V3 remove triton v1 (huggingface#15)

* pkg depends update

* remove fused attention/mlp

* Remove triton v1 and cleanup unused fused files

auto_gptq/modeling/_base.py

@@ -790,7 +790,6 @@ def from_quantized(
         trainable: bool = False,
         disable_exllama: Optional[bool] = None,
         disable_exllamav2: bool = False,
-        use_tritonv2: bool = False,
         checkpoint_format: Optional[str] = None,
         **kwargs,
     ):
@@ -828,15 +827,9 @@ def from_quantized(
         if use_qigen and not QIGEN_AVAILABLE:
             logger.warning("Qigen is not installed, reset use_qigen to False.")
             use_qigen = False
-        if use_triton and use_tritonv2:
-            logging.warn(
-                "Both use_triton and use_tritonv2 are set to True. Defaulting to use_triton"
-            )
-            use_tritonv2 = False
-        if (use_triton or use_tritonv2) and not TRITON_AVAILABLE:
+        if use_triton and not TRITON_AVAILABLE:
             logger.warning("Triton is not installed, reset use_triton to False.")
             use_triton = False
-            use_tritonv2 = False
         if not disable_exllama and not EXLLAMA_KERNELS_AVAILABLE:
             logger.warning(
                 "Exllama kernel is not installed, reset disable_exllama to True. "
@@ -942,7 +935,7 @@ def from_quantized(
             disable_exllama = True
             disable_exllamav2 = True
 
-        elif not (use_triton or use_tritonv2) and trainable:
+        elif not use_triton and trainable:
             logger.warning(
                 "QuantLinear with cuda backend not support trainable mode yet, Switch to the pytorch backend."
             )
@@ -1004,7 +997,6 @@ def skip(*args, **kwargs):
                     use_cuda_fp16=use_cuda_fp16,
                     desc_act=quantize_config.desc_act,
                     trainable=trainable,
-                    use_tritonv2=use_tritonv2,
                 )
                 model.tie_weights()
 
@@ -1051,7 +1043,6 @@ def skip(*args, **kwargs):
                     bits=quantize_config.bits,
                     disable_exllama=disable_exllama,
                     disable_exllamav2=disable_exllamav2,
-                    use_tritonv2=use_tritonv2,
                 )
 
             # TODO: move this logic in an awq_utils.py file.
@@ -1172,7 +1163,6 @@ def skip(*args, **kwargs):
                     disable_exllama=disable_exllama,
                     disable_exllamav2=disable_exllamav2,
                     use_marlin=False,
-                    use_tritonv2=use_tritonv2,  # Get the "original" QuantLinear class
                 )
 
                 # Prepare model for marlin load.
@@ -1234,7 +1224,6 @@ def skip(*args, **kwargs):
                 desc_act=quantize_config.desc_act,
                 trainable=trainable,
                 use_qigen=True,
-                use_tritonv2=use_tritonv2,
                 use_marlin=quantize_config.checkpoint_format == CHECKPOINT_FORMAT.MARLIN,
             )
             preprocess_checkpoint_qigen(
@@ -1248,7 +1237,6 @@ def skip(*args, **kwargs):
 
         qlinear_kernel = dynamically_import_QuantLinear(
             use_triton=use_triton,
-            use_tritonv2=use_tritonv2,
             desc_act=quantize_config.desc_act,
             group_size=quantize_config.group_size,
             bits=quantize_config.bits,
@@ -1293,12 +1281,8 @@ def skip(*args, **kwargs):
         model.eval()
 
         # == step6: (optional) warmup triton == #
-        if (use_triton or use_tritonv2) and warmup_triton:
-            if use_tritonv2:
-                from ..nn_modules.qlinear.qlinear_tritonv2 import QuantLinear
-            else:
-                from ..nn_modules.qlinear.qlinear_triton import QuantLinear
-
+        if use_triton and warmup_triton:
+            from ..nn_modules.qlinear.qlinear_tritonv2 import QuantLinear
             QuantLinear.warmup(model, seqlen=model.seqlen)
 
 
@@ -1319,7 +1303,7 @@ def skip(*args, **kwargs):
             model,
             True,
             quantize_config,
-            is_triton_backend=use_triton or use_tritonv2,
+            is_triton_backend=use_triton,
             trainable=trainable,
             qlinear_kernel=qlinear_kernel,
         )
@@ -1331,8 +1315,7 @@ def warmup_triton(self, enabled: bool = True):
             logger.warning("triton is not available, skip warmup stage directly.")
             return
 
