Add support for AWQ-quantized Idefics2 (huggingface#2233)

Fixes huggingface#2036.

server/text_generation_server/models/custom_modeling/idefics2.py

@@ -34,6 +34,7 @@
     TensorParallelEmbedding,
     TensorParallelRowLinear,
 )
+from text_generation_server.utils.weights import DefaultWeightsLoader
 
 
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
@@ -682,7 +683,7 @@ def forward(self, image_hidden_states, attention_mask):
 class Idefics2ForConditionalGeneration(nn.Module):
     def __init__(self, prefix, config, weights):
         super().__init__()
-        config.vision_config.quantize = config.quantize
+        config.vision_config.quantize = None
         config.vision_config.speculator = config.speculator
         config.text_config.quantize = config.quantize
         config.text_config.speculator = config.speculator
@@ -695,16 +696,28 @@ def __init__(self, prefix, config, weights):
             name="text_model",
         )
         self.dtype = weights.dtype
-        self.vision_model = Idefics2VisionTransformer(
-            prefix=f"{prefix}.model.vision_model" if prefix else "model.vision_model",
-            config=vision_config,
-            weights=weights,
-        )
-        self.connector = Idefics2Connector(
-            prefix=f"{prefix}.model.connector" if prefix else "model.connector",
-            config=config,
-            weights=weights,
-        )
+
+        # The vision and connector models are not quantized.
+        with weights.use_loader(DefaultWeightsLoader()):
+            self.vision_model = Idefics2VisionTransformer(
+                prefix=(
+                    f"{prefix}.model.vision_model" if prefix else "model.vision_model"
+                ),
+                config=vision_config,
+                weights=weights,
+            )
+
+            quantize = config.quantize
+            try:
+                config.quantize = None
+                self.connector = Idefics2Connector(
+                    prefix=f"{prefix}.model.connector" if prefix else "model.connector",
+                    config=config,
+                    weights=weights,
+                )
+            finally:
+                config.quantize = quantize
+
         self.config = config
         self.image_seq_len = config.perceiver_config.resampler_n_latents
         self.image_token_id = config.image_token_id

server/text_generation_server/utils/weights.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from contextlib import contextmanager
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 from safetensors import safe_open
@@ -306,6 +307,20 @@ def get_tensor_shard(self, var, dim):
     def get_weights_row(self, prefix: str):
         return self.weights_loader.get_weights_row(self, prefix)
 
+    @contextmanager
+    def use_loader(self, weights_loader: WeightsLoader):
+        """
+        This method is a context manager that can be used to use `Weights` with
+        a different loader for the duration of the context.
+        """
+
+        old_loader = self.weights_loader
+        self.weights_loader = weights_loader
+        try:
+            yield
+        finally:
+            self.weights_loader = old_loader
+
 
 def _blocks_to_block_sizes(total_size: int, blocks: Union[int, List[int]]) -> List[int]:
     """