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from dataclasses import dataclass |
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from typing import List, Optional, Tuple, Union |
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import ast |
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import re |
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import torch |
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import torch.utils.checkpoint |
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from torch import nn, Tensor |
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from torch.nn import functional as F |
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from transformers import PreTrainedModel |
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from transformers.modeling_outputs import CausalLMOutputWithPast |
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from transformers.generation.utils import GenerateOutput |
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from transformers import CLIPVisionModel, CLIPImageProcessor,SiglipVisionModel, SiglipImageProcessor |
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from .configuration import TinyLlavaConfig, IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN |
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from transformers import AutoConfig, AutoModelForCausalLM |
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CONTROLLER_HEART_BEAT_EXPIRATION = 30 |
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WORKER_HEART_BEAT_INTERVAL = 15 |
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LOGDIR = "." |
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import os |
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from torch.nn import CrossEntropyLoss |
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from transformers.activations import ACT2FN |
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from transformers.cache_utils import Cache, DynamicCache, StaticCache |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPast, |
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) |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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from numbers import Number |
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from typing import List, Optional, Union |
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import numpy as np |
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from transformers import PretrainedConfig, AutoTokenizer |
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def make_divisible( |
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v: Union[float, int], |
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divisor: Optional[int] = 8, |
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min_value: Optional[Union[float, int]] = None, |
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) -> Union[float, int]: |
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""" |
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This function is taken from the original tf repo. |
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It ensures that all layers have a channel number that is divisible by the divisor |
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It can be seen at: |
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https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62 |
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Args: |
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v: input value |
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divisor: default to 8 |
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min_value: minimum divisor value |
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Returns: |
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new_v: new divisible value |
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""" |
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if min_value is None: |
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min_value = divisor |
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) |
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if new_v < 0.9 * v: |
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new_v += divisor |
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return new_v |
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def compute_heads(model_dim: int, head_dim: int) -> int: |
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"""Compute the number of heads. |
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Args: |
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model_dim: Model dimension. |
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head_dim: Head dimension. |
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Returns: |
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An integer denoting number of heads in multi-head attention is returned. |
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Raises: |
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ValueError: if model dimension is not divisible by head dimension. |
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""" |
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if model_dim % head_dim == 0: |
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return model_dim // head_dim |
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else: |
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raise ValueError( |
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f"Model dimension should be divisible by head dimension. Got: {model_dim} and {head_dim}." |
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) |
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OpenELM_CONFIGS = { |
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"OpenELM-270M": dict( |
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num_transformer_layers=16, |
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model_dim=1280, |
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head_dim=64, |
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num_gqa_groups=4, |
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normalize_qk_projections=True, |
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share_input_output_layers=True, |
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ffn_multipliers=(0.5, 4.0), |
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qkv_multipliers=(0.5, 1.0), |
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), |
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"OpenELM-450M": dict( |
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num_transformer_layers=20, |
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model_dim=1536, |
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head_dim=64, |
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num_gqa_groups=4, |
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normalize_qk_projections=True, |
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share_input_output_layers=True, |
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ffn_multipliers=(0.5, 4.0), |
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qkv_multipliers=(0.5, 1.0), |
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), |
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"OpenELM-1_1B": dict( |
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num_transformer_layers=28, |
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model_dim=2048, |
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head_dim=64, |
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num_gqa_groups=4, |
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normalize_qk_projections=True, |
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share_input_output_layers=True, |
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ffn_multipliers=(0.5, 4.0), |
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qkv_multipliers=(0.5, 1.0), |
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), |
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"OpenELM-3B": dict( |
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num_transformer_layers=36, |
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model_dim=3072, |
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head_dim=128, |
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num_gqa_groups=4, |
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normalize_qk_projections=True, |
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share_input_output_layers=True, |
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ffn_multipliers=(0.5, 4.0), |
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qkv_multipliers=(0.5, 1.0), |
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), |
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} |
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class OpenELMConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`OpenELMModel`]. It is used to instantiate an OpenELM model according to the specified arguments, defining the model architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 32000): |
