# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # 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 json from pathlib import Path from typing import List, Optional, Union from ...functional import LayerNormPositionType, LayerNormType, MLPType from ...mapping import Mapping from ..convert_utils import infer_dtype from ..modeling_utils import PretrainedConfig, QuantConfig class MLLaMAConfig(PretrainedConfig): def __init__(self, *, mlp_bias: bool = False, attn_bias: bool = False, rotary_base: float = 10000.0, rotary_scaling: Optional[dict] = None, residual_mlp: bool = False, disable_weight_only_quant_plugin: bool = False, cross_attention: bool = True, cross_attention_layers: List[int] = None, vision_output_dim: int = 0, has_position_embedding=False, type_vocab_size=None, rescale_before_lm_head=False, layernorm_type=LayerNormType.RmsNorm, layernorm_position=LayerNormPositionType.pre_layernorm, has_attention_qkvo_bias=False, has_mlp_bias=False, has_model_final_layernorm=True, model_type='MLLaMAForCausalLM', skip_cross_kv=False, mlp_type=MLPType.GatedMLP, has_embedding_scale=False, residual_scaling=1.0, has_lm_head_bias=False, num_buckets=None, max_distance=0, relative_attention=False, **kwargs): self.mlp_bias = mlp_bias self.attn_bias = attn_bias self.rotary_base = rotary_base self.rotary_scaling = rotary_scaling self.residual_mlp = residual_mlp self.disable_weight_only_quant_plugin = disable_weight_only_quant_plugin assert cross_attention self.cross_attention = cross_attention self.cross_attention_layers = cross_attention_layers assert vision_output_dim != 0 self.vision_output_dim = vision_output_dim self.has_position_embedding = has_position_embedding self.type_vocab_size = type_vocab_size self.rescale_before_lm_head = rescale_before_lm_head self.layernorm_type = layernorm_type self.layernorm_position = layernorm_position self.has_attention_qkvo_bias = has_attention_qkvo_bias self.has_mlp_bias = has_mlp_bias self.has_model_final_layernorm = has_model_final_layernorm self.model_type = model_type self.skip_cross_kv = skip_cross_kv self.mlp_type = mlp_type self.has_embedding_scale = has_embedding_scale self.residual_scaling = residual_scaling self.has_lm_head_bias = has_lm_head_bias self.num_buckets = num_buckets self.max_distance = max_distance self.relative_attention = relative_attention self.skip_cross_attn_blocks = True kwargs.pop('embed_vocab_size', None) kwargs.pop('num_kv_heads_per_layer', None) kwargs.pop('num_kv_heads_per_cross_attn_layer', None) super().__init__(**kwargs) @property def embed_vocab_size(self): return self.vocab_size + 8 #FIXME The vocab_size of embedding contains the special tokens for image @property def num_kv_heads_per_layer(self): num_kv_heads_per_layer = [ self.num_key_value_heads for _ in range(self.num_hidden_layers) ] for layer_idx in self.cross_attention_layers: num_kv_heads_per_layer[layer_idx] = 0 return num_kv_heads_per_layer @property def num_kv_heads_per_cross_attn_layer(self): num_kv_heads_per_cross_attn_layer = [ 0 for _ in range(self.num_hidden_layers) ] for layer_idx in self.cross_attention_layers: num_kv_heads_per_cross_attn_layer[ layer_idx] = self.num_key_value_heads return num_kv_heads_per_cross_attn_layer def to_dict(self): output = super().to_dict() # Serialize the fields added in MLLaMAConfig output['mlp_bias'] = self.mlp_bias output['attn_bias'] = self.attn_bias output['rotary_base'] = self.rotary_base output['rotary_scaling'] = self.rotary_scaling output['residual_mlp'] = self.residual_mlp output[ 'disable_weight_only_quant_plugin'] = self.disable_weight_only_quant_plugin output['cross_attention'] = self.cross_attention output['cross_attention_layers'] = self.cross_attention_layers output['vision_output_dim'] = self.vision_output_dim output['embed_vocab_size'] = self.embed_vocab_size output['num_kv_heads_per_layer'] = self.num_kv_heads_per_layer output[ 'num_kv_heads_per_cross_attn_layer'] = self.num_kv_heads_per_cross_attn_layer