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# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model Architecture."""
import functools
from typing import Any, Callable
from flax import linen as nn
from flax import struct
from jax import numpy as jnp
@struct.dataclass
class TransformerConfig:
"""Global hyperparameters used to minimize obnoxious kwarg plumbing."""
vocab_size: int = 1
dtype: Any = jnp.float32
emb_dim: int = 512
num_heads: int = 8
num_layers: int = 6
qkv_dim: int = 512
mlp_dim: int = 2048
seq_len: int = 2048 # Maximum sequence length
dropout_rate: float = 0.1
attention_dropout_rate: float = 0.1
deterministic: bool = False
num_latent_slots: int = 0 # K continuous latent thought slots
# When False, the fed-back latent thought vectors are NOT injected into the
# slot positions (they keep their placeholder token embedding). This turns
# the K slots into static, parallel per-stage readouts with NO carried
# recurrent state -- the "stagewise supervision, no recurrence" control.
inject_latents: bool = True
class TransformerBlock(nn.Module):
config: Any = None
def setup(self):
self.vocab_size = self.config.vocab_size
self.emb_dim = self.config.emb_dim
self.num_layers = self.config.num_layers
@nn.compact
def __call__(self, inputs, causal_mask_inputs, training=True):
"""
Transformer Block call function.
Args:
inputs: Input tensor.
causal_mask_inputs: Causal mask for the inputs.
training: Whether the model is in training mode.
Returns:
Transformed tensor after self-attention and MLP layers.
"""
x = inputs + nn.SelfAttention(
num_heads=self.config.num_heads, dtype=self.config.dtype,
qkv_features=self.config.qkv_dim,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6),
use_bias=False, broadcast_dropout=False,
dropout_rate=self.config.attention_dropout_rate, normalize_qk=True,
deterministic=self.config.deterministic)(inputs, causal_mask_inputs)
def mlp(x):
"""
Multi-Layer Perceptron function.
Args:
x: Input tensor.
Returns:
Transformed tensor after applying MLP layers.
"""
dense_with_init = functools.partial(
nn.Dense,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6)
)
x = dense_with_init(features=self.config.mlp_dim)(x)
x = nn.gelu(x)
x = dense_with_init(features=self.config.emb_dim)(x)
x = nn.Dropout(rate=self.config.dropout_rate,
deterministic=self.config.deterministic)(x)
return x
x = x + mlp(x)
return x
class TransformerLMHeadModel(nn.Module):
config: Any = None
def setup(self):
self.vocab_size = self.config.vocab_size
self.emb_dim = self.config.emb_dim
self.num_layers = self.config.num_layers
@nn.compact
def __call__(self, inputs, latent_values=None, latent_positions=None,
latent_active=None, training=True):
"""
Transformer LM Head call function.
Args:
inputs: Input token ids (batch, seq).
latent_values: Optional (batch, K, emb_dim) continuous thought
vectors (raw last-layer hiddens fed back, Coconut/ATC style).
latent_positions: Optional (batch, K) int positions of the latent
slots in the sequence (per-example, after the clue block).
latent_active: Optional (batch, K) bool; slot j uses the projected
latent vector when True, otherwise keeps the placeholder
token embedding.
training: Whether the model is in training mode.
Returns:
(logits, hidden): LM logits and final (post-LayerNorm) hidden
states, the latter used to build the next continuous thought.
"""
batch_size, seq_size = inputs.shape
causal_mask_x = nn.make_causal_mask(inputs, dtype=self.config.dtype)
# Embed the input tensor using a learnable embedding matrix.
embed_with_init = functools.partial(
nn.Embed, embedding_init=nn.initializers.normal(stddev=0.02))
token_embeddings = embed_with_init(
num_embeddings=self.config.vocab_size,
features=self.config.emb_dim,
)(inputs)
# Check the shape of the embedded tensor.
assert token_embeddings.shape == (batch_size, seq_size,
self.config.emb_dim)
# Continuous latent thoughts: project fed-back hidden states and
# scatter them into the latent slot positions, replacing the
# placeholder token embedding (position embeddings still added below).
# Skipped when inject_latents is False (no-recurrence control: the slots
# stay as static placeholders and the candidate heads become parallel
# per-stage readouts with no carried state).
if latent_values is not None and self.config.inject_latents:
proj = nn.Dense(features=self.config.emb_dim,
kernel_init=nn.initializers.xavier_uniform(),
name="latent_proj_in")(latent_values)
proj = nn.gelu(proj)
proj = nn.Dense(features=self.config.emb_dim,
kernel_init=nn.initializers.xavier_uniform(),
name="latent_proj_out")(proj)
bidx = jnp.arange(batch_size)[:, None]
cur = token_embeddings[bidx, latent_positions]
new = jnp.where(latent_active[..., None],
proj.astype(cur.dtype), cur)
token_embeddings = token_embeddings.at[
bidx, latent_positions].set(new)
# Initialize the positional embedding variable.
pos_embedding_variable = self.variable(
"params",
"position_embeddings",
jnp.zeros,
(self.config.seq_len, self.config.emb_dim),
)
# Slice the positional embedding array to the correct sequence length.
pos_embeddings = pos_embedding_variable.value[:seq_size, :]
# Check the shape of the positional embedding array.
output_tuple = (pos_embeddings.shape, token_embeddings.shape[1:])
assert pos_embeddings.shape == token_embeddings.shape[1:], output_tuple
# Add the positional embeddings to the token embeddings.
x = token_embeddings + pos_embeddings[None, :, :]
# Apply dropout to the input.
x = nn.Dropout(rate=self.config.dropout_rate,
deterministic=self.config.deterministic)(x)
# Apply the Transformer layers. remat (gradient checkpointing) keeps
# the multi-pass latent recurrence within GPU memory under full BPTT.
RematBlock = nn.remat(TransformerBlock)
for i in range(self.num_layers):
x = RematBlock(config=self.config)(
x, causal_mask_x, training=training)
self.sow('intermediates', 'feature_' + str(i), x)
# Apply the final layer normalization.
x = nn.LayerNorm()(x)
# Apply the LM head.
logits = nn.Dense(features=self.config.vocab_size,
kernel_init=nn.initializers.xavier_uniform(),
bias_init=nn.initializers.normal(stddev=1e-6),
use_bias=False)(x)
# Check the shape of the output tensor.
assert logits.shape == (batch_size, seq_size, self.config.vocab_size)
# ---- Auxiliary multi-candidate head ----
# Each latent slot reads its (post-LayerNorm) hidden and predicts the
# full 81x9 candidate grid for its reasoning stage. Trained with BCE
# (independent per-digit sigmoids = candidate-set membership), NOT
# softmax, so multiple digits can be "on" at intermediate stages.
cand_logits = None
if latent_positions is not None:
bidx = jnp.arange(batch_size)[:, None]
slot_hidden = x[bidx, latent_positions] # (bs, K, emb)
h = nn.Dense(features=self.config.emb_dim,
kernel_init=nn.initializers.xavier_uniform(),
name="cand_head_in")(slot_hidden)
h = nn.gelu(h)
cand_logits = nn.Dense(features=81 * 9,
kernel_init=nn.initializers.xavier_uniform(),
name="cand_head_out")(h) # (bs, K, 729)
cand_logits = cand_logits.reshape(
batch_size, -1, 81, 9) # (bs, K, 81, 9)
return logits, x, cand_logits
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