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Vocabulary size of the OpenELM model. |
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max_context_length (`int`, *optional*, defaults to 2048): |
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Maximum number of input tokens. |
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num_transformer_layers (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer decoder. |
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model_dim (`int`, *optional*, defaults to 2048): |
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Dimension of the hidden representations. |
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head_dim (`int`, *optional*, defaults to 128): |
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The attention head dimension. |
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qkv_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 1.0): |
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If the qkv_multipliers is a Number, then all attention layers have the same latent dimensions, |
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resulting in uniform allocation of parameters. |
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If the qkv_multipliers is a List of Number, then each attention layer have different latent dimensions |
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assuming qkv_multipliers[0] != qkv_multipliers[1]. This results in variable allocation of parameters in attention layer. |
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This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623 |
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num_query_heads (`Union[int, None]`, *optional*, defaults to None): |
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The number of query heads, computed from `compute_heads(model_dim=model_dim, head_dim=head_dim)`. |
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num_gqa_groups (`int`, *optional*, defaults to 1): |
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This variable allows to switch between multi-head attention, group query attention, and multi-query attention. |
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When num_gqa_groups == 1, then it is multi-head attention. |
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When 1 < num_gqa_groups < num_heads and num_heads is divisible by num_gqa_groups, then it is group query attention |
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When num_gqa_groups == num_heads, then it is multi-query attention |
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ffn_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 4.0): |
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Feed-forward network (FFN) multipliers. |
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If the ffn_multipliers is a Number, then all FFN layers have the same latent dimensions, |
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resulting in uniform allocation of parameters. |
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If the ffn_multipliers is a List of Number, then each FFN layer have different latent dimensions |
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assuming ffn_multipliers[0] != ffn_multipliers[1]. This results in variable allocation of parameters in FFN layer. |
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This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623 |
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ffn_with_glu (`bool`, *optional*, defaults to True): |
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Whether to use FFN with Gated Linear Unit (GLU) |
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ffn_dim_divisor (`int`, *optional*, defaults to 256): |
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The ffn layer dimension divisor. |
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activation_fn_name (`str` or `function`, *optional*, defaults to `"swish"`): |
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The non-linear activation function (function or string) in the decoder. |
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normalization_layer_name (`str` or `function`, *optional*, defaults to `"rms_norm"`): |
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Type of normalization layer. |
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normalize_qk_projections (`bool`, *optional*, defaults to False): |
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Whether to normalize queries and keys after projections |
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share_input_output_layers (`bool`, *optional*, defaults to False): |
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Whether to share the embedding between input and output linear layer |
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rope_freq_constant (`int`, *optional*, defaults to 10000): |
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The base period of the RoPE embeddings. |
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rope_max_length (`int`, *optional*, defaults to 4096): |
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That rope_max_length is set to twice of max_context_length. |
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This allows flexibility in token lengths during training or fine-tuning. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). Only |
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relevant if `config.is_decoder=True`. |
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bos_token_id (`int`, *optional*, defaults to 2): |
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Beginning of stream token id. |
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eos_token_id (`int`, *optional*, defaults to 1): |
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End of stream token id. |
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""" |
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model_type = "openelm" |
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def __init__( |
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self, |
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vocab_size: int = 32000, |
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max_context_length: int = 2048, |
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num_transformer_layers: int = 12, |
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model_dim: int = 2048, |
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head_dim: int = 128, |
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qkv_multipliers: Union[Number, List[Number]] = 1.0, |
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num_query_heads: Union[int, None] = None, |
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num_gqa_groups: int = 1, |
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ffn_multipliers: Union[Number, List[Number]] = 4.0, |
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ffn_with_glu: bool = True, |
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ffn_dim_divisor: int = 256, |
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activation_fn_name: str = "swish", |
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normalization_layer_name: str = "rms_norm", |
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normalize_qk_projections: bool = False, |
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share_input_output_layers: bool = False, |
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rope_freq_constant: int = 10000, |
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rope_max_length: int = 4096, |
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initializer_range: float = 0.02, |
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use_cache: bool = True, |
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bos_token_id: int = 1, |
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eos_token_id: int = 2, |
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**kwargs, |
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) -> None: |
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self.vocab_size = vocab_size |
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self.max_context_length = max_context_length |
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self.num_transformer_layers = num_transformer_layers |
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self.model_dim = model_dim |
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self.head_dim = head_dim |
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self.qkv_multipliers = qkv_multipliers |
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self.num_query_heads = num_query_heads |
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self.num_gqa_groups = num_gqa_groups |
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self.ffn_multipliers = ffn_multipliers |
