Molmo-7B-D-0924 / modeling_molmo.py
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"""
Adapted from
[MosaiclML](https://github.com/mosaicml/examples.git) and
[minGPT](https://github.com/karpathy/minGPT.git)
"""
from __future__ import annotations
import logging
import math
import sys
from abc import abstractmethod
from pathlib import Path
from typing import (
Callable,
Dict,
List,
NamedTuple,
Optional,
Sequence,
Tuple,
Union,
Any,
)
from copy import deepcopy
import torch
import torch.backends.cuda
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
import einops
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast, ModelOutput
# from olmo.util import resource_path
from .config_molmo import (
MolmoConfig,
VisionBackboneConfig,
VisionBackboneType,
ImagePooling2DType,
ImageProjectType,
AttentionType,
MolmoConfigurationError,
)
if sys.version_info.minor > 8:
from collections.abc import MutableMapping
elif sys.version_info.minor == 8:
from typing import MutableMapping
else:
raise SystemExit("This script supports Python 3.8 or higher")
log = logging.getLogger(__name__)
def resource_path(
folder: Union[str, Path],
fname: str,
local_cache: Optional[Union[str, Path]] = None,
) -> Path:
if local_cache is not None and (local_path := Path(local_cache) / fname).is_file():
log.info(f"Found local cache of {fname} at {local_path}")
return local_path
else:
from cached_path import cached_path
return cached_path(f"{str(folder).rstrip('/')}/{fname}")
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 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.
"""
def _non_meta_init_device(config: MolmoConfig) -> 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 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, drop_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
:param input: A tensor of shape `(batch_size, seq_len, embed_dim)`
:param drop_mask: A tensor of shape `(batch_size, seq_len)` with values of zero or one.
"""
if self.p == 0.0 and (self.mask_p is None or self.mask_p == 0.0):
return input
else:
if self.mask_p > 0.0 and self.training:
assert drop_mask is not None
drop_mask = drop_mask.to(input.dtype)
keep_prob = 1.0 - self.p
keep_prob2 = 1.0 - self.mask_p
keep_prob = drop_mask * keep_prob2 + (1 - drop_mask) * keep_prob
keep_prob = keep_prob.unsqueeze(-1)
dropout_shape = list(input.shape)
keep_prob = keep_prob.broadcast_to(dropout_shape)
multiplier = input.new_empty(dropout_shape).bernoulli_(keep_prob)
multiplier.div_(keep_prob)
return input * multiplier
elif self.p > 0.0 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)
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)
class LayerNorm(LayerNormBase):
"""
The default :class:`LayerNorm` implementation which can optionally run in low precision.
"""
def __init__(
self,
config: MolmoConfig,
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 _cast_if_autocast_enabled(self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None) -> torch.Tensor:
# 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 tensor.device.type == "cuda" and torch.is_autocast_enabled():
return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_gpu_dtype())
elif tensor.device.type == "cpu" and torch.is_autocast_cpu_enabled():
return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_cpu_dtype())
else:
return tensor
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)
def reset_parameters(self):
if self.weight is not None:
torch.nn.init.ones_(self.weight) # type: ignore
if self.bias is not None:
torch.nn.init.zeros_(self.bias) # type: ignore
class RMSLayerNorm(LayerNormBase):
"""
RMS layer norm, a simplified :class:`LayerNorm` implementation
"""
def __init__(
self,
config: MolmoConfig,
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
def _cast_if_autocast_enabled(self, tensor: torch.Tensor, dtype: Optional[torch.dtype] = None) -> torch.Tensor:
# 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 tensor.device.type == "cuda" and torch.is_autocast_enabled():
return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_gpu_dtype())
elif tensor.device.type == "cpu" and torch.is_autocast_cpu_enabled():
return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_cpu_dtype())
else:
return tensor
def reset_parameters(self):
if self.weight is not None:
torch.nn.init.ones_(self.weight) # type: ignore
if self.bias is not None:
torch.nn.init.zeros_(self.bias) # type: ignore
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.head_dim
if self.config.head_dim is not None
else 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 = einsum("i , j -> i j", seq, inv_freq)
if self.config.rope_impl == "cockatoo":
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 == "cockatoo":
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 Activation(nn.Module):
def __init__(self, config: MolmoConfig):
super().__init__()
self.config = config
@abstractmethod
def forward(self, x: torch.Tensor) -> torch.Tensor:
raise NotImplementedError
@property
@abstractmethod
def output_multiplier(self) -> float:
raise NotImplementedError
class GELU(nn.GELU):
@property
def output_multiplier(self) -> float:
return 1.0
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 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
class SwiGLU(Activation):
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 LlamaSwiGLU(Activation):
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
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 MolmoAttention(nn.Module):
def __init__(self, config: MolmoConfig, cache: BufferCache):
super().__init__()
self.config = config
self.__cache = cache
self.rotary_emb = RotaryEmbedding(config, self.__cache)
self.k_norm: Optional[LayerNormBase] = None
self.q_norm: Optional[LayerNormBase] = None
self.hidden_size = (
config.mlp_hidden_size if config.mlp_hidden_size is not None else config.mlp_ratio * config.d_model
)
if config.attention_layer_norm:
assert config.n_kv_heads is not None
self.k_norm = LayerNormBase.build(
config,
size=(config.d_model // config.n_heads) * config.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 = SwiGLU(config)