output['skip_cross_attn_blocks'] = self.skip_cross_attn_blocks return output @classmethod def from_hugging_face( cls, hf_config_or_dir: Union[str, 'transformers.PretrainedConfig'], dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, **kwargs): import transformers if isinstance(hf_config_or_dir, transformers.PretrainedConfig): hf_config = hf_config_or_dir else: hf_config_dir = str(hf_config_or_dir) hf_config = transformers.AutoConfig.from_pretrained( hf_config_dir, trust_remote_code=True) hf_text_config = hf_config.text_config hf_vision_config = hf_config.vision_config num_key_value_heads = getattr(hf_text_config, "num_key_value_heads", hf_text_config.num_attention_heads) hidden_act = hf_text_config.hidden_act if hasattr( hf_text_config, "hidden_act") else hf_text_config.hidden_activation norm_epsilon = hf_text_config.rms_norm_eps head_dim = getattr( hf_text_config, "head_dim", hf_text_config.hidden_size // hf_text_config.num_attention_heads) head_size = getattr(hf_text_config, "kv_channels", head_dim) attn_bias = getattr(hf_text_config, 'bias', False) or getattr( hf_text_config, 'attention_bias', False) rotary_scaling = getattr(hf_text_config, "rope_scaling", None) rotary_base = getattr(hf_text_config, "rope_theta", 10000.0) residual_mlp = getattr(hf_text_config, "parallel_attn_mlp_res", False) disable_weight_only_quant_plugin = kwargs.pop( 'disable_weight_only_quant_plugin', False) dtype = infer_dtype(dtype, getattr(hf_config, 'torch_dtype', None)) return cls( architecture=hf_config.architectures[0], dtype=dtype, num_hidden_layers=hf_text_config.num_hidden_layers, num_attention_heads=hf_text_config.num_attention_heads, hidden_size=hf_text_config.hidden_size, intermediate_size=hf_text_config.intermediate_size, num_key_value_heads=num_key_value_heads, head_size=head_size, vocab_size=hf_text_config.vocab_size, position_embedding_type='rope_gpt_neox', max_position_embeddings=hf_text_config.max_position_embeddings, hidden_act=hidden_act, norm_epsilon=norm_epsilon, attn_bias=attn_bias, rotary_base=rotary_base, rotary_scaling=rotary_scaling, residual_mlp=residual_mlp, disable_weight_only_quant_plugin=disable_weight_only_quant_plugin, mapping=mapping, quantization=quant_config, cross_attention_layers=hf_text_config.cross_attention_layers, vision_output_dim=hf_vision_config.vision_output_dim, **kwargs) @classmethod def from_meta_ckpt(cls, meta_ckpt_dir: str, dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, **kwargs): with open(Path(meta_ckpt_dir, "params.json")) as fp: meta_config: dict = json.load(fp) n_embd = meta_config["dim"] n_head = meta_config["n_heads"] n_kv_head = meta_config.get("n_kv_heads", n_head) vocab_size = meta_config.get("vocab_size", 32000) # Reset vocab_size to 32000 for LLama v2 checkpoint. if vocab_size == -1: vocab_size = 32000 if "hidden_dim" in meta_config: inter_size = meta_config["hidden_dim"] else: multiple_of = meta_config.get("multiple_of", 1) n_embd_ = int(4 * n_embd * 2 / 3) ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1) inter_size = multiple_of * ( (int(n_embd_ * ffn_dim_multiplier) + multiple_of - 1) // multiple_of) dtype = infer_dtype(dtype, 'bfloat16') if meta_config.get('use_scaled_rope'): rotary_scaling = {"type": "llama3"} else: rotary_scaling = meta_config.get("rope_scaling") # meta checkpoint don't have vocab_size|hidden_act|rotary_base specified, use same default value as HF return cls(architecture="MLLaMAForCausalLM", dtype=dtype, num_hidden_layers=meta_config["n_layers"], num_attention_heads=n_head, hidden_size=n_embd, intermediate_size=inter_size, num_key_value_heads=n_kv_head, vocab_size=vocab_size, position_embedding_type='rope_gpt_neox', max_position_embeddings=2048, hidden_act='silu', rotary_scaling=rotary_scaling, rotary_base=meta_config.get('rope_theta', 10000), norm_epsilon=meta_config["norm_eps"], mapping=mapping, quantization=quant_config, **kwargs)