# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import re
from typing import Tuple
import torch
from cerebras.modelzoo.tools.checkpoint_converters.base_converter import (
BaseCheckpointConverter_HF_CS,
BaseConfigConverter,
BaseConfigConverter_HF_CS,
ConfigConversionError,
ConversionRule,
EquivalentSubkey,
FormatVersions,
)
from cerebras.modelzoo.tools.checkpoint_converters.gpt2_hf_cs import (
ConfigConverter_GPT2Model_CS18_CS20,
Converter_GPT2LMHeadModel_CS18_CS20,
Converter_GPT2LMHeadModel_CS20_CS21,
)
[docs]class Converter_LlamaAttention_HF_CS(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
[
EquivalentSubkey("q_proj", "proj_q_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.convert_with_interleaving_query,
),
ConversionRule(
[
EquivalentSubkey("k_proj", "proj_k_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.convert_with_interleaving_key,
),
ConversionRule(
[
EquivalentSubkey("v_proj", "proj_v_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("o_proj", "proj_output_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.convert_output_and_inv_freq,
),
]
def convert_with_interleaving_query(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
# Query & Keys should be interleaved since HF and CS RoPE differ
cs_config = action_fn_args["configs"][1]
tensor = old_state_dict[old_key]
initial_shape = tensor.size()
num_heads = cs_config["model"]["num_heads"]
if from_index == 0:
if len(tensor.size()) == 2:
tensor = tensor.view(
num_heads, tensor.size(0) // num_heads, tensor.size(-1)
)
elif len(tensor.size()) == 1:
tensor = tensor.view(num_heads, tensor.size(0) // num_heads)
tensor = self.interleave_helper(tensor, cs_config)
else:
tensor = self.reverse_interleave_helper(
tensor, cs_config, num_heads
)
tensor = tensor.view(*initial_shape)
new_state_dict[new_key] = tensor
def convert_with_interleaving_key(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
# Query & Keys should be interleaved since HF and CS RoPE differ
cs_config = action_fn_args["configs"][1]
if (
cs_config["model"].get("attention_module", "aiayn_attention")
== "aiayn_attention"
):
self.convert_with_interleaving_query(
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
)
return
elif cs_config["model"]["attention_module"] == "multiquery_attention":
tensor = old_state_dict[old_key]
initial_shape = tensor.size()
num_group = cs_config["model"]["extra_attention_params"][
"num_kv_groups"
]
if from_index == 0:
if len(tensor.size()) == 2:
tensor = tensor.view(
num_group, tensor.size(0) // num_group, tensor.size(-1)
)
elif len(tensor.size()) == 1:
tensor = tensor.view(num_group, tensor.size(0) // num_group)
tensor = self.interleave_helper(tensor, cs_config)
else:
tensor = self.reverse_interleave_helper(
tensor, cs_config, num_group
)
tensor = tensor.view(*initial_shape)
new_state_dict[new_key] = tensor
else:
assert False, (
f"attention_module {cs_config['model']['attention_module']} is not supported for "
f"llama"
)
def interleave_helper(self, t, cs_config):
rotary_dim = cs_config["model"]["rotary_dim"]
if len(t.shape) == 3:
to_rotate = t[:, :rotary_dim, :]
to_pass = t[:, rotary_dim:, :]
to_rotate = (
to_rotate.reshape(t.shape[0], 2, -1, t.shape[-1])
.permute(0, 2, 1, 3)
.reshape(t.shape[0], -1, t.shape[-1])
)
interleaved = torch.cat((to_rotate, to_pass), dim=1)
elif len(t.shape) == 2:
to_rotate = t[:, :rotary_dim]
to_pass = t[:, rotary_dim:]
to_rotate = (
to_rotate.reshape(t.shape[0], 2, -1)
.permute(0, 2, 1)
.reshape(t.shape[0], -1)
)
interleaved = torch.cat((to_rotate, to_pass), dim=1)
else:
assert False, (
"shape of query, key, value projection tensor has to have shape of length 2 "
"(biases) or 3 (weights) when converting from HF to CS."
