HRM-anchoring-bias-model / hrm_act_v1.py
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from typing import Tuple, List, Dict, Optional
from dataclasses import dataclass
import math
import torch
import torch.nn.functional as F
from torch import nn
from pydantic import BaseModel
from models.common import trunc_normal_init_
from models.layers import rms_norm, SwiGLU, Attention, RotaryEmbedding, CosSin, CastedEmbedding, CastedLinear
from models.sparse_embedding import CastedSparseEmbedding
@dataclass
class HierarchicalReasoningModel_ACTV1InnerCarry:
z_H: torch.Tensor
z_L: torch.Tensor
@dataclass
class HierarchicalReasoningModel_ACTV1Carry:
inner_carry: HierarchicalReasoningModel_ACTV1InnerCarry
steps: torch.Tensor
halted: torch.Tensor
current_data: Dict[str, torch.Tensor]
class HierarchicalReasoningModel_ACTV1Config(BaseModel):
batch_size: int
seq_len: int
puzzle_emb_ndim: int = 0
num_puzzle_identifiers: int
vocab_size: int
H_cycles: int
L_cycles: int
H_layers: int
L_layers: int
# Transformer config
hidden_size: int
expansion: float
num_heads: int
pos_encodings: str
rms_norm_eps: float = 1e-5
rope_theta: float = 10000.0
# Halting Q-learning config
halt_max_steps: int
halt_exploration_prob: float
forward_dtype: str = "bfloat16"
class HierarchicalReasoningModel_ACTV1Block(nn.Module):
def __init__(self, config: HierarchicalReasoningModel_ACTV1Config) -> None:
super().__init__()
self.self_attn = Attention(
hidden_size=config.hidden_size,
head_dim=config.hidden_size // config.num_heads,
num_heads=config.num_heads,
num_key_value_heads=config.num_heads,
causal=False
)
self.mlp = SwiGLU(
hidden_size=config.hidden_size,
expansion=config.expansion,
)
self.norm_eps = config.rms_norm_eps
def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
# Post Norm
# Self Attention
hidden_states = rms_norm(hidden_states + self.self_attn(cos_sin=cos_sin, hidden_states=hidden_states), variance_epsilon=self.norm_eps)
# Fully Connected
hidden_states = rms_norm(hidden_states + self.mlp(hidden_states), variance_epsilon=self.norm_eps)
return hidden_states
class HierarchicalReasoningModel_ACTV1ReasoningModule(nn.Module):
def __init__(self, layers: List[HierarchicalReasoningModel_ACTV1Block]):
super().__init__()
self.layers = torch.nn.ModuleList(layers)
def forward(self, hidden_states: torch.Tensor, input_injection: torch.Tensor, **kwargs) -> torch.Tensor:
# Input injection (add)
hidden_states = hidden_states + input_injection
# Layers
for layer in self.layers:
hidden_states = layer(hidden_states=hidden_states, **kwargs)
return hidden_states
class HierarchicalReasoningModel_ACTV1_Inner(nn.Module):
def __init__(self, config: HierarchicalReasoningModel_ACTV1Config) -> None:
super().__init__()
self.config = config
self.forward_dtype = getattr(torch, self.config.forward_dtype)
# I/O
self.embed_scale = math.sqrt(self.config.hidden_size)
embed_init_std = 1.0 / self.embed_scale
self.embed_tokens = CastedEmbedding(self.config.vocab_size, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
self.lm_head = CastedLinear(self.config.hidden_size, self.config.vocab_size, bias=False)
self.q_head = CastedLinear(self.config.hidden_size, 2, bias=True)
self.puzzle_emb_len = -(self.config.puzzle_emb_ndim // -self.config.hidden_size) # ceil div
if self.config.puzzle_emb_ndim > 0:
# Zero init puzzle embeddings
self.puzzle_emb = CastedSparseEmbedding(self.config.num_puzzle_identifiers, self.config.puzzle_emb_ndim,
batch_size=self.config.batch_size, init_std=0, cast_to=self.forward_dtype)
# LM Blocks
if self.config.pos_encodings == "rope":
self.rotary_emb = RotaryEmbedding(dim=self.config.hidden_size // self.config.num_heads,
max_position_embeddings=self.config.seq_len + self.puzzle_emb_len,
base=self.config.rope_theta)
elif self.config.pos_encodings == "learned":
