feat: support lora revisions and qkv_proj weights (#2482)

* feat: support lora revisions and qkv_proj weights

* fix: add qkv_proj weights to weight test

server/tests/utils/test_adapter.py

 import pytest
 from unittest.mock import Mock
-from text_generation_server.utils.adapter import get_attn_weights, get_mlp_weights
+from text_generation_server.utils.adapter import (
+    get_attn_weights,
+    get_mlp_weights,
+    parse_lora_adapters,
+    AdapterInfo,
+)
+
+
+def test_parse_lora_adapters_empty():
+    assert parse_lora_adapters(None) == []
+    assert parse_lora_adapters("") == []
+
+
+def test_parse_lora_adapters_single():
+    result = parse_lora_adapters("adapter1")
+    assert result == [AdapterInfo(id="adapter1", path=None, revision=None)]
+
+
+def test_parse_lora_adapters_with_path():
+    result = parse_lora_adapters("adapter1=path/to/adapter1")
+    assert result == [
+        AdapterInfo(id="adapter1", path="path/to/adapter1", revision=None)
+    ]
+
+
+def test_parse_lora_adapters_with_path_and_revision():
+    result = parse_lora_adapters("adapter1=path/to/adapter1@main")
+    assert result == [
+        AdapterInfo(id="adapter1", path="path/to/adapter1", revision="main")
+    ]
+
+
+def test_parse_lora_adapters_multiple():
+    result = parse_lora_adapters(
+        "adapter1,adapter2=path/to/adapter2,adapter3=path/to/adapter3@dev"
+    )
+    assert result == [
+        AdapterInfo(id="adapter1", path=None, revision=None),
+        AdapterInfo(id="adapter2", path="path/to/adapter2", revision=None),
+        AdapterInfo(id="adapter3", path="path/to/adapter3", revision="dev"),
+    ]
+
+
+def test_parse_lora_adapters_invalid_format():
+    try:
+        parse_lora_adapters("adapter1,invalid=format=test,adapter3")
+        assert False, "Should have raised ValueError"
+    except ValueError as e:
+        assert str(e) == "Invalid LoRA adapter format: invalid=format=test"
 
 
 def test_get_attn_weights():
@@ -22,6 +70,10 @@ def test_get_attn_weights():
             "model.layers.2.self_attn.k_proj",
             mock_layer.self_attn.query_key_value,
         ),
+        (2, "qkv_proj"): (
+            "model.layers.2.self_attn.qkv_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
         (2, "v_proj"): (
             "model.layers.2.self_attn.v_proj",
             mock_layer.self_attn.query_key_value,
@@ -115,6 +167,10 @@ def test_get_attn_weights_llama_compatibility():
             "model.layers.2.self_attn.k_proj",
             mock_layer.self_attn.query_key_value,
         ),
+        (2, "qkv_proj"): (
+            "model.layers.2.self_attn.qkv_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
         (2, "v_proj"): (
             "model.layers.2.self_attn.v_proj",
             mock_layer.self_attn.query_key_value,
@@ -155,6 +211,10 @@ def test_get_attn_weights_gemma_compatibility():
             "model.layers.2.self_attn.k_proj",
             mock_layer.self_attn.query_key_value,
         ),
+        (2, "qkv_proj"): (
+            "model.layers.2.self_attn.qkv_proj",
+            mock_layer.self_attn.query_key_value,
+        ),
         (2, "v_proj"): (
             "model.layers.2.self_attn.v_proj",
             mock_layer.self_attn.query_key_value,

server/text_generation_server/models/__init__.py

@@ -1259,6 +1259,7 @@ def get_model_with_lora_adapters(
                 "gate_proj",
                 "up_proj",
                 "down_proj",
+                "qkv_proj",
             ]
 
             for layer_name in adapter_layers:
@@ -1286,7 +1287,7 @@ def get_model_with_lora_adapters(
 
             if len(unused_weight_names) > 0:
                 logger.warning(
-                    f"{','.join(adapter_parameters.adapter_ids)} unused adapter weights: {unused_weight_names}"
+                    f"{','.join([a.id for a in lora_adapters])} unused adapter weights: {unused_weight_names}"
                 )
 
             if adapter_tokenizer is not None:

