Fixing indirect GPTQ loads.

Loading GPTQ (or any kernel) should always be lazy.
Some platforms don't have GPTQ support and shouldn't fail because of
imports.

server/text_generation_server/layers/gptq/__init__.py

@@ -6,6 +6,8 @@ from text_generation_server.utils.import_utils import (
     SYSTEM,
 )
 
+raise RuntimeError("No")
+
 
 @dataclass
 class GPTQWeight:

server/text_generation_server/layers/linear.py

@@ -2,8 +2,6 @@ from typing import Optional
 import torch
 from torch.nn import functional as F
 from text_generation_server.utils.import_utils import SYSTEM
-from text_generation_server.layers.exl2 import Exl2Weight
-from text_generation_server.layers.gptq import GPTQWeight
 
 if SYSTEM == "rocm":
     try:
@@ -155,6 +153,8 @@ def get_linear(weight, bias, quantize):
             quant_type="nf4",
         )
     elif quantize == "exl2":
+        from text_generation_server.layers.exl2 import Exl2Weight
+
         if not isinstance(weight, Exl2Weight):
             raise NotImplementedError(
                 f"The passed weight is not `exl2` compatible, loader needs to be updated."
@@ -165,6 +165,8 @@ def get_linear(weight, bias, quantize):
         linear = ExllamaQuantLinear(weight, bias)
 
     elif quantize == "gptq":
+        from text_generation_server.layers.gptq import GPTQWeight
+
         if not isinstance(weight, GPTQWeight):
             raise NotImplementedError(
                 f"The passed weight is not `gptq` compatible, loader needs to be updated."

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -21,7 +21,6 @@ from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple, Any
 from loguru import logger
-from text_generation_server.layers.gptq import GPTQWeight
 from text_generation_server.utils.import_utils import SYSTEM
 
 if SYSTEM != "xpu":
@@ -198,6 +197,8 @@ def _load_gqa(config, prefix: str, weights):
     v_stop = v_offset + (rank + 1) * kv_block_size
 
     if config.quantize in ["gptq", "awq"]:
+        from text_generation_server.layers.gptq import GPTQWeight
+
         try:
             qweight_slice = weights._get_slice(f"{prefix}.qweight")
             q_qweight = qweight_slice[:, q_start:q_stop]

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -5,7 +5,6 @@ from torch import nn
 from transformers.activations import ACT2FN
 from typing import Optional, List, Tuple
 
-from text_generation_server.layers.gptq import GPTQWeight
 from text_generation_server.layers.attention import (
     paged_attention,
     attention,
@@ -39,6 +38,8 @@ def load_multi_mqa(
 def _load_multi_mqa_gptq(
     config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
 ):
+    from text_generation_server.layers.gptq import GPTQWeight
+
     if any("c_attn" in k for k in weights.routing.keys()) and not config.transpose:
         world_size = weights.process_group.size()
         rank = weights.process_group.rank()

server/text_generation_server/utils/weights.py

@@ -7,8 +7,6 @@ import torch
 from loguru import logger
 from huggingface_hub import hf_hub_download
 import json
-from text_generation_server.layers.exl2 import Exl2Weight
-from text_generation_server.layers.gptq import GPTQWeight
 from text_generation_server.utils.log import log_once
 
 
@@ -221,6 +219,8 @@ class Weights:
 
     def get_weights_col(self, prefix: str, quantize: str):
         if quantize == "exl2":
+            from text_generation_server.layers.exl2 import Exl2Weight
+
             try:
                 q_weight = self.get_tensor(f"{prefix}.q_weight")
             except RuntimeError:
@@ -247,6 +247,8 @@ class Weights:
         if quantize == "exl2":
             raise ValueError("get_multi_weights_col is not supported for exl2")
         elif quantize in ["gptq", "awq"]:
+            from text_generation_server.layers.gptq import GPTQWeight
+
             try:
                 qweight = torch.cat(
                     [self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1