# 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.gptj_hf_cs import (
ConfigConverter_GPTJModel_CS18_CS20,
Converter_GPTJ_LMHeadModel_CS18_CS20,
Converter_GPTJ_LMHeadModel_CS20_CS21,
)
[docs]class Converter_Codegen_Attention_HF_CS17(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.mp_num = 4
self.rules = [
ConversionRule(
[
EquivalentSubkey("qkv_proj", "proj_q_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.qkv_converter,
),
ConversionRule(
[
EquivalentSubkey("qkv_proj", "proj_k_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.assert_already_converted,
),
ConversionRule(
[
EquivalentSubkey("qkv_proj", "proj_v_dense_layer"),
r"\.(?:weight|bias)",
],
action=self.assert_already_converted,
),
ConversionRule(
[
EquivalentSubkey("out_proj", "proj_output_dense_layer"),
r"\.(?:weight|bias)",
],
# This is a hacky way to initialize bias and masked_bias when converting from CS to
# Huggingface. However, based on huggingface implementation this is unavoidable in
# order to initialize attn.bias and attn.masked_bias, which we intiantiate when we
# capture the `out_proj` key
action=self.replace_or_fill_masked_bias,
),
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.7"))
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return None
def qkv_converter(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
if from_index == 0:
self.qkv_converter_hf_to_cs17(
old_key,
new_key,
old_state_dict,
new_state_dict,
action_fn_args,
)
else:
self.qkv_converter_cs17_to_hf(
old_key,
new_key,
old_state_dict,
new_state_dict,
action_fn_args,
)
def qkv_converter_hf_to_cs17(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
action_fn_args,
):
# HF represents Q, K, and V in a packed format (torch.Size(3*hidden, hidden)). We need to
# unpack the weight and bias tensor for CS 1.7 format.
q_key = new_key
k_key = re.sub(r"\.proj_q_dense_layer\.", ".proj_k_dense_layer.", q_key)
v_key = re.sub(r"\.proj_q_dense_layer\.", ".proj_v_dense_layer.", q_key)
if new_key.endswith(".bias"):
assert (
False
), "Codegen model doesn't support bias with attention projection"
elif new_key.endswith(".weight"):
packed_dim, embed_dim = old_state_dict[old_key].shape
assert 3 * embed_dim == packed_dim, (
f"Invalid tensor shape {old_state_dict[old_key].shape} at {old_key}. The first "
f"dimension (packed_dim) should be 3x the second dimension (embed_dim) since "
f"Q, K, and V are packed"
)
packed_dim = old_state_dict[old_key].shape[0]
dim = old_state_dict[old_key].shape[1]
split_by_mp_num = old_state_dict[old_key].reshape(
self.mp_num, -1, dim
)
local_dim = dim // self.mp_num
query, value, key = torch.split(split_by_mp_num, local_dim, dim=1)
query = query.reshape(-1, dim)
value = value.reshape(-1, dim)
key = key.reshape(-1, dim)
new_state_dict[q_key] = query
new_state_dict[k_key] = key
new_state_dict[v_key] = value
else:
raise ValueError("Invalid key after conversion: {}".format(new_key))
def qkv_converter_cs17_to_hf(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
action_fn_args,
):
# HF represents Q, K, and V in a packed format. It also contains
# special ".bias" and ".masked_bias" register buffers that need to be
# initalized
q_key = old_key
k_key = re.sub(r"\.proj_q_dense_layer\.", ".proj_k_dense_layer.", q_key)
v_key = re.sub(r"\.proj_q_dense_layer\.", ".proj_v_dense_layer.", q_key)
assert (
k_key in old_state_dict
), "Expected the following key to exist! {}".format(k_key)
assert (
v_key in old_state_dict
), "Expected the following key to exist! {}".format(v_key)
query = old_state_dict[q_key]
value = old_state_dict[v_key]
key = old_state_dict[k_key]
hidden_size = old_state_dict[q_key].shape[-1]
query = query.reshape(self.mp_num, -1, old_state_dict[q_key].shape[-1])
value = value.reshape(self.mp_num, -1, old_state_dict[v_key].shape[-1])
key = key.reshape(self.mp_num, -1, old_state_dict[k_key].shape[-1])
cat_dim = 1
packed_qkv = torch.cat(
(
query,
value,
key,
),
dim=cat_dim,
)
packed_qkv = packed_qkv.reshape(-1, hidden_size)
new_state_dict[new_key] = packed_qkv
if new_key.endswith(".bias"):
cs_config = action_fn_args["configs"][1]
max_positions = cs_config["model"]["max_position_embeddings"]
rotary_dim = cs_config["model"]["rotary_dim"]
hf_config = action_fn_args["configs"][0]
rotary_emb_base = hf_config["rotary_emb_base"]
