import logging import math from copy import deepcopy from dataclasses import fields, dataclass, replace from enum import Enum from typing import List, Optional, Tuple, Union, Dict, Any, Sequence, Callable, cast, MutableMapping import torch from einops import einsum, einops from transformers import PreTrainedModel, GenerationConfig from transformers.cache_utils import Cache from transformers.modeling_outputs import CausalLMOutputWithPast, ModelOutput from transformers.models.auto import AutoModelForCausalLM from torch import nn from .config_molmo import MolmoConfig from torch.nn import functional as F log = logging.getLogger(__name__) class BufferCache(dict, MutableMapping[str, torch.Tensor]): """ Cache for attention biases and other things that would normally be stored as buffers. We avoid using buffers because we've run into various issues doing so with FSDP. In general it appears the way FSDP handles buffers is not well-defined. It doesn't shard them but apparently it does synchronize them across processes, which we want to avoid since (A) it isn't necessary, and (B) we sometimes have `-inf` in these biases which might get turned into NaNs when they're synchronized due to casting or some other issue. """ class StrEnum(str, Enum): def __str__(self) -> str: return self.value def __repr__(self) -> str: return f"'{str(self)}'" class ImageProjectType(StrEnum): mlp = "mlp" mlpx2 = "2mlp" linear = "linear" class ImagePooling2DType(StrEnum): attention = "attention" attention_meanq = "attention-meanq" attention_2wide = "attention_2wide" attention_v2 = "attention-v2" none = "none" stack = "stack" class ActivationType(StrEnum): quick_gelu = "quick_gelu" gelu = "gelu" gelu_tanh = "gelu_tanh" relu = "relu" silu = "silu" llama_geglu = "llama_geglu" llama_geglu_tanh = "llama_geglu_tanh" llama_swiglu = "llama_swiglu" swiglu = "swiglu" def ensure_finite_(x: torch.Tensor, check_neg_inf: bool = True, check_pos_inf: bool = False): """ Modify ``x`` in place to replace ``float("-inf")`` with the minimum value of the dtype when ``check_neg_inf`` is ``True`` and to replace ``float("inf")`` with the maximum value of the dtype when ``check_pos_inf`` is ``True``. """ if check_neg_inf: x.masked_fill_(x == float("-inf"), torch.finfo(x.dtype).min) if check_pos_inf: x.masked_fill_(x == float("inf"), torch.finfo(x.dtype).max) class MolmoConfigurationError(Exception): pass def _non_meta_init_device(config) -> torch.device: if config.init_device is not None and config.init_device != "meta": return torch.device(config.init_device) else: return torch.device("cuda" if torch.cuda.is_available() else "cpu") class RotaryEmbedding(nn.Module): """ [Rotary positional embeddings (RoPE)](https://arxiv.org/abs/2104.09864). """ def __init__(self, config: MolmoConfig, cache: BufferCache): super().__init__() self.config = config self.__cache = cache # Warm up cache. self.get_rotary_embedding( config.max_position_embeddings or config.max_sequence_length, _non_meta_init_device(config) ) def get_rotary_embedding(self, seq_len: int, device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]: if ( (pos_sin := self.__cache.get("rope_pos_sin")) is not None and (pos_cos := self.__cache.get("rope_pos_cos")) is not None and pos_sin.shape[-2] >= seq_len and pos_cos.shape[-2] >= seq_len ): if pos_sin.device != device: pos_sin = pos_sin.to(device) self.__cache["rope_pos_sin"] = pos_sin if pos_cos.device != device: pos_cos = pos_cos.to(device) self.__cache["rope_pos_cos"] = pos_cos return pos_sin[:, :, :seq_len, :], pos_cos[:, :, :seq_len, :] with torch.autocast(device.type, enabled=False): dim = self.config.d_model // self.config.n_heads inv_freq = 1.0 / (self.config.rope_theta ** (torch.arange(0, dim, 2, device=device, dtype=torch.float) / dim)) seq = torch.arange(seq_len, device=device, dtype=torch.float) freqs = torch.einsum("i , j -> i j", seq, inv_freq) if self.config.rope_impl == "interleave": positions = freqs.repeat_interleave(2, dim=-1) else: positions = torch.cat((freqs, freqs), dim=-1) pos_sin, pos_cos = positions.sin()[None, None, :, :], positions.cos()[None, None, :, :] self.__cache["rope_pos_sin"] = pos_sin self.__cache["rope_pos_cos"] = pos_cos return pos_sin, pos_cos def rotate_half(self, x: torch.Tensor) -> torch.Tensor: B, nh, T, hs = x.size() x = x.view(B, nh, T, 2, hs // 2) x1, x2 = x.unbind(dim=-2) return torch.cat((-x2, x1), dim=-1) def rotate_every_two(self, x: torch.Tensor) -> torch.Tensor: B, nh, T, hs = x.size() x = x.view(B, nh, T, hs // 2, 2) x1, x2 = x.unbind(dim=-1) x = torch.stack((-x2, x1), dim=-1) return x.view(B, nh, T, hs) def apply_rotary_pos_emb(self, pos_sin: torch.Tensor, pos_cos: torch.Tensor, t: torch.Tensor) -> torch.Tensor: if self.config.rope_impl == "interleave": return ((t * pos_cos) + (self.rotate_every_two(t) * pos_sin)).to(t.dtype) else: return ((t * pos_cos) + (self.rotate_half(t) * pos_sin)).to(t.dtype) def forward( self, q: torch.Tensor, k: torch.Tensor, position_ids: Optional[torch.Tensor] = None ) -> Tuple[torch.Tensor, torch.Tensor]: if self.config.rope_full_precision: q_, k_ = q.float(), k.float() else: q_, k_ = q, k with torch.autocast(q.device.type, enabled=False): batch_size = q_.shape[0] query_len, key_len = q_.shape[-2], k_.shape[-2] # could be different if layer_past not None if position_ids is not None: freqs_cis_len = (self.config.max_position_embeddings or self.config.max_sequence_length) else: freqs_cis_len = key_len pos_sin, pos_cos = self.get_rotary_embedding(freqs_cis_len, q_.device) pos_sin = pos_sin.type_as(q_) pos_cos = pos_cos.type_as(q_) if position_ids is not None: assert query_len == key_len, "Query and key lengths must be equal when using position IDs." pos_sin = pos_sin[0, 0][position_ids].view( (batch_size, 1, key_len, pos_sin.shape[-1]) ) pos_cos = pos_cos[0, 0][position_ids].view( (batch_size, 1, key_len, pos_cos.shape[-1]) ) q_ = self.apply_rotary_pos_emb( pos_sin[:, :, key_len - query_len : key_len, :], pos_cos[:, :, key_len - query_len : key_len, :], q_, ) k_ = self.apply_rotary_pos_emb(pos_sin, pos_cos, k_) return q_.type_as(q), k_.type_as(k) class MolmoBlock(nn.Module): """ A base class for transformer block implementations. """ def __init__(self, layer_id: int, config: MolmoConfig, cache: BufferCache): super().__init__() self.layer_id = layer_id self.config = config self.hidden_size = ( config.mlp_hidden_size if config.mlp_hidden_size is not None else config.mlp_ratio * config.d_model ) self.__cache = cache self._activation_checkpoint_fn = None # Dropout. self.dropout = Dropout(config.residual_dropout) # Layer norms. self.k_norm: Optional[LayerNormBase] = None self.q_norm: Optional[LayerNormBase] = None if config.attention_layer_norm: assert config.effective_n_kv_heads is not None self.k_norm = LayerNormBase.build( config, size=(config.d_model // config.n_heads) * config.effective_n_kv_heads, elementwise_affine=config.attention_layer_norm_with_affine, ) self.q_norm = LayerNormBase.build(config, elementwise_affine=config.attention_layer_norm_with_affine) # Make sure QKV clip coefficient is positive, otherwise it's not well-defined. if config.clip_qkv is not None: assert config.clip_qkv > 0 # Activation function. self.act = Activation.build(config) assert (self.act.output_multiplier * self.hidden_size) % 1 == 0 # Attention output projection. input_dim = config.d_model self.attn_out = nn.Linear( input_dim, config.d_model, bias=config.include_bias, device=config.init_device ) # Feed-forward output projection. self.ff_out = nn.Linear( int(self.act.output_multiplier * self.hidden_size), config.d_model, bias=config.include_bias, device=config.init_device, ) self.ff_out._is_residual = True # type: ignore # Rotary embeddings. if self.config.rope: self.rotary_emb = RotaryEmbedding(config, self.