self.embed_pos = CastedEmbedding(self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, init_std=embed_init_std, cast_to=self.forward_dtype)
else:
raise NotImplementedError()
# Reasoning Layers
self.H_level = HierarchicalReasoningModel_ACTV1ReasoningModule(layers=[HierarchicalReasoningModel_ACTV1Block(self.config) for _i in range(self.config.H_layers)])
self.L_level = HierarchicalReasoningModel_ACTV1ReasoningModule(layers=[HierarchicalReasoningModel_ACTV1Block(self.config) for _i in range(self.config.L_layers)])
# --- CORRECTED CODE BLOCK ---
# Initial states
h_init_tensor = trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1)
self.register_buffer('H_init', h_init_tensor)
l_init_tensor = trunc_normal_init_(torch.empty(self.config.hidden_size, dtype=self.forward_dtype), std=1)
self.register_buffer('L_init', l_init_tensor)
# --- END OF CORRECTION ---
# Q head special init
# Init Q to (almost) zero for faster learning during bootstrapping
with torch.no_grad():
self.q_head.weight.zero_()
self.q_head.bias.fill_(-5) # type: ignore
def _input_embeddings(self, input: torch.Tensor, puzzle_identifiers: torch.Tensor):
# Token embedding
embedding = self.embed_tokens(input.to(torch.int32))
# Puzzle embeddings
if self.config.puzzle_emb_ndim > 0:
puzzle_embedding = self.puzzle_emb(puzzle_identifiers)
pad_count = self.puzzle_emb_len * self.config.hidden_size - puzzle_embedding.shape[-1]
if pad_count > 0:
puzzle_embedding = F.pad(puzzle_embedding, (0, pad_count))
embedding = torch.cat((puzzle_embedding.view(-1, self.puzzle_emb_len, self.config.hidden_size), embedding), dim=-2)
# Position embeddings
if self.config.pos_encodings == "learned":
# scale by 1/sqrt(2) to maintain forward variance
embedding = 0.707106781 * (embedding + self.embed_pos.embedding_weight.to(self.forward_dtype))
# Scale
return self.embed_scale * embedding
def empty_carry(self, batch_size: int):
return HierarchicalReasoningModel_ACTV1InnerCarry(
z_H=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
z_L=torch.empty(batch_size, self.config.seq_len + self.puzzle_emb_len, self.config.hidden_size, dtype=self.forward_dtype),
)
def reset_carry(self, reset_flag: torch.Tensor, carry: HierarchicalReasoningModel_ACTV1InnerCarry):
return HierarchicalReasoningModel_ACTV1InnerCarry(
z_H=torch.where(reset_flag.view(-1, 1, 1), self.H_init, carry.z_H),
z_L=torch.where(reset_flag.view(-1, 1, 1), self.L_init, carry.z_L),
)
def forward(self, carry: HierarchicalReasoningModel_ACTV1InnerCarry, batch: Dict[str, torch.Tensor]) -> Tuple[HierarchicalReasoningModel_ACTV1InnerCarry, torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
seq_info = dict(
cos_sin=self.rotary_emb() if hasattr(self, "rotary_emb") else None,
)
# Input encoding
input_embeddings = self._input_embeddings(batch["inputs"], batch["puzzle_identifiers"])
# Forward iterations
with torch.no_grad():
z_H, z_L = carry.z_H, carry.z_L
for _H_step in range(self.config.H_cycles):
for _L_step in range(self.config.L_cycles):
if not ((_H_step == self.config.H_cycles - 1) and (_L_step == self.config.L_cycles - 1)):
z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
if not (_H_step == self.config.H_cycles - 1):
z_H = self.H_level(z_H, z_L, **seq_info)
assert not z_H.requires_grad and not z_L.requires_grad
# 1-step grad
z_L = self.L_level(z_L, z_H + input_embeddings, **seq_info)
z_H = self.H_level(z_H, z_L, **seq_info)
# LM Outputs
new_carry = HierarchicalReasoningModel_ACTV1InnerCarry(z_H=z_H.detach(), z_L=z_L.detach()) # New carry no grad
output = self.lm_head(z_H)[:, self.puzzle_emb_len:]
# Q head
q_logits = self.q_head(z_H[:, 0]).to(torch.float32)
return new_carry, output, (q_logits[..., 0], q_logits[..., 1])
class HierarchicalReasoningModel_ACTV1(nn.Module):
"""ACT wrapper."""