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self.ffn_with_glu = ffn_with_glu |
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self.ffn_dim_divisor = ffn_dim_divisor |
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self.activation_fn_name = activation_fn_name |
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self.normalization_layer_name = normalization_layer_name |
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self.normalize_qk_projections = normalize_qk_projections |
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self.share_input_output_layers = share_input_output_layers |
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self.rope_freq_constant = rope_freq_constant |
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self.rope_max_length = rope_max_length |
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self.num_query_heads = ( |
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compute_heads(model_dim=model_dim, head_dim=head_dim) |
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if num_query_heads is None |
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else num_query_heads |
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) |
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self.initializer_range = initializer_range |
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self.__post_init__() |
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super().__init__( |
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use_cache=use_cache, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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**kwargs, |
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) |
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def __post_init__(self) -> None: |
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if self.num_gqa_groups is not None: |
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head_multiple_of = self.num_gqa_groups |
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else: |
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head_multiple_of = 2 |
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|
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if isinstance(self.qkv_multipliers, Number): |
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qkv_dim = make_divisible( |
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self.model_dim * self.qkv_multipliers, |
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divisor=self.head_dim * head_multiple_of, |
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) |
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query_dims = [int(qkv_dim)] * self.num_transformer_layers |
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|
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elif ( |
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isinstance(self.qkv_multipliers, (tuple, list)) |
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and len(self.qkv_multipliers) == 2 |
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): |
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qkv_multipliers = [ |
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round(v, 2) |
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for v in np.linspace( |
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self.qkv_multipliers[0], |
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self.qkv_multipliers[1], |
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num=self.num_transformer_layers, |
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dtype=float, |
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) |
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] |
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query_dims = [ |
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int( |
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make_divisible( |
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self.model_dim * m, divisor=self.head_dim * head_multiple_of |
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) |
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) |
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for m in qkv_multipliers |
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] |
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else: |
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raise NotImplementedError( |
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f"QKV multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}." |
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) |
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self.num_query_heads = [ |
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int(compute_heads(q_dim, self.head_dim)) for q_dim in query_dims |
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] |
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self.num_kv_heads = [ |
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q_heads // self.num_gqa_groups for q_heads in self.num_query_heads |
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] |
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if isinstance(self.ffn_multipliers, Number): |
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self.ffn_multipliers = [self.ffn_multipliers] * self.num_transformer_layers |
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elif isinstance(self.ffn_multipliers, (tuple, list)): |
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|
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if len(self.ffn_multipliers) == 2: |
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self.ffn_multipliers = [ |
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round(v, 2) |
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for v in np.linspace( |
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self.ffn_multipliers[0], |
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self.ffn_multipliers[1], |
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num=self.num_transformer_layers, |
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dtype=float, |
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) |
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] |
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else: |
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assert ( |
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len(self.ffn_multipliers) == self.num_transformer_layers |
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), f"{len(self.ffn_multipliers)=}!={self.num_transformer_layers=}" |
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else: |
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raise NotImplementedError( |
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f"FFN multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}." |
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) |
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for layer_idx in range(len(query_dims)): |
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assert self.num_query_heads[layer_idx] % self.num_kv_heads[layer_idx] == 0 |
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|
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class OpenELMRMSNorm(nn.Module): |
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def __init__(self, num_features: int, eps: float = 1e-6): |
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""" |
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Initialize the OpenELMRMSNorm normalization layer. |
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Args: |
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dim (int): The dimension of the input tensor. |
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eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. |
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Attributes: |
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eps (float): A small value added to the denominator for numerical stability. |
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weight (nn.Parameter): Learnable scaling parameter. |
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""" |
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super().__init__() |
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self.eps = eps |
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self.weight = nn.Parameter(torch.ones(num_features)) |
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self.num_features = num_features |
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|
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def _norm(self, x: Tensor) -> Tensor: |
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""" |
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Apply the OpenELMRMSNorm normalization to the input tensor. |
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Args: |
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x (torch.Tensor): The input tensor. |
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Returns: |
