scenic/projects/knowledge_visual_language/models/vit.py

# Copyright 2025 The Scenic Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""A refactored and simplified ViT.

However, the names of modules are made to match the old ones for easy loading.
"""

from typing import Optional, Sequence, Union

from big_vision.models import vit as bv_vit
import flax.linen as nn
import jax
import jax.numpy as jnp


class MlpBlock(nn.Module):
  """Transformer MLP / feed-forward block."""
  mlp_dim: Optional[int] = None  # Defaults to 4x input dim
  dropout: float = 0.0
  dtype: str = jnp.bfloat16

  @nn.compact
  def __call__(self, x, deterministic=True):
    """Applies Transformer MlpBlock module."""
    inits = dict(
        kernel_init=nn.initializers.xavier_uniform(),
        bias_init=nn.initializers.normal(stddev=1e-6),
    )

    n, l, d = x.shape  # pylint: disable=unused-variable
    x = jnp.array(x, dtype=self.dtype)
    x = nn.Dense(self.mlp_dim or 4 * d, **inits, dtype=self.dtype)(x)
    x = nn.gelu(x)
    x = nn.Dropout(rate=self.dropout)(x, deterministic)
    x = nn.Dense(d, **inits, dtype=self.dtype)(x)
    return x


class Encoder1DBlock(nn.Module):
  """Single transformer encoder block (MHSA + MLP)."""
  mlp_dim: Optional[int] = None  # Defaults to 4x input dim
  num_heads: int = 12
  dropout: float = 0.0
  dtype: str = jnp.bfloat16

  @nn.compact
  def __call__(self, x, deterministic=True):
    out = {}
    y = nn.LayerNorm()(x)
    y = out["sa"] = nn.MultiHeadDotProductAttention(
        num_heads=self.num_heads,
        kernel_init=nn.initializers.xavier_uniform(),
        deterministic=deterministic,
        dtype=self.dtype)(y, y)
    y = nn.Dropout(rate=self.dropout)(y, deterministic)
    x = out["+sa"] = x + y

    y = nn.LayerNorm()(x)
    y = out["mlp"] = MlpBlock(
        mlp_dim=self.mlp_dim, dropout=self.dropout,
        dtype=self.dtype)(y, deterministic)
    y = nn.Dropout(rate=self.dropout)(y, deterministic)
    x = out["+mlp"] = x + y
    return x, out


class Encoder(nn.Module):
  """Transformer Model Encoder for sequence to sequence translation."""
  depth: int
  mlp_dim: Optional[int] = None  # Defaults to 4x input dim
  num_heads: int = 12
  dropout: float = 0.0
  dtype: str = jnp.bfloat16
  num_frozen_layers: int = -1

  @nn.compact
  def __call__(self, x, deterministic=True):
    out = {}

    # Input Encoder
    for lyr in range(self.depth):
      block = Encoder1DBlock(
          name=f"encoderblock_{lyr}",
          mlp_dim=self.mlp_dim,
          num_heads=self.num_heads,
          dropout=self.dropout,
          dtype=self.dtype)
      x, out[f"block{lyr:02d}"] = block(x, deterministic)
      if self.num_frozen_layers > 0 and lyr == self.num_frozen_layers - 1:
        x = jax.lax.stop_gradient(x)
    out["pre_ln"] = x  # Alias for last block, but without the number in it.

    return nn.LayerNorm(name="encoder_norm")(x), out


class _Model(nn.Module):
  """ViT model."""

  num_classes: int
  patch_size: Sequence[int] = (16, 16)
  width: int = 768
  depth: int = 12
  mlp_dim: Optional[int] = None  # Defaults to 4x input dim
  num_heads: int = 12
  posemb: str = "learn"  # Can also be "sincos2d"
  rep_size: Union[int, bool] = False
  dropout: float = 0.0
  pool_type: str = "gap"  # Can also be "map" or "tok"
  head_zeroinit: bool = True
  dtype: str = jnp.bfloat16
  num_frozen_layers: int = -1

  @nn.compact
  def __call__(self, image, *, train=False):
    out = {}

    # Patch extraction
    x = out["stem"] = nn.Conv(
        self.width,
        self.patch_size,
        strides=self.patch_size,
        padding="VALID",
        name="embedding")(
            image)
    n, h, w, c = x.shape
    x = jnp.reshape(x, [n, h * w, c])

    # Add posemb before adding extra token.
    x = out["with_posemb"] = x + bv_vit.get_posemb(self, self.posemb, (h, w), c,
                                                   "pos_embedding", x.dtype)

    if self.pool_type == "tok":
      cls = self.param("cls", nn.initializers.zeros, (1, 1, c), x.dtype)
      x = jnp.concatenate([jnp.tile(cls, [n, 1, 1]), x], axis=1)

    n, l, c = x.shape  # pylint: disable=unused-variable
    x = nn.Dropout(rate=self.dropout)(x, not train)
    x, out["encoder"] = Encoder(
        depth=self.depth,
        mlp_dim=self.mlp_dim,
        num_heads=self.num_heads,
        dropout=self.dropout,
        dtype=self.dtype,
        num_frozen_layers=self.num_frozen_layers,
        name="Transformer")(
            x, deterministic=not train)

    encoded = out["encoded"] = x

    if self.pool_type == "map":
      x = out["head_input"] = bv_vit.MAPHead(
          num_heads=self.num_heads, mlp_dim=self.mlp_dim)(
              x)
    elif self.pool_type == "gap":
      x = out["head_input"] = jnp.mean(x, axis=1)
    elif self.pool_type == "0":
      x = out["head_input"] = x[:, 0]
    elif self.pool_type == "tok":
      x = out["head_input"] = x[:, 0]
      encoded = encoded[:, 1:]
    else:
      raise ValueError(f"Unknown pool type: '{self.pool_type}'")

    x_2d = jnp.reshape(encoded, [n, h, w, -1])

    if self.rep_size:
      rep_size = self.width if self.rep_size is True else self.rep_size  # pylint: disable=g-bool-id-comparison
      hid = nn.Dense(rep_size, name="pre_logits", dtype=self.dtype)
      # NOTE: In the past we did not include tanh in pre_logits.
      # For few-shot, it should not matter much, as it whitens anyways.
      x_2d = nn.tanh(hid(x_2d))
      x = nn.tanh(hid(x))

    out["pre_logits_2d"] = x_2d
    out["pre_logits"] = x

    if self.num_classes:
      kw = {"kernel_init": nn.initializers.zeros} if self.head_zeroinit else {}
      head = nn.Dense(self.num_classes, name="head", dtype=self.dtype, **kw)
      x_2d = out["logits_2d"] = head(x_2d)
      x = out["logits"] = head(x)
    x = jnp.array(x, dtype=self.dtype)
    return x, out


def Model(num_classes,
          num_frozen_layers=-1,
          dtype=jnp.bfloat16,
          variant=None,
          **kw):  # pylint: disable=invalid-name
  """Factory function, because linen really don't like what I'm doing!"""
  vit_config = bv_vit.decode_variant(variant)
  if isinstance(num_frozen_layers, float):
    num_frozen_layers = int(num_frozen_layers * vit_config["depth"])
  return _Model(
      num_classes=num_classes,
      num_frozen_layers=num_frozen_layers,
      dtype=dtype,
      **{
          **vit_config,
          **kw
      })