "transformer.h.{bid}.mlp.fc1",                            # phi2
+            "model.layers.layers.{bid}.mlp.up_proj",                  # plamo
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -186,6 +193,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.gate_proj",           # llama-hf refact
             "layers.{bid}.feed_forward.w1",               # llama-pth
             "transformer.h.{bid}.mlp.w2",                 # qwen
+            "model.layers.layers.{bid}.mlp.gate_proj",    # plamo
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
@@ -206,6 +214,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.fc_out",                         # gpt-j
             "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h",  # persimmon
             "transformer.h.{bid}.mlp.fc2",                            # phi2
+            "model.layers.layers.{bid}.mlp.down_proj",                # plamo
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (