From 5e8db7c14f7ca2709cd108fb68c41ed27bb2ed86 Mon Sep 17 00:00:00 2001
From: Nilabhra <nilabhraroy@gmail.com>
Date: Mon, 15 Apr 2024 13:52:20 +0400
Subject: [PATCH] add: support for falcon-10B architecture.

---
 .../custom_modeling/flash_rw_modeling.py      | 41 ++++++++++++++++++-
 1 file changed, 39 insertions(+), 2 deletions(-)

diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
index a2236a3d..bb53ee20 100644
--- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
@@ -7,8 +7,7 @@ from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_utils import PreTrainedModel
 
 from text_generation_server.utils import paged_attention, flash_attn
-from text_generation_server.utils.flash_attn import attention
-from text_generation_server.layers import (
+from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
@@ -480,6 +479,44 @@ class FlashRWLayer(nn.Module):
 
             return mlp_output, residual
 
+class FlashRWLayerNorm(nn.Module):
+    def __init__(self, config, prefix, weights):
+        super().__init__()
+        self.num_ln = config.num_ln_in_parallel_attn
+
+        if self.num_ln == 1:
+            self.input_ln = FastLayerNorm.load(
+                prefix=f"{prefix}.input_layernorm",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+        elif self.num_ln == 2:
+            self.ln_attn = FastLayerNorm.load(
+                prefix=f"{prefix}.ln_attn",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+            self.ln_mlp = FastLayerNorm.load(
+                prefix=f"{prefix}.ln_mlp",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+        else:
+            raise ValueError("Number of layer norms can either be 1 or 2.")
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+    ):
+        if self.num_ln == 1:
+            ln_hidden_states, residual = self.input_ln(hidden_states, residual)
+            return ln_hidden_states, ln_hidden_states, residual
+        elif self.num_ln == 2:
+            ln_attn, residual = self.ln_attn(hidden_states, residual)
+            ln_mlp, _ = self.ln_mlp(residual)
+            return ln_attn, ln_mlp, residual
+
 
 class FlashRWLayerNorm(nn.Module):
     def __init__(self, config, prefix, weights):