__cache) self.flash_attn_func = None if config.attention_type == "flash": try: from flash_attn import flash_attn_func # type: ignore self.flash_attn_func = flash_attn_func except ModuleNotFoundError: pass def reset_parameters(self): if self.k_norm is not None: self.k_norm.reset_parameters() if self.q_norm is not None: self.q_norm.reset_parameters() init_weights( self.config, self.attn_out, d=self.config.d_model, layer_id=self.layer_id, type_of_module=ModuleType.out_module, ) init_weights( self.config, self.ff_out, d=self.ff_out.in_features, layer_id=self.layer_id, type_of_module=ModuleType.out_module, ) @classmethod def _cast_attn_bias(cls, bias: torch.Tensor, input_dtype: torch.dtype) -> torch.Tensor: target_dtype = input_dtype # NOTE: `is_autocast_enabled()` only checks for CUDA autocast, so we use the separate function # `is_autocast_cpu_enabled()` for CPU autocast. # See https://github.com/pytorch/pytorch/issues/110966. if bias.device.type == "cuda" and torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() elif bias.device.type == "cpu" and torch.is_autocast_cpu_enabled(): target_dtype = torch.get_autocast_cpu_dtype() if bias.dtype != target_dtype: bias = bias.to(target_dtype) ensure_finite_(bias, check_neg_inf=True, check_pos_inf=False) return bias def _scaled_dot_product_attention( self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: Optional[torch.Tensor] = None, dropout_p: float = 0.0, response_dropout_p: float = 0.0, is_causal: bool = False, ) -> torch.Tensor: """ Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed, and applying dropout if a probability greater than 0.0 is specified. """ if attn_mask is not None: attn_mask = attn_mask.to(q.device) if self.flash_attn_func is not None and attn_mask is None: r = self.flash_attn_func( q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), dropout_p=dropout_p, causal=is_causal ) return r.transpose(1, 2) else: # torch's sdpa doesn't support GQA, so we're doing this assert k.size(1) == v.size(1) num_kv_heads = k.size(1) num_q_heads = q.size(1) if num_q_heads != num_kv_heads: assert num_q_heads % num_kv_heads == 0 k = k.repeat_interleave(num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads) v = v.repeat_interleave(num_q_heads // num_kv_heads, dim=1, output_size=num_q_heads) return F.scaled_dot_product_attention( q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, ) def attention( self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attention_bias: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: B, T, C = q.size() # batch size, sequence length, d_model dtype = k.dtype # Optionally apply layer norm to keys and queries. if self.q_norm is not None and self.k_norm is not None: q = self.q_norm(q).to(dtype=dtype) k = self.k_norm(k).to(dtype=dtype) # Move head forward to be next to the batch dim. # shape: (B, nh, T, hs) q = q.view(B, T, self.config.n_heads, C // self.config.n_heads).transpose(1, 2) # shape: (B, n_kv_h, T, hs) k = k.view(B, T, self.config.effective_n_kv_heads, C // self.config.n_heads).transpose(1, 2) # shape: (B, n_kv_h, T, hs) v = v.view(B, T, self.config.effective_n_kv_heads, C // self.config.n_heads).transpose(1, 2) if self.config.use_position_ids and self.config.rope: # Apply rotary embeddings q, k = self.rotary_emb(q, k, position_ids=position_ids) if layer_past is not None: past_key, past_value = layer_past k = torch.cat((past_key.to(k.device), k), dim=-2) v = torch.cat((past_value.to(v.device), v), dim=-2) present = (k, v) if use_cache else None query_len, key_len = q.shape[-2], k.shape[-2] # could be different if layer_past not None if not self.config.use_position_ids and self.config.rope: # Apply rotary embeddings q, k = self.rotary_emb(q, k) if attention_bias is not None: # Resize and cast attention bias. # The current dtype of the attention bias might not match the dtype that the SDP attn function will # run in if AMP is enabled, and this can be a problem if some tokens are masked out due to padding # as down-casting the attention bias to the autocast precision will result in -infs, which will # cause the SDP attn function to produce NaNs. attention_bias = self._cast_attn_bias( attention_bias[:, :, key_len - query_len : key_len, :key_len], dtype ) # Get the attention scores. # shape: (B, nh, T, hs) att = self._scaled_dot_product_attention( q, k, v, attn_mask=attention_bias, dropout_p=0.0 if not self.training else self.config.attention_dropout, response_dropout_p=0.0 if not self.training else self.config.response_attention_dropout, is_causal=attention_bias is None, ) # Re-assemble all head outputs side-by-side. att = att.transpose(1, 2).contiguous().view(B, T, C) # Apply output projection. return self.attn_out(att), present def forward( self, x: torch.Tensor, attention_bias: Optional[torch.FloatTensor] = None, position_ids: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: raise NotImplementedError @classmethod def build(cls, layer_id: int, config: MolmoConfig, cache: BufferCache): return MolmoSequentialBlock(layer_id, config, cache) class MolmoSequentialBlock(MolmoBlock): """ This is a typical transformer block where the output is computed as ``MLP(LN(x + Attention(LN(x))))`` (plus another skip connection). """ def __init__(self, layer_id: int, config: MolmoConfig, cache: BufferCache): super().__init__(layer_id, config, cache) # Layer norms. self.attn_norm = LayerNorm.build(config) self.ff_norm = LayerNorm.build(config) # Attention input projection. Projects x -> (q, k, v) head_dim = config.d_model // config.n_heads self.fused_dims = ( config.d_model, config.effective_n_kv_heads * head_dim, config.effective_n_kv_heads * head_dim, ) self.att_proj = nn.Linear( config.d_model, sum(self.fused_dims), bias=config.include_bias or config.qkv_bias, device=config.init_device ) # Feed-forward input projection. self.ff_proj = nn.Linear( config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device ) def reset_parameters(self): super().reset_parameters() self.attn_norm.reset_parameters() self.ff_norm.reset_parameters() # NOTE: the standard deviation for these weights does not depend on the layer. init_weights( self.config, self.att_proj, d=self.config.d_model, layer_id=None, type_of_module=ModuleType.in_module ) init_weights( self.config, self.ff_proj, d=self.config.d_model, layer_id=None, type_of_module=ModuleType.in_module ) def forward( self, x: torch.Tensor, attention_bias: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: # Get query, key, value projections. # shape: # - for regular attn q, k, v: (batch_size, seq_len, d_model) # - for multi-query attn q: (batch_size, seq_len, d_model) # k, v: (batch_size, seq_len, d_model // n_heads) # - for group query attn q: (batch_size, seq_len, d_model) # k, v: (batch_size, seq_len, d_model // n_kv_heads) if not self.config.norm_after: if self._activation_checkpoint_fn is not None: atten_in = self._activation_checkpoint_fn(self.attn_norm, x) else: atten_in = self.attn_norm(x) else: atten_in = x qkv = self.att_proj(atten_in) if self.config.clip_qkv is not None: qkv.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) q, k, v = qkv.split(self.fused_dims, dim=-1) # Get attention scores. if self._activation_checkpoint_fn is not None: att, cache = self._activation_checkpoint_fn( # type: ignore self.attention, q, k, v, attention_bias, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache ) else: att, cache = self.attention(q, k, v, attention_bias, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache) if self.config.norm_after: if self._activation_checkpoint_fn is not None: att = self._activation_checkpoint_fn(self.attn_norm, att) else: att = self.attn_norm(att) # Add attention scores. # shape: (B, T, C) x = x + self.dropout(att) # Add feed-forward projection. # shape: (batch_size, seq_len, d_model) og_x = x if not self.config.norm_after: if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore else: x = self.ff_norm(x) x = self.ff_proj(x) if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.act, x) # type: ignore else: x = self.act(x) x = self.ff_out(x) if self.config.norm_after: if self._activation_checkpoint_fn is not None: x = self._activation_checkpoint_fn(self.ff_norm, x) # type: ignore else: x = self.ff_norm(x) x = self.dropout(x) x = og_x + x return x, cache class Embedding(nn.Module): def __init__( self, num_embeddings: int, num_new_embeddings: int, features: int, device: Union[str, torch.device], initializer_range: float = 0.02, new_embed_initializer_range: float = 0.02, ): super().__init__() self.initializer_range = initializer_range self.new_embed_initializer_range = new_embed_initializer_range self.embedding = nn.Parameter( torch.zeros(num_embeddings, features, device=device), ) self.new_embedding = nn.Parameter( torch.zeros(num_new_embeddings, features, device=device), ) def reset_parameters(self): nn.init.normal_(self.embedding, std=self.initializer_range) nn.init.normal_(self.new_embedding, std=self.new_embed_initializer_range) def forward(self, x: torch.Tensor) -> torch.Tensor: return F.embedding(x, torch.cat([self.embedding, self.new_embedding], dim=0)) class Dropout(nn.Dropout): def __init__( self, p: float = 0.5, inplace: bool = False, mask_p: float = 0, broadcast_dims: Sequence[int] = (), ): super().