From 1445b9517df85e24d3f19209248e9f67df38f978 Mon Sep 17 00:00:00 2001
From: Nicolas Patry <patry.nicolas@protonmail.com>
Date: Mon, 26 Feb 2024 15:15:02 +0100
Subject: [PATCH] Remove dead file.

---
 server/text_generation_server/utils/medusa.py | 53 -------------------
 1 file changed, 53 deletions(-)
 delete mode 100644 server/text_generation_server/utils/medusa.py

diff --git a/server/text_generation_server/utils/medusa.py b/server/text_generation_server/utils/medusa.py
deleted file mode 100644
index 9f66ba10..00000000
--- a/server/text_generation_server/utils/medusa.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-from dataclasses import dataclass
-from text_generation_server.utils.layers import TensorParallelHead, FastLinear
-
-
-class ResBlock(torch.nn.Module):
-    def __init__(self, config, prefix, weights):
-        super().__init__()
-        self.linear = FastLinear.load(
-            config, prefix=f"{prefix}.linear", weights=weights, bias=True
-        )
-        self.act = torch.nn.SiLU()
-
-    def forward(self, x):
-        return x + self.act(self.linear(x))
-
-
-class MedusaModel(torch.nn.Module):
-    def __init__(self, config, weights, lm_head):
-        super().__init__()
-        self.heads = torch.nn.ModuleList(
-            [
-                MedusaHead(config, prefix=f"{i}", weights=weights)
-                for i in range(config["medusa_num_heads"])
-            ]
-        )
-        self.lm_head = lm_head
-
-    def forward(self, x):
-        logits = self.lm_head(x)
-        speculative_logits = torch.stack([head(x) for head in self.heads], dim=1)
-        return logits, speculative_logits
-
-
-class MedusaHead(torch.nn.Module):
-    def __init__(self, config, prefix, weights):
-        super().__init__()
-        self.blocks = torch.nn.ModuleList(
-            [
-                ResBlock(config, prefix=f"{prefix}.{i}", weights=weights)
-                for i in range(config["medusa_num_layers"])
-            ]
-        )
-        n = len(self.blocks)
-        self.out = FastLinear.load(
-            config, prefix=f"{prefix}.{n}", weights=weights, bias=False
-        )
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-        x = self.out(x)
-        return x