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torch.Tensor: The normalized tensor. |
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""" |
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) |
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|
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def forward(self, x: Tensor) -> Tensor: |
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""" |
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Forward pass through the OpenELMRMSNorm layer. |
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Args: |
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x (torch.Tensor): The input tensor. |
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Returns: |
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torch.Tensor: The output tensor after applying OpenELMRMSNorm. |
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""" |
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output = self._norm(x.float()).type_as(x) |
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return output * self.weight |
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|
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def extra_repr(self) -> str: |
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return ( |
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super().extra_repr() + f"num_features={self.num_features}, eps={self.eps}" |
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) |
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|
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class OpenELMPreTrainedModel(PreTrainedModel): |
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config_class = OpenELMConfig |
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base_model_prefix = "transformer" |
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supports_gradient_checkpointing = True |
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_no_split_modules = ["OpenELMDecoderLayer"] |
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_skip_keys_device_placement = "past_key_values" |
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|
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def __init__(self, *inputs, **kwargs) -> None: |
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super().__init__(*inputs, **kwargs) |
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|
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def _init_weights(self, module: nn.Module) -> None: |
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"""Initialize the weights.""" |
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if isinstance(module, nn.Linear): |
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|
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|
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.bias is not None: |
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module.bias.data.zero_() |
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elif isinstance(module, nn.Embedding): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.padding_idx is not None: |
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module.weight.data[module.padding_idx].zero_() |
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elif isinstance(module, OpenELMRMSNorm): |
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module.weight.data.fill_(1.0) |
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|
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def _rotate_half(x: Tensor) -> Tensor: |
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x1, x2 = x.chunk(2, dim=-1) |
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return torch.cat((-x2, x1), dim=-1) |
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|
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def _apply_rotary_pos_emb(x: Tensor, pos_sin: Tensor, pos_cos: Tensor) -> Tensor: |
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return (x * pos_cos) + (_rotate_half(x) * pos_sin) |
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|
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|
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class OpenELMRotaryEmbedding(torch.nn.Module): |
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""" |
|
The rotary position embeddings (aka RoPE) from `RoFormer <https://arxiv.org/abs/2104.09864>`_. |
|
RoPE encodes the position information of tokens using a rotation matrix, and is able to capture |
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explicit relative positional dependencies. |
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Args: |
|
model_dim: The dimensionality of the model's hidden state. |
|
max_seq_length: Maximum sequence length. |
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freq_constant: A constant used for computing frequencies. |
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""" |
|
|
|
def __init__( |
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self, model_dim: int, max_seq_length: int, freq_constant: int = 10000 |
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) -> None: |
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inv_freq = 1.0 / ( |
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freq_constant |
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** (torch.arange(0, model_dim, 2, dtype=torch.float32) / model_dim) |
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) |
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super().__init__() |
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|
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self.model_dim = model_dim |
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self.freq_constant = freq_constant |
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self.max_seq_length = max_seq_length |
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|
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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self._cached_cos = None |
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self._cached_sin = None |
|
self._cached_seq_length = max_seq_length |
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self._compute_sin_cos_embeddings(max_seq_length) |
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|
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def extra_repr(self) -> str: |
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return f"\tmodel_dim={self.model_dim}, max_seq_length={self.max_seq_length}, freq_constant={self.freq_constant}" |
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|
|
def _compute_sin_cos_embeddings( |
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self, |
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key_len: int, |
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key_device: torch.device = torch.device("cpu"), |
|
key_dtype: torch.dtype = torch.float32, |
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) -> None: |
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""" |
|
Compute sine and cos embeddings. |
|
Args: |
|
key_len: Number of tokens in the key embeddings in the transformer model. |
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device: Device where the key embeddings are stored. |
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key_dtype: Data type of the key embeddings. |
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Returns: |
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None |
|
...note: |
|
We recalculate the sine and cosine embeddings if any of the following conditions are met: |
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1. The number of tokens in key embeddings are greater than the cached sequence length. |
|
2. Sine and cosine caches are empty. |
|
3. The device and data type of sine and cosine embeddings does not match with the key embeddings. |
|
""" |
|
if ( |
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key_len > self._cached_seq_length |
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or self._cached_cos is None |
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or (self._cached_cos is not None and self._cached_cos.device != key_device) |
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or (self._cached_cos is not None and self._cached_cos.dtype != key_dtype) |
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or self._cached_sin is None |
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or (self._cached_sin is not None and self._cached_sin.device != key_device) |
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or (self._cached_sin is not None and self._cached_sin.dtype != key_dtype) |
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): |
|
self._cached_seq_length = max(key_len, self._cached_seq_length) |
|
|
|
|
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pos_index = torch.arange( |
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self._cached_seq_length, |
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dtype=torch.float32, |