attn_bias_key = re.sub(r"\.query_key_value\.", ".", new_key)
new_state_dict[attn_bias_key] = torch.tril(
torch.ones((max_positions, max_positions), dtype=torch.uint8)
).view(1, 1, max_positions, max_positions)
masked_bias_key = re.sub(
r"\.query_key_value\.", ".masked_", new_key
)
new_state_dict[masked_bias_key] = torch.tensor(-1e9)
inv_freq_key = re.sub(
r"\.query_key_value\.bias", ".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)
)
def replace_or_fill_masked_bias(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
# copy between `out_proj` and `proj_output_dense_layer`
new_state_dict[new_key] = old_state_dict[old_key]
# take care of bias and masked_bias
if from_index == 1:
max_positions = action_fn_args["configs"][1]["model"][
"max_position_embeddings"
]
causal_mask_key = re.sub(
r"out_proj\.weight", "causal_mask", new_key
)
new_state_dict[causal_mask_key] = torch.tril(
torch.ones((max_positions, max_positions), dtype=torch.uint8)
).view(1, 1, max_positions, max_positions)
def assert_already_converted(
self,
old_key,
new_key,
old_state_dict,
new_state_dict,
from_index,
action_fn_args,
):
if from_index == 0:
# We should never hit this case as this key should have been matched
# already
assert False, "Invalid key: {}".format(old_key)
else:
# When we convert from CS -> HF, the proj_q_dense_layer should also handle
# conversion of proj_k_dense_layer and proj_v_dense_layer since HF
# represents these three layers in a packed format. We simply need
# to test that the key containing the packed format has already
# been converted.
assert (
new_key in new_state_dict
), "Key should've been already converted: {} -> {}".format(
old_key, new_key
)
[docs]class Converter_Codegen_Headless_HF_CS17(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
# word embeddings
ConversionRule(
[
EquivalentSubkey("wte", "embedding_layer.word_embeddings"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
# final layer norm
ConversionRule(
[
EquivalentSubkey("ln_f", "transformer_decoder.norm"),
r"\.(?:weight|bias)",
],
action=self.replace_final_norm,
),
# attention
ConversionRule(
[
EquivalentSubkey("h", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("attn.", "self_attn."),
Converter_Codegen_Attention_HF_CS17(),
],
action=None,
),
# attention norm
ConversionRule(
[
EquivalentSubkey("h", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("ln_1", "norm1"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
# intermediate ffn
ConversionRule(
[
EquivalentSubkey("h", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("mlp.fc_in", "ffn.ffn.0.linear_layer"),
r"\.(?:weight|bias)",
],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("h", "transformer_decoder.layers"),
r"\.\d+\.",
EquivalentSubkey("mlp.fc_out", "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"),
ConversionRule(
[
r"h\.\d+\.attn\.(?:masked_bias|bias)",
],
exists="left",
),
ConversionRule(
[
r"h\.\d+\.attn\.causal_mask",
],
exists="left",
),
]
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 pre_model_convert(
self,
old_state_dict,
new_state_dict,
configs,
converter_indices,
drop_unmatched_keys,
):
if converter_indices.direction == 0:
logging.warning(
"{} GPTJ has a language model head (lm_head) "
"while {} CodeGenModel does not. Initializing lm_head to default.".format(
self.formats()[1], self.formats()[0]
)
)
# Manually tie weights
if (
converter_indices.direction == 1
and configs[1]["model"]["share_embedding_weights"]
):
if (
old_state_dict.get("embedding_layer.word_embeddings.weight", 0)
is None
):
old_state_dict[
"embedding_layer.word_embeddings.weight"
] = old_state_dict["lm_head.weight"]
[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 CodeGenModel (which is headless) -> CS GPTJModel
# (which has a head). We need to create 'lm_head' and init to default values
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['wte.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-1.7"))
@classmethod
def converter_note(cls) -> str:
return (
"{} CodeGenModel <-> {} GPTJModel (configured as codegen)\n"
"The HF model doesn't contain a language model head while the CS "
"one does. When converting to CS, the exported checkpoint will "
"contain a language model head initialized to default random "
"values. When converting to HF, the language model head will be "
"dropped."