assert (self.act.output_multiplier * self.hidden_size) % 1 == 0
# Attention output projection.
input_dim = config.head_dim * config.n_heads if config.head_dim is not None else config.d_model
head_dim = config.d_model // config.n_heads
self.fused_dims = (
config.d_model,
config.n_kv_heads * head_dim,
config.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,
)
self.attn_out = nn.Linear(input_dim, config.d_model, bias=config.include_bias, device=config.init_device)
self.attn_norm = RMSLayerNorm(config, size=config.d_model, eps=config.layer_norm_eps)
self.flash_attn_func = None
if self.config.attention_type == AttentionType.flash:
try:
from flash_attn import flash_attn_func
self.flash_attn_func = flash_attn_func
except ModuleNotFoundError:
pass
def attention(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
attention_bias: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
drop_mask: 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.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.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,
drop_mask=drop_mask,
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
@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,
drop_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 forward(self, x, attention_bias, position_ids, drop_mask, layer_past, use_cache):
if not self.config.norm_after:
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.
att, cache = self.attention(
q,
k,
v,
attention_bias,
position_ids=position_ids,
drop_mask=drop_mask,
layer_past=layer_past,
use_cache=use_cache,
)
if self.config.norm_after:
att = self.attn_norm(att)
return att, cache
class MolmoMLP(nn.Module):
def __init__(self, config: MolmoConfig):
# Feed-forward input projection.
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.act = SwiGLU(config)
self.ff_proj = nn.Linear(
config.d_model, self.hidden_size, bias=config.include_bias, device=config.init_device
)
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_norm = RMSLayerNorm(config, size=config.d_model, eps=config.layer_norm_eps)
def forward(self, x):
if not self.config.norm_after:
x = self.ff_norm(x)
x = self.ff_proj(x)
x = self.act(x)
x = self.ff_out(x)
if self.config.norm_after:
x = self.ff_norm(x)
return x
class MolmoDecoderLayer(nn.Module):
"""
A base class for transformer block implementations.
"""
def __init__(self, layer_id: int, config: MolmoConfig, cache: BufferCache):
super().__init__()
self.self_attn = MolmoAttention(config, cache)
self.mlp = MolmoMLP(config)
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
if config.head_dim is None:
assert config.d_model % config.n_heads == 0
# Dropout.
self.dropout = Dropout(config.residual_dropout, mask_p=config.response_residual_dropout)
def forward(
self,
x: torch.Tensor,
attention_bias: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.Tensor] = None,
drop_mask: 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)
"""
att, cache = self.self_attn(
x,
attention_bias=attention_bias,
position_ids=position_ids,
drop_mask=drop_mask,
layer_past=layer_past,
use_cache=use_cache,
)
x = x + self.dropout(att, drop_mask=drop_mask)
og_x = x
x = self.mlp(x)
x = self.dropout(x, drop_mask=drop_mask)
x = og_x + x
return x, cache
class MolmoOutput(NamedTuple):
attn_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]
"""
Attention keys and values from each block.
"""
hidden_states: Optional[Tuple[torch.Tensor]]
"""
Hidden states from each block.