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device=self.inv_freq.device, |
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) |
|
|
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pos_index_theta = torch.einsum("i,j->ij", pos_index, self.inv_freq) |
|
|
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emb = torch.cat((pos_index_theta, pos_index_theta), dim=-1) |
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|
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|
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cos_emb = emb.cos().to(dtype=key_dtype, device=key_device) |
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sin_emb = emb.sin().to(dtype=key_dtype, device=key_device) |
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|
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|
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self._cached_cos = cos_emb[None, None, :, :] |
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self._cached_sin = sin_emb[None, None, :, :] |
|
|
|
def forward( |
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self, |
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query: torch.Tensor, |
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key: torch.Tensor, |
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) -> Tuple[torch.Tensor, torch.Tensor]: |
|
""" |
|
The forward function of RoPE embeddings. |
|
Args: |
|
query: Query embeddings in the transformer model. The shape of query embeddings is |
|
[Batch, number of query heads, number of query tokens, model dimension]. |
|
key: Key embeddings in the transformer model. The shape of key embeddings is |
|
[Batch, number of key heads, number of key tokens, model dimension]. |
|
Returns: |
|
A tuple containing the query and key embeddings with positional information. The shape of the returned query |
|
and key embeddings is the same as the input query and key embeddings respectively. |
|
...note: |
|
The RoPE embedding computation is done in full-precision. After the computation, input query and key tensors |
|
are casted to original input datatype. |
|
""" |
|
dim = key.shape[-1] |
|
key_len = key.shape[2] |
|
query_len = query.shape[2] |
|
|
|
assert dim == self.model_dim |
|
assert key.device == query.device |
|
assert key.dtype == query.dtype |
|
|
|
|
|
|
|
|
|
|
|
|
|
assert ( |
|
key_len >= query_len |
|
), "Number of keys has to be greater than or equal to number of queries." |
|
|
|
query_float = query.float() |
|
key_float = key.float() |
|
|
|
self._compute_sin_cos_embeddings( |
|
key_len, key_device=key_float.device, key_dtype=key_float.dtype |
|
) |
|
query_float = _apply_rotary_pos_emb( |
|
x=query_float, |
|
pos_sin=self._cached_sin[..., key_len - query_len : key_len, :], |
|
pos_cos=self._cached_cos[..., key_len - query_len : key_len, :], |
|
) |
|
key_float = _apply_rotary_pos_emb( |
|
x=key_float, |
|
pos_sin=self._cached_sin[..., :key_len, :], |
|
pos_cos=self._cached_cos[..., :key_len, :], |
|
) |
|
|
|
return query_float.type_as(query), key_float.type_as(key) |
|
|
|
|
|
class OpenELMMultiHeadCausalAttention(nn.Module): |
|
def __init__(self, config: OpenELMConfig, layer_idx: int) -> None: |
|
super().__init__() |
|
self.layer_idx = layer_idx |
|
head_dim = config.head_dim |
|
q_heads = config.num_query_heads[layer_idx] |
|
k_heads = config.num_kv_heads[layer_idx] |
|
v_heads = config.num_kv_heads[layer_idx] |
|
|
|
self.qkv_proj = nn.Linear( |
|
in_features=config.model_dim, |
|
out_features=(q_heads + k_heads + v_heads) * head_dim, |
|
bias=False, |
|
) |
|
|
|
self.pos_embedding = OpenELMRotaryEmbedding( |
|
model_dim=config.head_dim, |
|
max_seq_length=config.rope_max_length, |
|
freq_constant=config.rope_freq_constant, |
|
) |
|
|
|
if config.normalize_qk_projections: |
|
self.q_norm = OpenELMRMSNorm( |
|
num_features=config.head_dim, |
|
) |
|
self.k_norm = OpenELMRMSNorm( |
|
num_features=config.head_dim, |
|
) |
|
else: |
|
self.q_norm = None |
|
self.k_norm = None |
|
|
|
self.out_proj = nn.Linear( |
|
in_features=q_heads * head_dim, |
|
out_features=config.model_dim, |
|
bias=False, |
|
) |
|
|
|
self.head_dim = config.head_dim |
|
self.num_q_heads = q_heads |
|
self.num_k_heads = k_heads |
|
self.num_v_heads = v_heads |
|
self.transformer_dim = config.model_dim |
|
self.num_groups = self.num_q_heads // self.num_k_heads |
|
|
|
def extra_repr(self) -> str: |
|
return ( |
|
super().extra_repr() |
|
+ f"query_heads={self.num_q_heads}, key_heads={self.num_k_heads}, value_heads={self.num_v_heads}" |
|
) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
past_key_value: Optional[Cache] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
""" |
|
Forward pass of multi-head self-attention. |
|
Args: |
|
hidden_states: Input tensor of the shape [batch size, sequence length, model dimension]. |
|
past_key_value: Tensor storing the cached keys and values. |
|
output_attentions: output attention weights. |
|
use_cache: Specifies whether to use kv-cache for generation. |
|
cache_position: used for updating the kv-cache. |
|
Returns: |
|
The output of the same shape as the input, optionally with a tensor containing cached keys and values. |
|
""" |
|
|
|
|
|
output_attentions = False |
|
batch_size, seq_length, d_model = hidden_states.size() |
|
|
|
|
|
qkv = self.qkv_proj(hidden_states) |
|
|
|
qkv = qkv.reshape( |
|
batch_size, |
|
seq_length, |
|
self.num_q_heads + self.num_k_heads + self.num_v_heads, |
|
self.head_dim, |
|
) |
|
|
|
qkv = qkv.transpose(1, 2) |
|
|
|
queries, keys, values = qkv.split( |
|
[self.num_q_heads, self.num_k_heads, self.num_v_heads], dim=1 |
|
) |
|
|
|
if self.q_norm is not None: |
|
queries = self.q_norm(queries) |
|
|
|
if self.k_norm is not None: |
|
keys = self.k_norm(keys) |
|
|
|
past_key_value = getattr(self, "past_key_value", past_key_value) |
|
|
|
if past_key_value is not None: |
|
|
|
|
|
cache_kwargs = {"cache_position": cache_position} |
|
keys, values = past_key_value.update( |
|
keys, values, self.layer_idx, cache_kwargs |
|
) |
|
|
|
|
|
queries, keys = self.pos_embedding(queries, keys) |
|
|
|
if self.num_groups != 1: |
|
|
|
|
|
keys = keys.repeat_interleave(self.num_groups, dim=1) |
|
|
|
values = values.repeat_interleave(self.num_groups, dim=1) |
|
|
|
causal_mask = attention_mask |
|
if attention_mask is not None and cache_position is not None: |
|
causal_mask = causal_mask[:, :, cache_position, : keys.shape[-2]] |
|
|
|
attn_output = F.scaled_dot_product_attention( |
|
queries, |
|
keys, |
|
values, |
|
attn_mask=causal_mask, |
|
dropout_p=0, |
|
) |
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous() |
|
attn_output = attn_output.reshape( |
|
batch_size, seq_length, self.num_q_heads * self.head_dim |
|
) |
|
attn_output = self.out_proj(attn_output) |
|
if not output_attentions: |
|
attn_weights = None |
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
class OpenELMFeedForwardNetwork(nn.Module): |
|
def __init__(self, config: OpenELMConfig, layer_idx: int) -> None: |
|
super().__init__() |
|
ffn_multiplier = config.ffn_multipliers[layer_idx] |
|
intermediate_dim = int( |
|
make_divisible( |
|
ffn_multiplier * config.model_dim, |
|
divisor=config.ffn_dim_divisor, |
|
) |
|
) |
|
if config.ffn_with_glu: |
|
|
|
self.proj_1 = nn.Linear( |
|
in_features=config.model_dim, |
|
out_features=2 * intermediate_dim, |
|
bias=False, |
|
) |
|
self.proj_2 = nn.Linear( |
|
in_features=intermediate_dim, |
|
out_features=config.model_dim, |
|
bias=False, |
|
) |
|
self.ffn_with_glu = True |
|
else: |
|
|
|
self.proj_1 = nn.Linear( |
|
in_features=config.model_dim, |
|
out_features=intermediate_dim, |
|
bias=False, |
|
) |
|
self.proj_2 = nn.Linear( |
|
in_features=intermediate_dim, |
|
out_features=config.model_dim, |
|
bias=False, |
|
) |
|
self.ffn_with_glu = False |
|
|
|
self.act = ACT2FN[config.activation_fn_name] |
|
|
|
def extra_repr(self) -> str: |
|
return super().extra_repr() + f"(ffn_with_glu) : {self.ffn_with_glu}" |
|
|
|
def forward(self, x: Tensor) -> Tensor: |
|
"""Forward function of FFN layer. |
|
Args: |
|
x: Input tensor of the shape [batch size, sequence length, model dimension]. |
|
Returns: |
|
A tensor of the same shape as the input. |
|
""" |
|
if self.ffn_with_glu: |
|
y_12 = self.proj_1(x) |
|
y_1, y_2 = y_12.chunk(2, dim=-1) |
|
y = self.act(y_1) * y_2 |
|
return self.proj_2(y) |
|
else: |
|
return self.proj_2(self.act(self.proj_1(x))) |
|
|
|
|
|
class OpenELMDecoderLayer(nn.Module): |
|
def __init__(self, config: OpenELMConfig, layer_idx: int) -> None: |
|
super().__init__() |
|
self.attn = OpenELMMultiHeadCausalAttention(config=config, layer_idx=layer_idx) |
|
self.ffn = OpenELMFeedForwardNetwork(config=config, layer_idx=layer_idx) |
|
self.ffn_norm = OpenELMRMSNorm( |
|
num_features=config.model_dim, |
|
) |
|
self.attn_norm = OpenELMRMSNorm( |
|
num_features=config.model_dim, |
|
) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
output_attentions: Optional[bool] = False, |
|
use_cache: Optional[bool] = False, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
**kwargs, |
|
) -> Tuple[ |
|
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] |