__init__(p, inplace) self.mask_p = mask_p self.broadcast_dims = broadcast_dims def forward(self, input: torch.Tensor) -> torch.Tensor: """ :param input: A tensor of shape `(batch_size, seq_len, embed_dim)` """ if self.p == 0.0 and (self.mask_p is None or self.mask_p == 0.0): return input else: if self.p > 0. and len(self.broadcast_dims) > 0 and self.training: keep_prob = 1.0 - self.p dropout_shape = list(input.shape) for dim in self.broadcast_dims: dropout_shape[dim] = 1 keep = input.new_empty(dropout_shape).bernoulli_(keep_prob) multiplier = keep.broadcast_to(input.shape) multiplier.div_(keep_prob) input = input * multiplier else: return F.dropout(input, self.p, self.training, self.inplace) @dataclass class VisionBackboneConfig: image_default_input_size: Tuple[int, int] = (336, 336) image_patch_size: int = 14 image_pos_patch_size: int = 14 image_emb_dim: int = 1024 image_num_heads: int = 16 image_num_key_value_heads: int = 16 image_num_layers: int = 24 image_head_dim: int = 64 image_mlp_dim: int = 4096 image_mlp_activations: str = "gelu" image_dropout_rate: float = 0.0 image_num_pos: int = 577 image_norm_eps: float = 1e-5 attention_dropout: float = 0.0 residual_dropout: float = 0.0 initializer_range: float = 0.02 fsdp_wrap: bool = False resize_mode: str = "default" def __post_init__(self): self.image_default_input_size = tuple(self.image_default_input_size) # type: ignore[assignment] @property def image_num_patch(self): h, w = self.image_default_input_size return h // self.image_patch_size, w // self.image_patch_size @dataclass class FullMolmoConfig: d_model: int = 768 n_heads: int = 12 n_kv_heads: Optional[int] = None qkv_bias: bool = False clip_qkv: Optional[float] = None n_layers: int = 12 mlp_ratio: int = 4 mlp_hidden_size: Optional[int] = None activation_type: str = "swiglu" block_group_size: int = 1 rope: bool = True rope_full_precision: bool = True rope_theta: float = 10000. rope_impl: str = "interleave" vision_backbone: Optional[VisionBackboneConfig] = None attention_type: str = "sdpa" float32_attention: bool = True attention_dropout: float = 0.1 response_attention_dropout: float = 0.0 multi_query_attention: Optional[bool] = None attention_layer_norm: bool = False residual_dropout: float = 0.1 embedding_dropout: float = 0.1 layer_norm_type: str = "default" layer_norm_with_affine: bool = True layer_norm_eps: Optional[float] = None attention_layer_norm_with_affine: bool = True max_sequence_length: int = 1024 max_position_embeddings: Optional[int] = None include_bias: bool = True bias_for_layer_norm: Optional[bool] = None scale_logits: bool = False vocab_size: int = 50257 embedding_size: Optional[int] = 50304 additional_vocab_size: Optional[int] = None new_embedding_init_range: float = 0.02 weight_tying: bool = True pad_token_id: int = -1 init_device: Optional[str] = None init_std: float = 0.02 init_cutoff_factor: Optional[float] = None norm_after: bool = False precision: Optional[str] = None image_padding_embed: Optional[str] = None vit_layers: Tuple = (-1,) image_pooling_h: int = 2 image_pooling_w: int = 2 image_pooling_2d: str = "attention" image_projector: str = "mlp" image_feature_dropout: float = 0.0 initializer_range: float = 0.02 normalize_input_embeds: bool = False use_position_ids: bool = True @property def effective_n_kv_heads(self) -> int: if self.n_kv_heads is None: if self.multi_query_attention is True: return 1 else: return self.n_heads else: if self.multi_query_attention is None: return self.n_kv_heads if self.multi_query_attention: n_kv_heads_should_be = 1 else: n_kv_heads_should_be = self.n_heads if self.n_kv_heads == n_kv_heads_should_be: return n_kv_heads_should_be else: raise MolmoConfigurationError( "You can't set `multi_query_attention` and `n_kv_heads` at the same time." ) @property def image_num_patch(self): assert self.vision_backbone is not None return self.vision_backbone.image_num_patch @property def image_patch_size(self): assert self.vision_backbone is not None return self.visoin_backbone.image_patch_size def llm_patches_per_crop(self): h, w = self.image_num_patch # Round up in case we need to pad the image features for pooling h = (h + self.image_pooling_h - 1) // self.image_pooling_h w = (w + self.image_pooling_w - 1) // self.image_pooling_w return h, w def _expand_token(token, batch_size: int): return token.view(1, 1, -1).expand(batch_size, -1, -1) class LayerNormFp32(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back). Derived from https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py. """ def forward(self, x: torch.Tensor) -> torch.Tensor: orig_type = x.dtype x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps) return x.to(orig_type) class ViTMLP(nn.Module): def __init__(self, config: FullMolmoConfig): super().__init__() self.config = config v_cfg = config.vision_backbone self.w1 = nn.Linear( v_cfg.image_emb_dim, v_cfg.image_mlp_dim, bias=True, device=config.init_device, ) # Activation function. cfg = deepcopy(config) cfg.activation_type = v_cfg.image_mlp_activations self.act = Activation.build(cfg) self.w2 = nn.Linear( v_cfg.image_mlp_dim, v_cfg.image_emb_dim, bias=True, device=config.init_device, ) def reset_parameters(self): v_cfg = self.config.vision_backbone nn.init.trunc_normal_(self.w1.weight, std=math.sqrt(1 / v_cfg.image_emb_dim), a=-2.0, b=2.0) nn.init.trunc_normal_(self.w2.weight, std=math.sqrt(1 / v_cfg.image_mlp_dim), a=-2.0, b=2.0) nn.init.zeros_(self.w1.bias) nn.init.zeros_(self.w2.bias) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.w1(x) x = self.act(x) x = self.w2(x) return x class ResidualAttentionBlock(nn.Module): def __init__(self, config: FullMolmoConfig): super().__init__() self.config = config v_cfg = config.vision_backbone self.attention = MultiHeadDotProductAttention(config) self.feed_forward = ViTMLP(config) self.attention_norm = nn.LayerNorm( v_cfg.image_emb_dim, eps=v_cfg.image_norm_eps, device=config.init_device, ) self.ffn_norm = nn.LayerNorm( v_cfg.image_emb_dim, eps=v_cfg.image_norm_eps, device=config.init_device, ) def reset_parameters(self): self.attention.reset_parameters() self.feed_forward.reset_parameters() self.attention_norm.reset_parameters() self.ffn_norm.reset_parameters() def forward(self, x: torch.Tensor) -> torch.Tensor: x = x + self.attention(self.attention_norm(x)) x = x + self.feed_forward(self.ffn_norm(x)) return x class BlockCollection(nn.Module): def __init__(self, config: FullMolmoConfig): super().__init__() self.config = config self.grad_checkpointing: bool = False v_cfg = config.vision_backbone self.resblocks = nn.ModuleList([ ResidualAttentionBlock(config) for _ in range(v_cfg.image_num_layers) ]) def reset_parameters(self): for r in self.resblocks: r.reset_parameters() def forward(self, x: torch.Tensor) -> List[torch.Tensor]: hidden_states = [] for r in self.resblocks: x = r(x) hidden_states.append(x) return hidden_states class VisionTransformer(nn.Module): def __init__(self, config: FullMolmoConfig): super().__init__() self.config = config v_cfg = config.vision_backbone # class embeddings and positional embeddings self.scale = v_cfg.image_emb_dim ** -0.5 self.class_embedding = nn.Parameter( torch.zeros(v_cfg.image_emb_dim, device=config.init_device), ) self.num_prefix_tokens: int = 1 self.positional_embedding = nn.Parameter( torch.zeros(v_cfg.image_num_pos, v_cfg.image_emb_dim, device=config.init_device), ) image_patch_size = v_cfg.image_patch_size self.patch_embedding = nn.Linear( image_patch_size * image_patch_size * 3, v_cfg.image_emb_dim, bias=False, device=config.init_device, ) self.pre_ln = LayerNormFp32( v_cfg.image_emb_dim, eps=v_cfg.image_norm_eps, device=config.init_device, ) self.transformer = BlockCollection(config) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.transformer.grad_checkpointing = enable def reset_parameters(self): nn.init.normal_(self.class_embedding, std=self.scale) nn.init.normal_(self.positional_embedding, std=self.scale) nn.init.normal_(self.patch_embedding.weight, std=0.02) self.pre_ln.reset_parameters() self.transformer.reset_parameters() def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor: cls_emb = self.positional_embedding[0:1] pos_emb = self.positional_embedding[1:] pos_emb = pos_emb.reshape( (int(math.sqrt(pos_emb.shape[0])), int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1]) ) (patch_num_0, patch_num_1) = patch_num if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1: # Dervied from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py # antialias: default True in jax.image.resize pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2) pos_emb = F.interpolate( pos_emb, size=(patch_num_0, patch_num_1), mode="bicubic", align_corners=False, antialias=True, ) pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0) pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1]) x = x + torch.cat([cls_emb[None, :, :], pos_emb[None, :, :]], dim=1).to(x.dtype) return x def forward(self, x: torch.Tensor, patch_num: int = None) -> List[torch.Tensor]: """ : param x: (batch_size, num_patch, n_pixels) """ if patch_num is None: patch_num = self.config.vision_backbone.image_num_patch B, N, D = x.shape x = self.patch_embedding(x) # class embeddings and positional embeddings x = torch.cat([_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], dim=1) x = self.add_pos_emb(x, patch_num) x = self.pre_ln(x) hidden_states = self.transformer(x) return hidden_states class MultiHeadDotProductAttention(nn.Module): def __init__(self, config: FullMolmoConfig, use_bias: bool = True, is_vit_layer: Optional[bool] = True): super().