# Copyright (c) 2022, Tri Dao. # This BERT implementation is based on our MLPerf 2.0 and MLPerf 2.1 BERT implementation. # https://github.com/mlcommons/training_results_v2.0/blob/main/HazyResearch/benchmarks/bert/implementations/pytorch/modeling.py # https://github.com/mlcommons/training_results_v2.1/blob/main/Azure-HazyResearch/benchmarks/bert/implementations/ND96amsr_A100_v4/modeling.py import collections import logging # Inspired by https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py import math import os import re from collections import OrderedDict from functools import partial from typing import List, Optional, Tuple, Union import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from safetensors.torch import load_file as safe_load_file from torch.nn.modules.utils import _pair from transformers import GPT2Config, PreTrainedModel, ViTConfig, ViTModel from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.models.bert.modeling_bert import ( BaseModelOutputWithPoolingAndCrossAttentions, MaskedLMOutput, SequenceClassifierOutput, ) from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME from transformers.utils.hub import cached_file, get_checkpoint_shard_files from .configuration_hf_nomic_bert import NomicBertConfig try: from torch.nn.functional import scaled_dot_product_attention except ImportError: scaled_dot_product_attention = None logger = logging.getLogger(__name__) # adapted from flash attention, added safe serialization option for hf models def state_dict_from_pretrained(model_name, safe_serialization=False, device=None, dtype=None): # If not fp32, then we don't want to load directly to the GPU mapped_device = "cpu" if dtype not in [torch.float32, None] else device is_sharded = False load_safe = False resolved_archive_file = None weights_path = os.path.join(model_name, WEIGHTS_NAME) weights_index_path = os.path.join(model_name, WEIGHTS_INDEX_NAME) safe_weights_path = os.path.join(model_name, SAFE_WEIGHTS_NAME) safe_weights_index_path = os.path.join(model_name, SAFE_WEIGHTS_INDEX_NAME) if os.path.isfile(weights_path): resolved_archive_file = cached_file(model_name, WEIGHTS_NAME, _raise_exceptions_for_missing_entries=False) elif os.path.isfile(weights_index_path): resolved_archive_file = cached_file(model_name, WEIGHTS_INDEX_NAME, _raise_exceptions_for_missing_entries=False) is_sharded = True elif os.path.isfile(safe_weights_path): resolved_archive_file = cached_file(model_name, SAFE_WEIGHTS_NAME, _raise_exceptions_for_missing_entries=False) load_safe = True elif os.path.isfile(safe_weights_index_path): resolved_archive_file = cached_file( model_name, SAFE_WEIGHTS_INDEX_NAME, _raise_exceptions_for_missing_entries=False ) is_sharded = True load_safe = True else: # Try loading from HF hub instead of from local files resolved_archive_file = None for weight_name in [WEIGHTS_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME]: resolved_archive_file = cached_file(model_name, weight_name, _raise_exceptions_for_missing_entries=False) if resolved_archive_file is not None: if weight_name in [SAFE_WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME]: load_safe = True if weight_name in [WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_INDEX_NAME]: is_sharded = True break if resolved_archive_file is None: raise EnvironmentError(f"Model name {model_name} was not found.") if load_safe: loader = partial(safe_load_file, device=mapped_device) else: loader = partial(torch.load, map_location=mapped_device) if is_sharded: # resolved_archive_file becomes a list of files that point to the different # checkpoint shards in this case. resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(model_name, resolved_archive_file) state_dict = {} for sharded_file in resolved_archive_file: state_dict.update(loader(sharded_file)) else: state_dict = loader(resolved_archive_file) # Convert dtype before moving to GPU to save memory if dtype is not None: state_dict = {k: v.to(dtype=dtype) for k, v in state_dict.items()} state_dict = {k: v.to(device=device) for k, v in state_dict.items()} return state_dict def filter_shapes(state_dict, model): """ Filters the state dict to match the current model shape. """ filtered_state_dict = {} for key, value in state_dict.items(): if key in model.state_dict(): if value.shape == model.state_dict()[key].shape: filtered_state_dict[key] = value return filtered_state_dict def remap_bert_state_dict( state_dict, config, remove_bert=False, remove_cls_weights=False, add_pooling_layer=False, ): """ Map the state_dict of a Huggingface BERT model to be flash_attn compatible. """ def add_bert_prefix(key): # prepend bert. to the key if key.startswith("bert.") or key.startswith("cls."): return key return f"bert.{key}" state_dict = OrderedDict((add_bert_prefix(k), v) for k, v in state_dict.items()) # LayerNorm def key_mapping_ln_gamma_beta(key): key = re.sub(r"LayerNorm.gamma$", "LayerNorm.weight", key) key = re.sub(r"LayerNorm.beta$", "LayerNorm.bias", key) return key state_dict = OrderedDict((key_mapping_ln_gamma_beta(k), v) for k, v in state_dict.items()) # Layers def key_mapping_layers(key): return re.sub(r"^bert.encoder.layer\.", "bert.encoder.layers.", key) state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items()) # LayerNorm def key_mapping_ln(key): key = re.sub(r"^bert.embeddings.LayerNorm.", "bert.emb_ln.", key) key = re.sub( r"^bert.encoder.layers.(\d+).attention.output.LayerNorm.(weight|bias)", r"bert.encoder.layers.\1.norm1.\2", key, ) key = re.sub( r"^bert.encoder.layers.(\d+).output.LayerNorm.(weight|bias)", r"bert.encoder.layers.\1.norm2.\2", key, ) key = re.sub( r"^cls.predictions.transform.LayerNorm.(weight|bias)", r"cls.predictions.transform.layer_norm.\1", key, ) return key state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items()) # MLP def key_mapping_mlp(key): key = re.sub( r"^bert.encoder.layers.(\d+).intermediate.dense.(weight|bias)", r"bert.encoder.layers.\1.mlp.fc1.\2", key, ) key = re.sub( r"^bert.encoder.layers.(\d+).output.dense.(weight|bias)", r"bert.encoder.layers.\1.mlp.fc2.\2", key, ) return key state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items()) # Attention last_layer_subset = getattr(config, "last_layer_subset", False) for d in range(config.num_hidden_layers): if f"bert.encoder.layers.{d}.attention.self.query.weight" not in state_dict: continue Wq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.weight") Wk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.weight") Wv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.weight") bq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.bias") bk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.bias") bv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.bias") if not (last_layer_subset and d == config.num_hidden_layers - 1): state_dict[f"bert.encoder.layers.{d}.attn.Wqkv.weight"] = torch.cat([Wq, Wk, Wv], dim=0) state_dict[f"bert.encoder.layers.{d}.attn.Wqkv.bias"] = torch.cat([bq, bk, bv], dim=0) else: state_dict[f"bert.encoder.layers.{d}.attn.Wq.weight"] = Wq state_dict[f"bert.encoder.layers.{d}.attn.Wkv.weight"] = torch.cat([Wk, Wv], dim=0) state_dict[f"bert.encoder.layers.{d}.attn.Wq.bias"] = bq state_dict[f"bert.encoder.layers.{d}.attn.Wkv.bias"] = torch.cat([bk, bv], dim=0) def key_mapping_attn(key): return re.sub( r"^bert.encoder.layers.(\d+).attention.output.dense.(weight|bias)", r"bert.encoder.layers.\1.attn.out_proj.\2", key, ) state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items()) def key_mapping_decoder_bias(key): return re.sub(r"^cls.predictions.bias", "cls.predictions.decoder.bias", key) # remove nsp weights, we don't use state_dict.pop("cls.seq_relationship.weight", None) state_dict.pop("cls.seq_relationship.bias", None) state_dict.pop("bert.embeddings.position_ids", None) state_dict = OrderedDict((key_mapping_decoder_bias(k), v) for k, v in state_dict.items()) if remove_cls_weights: cls_weights = [ "cls.predictions.decoder.bias", "cls.predictions.transform.dense.weight", "cls.predictions.transform.dense.bias", "cls.predictions.transform.layer_norm.weight", "cls.predictions.transform.layer_norm.bias", "cls.predictions.decoder.weight", ] for weight in cls_weights: state_dict.pop(weight, None) # Word embedding pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1) if pad_vocab_size_multiple > 1: word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"] state_dict["bert.embeddings.word_embeddings.weight"] = F.pad( word_embeddings, (0, 0, 0, config.vocab_size - word_embeddings.shape[0]) ) if not remove_cls_weights: decoder_weight = state_dict["cls.predictions.decoder.weight"] state_dict["cls.predictions.decoder.weight"] = F.pad( decoder_weight, (0, 0, 0, config.vocab_size - decoder_weight.shape[0]) ) # If the vocab was padded, we want to set the decoder bias for those padded indices to be # strongly negative (i.e. the decoder shouldn't predict those indices). # TD [2022-05-09]: I don't think it affects the MLPerf training. if "cls.predictions.decoder.bias" in state_dict: decoder_bias = state_dict["cls.predictions.decoder.bias"] state_dict["cls.predictions.decoder.bias"] = F.pad( decoder_bias, (0, config.vocab_size - decoder_bias.shape[0]), value=-100.0 ) if add_pooling_layer is False: pooler_weights = [ "bert.pooler.dense.weight", "bert.pooler.dense.bias", ] for key in pooler_weights: state_dict.pop(key, None) if remove_bert: def remove_bert_prefix(key): key = re.sub(r"^bert.", "", key) return key state_dict = OrderedDict((remove_bert_prefix(k), v) for k, v in state_dict.items()) return state_dict def _trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 if (mean < a - 2 * std) or (mean > b + 2 * std): print( "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2, ) # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.0)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) return tensor def trunc_normal_tf_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \leq \text{mean} \leq b`. NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 and the result is subsquently scaled and shifted by the mean and std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value Examples: >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w) """ with torch.no_grad(): _trunc_normal_(tensor, 0, 1.0, a, b) tensor.mul_(std).add_(mean) return tensor class NomicBertPreTrainedModel(PreTrainedModel): """An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models. """ config_class = NomicBertConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Block"] _skip_keys_device_placement = "past_key_values" def __init__(self, config, *inputs, **kwargs): super().__init__(config) if not isinstance(config, GPT2Config): raise ValueError( "Parameter config in `{}(config)` should be an instance of class `GPT2Config`. " "To create a model from a Google pretrained model use " "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( self.__class__.__name__, self.__class__.__name__ ) ) self.config = config @classmethod def from_pretrained(cls, model_name, config=None, *inputs, **kwargs): """ Instantiate a NomicBertPreTrainedModel from a pre-trained model file or a pytorch state dict. Download and cache the pre-trained model file if needed. Params: pretrained_model_name_or_path: either: - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `pytorch_model.bin` a PyTorch dump of a NomicBertForPretraining instance - a path or url to a pretrained model archive containing: . `bert_config.json` a configuration file for the model . `model.chkpt` a TensorFlow checkpoint *inputs, **kwargs: additional input for the specific NomicBert class (ex: num_labels for NomicBertForSequenceClassification) """ # Instantiate model. if config is None: config = cls.config_class.from_pretrained(model_name) remove_cls = cls != NomicBertForPreTraining remove_bert_prefix = cls != NomicBertForPreTraining and cls != NomicBertForSequenceClassification ignore_mismatched_shapes = kwargs.pop("ignore_mismatched_sizes", False) num_labels = kwargs.pop("num_labels", None) rotary_scaling_factor = kwargs.pop("rotary_scaling_factor", None) strict = kwargs.pop("strict", True) dtype = kwargs.pop("torch_dtype", None) if rotary_scaling_factor: config.rotary_scaling_factor = rotary_scaling_factor if config.n_positions <= 0 and config.rotary_emb_fraction > 0: config.n_positions = 2048 if num_labels: config.num_labels = num_labels if "add_pooling_layer" in kwargs: model = cls(config, *inputs, add_pooling_layer=kwargs.pop("add_pooling_layer")) else: if cls == NomicBertModel: model = cls(config, *inputs, add_pooling_layer=False) else: model = cls(config, *inputs) if dtype is not None: model = model.to(dtype=dtype) # TODO: fix this # Assuming we know what we're doing when loading from disk # Prob a bad assumption but i'm tired and want to train this asap if os.path.exists(model_name): model_path = f"{model_name}/pytorch_model.bin" if os.path.exists(model_path): state_dict = torch.load(f"{model_name}/pytorch_model.bin") else: model_path = f"{model_name}/model.safetensors" if not os.path.exists(model_path): raise ValueError(f"Model path {model_path} not found") state_dict = safe_load_file(model_path) if ignore_mismatched_shapes: state_dict = filter_shapes(state_dict, model) load_return = model.load_state_dict(state_dict, strict=False) else: # TODO: can probably check config class and see if we need to remap from a bert model state_dict = state_dict_from_pretrained(model_name, dtype=dtype) state_dict = remap_bert_state_dict( state_dict, config, remove_bert=remove_bert_prefix, remove_cls_weights=remove_cls, add_pooling_layer=getattr(config, "add_pooling_layer", False), ) if ignore_mismatched_shapes: state_dict = filter_shapes(state_dict, model) load_return = model.load_state_dict(state_dict, strict=strict) logger.warning(load_return) return model def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, NomicBertEncoder): module.gradient_checkpointing = value # https://github.com/huggingface/transformers/blob/7032e0203262ebb2ebf55da8d2e01f873973e835/src/transformers/models/bert/modeling_bert.py#L748 def _init_weights(module, initializer_range=0.02): if isinstance(module, nn.Linear): nn.init.normal_(module.weight, std=initializer_range) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): nn.init.normal_(module.weight, std=initializer_range) if module.padding_idx is not None: nn.init.zeros_(module.weight[module.padding_idx]) def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): return tuple(x) return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) to_3tuple = _ntuple(3) to_4tuple = _ntuple(4) to_ntuple = _ntuple def get_2d_sincos_pos_embed(embed_dim, grid_size, add_cls_token=False): """ Create 2D sin/cos positional embeddings. Args: embed_dim (`int`): Embedding dimension. grid_size (`int`): The grid height and width. add_cls_token (`bool`, *optional*, defaults to `False`): Whether or not to add a classification (CLS) token. Returns: (`torch.FloatTensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_size*grid_size, embed_dim): the