install check qigen

auto_gptq/modeling/_base.py

@@ -26,7 +26,7 @@ from ..nn_modules._fused_base import FusedBaseAttentionModule, FusedBaseMLPModul
 from ..quantization import GPTQ
 from ..utils.data_utils import collate_data
 from ..utils.import_utils import (
-    dynamically_import_QuantLinear, TRITON_AVAILABLE, AUTOGPTQ_CUDA_AVAILABLE, EXLLAMA_KERNELS_AVAILABLE
+    dynamically_import_QuantLinear, TRITON_AVAILABLE, AUTOGPTQ_CUDA_AVAILABLE, EXLLAMA_KERNELS_AVAILABLE, QIGEN_AVAILABLE
 )
 
 logger = getLogger(__name__)
@@ -727,13 +727,9 @@ class BaseGPTQForCausalLM(nn.Module, PushToHubMixin):
             "_raise_exceptions_for_missing_entries": False,
             "_commit_hash": commit_hash,
         }
-        if use_qigen:
+        if use_qigen and not QIGEN_AVAILABLE:
-            logger.warning("QIgen is active. Ignores all settings related to cuda.")
+            logger.warning("Qigen is not installed, reset use_qigen to False.")
-            inject_fused_attention = False
+            use_qigen = False    
-            inject_fused_mlp = False
-            use_triton = False
-            disable_exllama = True
-            
         if use_triton and not TRITON_AVAILABLE:
             logger.warning("Triton is not installed, reset use_triton to False.")
             use_triton = False
@@ -755,6 +751,13 @@ class BaseGPTQForCausalLM(nn.Module, PushToHubMixin):
                 "3. CUDA and nvcc are not installed in your device."
             )
             
+        if use_qigen and QIGEN_AVAILABLE:
+            logger.warning("QIgen is active. Ignores all settings related to cuda.")
+            inject_fused_attention = False
+            inject_fused_mlp = False
+            use_triton = False
+            disable_exllama = True
+            
         # == step1: prepare configs and file names == #
         config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=trust_remote_code, **cached_file_kwargs)
 

auto_gptq/modeling/_utils.py

@@ -6,7 +6,6 @@ import torch
 import torch.nn as nn
 from transformers import AutoConfig
 import transformers
-import cQIGen as qinfer
 
 from ._const import SUPPORTED_MODELS, CPU, CUDA_0, EXLLAMA_DEFAULT_MAX_INPUT_LENGTH
 from ..utils.import_utils import dynamically_import_QuantLinear
@@ -105,28 +104,6 @@ def make_quant(
             use_qigen=use_qigen
         )
 
-def process_zeros_scales(zeros, scales, bits, out_features):
-    if zeros.dtype != torch.float32:
-        new_zeros = torch.zeros_like(scales).float().contiguous()
-        if bits == 4:
-            qinfer.unpack_zeros4(zeros, new_zeros, new_zeros.shape[0], new_zeros.shape[1])
-        elif bits == 2:
-            qinfer.unpack_zeros2(zeros, new_zeros, new_zeros.shape[0], new_zeros.shape[1])
-        elif bits == 3:
-            logger.info("Unpacking zeros for 3 bits")
-        new_scales = scales.contiguous()
-    else:
-        if scales.shape[1] != out_features:
-            new_scales = scales.transpose(0,1).contiguous()
-        else:
-            new_scales = scales.contiguous()
-        if zeros.shape[1] != out_features:
-            new_zeros = zeros.transpose(0,1).contiguous()
-        else:
-            new_zeros = zeros.contiguous()
-
-    return new_zeros, new_scales
-
 def preprocess_checkpoint_qigen(
     module,
     names,
@@ -135,12 +112,40 @@ def preprocess_checkpoint_qigen(
     checkpoint,
     name='',
 ):
+    try:
+        import cQIGen as qinfer
+    except ImportError:
+        logger.error('cQIGen not installed.')
+        raise
+
     QuantLinear = dynamically_import_QuantLinear(use_triton=False, desc_act=False, group_size=group_size, bits=bits, disable_exllama=False, use_qigen=True)
     if isinstance(module, QuantLinear):
         in_features = module.infeatures
         out_features = module.outfeatures
         
-        checkpoint[name + '.zeros'],checkpoint[name + '.scales'] = process_zeros_scales(checkpoint[name + '.qzeros'],checkpoint[name + '.scales'].float(), bits, out_features)
+        zeros = checkpoint[name + '.qzeros']
+        scales = checkpoint[name + '.scales'].float()
+        
+        if zeros.dtype != torch.float32:
+            new_zeros = torch.zeros_like(scales).float().contiguous()
+            if bits == 4:
+                qinfer.unpack_zeros4(zeros, new_zeros, new_zeros.shape[0], new_zeros.shape[1])
+            elif bits == 2:
+                qinfer.unpack_zeros2(zeros, new_zeros, new_zeros.shape[0], new_zeros.shape[1])
+            elif bits == 3:
+                logger.info("Unpacking zeros for 3 bits")
+            new_scales = scales.contiguous()
+        else:
+            if scales.shape[1] != out_features:
+                new_scales = scales.transpose(0,1).contiguous()
+            else:
+                new_scales = scales.contiguous()
+            if zeros.shape[1] != out_features:
+                new_zeros = zeros.transpose(0,1).contiguous()
+            else:
+                new_zeros = zeros.contiguous()
+
+        checkpoint[name + '.zeros'],checkpoint[name + '.scales'] = new_zeros, new_scales
         del checkpoint[name + '.qzeros']
         del checkpoint[name + '.g_idx']
         if name + '.bias' in checkpoint:

auto_gptq/nn_modules/qlinear/qlinear_qigen.py

@@ -4,7 +4,6 @@ from torch import nn
 from tqdm import tqdm
 import gc
 
-import cQIGen as qinfer
 import math
 import numpy as np
 from gekko import GEKKO
@@ -12,6 +11,11 @@ from logging import getLogger
 
 logger = getLogger(__name__)
 
+try:
+    import cQIGen as qinfer
+except ImportError:
+    logger.error('cQIGen not installed.')
+    raise
 
 def mem_model(N, M, T, mu, tu, bits, l1, p, gs):
     m = GEKKO() # create GEKKO model

auto_gptq/utils/import_utils.py

@@ -25,6 +25,13 @@ try:
 except:
     EXLLAMA_KERNELS_AVAILABLE = False
 
+try:
+    import cQIGen as qinfer
+
+    QIGEN_AVAILABLE = True
+except:
+    QIGEN_AVAILABLE = False
+
 logger = getLogger(__name__)