rrm workspace: TRM/HRM/SRM code, Maze dataset, dynamical-analysis pipelineHEADmain

Curated export for clone-and-run Maze training (2x A6000) + diagnostics.
trm/hrm pretrain.py carry trajectory-augmentation code (backward-compatible).
Heavy artifacts (checkpoints/wandb/npz) gitignored; see PROVENANCE.md.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

1 files changed, 157 insertions, 0 deletions

diff --git a/hrm/models/layers.py b/hrm/models/layers.py
new file mode 100644
index 0000000..0394744
--- /dev/null
+++ b/hrm/models/layers.py
@@ -0,0 +1,157 @@
+from typing import Tuple
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+try:
+    from flash_attn_interface import flash_attn_func  # type: ignore[import]
+except ImportError:
+    # Fallback to FlashAttention 2
+    from flash_attn import flash_attn_func  # type: ignore[import]
+
+from models.common import trunc_normal_init_
+
+
+CosSin = Tuple[torch.Tensor, torch.Tensor]
+
+
+def _find_multiple(a, b):
+    return (-(a // -b)) * b
+
+
+def rotate_half(x: torch.Tensor):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+    # q, k: [bs, seq_len, num_heads, head_dim]
+    # cos, sin: [seq_len, head_dim]
+    orig_dtype = q.dtype
+    q = q.to(cos.dtype)
+    k = k.to(cos.dtype)
+
+    q_embed = (q * cos.unsqueeze(-2)) + (rotate_half(q) * sin.unsqueeze(-2))
+    k_embed = (k * cos.unsqueeze(-2)) + (rotate_half(k) * sin.unsqueeze(-2))
+
+    return q_embed.to(orig_dtype), k_embed.to(orig_dtype)
+
+
+class CastedLinear(nn.Module):
+    def __init__(self,
+                 in_features: int,
+                 out_features: int,
+                 bias: bool):
+        super().__init__()
+        # Truncated LeCun normal init
+        self.weight = nn.Parameter(
+            trunc_normal_init_(torch.empty((out_features, in_features)), std=1.0 / (in_features ** 0.5))
+        )
+        self.bias = None
+        if bias:
+            # Zero init bias
+            self.bias = nn.Parameter(torch.zeros((out_features, )))
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return F.linear(input, self.weight.to(input.dtype), bias=self.bias.to(input.dtype) if self.bias is not None else None)
+
+
+class CastedEmbedding(nn.Module):
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 init_std: float,
+                 cast_to: torch.dtype):
+        super().__init__()
+        self.cast_to = cast_to
+
+        # Truncated LeCun normal init
+        self.embedding_weight = nn.Parameter(
+            trunc_normal_init_(torch.empty((num_embeddings, embedding_dim)), std=init_std)
+        )
+        
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return F.embedding(input, self.embedding_weight.to(self.cast_to))
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings, base, device=None):
+        super().__init__()
+
+        # RoPE
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim))
+        t = torch.arange(max_position_embeddings, dtype=torch.float32, device=device)
+        freqs = torch.outer(t, inv_freq)
+
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.cos_cached = nn.Buffer(emb.cos(), persistent=False)
+        self.sin_cached = nn.Buffer(emb.sin(), persistent=False)
+
+    def forward(self):
+        return self.cos_cached, self.sin_cached
+
+
+class Attention(nn.Module):
+    def __init__(self, hidden_size, head_dim, num_heads, num_key_value_heads, causal=False):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.head_dim = head_dim
+        self.output_size = head_dim * num_heads
+        self.num_heads = num_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.causal = causal
+
+        self.qkv_proj = CastedLinear(self.hidden_size, (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim, bias=False)
+        self.o_proj = CastedLinear(self.output_size, self.hidden_size, bias=False)
+
+    def forward(self, cos_sin: CosSin, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, seq_len, _ = hidden_states.shape
+
+        # hidden_states: [bs, seq_len, num_heads, head_dim]
+        qkv = self.qkv_proj(hidden_states)
+
+        # Split head
+        qkv = qkv.view(batch_size, seq_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
+        query = qkv[:, :, :self.num_heads]
+        key = qkv[:, :, self.num_heads: self.num_heads + self.num_key_value_heads]
+        value = qkv[:, :, self.num_heads + self.num_key_value_heads:]
+
+        # RoPE
+        if cos_sin is not None:
+            cos, sin = cos_sin
+            query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+        # flash attn
+        attn_output = flash_attn_func(q=query, k=key, v=value, causal=self.causal)
+        if isinstance(attn_output, tuple):  # fa2 and fa3 compatibility
+            attn_output = attn_output[0]
+
+        attn_output = attn_output.view(batch_size, seq_len, self.output_size)  # type: ignore
+        return self.o_proj(attn_output)
+
+
+class SwiGLU(nn.Module):
+    def __init__(self, hidden_size: int, expansion: float):
+        super().__init__()
+        inter = _find_multiple(round(expansion * hidden_size * 2 / 3), 256)
+
+        self.gate_up_proj = CastedLinear(hidden_size, inter * 2, bias=False)
+        self.down_proj    = CastedLinear(inter, hidden_size, bias=False)
+
+    def forward(self, x):
+        gate, up = self.gate_up_proj(x).chunk(2, dim=-1)
+        return self.down_proj(F.silu(gate) * up)
+
+
+def rms_norm(hidden_states: torch.Tensor, variance_epsilon: float) -> torch.Tensor:
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.to(torch.float32)
+
+    variance = hidden_states.square().mean(-1, keepdim=True)
+    hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
+    return hidden_states.to(input_dtype)