position embeddings (with or without classification token) """ grid_h = np.arange(grid_size, dtype=np.float32) grid_w = np.arange(grid_size, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) grid = grid.reshape([2, 1, grid_size, grid_size]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if add_cls_token: pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): if embed_dim % 2 != 0: raise ValueError("embed_dim must be even") # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ if embed_dim % 2 != 0: raise ValueError("embed_dim must be even") omega = np.arange(embed_dim // 2, dtype=float) omega /= embed_dim / 2.0 omega = 1.0 / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]: """generate N-D grid in dimension order. The ndgrid function is like meshgrid except that the order of the first two input arguments are switched. That is, the statement [X1,X2,X3] = ndgrid(x1,x2,x3) produces the same result as [X2,X1,X3] = meshgrid(x2,x1,x3) This naming is based on MATLAB, the purpose is to avoid confusion due to torch's change to make torch.meshgrid behaviour move from matching ndgrid ('ij') indexing to numpy meshgrid defaults of ('xy'). """ try: return torch.meshgrid(*tensors, indexing='ij') except TypeError: # old PyTorch < 1.10 will follow this path as it does not have indexing arg, # the old behaviour of meshgrid was 'ij' return torch.meshgrid(*tensors) def build_fourier_pos_embed( feat_shape: List[int], bands: Optional[torch.Tensor] = None, num_bands: int = 64, max_res: int = 224, temperature: float = 10000.0, linear_bands: bool = False, include_grid: bool = False, in_pixels: bool = True, ref_feat_shape: Optional[List[int]] = None, dtype: torch.dtype = torch.float32, device: Optional[torch.device] = None, ) -> List[torch.Tensor]: """ Args: feat_shape: Feature shape for embedding. bands: Pre-calculated frequency bands. num_bands: Number of frequency bands (determines output dim). max_res: Maximum resolution for pixel based freq. temperature: Temperature for non-pixel freq. linear_bands: Linear band spacing for pixel based freq. include_grid: Include the spatial grid in output. in_pixels: Output in pixel freq. ref_feat_shape: Reference feature shape for resize / fine-tune. dtype: Output dtype. device: Output device. Returns: """ if bands is None: if in_pixels: bands = pixel_freq_bands( num_bands, float(max_res), linear_bands=linear_bands, device=device, ) else: bands = freq_bands( num_bands, temperature=temperature, step=1, device=device, ) else: if device is None: device = bands.device if dtype is None: dtype = bands.dtype if in_pixels: t = [torch.linspace(-1.0, 1.0, steps=s, device=device, dtype=torch.float32) for s in feat_shape] else: t = [torch.arange(s, device=device, dtype=torch.int64).to(torch.float32) for s in feat_shape] if ref_feat_shape is not None: # eva's scheme for resizing rope embeddings (ref shape = pretrain) t = [x / f * r for x, f, r in zip(t, feat_shape, ref_feat_shape)] grid = torch.stack(ndgrid(t), dim=-1) grid = grid.unsqueeze(-1) pos = grid * bands pos_sin, pos_cos = pos.sin().to(dtype=dtype), pos.cos().to(dtype) out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos] return out def build_rotary_pos_embed( feat_shape: List[int], bands: Optional[torch.Tensor] = None, dim: int = 64, max_res: int = 224, temperature: float = 10000.0, linear_bands: bool = False, in_pixels: bool = True, ref_feat_shape: Optional[List[int]] = None, dtype: torch.dtype = torch.float32, device: Optional[torch.device] = None, ): """ Args: feat_shape: Spatial shape of the target tensor for embedding. bands: Optional pre-generated frequency bands dim: Output dimension of embedding tensor. max_res: Maximum resolution for pixel mode. temperature: Temperature (inv freq) for non-pixel mode linear_bands: Linearly (instead of log) spaced bands for pixel mode in_pixels: Pixel vs language (inv freq) mode. dtype: Output dtype. device: Output device. Returns: """ sin_emb, cos_emb = build_fourier_pos_embed( feat_shape, bands=bands, num_bands=dim // 4, max_res=max_res, temperature=temperature, linear_bands=linear_bands, in_pixels=in_pixels, ref_feat_shape=ref_feat_shape, device=device, dtype=dtype, ) num_spatial_dim = 1 # this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks for x in feat_shape: num_spatial_dim *= x sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1) cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1) return sin_emb, cos_emb def freq_bands( num_bands: int, temperature: float = 10000.0, step: int = 2, device: Optional[torch.device] = None, ) -> torch.Tensor: exp = torch.arange(0, num_bands, step, dtype=torch.int64, device=device).to(torch.float32) / num_bands bands = 1.0 / (temperature**exp) return bands def pixel_freq_bands( num_bands: int, max_freq: float = 224.0, linear_bands: bool = True, device: Optional[torch.device] = None, ): if linear_bands: bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=torch.float32, device=device) else: bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=torch.float32, device=device) return bands * torch.pi def rot(x): return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape) def apply_rot_embed_cat(x: torch.Tensor, emb): sin_emb, cos_emb = emb.tensor_split(2, -1) if sin_emb.ndim == 3: return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x) return x * cos_emb + rot(x) * sin_emb # taken from https://github.com/huggingface/pytorch-image-models/blob/cb0e4391beedcc5ac3ae4bce16561b95c326f32c/timm/layers/pos_embed_sincos.py#L363 class NomicVisionRotaryEmbeddingCat(nn.Module): """Rotary position embedding w/ concatenatd sin & cos The following impl/resources were referenced for this impl: * https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py * https://blog.eleuther.ai/rotary-embeddings/ """ def __init__( self, dim, max_res=224, temperature=10000, in_pixels=True, linear_bands: bool = False, feat_shape: Optional[List[int]] = None, ref_feat_shape: Optional[List[int]] = None, ): super().__init__() self.dim = dim self.max_res = max_res self.temperature = temperature self.in_pixels = in_pixels self.feat_shape = feat_shape self.ref_feat_shape = ref_feat_shape if feat_shape is None: # only cache bands if in_pixels: bands = pixel_freq_bands( dim // 4, float(max_res), linear_bands=linear_bands, ) else: bands = freq_bands( dim // 4, temperature=temperature, step=1, ) self.register_buffer( 'bands', bands, persistent=False, ) self.pos_embed = None else: # cache full sin/cos embeddings if shape provided up front embeds = build_rotary_pos_embed( feat_shape=feat_shape, dim=dim, max_res=max_res, linear_bands=linear_bands, in_pixels=in_pixels, ref_feat_shape=self.ref_feat_shape, ) self.bands = None self.register_buffer( 'pos_embed', torch.cat(embeds, -1), persistent=False, ) def get_embed(self, shape: Optional[List[int]] = None): if self.bands is not None and shape is not None: # rebuild embeddings every call, use if target shape changes embeds = build_rotary_pos_embed( shape, self.bands, in_pixels=self.in_pixels, ref_feat_shape=self.ref_feat_shape, ) return torch.cat(embeds, -1) elif self.pos_embed is not None: return self.pos_embed else: assert False, "get_embed() requires pre-computed pos_embed or valid shape w/ pre-computed bands" def forward(self, x): # assuming channel-first tensor where spatial dim are >= 2 pos_embed = self.get_embed(x.shape[2:]) return apply_rot_embed_cat(x, pos_embed) class NomicVisionPatchEmbeddings(nn.Module): def __init__( self, config, ): super().__init__() img_size = _pair(config.img_size) patch_size = _pair(config.patch_size) self.img_size = img_size self.patch_size = patch_size self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.proj = nn.Linear( config.num_channels * patch_size[0] * patch_size[1], config.n_embd, bias=config.patch_embed_bias ) self.learned_pos_embedding = False self.sinusoidal_pos_embedding = False self.no_embed_class = getattr(config, "no_embed_class", False) self.cls_token = ( nn.Parameter(torch.zeros(1, 1, config.n_embd)) if not getattr(config, "no_cls_token", False) else None ) if config.learned_pos_embedding: # this is the default in DINO self.learned_pos_embedding = True # hack for timm dinov2 with registers num_patches = self.num_patches if getattr(config, "register_tokens", 0) > 0 else self.num_patches + 1 self.pos_embed = ( nn.Parameter(torch.randn(1, num_patches, config.n_embd) * 0.02) if getattr(config, "use_pos_embed", True) else None ) elif getattr(config, "sinusoidal_pos_embedding", False): self.sinusoidal_pos_embedding = True if getattr(config, "use_pos_embed", True): self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, config.n_embd), requires_grad=False) pos_embed = get_2d_sincos_pos_embed(config.n_embd, self.grid_size[0], add_cls_token=True) self.pos_embed.data.copy_(torch.from_numpy(pos_embed).to(self.pos_embed)) else: self.pos_embed = None else: self.pos_embed = ( nn.Parameter(torch.randn(1, self.num_patches + 1, config.n_embd) * 0.02) if getattr(config, "use_pos_embed", True) else None ) if getattr(config, "register_tokens", 0) > 0: self.reg_token = nn.Parameter(torch.randn(1, config.register_tokens, config.n_embd) * 0.02) else: self.reg_token = None if config.mask_token: self.mask_token = nn.Parameter(torch.zeros(1, config.n_embd)) self.patch_dropout = nn.Identity() if getattr(config, "use_rotary_pos_emb", False): ref_feat_shape = getattr(config, "ref_feat_shape", None) ref_feat_shape = to_2tuple(ref_feat_shape) if ref_feat_shape is not None else None self.rope = NomicVisionRotaryEmbeddingCat( config.n_embd // config.n_head, in_pixels=False, feat_shape=self.grid_size, ref_feat_shape=ref_feat_shape, ) else: self.rope = None def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 """ num_patches = embeddings.shape[1] - 1 num_positions = self.pos_embed.shape[1] - 1 if num_patches == num_positions and height == width: return self.pos_embed class_pos_embed = self.pos_embed[:, 0] patch_pos_embed = self.pos_embed[:, 1:] dim = embeddings.shape[-1] height = height // self.patch_size[0] width = width // self.patch_size[1] # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 height, width = height + 0.1, width + 0.1 patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=(height / math.sqrt(num_positions), width / math.sqrt(num_positions)), mode="bicubic", align_corners=False, ) if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]: raise ValueError("Width or height does not match with the interpolated position embeddings") patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward(self, x): # deepspeed case where the input is in fp32 if x.dtype != self.proj.weight.dtype: x = x.to(dtype=self.proj.weight.dtype) _, _, height, width = x.shape x = self.proj( rearrange( x, "b c (h p1) (w p2) -> b h w (c p1 p2)", p1=self.patch_size[0], p2=self.patch_size[1], ) ) embeddings = rearrange(x, "b h w c -> b (h w) c") to_cat = [] if self.cls_token is not None: if self.sinusoidal_pos_embedding: cls_token = self.cls_token + self.pos_embed[:, 0] cls_token = cls_token.expand(embeddings.shape[0], -1, -1) to_cat += [cls_token] else: cls_token = self.cls_token.expand(embeddings.shape[0], 1, -1) to_cat += [cls_token] if self.reg_token is not None: to_cat += [self.reg_token.expand(embeddings.shape[0], -1, -1)] rot_pos_embed = self.rope.get_embed() if self.rope is not None else None if self.no_embed_class: if self.learned_pos_embedding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: if self.pos_embed is not None: embeddings = embeddings + self.pos_embed if to_cat: embeddings = torch.cat(to_cat + [embeddings], dim=1) else: if to_cat: embeddings = torch.cat(to_cat + [embeddings], dim=1) if self.learned_pos_embedding: if self.pos_embed is not None: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: if self.pos_embed is not None: embeddings = embeddings + self.pos_embed embeddings = self.patch_dropout(embeddings) return embeddings, rot_pos_embed class NomicBertEmbeddings(nn.Module): def __init__(self, config): """ If max_position_embeddings <= 0, there's no position embeddings If type_vocab_size <= 0, there's no token type embeddings """ super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.max_position_embeddings = config.max_position_embeddings if config.rotary_emb_fraction <= 0 else 0 self.type_vocab_size = config.type_vocab_size if self.max_position_embeddings > 0 and config.rotary_emb_fraction <= 0: self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, ) if self.type_vocab_size > 0: self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) def forward(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None): """ input_ids: (batch, seqlen) position_ids: (batch, seqlen) token_type_ids: (batch, seqlen) """ if inputs_embeds is None: embeddings = self.word_embeddings(input_ids) else: embeddings = inputs_embeds batch_size, seqlen, _ = embeddings.shape if self.type_vocab_size > 0: if token_type_ids is None: token_type_ids = torch.zeros(seqlen, dtype=torch.long, device=embeddings.device) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = embeddings + token_type_embeddings if self.max_position_embeddings > 0: if position_ids is None: position_ids = torch.arange(seqlen, dtype=torch.long, device=embeddings.device) position_embeddings = self.position_embeddings(position_ids) embeddings = embeddings + position_embeddings return embeddings class NomicBertMLP(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, activation=F.gelu, bias1=True, bias2=True, return_residual=False, fused_bias_fc=False, ): super().__init__() out_features = out_features if out_features is not None else in_features hidden_features = hidden_features if hidden_features is not None else in_features * 4 self.return_residual = return_residual self.fc1 = nn.Linear(in_features, hidden_features, bias=bias1) approximate = "tanh" if activation in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"] else "none" self.activation = nn.GELU(approximate=approximate) if activation == "gelu" else activation self.fc2 = nn.Linear(hidden_features, out_features, bias=bias2) def forward(self, x): y = self.fc1(x) y = self.activation(y) y = self.fc2(y) return y if not self.return_residual else (y, x) class NomciBertGatedMLP(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, activation=F.sigmoid, bias1=True, bias2=True, multiple_of=256, return_residual=False, fused_bias_fc=True, device=None, dtype=None, norm_layer=False, ): super().__init__() out_features = out_features if out_features is not None else in_features hidden_features = hidden_features if hidden_features is not None else int(8 * in_features / 3) hidden_features = int((hidden_features + multiple_of - 1) // multiple_of * multiple_of) self.return_residual = return_residual self.fc11 = nn.Linear(in_features, hidden_features, bias=bias1) self.fc12 = nn.Linear(in_features, hidden_features, bias=bias1) self.activation = activation self.fc2 = nn.Linear(hidden_features, out_features, bias=bias2) self.norm = nn.LayerNorm(hidden_features) if norm_layer else nn.Identity() def forward(self, x): y = self.fc11(x) gate = self.fc12(x) if self.activation == F.sigmoid: # Special case for GLU y = F.glu(torch.cat([y, gate], dim=-1), dim=-1) else: y = y * self.activation(gate) # eva uses layer norm after the activation y = self.norm(y) y = self.fc2(y) return y if not self.return_residual else (y, x) def rotate_half(x, interleaved=False): if not interleaved: x1, x2 = x.chunk(2, dim=-1) return torch.cat((-x2, x1), dim=-1) else: x1, x2 = x[..., ::2], x[..., 1::2] return rearrange(torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2) def apply_rotary_emb(x, cos, sin, offset=0, interleaved=False): """ x: (batch_size, seqlen, nheads, headdim) cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2) """ ro_dim = cos.shape[-1] * 2 assert ro_dim <= x.shape[-1] cos, sin = ( cos[offset : offset + x.shape[1]], sin[offset : offset + x.shape[1]], ) cos = repeat(cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)") sin = repeat(sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)") return torch.cat( [x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:]], dim=-1, ) class NomicBertRotaryEmbedding(nn.Module): def __init__( self, dim: int, base=10000.0, interleaved=False, scale_base=None, pos_idx_in_fp32=True, device=None, ): """ interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead of 1st half and 2nd half (GPT-NeoX style). pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32, otherwise they might be in lower precision. This option was added because previously (before 2023-07-02), when we construct the position indices, we use the dtype of self.inv_freq. In most cases this would be fp32, but if the model is trained in pure bf16 (not mixed precision), then self.inv_freq would be bf16, and the position indices are also in bf16. Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the embeddings for some positions will coincide. To maintain compatibility with models previously trained in pure bf16, we add this option. """ super().__init__() self.dim = dim self.base = float(base) self.pos_idx_in_fp32 = pos_idx_in_fp32 # Generate and save the inverse frequency buffer (non trainable) inv_freq = self._compute_inv_freq(device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.interleaved = interleaved self.scale_base = scale_base scale = ( (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim) if scale_base is not None else None ) self.register_buffer("scale", scale, persistent=False) self._seq_len_cached = 0 self._cos_cached = None self._sin_cached = None self._cos_k_cached = None self._sin_k_cached = None def _compute_inv_freq(self, device=None): return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim)) def _update_cos_sin_cache(self, seqlen, device=None, dtype=None): # Reset the tables if the sequence length has changed, # if we're on a new device (possibly due to tracing for instance), # or if we're switching from inference mode to training if ( seqlen > self._seq_len_cached or self._cos_cached is None or self._cos_cached.device != device or self._cos_cached.dtype != dtype or (self.training and self._cos_cached.is_inference()) ): self._seq_len_cached = seqlen # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16 # And the output of arange can be quite large, so bf16 would lose a lot of precision. # However, for compatibility reason, we add an option to use the dtype of self.inv_freq. if self.pos_idx_in_fp32: t = torch.arange(seqlen, device=device, dtype=torch.float32) # We want fp32 here as well since inv_freq will be multiplied with t, and the output # will be large. Having it in bf16 will lose a lot of precision and cause the # cos & sin output to change significantly. # We want to recompute self.inv_freq if it was not loaded in fp32 if self.inv_freq.dtype != torch.float32: inv_freq = self._compute_inv_freq(device=device) else: inv_freq = self.inv_freq else: t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) inv_freq = self.inv_freq # Don't do einsum, it converts fp32 to fp16 under AMP # freqs = torch.einsum("i,j->ij", t, self.inv_freq) freqs = torch.outer(t, inv_freq) self._cos_cached = torch.cos(freqs).to(dtype) self._sin_cached = torch.sin(freqs).to(dtype) def forward( self, qkv: torch.Tensor, kv: Optional[torch.Tensor] = None, seqlen_offset: Union[int, torch.Tensor] = 0, max_seqlen: Optional[int] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ qkv: (batch, seqlen, 3, nheads, headdim) if kv is none, else it's just q of shape (batch, seqlen, nheads, headdim) kv: (batch, seqlen, 2, nheads, headdim) seqlen_offset: (batch_size,) or int. Each sequence in x is shifted by this amount. Most commonly used in inference when we have KV cache. If it's a tensor of shape (batch_size,), then to update the cos / sin cache, one should pass in max_seqlen, which will update the cos / sin cache up to that length. Apply rotary embedding *inplace* to qkv and / or kv. """ seqlen = qkv.shape[1] if seqlen > self._seq_len_cached: self._update_cos_sin_cache(seqlen, device=qkv.device, dtype=qkv.dtype) elif max_seqlen is not None: self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype) elif isinstance(seqlen_offset, int): self._update_cos_sin_cache(seqlen + seqlen_offset, device=qkv.device, dtype=qkv.dtype) q_rot = apply_rotary_emb(qkv[:, :, 0], self._cos_cached, self._sin_cached, seqlen_offset, self.interleaved) k_rot = apply_rotary_emb(qkv[:, :, 1], self._cos_cached, self._sin_cached, seqlen_offset, self.interleaved) return torch.stack((q_rot, k_rot, qkv[:, :, 2]), dim=2) class NomicBertDynamicNTKRotaryEmbedding(NomicBertRotaryEmbedding): def __init__(self, rotary_scaling_factor, max_position_embeddings, **kwargs): super().__init__(**kwargs) self.rotary_scaling_factor = rotary_scaling_factor self.max_position_embeddings = max_position_embeddings def _compute_inv_freq(self, base=None, device=None): if base is None: base = self.base return 1.0 / (base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim)) def _update_cos_sin_cache(self, seqlen, device=None, dtype=None): # Reset the tables if the sequence length has changed, # if we're on a new device (possibly due to tracing for instance), # or if we're switching from inference mode to training if seqlen > self.max_position_embeddings: base = self.base * ( (self.rotary_scaling_factor * seqlen / self.max_position_embeddings) - (self.rotary_scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = self._compute_inv_freq(base=base, device=device) self.register_buffer("inv_freq", inv_freq, persistent=False) if ( seqlen > self._seq_len_cached or self._cos_cached is None or self._cos_cached.device != device or self._cos_cached.dtype != dtype or (self.training and self._cos_cached.is_inference()) ): self._seq_len_cached = seqlen # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16 # And the output of arange can be quite large, so bf16 would lose a lot of precision. # However, for compatibility reason, we add an option to use the dtype of self.inv_freq. if self.pos_idx_in_fp32: t = torch.arange(seqlen, device=device, dtype=torch.float32) # We want fp32 here as well since inv_freq will be multiplied with t, and the output # will be large. Having it in bf16 will lose a lot of precision and cause the # cos & sin output to change significantly. # We want to recompute self.inv_freq if it was not loaded in fp32 if self.inv_freq.dtype != torch.float32: if seqlen > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seqlen / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) else: base = self.base inv_freq = self._compute_inv_freq(device=device, base=base) else: inv_freq = self.inv_freq else: t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) inv_freq = self.inv_freq # Don't do einsum, it converts fp32 to fp16 under AMP # freqs = torch.einsum("i,j->ij", t, self.inv_freq) freqs = torch.outer(t, inv_freq) if self.scale is None: self._cos_cached = torch.cos(freqs).to(dtype) self._sin_cached = torch.sin(freqs).to(dtype) else: power = ( torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2 ) / self.scale_base scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1") # We want the multiplication by scale to happen in fp32 self._cos_cached = (torch.cos(freqs) * scale).to(dtype) self._sin_cached = (torch.sin(freqs) * scale).to(dtype) self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype) self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype) class NomicBertAttention(nn.Module): """Multi-head self-attention and cross-attention""" def __init__( self, config, ) -> None: """ num_heads_kv: can be used to toggle MQA / GQA. If None, use num_heads. return_residual: whether to return the input x along with the output. This is for performance reason: for post-norm architecture, returning the input allows us to fuse the backward of nn.Linear with the residual connection. """ super().__init__() self.embed_dim = config.n_embd self.use_flash_attn = config.use_flash_attn self.fused_bias_fc = config.fused_bias_fc self.num_heads = config.n_head self.num_heads_kv = config.num_heads_kv if getattr(config, "num_heads_kv", None) is not None else self.num_heads assert self.embed_dim % self.num_heads == 0, "embed_dim must be divisible by num_heads" self.head_dim = self.embed_dim // self.num_heads # we don't really support mqa / gqa for now qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv) self.register_buffer( "norm_factor", torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()), persistent=False, ) self.rotary_emb_dim = self.head_dim * config.rotary_emb_fraction if self.rotary_emb_dim > 0: if getattr(config, "rotary_scaling_factor", None): self.rotary_emb = NomicBertDynamicNTKRotaryEmbedding( dim=self.rotary_emb_dim, base=config.rotary_emb_base, scale_base=config.rotary_emb_scale_base, interleaved=config.rotary_emb_interleaved, rotary_scaling_factor=config.rotary_scaling_factor, max_position_embeddings=config.max_trained_positions, ) else: self.rotary_emb = NomicBertRotaryEmbedding( dim=self.rotary_emb_dim, base=config.rotary_emb_base, scale_base=config.rotary_emb_scale_base, interleaved=config.rotary_emb_interleaved, ) # bug in xformers: https://github.com/facebookresearch/xformers/issues/841 # uses the head dimension instead of the sequence dimension self.rotary_head_dim = getattr(config, "rotary_head_dim", False) self.Wqkv = nn.Linear(self.embed_dim, qkv_dim, bias=config.qkv_proj_bias) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_proj_bias) self.causal = config.causal self.drop = nn.Dropout(config.attn_pdrop) self.num_prefix_tokens = max(getattr(config, "register_tokens", 1), 1) 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: bool = False, use_cache: bool = False, is_padded_inputs: Optional[bool] = True, cu_seqlens: Optional[torch.Tensor] = None, max_seq_len: Optional[int] = None, rope: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: has_layer_past = past_key_value is not None if has_layer_past: past_key_value = past_key_value[0] past_len = past_key_value[1] else: past_len = 0 qkv = self.Wqkv(hidden_states) qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim) past_key_value = (past_key_value, past_len + qkv.size(1)) if use_cache else None if self.rotary_emb_dim > 0: if self.rotary_head_dim: qkv = rearrange(qkv, "b s three h d -> b h three s d") qkv = self.rotary_emb(qkv, seqlen_offset=past_len) if self.rotary_head_dim: qkv = rearrange(qkv, "b h three s d -> b s three h d") elif rope is not None: q, k, v = qkv.permute(0, 3, 1, 2, 4).unbind(dim=-2) q = torch.cat( [q[:, :, : self.num_prefix_tokens], apply_rot_embed_cat(q[:, :, self.num_prefix_tokens :], rope)], dim=2 ).type_as(q) k = torch.cat( [k[:, :, : self.num_prefix_tokens], apply_rot_embed_cat(k[:, :, self.num_prefix_tokens :], rope)], dim=2 ).type_as(q) qkv = torch.stack([q, k, v], dim=-2) qkv = rearrange(qkv, "b h s three d -> b s three h d") query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2] query = query.permute(0, 2, 1, 3) key = key.permute(0, 2, 1, 3) value = value.permute(0, 2, 1, 3) if scaled_dot_product_attention is not None: attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, dropout_p=self.drop.p, is_causal=False ) else: attention_scores = torch.matmul(query, key.transpose(-1, -2)) / self.norm_factor if attention_mask is not None: attention_scores = attention_scores + attention_mask attentions_probs = F.softmax(attention_scores, dim=-1) attentions_probs = self.drop(attentions_probs) attn_output = torch.matmul(attentions_probs, value) attn_output = rearrange(attn_output.permute(0, 2, 1, 3), "... h d -> ... (h d)") attn_output = self.out_proj(attn_output) return attn_output class NomicBertBlock(NomicBertPreTrainedModel): def __init__( self, config, ): super().__init__(config=config) self.prenorm = config.prenorm self.fused_dropout_add_ln = config.fused_dropout_add_ln self.attn = NomicBertAttention(config) activation = ( F.sigmoid if config.activation_function == "glu" else (F.silu if config.activation_function == "swiglu" else F.gelu) ) if config.activation_function in ["glu", "swiglu", "geglu"]: self.mlp = NomciBertGatedMLP( config.n_embd, hidden_features=config.n_inner, bias1=config.mlp_fc1_bias, bias2=config.mlp_fc2_bias, activation=activation, fused_bias_fc=config.fused_bias_fc, norm_layer=getattr(config, "norm_mlp", False), ) else: self.mlp = NomicBertMLP( config.n_embd, hidden_features=config.n_inner, bias1=config.mlp_fc1_bias, bias2=config.mlp_fc2_bias, activation=activation, fused_bias_fc=config.fused_bias_fc, ) self.dropout1 = nn.Dropout(config.resid_pdrop) self.norm1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.norm2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.dropout2 = nn.Dropout(config.resid_pdrop) def forward( self, hidden_states: torch.Tensor, hidden_states2: torch.Tensor, residual: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, is_padded_inputs: Optional[bool] = True, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cu_seqlens: Optional[torch.Tensor] = None, max_seq_len: Optional[int] = None, rope: Optional[torch.Tensor] = None, ): r"""Pass the input through the encoder layer. Args: hidden_states: the sequence to the encoder layer (required). residual: if postnorm, residual=None, If prenorm, hidden_states = Attn/MLP(LN(residual)) mixer_subset: for cross-attention only. If not None, will take a subset of x before applying the query projection. Useful for e.g., ViT where we only care about the CLS token in the last layer. """ if self.prenorm: dropped = self.dropout1(hidden_states) residual = (dropped + residual) if residual is not None else dropped hidden_states = self.norm1(residual.to(dtype=self.norm1.weight.dtype)) hidden_states = self.attn( hidden_states, attention_mask=attention_mask, is_padded_inputs=is_padded_inputs, cu_seqlens=cu_seqlens, max_seq_len=max_seq_len, rope=rope, ) dropped = self.dropout2(hidden_states) residual = (dropped + residual) if residual is not None else dropped hidden_states = self.norm2(residual.to(dtype=self.norm2.weight.dtype)) hidden_states = self.mlp(hidden_states) return hidden_states, None, residual else: assert residual is None attn_outputs = self.attn( hidden_states, attention_mask=attention_mask, is_padded_inputs=is_padded_inputs, cu_seqlens=cu_seqlens, max_seq_len=max_seq_len, rope=rope, ) hidden_states = self.norm1((self.dropout1(attn_outputs) + hidden_states).to(dtype=self.norm1.weight.dtype)) mlp_out = self.mlp(hidden_states) hidden_states = self.norm2((self.dropout2(mlp_out) + hidden_states).to(dtype=self.norm2.weight.dtype)) return hidden_states, None, None class NomicBertEncoder(nn.Module): def __init__(self, config: GPT2Config): super().