|
]: |
|
""" |
|
Args: |
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` |
|
attention_mask (`torch.FloatTensor`, *optional*): |
|
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, |
|
query_sequence_length, key_sequence_length)` if default attention is used. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding |
|
(see `past_key_values`). |
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
|
""" |
|
residual = hidden_states |
|
hidden_states = self.attn_norm(hidden_states) |
|
|
|
|
|
hidden_states, self_attn_weights, present_key_value = self.attn( |
|
hidden_states=hidden_states, |
|
attention_mask=attention_mask, |
|
past_key_value=past_key_value, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
cache_position=cache_position, |
|
**kwargs, |
|
) |
|
hidden_states = residual + hidden_states |
|
|
|
|
|
residual = hidden_states |
|
hidden_states = self.ffn_norm(hidden_states) |
|
hidden_states = self.ffn(hidden_states) |
|
hidden_states = residual + hidden_states |
|
|
|
outputs = (hidden_states,) |
|
|
|
if output_attentions: |
|
outputs += (self_attn_weights,) |
|
|
|
if use_cache: |
|
outputs += (present_key_value,) |
|
|
|
return outputs |
|
|
|
|
|
class OpenELMModel(OpenELMPreTrainedModel): |
|
config_class = OpenELMConfig |
|
|
|
def __init__(self, config: OpenELMConfig): |
|
super().__init__(config) |
|
self.config = config |
|
|
|
self.token_embeddings = nn.Embedding( |
|
embedding_dim=config.model_dim, |
|
num_embeddings=config.vocab_size, |
|
) |
|
|
|
self.layers = nn.ModuleList( |
|
OpenELMDecoderLayer(config=config, layer_idx=layer_idx) |
|
for layer_idx in range(config.num_transformer_layers) |
|
) |
|
self.norm = OpenELMRMSNorm(num_features=config.model_dim) |
|
if config.share_input_output_layers: |
|
self.classifier = None |
|
else: |
|
self.classifier = nn.Linear( |
|
in_features=config.model_dim, |
|
out_features=config.vocab_size, |
|
bias=False, |
|
) |
|
self.num_transformer_layers = config.num_transformer_layers |
|
self.gradient_checkpointing = False |
|
|
|
|
|
|
|
causal_mask = torch.full( |
|
(config.max_context_length, config.max_context_length), |
|
fill_value=True, |
|
dtype=torch.bool, |
|
) |
|
self.register_buffer( |
|
"causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False |
|
) |
|
|
|
|
|
self.post_init() |
|
self.reset_parameters(config=config) |
|
|
|
def get_input_embeddings(self): |
|
return self.token_embeddings |
|
|
|
def set_input_embeddings(self, new_embeddings: torch.Tensor): |
|
self.token_embeddings = new_embeddings |
|
|
|
def reset_parameters(self, config: OpenELMConfig) -> None: |
|
"""Initialize the layers in Language Model |
|
The initialization scheme is followed, following `OPT <https://arxiv.org/pdf/2205.01068.pdf>`_. |
|
Args: |
|
use_megatron_std: Use standard deviation as described in Megatron-LM. |
|
Returns: |
|
None |
|
""" |
|
for module in self.modules(): |
|
if isinstance(module, nn.Linear): |
|
std = module.in_features**-0.5 |
|
torch.nn.init.normal_(module.weight, mean=0.0, std=std) |
|
if module.bias is not None: |
|
torch.nn.init.zeros_(module.bias) |
|
elif isinstance(module, nn.Embedding): |
|
std = module.embedding_dim**-0.5 |
|
torch.nn.init.normal_(module.weight, mean=0.0, std=std) |
|
elif isinstance(module, OpenELMRMSNorm): |
|
if module.weight is not None: |
|
torch.nn.init.ones_(module.weight) |
|
if hasattr(module, "bias") and module.bias is not None: |
|
torch.nn.init.zeros_(module.bias) |
|
|
|
model_dim = config.model_dim |
|
n_layers = config.num_transformer_layers |
|
std = (model_dim**-0.5) * ((2 * n_layers) ** -0.5) |
|
for param_name, param in self.named_parameters(): |
|
if param_name.endswith("out_proj.weight") or param_name.endswith( |
|
"ffn.proj_2.weight" |
|
): |
|
torch.nn.init.normal_(param, mean=0.0, std=std) |
|
|
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPast]: |
|
output_attentions = ( |
|
output_attentions |
|
if output_attentions is not None |
|
else self.config.output_attentions |
|
) |
|
output_hidden_states = ( |
|
output_hidden_states |
|
if output_hidden_states is not None |
|
else self.config.output_hidden_states |
|
) |
|
use_cache = use_cache if use_cache is not None else self.config.use_cache |
|
return_dict = ( |
|
return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None): |
|
raise ValueError( |
|
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" |
|
) |
|
|
|
if self.gradient_checkpointing and self.training and use_cache: |
|
logger.warning_once( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." |
|
) |
|
use_cache = False |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.token_embeddings(input_ids) |
|
|
|
past_seen_tokens = 0 |
|
if use_cache: |
|
if not isinstance(past_key_values, StaticCache): |
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values) |
|
past_seen_tokens = past_key_values.get_seq_length() |
|
|
|
if cache_position is None: |
|
cache_position = torch.arange( |
|
past_seen_tokens, |
|
past_seen_tokens + inputs_embeds.shape[1], |
|
device=inputs_embeds.device, |
|
) |
|
|
|
if position_ids is None: |
|
position_ids = cache_position.unsqueeze(0) |
|
|
|
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds) |
|
|
|
|
|
hidden_states = inputs_embeds |
|
|
|
|
|
all_hidden_states = () if output_hidden_states else None |
|
all_self_attns = () if output_attentions else None |
|
next_decoder_cache = None |
|
|
|
for decoder_layer in self.layers: |
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
layer_outputs = self._gradient_checkpointing_func( |
|
decoder_layer.__call__, |
|
hidden_states, |
|
causal_mask, |
|
position_ids, |
|
past_key_values, |
|
output_attentions, |
|
use_cache, |
|
cache_position, |
|
) |
|
else: |
|
layer_outputs = decoder_layer( |
|
hidden_states, |
|
attention_mask=causal_mask, |
|
position_ids=position_ids, |
|
past_key_value=past_key_values, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
cache_position=cache_position, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
|
|
if use_cache: |
|
next_decoder_cache = layer_outputs[2 if output_attentions else 1] |
|
|
|
if output_attentions: |
|
all_self_attns += (layer_outputs[1],) |
|
|
|
hidden_states = self.norm(hidden_states) |
|
|
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
next_cache = None |
|
if use_cache: |
|
next_cache = ( |
|
next_decoder_cache.to_legacy_cache() |
|
if isinstance(next_decoder_cache, Cache) |
|
else next_decoder_cache |
|
) |
|
if not return_dict: |
|
return tuple( |
|
v |
|
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] |
|
if v is not None |
|
) |
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_self_attns, |
|
) |
|
|
|
def _update_causal_mask(self, attention_mask, input_tensor): |
|
if self.config._attn_implementation == "flash_attention_2": |
|
if attention_mask is not None and 0.0 in attention_mask: |
|
return attention_mask |
|
return None |
|
|
|
batch_size, seq_length = input_tensor.shape[:2] |
|
dtype = input_tensor.dtype |
|
device = input_tensor.device |
|
|
|
|
|
if seq_length > self.causal_mask.shape[-1]: |
|
causal_mask = torch.full( |
|
(2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), |
|
fill_value=1, |
|
) |
|
self.register_buffer( |
|
"causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False |
|
) |
|
|
|
|
|
min_dtype = torch.finfo(dtype).min |
|
causal_mask = ( |
|
self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) |
|
* min_dtype |
|
) |
|
|
|
causal_mask = causal_mask.to(dtype=dtype, device=device) |
|
if attention_mask is not None and attention_mask.dim() == 2: |
|
mask_length = attention_mask.shape[-1] |
|
padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[ |
|
:, None, None, : |
|
].eq(0.0) |
|
causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill( |
|
padding_mask, min_dtype |
|
) |
|
|
|
if self.config._attn_implementation == "sdpa" and attention_mask is not None: |
|
|
|
is_tracing = ( |
|
torch.jit.is_tracing() |
|
or isinstance(input_tensor, torch.fx.Proxy) |
|
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling()) |
|
) |
|
if not is_tracing and torch.any(attention_mask != 1): |
|
|
|
|
|
|
|
causal_mask = causal_mask.mul( |
|
~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True) |
|
).to(dtype) |
|
|
|
return causal_mask |
|
|
|
|
|
class OpenELMForCausalLM(OpenELMPreTrainedModel): |
|
_tied_weights_keys = ["lm_head.weight"] |
|
|
|
def __init__(self, config: OpenELMConfig): |
|
super().__init__(config) |
|
self.transformer = OpenELMModel(config) |
|
self.vocab_size = config.vocab_size |
|
if config.share_input_output_layers: |
|
self.lm_head = None |
|
else: |
|
self.lm_head = nn.Linear(config.model_dim, config.vocab_size, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.transformer.token_embeddings |
|
|
|
def set_input_embeddings(self, value): |
|
self.transformer.token_embeddings = value |
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
def set_decoder(self, decoder): |
|
self.transformer = decoder |
|
|
|
def get_decoder(self): |
|
return self.transformer |
|
|
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