__init__() self.config = config self.use_bias = use_bias v_cfg = config.vision_backbone self.embed_dim = v_cfg.image_emb_dim self.num_heads = v_cfg.image_num_heads self.head_dim = v_cfg.image_head_dim self.num_key_value_heads = v_cfg.image_num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.initializer_range = v_cfg.initializer_range self.is_vit_layer = is_vit_layer nlayers = 1 if (is_vit_layer or config.vit_layers is None) else len(config.vit_layers) self.wq = nn.Linear( nlayers * self.embed_dim, self.num_heads * self.head_dim, bias=use_bias, device=config.init_device, ) self.wk = nn.Linear( nlayers * self.embed_dim, self.num_key_value_heads * self.head_dim, bias=use_bias, device=config.init_device, ) self.wv = nn.Linear( nlayers * self.embed_dim, self.num_key_value_heads * self.head_dim, bias=use_bias, device=config.init_device, ) self.wo = nn.Linear( self.num_heads * self.head_dim, self.embed_dim, bias=use_bias, device=config.init_device, ) self.attention_dropout: Optional[Dropout] = None if v_cfg.attention_dropout > 0: self.attention_dropout = Dropout(v_cfg.attention_dropout, broadcast_dims=(0, 1)) self.residual_dropout = Dropout(v_cfg.residual_dropout) def reset_parameters(self): nn.init.normal_(self.wq.weight, std=self.initializer_range) nn.init.normal_(self.wk.weight, std=self.initializer_range) nn.init.normal_(self.wv.weight, std=self.initializer_range) nn.init.normal_(self.wo.weight, std=self.initializer_range) if self.use_bias: nn.init.constant_(self.wq.bias, 0) nn.init.constant_(self.wk.bias, 0) nn.init.constant_(self.wv.bias, 0) nn.init.constant_(self.wo.bias, 0) def _split_heads(self, hidden_states, num_heads) -> torch.Tensor: return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim)) def _merge_heads(self, hidden_states) -> torch.Tensor: return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def forward(self, inputs_q: torch.Tensor, inputs_kv: Optional[torch.Tensor] = None) -> torch.Tensor: if inputs_kv is not None: inputs_k = inputs_kv inputs_v = inputs_kv else: inputs_k = inputs_q inputs_v = inputs_q xq, xk, xv = self.wq(inputs_q), self.wk(inputs_k), self.wv(inputs_v) xq = self._split_heads(xq, self.num_heads) xk = self._split_heads(xk, self.num_key_value_heads) xv = self._split_heads(xv, self.num_key_value_heads) if self.num_heads != self.num_key_value_heads: xk = xk.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads) xv = xv.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads) og_dtype = xq.dtype if self.config.float32_attention: xq = xq.to(torch.float) xk = xk.to(torch.float) if self.config.attention_type == "direct": attn_weights = torch.einsum("...qhd,...khd->...hqk", xq / math.sqrt(xq.size(-1)), xk) attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(xq.dtype) if self.attention_dropout is not None: attn_weights = self.attention_dropout(attn_weights) attn_output = torch.einsum("...hqk,...khd->...qhd", attn_weights.to(xv.dtype), xv) elif self.config.attention_type == "sdpa": attn_output = F.scaled_dot_product_attention( xq.transpose(1, 2).contiguous(), xk.transpose(1, 2).contiguous(), xv.transpose(1, 2).contiguous(), is_causal=False, dropout_p=self.config.vision_backbone.attention_dropout ).transpose(1, 2) else: raise NotImplementedError(self.config.attention_type) attn_output = attn_output.to(og_dtype) attn_output = self._merge_heads(attn_output) attn_output = self.wo(attn_output) attn_output = self.residual_dropout(attn_output) return attn_output class MultiHeadAttentionPool(nn.Module): def __init__( self, config: FullMolmoConfig, factor: int = 1, use_bias: bool = True, dropout: bool = True, output_layer: bool = True, mean_residual: bool = False, query: str = "mean", is_vit_layer: Optional[bool] = True ): super().__init__() self.config = config self.factor = factor self.use_bias = use_bias self.dropout = dropout self.output_layer = output_layer self.mean_residual = mean_residual self.query = query v_cfg = config.vision_backbone input_dim = v_cfg.image_emb_dim self.embed_dim = v_cfg.image_emb_dim * factor self.num_heads = v_cfg.image_num_heads self.head_dim = v_cfg.image_head_dim * factor self.num_key_value_heads = v_cfg.image_num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.initializer_range = v_cfg.initializer_range nlayers = 1 if (is_vit_layer or config.vit_layers is None) else len(config.vit_layers) if query != "vector": self.wq = nn.Linear( nlayers * input_dim, self.num_heads * self.head_dim, bias=use_bias, device=config.init_device, ) self.wk = nn.Linear( nlayers * input_dim, self.num_key_value_heads * self.head_dim, bias=use_bias, device=config.init_device, ) self.wv = nn.Linear( nlayers * input_dim, self.num_key_value_heads * self.head_dim, bias=use_bias, device=config.init_device, ) if query == "vector": self.attention_query = nn.Parameter( torch.zeros( 1, self.num_key_value_heads * self.head_dim, device=config.init_device, ), ) if output_layer: self.wo = nn.Linear( self.num_heads * self.head_dim, self.embed_dim, bias=use_bias, device=config.init_device, ) self.attention_dropout = Dropout(v_cfg.attention_dropout, broadcast_dims=(0, 1)) if dropout: self.residual_dropout = Dropout(v_cfg.residual_dropout) def reset_parameters(self): if self.query != "vector": nn.init.normal_(self.wq.weight, std=self.initializer_range) nn.init.normal_(self.wk.weight, std=self.initializer_range) nn.init.normal_(self.wv.weight, std=self.initializer_range) if self.output_layer: nn.init.normal_(self.wo.weight, std=self.initializer_range) if self.use_bias: if self.query != "vector": nn.init.constant_(self.wq.bias, 0) nn.init.constant_(self.wk.bias, 0) nn.init.constant_(self.wv.bias, 0) if self.output_layer: nn.init.constant_(self.wo.bias, 0) if self.query == "vector": nn.init.normal_(self.attention_query, std=self.initializer_range) def _split_heads(self, hidden_states, num_heads): return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def forward(self, inputs_kv: torch.Tensor) -> torch.Tensor: xk, xv = self.wk(inputs_kv), self.wv(inputs_kv) if self.query == "mean": inputs_q = inputs_kv.mean(dim=1, keepdim=True) xq = self.wq(inputs_q) elif self.query == "first": inputs_q = inputs_kv[:, :1] xq = self.wq(inputs_q) elif self.query == "vector": xq = self.attention_query.expand(inputs_kv.size(0), -1, -1) elif self.query == "constant": inputs_q = torch.ones_like(inputs_kv[:, :1]) / math.sqrt(inputs_kv.shape[-1]) xq = self.wq(inputs_q) else: raise ValueError(f"Unknown query type: {self.query}") xq = self._split_heads(xq, self.num_heads) xk = self._split_heads(xk, self.num_key_value_heads) xv = self._split_heads(xv, self.num_key_value_heads) if self.num_heads != self.num_key_value_heads: xk = xk.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads) xv = xv.repeat_interleave(self.num_key_value_groups, dim=2, output_size=self.num_heads) xq = xq.to(torch.float) xk = xk.to(torch.float) xq = xq / math.sqrt(xq.size(-1)) attn_weights = torch.einsum("...qhd,...khd->...hqk", xq, xk) attn_weights = F.softmax(attn_weights, dim=-1).to(xq.dtype) attn_weights = self.attention_dropout(attn_weights).to(xv.dtype) attn_output = torch.einsum("...hqk,...khd->...qhd", attn_weights, xv) attn_output = self._merge_heads(attn_output) if self.output_layer: attn_output = self.wo(attn_output) if self.dropout: attn_output = self.residual_dropout(attn_output) if self.mean_residual: attn_output += inputs_kv.mean(dim=1, keepdim=True) return attn_output class MLP(nn.Module): def __init__(self, config: FullMolmoConfig, input_dim: int, dropout: float = 0.0): super().__init__() self.config = config self.hidden_size = ( config.mlp_hidden_size if config.mlp_hidden_size is not None else config.mlp_ratio * config.d_model ) self.initializer_range = config.initializer_range self.w1 = nn.Linear( input_dim, self.hidden_size // 2, bias=False, device=config.init_device, ) self.w2 = nn.Linear( self.hidden_size // 2, config.d_model, bias=False, device=config.init_device, ) self.w3 = nn.Linear( input_dim, self.hidden_size // 2, bias=False, device=config.init_device, ) # Activation function. self.act = Activation.build(config) self.dropout = Dropout(dropout) def reset_parameters(self): nn.init.normal_(self.w1.weight, std=self.initializer_range) nn.init.normal_(self.w2.weight, std=self.initializer_range) nn.init.normal_(self.w3.weight, std=self.initializer_range) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.w2(self.act(self.w1(x), self.w3(x))) x = self.dropout(x) return x class Residual(nn.Module): def __init__(self, submodule: nn.Module): super().__init__() self.submodule = submodule def reset_parameters(self): self.submodule.reset_parameters() def forward(self, x: torch.Tensor) -> torch.Tensor: return x + self.submodule(x) class OLMoVisionBackbone(nn.Module): def __init__(self, config: FullMolmoConfig): super().