__init__() self.layers = nn.ModuleList([NomicBertBlock(config) for _ in range(config.n_layer)]) self.gradient_checkpointing = False self.config = config def forward( self, hidden_states: 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, is_padded_inputs: Optional[bool] = True, rope: Optional[torch.Tensor] = None, ): """If subset_mask is not None, we only want output for the subset of the sequence. This means that we only compute the last layer output for these tokens. subset_mask: (batch, seqlen), dtype=torch.bool """ hidden_states2 = None residual = None for _, layer in enumerate(self.layers): if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs) return custom_forward hidden_states, hidden_states2, residual = torch.utils.checkpoint.checkpoint( create_custom_forward(layer), hidden_states, hidden_states2, residual, attention_mask, position_ids, past_key_values, is_padded_inputs, output_attentions, use_cache, None, None, rope, # if you freeze ANY layers, you need `use_reentrant=False` # https://github.com/huggingface/transformers/issues/21381 # https://discuss.pytorch.org/t/checkpoint-with-no-grad-requiring-inputs-problem/19117/7 use_reentrant=False, ) else: hidden_states, hidden_states2, residual = layer( hidden_states, hidden_states2, residual, attention_mask, position_ids, None, is_padded_inputs, output_attentions, use_cache, rope=rope, ) return hidden_states class NomicBertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.n_embd, config.n_embd) self.activation = nn.Tanh() def forward(self, hidden_states, pool=True): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] if pool else hidden_states pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class NomicBertPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.n_embd, config.n_embd, bias=config.mlp_fc1_bias) approximate = "tanh" if config.activation_function in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"] else "none" if config.activation_function == "swiglu": self.transform_act_fn = F.silu else: self.transform_act_fn = nn.GELU(approximate=approximate) self.layer_norm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.layer_norm(hidden_states) return hidden_states class NomicBertLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = NomicBertPredictionHeadTransform(config) self.decoder = nn.Linear(config.n_embd, config.vocab_size, bias=config.mlp_fc1_bias) def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class NomicBertPreTrainingHeads(nn.Module): def __init__(self, config): super().__init__() self.predictions = NomicBertLMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores class NomicBertModel(NomicBertPreTrainedModel): def __init__(self, config: GPT2Config, add_pooling_layer=True): super().__init__(config) self.pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1) if config.vocab_size % self.pad_vocab_size_multiple != 0: config.vocab_size += self.pad_vocab_size_multiple - (config.vocab_size % self.pad_vocab_size_multiple) assert config.activation_function in [ "gelu", "gelu_new", "gelu_fast", "gelu_pytorch_tanh", "swiglu", "geglu", "glu", ] self.embeddings = NomicBertEmbeddings(config) self.emb_drop = nn.Dropout(config.resid_pdrop) self.emb_ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.encoder = NomicBertEncoder(config) self.pooler = NomicBertPooler(config) if add_pooling_layer else None self.apply(partial(_init_weights, initializer_range=config.initializer_range)) def forward( self, input_ids=None, attention_mask=None, position_ids=None, token_type_ids=None, return_dict=None, matryoshka_dim=None, inputs_embeds=None, ): if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") hidden_states = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) hidden_states = self.emb_ln(hidden_states) hidden_states = self.emb_drop(hidden_states) attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1]) sequence_output = self.encoder(hidden_states, attention_mask=attention_mask, return_dict=return_dict) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if matryoshka_dim: sequence_output = sequence_output[:, :matryoshka_dim] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, ) class NomicBertForPreTraining(NomicBertPreTrainedModel): _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config: GPT2Config): super().__init__(config) self.bert = NomicBertModel(config, add_pooling_layer=getattr(config, "add_pooling_layer", False)) self.cls = NomicBertPreTrainingHeads(config) self.mlm_loss = nn.CrossEntropyLoss() # Initialize weights and apply final processing self.apply(partial(_init_weights, initializer_range=config.initializer_range)) self.tie_weights() def tie_weights(self): self.cls.predictions.decoder.weight = self.bert.embeddings.word_embeddings.weight def forward( self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, labels=None, ): """ If labels are provided, they must be -100 for masked out tokens (as specified in the attention mask). Outputs: if `labels` and `next_sentence_label` are not `None`: Outputs the total_loss which is the sum of the masked language modeling loss and the next sentence classification loss. if `labels` or `next_sentence_label` is `None`: Outputs a tuple comprising - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and - the next sentence classification logits of shape [batch_size, 2]. """ outputs = self.bert( input_ids, position_ids=position_ids, token_type_ids=token_type_ids, attention_mask=attention_mask.bool() if attention_mask is not None else None, ) sequence_output, _ = outputs.last_hidden_state, outputs.pooler_output prediction_scores = self.cls(sequence_output) total_loss = None if labels is not None: masked_lm_loss = self.mlm_loss( rearrange(prediction_scores, "... v -> (...) v"), rearrange(labels, "... -> (...)"), ) total_loss = masked_lm_loss.float() return MaskedLMOutput( loss=total_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=None, ) class NomicBertForSequenceClassification(NomicBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.config = config self.bert = NomicBertModel(config) classifier_dropout = getattr(config, "classifier_dropout", config.embd_pdrop) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.n_embd, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.bert( input_ids, position_ids=position_ids, token_type_ids=token_type_ids, attention_mask=attention_mask.bool() if attention_mask is not None else None, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = nn.MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = nn.CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = nn.BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def hf_vit_config_to_vit_config(vit_config: ViTConfig) -> GPT2Config: return