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from typing import Any, Optional, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
from .configuration_aimv2 import AIMv2Config
from flax.core import frozen_dict
from transformers import FlaxPreTrainedModel
from transformers.modeling_flax_outputs import FlaxBaseModelOutput
__all__ = ["FlaxAIMv2Model"]
class FlaxRMSNorm(nn.Module):
eps: float = 1e-6
@nn.compact
def __call__(self, x: jax.Array) -> jax.Array:
dim = x.shape[-1]
scale = self.param("scale", nn.initializers.ones_init(), (dim,))
output = self._norm(x.astype(jnp.float32)).astype(x.dtype)
output = output * scale.astype(x.dtype)
return output
def _norm(self, x: jax.Array) -> jax.Array:
return x * jax.lax.rsqrt(jnp.power(x, 2).mean(-1, keepdims=True) + self.eps)
class FlaxAIMv2SwiGLUFFN(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(self, x: jax.Array) -> jax.Array:
hidden_features = self.config.intermediate_size
in_features = self.config.hidden_size
bias = self.config.use_bias
x1 = nn.Dense(hidden_features, use_bias=bias, dtype=self.dtype, name="fc1")(x)
x2 = nn.Dense(hidden_features, use_bias=bias, dtype=self.dtype, name="fc3")(x)
x = nn.silu(x1) * x2
x = nn.Dense(in_features, use_bias=bias, dtype=self.dtype, name="fc2")(x)
return x
class FlaxAIMv2PatchEmbed(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(self, x: jax.Array) -> jax.Array:
patch_size = (self.config.patch_size, self.config.patch_size)
x = x.transpose(0, 2, 3, 1) # (N C H W) -> (N H W C)
x = nn.Conv(
self.config.hidden_size,
kernel_size=patch_size,
strides=patch_size,
padding=(0, 0),
dtype=self.dtype,
name="proj",
)(x)
x = jax.lax.collapse(x, 1, 3) # (N, H * W, F)
x = FlaxRMSNorm(self.config.rms_norm_eps, name="norm")(x)
return x
class FlaxAIMv2ViTPreprocessor(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(self, x: jax.Array) -> jax.Array:
tokens = FlaxAIMv2PatchEmbed(self.config, dtype=self.dtype, name="patchifier")(
x
)
_, N, _ = tokens.shape
pos_embed = self.param(
"pos_embed",
nn.initializers.normal(stddev=0.02),
(1, self.num_patches, self.config.hidden_size),
)
tokens = tokens + pos_embed[:, :N].astype(tokens.dtype)
return tokens
@property
def num_patches(self) -> int:
return (self.config.image_size // self.config.patch_size) ** 2
class FlaxAIMv2Attention(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(
self,
x: jax.Array,
mask: Optional[jax.Array] = None,
deterministic: bool = True,
output_attentions: bool = False,
) -> Tuple[jax.Array, Optional[jax.Array]]:
B, N, C = x.shape
dim, num_heads = self.config.hidden_size, self.config.num_attention_heads
qkv = nn.Dense(
dim * 3, use_bias=self.config.qkv_bias, dtype=self.dtype, name="qkv"
)(x)
qkv = qkv.reshape(B, N, 3, num_heads, C // num_heads).transpose(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn_weights = nn.dot_product_attention_weights(
q.swapaxes(-3, -2), # [B, N, H, C]
k.swapaxes(-3, -2),
mask=mask,
deterministic=deterministic,
dtype=self.dtype,
)
attn_weights = nn.Dropout(
self.config.attention_dropout, deterministic=deterministic, name="attn_drop"
)(attn_weights)
x = (attn_weights @ v).swapaxes(1, 2).reshape(B, N, C)
x = nn.Dense(dim, use_bias=self.config.use_bias, dtype=self.dtype, name="proj")(
x
)
x = nn.Dropout(
self.config.projection_dropout,
deterministic=deterministic,
name="proj_drop",
)(x)
return (x, attn_weights) if output_attentions else (x, None)
class FlaxAIMv2Block(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attn = FlaxAIMv2Attention(self.config, dtype=self.dtype, name="attn")
self.norm_1 = FlaxRMSNorm(self.config.rms_norm_eps, name="norm_1")
self.mlp = FlaxAIMv2SwiGLUFFN(self.config, dtype=self.dtype, name="mlp")
self.norm_2 = FlaxRMSNorm(self.config.rms_norm_eps, name="norm_2")
def __call__(
self,
x: jax.Array,
mask: Optional[jax.Array] = None,
deterministic: bool = True,
output_attentions: bool = False,
) -> Tuple[jax.Array, Optional[jax.Array]]:
features, attention = self.attn(
self.norm_1(x),
mask,
deterministic=deterministic,
output_attentions=output_attentions,
)
x = x + features
x = x + self.mlp(self.norm_2(x))
return x, attention
class FlaxAIMv2Transformer(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(
self,
tokens: jax.Array,
mask: Optional[jax.Array] = None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
) -> Tuple[
jax.Array, Optional[Tuple[jax.Array, ...]], Optional[Tuple[jax.Array, ...]]