output_attentions = ( |
|
output_attentions |
|
if output_attentions is not None |
|
else self.config.output_attentions |
|
) |
|
output_hidden_states = ( |
|
output_hidden_states |
|
if output_hidden_states is not None |
|
else self.config.output_hidden_states |
|
) |
|
return_dict = ( |
|
return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
|
|
outputs = self.transformer( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
cache_position=cache_position, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
if self.lm_head is None: |
|
|
|
logits = F.linear( |
|
hidden_states, weight=self.transformer.token_embeddings.weight |
|
) |
|
else: |
|
logits = self.lm_head(hidden_states) |
|
logits = logits[:, : self.config.vocab_size] |
|
loss = None |
|
if labels is not None: |
|
|
|
shift_logits = logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss() |
|
shift_logits = shift_logits.view(-1, self.config.vocab_size) |
|
shift_labels = shift_labels.view(-1) |
|
|
|
shift_labels = shift_labels.to(shift_logits.device) |
|
loss = loss_fct(shift_logits, shift_labels) |
|
|
|
if not return_dict: |
|
output = (logits,) + outputs[1:] |
|
return (loss,) + output if loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=loss, |
|
logits=logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
def prepare_inputs_for_generation( |
|
self, |
|
input_ids, |
|
past_key_values=None, |
|
attention_mask=None, |
|
inputs_embeds=None, |
|
**kwargs, |
|
): |
|
past_length = 0 |
|
if past_key_values is not None: |
|
if isinstance(past_key_values, Cache): |
|
cache_length = past_key_values.get_seq_length() |
|
past_length = past_key_values.seen_tokens |
|
max_cache_length = past_key_values.get_max_length() |
|
else: |
|
cache_length = past_length = past_key_values[0][0].shape[2] |
|
max_cache_length = None |
|
|
|
|
|
|
|
|
|
|
|
if ( |
|
attention_mask is not None |
|
and attention_mask.shape[1] > input_ids.shape[1] |
|
): |
|
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] |
|
|
|
|
|
elif past_length < input_ids.shape[1]: |
|
input_ids = input_ids[:, past_length:] |
|
|
|
|
|
|
|
if ( |
|
max_cache_length is not None |
|
and attention_mask is not None |
|
and cache_length + input_ids.shape[1] > max_cache_length |
|
): |
|
attention_mask = attention_mask[:, -max_cache_length:] |
|
|
|
position_ids = kwargs.get("position_ids", None) |
|
if attention_mask is not None and position_ids is None: |
|
|
|
position_ids = attention_mask.long().cumsum(-1) - 1 |
|
position_ids.masked_fill_(attention_mask == 0, 1) |
|
if past_key_values: |
|
position_ids = position_ids[:, -input_ids.shape[1] :] |
|
|
|
if self.generation_config.cache_implementation == "static": |
|
|
|
cache_position = kwargs.get("cache_position", None) |
|
if cache_position is None: |
|
past_length = 0 |
|
else: |
|
past_length = cache_position[-1] + 1 |
|
input_ids = input_ids[:, past_length:] |
|
position_ids = position_ids[:, past_length:] |
|
|
|
|
|
|
|
cache_position = torch.arange( |
|
past_length, |
|
past_length + position_ids.shape[-1], |
|
device=position_ids.device, |
|
) |
|
|
|
|
|
if inputs_embeds is not None and past_key_values is None: |
|
model_inputs = {"inputs_embeds": inputs_embeds} |
|
else: |
|
|
|
|
|
|
|
model_inputs = {"input_ids": input_ids.contiguous()} |
|
|
|
model_inputs.update( |
|
{ |
|
"position_ids": position_ids.contiguous(), |
|
"cache_position": cache_position, |
|
"past_key_values": past_key_values, |
|
"use_cache": kwargs.get("use_cache"), |
|
"attention_mask": attention_mask, |
|
} |
|
) |
|
return model_inputs |
|
|
|
@staticmethod |
|
def _reorder_cache(past_key_values, beam_idx): |
|
reordered_past = () |
|
for layer_past in past_key_values: |
|
reordered_past += ( |
|
tuple( |
|
past_state.index_select(0, beam_idx.to(past_state.device)) |
|
for past_state in layer_past |
|
), |
|
) |
|
return reordered_past |
|
|
|
|
|
ACT_TYPE = { |
|
'relu': nn.ReLU, |
|
'gelu': nn.GELU |
|
} |
|
|
|
class Connector(nn.Module): |
|
def __init__(self, config=None): |
|
super().__init__() |
|
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', config.connector_type) |
|
act_type = config.connector_type.split('_')[-1] |
|
mlp_depth = int(mlp_gelu_match.group(1)) |
|
modules = [nn.Linear(config.vision_hidden_size, config.hidden_size)] |
|
for _ in range(1, mlp_depth): |
|
modules.append(ACT_TYPE[act_type]()) |
|
modules.append(nn.Linear(config.hidden_size, config.hidden_size)) |
|
|
|
self._connector = nn.Sequential(*modules) |
|
|
|
def forward(self, x): |
|
return self._connector(x) |
|
|
|
class VisionTower(nn.Module): |
|
def __init__(self, cfg, model_name_or_path = 'clip'): |
|
super().__init__() |
|
if 'clip' in model_name_or_path: |
|
self._vision_tower = CLIPVisionModel(cfg) |
|
self._image_processor = CLIPImageProcessor.from_pretrained(cfg.model_name_or_path) |
|
else: |
|
self._vision_tower = SiglipVisionModel(cfg) |
|
self._image_processor = SiglipImageProcessor.from_pretrained(cfg.model_name_or_path) |
|
|
|
self.config = cfg |
|
|
|
|
|
|
|
def forward(self, x, **kwargs): |
|
image_features = self._vision_tower(x, output_hidden_states=True) |
|
image_features = image_features.hidden_states[kwargs.get('vision_feature_layer', -2)] |
|
|
|
if kwargs.get('vision_feature_select_strategy', 'patch') == 'patch': |
|
image_features = image_features[:, 1:] |
|
elif kwargs.get('vision_feature_select_strategy', 'patch') == 'cls_patch': |
|
image_features = image_features |
|
else: |
|
raise ValueError(f"Unexpected select feature: {kwargs.get('vision_feature_select_strategy')}") |
|
|
|
return image_features |
|
|
|
|
|
|
|
@property |
|
def vision_tower(self): |
|
return self._vision_tower |
|
|
|
@vision_tower.setter |
|
def vision_tower(self, vision_tower): |
|
self._vision_tower = vision_tower |
|
|
|
def get_value_from_kwargs(kwargs, name): |
|
if name in kwargs: |
|
return kwargs.pop(name) |
|
else: |
|
return None |
|
|
|
|
|
|
|
class TinyLlavaPreTrainedModel(PreTrainedModel): |
|
config_class = TinyLlavaConfig |
|
base_model_prefix = "model" |
|
supports_gradient_checkpointing = True |
|
_no_split_modules = ["LlavaVisionAttention"] |
|
_skip_keys_device_placement = "past_key_values" |
|
_supports_flash_attn_2 = True |
|
|
|
def _init_weights(self, module): |
|
std = ( |
|
self.config.initializer_range |
|
if hasattr(self.config, "initializer_range") |
|
else self.config.text_config.initializer_range |
|
) |
|
|
|
if hasattr(module, "class_embedding"): |
|
module.class_embedding.data.normal_(mean=0.0, std=std) |
|
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.bias is not None: |
|
module.bias.data.zero_() |
|
elif isinstance(module, nn.Embedding): |
|
module.weight.data.normal_(mean=0.0, std=std) |
|
if module.padding_idx is not None: |
|
module.weight.data[module.padding_idx].zero_() |
|
|
|
@property |
|
def _supports_sdpa(self): |
|
return self.language_model._supports_sdpa |
|
|
|
|
|
class TinyLlavaForConditionalGeneration(TinyLlavaPreTrainedModel): |
|
def __init__(self, config: TinyLlavaConfig): |
|
|
|
super().__init__(config) |
|
|
|
self.language_model = OpenELMForCausalLM(config.text_config) |
|
self.vision_tower = VisionTower(config.vision_config, config.vision_model_name_or_path) |
|
self.connector = Connector(config) |
|
self.post_init() |
|
|
|
|
|
def get_input_embeddings(self): |
|
return self.language_model.get_input_embeddings() |
|
|
|
def set_input_embeddings(self, value): |
|
self.language_model.set_input_embeddings(value) |
|
|
|
def get_output_embeddings(self): |
|
return self.language_model.get_output_embeddings() |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.language_model.set_output_embeddings(new_embeddings) |
|
|
|
def set_decoder(self, decoder): |
|
self.language_model.set_decoder(decoder) |
|
|
|
def get_decoder(self): |
|
return self.language_model.get_decoder() |
|
|
|
def tie_weights(self): |
|
return self.language_model.tie_weights() |
|
|
|
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding: |
|
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of) |
|
|
|
self.config.text_config.vocab_size = model_embeds.num_embeddings |
|
self.config.vocab_size = model_embeds.num_embeddings |
|
self.vocab_size = model_embeds.num_embeddings |
|
return model_embeds |
|
|
|
|
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[List[torch.FloatTensor]] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
images: Optional[torch.FloatTensor] = None, |
|
image_sizes: Optional[List[List[int]]] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
use_cache = use_cache if use_cache is not None else self.config.use_cache |
|
if inputs_embeds is None: |
|
( |
|
input_ids, |
|
position_ids, |
|
attention_mask, |
|
past_key_values, |
|
inputs_embeds, |
|
labels |
|
) = self.prepare_inputs_labels_for_multimodal( |
|
input_ids, |
|
position_ids, |
|
attention_mask, |
|
past_key_values, |
|
labels, |
|
images, |
|
image_sizes |
|
) |
|
return self.language_model.forward( |
|
input_ids=input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_values=past_key_values, |
|
inputs_embeds=inputs_embeds, |
|