)
return interleaved
def reverse_interleave_helper(self, t, cs_config, num_heads):
rotary_dim = cs_config["model"]["rotary_dim"]
if len(t.shape) == 2:
t = t.reshape(num_heads, -1, t.shape[-1])
to_rotate = t[:, :rotary_dim, :]
to_pass = t[:, rotary_dim:, :]
# pylint: disable=redefined-builtin
reversed = (
to_rotate.reshape(num_heads, -1, 2, t.shape[-1])
.permute(0, 2, 1, 3)
.reshape(num_heads, rotary_dim, t.shape[-1])
)
reversed = torch.cat((reversed, to_pass), dim=1)
elif len(t.shape) == 1:
t = t.reshape(num_heads, -1)
to_rotate = t[:, :rotary_dim]
to_pass = t[:, rotary_dim:]
reversed = (
to_rotate.reshape(num_heads, -1, 2)
.permute(0, 2, 1)
.reshape(num_heads, -1)
)
reversed = torch.cat((reversed, to_pass), dim=1)
else:
assert False, (
"shape of query, key, value projection tensor has to have shape of length 1 "
"(biases) or 2 (weights) when converting from CS to HF."
)
return reversed
def convert_output_and_inv_freq(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
# Convert output projection:
self.replaceKey(
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
)
# HF also has inv_freq buffer saved which we need to recreate:
if from_index == 1 and old_key.endswith(".weight"):
rotary_emb_base = 10000 # hardcoded in HF's llama
cs_config = action_fn_args["configs"][1]
rotary_dim = cs_config["model"]["rotary_dim"]
inv_freq_key = re.sub(
r"\.o_proj\.weight", ".rotary_emb.inv_freq", new_key
)
new_state_dict[inv_freq_key] = 1.0 / (
rotary_emb_base
** (torch.arange(0, rotary_dim, 2).float() / rotary_dim)
)
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return None
[docs]class Converter_LlamaModel_HF_CS(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
# word embeddings
ConversionRule(
[
EquivalentSubkey(
"embed_tokens", "embedding_layer.word_embeddings"
),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
# final layer norm
ConversionRule(
[
EquivalentSubkey("norm", "transformer_decoder.norm"),
r"\.(?:weight|bias)",
],
action=self.replace_final_norm,
),
# attention
ConversionRule(
[
EquivalentSubkey("layers", "transformer_decoder.layers"),
r"\.\d+\.self_attn\.",
Converter_LlamaAttention_HF_CS(),
],
action=None,
),
# Rotary embedding
ConversionRule(
[r"layers\.\d+\.self_attn\.rotary_emb\.inv_freq"],
exists="left",
action=None,
),
# attention norm
ConversionRule(
[
EquivalentSubkey("layers", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("input_layernorm", "norm1"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("layers", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("post_attention_layernorm", "norm3"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
# intermediate ffn
ConversionRule(
[
EquivalentSubkey("layers", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("mlp.up_proj", "ffn.ffn.0.linear_layer"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("layers", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey(
"mlp.gate_proj", "ffn.ffn.0.linear_layer_for_glu"
),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("layers", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("mlp.down_proj", "ffn.ffn.1.linear_layer"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
ConversionRule([r"lm_head\.(?:weight|bias)"], exists="right"),
ConversionRule([r"ln_f\.(?:weight|bias)"], exists="right"),
]
def replace_final_norm(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
new_state_dict[new_key] = old_state_dict[old_key]
# CS 1.7 has both "ln_f" and "transformer_decoder.norm"
# we need to copy the original ("ln_f") too:
if from_index == 0:
ln_f_key = re.sub(r"transformer_decoder\.norm\.", "ln_f.", new_key)
new_state_dict[ln_f_key] = old_state_dict[old_key]
[docs] def post_model_convert(
self,
old_state_dict,
new_state_dict,
configs,
converter_indices,
drop_unmatched_keys,
key_prefix="",
):
if converter_indices.direction == 0:
# We are converting from HF LlamaModel (which is headless) ->
# CS GPT2LMHeadModel configured as llama (which has a head)
# We need to create 'lm_head' and init to default values
logging.warning(
f"{self.formats()[1]} has a language model head (lm_head) "
f"while {self.formats()[0]} does not. Initializing lm_head to default."