]:
hidden_states = () if output_hidden_states else None
attentions = () if output_attentions else None
for blk_id, block in enumerate(range(self.config.num_hidden_layers)):
tokens, attention = FlaxAIMv2Block(
self.config, dtype=self.dtype, name=f"layers_{blk_id}"
)(
tokens,
mask,
deterministic=deterministic,
output_attentions=output_attentions,
)
if output_hidden_states:
hidden_states += (tokens,)
if output_attentions:
attentions += (attention,)
tokens = FlaxRMSNorm(self.config.rms_norm_eps, name="post_trunk_norm")(tokens)
return tokens, hidden_states, attentions
class FlaxAIMv2Module(nn.Module):
config: AIMv2Config
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(
self,
x: jax.Array,
mask: Optional[jax.Array] = None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
) -> Tuple[
jax.Array, Optional[Tuple[jax.Array, ...]], Optional[Tuple[jax.Array, ...]]
]:
x = FlaxAIMv2ViTPreprocessor(
self.config, dtype=self.dtype, name="preprocessor"
)(x)
x, hidden_states, attentions = FlaxAIMv2Transformer(
self.config, dtype=self.dtype, name="trunk"
)(
x,
mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
return x, hidden_states, attentions
class FlaxAIMv2PretrainedModel(FlaxPreTrainedModel):
config_class = AIMv2Config
base_model_prefix = "aimv2"
main_input_name = "pixel_values"
def __init__(
self,
config: AIMv2Config,
input_shape: Optional[Tuple[int, int, int, int]] = None, # [B, C, H, W]
dtype: jnp.dtype = jnp.float32,
**kwargs: Any,
):
if input_shape is None:
input_shape = (1, 3, config.image_size, config.image_size)
super().__init__(
config,
module=FlaxAIMv2Module(config, dtype=dtype),
input_shape=input_shape,
dtype=dtype,
**kwargs,
)
def init_weights(
self,
rng: jax.Array,
input_shape: Tuple[int, ...],
params: Optional[frozen_dict.FrozenDict] = None,
) -> frozen_dict.FrozenDict:
del params
input_pixels = jnp.empty(input_shape)
params = self.module.init(rng, input_pixels, deterministic=True)
return params["params"]
class FlaxAIMv2Model(FlaxAIMv2PretrainedModel):
def __call__(
self,
pixel_values: jax.Array,
params: Optional[frozen_dict.FrozenDict] = None,
mask: Optional[jax.Array] = None,
dropout_rng: Optional[jax.Array] = None,
deterministic: bool = True,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[
Tuple[jax.Array],
Tuple[jax.Array, Tuple[jax.Array, ...]],
Tuple[jax.Array, Tuple[jax.Array, ...], Tuple[jax.Array, ...]],
FlaxBaseModelOutput,
]:
if params is None:
params = self.params
if output_attentions is None:
output_attentions = self.config.output_attentions
if output_hidden_states is None:
output_hidden_states = self.config.output_hidden_states
if return_dict is None:
return_dict = self.config.use_return_dict
rngs = None if deterministic else {"dropout": dropout_rng}
x, hidden_states, attentions = self.module.apply(
{"params": params},
pixel_values,
mask,
rngs=rngs,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
if not return_dict:
res = (x,)
res += (hidden_states,) if output_hidden_states else ()
res += (attentions,) if output_attentions else ()
return res
return FlaxBaseModelOutput(
last_hidden_state=x,
hidden_states=hidden_states,
attentions=attentions,
)
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