__init__() self.config = config self.image_vit = VisionTransformer(config) input_dim: int = None self.image_pooling_2d: nn.Module = None if config.image_pooling_2d in {ImagePooling2DType.attention, ImagePooling2DType.attention_meanq}: self.image_pooling_2d = MultiHeadDotProductAttention(config, is_vit_layer=False) input_dim = config.vision_backbone.image_emb_dim elif config.image_pooling_2d == ImagePooling2DType.attention_2wide: cfg = deepcopy(config) cfg.vision_backbone.image_emb_dim *= 2 cfg.vision_backbone.image_head_dim *= 2 self.image_pooling_2d = MultiHeadDotProductAttention(cfg, is_vit_layer=False) input_dim = cfg.vision_backbone.image_emb_dim elif config.image_pooling_2d == ImagePooling2DType.attention_v2: assert config.vit_layers is not None use_bias = True dropout = True output_layer = True query = "mean" mean_residual = False factor = len(config.vit_layers) self.image_pooling_2d = MultiHeadAttentionPool( config, factor=factor, use_bias=use_bias, dropout=dropout, output_layer=output_layer, mean_residual=mean_residual, query=query, is_vit_layer=False, ) input_dim = config.vision_backbone.image_emb_dim * factor elif config.image_pooling_2d in [ImagePooling2DType.none, ImagePooling2DType.stack]: self.image_pooling_2d = None nlayers = 1 if config.vit_layers is None else len(config.vit_layers) input_dim = nlayers * config.vision_backbone.image_emb_dim else: raise NotImplementedError(f"Unknown image pooling 2D method: {config.image_pooling_2d}") self.input_dim = input_dim # `MLP` assume the activation takes two inputs, so it must be a 'llama' version if config.activation_type == ActivationType.swiglu: mlp_config = replace(config, activation_type=ActivationType.llama_swiglu) elif config.activation_type == ActivationType.gelu: mlp_config = replace(config, activation_type=ActivationType.llama_geglu) else: mlp_config = config if config.image_projector == ImageProjectType.mlpx2: self.image_projector = nn.ModuleList( [MLP(mlp_config, input_dim), Residual(MLP(config, input_dim))] ) elif config.image_projector == ImageProjectType.mlp: self.image_projector = MLP(mlp_config, input_dim) elif config.image_projector == ImageProjectType.linear: self.image_projector = nn.Linear( input_dim, config.d_model, bias=False, device=config.init_device, ) else: raise NotImplementedError(f"Unknown image projector: {config.image_projector}") self.image_feature_dropout = Dropout(config.image_feature_dropout) def reset_parameters(self): if self.image_pooling_2d is not None: self.image_pooling_2d.reset_parameters() if self.config.image_projector == "2mlp": for module in self.image_projector: module.reset_parameters() elif self.config.image_projector == "linear": nn.init.xavier_uniform_(self.image_projector.weight) else: self.image_projector.reset_parameters() def forward(self, images: torch.Tensor, image_masks: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: raise NotImplementedError class OLMoPretrainedVisionBackbone(OLMoVisionBackbone): def __init__(self, config: FullMolmoConfig): super().__init__(config) v_cfg = self.config.vision_backbone self.grad_checkpointing = False self.num_prefix_tokens = self.image_vit.num_prefix_tokens assert self.num_prefix_tokens in {0, 1}, "Only 0 or 1 prefix tokens are supported" self.pad_embed = None if config.image_padding_embed: image_dim = v_cfg.image_emb_dim*len(self.config.vit_layers) if config.image_padding_embed in ["pad_embed", "regress"]: self.pad_embed = nn.Parameter( torch.zeros((image_dim,), device=config.init_device)) elif config.image_padding_embed == "pad_and_partial_pad": self.pad_embed = nn.Parameter( torch.zeros((2, image_dim), device=config.init_device)) else: raise ValueError(config.image_padding_embed) def reset_parameters(self): super().reset_parameters() self.image_vit.reset_parameters() def encode_image(self, images: torch.Tensor) -> torch.Tensor: """ : param images: (batch_size, num_crops, num_patch, n_pixels) """ cfg = self.config v_cfg = self.config.vision_backbone B, T, N, D = images.shape mask = ~torch.all(images.view(B * T, N, D) == -1, dim=(1, 2), keepdim=True) # Output all hidden states # n_layers x (batch_num_crops, (1+)n_tokens, image_emb_dim) images = images.view(B * T, N, D) image_features = self.image_vit(images) if cfg.vit_layers is not None: features = [] for layer in cfg.vit_layers: features.append(image_features[layer]) image_features = torch.cat(features, dim=-1) else: image_features = image_features[-1] cls_embed: torch.Tensor = None if self.num_prefix_tokens > 0: cls_embed = image_features[:, 0] image_features = image_features[:, 1:] image_features = image_features * mask image_features = image_features.view(B, T, N, -1) cls_embed = cls_embed.view(B, T, -1) if cls_embed is not None else None return image_features, cls_embed def forward(self, images: torch.Tensor, image_masks: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: cfg = self.config # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim) batch_size, num_image = images.shape[:2] image_features, cls_embed = self.encode_image(images) if cfg.image_padding_embed: assert image_masks is not None if cfg.image_padding_embed == "pad_embed": all_pad = (image_masks == 0).to(dtype=torch.float32) pad_embed = self.pad_embed[None, None, None, :] image_features = image_features + pad_embed * torch.unsqueeze(all_pad, -1) elif cfg.image_padding_embed == "regress": pad_embed = self.pad_embed[None, None, None, :] image_features = image_features + pad_embed * torch.unsqueeze(torch.maximum(image_masks, torch.zeros_like(image_masks)), -1) elif cfg.image_padding_embed == "pad_and_partial_pad": pad_embed = self.pad_embed[:, None, None, None, :] all_pad = image_masks == 0 partial_pad = torch.logical_and(image_masks < 1, torch.logical_not(all_pad)).to(dtype=torch.float32) all_pad = all_pad.to(dtype=torch.float32) image_features = image_features + pad_embed[0] * torch.unsqueeze(all_pad, -1) image_features = image_features + pad_embed[1] * torch.unsqueeze(partial_pad, -1) else: raise ValueError(cfg.image_padding_embed) image_features = self.image_feature_dropout(image_features) if cls_embed is not None: cls_embed = self.image_feature_dropout(cls_embed) image_features = image_features.reshape( (batch_size, num_image) + cfg.image_num_patch + (-1,), ) if cfg.image_num_patch[0] % cfg.image_pooling_h == 1: # Pad so we can still pool 2x2 patches image_features = F.pad( image_features, (0, 0, 0, 1, 0, 1, 0, 0, 0, 0), ) # image pooling image_features = einops.rearrange( image_features, 'b n (h dh) (w dw) c -> (b n h w) (dh dw) c', dh=cfg.image_pooling_h, dw=cfg.image_pooling_w, ) if cfg.image_pooling_2d == ImagePooling2DType.attention_meanq: query = image_features.mean(-2, keepdim=True) image_features = self.image_pooling_2d(query, image_features) elif cfg.image_pooling_2d not in {ImagePooling2DType.none, ImagePooling2DType.stack}: if self.grad_checkpointing: from torch.utils.checkpoint import checkpoint image_features = checkpoint(self.image_pooling_2d, image_features[:, :1, :], image_features, use_reentrant=False) else: image_features = self.image_pooling_2d(image_features[:, :1, :], image_features) h, w = cfg.llm_patches_per_crop() image_features = image_features.reshape(batch_size, num_image, h * w, -1) # MLP layer to map the feature. if self.grad_checkpointing: from torch.utils.checkpoint import checkpoint image_features = checkpoint(self.image_projector, image_features, use_reentrant=False) else: image_features = self.image_projector(image_features) # image_features: (batch_size, num_image, num_patch, d_model) # cls_embed: (batch_size, num_image, d_model) return image_features, cls_embed class ModuleType(str, Enum): in_module = "in" out_module = "out" emb = "emb" final_out = "final_out" def init_weights( config: FullMolmoConfig, module: Union[nn.Linear, nn.Embedding], d: Optional[int] = None, layer_id: Optional[int] = None, std_factor: float = 1.0, type_of_module: Optional[ModuleType] = None, ) -> None: d = d if d is not None else config.d_model std = config.init_std * std_factor if config.init_cutoff_factor is not None: cutoff_value = config.init_cutoff_factor * std nn.init.trunc_normal_(module.weight, mean=0.0, std=std, a=-cutoff_value, b=cutoff_value) else: nn.init.normal_(module.weight, mean=0.0, std=std) class LlamaSwiGLU(nn.Module): def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor: return F.silu(x1) * x2 @property def output_multiplier(self) -> float: return 0.5 class SwiGLU(nn.Module): def forward(self, x: torch.Tensor) -> torch.Tensor: x, gate = x.chunk(2, dim=-1) return F.silu(gate) * x @property def output_multiplier(self) -> float: return 0.5 class Activation(nn.Module): def __init__(self, config: FullMolmoConfig): super().__init__() self.config = config def forward(self, x: torch.Tensor) -> torch.Tensor: raise NotImplementedError @property def output_multiplier(self) -> float: raise NotImplementedError @classmethod def build(cls, config: FullMolmoConfig) -> 'Activation': if config.activation_type == "quick_gelu": return QuickGELU(config) elif config.activation_type == "gelu": return cast(Activation, GELU(approximate="none")) elif config.activation_type == "gelu_tanh": return cast(Activation, GELU(approximate="tanh")) elif config.activation_type == "relu": return cast(Activation, ReLU(inplace=False)) elif config.activation_type == "silu": return cast(Activation, SiLU(inplace=False)) # elif config.activation_type == "llama_geglu": # return LlamaGEGLU(config) # elif config.activation_type == "llama_geglu_tanh": # return LlamaGEGLUTanh(config) elif config.activation_type == "llama_swiglu": return LlamaSwiGLU() elif config.activation_type == "swiglu": return SwiGLU() else: raise NotImplementedError(f"Unknown activation: '{config.activation_type}'") class QuickGELU(Activation): def forward(self, x: torch.Tensor) -> torch.Tensor: return x * torch.sigmoid(1.702 * x) @property def output_multiplier(self) -> float: return 1.0 class GELU(nn.GELU): @property def output_multiplier(self) -> float: return 1.0 class ReLU(nn.ReLU): @property def output_multiplier(self) -> float: return 1.0 class SiLU(nn.SiLU): @property def output_multiplier(self) -> float: return 1.0 def causal_attention_bias(seq_len: int, device: torch.device) -> torch.FloatTensor: att_bias = torch.triu( torch.ones(seq_len, seq_len, device=device, dtype=torch.float), diagonal=1, ) att_bias.masked_fill_(att_bias == 1, torch.finfo(att_bias.dtype).min) return att_bias.view(1, 1, seq_len, seq_len) # type: ignore def get_causal_attention_bias(cache: BufferCache, seq_len: int, device: torch.device) -> torch.Tensor: if (causal_bias := cache.get("causal_attention_bias")) is not None and causal_bias.shape[-1] >= seq_len: if causal_bias.device != device: causal_bias = causal_bias.to(device) cache["causal_attention_bias"] = causal_bias return causal_bias with torch.autocast(device.type, enabled=False): causal_bias = causal_attention_bias(seq_len, device) cache["causal_attention_bias"] = causal_bias return causal_bias class LayerNormBase(nn.Module): def __init__( self, config: MolmoConfig, *, size: Optional[int] = None, elementwise_affine: Optional[bool] = True, eps: float = 1e-05, weight_initializer: Optional[Callable] = torch.ones, bias_initializer: Optional[Callable] = torch.zeros, ): super().