"""
last_hidden_states: torch.Tensor
class MultiHeadDotProductAttention(nn.Module):
def __init__(self, config: MolmoConfig, 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)
xv = xv.to(torch.float)
if self.config.attention_type == AttentionType.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 == AttentionType.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: MolmoConfig,
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 ViTMLP(nn.Module):
def __init__(self, config: MolmoConfig):
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 = QuickGELU(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 MLP(nn.Module):
def __init__(self, config: MolmoConfig, 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,
)
# `MLP` assume the activation takes two inputs, so it must be a 'llama' version.
self.act = LlamaSwiGLU(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 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
if self.training:
x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
return x.to(orig_type)
class ResidualAttentionBlock(nn.Module):
def __init__(self, config: MolmoConfig):
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: MolmoConfig):
super().__init__()
self.config = config
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
def _expand_token(token, batch_size: int):
return token.view(1, 1, -1).expand(batch_size, -1, -1)
class VisionTransformer(nn.Module):
def __init__(self, config: MolmoConfig):
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)
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 MolmoVisionBackbone(nn.Module):
def __init__(self, config: VisionBackboneConfig):
super().__init__()
self.config = 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
self.image_projector = MLP(config, input_dim)
self.image_feature_dropout = Dropout(config.image_feature_dropout)
@classmethod
def build(cls, config: MolmoConfig) -> MolmoVisionBackbone:
v_cfg = config.vision_backbone
assert v_cfg is not None
return MolmoPretrainedVisionBackbone(config)
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()
@abstractmethod
def forward(
self, images: torch.Tensor, image_masks: torch.Tensor
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
raise NotImplementedError
class MolmoPretrainedVisionBackbone(MolmoVisionBackbone):
def __init__(self, config: MolmoConfig):
super().__init__(config)
v_cfg = self.config.vision_backbone
if v_cfg.image_model_type == VisionBackboneType.openai:
self.image_vit = VisionTransformer(config)
else:
raise NotImplementedError(f"Unknown image model type: {v_cfg.image_model_type}")
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"
if config.use_cls_feature:
assert self.num_prefix_tokens > 0, "The model does not have a CLS token"
nlayers = 1 if config.vit_layers is None else len(config.vit_layers)
self.cls_projector = nn.Linear(
nlayers * v_cfg.image_emb_dim,
self.input_dim,
bias=False,
device=config.init_device,
)
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_with_pretrained_weights(self):
super().reset_parameters() # resets the connector
if self.config.vit_load_path:
vit_load_path = Path(self.config.vit_load_path)
state_dict_path = resource_path(
vit_load_path.parent,
vit_load_path.name,
local_cache=vit_load_path.parent,
)
assert state_dict_path.is_file(), f"Model file {str(state_dict_path)} not found"
state_dict = torch.load(state_dict_path, map_location="cpu")
self.image_vit.load_state_dict(state_dict)
else:
self.image_vit.reset_parameters()
if self.config.use_cls_feature:
nn.init.xavier_uniform_(self.cls_projector.weight)
if self.pad_embed is not None:
nn.init.zeros_(self.pad_embed)
def reset_parameters(self):
super().reset_parameters()
self.image_vit.reset_parameters()
if self.config.use_cls_feature:
nn.init.xavier_uniform_(self.cls_projector.weight)
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.logical_not(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)
og_dtype = image_features.dtype
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":
og_dtype = image_features.dtype
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 = image_features.to(og_dtype)
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.vision_backbone.image_num_patch + (-1,),
)
if cfg.vision_backbone.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 == ImagePooling2DType.attention_v2:
image_features = self.image_pooling_2d(image_features)
elif cfg.image_pooling_2d not in {ImagePooling2DType.none, ImagePooling2DType.stack}:
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 cfg.image_projector == ImageProjectType.mlpx2:
for module in self.image_projector:
image_features = module(image_features)
else:
image_features = self.image_projector(image_features)
if self.config.use_cls_feature:
cls_embed = self.cls_projector(cls_embed)
if cfg.image_projector == ImageProjectType.mlpx2:
for module in self.image_projector:
cls_embed = module(cls_embed)
else:
cls_embed = self.image_projector(cls_embed)
# image_features: (batch_size, num_image, num_patch, d_model)
# cls_embed: (batch_size, num_image, d_model)
return image_features, cls_embed
class MolmoPretrainedModel(PreTrainedModel):
config_class = MolmoConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MolmoDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
def _init_weights(self, module):
if self.vision_backbone is not None:
self.vision_backbone.reset_parameters()
