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

Summary:
The 730M dense model checkpoint uses several 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

@@ -7,7 +7,11 @@
 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
 
 
@@ -375,6 +379,9 @@ 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).
@@ -417,17 +424,26 @@ 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."""
@@ -478,13 +494,17 @@ def _prepare_qkv_shared(
 
         if self.use_qk_norm and self.qk_norm_before_rope:
             q = self.q_norm_fn(q)
+            if self.scale_query_by != 1.0:
+                q = 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)
+            if self.scale_query_by != 1.0:
+                q = q * self.scale_query_by
 
         return q, k, v
 
@@ -508,6 +528,8 @@ def _prepare_qkv(
 
         if self.use_qk_norm and self.qk_norm_before_rope:
             q = self.q_norm_fn(q)
+            if self.scale_query_by != 1.0:
+                q = q * self.scale_query_by
             k = self.k_norm_fn(k)
 
         q, k = self.rope.forward(q, k, freqs_cos, freqs_sin)
@@ -518,6 +540,8 @@ def _prepare_qkv(
 
         if self.use_qk_norm and not self.qk_norm_before_rope:
             q = self.q_norm_fn(q)
+            if self.scale_query_by != 1.0:
+                q = q * self.scale_query_by
             k = self.k_norm_fn(k)
 
         return q, k, v
@@ -582,8 +606,7 @@ 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)}
@@ -602,13 +625,27 @@ 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 +21,11 @@
 )
 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 +56,26 @@ 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 +124,11 @@ 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 +139,7 @@ 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 +148,7 @@ 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 +181,20 @@ 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 +214,38 @@ 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 +433,9 @@ 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 +450,17 @@ 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 +102,9 @@ 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 +165,15 @@ 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 +32,38 @@ 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
+        return F.rms_norm(scaled, (self.dim,), None, self.eps)
 
 
 class RMSNormGated(nn.Module):