__init__() self.config = config self.eps = self.config.layer_norm_eps or eps self.normalized_shape = (size or config.d_model,) if elementwise_affine or (elementwise_affine is None and self.config.layer_norm_with_affine): self.weight = nn.Parameter(weight_initializer(self.normalized_shape, device=config.init_device)) use_bias = self.config.bias_for_layer_norm if use_bias is None: use_bias = self.config.include_bias if use_bias: self.bias = nn.Parameter(bias_initializer(self.normalized_shape, device=config.init_device)) else: self.register_parameter("bias", None) else: self.register_parameter("bias", None) self.register_parameter("weight", None) @classmethod def build(cls, config: FullMolmoConfig, size: Optional[int] = None, **kwargs): if config.layer_norm_type == "default": return LayerNorm(config, size=size, low_precision=False, **kwargs) elif config.layer_norm_type == "low_precision": return LayerNorm(config, size=size, low_precision=True, **kwargs) elif config.layer_norm_type == "rms": return RMSLayerNorm(config, size=size, **kwargs) else: raise NotImplementedError(f"Unknown LayerNorm type: '{config.layer_norm_type}'") class RMSLayerNorm(LayerNormBase): """ RMS layer norm, a simplified :class:`LayerNorm` implementation """ def __init__( self, config: FullMolmoConfig, size: Optional[int] = None, elementwise_affine: Optional[bool] = None, eps: float = 1e-5, ): super().__init__(config, size=size, elementwise_affine=elementwise_affine, eps=eps) def forward(self, x: torch.Tensor) -> torch.Tensor: with torch.autocast(enabled=False, device_type=x.device.type): og_dtype = x.dtype x = x.to(torch.float32) variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.eps) x = x.to(og_dtype) if self.weight is not None: if self.bias is not None: return self.weight * x + self.bias else: return self.weight * x else: return x class LayerNorm(LayerNormBase): """ The default :class:`LayerNorm` implementation which can optionally run in low precision. """ def __init__( self, config: FullMolmoConfig, size: Optional[int] = None, low_precision: bool = False, elementwise_affine: Optional[bool] = None, eps: float = 1e-05, ): super().__init__(config, size=size, elementwise_affine=elementwise_affine, eps=eps) self.low_precision = low_precision def forward(self, x: torch.Tensor) -> torch.Tensor: if self.low_precision: module_device = x.device downcast_x = self._cast_if_autocast_enabled(x) downcast_weight = ( self._cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight ) downcast_bias = self._cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias with torch.autocast(enabled=False, device_type=module_device.type): return F.layer_norm( downcast_x, self.normalized_shape, weight=downcast_weight, bias=downcast_bias, eps=self.eps ) else: return F.layer_norm(x, self.normalized_shape, weight=self.weight, bias=self.bias, eps=self.eps) class Molmo(nn.Module): def __init__(self, config: FullMolmoConfig, init_params: bool = True): super().__init__() self.config = config self.__cache = BufferCache() # Validate config. if self.config.embedding_size is not None and self.config.embedding_size != self.config.vocab_size: if self.config.embedding_size < self.config.vocab_size: raise MolmoConfigurationError("embedding size should be at least as big as vocab size") elif self.config.embedding_size % 128 != 0: import warnings warnings.warn( "Embedding size is not a multiple of 128! This could hurt throughput performance.", UserWarning ) torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(False) # this is super slow so make sure torch won't use it wte = None if self.config.additional_vocab_size is not None: wte = Embedding( config.embedding_size or config.vocab_size, config.additional_vocab_size, config.d_model, device=config.init_device, initializer_range=config.initializer_range, new_embed_initializer_range=config.new_embedding_init_range ) else: wte=nn.Embedding( config.embedding_size or config.vocab_size, config.d_model, device=config.init_device ) self.transformer = nn.ModuleDict( dict( wte=wte, emb_drop=Dropout(config.embedding_dropout), ln_f=LayerNorm.build(config), ) ) blocks = [MolmoBlock.build(i, config, self.__cache) for i in range(config.n_layers)] if self.config.block_group_size > 1: raise NotImplementedError() else: self.transformer.update({"blocks": nn.ModuleList(blocks)}) if not self.config.rope: self.transformer.update( {"wpe": nn.Embedding(config.max_sequence_length, config.d_model, device=config.init_device)} ) if not config.weight_tying: self.transformer.update( { "ff_out": nn.Linear( config.d_model, config.embedding_size or config.vocab_size, bias=config.include_bias, device=config.init_device, ) } ) self.vision_backbone: Optional[OLMoVisionBackbone] = None if config.vision_backbone is not None: self.vision_backbone = OLMoPretrainedVisionBackbone(config) self.__num_fwd_flops: Optional[int] = None def reset_parameters(self): if self.vision_backbone is not None: self.vision_backbone.reset_parameters() self.reset_non_vision_parameters() def reset_non_vision_parameters(self): self.transformer.wte.reset_parameters() if hasattr(self.transformer.wte, "new_embedding"): nn.init.normal_(self.transformer.wte.new_embedding, std=self.config.new_embedding_init_range) if hasattr(self.transformer, "wpe"): nn.init.normal_(self.transformer.wpe, mean=0.0, std=1.0) self.transformer.ln_f.reset_parameters() # type: ignore if hasattr(self.transformer, "ff_out"): nn.init.normal_(self.transformer.ff_out, mean=0.0, std=0.02) if self.config.block_group_size == 1: for block in self.transformer.blocks: block.reset_parameters() else: for block_group in self.transformer.block_groups: block_group.reset_parameters() def forward( self, input_ids: torch.LongTensor, input_embeddings: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, attention_bias: Optional[torch.Tensor] = None, response_mask: Optional[torch.Tensor] = None, images: Optional[torch.Tensor] = None, image_masks: Optional[torch.Tensor] = None, image_input_idx: Optional[torch.Tensor] = None, subsegment_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Sequence[Tuple[torch.Tensor, torch.Tensor]]] = None, use_cache: bool = False, last_logits_only: bool = False, output_hidden_states: Optional[bool] = None, append_last_valid_logits: Optional[torch.Tensor] = None, ) -> ModelOutput: """ :param input_ids: A tensor of shape `(batch_size, seq_len)`. :param input_embeddings: A tensor of shape `(batch_size, seq_len, d_model)` with input embeddings. When provided, it is treated as the output of the input embedding layer. :param attention_mask: A tensor of shape `(batch_size, seq_len)` that indicates which input IDs are masked. A `1` value in the mask means that the corresponding input ID should *not* be ignored. A `0` means that the corresponding input ID is masked. This has the same meaning as the `attention_mask` in HuggingFace's `transformers` library. :param attention_bias: A tensor of shape `(batch_size, 1, seq_len, seq_len)`, `(1, 1, seq_len, seq_len)`, or `(seq_len, seq_len)`. This is used to introduce causal or other biases. If the tensor is a bool or byte tensor, a `True` or `1` at `attention_bias[:, :, i, j]` indicates that the i-th element in the sequence is allowed to attend to the j-th element in the sequence. If the tensor is a float tensor, it will just be added to the attention scores before the softmax. The default is causal, which corresponds to a lower-diagonal byte matrix of ones. :param response_mask: A tensor of shape `(batch_size, seq_len)` that indicates the response mask. A `1` value in the mask means that the corresponding token is a response token. A `0` means that the corresponding token is not a response token. :param past_key_values: Pre-computed keys and values for each attention block. Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. :param use_cache: If `True`, return key and value tensors for each block. :param last_logits_only: If `True`, only compute the logits for the last token of each sequence. This can speed up decoding when you only care about the next token. """ output_hidden_states = output_hidden_states if output_hidden_states is not None else False if past_key_values: assert len(past_key_values) == self.config.n_layers has_image = images is not None assert not (has_image and input_embeddings is not None), "Cannot provide both images and input embeddings." assert not (has_image and past_key_values is not None), "Cached key and values should not be used with images." batch_size, seq_len = input_ids.size() if input_embeddings is None else input_embeddings.size()[:2] if past_key_values is None: past_length = 0 else: past_length = past_key_values[0][0].size(-2) if self.config.use_position_ids and attention_mask is None: attention_mask = input_ids != -1 if subsegment_ids is not None: assert not use_cache, "Subsegment_ids cannot be used with cache." subsegment_mask = subsegment_ids.unsqueeze(2) <= subsegment_ids.unsqueeze(1) attention_mask = ( subsegment_mask.to(attention_mask.dtype) * attention_mask.unsqueeze(2) * attention_mask.unsqueeze(1)) if position_ids is None: raise ValueError(f"Positioned ids must be given if using subsegment_ids") else: if self.config.use_position_ids and position_ids is None: position_ids = torch.clamp( torch.cumsum(attention_mask.to(torch.int32), dim=-1) - 1, min=0, ).broadcast_to((batch_size, attention_mask.shape[-1])) # Get embeddings of input. # shape: (batch_size, seq_len, d_model) if input_ids is not None: input_ids = input_ids * (input_ids != -1).to(input_ids.dtype) x = self.transformer.wte(input_ids) if input_embeddings is None else input_embeddings # type: ignore num_image: Optional[int] = None if images is not None: # shape: (batch_size, num_image, num_patch, d_model) # cls_embed: (batch_size, num_image, d_model) image_features, cls_embed = self.vision_backbone(images, image_masks) num_image, num_patch = image_features.shape[1:3] assert image_input_idx.shape == (batch_size, num_image, num_patch) # inster the image feature into the embedding. image_features = image_features.view(batch_size, num_image * num_patch, -1) image_input_idx = image_input_idx.view(batch_size, num_image * num_patch) valid = image_input_idx >= 0 batch_idx = torch.arange(batch_size, device=x.device) batch_idx = torch.tile(batch_idx[:, None], [1, image_features.shape[1]]) # For hf demo/endpoint image_features = image_features.to(x.device) x[batch_idx[valid], image_input_idx[valid]] += image_features[valid] if not self.config.rope: # Get positional embeddings. # shape: (1, seq_len) pos = torch.arange(past_length, past_length + seq_len, dtype=torch.long, device=x.device).unsqueeze(0) # shape: (1, seq_len, d_model) pos_emb = self.transformer.wpe(pos) # type: ignore x = pos_emb + x # Add input + positional embeddings and apply dropout. # shape: (batch_size, seq_len, d_model) x = self.transformer.emb_drop(x) # type: ignore # normalized if self.config.normalize_input_embeds: x = x * (self.config.d_model ** 0.5) # Transform the attention mask into what the blocks expect. if attention_mask is not None: # shape: (batch_size, 1, 1, seq_len) if len(attention_mask.shape) == 2: attention_mask = attention_mask[:, :past_length + seq_len] attention_mask = attention_mask.to(dtype=torch.float).view(batch_size, -1)[:, None, None, :] else: attention_mask = attention_mask.unsqueeze(1).to(dtype=torch.float) attention_mask = (1.0 - attention_mask) * torch.finfo(attention_mask.dtype).min # Merge attention mask with attention bias. if ( attention_bias is not None or attention_mask is not None # NOTE (epwalsh): we need to initialize the attn bias in order for attn to work properly # with key+value cache. Otherwise `F.scaled_dot_product_attention()` doesn't seem to compute # scores correctly. or past_key_values is not None ): if attention_bias is None: attention_bias = get_causal_attention_bias(self.__cache, past_length + seq_len, x.device) elif attention_bias.dtype in (torch.int8, torch.bool): attention_bias = attention_bias.to(dtype=torch.float) attention_bias.masked_fill_(attention_bias == 0.0, torch.finfo(attention_bias.dtype).min) # Transform to the right shape and data type. mask_len = seq_len if attention_mask is not None: mask_len = attention_mask.shape[-1] elif past_key_values is not None: mask_len = past_key_values[0][0].shape[-2] + seq_len attention_bias = attention_bias[:, :, :mask_len, :mask_len].to(dtype=torch.float) # Add in the masking bias. if attention_mask is not None: attention_bias = attention_bias + attention_mask # Might get -infs after adding attention mask, since dtype.min + dtype.min = -inf. # `F.scaled_dot_product_attention()` doesn't handle -inf like you'd expect, instead # it can produce NaNs. ensure_finite_(attention_bias, check_neg_inf=True, check_pos_inf=False) attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = [] if use_cache else None # decoder layers all_hidden_states = [] # Apply blocks one-by-one. if self.config.block_group_size == 1: for block_idx, block in enumerate(self.transformer.blocks): if output_hidden_states: # add hidden states all_hidden_states.append(x) layer_past = None if past_key_values is None else past_key_values[block_idx] x, cache = block(x, attention_bias=attention_bias, position_ids=position_ids, layer_past=layer_past, use_cache=use_cache) if attn_key_values is not None: assert cache is not None attn_key_values.append(cache) else: for group_idx, block_group in enumerate(self.transformer.block_groups): if output_hidden_states: # add hidden states all_hidden_states.append(x) layers_past = ( None if past_key_values is None else past_key_values[ group_idx * self.config.block_group_size : (group_idx + 1) * self.config.block_group_size ] ) x, cache = block_group( x, attention_bias=attention_bias, position_ids=position_ids, layers_past=layers_past, use_cache=use_cache ) if attn_key_values is not None: assert cache is not None attn_key_values.extend(cache) if last_logits_only: # shape: (batch_size, 1, d_model) if append_last_valid_logits is not None: last_valid_output = x[ torch.arange(x.shape[0], device=x.device), append_last_valid_logits.to(x.device)] x = last_valid_output.unsqueeze(1) else: x = x[:, -1, :].unsqueeze(1) # Apply final layer norm. # shape: (batch_size, seq_len or 1, d_model) x = self.transformer.ln_f(x) # type: ignore if output_hidden_states: # add final hidden state post-final-layernorm, following HuggingFace's convention all_hidden_states.append(x) # Get logits. # shape: (batch_size, seq_len or 1, vocab_size) if self.config.weight_tying: logits = F.linear(x, self.transformer.wte.weight, None) # type: ignore else: logits = self.transformer.ff_out(x) # type: ignore if self.config.scale_logits: logits.mul_(1 / math.sqrt(self.config.d_model)) if not last_logits_only and append_last_valid_logits is not None: last_valid_logit = logits[ torch.arange(logits.shape[0], device=logits.device), append_last_valid_logits] logits = torch.cat([logits[:, :-1], last_valid_logit[:, None]], dim=1) return ModelOutput(logits=logits, attn_key_values=attn_key_values, hidden_states=tuple(all_hidden_states) if output_hidden_states else None) # type: ignore[arg-type] class MolmoForCausalLM(PreTrainedModel): config_class = MolmoConfig base_model_prefix = "model" _no_split_modules = ["MolmoBlock"] def __init__(self, config: MolmoConfig, model: Optional[Molmo] = None, init_params: bool = False): super().__init__(config) if not model: full_config = FullMolmoConfig( image_padding_embed="pad_and_partial_pad", image_pooling_2d="attention-meanq", attention_layer_norm=config.attention_layer_norm, rope_impl="llama", vocab_size=config.vocab_size, max_sequence_length=config.max_position_embeddings, qkv_bias=config.qkv_bias, norm_after=config.norm_after, embedding_size=config.embedding_size, attention_type="sdpa", embedding_dropout=0, attention_dropout=0, residual_dropout=0, rope=True, weight_tying=False, include_bias=False, d_model=config.hidden_size, mlp_hidden_size=config.intermediate_size, n_layers=config.num_hidden_layers, additional_vocab_size=128, n_heads=config.num_attention_heads, n_kv_heads=config.num_key_value_heads, rope_theta=config.rope_theta, layer_norm_eps=config.layer_norm_eps, layer_norm_type=config.layer_norm_type, vit_layers=[-2, -9], vision_backbone=VisionBackboneConfig( image_default_input_size=(336, 336), image_patch_size=14, image_pos_patch_size=14, image_emb_dim=1024, image_num_heads=16, image_num_key_value_heads=16, image_num_layers=23, image_head_dim=64, image_mlp_dim=4096, image_mlp_activations="quick_gelu", image_dropout_rate=0.0, image_num_pos=577, image_norm_eps=1e-5, attention_dropout=0.0, residual_dropout=0.0, initializer_range=0.02, ) ) self.model = Molmo(full_config, init_params=init_params) else: self.model = model def forward( self, input_ids: torch.LongTensor = None, inputs_embeds: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, attention_bias: Optional[torch.Tensor] = None, response_mask: Optional[torch.Tensor] = None, images: Optional[torch.Tensor] = None, image_masks: Optional[torch.Tensor] = None, image_input_idx: Optional[torch.Tensor] = None, subsegment_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, labels: Optional[torch.LongTensor] = None, loss_masks: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, last_logits_only: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, append_last_valid_logits: Optional[torch.Tensor] = None, return_dict: Optional[bool] = None, cache_position: Optional[ Cache ] = None, # This is a hack mitigation of an issue in transformers `4.39.x` https://github.com/huggingface/transformers/issues/29426 ) -> Union[Tuple, CausalLMOutputWithPast]: if use_cache is None: use_cache = self.config.use_cache if output_attentions: raise ValueError("output_attentions is not yet supported in Molmo") return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model.forward( input_ids=input_ids, input_embeddings=inputs_embeds, attention_mask=attention_mask, attention_bias=attention_bias, response_mask=response_mask, images=images, image_masks=image_masks, image_input_idx=image_input_idx, subsegment_ids=subsegment_ids, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, last_logits_only=last_logits_only, output_hidden_states=output_hidden_states, append_last_valid_logits=append_last_valid_logits, ) logits = outputs.logits hidden_states = outputs.hidden_states loss = None if labels is not None: if loss_masks is not None: loss_masks = loss_masks * (loss_masks > 0) batch_size_in_tokens = max(loss_masks.sum().item(), 1) labels = labels.long() labels.masked_fill_(~(loss_masks > 0), -100) labels = labels.view(-1) logits_for_loss = logits.to(torch.float32).view(-1, logits.size(-1)) loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-100, reduction='none') loss = loss_fct(logits_for_loss, labels) loss = loss.view(input_ids.shape[0], -1) loss = loss * loss_masks loss = loss.sum() / batch_size_in_tokens use_zloss = getattr(self.config, "softmax_auxiliary_loss", False) if use_zloss: z_squared = logits_for_loss.logsumexp(-1).pow(2) z_loss = self.config.softmax_auxiliary_loss_scale * z_squared z_loss = z_loss.view(input_ids.shape[0], -1) z_loss = z_loss * loss_masks z_loss = z_loss.sum() / batch_size_in_tokens loss += z_loss else: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = torch.nn.CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.embedding_size) shift_labels = shift_labels.view(-1) # Enable model parallelism 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.attn_key_values, hidden_states=hidden_states, ) def can_generate(self) -> bool: return True @torch.no_grad() def generate_from_batch( self, batch: Dict[str, Any], generation_config: Optional[GenerationConfig] = None, **kwargs, ): if generation_config is not None: assert generation_config.use_cache images = batch.get("images") image_masks = batch.get("image_masks") image_input_idx = batch.get("image_input_idx") # Validate inputs. input_ids = batch["input_ids"] batch_size, seq_len = input_ids.shape attention_mask = batch.get("attention_mask", None) max_new_tokens = generation_config.max_new_tokens assert max_new_tokens is not None mask_len = seq_len + max_new_tokens if self.config.use_position_ids else seq_len position_ids: Optional[torch.Tensor] = None append_last_valid_logits: Optional[torch.Tensor] = None if self.config.use_position_ids and attention_mask is None: attention_mask = input_ids != -1 position_ids = torch.clamp( torch.cumsum(attention_mask.to(torch.int32), dim=-1) - 1, min=0 ) append_last_valid_logits = attention_mask.long().sum(dim=-1) - 1 attention_mask = torch.cat( [attention_mask, attention_mask.new_ones((batch_size, max_new_tokens))], dim=1, ) if attention_mask is not None: assert attention_mask.shape == (batch_size, mask_len) out = super().generate( batch["input_ids"], generation_config, attention_mask=attention_mask, images=images, image_masks=image_masks, image_input_idx=image_input_idx, position_ids=position_ids, append_last_valid_logits=append_last_valid_logits, **kwargs, ) return out def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple]] = None, **kwargs ): if past_key_values: # This is because we want the model to only process the last generated token. input_ids = input_ids[:, -1:] if self.config.use_position_ids: attention_mask = kwargs.get("attention_mask") images = kwargs.get("images") image_masks = kwargs.get("image_masks") image_input_idx = kwargs.get("image_input_idx") position_ids = kwargs.get("position_ids") append_last_valid_logits = kwargs.get("append_last_valid_logits") model_inputs = { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": True, "last_logits_only": True, } if past_key_values is None: model_inputs["images"] = images model_inputs["image_masks"] = image_masks model_inputs["image_input_idx"] = image_input_idx model_inputs["append_last_valid_logits"] = append_last_valid_logits else: model_inputs = {"input_ids": input_ids, "past_key_values": past_key_values} model_inputs.update(kwargs) model_inputs["use_cache"] = kwargs.pop("use_cache", self.config.use_cache) return model_inputs def _update_model_kwargs_for_generation( self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, num_new_tokens: int = 1, ) -> Dict[str, Any]: if self.config.use_position_ids: model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 if "append_last_valid_logits" in model_kwargs: del model_kwargs["append_last_valid_logits"] if "images" in model_kwargs: del model_kwargs["images"] del model_kwargs["image_masks"] del model_kwargs["image_input_idx"] cache_name, cache = super()._extract_past_from_model_output(outputs) model_kwargs[cache_name] = cache model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens return model_kwargs def get_input_embeddings(self) -> torch.nn.Module: return self.model.transformer.wte def set_input_embeddings(self, value: torch.nn.Module): self.model.transformer.wte = value def get_output_embeddings(self): if self.config.weight_tying: return self.model.transformer.wte else: return self.model.transformer.ff_out def set_output_embeddings(self, value: torch.nn.Module): if self.config.weight_tying: self.model.transformer.wte = value else: self.model.transformer.ff_out = value def tie_weights(self): """ This function is intentionally left as a no-op. Weight tying is handled as follows: - When the model is initialized, the `ff_out` layer is conditionally defined based on the `weight_tying` configuration. See: `if not config.weight_tying: self.transformer.update(...)` in `olmo/model.py`. - When computing logits, the `wte` weights are used directly if `weight_tying` is enabled. See: `if self.config.weight_tying: logits = F.linear(x, self.transformer.wte.weight, None)` in the `forward` method. Therefore, there is no need to explicitly tie the weights in this function. """ pass def resize_token_embeddings( self, new_num_tokens: Optional[int] = None, pad_to_multiple_of: Optional[int] = None ) -> torch.nn.Embedding: """ Resizes input token embeddings matrix of the model if `new_num_tokens != config.embedding_size`. Takes care of tying weights embeddings afterwards if the model class has a `tie_weights()` method. Arguments: new_num_tokens (`int`, *optional*): The new number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or `None`, just returns a pointer to the input tokens `torch.nn.Embedding` module of the model without doing anything. pad_to_multiple_of (`int`, *optional*): If set will pad the embedding matrix to a multiple of the provided value. If `new_num_tokens` is set to `None` will just pad the embedding to a multiple of `pad_to_multiple_of`. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc Return: `torch.nn.Embedding`: Pointer to the input tokens Embeddings Module of the model. Note: This method differs from the base class implementation by resizing the `embedding_size` attribute of the model configuration instead of the `vocab_size`. It also includes a warning if the resized `embedding_size` is less than the `vocab_size`. In OLMo, `embedding_size` refers to the dimensionality of the model's token embeddings, while `vocab_size` refers to the number of unique tokens in the vocabulary. """ model_embeds = self._resize_token_embeddings(new_num_tokens, pad_to_multiple_of) if new_num_tokens is None and pad_to_multiple_of is None: return model_embeds # Update base model and current model config self.config.embedding_size = model_embeds.weight.shape[0] self.model.config.embedding_size = model_embeds.weight.shape[0] # Check if the embedding size is less than the vocab size if self.config.embedding_size < self.config.vocab_size: warning_message = ( f"Resizing token embeddings to size {self.config.embedding_size}, which is less than the vocab size " f"{self.config.vocab_size} defined in the model configuration. Make sure your tokenizer's vocabulary " "size is less than or equal to the new token embedding size." ) log.warning(warning_message) # Tie weights again if needed self.tie_weights() return model_embeds # Always register for multi-modal features AutoModelForCausalLM.register(MolmoConfig, MolmoForCausalLM)