self.reset_non_vision_parameters()
class MolmoModel(MolmoPretrainedModel):
def __init__(self, config: MolmoConfig, init_params: bool = True):
super().__init__(config)
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
)
if not (
0 < self.config.block_group_size <= self.config.n_layers
and self.config.n_layers % self.config.block_group_size == 0
):
raise MolmoConfigurationError("n layers must be divisible by block group size")
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=RMSLayerNorm(config, size=config.d_model, eps=config.layer_norm_eps),
)
)
layers = [MolmoDecoderLayer(i, config, self.__cache) for i in range(config.n_layers)]
self.transformer.update({"layers": nn.ModuleList(layers)})
self.vision_backbone: Optional[MolmoVisionBackbone] = None
if config.vision_backbone is not None:
self.vision_backbone = MolmoVisionBackbone.build(config)
if self.vision_backbone is not None:
self.vision_backbone.reset_with_pretrained_weights()
@property
def device(self) -> torch.device:
device: torch.device = self.transformer.wte.weight.device # type: ignore
if device.type == "meta":
return _non_meta_init_device(self.config)
else:
return device
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,
) -> MolmoOutput:
"""
: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.unconditioned and input_embeddings is None:
images = None
image_input_idx = None
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("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 self.config.use_cls_feature:
x = torch.cat([x[:, :1], cls_embed, x[:, 1:-num_image]], dim=1)
valid_images = torch.any((image_input_idx >= 0).view(batch_size, num_image, num_patch), dim=-1)
valid_images = valid_images.to(attention_mask.dtype)
attention_mask = torch.cat(
[attention_mask[:, :1], valid_images, attention_mask[:, 1:-num_image]],
dim=1,
)
position_ids = torch.clamp(
torch.cumsum(attention_mask, dim=-1) - 1,
min=0,
).broadcast_to((batch_size, attention_mask.shape[-1]))
# 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.
for block_idx, layer in enumerate(self.transformer.layers):
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]
# shape: (batch_size, seq_len, d_model)
x, cache = layer(
x,
attention_bias=attention_bias,
position_ids=position_ids,
drop_mask=response_mask,
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)
if images is not None and self.config.use_cls_feature:
assert num_image is not None
x = torch.cat(
[x[:, :1], x[:, num_image + 1 :], torch.zeros_like(x[:, :num_image])],
dim=1,
)
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)
return MolmoOutput(
last_hidden_states=x,
attn_key_values=attn_key_values,
hidden_states=tuple(all_hidden_states) if output_hidden_states else None,
)
class MolmoForCausalLM(PreTrainedModel):
"""
Extremely barebones HF model wrapper.
"""
config_class = MolmoConfig
base_model_prefix = "model"
_no_split_modules = ["MolmoDecoderLayer"]
def __init__(self, config: MolmoConfig):
super().__init__(config)
# model_config = create_model_config_from_pretrained_config(config)
# Initialize model (always on CPU to start with so we don't run out of GPU memory).
config.init_device = "cpu"
v_cfg = config.vision_backbone
if v_cfg is not None:
v_cfg = VisionBackboneConfig(**v_cfg)
config.vision_backbone = v_cfg
self.model = MolmoModel(config)
if not config.weight_tying:
self.lm_head = nn.Linear(
config.d_model,
config.embedding_size or config.vocab_size,
bias=config.include_bias,
device=config.init_device,
)
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[
torch.Tensor
] = 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(
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,
)
x = outputs.last_hidden_states
if self.config.weight_tying:
logits = F.linear(x, self.model.transformer.wte.weight, None) # type: ignore
else:
logits = self.lm_head(x) # type: ignore
if self.config.scale_logits:
logits.mul_(1 / math.sqrt(self.config.d_model))
if self.config.final_logit_softcapping is not None:
logits = logits / self.config.final_logit_softcapping
logits = torch.tanh(logits)
logits = logits * self.config.final_logit_softcapping
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)
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=outputs.hidden_states,
)
def can_generate(self) -> bool:
return True
@torch.no_grad()
def generate(
self,
input_ids,
images=None,
attention_mask=None,
image_masks=None,
image_input_idx=None,
generation_config=None,
**kwargs,
):
if generation_config is not None:
assert generation_config.use_cache
# Validate inputs.
batch_size, seq_len = input_ids.shape
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(
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
# TODO: these are required to make the implementation complete.
# def resize_position_embeddings(self, new_num_position_embeddings: int):
# pass
#
# def get_position_embeddings(self) -> Union[nn.Embedding, Tuple[nn.Embedding]]:
# pass
#
# def _reorder_cache(self, past_key_values, beam_idx):
# pass
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.lm_head
def set_output_embeddings(self, value: torch.nn.Module):
if self.config.weight_tying:
self.model.transformer.wte = value
else:
self.lm_head = 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 `lm_head` 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 Molmo, `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