Source code for modelzoo.common.pytorch.model_utils.checkpoint_converters.llama

# 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 modelzoo.common.pytorch.model_utils.checkpoint_converters.base_converter import (
    BaseCheckpointConverter_CS_CS,
    BaseCheckpointConverter_HF_CS,
    BaseConfigConverter,
    BaseConfigConverter_CS_CS,
    BaseConfigConverter_HF_CS,
    ConfigConversionError,
    ConversionRule,
    EquivalentSubkey,
    FormatVersions,
)
from modelzoo.common.pytorch.model_utils.checkpoint_converters.helper import (
    convert_use_rms_layer_norm_helper,
)


[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"), "\.(?:weight|bias)", ], action=self.convert_with_interleaving_query, ), ConversionRule( [ EquivalentSubkey("k_proj", "proj_k_dense_layer"), "\.(?:weight|bias)", ], action=self.convert_with_interleaving_key, ), ConversionRule( [ EquivalentSubkey("v_proj", "proj_v_dense_layer"), "\.(?:weight|bias)", ], action=self.replaceKey, ), ConversionRule( [ EquivalentSubkey("o_proj", "proj_output_dense_layer"), "\.(?: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 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:, :] 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( "\.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" ), "\.(?:weight|bias)", ], action=self.replaceKey, ), # final layer norm ConversionRule( [ EquivalentSubkey("norm", "transformer_decoder.norm"), "\.(?:weight|bias)", ], action=self.replace_final_norm, ), # attention ConversionRule( [ EquivalentSubkey("layers", "transformer_decoder.layers"), "\.\d+\.self_attn\.", Converter_LlamaAttention_HF_CS(), ], action=None, ), # Rotary embedding ConversionRule( ["layers\.\d+\.self_attn\.rotary_emb\.inv_freq"], exists="left", action=None, ), # attention norm ConversionRule( [ EquivalentSubkey("layers", "transformer_decoder.layers"), "\.\d+\.", EquivalentSubkey("input_layernorm", "norm1"), "\.(?:weight|bias)", ], action=self.replaceKey, ), ConversionRule( [ EquivalentSubkey("layers", "transformer_decoder.layers"), "\.\d+\.", EquivalentSubkey("post_attention_layernorm", "norm3"), "\.(?:weight|bias)", ], action=self.replaceKey, ), # intermediate ffn ConversionRule( [ EquivalentSubkey("layers", "transformer_decoder.layers"), "\.\d+\.", EquivalentSubkey("mlp.up_proj", "ffn.ffn.0.linear_layer"), "\.(?:weight|bias)", ], action=self.replaceKey, ), ConversionRule( [ EquivalentSubkey("layers", "transformer_decoder.layers"), "\.\d+\.", EquivalentSubkey( "mlp.gate_proj", "ffn.ffn.0.linear_layer_for_glu" ), "\.(?:weight|bias)", ], action=self.replaceKey, ), ConversionRule( [ EquivalentSubkey("layers", "transformer_decoder.layers"), "\.\d+\.", EquivalentSubkey("mlp.down_proj", "ffn.ffn.1.linear_layer"), "\.(?:weight|bias)", ], action=self.replaceKey, ), ConversionRule(["lm_head\.(?:weight|bias)"], exists="right"), ConversionRule(["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("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, from_index, drop_unmatched_keys, ): if from_index == 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( "{} has a language model head (lm_head) " "while {} does not. Initializing lm_head to default.".format( self.formats()[1], self.formats()[0] ) ) 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["lm_head.weight"] = lm_head_weight if use_bias_in_output: lm_head_bias = torch.zeros(vocab_size) new_state_dict["lm_head.bias"] = lm_head_bias super().post_model_convert( old_state_dict, new_state_dict, configs, from_index, drop_unmatched_keys, )
@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 ( "{} LlamaModel <-> {} GPT2LMHeadModel (configured as Llama)\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_LLaMa_HF_CS19
[docs]class Converter_LlamaForCausalLM_HF_CS(BaseCheckpointConverter_HF_CS):
[docs] def __init__(self): super().__init__() self.rules = [ ConversionRule( ["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__() self.rules = [ ConversionRule( ["model_type"], action=BaseConfigConverter.assert_factory_fn(0, "llama"), ), # 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( [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.keys(): 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.keys(): 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, from_index, ): config = super().pre_config_convert(config, from_index) if from_index == 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, from_index, drop_unmatched_keys, ): if from_index == 0: new_config["rotary_dim"] = ( new_config["hidden_size"] // new_config["num_heads"] ) return super().post_config_convert( original_config, old_config, new_config, from_index, drop_unmatched_keys, ) @staticmethod def formats() -> Tuple[FormatVersions, FormatVersions]: return (FormatVersions("hf"), FormatVersions("cs-1.9"))
[docs]class Converter_LlamaForCausalLM_CS19_CS20(BaseCheckpointConverter_CS_CS):
[docs] def __init__(self): super().__init__() # Model didn't change between 1.9 and 2.0. Copy all keys. self.rules = [ ConversionRule([".*"], action=self.replaceKey), ]
@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(BaseConfigConverter_CS_CS):
[docs] def __init__(self): super().__init__() # Only difference between 1.9 and 2.0 is introduction of norm_type self.rules = [ ConversionRule( [EquivalentSubkey("use_rms_norm", "norm_type")], action=self.convert_use_rms_layer_norm, ), ConversionRule([".*"], action=self.replaceKey), ] self.pre_convert_defaults[0]["use_rms_norm"] = False self.pre_convert_defaults[1]["norm_type"] = "layernorm"
def convert_use_rms_layer_norm(self, *args): convert_use_rms_layer_norm_helper(self, *args) @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):
[docs] def __init__(self): super().__init__()
@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):
[docs] def __init__(self): super().__init__()
@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"
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 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[old_key]["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 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 llama" @staticmethod def formats() -> Tuple[FormatVersions, FormatVersions]: return (FormatVersions("hf"), FormatVersions("cs-2.0"))