def __init__(self, config_dict: dict):
super().__init__()
self.config = HierarchicalReasoningModel_ACTV1Config(**config_dict)
self.inner = HierarchicalReasoningModel_ACTV1_Inner(self.config)
@property
def puzzle_emb(self):
return self.inner.puzzle_emb
def initial_carry(self, batch: Dict[str, torch.Tensor]):
batch_size = batch["inputs"].shape[0]
return HierarchicalReasoningModel_ACTV1Carry(
inner_carry=self.inner.empty_carry(batch_size), # Empty is expected, it will be reseted in first pass as all sequences are halted.
steps=torch.zeros((batch_size, ), dtype=torch.int32),
halted=torch.ones((batch_size, ), dtype=torch.bool), # Default to halted
current_data={k: torch.empty_like(v) for k, v in batch.items()}
)
def forward(self, carry: HierarchicalReasoningModel_ACTV1Carry, batch: Dict[str, torch.Tensor]) -> Tuple[HierarchicalReasoningModel_ACTV1Carry, Dict[str, torch.Tensor]]:
# Update data, carry (removing halted sequences)
new_inner_carry = self.inner.reset_carry(carry.halted, carry.inner_carry)
new_steps = torch.where(carry.halted, 0, carry.steps)
new_current_data = {k: torch.where(carry.halted.view((-1, ) + (1, ) * (batch[k].ndim - 1)), batch[k], v) for k, v in carry.current_data.items()}
# Forward inner model
new_inner_carry, logits, (q_halt_logits, q_continue_logits) = self.inner(new_inner_carry, new_current_data)
outputs = {
"logits": logits,
"q_halt_logits": q_halt_logits,
"q_continue_logits": q_continue_logits
}
with torch.no_grad():
# Step
new_steps = new_steps + 1
is_last_step = new_steps >= self.config.halt_max_steps
halted = is_last_step
# if training, and ACT is enabled
if self.training and (self.config.halt_max_steps > 1):
# Halt signal
# NOTE: During evaluation, always use max steps, this is to guarantee the same halting steps inside a batch for batching purposes
halted = halted | (q_halt_logits > q_continue_logits)
# Exploration
min_halt_steps = (torch.rand_like(q_halt_logits) < self.config.halt_exploration_prob) * torch.randint_like(new_steps, low=2, high=self.config.halt_max_steps + 1)
halted = halted & (new_steps >= min_halt_steps)
# Compute target Q
# NOTE: No replay buffer and target networks for computing target Q-value.
# As batch_size is large, there're many parallel envs.
# Similar concept as PQN https://arxiv.org/abs/2407.04811
next_q_halt_logits, next_q_continue_logits = self.inner(new_inner_carry, new_current_data)[-1]
outputs["target_q_continue"] = torch.sigmoid(torch.where(is_last_step, next_q_halt_logits, torch.maximum(next_q_halt_logits, next_q_continue_logits)))
return HierarchicalReasoningModel_ACTV1Carry(new_inner_carry, new_steps, halted, new_current_data), outputs