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -66,15 +66,15 @@ def load_attention(config, prefix: str, weights, layer_id):
     prefixes = None
 
     if config.model_type == "phi3":
-        prefix = f"{prefix}.qkv_proj"
         base_layer = TensorParallelColumnLinear.load_qkv(
             config,
-            prefix=prefix,
+            prefix=f"{prefix}.qkv_proj",
             weights=weights,
             bias=bias,
             num_heads=config.num_attention_heads,
             num_key_value_heads=config.num_key_value_heads,
         )
+        prefixes = ["qkv_proj"]
     elif config.model_type == "baichuan":
         prefix = f"{prefix}.W_pack"
         base_layer = TensorParallelColumnLinear.load_qkv(
@@ -85,6 +85,7 @@ def load_attention(config, prefix: str, weights, layer_id):
             num_heads=config.num_attention_heads,
             num_key_value_heads=config.num_key_value_heads,
         )
+        prefixes = [prefix]
     else:
         prefixes = ["q_proj", "k_proj", "v_proj"]
         sizes = [

server/text_generation_server/utils/adapter.py

@@ -3,6 +3,7 @@
 # License:  Apache License Version 2.0, January 2004
 
 import warnings
+import re
 from dataclasses import dataclass
 from functools import lru_cache
 from typing import TYPE_CHECKING, Set, Tuple, Optional, List
@@ -27,6 +28,7 @@ BASE_MODEL_ADAPTER_ID = "__base_model__"
 class AdapterInfo:
     id: str
     path: Optional[str]
+    revision: Optional[str] = None
 
 
 @dataclass
@@ -51,11 +53,16 @@ def parse_lora_adapters(lora_adapters: Optional[str]) -> List[AdapterInfo]:
 
     adapter_list = []
     for adapter in lora_adapters.split(","):
-        parts = adapter.strip().split("=")
-        if len(parts) == 1:
-            adapter_list.append(AdapterInfo(id=parts[0], path=None))
-        elif len(parts) == 2:
-            adapter_list.append(AdapterInfo(id=parts[0], path=parts[1]))
+        adapter = adapter.strip()
+        if adapter.count("=") > 1 or adapter.count("@") > 1:
+            raise ValueError(f"Invalid LoRA adapter format: {adapter}")
+        match = re.match(r"^([^=@]+)(?:=([^@]+))?(?:@(.+))?$", adapter)
+
+        if match:
+            adapter_id, path, revision = match.groups()
+            adapter_list.append(
+                AdapterInfo(id=adapter_id, path=path, revision=revision)
+            )
         else:
             raise ValueError(f"Invalid LoRA adapter format: {adapter}")
     return adapter_list
@@ -73,6 +80,7 @@ def load_and_merge_adapters(
         adapter_info = next(iter(adapter_parameters.adapter_info))
         return load_module_map(
             model_id,
+            adapter_info.revision,
             adapter_info.id,
             adapter_info.path,
             weight_names,
@@ -80,7 +88,13 @@ def load_and_merge_adapters(
         )
 
     adapter_params = AdapterParametersContainer(adapter_parameters, adapter_index)
-    return _load_and_merge(model_id, adapter_params, weight_names, trust_remote_code)
+    return _load_and_merge(
+        model_id,
+        adapter_params.revision,
+        adapter_params,
+        weight_names,
+        trust_remote_code,
+    )
 
 
 @dataclass
@@ -95,6 +109,7 @@ class AdapterParametersContainer:
 @lru_cache(maxsize=32)
 def _load_and_merge(
     model_id: str,
+    revision: str,
     adapter_params: AdapterParametersContainer,
     weight_names: Tuple[str],
     trust_remote_code: bool = False,
@@ -171,12 +186,12 @@ def check_architectures(
 @lru_cache(maxsize=128)
 def load_module_map(
     model_id: str,
+    revision: str,
     adapter_id: str,
     adapter_path: Optional[str],
     weight_names: Tuple[str],
     trust_remote_code: bool = False,
 ) -> Tuple["ModuleMap", "AdapterConfig", Set[str], PreTrainedTokenizer]:
-    revision = "main"
 
     adapter_config = LoraConfig.load(adapter_path or adapter_id, None)
 
@@ -191,6 +206,12 @@ def load_module_map(
         )
     )
 
+    # throw an error if no adapter weights are found
+    if not adapter_filenames:
+        raise FileNotFoundError(
+            f"No adapter weights found for adapter '{adapter_id}' and revision '{revision}'."
+        )
+
     try:
         adapter_tokenizer = AutoTokenizer.from_pretrained(
             adapter_config.config_path,
@@ -221,6 +242,12 @@ def get_attn_weights(i, layer):
         value = (f"model.layers.{i}.self_attn.{k}_proj", qkv)
         weights[key] = value
 
+    # also add the qkv_proj weight for the adapter
+    weights[(i, "qkv_proj")] = (
+        f"model.layers.{i}.self_attn.qkv_proj",
+        qkv,
+    )
+
     weights[(i, "o_proj")] = (
         f"model.layers.{i}.self_attn.o_proj",
         layer.self_attn.o_proj,