fix: merge errors

ZackBradshaw · Apr 22, 2024 · 6882e22 · 6882e22
1 parent 6293809
commit 6882e22
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diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
@@ -214,14 +214,20 @@ def add_cli_args(
             choices=['outlines', 'lm-format-enforcer'],
             help='Which engine will be used for guided decoding'
 <<<<<<< HEAD
+<<<<<<< HEAD
+=======
+>>>>>>> cc74b2b (Updating lm-format-enforcer version and adding links to decoding libraries in docs (#4222))
             ' (JSON schema / regex etc) by default. Currently support '
             'https://github.com/outlines-dev/outlines and '
             'https://github.com/noamgat/lm-format-enforcer.'
             ' Can be overridden per request via guided_decoding_backend'
             ' parameter.')
+<<<<<<< HEAD
 =======
             ' (JSON schema / regex etc).')
 >>>>>>> 682789d (Fix missing docs and out of sync `EngineArgs` (#4219))
+=======
+>>>>>>> cc74b2b (Updating lm-format-enforcer version and adding links to decoding libraries in docs (#4222))
         # Parallel arguments
         parser.add_argument('--worker-use-ray',
                             action='store_true',

diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py
@@ -1,8 +1,12 @@
 <<<<<<< HEAD
+<<<<<<< HEAD
 from typing import Any, Dict, List, Optional, Tuple
 =======
 from typing import Any, Dict, List, Optional
 >>>>>>> a22cdea ([Kernel][FP8] Initial support with dynamic per-tensor scaling (#4118))
+=======
+from typing import Any, Dict, List, Optional, Tuple
+>>>>>>> cc74b2b (Updating lm-format-enforcer version and adding links to decoding libraries in docs (#4222))
 
 import torch
 from torch.nn import Module
@@ -118,11 +122,15 @@ def apply_weights(self,
         return output
 
 
+<<<<<<< HEAD
 <<<<<<< HEAD
 def per_tensor_quantize(tensor: torch.Tensor) -> Tuple[torch.Tensor, float]:
 =======
 def per_tensor_quantize(tensor: torch.Tensor) -> tuple[torch.Tensor, float]:
 >>>>>>> a22cdea ([Kernel][FP8] Initial support with dynamic per-tensor scaling (#4118))
+=======
+def per_tensor_quantize(tensor: torch.Tensor) -> Tuple[torch.Tensor, float]:
+>>>>>>> cc74b2b (Updating lm-format-enforcer version and adding links to decoding libraries in docs (#4222))
     """Quantize a tensor using per-tensor static scaling factor.
 
     Args: