Add rlformers forward-pass features to ExecuTorch backbone for on-device export parity

Summary:
The 730M dense model checkpoint (16node_IFT_avoDistilled730m) uses several rlformers features that the ExecuTorch XNNPACK export path did not implement. Without these, the exported model produces numerically incorrect output.

This diff adds support for 8 missing features:
1. `normalize_tok_embeddings` — scaleless RMSNorm after embedding lookup
2. `qk_norm_before_rope` — conversion from GenAI args (attention code already supported it)
3. `scale_query_by` — custom scalar multiplier on Q after QK norm
4. `use_attn_o_gate` — sigmoid gate on attention output using a learned linear projection of the layer input
5. `use_attn_o_norm` — scaleless per-head RMSNorm on attention output (applied before o_gate)
6. `use_residual_gate` — NormPreservingResidualConnection with learned per-dim gates for both attention and FFN residual connections
7. `use_ffn_learnable_scales` — RMSNormWithInputScale replacing standard post-FFN norm, computing `rms_norm(gamma * x)` instead of `gamma * rms_norm(x)`
8. `output_soft_cap_temp` — `tanh(logits/temp) * temp` soft capping on output logits

All features are off by default (backward compatible). They activate when the corresponding fields are set in the checkpoint's params.json and propagated through model_args_conversion.

Weight key mappings added for: `attention.og.weight`, `add_attn.gate`, `add_ffn.gate`, `post_ffn_norm.weight`.

Reviewed By: chinnadhurai, digantdesai

Differential Revision: D102030169

Author: ifed-ucsd

examples/models/llama/attention.py

@@ -8,7 +8,7 @@
 import torch.nn.functional as F
 from executorch.examples.models.llama.lora import LoRALinear
 from executorch.examples.models.llama.model_args import ModelArgs
-from executorch.examples.models.llama.norm import RMSNorm, RMSNormGated
+from executorch.examples.models.llama.norm import RMSNorm, RMSNormGated, ScalelessRMSNorm
 from executorch.examples.models.llama.rope import Rope
 
 
@@ -410,6 +410,9 @@ def __init__(
         self.qk_norm_before_rope = args.qk_norm_before_rope
         self.use_q_gate = args.use_q_gate
         self.enable_dynamic_shape = args.enable_dynamic_shape
+        self.scale_query_by = args.scale_query_by
+        self.use_attn_o_gate = args.use_attn_o_gate
+        self.use_attn_o_norm = args.use_attn_o_norm
         q_out_dim = self.n_heads * self.head_dim * (2 if self.use_q_gate else 1)
 
         # YOCO: Determine if this is a KV shared layer (receives shared KV from donor).
@@ -452,17 +455,26 @@ def __init__(
     def _init_norms(self, args: ModelArgs) -> None:
         """Initialize QK normalization layers."""
         if self.use_qk_norm:
-            self.q_norm_fn = RMSNorm(
-                self.head_dim,
-                eps=args.norm_eps,
-                add_unit_offset=args.rms_norm_add_unit_offset,
-            )
-            if self.has_kv_weights:
-                self.k_norm_fn = RMSNorm(
+            if args.qk_norm_affine:
+                self.q_norm_fn = RMSNorm(
                     self.head_dim,
                     eps=args.norm_eps,
                     add_unit_offset=args.rms_norm_add_unit_offset,
                 )
+                if self.has_kv_weights:
+                    self.k_norm_fn = RMSNorm(
+                        self.head_dim,
+                        eps=args.norm_eps,
+                        add_unit_offset=args.rms_norm_add_unit_offset,
+                    )
+            else:
+                self.q_norm_fn = ScalelessRMSNorm(self.head_dim, eps=args.norm_eps)
+                if self.has_kv_weights:
+                    self.k_norm_fn = ScalelessRMSNorm(self.head_dim, eps=args.norm_eps)
+        if self.use_attn_o_norm:
+            self.o_norm = ScalelessRMSNorm(self.head_dim, eps=args.norm_eps)
+        if self.use_attn_o_gate:
+            self.og = nn.Linear(args.dim, self.n_heads * self.head_dim, bias=False)
 
     def _init_projections(self, args: ModelArgs, q_out_dim: int) -> None:
         """Initialize Q/K/V/O projection layers."""
@@ -512,14 +524,14 @@ def _prepare_qkv_shared(
         k, v = shared_kv
 
         if self.use_qk_norm and self.qk_norm_before_rope:
-            q = self.q_norm_fn(q)
+            q = self.q_norm_fn(q) * self.scale_query_by
 
         # Apply RoPE to Q only (K already has RoPE from donor layer)
         q, _ = self.rope.forward(q, q, freqs_cos, freqs_sin)
         q = q.transpose(1, 2)
 
         if self.use_qk_norm and not self.qk_norm_before_rope:
-            q = self.q_norm_fn(q)
+            q = self.q_norm_fn(q) * self.scale_query_by
 
         return q, k, v
 
@@ -542,7 +554,7 @@ def _prepare_qkv(
         v = v.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
 
         if self.use_qk_norm and self.qk_norm_before_rope:
-            q = self.q_norm_fn(q)
+            q = self.q_norm_fn(q) * self.scale_query_by
             k = self.k_norm_fn(k)
 
         q, k = self.rope.forward(q, k, freqs_cos, freqs_sin)
@@ -552,7 +564,7 @@ def _prepare_qkv(
         v = v.transpose(1, 2)
 
         if self.use_qk_norm and not self.qk_norm_before_rope:
-            q = self.q_norm_fn(q)
+            q = self.q_norm_fn(q) * self.scale_query_by
             k = self.k_norm_fn(k)
 
         return q, k, v
@@ -617,8 +629,7 @@ def forward(
                 )
 
             output = self.SDPA(input_pos, q, k, v, bsz, seqlen, attn_mask)
-            if gate is not None:
-                output = output * torch.sigmoid(gate)
+            output = self._apply_output_transforms(output, x, gate, bsz, seqlen)
 
             if shared_kv is None and self.num_kv_shared_layers > 0:
                 update = {"kv_to_share": (k, v)}
@@ -637,13 +648,27 @@ def forward(
         output = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
 
         output = output.transpose(1, 2).reshape(bsz, seqlen, -1)
-        if gate is not None:
-            output = output * torch.sigmoid(gate)
+        output = self._apply_output_transforms(output, x, gate, bsz, seqlen)
 
         output = self.wo(output)
 
         return output, None
 
+    def _apply_output_transforms(
+        self, output: torch.Tensor, x: torch.Tensor, gate, bsz: int, seqlen: int
+    ) -> torch.Tensor:
+        if self.use_attn_o_norm or self.use_attn_o_gate:
+            output_4d = output.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+            if self.use_attn_o_norm:
+                output_4d = self.o_norm(output_4d)
+            if self.use_attn_o_gate:
+                og = self.og(x).view(bsz, seqlen, self.n_local_heads, self.head_dim)
+                output_4d = torch.sigmoid(og) * output_4d
+            output = output_4d.reshape(bsz, seqlen, -1)
+        if gate is not None:
+            output = output * torch.sigmoid(gate)
+        return output
+
 
 def _l2norm(x: torch.Tensor, dim: int = -1, eps: float = 1e-6) -> torch.Tensor:
     inv_norm = torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps)

examples/models/llama/llama_transformer.py

@@ -7,6 +7,7 @@
 
 # Please refer to README.md in the same folder for more information.
 
+import math
 from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
@@ -20,7 +21,7 @@
 )
 from executorch.examples.models.llama.feed_forward import FeedForward, LoRAFeedForward
 from executorch.examples.models.llama.model_args import ModelArgs
-from executorch.examples.models.llama.norm import RMSNorm
+from executorch.examples.models.llama.norm import RMSNorm, RMSNormWithInputScale, ScalelessRMSNorm
 from executorch.examples.models.llama.rope import Rope
 from torch import nn
 
@@ -51,6 +52,24 @@ def _is_kv_shared_layer(
     return layer_idx >= first_shared and first_shared > 0
 
 
+class NormPreservingResidualConnection(nn.Module):
+    def __init__(self, dim: int, init_scale: float, temperature: float = 0.3, eps: float = 1e-3):
+        super().__init__()
+        self.eps = eps
+        self.temperature = temperature
+        p = max(0.0 + eps, min(1.0 - eps, init_scale))
+        init_param = math.log(p / (1.0 - p)) * temperature
+        self.gate = nn.Parameter(torch.full((dim,), init_param))
+
+    def forward(self, stream: torch.Tensor, branch: torch.Tensor) -> torch.Tensor:
+        dtype = stream.dtype
+        w = self.gate.view(*([1] * (stream.ndim - 1)), -1).float()
+        beta = torch.sigmoid(w / self.temperature)
+        alpha_sq = torch.sigmoid(-w / self.temperature) * (1.0 + beta)
+        alpha = torch.sqrt(torch.clamp(alpha_sq, min=self.eps))
+        return (alpha * stream.float() + beta * branch.float()).to(dtype)
+
+
 class ConditionalFeedForward(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
@@ -99,7 +118,8 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 class TransformerBlock(nn.Module):
     def __init__(
-        self, args: ModelArgs, attention: Attention, mlp_type: str = "default"
+        self, args: ModelArgs, attention: Attention, mlp_type: str = "default",
+        layer_id: int = 0,
     ):
         """
         Transformer block with support for pre-norm and post-norm.
@@ -110,6 +130,7 @@ def __init__(
                 the attention type is registered in the ATTENTION_REGISTRY.
             mlp_type (str): MLP type for this layer. "default" for standard
                 FFN, "skip" for no FFN block.
+            layer_id (int): layer index, used for residual gate initialization.
         """
         super().__init__()
         self.use_kv_cache = args.use_kv_cache
@@ -118,6 +139,7 @@ def __init__(
         self.head_dim = args.head_dim
         self.attention = attention
         self.mlp_type = mlp_type.lower()
+        self.use_residual_gate = args.use_residual_gate
 
         assert (
             args.hidden_dim is not None
@@ -150,6 +172,16 @@ def __init__(
                 add_unit_offset=args.rms_norm_add_unit_offset,
             )
 
+        if args.use_residual_gate:
+            attn_init = 1.0 / (2 * layer_id + 1) if layer_id > 0 else 0.5
+            ffn_init = 1.0 / (2 * layer_id + 2)
+            self.add_attn = NormPreservingResidualConnection(dim=args.dim, init_scale=attn_init)
+            self.add_ffn = NormPreservingResidualConnection(dim=args.dim, init_scale=ffn_init)
+            self.post_attn_norm = ScalelessRMSNorm(args.dim, eps=args.norm_eps)
+
+        if args.use_ffn_learnable_scales and self.mlp_type != "skip":
+            self.post_ffn_norm = RMSNormWithInputScale(args.dim, eps=args.norm_eps)
+
     @classmethod
     def from_type(cls, layer_id, args, rope) -> "TransformerBlock":
         """
@@ -169,21 +201,38 @@ def from_type(cls, layer_id, args, rope) -> "TransformerBlock":
             mlp_type = args.mlp_type[layer_id]
         cls = ATTENTION_REGISTRY[args.attention_type]
         attention = cls(args, layer_id, rope, **args.attention_kwargs)
-        return TransformerBlock(args, attention, mlp_type=mlp_type)
+        return TransformerBlock(args, attention, mlp_type=mlp_type, layer_id=layer_id)
 
     def forward(self, x, freqs_cos, freqs_sin, attn_options: ForwardOptions):  # x: 1xN
         h, attn_options_update = self.attention(
             self.attention_norm(x), freqs_cos, freqs_sin, **attn_options
         )
         if not isinstance(self.attention, AttentionSkip):
-            h = x + h
+            if self.use_residual_gate:
+                if hasattr(self, "post_attn_norm"):
+                    h = self.post_attn_norm(h)
+                h = self.add_attn(stream=x, branch=h)
+            else:
+                h = x + h
 
         if self.mlp_type == "skip":
             out = h
         elif hasattr(self, "block_sparse_moe"):
-            out = h + self.block_sparse_moe(self.ffn_norm(h))
+            ffn_out = self.block_sparse_moe(self.ffn_norm(h))
+            if hasattr(self, "post_ffn_norm"):
+                ffn_out = self.post_ffn_norm(ffn_out)
+            if self.use_residual_gate:
+                out = self.add_ffn(stream=h, branch=ffn_out)
+            else:
+                out = h + ffn_out
         else:
-            out = h + self.feed_forward(self.ffn_norm(h))
+            ffn_out = self.feed_forward(self.ffn_norm(h))
+            if hasattr(self, "post_ffn_norm"):
+                ffn_out = self.post_ffn_norm(ffn_out)
+            if self.use_residual_gate:
+                out = self.add_ffn(stream=h, branch=ffn_out)
+            else:
+                out = h + ffn_out
         return out, attn_options_update
 
 
@@ -371,7 +420,7 @@ def construct_transformer(model_args: ModelArgs) -> Transformer:
             and model_args.layer_types[layer_id] == "skip_attention"
         ):
             attention = AttentionSkip()
-            transformer_block = TransformerBlock(model_args, attention)
+            transformer_block = TransformerBlock(model_args, attention, layer_id=layer_id)
             layers.append(transformer_block)
         elif (
             model_args.layer_types
@@ -386,13 +435,13 @@ def construct_transformer(model_args: ModelArgs) -> Transformer:
             attention = linear_cls(
                 model_args, layer_id, rope, **model_args.attention_kwargs
             )
-            transformer_block = TransformerBlock(model_args, attention)
+            transformer_block = TransformerBlock(model_args, attention, layer_id=layer_id)
             layers.append(transformer_block)
         else:
             attention = cls(
                 model_args, layer_id, rope, **model_args.attention_kwargs
             )  # pyre-ignore[45]
-            transformer_block = TransformerBlock(model_args, attention)
+            transformer_block = TransformerBlock(model_args, attention, layer_id=layer_id)
             layers.append(transformer_block)
 
     return Transformer(model_args, layers, rope)

examples/models/llama/model_args.py

@@ -102,6 +102,7 @@ class ModelArgs:
     apply_output: bool = True  # Use output layer (unembedding) inside the transformer
     use_qk_norm: bool = False  # apply normalization to q and k in the attention
     qk_norm_before_rope: bool = False  # when to apply qk norm
+    qk_norm_affine: bool = True  # whether QK norm has learnable weight (False = scaleless)
     residual_multiplier: Optional[float] = (
         None  # Scaling factor applied to the residual hidden states
     )
@@ -162,6 +163,15 @@ class ModelArgs:
     final_logit_softcapping: Optional[float] = None
     attn_logit_softcapping: Optional[float] = None
 
+    # rlformers forward-pass features for on-device model parity
+    normalize_tok_embeddings: bool = False
+    scale_query_by: float = 1.0
+    use_attn_o_gate: bool = False
+    use_attn_o_norm: bool = False
+    use_residual_gate: bool = False
+    use_ffn_learnable_scales: bool = False
+    output_soft_cap_temp: Optional[float] = None
+
     def __post_init__(self):  # noqa: C901
         if self.n_kv_heads is None:
             self.n_kv_heads = self.n_heads

examples/models/llama/norm.py

@@ -32,33 +32,36 @@ def __init__(self, dim: int, eps: float = 1e-6, add_unit_offset: bool = False):
         self.weight = nn.Parameter(torch.ones(dim))
 
     def _norm(self, x):
-        """
-        Apply the RMSNorm normalization to the input tensor.
+        return x * torch.rsqrt((x * x).mean(-1, keepdim=True) + self.eps)
 
-        Args:
-            x (torch.Tensor): The input tensor.
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        if self.add_unit_offset:
+            return output * (1.0 + self.weight.float()).type_as(x)
+        return output * self.weight.type_as(x)
 
-        Returns:
-            torch.Tensor: The normalized tensor.
 
-        """
-        return x * torch.rsqrt((x * x).mean(-1, keepdim=True) + self.eps)
+class ScalelessRMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
 
     def forward(self, x):
-        """
-        Forward pass through the RMSNorm layer.
+        x_float = x.float()
+        return (x_float * torch.rsqrt((x_float * x_float).mean(-1, keepdim=True) + self.eps)).type_as(x)
 
-        Args:
-            x (torch.Tensor): The input tensor.
 
-        Returns:
-            torch.Tensor: The output tensor after applying RMSNorm.
+class RMSNormWithInputScale(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.dim = dim
+        self.weight = torch.nn.Parameter(torch.ones(dim))
 
-        """
-        output = self._norm(x.float()).type_as(x)
-        if self.add_unit_offset:
-            return output * (1.0 + self.weight.float()).type_as(x)
-        return output * self.weight.type_as(x)
+    def forward(self, x):
+        scaled = (self.weight * x).float()
+        return (scaled * torch.rsqrt((scaled * scaled).mean(-1, keepdim=True) + self.eps)).type_as(x)
 
 
 class RMSNormGated(nn.Module):