)
hf_config = configs[0]
cs_config = configs[1]
use_bias_in_output = cs_config["model"].get(
"use_bias_in_output", False
)
vocab_size = cs_config["model"]["vocab_size"]
embed_dim = cs_config["model"]["hidden_size"]
if hf_config["tie_word_embeddings"]:
lm_head_weight = old_state_dict['embed_tokens.weight']
else:
lm_head_weight = torch.zeros((vocab_size, embed_dim))
lm_head_weight.normal_(mean=0.0, std=0.02)
new_state_dict[key_prefix + "lm_head.weight"] = lm_head_weight
if use_bias_in_output:
lm_head_bias = torch.zeros(vocab_size)
new_state_dict[key_prefix + "lm_head.bias"] = lm_head_bias
super().post_model_convert(
old_state_dict,
new_state_dict,
configs,
converter_indices,
drop_unmatched_keys,
key_prefix=key_prefix,
)
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return None
[docs]class Converter_LlamaModel_HF_CS19(Converter_LlamaModel_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
# Catch checkpoints from Pytorch 2.0 API
ConversionRule(
[
Converter_LlamaModel_HF_CS(),
],
action=None,
),
# Catch checkpoints from 1.7/1.8
ConversionRule(
[
EquivalentSubkey("", "model."),
Converter_LlamaModel_HF_CS(),
],
action=None,
),
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9"))
@classmethod
def converter_note(cls) -> str:
return (
f"{cls.formats()[0]} LlamaModel <-> {cls.formats()[1]} GPT2LMHeadModel (configured as "
f"Llama)\nThe HF model doesn't contain a language model head while the CS one does. "
f"When converting to CS, the exported checkpoint will contain a language model head "
f"initialized to default random values. When converting to HF, the language model head "
f"will be dropped."
).format(cls.formats()[0], cls.formats()[1])
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LLaMa_HF_CS19
[docs]class Converter_LlamaForCausalLM_HF_CS(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
[r"lm_head\.(?:weight|bias)"],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("model.", ""),
Converter_LlamaModel_HF_CS(),
],
action=None,
),
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return None
[docs]class Converter_LlamaForCausalLM_HF_CS19(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
# Catch checkpoints from Pytorch 2.0 API
ConversionRule(
[
Converter_LlamaForCausalLM_HF_CS(),
],
action=None,
),
# Catch checkpoints from 1.7/1.8
ConversionRule(
[
EquivalentSubkey("", "model."),
Converter_LlamaForCausalLM_HF_CS(),
],
action=None,
),
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9"))
@classmethod
def converter_note(cls) -> str:
return "{} LlamaForCausalLM <-> {} GPT2LMHeadModel (configured as Llama)".format(
cls.formats()[0], cls.formats()[1]
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LLaMa_HF_CS19
[docs]class ConfigConverter_LLaMa_HF_CS19(BaseConfigConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
if not hasattr(self, "model_type"):
self.model_type = "llama"
self.rules = [
ConversionRule(
["model_type"],
action=BaseConfigConverter.assert_factory_fn(
0, self.model_type
),
),
# Embedding
ConversionRule(["vocab_size"], action=self.replaceKey),
ConversionRule(
["position_embedding_type"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, "rotary"),
),
ConversionRule(
["use_position_embedding"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, True),
),
ConversionRule(
["embedding_dropout_rate"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, 0.0),
),
ConversionRule(
[
EquivalentSubkey(
"tie_word_embeddings", "share_embedding_weights"
)
],
action=self.replaceKey,
),
ConversionRule(
["embedding_layer_norm"],
action=BaseConfigConverter.assert_factory_fn(1, False),
),
# Decoder Block
ConversionRule(
["hidden_size"],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("num_attention_heads", "num_heads")],
action=self.replaceKey,
),
ConversionRule(
["num_hidden_layers"],
action=self.replaceKey,
),
ConversionRule(
["max_position_embeddings"],
action=self.replaceKey,
),
ConversionRule(
["attention_type"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(
1, "scaled_dot_product"
),
),
ConversionRule(
["use_projection_bias_in_attention"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, False),
),
ConversionRule(
["use_ffn_bias_in_attention"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, False),
),
ConversionRule(
["use_ffn_bias"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, False),
),
ConversionRule(
[EquivalentSubkey("intermediate_size", "filter_size")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("hidden_act", "nonlinearity")],
action=self.convert_nonlinearity,
),
ConversionRule(
["attention_dropout_rate"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, 0.0),
),
ConversionRule(
["dropout_rate"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, 0.0),
),
ConversionRule(
["rotary_dim"], exists="right", action=self.assert_rotary_dim
),
ConversionRule(["rope_theta"], action=self.replaceKey),
ConversionRule(
[EquivalentSubkey("rms_norm_eps", "layer_norm_epsilon")],
action=self.replaceKey,
),
ConversionRule(
["use_bias_in_output"],
action=BaseConfigConverter.assert_factory_fn(1, False),
),
ConversionRule(["initializer_range"], action=self.replaceKey),
ConversionRule(
["fixed_sparse_attention"],
action=BaseConfigConverter.assert_factory_fn(1, None),
),
ConversionRule(
["norm_first"],
action=BaseConfigConverter.assert_factory_fn(1, True),
),
ConversionRule(
["use_ff_layer1_dropout"],
action=BaseConfigConverter.assert_factory_fn(1, False),
),
ConversionRule(
["use_rms_norm"],
action=BaseConfigConverter.assert_factory_fn(1, True),
),
]
self.pre_convert_defaults[0].update(
{
"vocab_size": 32000,
"hidden_size": 4096,
"intermediate_size": 11008,
"num_hidden_layers": 32,
"num_attention_heads": 32,
"hidden_act": "silu",
"initializer_range": 0.02,
"rms_norm_eps": 1e-6,
"tie_word_embeddings": False,
"max_position_embeddings": 2048,
}
)
self.pre_convert_defaults[1].update(
{
"share_embedding_weights": True,
"use_rms_norm": False,
"max_position_embeddings": 1024,
"position_embedding_type": "learned",
"layer_norm_epsilon": 1.0e-5,
"use_projection_bias_in_attention": True,
"use_ffn_bias_in_attention": True,
"nonlinearity": "gelu",
"use_ffn_bias": True,
"use_bias_in_output": False,
"norm_first": True,
},
)
self.post_convert_defaults[0].update({"model_type": "llama"})
self.post_convert_defaults[1].update(
{
"use_position_embedding": True,
"position_embedding_type": "rotary",
"embedding_dropout_rate": 0.0,
"embedding_layer_norm": False,
"attention_type": "scaled_dot_product",
"use_projection_bias_in_attention": False,
"use_ffn_bias_in_attention": False,
"use_ffn_bias": False,
"attention_dropout_rate": 0.0,
"dropout_rate": 0.0,
"use_bias_in_output": False,
"norm_first": True,
"use_ff_layer1_dropout": False,
"use_rms_norm": True,
},
)
def convert_nonlinearity(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
activation = old_state_dict[old_key]
if from_index == 0:
gated_hf2cs = {"silu": "swiglu", "relu": "reglu", "gelu": "geglu"}
if activation not in gated_hf2cs:
raise ConfigConversionError(
"{} is not a GLU-able activation in CS".format(activation)
)
activation = gated_hf2cs[activation]
elif from_index == 1:
gated_cs2hf = {"swiglu": "silu", "reglu": "relu", "geglu": "gelu"}
if activation not in gated_cs2hf:
raise ConfigConversionError(
"{} is not a supported GLU activation in HF".format(
activation
)
)
activation = gated_cs2hf[activation]
new_state_dict[new_key] = activation
def assert_rotary_dim(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
assert from_index == 1, "{} should only exist in CS config".format(
old_key
)
if (
old_state_dict[old_key]
!= old_state_dict["hidden_size"] // old_state_dict["num_heads"]
):
raise ConfigConversionError(
"rotary_dim must be hidden_size // num_heads in order to be compatible with HF"
)
def pre_config_convert(
self,
config,
converter_indices,
):
config = super().pre_config_convert(config, converter_indices)
if converter_indices.direction == 1 and (
"rotary_dim" not in config or config["rotary_dim"] is None
):
raise ConfigConversionError("rotary_dim must be specified")
return config
def post_config_convert(
self,
original_config,
old_config,
new_config,
converter_indices,
drop_unmatched_keys,
):
if converter_indices.direction == 0:
new_config["rotary_dim"] = (
new_config["hidden_size"] // new_config["num_heads"]
)
return super().post_config_convert(
original_config,
old_config,
new_config,
converter_indices,
drop_unmatched_keys,
)
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.9"))
[docs]class Converter_LlamaForCausalLM_CS19_CS20(Converter_GPT2LMHeadModel_CS18_CS20):
r"""
Llama uses the GPT2 backbone
"""
@classmethod
def converter_note(cls) -> str:
return "GPT2LMHeadModel class (configured as Llama)"
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("cs-1.9"), FormatVersions("cs-2.0"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LlamaModel_CS19_CS20
[docs]class ConfigConverter_LlamaModel_CS19_CS20(ConfigConverter_GPT2Model_CS18_CS20):
r"""
Llama uses the GPT2 backbone
"""
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("cs-1.9"), FormatVersions("cs-2.0"))
