mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
llama : add Qwen support (#4281)
* enable qwen to llama.cpp * llama : do not GPU split bias tensors --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Shijie
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@ -10,7 +10,7 @@ import re
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import sys
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from enum import IntEnum
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
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from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
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import numpy as np
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import torch
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@ -168,6 +168,8 @@ class Model:
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return PersimmonModel
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if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
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return StableLMModel
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if model_architecture == "QWenLMHeadModel":
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return QwenModel
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return Model
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def _is_model_safetensors(self) -> bool:
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@ -203,6 +205,8 @@ class Model:
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return gguf.MODEL_ARCH.PERSIMMON
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if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
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return gguf.MODEL_ARCH.STABLELM
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if arch == "QWenLMHeadModel":
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return gguf.MODEL_ARCH.QWEN
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raise NotImplementedError(f'Architecture "{arch}" not supported!')
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@ -832,6 +836,131 @@ class StableLMModel(Model):
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self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
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self.gguf_writer.add_layer_norm_eps(1e-5)
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class QwenModel(Model):
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@staticmethod
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def token_bytes_to_string(b):
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from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
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byte_encoder = bytes_to_unicode()
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return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
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@staticmethod
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def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
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parts = [bytes([b]) for b in token]
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while True:
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min_idx = None
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min_rank = None
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for i, pair in enumerate(zip(parts[:-1], parts[1:])):
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rank = mergeable_ranks.get(pair[0] + pair[1])
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if rank is not None and (min_rank is None or rank < min_rank):
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min_idx = i
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min_rank = rank
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if min_rank is None or (max_rank is not None and min_rank >= max_rank):
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break
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assert min_idx is not None
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parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
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return parts
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def set_vocab(self):
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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 # type: ignore[attr-defined]
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tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
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vocab_size = hparams["vocab_size"]
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assert max(tokenizer.get_vocab().values()) < vocab_size
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merges = []
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vocab = {}
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mergeable_ranks = tokenizer.mergeable_ranks
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for token, rank in mergeable_ranks.items():
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vocab[self.token_bytes_to_string(token)] = rank
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if len(token) == 1:
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continue
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merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
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assert len(merged) == 2
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merges.append(' '.join(map(self.token_bytes_to_string, merged)))
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reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
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added_vocab = tokenizer.special_tokens
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for i in range(vocab_size):
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if i not in reverse_vocab:
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pad_token = f"[PAD{i}]".encode("utf-8")
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tokens.append(bytearray(pad_token))
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toktypes.append(gguf.TokenType.USER_DEFINED)
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elif reverse_vocab[i] in added_vocab:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.CONTROL)
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else:
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tokens.append(reverse_vocab[i])
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toktypes.append(gguf.TokenType.NORMAL)
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self.gguf_writer.add_tokenizer_model("gpt2")
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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, load_merges=False)
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special_vocab.merges = merges
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special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
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special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
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special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
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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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self.gguf_writer.add_name("Qwen")
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self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
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self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
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self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
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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(self.hparams["num_attention_heads"])
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self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
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def write_tensors(self):
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block_count = self.hparams["num_hidden_layers"]
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model_kv = dict(self.get_tensors())
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_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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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"{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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###### CONVERSION LOGIC ######
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@ -92,6 +92,7 @@ class MODEL_ARCH(IntEnum):
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BERT = auto()
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BLOOM = auto()
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STABLELM = auto()
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QWEN = auto()
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class MODEL_TENSOR(IntEnum):
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@ -132,6 +133,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.BERT: "bert",
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MODEL_ARCH.BLOOM: "bloom",
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MODEL_ARCH.STABLELM: "stablelm",
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MODEL_ARCH.QWEN: "qwen",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -317,6 +319,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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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.QWEN: [
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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_QKV,
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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.GPT2: [
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# TODO
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],
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@ -336,6 +352,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_ARCH.PERSIMMON: [
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MODEL_TENSOR.ROPE_FREQS,
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],
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MODEL_ARCH.QWEN: [
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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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@ -10,7 +10,7 @@ class TensorNameMap:
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# Token embeddings
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MODEL_TENSOR.TOKEN_EMBD: (
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"gpt_neox.embed_in", # gptneox
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"transformer.wte", # gpt2 gpt-j mpt refact
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"transformer.wte", # gpt2 gpt-j mpt refact qwen
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"transformer.word_embeddings", # falcon
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"word_embeddings", # bloom
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"model.embed_tokens", # llama-hf
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@ -38,7 +38,7 @@ class TensorNameMap:
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# Output
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MODEL_TENSOR.OUTPUT: (
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"embed_out", # gptneox
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"lm_head", # gpt2 mpt falcon llama-hf baichuan
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
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"output", # llama-pth bloom
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"word_embeddings_for_head", # persimmon
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),
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@ -51,7 +51,7 @@ class TensorNameMap:
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"norm", # llama-pth
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"embeddings.LayerNorm", # bert
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"transformer.norm_f", # mpt
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"ln_f", # refact bloom
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"ln_f", # refact bloom qwen
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"language_model.encoder.final_layernorm", # persimmon
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),
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@ -65,7 +65,7 @@ class TensorNameMap:
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# Attention norm
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MODEL_TENSOR.ATTN_NORM: (
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"gpt_neox.layers.{bid}.input_layernorm", # gptneox
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"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
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"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
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"transformer.blocks.{bid}.norm_1", # mpt
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"transformer.h.{bid}.input_layernorm", # falcon7b
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"h.{bid}.input_layernorm", # bloom
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@ -85,7 +85,7 @@ class TensorNameMap:
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# Attention query-key-value
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MODEL_TENSOR.ATTN_QKV: (
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"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
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"transformer.h.{bid}.attn.c_attn", # gpt2
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"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
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"transformer.blocks.{bid}.attn.Wqkv", # mpt
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"transformer.h.{bid}.self_attention.query_key_value", # falcon
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"h.{bid}.self_attention.query_key_value", # bloom
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@ -119,7 +119,7 @@ class TensorNameMap:
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# Attention output
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MODEL_TENSOR.ATTN_OUT: (
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"gpt_neox.layers.{bid}.attention.dense", # gptneox
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"transformer.h.{bid}.attn.c_proj", # gpt2 refact
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"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
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"transformer.blocks.{bid}.attn.out_proj", # mpt
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"transformer.h.{bid}.self_attention.dense", # falcon
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"h.{bid}.self_attention.dense", # bloom
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@ -139,7 +139,7 @@ class TensorNameMap:
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# Feed-forward norm
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MODEL_TENSOR.FFN_NORM: (
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"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
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"transformer.h.{bid}.ln_2", # gpt2 refact
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen
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"h.{bid}.post_attention_layernorm", # bloom
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"transformer.blocks.{bid}.norm_2", # mpt
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"model.layers.{bid}.post_attention_layernorm", # llama-hf
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@ -161,18 +161,20 @@ class TensorNameMap:
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"encoder.layer.{bid}.intermediate.dense", # bert
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"transformer.h.{bid}.mlp.fc_in", # gpt-j
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"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
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"transformer.h.{bid}.mlp.w1", # qwen
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),
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# Feed-forward gate
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MODEL_TENSOR.FFN_GATE: (
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"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
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"layers.{bid}.feed_forward.w1", # llama-pth
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"transformer.h.{bid}.mlp.w2", # qwen
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),
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# Feed-forward down
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MODEL_TENSOR.FFN_DOWN: (
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"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
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"transformer.h.{bid}.mlp.c_proj", # gpt2 refact
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"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
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"transformer.blocks.{bid}.ffn.down_proj", # mpt
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"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
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"h.{bid}.mlp.dense_4h_to_h", # bloom
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211
llama.cpp
211
llama.cpp
@ -192,6 +192,7 @@ enum llm_arch {
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LLM_ARCH_REFACT,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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LLM_ARCH_UNKNOWN,
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};
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@ -208,6 +209,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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};
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enum llm_kv {
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@ -518,6 +520,22 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
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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_QWEN,
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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_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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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_UNKNOWN,
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@ -2347,6 +2365,15 @@ 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_QWEN:
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{
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GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_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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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -3310,6 +3337,71 @@ static void llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_QWEN:
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{
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model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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{
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ggml_backend_type backend_norm;
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ggml_backend_type backend_output;
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if (n_gpu_layers > int(n_layer)) {
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// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
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// on Windows however this is detrimental unless everything is on the GPU
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#ifndef _WIN32
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backend_norm = llama_backend_offload;
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#else
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backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
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#endif // _WIN32
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backend_output = llama_backend_offload_split;
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} else {
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backend_norm = GGML_BACKEND_CPU;
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backend_output = GGML_BACKEND_CPU;
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}
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model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
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model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
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if (backend_norm == GGML_BACKEND_GPU) {
|
||||
vram_weights += ggml_nbytes(model.output_norm);
|
||||
}
|
||||
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
||||
vram_weights += ggml_nbytes(model.output);
|
||||
}
|
||||
}
|
||||
|
||||
const uint32_t n_ff = hparams.n_ff / 2;
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
|
||||
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
||||
|
||||
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd * 3}, backend_split);
|
||||
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd * 3}, backend);
|
||||
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
||||
|
||||
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
|
||||
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
|
||||
if (backend == GGML_BACKEND_GPU) {
|
||||
vram_weights +=
|
||||
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
|
||||
ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_gate) +
|
||||
ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
|
||||
}
|
||||
}
|
||||
} break;
|
||||
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
@ -4908,6 +5000,121 @@ struct llm_build_context {
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_qwen() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
|
||||
cb(inpL, "inp_embd", -1);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
cb(inp_pos, "inp_pos", -1);
|
||||
|
||||
// KQ_scale
|
||||
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
||||
cb(KQ_scale, "KQ_scale", -1);
|
||||
|
||||
// KQ_mask (mask for 1 head, it wil be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
||||
cb(KQ_mask, "KQ_mask", -1);
|
||||
|
||||
// shift the entire K-cache if needed
|
||||
if (do_rope_shift) {
|
||||
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
|
||||
}
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
// using mode = 2 for neox mode
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, Qcur, inp_pos, n_embd_head, 2, 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, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
|
||||
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||
|
||||
cur = llm_build_kqv(ctx0, hparams, kv_self,
|
||||
model.layers[il].wo, NULL,
|
||||
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward forward
|
||||
{
|
||||
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,
|
||||
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;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
@ -5382,6 +5589,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_stablelm();
|
||||
} break;
|
||||
case LLM_ARCH_QWEN:
|
||||
{
|
||||
result = llm.build_qwen();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
1
prompts/chat-with-qwen.txt
Normal file
1
prompts/chat-with-qwen.txt
Normal file
@ -0,0 +1 @@
|
||||
You are a helpful assistant.
|
||||
Loading…
Reference in New Issue
Block a user