).format(cls.formats()[0], cls.formats()[1])
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_HF_CS17
[docs]class Converter_Codegen_Headless_HF_CS18(Converter_Codegen_Headless_HF_CS17):
[docs] def __init__(self):
super().__init__()
self.rules = [
# Catch checkpoints from Pytorch 2.0 API
ConversionRule(
[
Converter_Codegen_Headless_HF_CS17(),
],
action=None,
),
# Catch checkpoints from 1.7/1.8
ConversionRule(
[
EquivalentSubkey("", "model."),
Converter_Codegen_Headless_HF_CS17(),
],
action=None,
),
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.8", "cs-1.9"))
@classmethod
def converter_note(cls) -> str:
return (
"{} CodeGenModel <-> {} GPTJModel (configured as codegen)\n"
"The HF model doesn't contain a language model head while the CS "
"one does. When converting to CS, the exported checkpoint will "
"contain a language model head initialized to default random "
"values. When converting to HF, the language model head will be "
"dropped."
).format(cls.formats()[0], cls.formats()[1])
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_HF_CS18
[docs]class Converter_Codegen_LMHeadModel_HF_CS17(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
[r"lm_head\.(?:weight|bias)"],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey("transformer.", ""),
Converter_Codegen_Headless_HF_CS17(),
],
action=None,
),
]
[docs] def pre_model_convert(
self,
old_state_dict,
new_state_dict,
configs,
converter_indices,
drop_unmatched_keys,
):
# Manually tie weights
if (
converter_indices.direction == 1
and configs[1]["model"]["share_embedding_weights"]
):
if (
old_state_dict.get("embedding_layer.word_embeddings.weight", 0)
is None
):
old_state_dict[
"embedding_layer.word_embeddings.weight"
] = old_state_dict["lm_head.weight"]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.7"))
@classmethod
def converter_note(cls) -> str:
return "{} CodeGenForCausalLM <-> {} GPTJModel (configured as codegen)".format(
cls.formats()[0], cls.formats()[1]
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_HF_CS17
[docs]class Converter_Codegen_LMHeadModel_HF_CS18(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
# Catch checkpoints from Pytorch 2.0 API
ConversionRule(
[
Converter_Codegen_LMHeadModel_HF_CS17(),
],
action=None,
),
# Catch checkpoints from 1.7/1.8
ConversionRule(
[
EquivalentSubkey("", "model."),
Converter_Codegen_LMHeadModel_HF_CS17(),
],
action=None,
),
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.8", "cs-1.9"))
@classmethod
def converter_note(cls) -> str:
return "{} CodeGenForCausalLM <-> {} GPTJModel (configured as codegen)".format(
cls.formats()[0], cls.formats()[1]
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_HF_CS18
[docs]class ConfigConverter_Codegen_Model_HF_CS17(BaseConfigConverter_HF_CS):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
["model_type"],
action=BaseConfigConverter.assert_factory_fn(0, "codegen"),
),
# Embedding
ConversionRule(["vocab_size"], action=self.replaceKey),
ConversionRule(["rotary_dim"], action=self.replaceKey),
ConversionRule(
["position_embedding_type"],
action=BaseConfigConverter.assert_factory_fn(1, "rotary"),
),
ConversionRule(
["use_position_embedding"],
exists="right",
action=BaseConfigConverter.assert_factory_fn(1, True),
),
ConversionRule(
[EquivalentSubkey("embd_pdrop", "embedding_dropout_rate")],
action=self.replaceKey,
),
ConversionRule(
[
EquivalentSubkey(
"tie_word_embeddings", "share_embedding_weights"
)
],
action=self.replaceKey,
),
# Decoder Block
ConversionRule(
[EquivalentSubkey("n_embd", "hidden_size")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("n_head", "num_heads")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("n_layer", "num_hidden_layers")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("n_positions", "max_position_embeddings")],
action=self.replaceKey,
),
ConversionRule(
["scale_attn_weights"],
action=BaseConfigConverter.assert_factory_fn(0, True),
),
ConversionRule(
["attention_type"],
action=BaseConfigConverter.assert_factory_fn(
1, "scaled_dot_product"
),
),
ConversionRule(
["use_projection_bias_in_attention"],
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, True),
),
ConversionRule(
["use_bias_in_output"],
action=BaseConfigConverter.assert_factory_fn(1, True),
),
ConversionRule(