-        from ..nn_modules.qlinear.qlinear_triton import QuantLinear
-
+        from ..nn_modules.qlinear.qlinear_tritonv2 import QuantLinear
         QuantLinear.warmup(self.model, seqlen=self.model.seqlen)
 
     def enable_trainable_mode(self, enabled: bool = True):
@@ -1356,7 +1339,6 @@ def make_sure_compatible_with_peft(
         disable_exllamav2: bool = False,
         use_marlin: bool = False,
         use_qigen: bool = False,
-        use_tritonv2: bool = False,
     ):
         GeneralQuantLinear.inject_to_model(
             model,

auto_gptq/modeling/_utils.py

@@ -81,7 +81,6 @@ def make_quant(
     use_cuda_fp16: bool = True,
     desc_act: bool = False,
     trainable: bool = False,
-    use_tritonv2: bool = False,
 ):
     # If disable_exllamav2 is True, we want to fall back on the exllama kernel and not the cuda/cuda_old ones.
     if disable_exllama is None:
@@ -99,7 +98,6 @@ def make_quant(
         disable_exllama=disable_exllama,
         disable_exllamav2=disable_exllamav2,
         use_qigen=use_qigen,
-        use_tritonv2=use_tritonv2,
     )
 
     if isinstance(module, QuantLinear):
@@ -123,7 +121,6 @@ def make_quant(
                 (not (desc_act) or group_size == -1)
                 and not use_triton
                 and not use_qigen
-                and not use_tritonv2
             ):
                 new_layer = QuantLinear(
                     bits,
@@ -331,7 +328,6 @@ def pack_model(
     warmup_triton: bool = False,
     force_layer_back_to_cpu: bool = False,
     use_marlin: bool = False,
-    use_tritonv2: bool = False,
 ):
     QuantLinear = dynamically_import_QuantLinear(
         use_triton=use_triton,
@@ -341,7 +337,6 @@ def pack_model(
         disable_exllama=False,
         disable_exllamav2=True,
         use_marlin=use_marlin,
-        use_tritonv2=use_tritonv2,
     )
 
     if force_layer_back_to_cpu:
@@ -577,9 +572,9 @@ def autogptq_post_init(model, use_act_order: bool, max_input_length: Optional[in
 
 
 def make_sure_no_tensor_in_meta_device(
-    model, use_triton: bool, desc_act: bool, group_size: int, bits: int, disable_exllama: bool, disable_exllamav2: bool, use_marlin: bool = False, use_tritonv2: bool = False,
+    model, use_triton: bool, desc_act: bool, group_size: int, bits: int, disable_exllama: bool, disable_exllamav2: bool, use_marlin: bool = False,
 ):
-    QuantLinear = dynamically_import_QuantLinear(use_triton, desc_act, group_size, bits=bits, disable_exllama=disable_exllama, disable_exllamav2=disable_exllamav2, use_marlin=use_marlin, use_tritonv2=use_tritonv2)
+    QuantLinear = dynamically_import_QuantLinear(use_triton, desc_act, group_size, bits=bits, disable_exllama=disable_exllama, disable_exllamav2=disable_exllamav2, use_marlin=use_marlin)
     for n, m in model.named_modules():
         if isinstance(m, QuantLinear) and m.bias.device == torch.device("meta"):
             m.register_buffer("bias", torch.zeros((m.outfeatures), dtype=torch.float16, device="cpu"))

auto_gptq/modeling/auto.py

@@ -110,7 +110,6 @@ def from_quantized(
         disable_exllama: Optional[bool] = None,
         disable_exllamav2: bool = False,
         use_marlin: bool = False,
-        use_tritonv2: bool = False,
         **kwargs,
     ) -> BaseGPTQForCausalLM:
         # If disable_exllamav2 is True, we want to fall back on the exllama kernel and not the cuda/cuda_old ones.
@@ -158,7 +157,6 @@ def from_quantized(
             disable_exllama=disable_exllama,
             disable_exllamav2=disable_exllamav2,
             use_marlin=use_marlin,
-            use_tritonv2=use_tritonv2,
             **keywords,
         )