Skip to content

Commit

Permalink
llama : support for Llama-3_1-Nemotron-51B (#10669)
Browse files Browse the repository at this point in the history 
* conflict resolution

* move comments after bracket to its own line
  • Loading branch information
@ymcki
ymcki authored Dec 23, 2024
1 parent dab76c9 commit 6f0c9e0
Show file tree
Hide file tree
Showing 4 changed files with 471 additions and 1 deletion.
178 changes: 178 additions & 0 deletions convert_hf_to_gguf.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1692,6 1692,184 @@ def prepare_tensors(self):
raise ValueError(f"Unprocessed experts: {experts}")


@Model.register("DeciLMForCausalLM")
class DeciModel(Model):
model_arch = gguf.MODEL_ARCH.DECI

@staticmethod
def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
# DeciLM-specific code
intermediate_size = int(2 * ffn_mult * n_embd / 3)
return DeciModel._find_multiple(intermediate_size, 256)

@staticmethod
def _find_multiple(n: int, k: int) -> int:
# DeciLM-specific code
if n % k == 0:
return n
return n k - (n % k)

def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)

if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
_block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
assert self.block_count == len(_block_configs)
self._num_kv_heads = list()
self._num_heads = list()
_ffn_multipliers = list()
# ***linear attention layer***
# if n_heads_in_group is None and replace_with_linear is True
# then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
# ***attention-free layer***
# if n_heads_in_group is None and replace_with_linear is False
# then _num_kv_heads[il] is 0 and _num_heads[il] is 0
# ***normal attention-layer***
# if n_heads_in_group is not None, then
# _num_kv_heads[il] is num_attention_head // n_heads_in_group and
# _num_heads[il] is num_attention_head
for il in range(len(_block_configs)):
if _block_configs[il]["attention"]["n_heads_in_group"] is None:
if _block_configs[il]["attention"]["replace_with_linear"] is True:
self._num_kv_heads.append(0)
self._num_heads.append(self.hparams["num_attention_heads"])
else:
self._num_kv_heads.append(0)
self._num_heads.append(0)
else:
self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
self._num_heads.append(self.hparams["num_attention_heads"])
_ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(_ffn_multipliers)
assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
self._ffn_dims: list[int] = [
DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
for multiplier in _ffn_multipliers
]

def set_vocab(self):
# Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
# eos_token from '|eot_id|' to '|end_of_text|'
if self.hparams.get("vocab_size", 128256) == 128256:
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(
self.dir_model, load_merges=True,
special_token_types = ['bos', 'eos', 'eom', 'eot']
)
special_vocab._set_special_token("bos", 128000)
special_vocab._set_special_token("eos", 128001)
special_vocab._set_special_token("eom", 128008)
special_vocab._set_special_token("eot", 128009)
special_vocab.add_to_gguf(self.gguf_writer)
else:
# DeciLM-7B
self._set_vocab_llama_hf()
# self._set_vocab_gpt2()

def set_gguf_parameters(self):
if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(self._ffn_dims)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
self.gguf_writer.add_head_count(self._num_heads)
self.gguf_writer.add_feed_forward_length(self._ffn_dims)
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_file_type(self.ftype)
else: # DeciLM-7B
super().set_gguf_parameters()
if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
assert self.block_count == len(self._num_kv_heads)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])

if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)

if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])

@staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
if bid is not None:
if "num_key_value_heads_per_layer" in self.hparams:
n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
elif "block_configs" in self.hparams:
n_kv_head = self._num_kv_heads[bid]
n_head = self._num_heads[bid]
else:
n_kv_head = self.hparams.get("num_key_value_heads")
else:
n_kv_head = self.hparams.get("num_key_value_heads")

if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = DeciModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
return [(self.map_tensor_name(name), data_torch)]

def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))

factor = rope_scaling.get("factor", 8.0)
low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
old_context_len = self.hparams.get("original_max_position_embeddings", 8192)

low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
assert low_freq_wavelen != high_freq_wavelen

rope_factors = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
rope_factors.append(1)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor smooth))

yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))

def prepare_tensors(self):
super().prepare_tensors()


@Model.register("BitnetForCausalLM")
class BitnetModel(Model):
model_arch = gguf.MODEL_ARCH.BITNET
Expand Down
26 changes: 26 additions & 0 deletions gguf-py/gguf/constants.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -221,6 221,7 @@ class GGUFType:

class MODEL_ARCH(IntEnum):
LLAMA = auto()
DECI = auto()
FALCON = auto()
BAICHUAN = auto()
GROK = auto()
Expand Down Expand Up @@ -402,6 403,7 @@ class MODEL_TENSOR(IntEnum):

MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.DECI: "deci",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.BAICHUAN: "baichuan",
MODEL_ARCH.GROK: "grok",
Expand Down Expand Up @@ -602,6 604,26 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
MODEL_ARCH.DECI: [
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_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
MODEL_ARCH.GROK: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
Expand Down Expand Up @@ -1448,6 1470,10 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.DECI: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.BAICHUAN: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
Expand Down
1 change: 1 addition & 0 deletions gguf-py/gguf/tensor_mapping.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -198,6 198,7 @@ class TensorNameMap:
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2
"model.layers.{bid}.self_attn.linear_attn", # deci
"layers.{bid}.attention.wo", # llama-pth
"encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j
Expand Down
Loading

0 comments on commit 6f0c9e0

Please sign in to comment.