GPT2Config( n_embd=vit_config.hidden_size, n_layer=vit_config.num_hidden_layers, n_head=vit_config.num_attention_heads, n_inner=vit_config.intermediate_size, activation_function=vit_config.hidden_act, vocab_size=0, # no vocab since using patches n_positions=0, # No absolute position embedding resid_pdrop=0.0, # No dropout embd_pdrop=getattr(vit_config, "dropout", 0.0), attn_pdrop=vit_config.attention_probs_dropout_prob, layer_norm_epsilon=vit_config.layer_norm_eps, initializer_range=vit_config.initializer_range, bos_token_id=None, eos_token_id=None, # These are new arguments not in the original GPT2Config drop_path_rate=0.0, # Why is there double layer norm?? prepre_layernom=False, layer_scale=False, layer_scale_init=None, img_size=vit_config.image_size, patch_size=vit_config.patch_size, num_channels=vit_config.num_channels, prenorm=True, parallel_block=False, parallel_block_tied_norm=False, rotary_emb_fraction=0, tie_word_embeddings=False, fused_dropout_add_ln=True, fused_bias_fc=True, patch_embed_bias=True, use_flash_attn=True, qkv_proj_bias=True, mlp_fc1_bias=getattr(vit_config, "mlp_fc1_bias", True), mlp_fc2_bias=getattr(vit_config, "mlp_fc2_bias", True), use_rms_norm=False, causal=False, hidden_features_scaling_factor=1.0, mask_token=False, learned_pos_embedding=False, patch_dropout=0, sinusoidal_pos_embedding=vit_config.model_type == "vit_mae", ) class NomicAttentionPooling(nn.Module): def __init__(self, config): super().__init__() self.embed_dim = config.n_embd self.use_flash_attn = config.use_flash_attn self.fused_bias_fc = config.fused_bias_fc self.num_heads = config.n_head self.num_heads_kv = config.num_heads_kv if getattr(config, "num_heads_kv", None) is not None else self.num_heads assert self.embed_dim % self.num_heads == 0, "embed_dim must be divisible by num_heads" self.head_dim = self.embed_dim // self.num_heads # we don't really support mqa / gqa for now kv_dim = 2 * self.head_dim * self.num_heads_kv self.register_buffer( "norm_factor", torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()), persistent=False, ) self.Wq = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_proj_bias) self.Wkv = nn.Linear(self.embed_dim, kv_dim, bias=config.qkv_proj_bias) self.latent = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_proj_bias) self.causal = config.causal self.drop = nn.Dropout(config.attn_pdrop) def init_weights(self): trunc_normal_tf_(self.latent, std=self.embed_dim**-0.5) def forward( self, kv, attention_mask=None, cu_seqlens_k=None, max_seqlen_k=None, is_padded_inputs: Optional[bool] = True, output_attentions: bool = False, ): """Implements the multihead softmax attention. Arguments --------- q: The tensor containing the query. (B, Sq, H, D) kv: The tensor containing the key and value. (B, Sk, 2, H_k, D) causal: if passed, will override self.causal cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into q. max_seqlen: int. Maximum sequence length in the batch of q. cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths of the sequences in the batch, used to index into kv. max_seqlen_k: int. Maximum sequence length in the batch of k and v. """ q_latent = self.latent.expand(kv.size(0), -1, -1) q = self.Wq(q_latent) bsz, q_len, h_size = q.shape kv = self.Wkv(kv) query = rearrange(q, "... (h d) -> ... h d", d=self.head_dim) kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim) key, value = kv[:, :, 0], kv[:, :, 1] query = query.permute(0, 2, 1, 3) key = key.permute(0, 2, 1, 3) value = value.permute(0, 2, 1, 3) attention_scores = torch.matmul(query, key.transpose(-1, -2)) / self.norm_factor if attention_mask is not None: attention_scores = attention_scores + attention_mask attentions_probs = F.softmax(attention_scores, dim=-1) attentions_probs = self.drop(attentions_probs) attn_output = torch.matmul(attentions_probs, value) attn_output = rearrange(attn_output.permute(0, 2, 1, 3), "... h d -> ... (h d)") attn_output = self.out_proj(attn_output) return attn_output class NomicMultiHeadAttentionPooling(nn.Module): def __init__( self, config, ): super().__init__() self.prenorm = config.prenorm self.fused_dropout_add_ln = config.fused_dropout_add_ln self.attn = NomicAttentionPooling(config) activation = ( F.sigmoid if config.activation_function == "glu" else (F.silu if config.activation_function == "swiglu" else F.gelu) ) if config.activation_function in ["glu", "swiglu", "geglu"]: self.mlp = NomciBertGatedMLP( config.n_embd, hidden_features=config.n_inner, bias1=config.mlp_fc1_bias, bias2=config.mlp_fc2_bias, activation=activation, fused_bias_fc=config.fused_bias_fc, ) else: self.mlp = NomicBertMLP( config.n_embd, hidden_features=config.n_inner, bias1=config.mlp_fc1_bias, bias2=config.mlp_fc2_bias, activation=activation, fused_bias_fc=config.fused_bias_fc, ) self.dropout1 = nn.Dropout(config.resid_pdrop) self.norm1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.dropout2 = nn.Dropout(config.resid_pdrop) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, ): r"""Pass the input through the encoder layer. Args: hidden_states: the sequence to the encoder layer (required). residual: if postnorm, residual=None, If prenorm, hidden_states = Attn/MLP(LN(residual)) mixer_subset: for cross-attention only. If not None, will take a subset of x before applying the query projection. Useful for e.g., ViT where we only care about the CLS token in the last layer. """ attn_outputs = self.attn( hidden_states, attention_mask=attention_mask, ) normed = self.norm1(attn_outputs) hidden_states = hidden_states + self.mlp(normed) return hidden_states class NomicVisionPreTrainedModel(PreTrainedModel): """An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models. """ config_class = NomicBertConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["Block"] _skip_keys_device_placement = "past_key_values" def __init__(self, config, *inputs, **kwargs): super().__init__(config) if not isinstance(config, GPT2Config): raise ValueError( "Parameter config in `{}(config)` should be an instance of class `GPT2Config`. " "To create a model from a Google pretrained model use " "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( self.__class__.__name__, self.__class__.__name__ ) ) self.config = config class NomicVisionModel(NomicVisionPreTrainedModel): def __init__(self, config): super().__init__(config) self.embeddings = NomicVisionPatchEmbeddings(config) self.layers = nn.ModuleList([NomicBertBlock(config) for _ in range(config.n_layer)]) self.selector = NomicMultiHeadAttentionPooling(config) self.global_pool = getattr(config, "global_pool", None) self.num_prefix_tokens = (1 if not getattr(config, "no_cls_token", False) else 0) + getattr( config, "register_tokens", 0 ) self.apply(partial(_init_weights, initializer_range=config.initializer_range)) def forward( self, pixel_values, attention_mask=None, position_ids=None, token_type_ids=None, return_dict=None, matryoshka_dim=None, ): embeddings, rope = self.embeddings(pixel_values) original_dtype = embeddings.dtype hidden_states = embeddings # unused but easier to pass to gradient checkpointing as words residual = None for layer in self.layers: # need to pass none for backwards compatability hidden_states, _, residual = layer( hidden_states, None, residual=residual, is_padded_inputs=False, rope=rope ) hidden_states = hidden_states + residual if self.global_pool == "avg": hidden_states = hidden_states[:, self.num_prefix_tokens :].mean(dim=1) pooled_output = self.selector(hidden_states) return BaseModelOutputWithPast( last_hidden_state=pooled_output, hidden_states=hidden_states, )