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[Model] Add support for xverse (#6301)
* Support xverse model convert to gguf format. * 1. Convert xverse models to gguf; 2. Add LLM_ARCH_XVERSE inference in llama.cpp; 3. Add xverse item in Supported models in README.md; * * gguf-py: remove redundant logs * llama: remove the init_mapping_prefetch custom parameter * llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers. * - Fix format issues - Remove duplicate set kqv_out to llm_build_kv * Update llama.cpp --------- Co-authored-by: willhe <willhe@xverse.cn> Co-authored-by: willhe <hexin@xverse.cn>
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@ -115,6 +115,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
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- [x] [Gemma](https://ai.google.dev/gemma)
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- [x] [Mamba](https://github.com/state-spaces/mamba)
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- [x] [Xverse](https://huggingface.co/models?search=xverse)
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- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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**Multimodal models:**
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@ -773,6 +773,148 @@ class BaichuanModel(Model):
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return weights[r * n_part:r * n_part + r, ...]
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@Model.register("XverseForCausalLM")
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class XverseModel(Model):
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model_arch = gguf.MODEL_ARCH.XVERSE
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def set_vocab(self):
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assert (self.dir_model / "tokenizer.json").is_file()
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dir_model = self.dir_model
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hparams = self.hparams
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tokens: list[bytearray] = []
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toktypes: list[int] = []
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
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assert max(tokenizer.vocab.values()) < vocab_size
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reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
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added_vocab = tokenizer.get_added_vocab()
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for token_id in range(vocab_size):
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token_text = reverse_vocab[token_id].encode('utf-8')
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# replace "\x00" to string with length > 0
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if token_text == b"\x00":
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toktype = gguf.TokenType.BYTE # special
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token_text = f"<{token_text}>".encode('utf-8')
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elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
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toktype = gguf.TokenType.BYTE # special
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elif reverse_vocab[token_id] in added_vocab:
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if tokenizer.added_tokens_decoder[token_id].special:
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toktype = gguf.TokenType.CONTROL
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else:
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toktype = gguf.TokenType.USER_DEFINED
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else:
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toktype = gguf.TokenType.NORMAL
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tokens.append(token_text)
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toktypes.append(toktype)
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self.gguf_writer.add_tokenizer_model("llama")
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
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special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
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block_count = self.hparams["num_hidden_layers"]
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head_count = self.hparams["num_attention_heads"]
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head_count_kv = self.hparams.get("num_key_value_heads", head_count)
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hf_repo = self.hparams.get("_name_or_path", "")
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ctx_length = 0
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if "max_sequence_length" in self.hparams:
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ctx_length = self.hparams["max_sequence_length"]
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elif "max_position_embeddings" in self.hparams:
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ctx_length = self.hparams["max_position_embeddings"]
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elif "model_max_length" in self.hparams:
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ctx_length = self.hparams["model_max_length"]
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else:
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print("gguf: can not find ctx length parameter.")
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sys.exit()
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self.gguf_writer.add_name(self.dir_model.name)
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self.gguf_writer.add_source_hf_repo(hf_repo)
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self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
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self.gguf_writer.add_context_length(ctx_length)
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self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
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self.gguf_writer.add_head_count(head_count)
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self.gguf_writer.add_head_count_kv(head_count_kv)
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
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if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
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if self.hparams["rope_scaling"].get("type") == "linear":
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
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def write_tensors(self):
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# Collect tensors from generator object
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model_kv = dict(self.get_tensors())
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block_count = self.hparams["num_hidden_layers"]
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head_count = self.hparams["num_attention_heads"]
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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head_count_kv = self.hparams.get("num_key_value_heads", head_count)
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for name, data_torch in model_kv.items():
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# we don't need these
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if name.endswith(".rotary_emb.inv_freq"):
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continue
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old_dtype = data_torch.dtype
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# convert any unsupported data types to float32
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if data_torch.dtype not in (torch.float16, torch.float32):
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data_torch = data_torch.to(torch.float32)
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# HF models permute some of the tensors, so we need to undo that
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if name.endswith(("q_proj.weight")):
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data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
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if name.endswith(("k_proj.weight")):
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data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
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data = data_torch.squeeze().numpy()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print(f"Can not map tensor {name!r}")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if self.ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
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self.gguf_writer.add_tensor(new_name, data)
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def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
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if n_kv_head is not None and n_head != n_kv_head:
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n_head //= n_kv_head
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return (
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weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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.reshape(weights.shape)
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)
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@Model.register("FalconForCausalLM", "RWForCausalLM")
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class FalconModel(Model):
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model_arch = gguf.MODEL_ARCH.FALCON
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@ -123,6 +123,7 @@ class MODEL_ARCH(IntEnum):
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GEMMA = auto()
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STARCODER2 = auto()
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MAMBA = auto()
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XVERSE = auto()
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COMMAND_R = auto()
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@ -191,6 +192,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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}
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@ -606,6 +608,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.SSM_D,
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MODEL_TENSOR.SSM_OUT,
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],
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MODEL_ARCH.XVERSE: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.COMMAND_R: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -650,6 +668,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.XVERSE: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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}
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#
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165
llama.cpp
165
llama.cpp
@ -218,6 +218,7 @@ enum llm_arch {
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LLM_ARCH_GEMMA,
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LLM_ARCH_STARCODER2,
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LLM_ARCH_MAMBA,
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LLM_ARCH_XVERSE,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_UNKNOWN,
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};
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@ -249,6 +250,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -878,6 +880,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
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},
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},
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{
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LLM_ARCH_XVERSE,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_COMMAND_R,
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{
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@ -3847,6 +3868,16 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_XVERSE:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_7B; break;
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case 40: model.type = e_model::MODEL_13B; break;
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case 80: model.type = e_model::MODEL_65B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_COMMAND_R:
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{
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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@ -5200,6 +5231,28 @@ static bool llm_load_tensors(
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layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
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}
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} break;
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case LLM_ARCH_XVERSE:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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}
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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}
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} break;
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case LLM_ARCH_COMMAND_R:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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@ -5238,7 +5291,7 @@ static bool llm_load_tensors(
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ml.done_getting_tensors();
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ml.init_mappings(true, &model.mlock_mmaps);
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ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
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model.mappings.reserve(ml.mappings.size());
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// create the backend buffers
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@ -6411,6 +6464,111 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_xverse() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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// positions of the tokens in the KV cache
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struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
||||
model.layers[il].wo, NULL,
|
||||
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, NULL,
|
||||
model.layers[il].ffn_gate, NULL,
|
||||
model.layers[il].ffn_down, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_falcon() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
@ -9389,6 +9547,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_mamba();
|
||||
} break;
|
||||
case LLM_ARCH_XVERSE:
|
||||
{
|
||||
result = llm.build_xverse();
|
||||
} break;
|
||||
case LLM_ARCH_COMMAND_R:
|
||||
{
|
||||
result = llm.build_command_r();
|
||||
@ -14188,6 +14350,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_ORION:
|
||||
case LLM_ARCH_INTERNLM2:
|
||||
case LLM_ARCH_MINICPM:
|
||||
case LLM_ARCH_XVERSE:
|
||||
case LLM_ARCH_COMMAND_R:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user