[docs]class Converter_LlamaModel_HF_CS20(Converter_LlamaModel_HF_CS19):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-2.0"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LLaMa_HF_CS20
[docs]class Converter_LlamaForCausalLM_HF_CS20(Converter_LlamaForCausalLM_HF_CS19):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-2.0"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LLaMa_HF_CS20
[docs]class ConfigConverter_LLaMa_HF_CS20(ConfigConverter_LLaMa_HF_CS19):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
["norm_type"],
action=BaseConfigConverter.assert_factory_fn(1, "rmsnorm"),
),
ConversionRule(
[
EquivalentSubkey(
"num_key_value_heads", "extra_attention_params"
)
],
action=self.convert_gqa,
),
*self.rules,
]
del self.pre_convert_defaults[1]["use_rms_norm"]
del self.post_convert_defaults[1]["use_rms_norm"]
self.pre_convert_defaults[1]["norm_type"] = "layernorm"
self.post_convert_defaults[1]["norm_type"] = "rmsnorm"
del self.post_convert_defaults[1]["use_position_embedding"]
def convert_gqa(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
if from_index == 0:
# check mha or gqa
if old_state_dict[old_key] == old_state_dict["num_attention_heads"]:
new_state_dict["attention_module"] = "aiayn_attention"
else:
assert (
old_state_dict["num_attention_heads"]
% old_state_dict[old_key]
== 0
), (
f"number of attention heads should be divisible by num_key_value_heads but "
f"got {old_state_dict['num_attention_heads']} and {old_state_dict[old_key]},"
)
extra = {"num_kv_groups": old_state_dict[old_key]}
new_state_dict[new_key] = extra
new_state_dict["attention_module"] = "multiquery_attention"
elif from_index == 1:
if (
old_state_dict.get("attention_module", "aiayn_attention")
== "aiayn_attention"
):
assert (
old_key not in old_state_dict
or "num_kv_groups" not in old_state_dict[old_key]
), "Conflict between use of multi-query and multi-head attention"
new_state_dict[new_key] = old_state_dict["num_heads"]
elif old_state_dict["attention_module"] == "multiquery_attention":
num_heads = old_state_dict["num_heads"]
num_kv_groups = old_state_dict[old_key]["num_kv_groups"]
assert num_heads % num_kv_groups == 0, (
f"number of attention heads should be divisible by num_key_value_heads but "
f"got {num_heads} and {num_kv_groups}."
)
new_state_dict[new_key] = old_state_dict[old_key][
"num_kv_groups"
]
else:
assert False, (
f"attention_module {old_state_dict['attention_module']} is not supported for "
f"llama"
)
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-2.0"))
###########################################################
# In CS 2.1, we refactored the embedding layer.
# CS 2.0 <> CS 2.1. We don't need a separate HF <> CS 2.1 converters since
# HF only supports RoPE which doesn't produce any checkpoint keys.
###########################################################
[docs]class Converter_LlamaForCausalLM_CS20_CS21(Converter_GPT2LMHeadModel_CS20_CS21):
@classmethod
def converter_note(cls) -> str:
return "GPT2LMHeadModel class (configured as Llama)"
[docs]class Converter_LlamaModel_HF_CS21(Converter_LlamaModel_HF_CS20):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-2.1", "cs-2.2"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LLaMa_HF_CS21
[docs]class Converter_LlamaForCausalLM_HF_CS21(Converter_LlamaForCausalLM_HF_CS20):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-2.1", "cs-2.2"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_LLaMa_HF_CS21
def supports_mup_conversion(self):
return True
[docs]class ConfigConverter_LLaMa_HF_CS21(ConfigConverter_LLaMa_HF_CS20):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
[EquivalentSubkey("rope_scaling", "pos_scaling_factor")],
action=self.convert_pi,
),
*self.rules,
]
self.pre_convert_defaults[0].update(
{
"rope_scaling": None,
}
)
self.pre_convert_defaults[1].update(
{
"pos_scaling_factor": 1.0,
},
)
def convert_pi(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
if from_index == 0:
if old_state_dict[old_key] is None:
new_state_dict[new_key] = 1.0
else:
scaling_type = old_state_dict[old_key]["type"].lower()
if scaling_type != "linear":
raise ConfigConversionError(
f"Only `rope_scaling` type `linear` is currently supported, "
f"but got type `{scaling_type}`."
)
new_state_dict[new_key] = old_state_dict[old_key]["factor"]
else:
if old_state_dict[old_key] == 1.0:
new_state_dict[new_key] = None
else:
new_state_dict[new_key] = {
"type": "linear",
"factor": old_state_dict[old_key],
}
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-2.1", "cs-2.2"))
[docs] def supports_mup_conversion(self):
return True