[EquivalentSubkey("n_inner", "filter_size")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("activation_function", "nonlinearity")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("attn_pdrop", "attention_dropout_rate")],
action=self.replaceKey,
),
ConversionRule(
[EquivalentSubkey("resid_pdrop", "residual_dropout_rate")],
action=self.replaceKey,
),
ConversionRule(
["layer_norm_epsilon"],
action=self.replaceKey,
),
ConversionRule(["initializer_range"], action=self.replaceKey),
ConversionRule(
["embedding_layer_norm"],
action=BaseConfigConverter.assert_factory_fn(1, False),
),
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_untied_layer_norm"],
action=BaseConfigConverter.assert_factory_fn(1, False),
),
]
self.pre_convert_defaults[0].update(
{
"vocab_size": 50400,
"n_positions": 2048,
"n_embd": 4096,
"n_layer": 28,
"n_head": 16,
"rotary_dim": 64,
"activation_function": "gelu_new",
"resid_pdrop": 0.0,
"embd_pdrop": 0.0,
"attn_pdrop": 0.0,
"initializer_range": 0.02,
"layer_norm_epsilon": 1.0e-5,
"tie_word_embeddings": False,
}
)
self.pre_convert_defaults[1].update(
{
"max_position_embeddings": 1024,
"embedding_dropout_rate": 0.1,
"share_embedding_weights": True,
"residual_dropout_rate": 0.1,
"nonlinearity": "gelu",
"layer_norm_epsilon": 1.0e-5,
"use_ffn_bias": False,
"use_untied_layer_norm": False,
"attention_dropout_rate": 0.1,
"use_projection_bias_in_attention": True,
"use_ffn_bias_in_attention": True,
"initializer_range": 0.02,
"use_bias_in_output": False,
"norm_first": True,
},
)
self.post_convert_defaults[0].update({"model_type": "codegen"})
self.post_convert_defaults[1].update(
{
"use_ffn_bias_in_attention": False,
"use_projection_bias_in_attention": False,
"use_ffn_bias": True,
"use_bias_in_output": True,
"attention_type": "scaled_dot_product",
"use_untied_layer_norm": False,
},
)
def pre_config_convert(
self,
config,
converter_indices,
):
config = super().pre_config_convert(config, converter_indices)
if converter_indices.direction == 0:
if "n_inner" not in config or config["n_inner"] is None:
config["n_inner"] = 4 * config["n_embd"]
if config["n_head"] < 4:
raise ConfigConversionError(
"HF Model doesn't support n_head < 4 because of hardcoded TPU constraint"
)
else:
if config["num_heads"] < 4:
raise ConfigConversionError(
"HF Model doesn't support n_head < 4 because of hardcoded TPU constraint"
)
return config
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.7"))
[docs]class ConfigConverter_Codegen_Model_HF_CS18(
ConfigConverter_Codegen_Model_HF_CS17
):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (FormatVersions("hf"), FormatVersions("cs-1.8", "cs-1.9"))
[docs]class Converter_Codegen_LMHeadModel_CS18_CS20(
Converter_GPTJ_LMHeadModel_CS18_CS20
):
r"""
Codegen uses the GPTJ backbone
"""
@classmethod
def converter_note(cls) -> str:
return "GPTJModel (configured as codegen)"
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_CS18_CS20
[docs]class ConfigConverter_Codegen_Model_CS18_CS20(
ConfigConverter_GPTJModel_CS18_CS20
):
r"""
Codegen uses the GPTJ backbone
"""
[docs]class Converter_Codegen_Headless_HF_CS20(Converter_Codegen_Headless_HF_CS18):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (
FormatVersions("hf"),
FormatVersions("cs-2.0", "cs-2.1", "cs-2.2"),
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_HF_CS20
[docs]class Converter_Codegen_LMHeadModel_HF_CS20(
Converter_Codegen_LMHeadModel_HF_CS18
):
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (
FormatVersions("hf"),
FormatVersions("cs-2.0", "cs-2.1", "cs-2.2"),
)
@staticmethod
def get_config_converter_class() -> BaseConfigConverter:
return ConfigConverter_Codegen_Model_HF_CS20
[docs]class ConfigConverter_Codegen_Model_HF_CS20(
ConfigConverter_Codegen_Model_HF_CS18
):
[docs] def __init__(self):
super().__init__()
self.rules = [
ConversionRule(
["norm_type"],
action=BaseConfigConverter.assert_factory_fn(1, "layernorm"),
),
*self.rules,
]
@staticmethod
def formats() -> Tuple[FormatVersions, FormatVersions]:
return (
FormatVersions("hf"),
FormatVersions("cs-2.0", "cs-2.1", "cs-2.2"),
)
###########################################################
# 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_Codegen_LMHeadModel_CS20_CS21(
Converter_GPTJ_LMHeadModel_CS20_CS21
):
[docs] def __init__(self):
super().__init__()
@classmethod
def converter_note(cls) -> str:
return "GPTJLMHeadModel class (configured as codegen)"