labels=labels, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict |
|
) |
|
|
|
@torch.no_grad() |
|
def generate( |
|
self, |
|
inputs: Optional[torch.Tensor] = None, |
|
images: Optional[torch.Tensor] = None, |
|
image_sizes: Optional[torch.Tensor] = None, |
|
**kwargs, |
|
) -> Union[GenerateOutput, torch.LongTensor]: |
|
position_ids = kwargs.pop("position_ids", None) |
|
attention_mask = kwargs.pop("attention_mask", None) |
|
if "inputs_embeds" in kwargs: |
|
raise NotImplementedError("`inputs_embeds` is not supported") |
|
|
|
if images is not None: |
|
( |
|
inputs, |
|
position_ids, |
|
attention_mask, |
|
_, |
|
inputs_embeds, |
|
_ |
|
) = self.prepare_inputs_labels_for_multimodal( |
|
inputs, |
|
position_ids, |
|
attention_mask, |
|
None, |
|
None, |
|
images, |
|
image_sizes=image_sizes |
|
) |
|
else: |
|
inputs_embeds = self.language_model.get_input_embeddings()(inputs) |
|
|
|
return self.language_model.generate( |
|
position_ids=position_ids, |
|
attention_mask=attention_mask, |
|
inputs_embeds=inputs_embeds, |
|
**kwargs |
|
) |
|
|
|
def encode_images(self, images): |
|
kwargs = {} |
|
kwargs['vision_feature_layer'] = self.config.vision_feature_layer |
|
kwargs['vision_feature_select_strategy'] = self.config.vision_feature_select_strategy |
|
images = images.to(device=self.device, dtype=self.dtype) |
|
image_features = self.vision_tower(images, **kwargs) |
|
image_features = self.connector(image_features) |
|
return image_features |
|
|
|
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, |
|
inputs_embeds=None, **kwargs): |
|
images = kwargs.pop("images", None) |
|
image_sizes = kwargs.pop("image_sizes", None) |
|
inputs = self.language_model.prepare_inputs_for_generation( |
|
input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs |
|
) |
|
if images is not None: |
|
inputs['images'] = images |
|
if image_sizes is not None: |
|
inputs['image_sizes'] = image_sizes |
|
return inputs |
|
|
|
def prepare_inputs_labels_for_multimodal( |
|
self, input_ids, position_ids, attention_mask, past_key_values, labels, |
|
images, image_sizes=None |
|
): |
|
vision_tower = self.vision_tower |
|
if vision_tower is None or images is None or input_ids.shape[1] == 1: |
|
return input_ids, position_ids, attention_mask, past_key_values, None, labels |
|
|
|
|
|
image_features = self.encode_images(images) |
|
|
|
|
|
if getattr(self.config, 'tune_mm_mlp_adapter', False): |
|
raise NotImplementedError |
|
|
|
|
|
|
|
|
|
|
|
_labels = labels |
|
_position_ids = position_ids |
|
_attention_mask = attention_mask |
|
if attention_mask is None: |
|
attention_mask = torch.ones_like(input_ids, dtype=torch.bool) |
|
else: |
|
attention_mask = attention_mask.bool() |
|
if position_ids is None: |
|
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device) |
|
if labels is None: |
|
labels = torch.full_like(input_ids, IGNORE_INDEX) |
|
|
|
|
|
_input_ids = input_ids |
|
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)] |
|
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)] |
|
|
|
new_input_embeds = [] |
|
new_labels = [] |
|
cur_image_idx = 0 |
|
for batch_idx, cur_input_ids in enumerate(input_ids): |
|
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum() |
|
if num_images == 0: |
|
cur_image_features = image_features[cur_image_idx] |
|
cur_input_embeds_1 = self.language_model.get_input_embeddings()(cur_input_ids) |
|
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0) |
|
new_input_embeds.append(cur_input_embeds) |
|
new_labels.append(labels[batch_idx]) |
|
cur_image_idx += 1 |
|
continue |
|
|
|
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]] |
|
cur_input_ids_noim = [] |
|
cur_labels = labels[batch_idx] |
|
cur_labels_noim = [] |
|
for i in range(len(image_token_indices) - 1): |
|
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]]) |
|
cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]]) |
|
split_sizes = [x.shape[0] for x in cur_labels_noim] |
|
cur_input_embeds = self.language_model.get_input_embeddings()(torch.cat(cur_input_ids_noim)) |
|
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0) |
|
cur_new_input_embeds = [] |
|
cur_new_labels = [] |
|
|
|
for i in range(num_images + 1): |
|
cur_new_input_embeds.append(cur_input_embeds_no_im[i]) |
|
cur_new_labels.append(cur_labels_noim[i]) |
|
if i < num_images: |
|
cur_image_features = image_features[cur_image_idx] |
|
cur_image_idx += 1 |
|
cur_new_input_embeds.append(cur_image_features) |
|
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype)) |
|
|
|
cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds] |
|
|
|
cur_new_input_embeds = torch.cat(cur_new_input_embeds) |
|
cur_new_labels = torch.cat(cur_new_labels) |
|
|
|
new_input_embeds.append(cur_new_input_embeds) |
|
new_labels.append(cur_new_labels) |
|
|
|
|
|
tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None) |
|
if tokenizer_model_max_length is not None: |
|
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds] |
|
new_labels = [x[:tokenizer_model_max_length] for x in new_labels] |
|
|
|
|
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max_len = max(x.shape[0] for x in new_input_embeds) |
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batch_size = len(new_input_embeds) |
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|
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new_input_embeds_padded = [] |
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new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device) |
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attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device) |
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position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device) |
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|
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for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)): |
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cur_len = cur_new_embed.shape[0] |
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if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": |
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new_input_embeds_padded.append(torch.cat(( |
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torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), |
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cur_new_embed |
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), dim=0)) |
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if cur_len > 0: |
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new_labels_padded[i, -cur_len:] = cur_new_labels |
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attention_mask[i, -cur_len:] = True |
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position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device) |
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else: |
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new_input_embeds_padded.append(torch.cat(( |
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cur_new_embed, |
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torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device) |
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), dim=0)) |
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if cur_len > 0: |
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new_labels_padded[i, :cur_len] = cur_new_labels |
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attention_mask[i, :cur_len] = True |
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position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device) |
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|
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new_input_embeds = torch.stack(new_input_embeds_padded, dim=0) |
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|
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if _labels is None: |
|
new_labels = None |
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else: |
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new_labels = new_labels_padded |
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|
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if _attention_mask is None: |
|
attention_mask = None |
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else: |
|
attention_mask = attention_mask.to(dtype=_attention_mask.dtype) |
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|
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if _position_ids is None: |
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position_ids = None |
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|
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return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels |
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|
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AutoConfig.register("tinyllava", TinyLlavaConfig) |
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AutoModelForCausalLM.register(TinyLlavaConfig, TinyLlavaForConditionalGeneration) |
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