updated

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py

@@ -102,30 +102,6 @@ def apply_weights(self,
 
         return out
 
-def quantize_with_max_scale(
-        weight: torch.Tensor, weight_scale: torch.Tensor,
-        logical_widths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]:
-    # Max scale to be used for quanitzation.
-    max_w_scale = weight_scale.max()
-
-    # QKV / MLP is fused in the on disk checkpoint if any of the
-    # weight scales are still set to the default since we initialize
-    # N weight scales for N shards but we only load 1 weight scale
-    # from disk in this case. Skip requantization in this case (since)
-    # we already are quantized with the single scale.
-    # * Sample Model: nm-testing/Phi-3-mini-128k-instruct-FP8
-    unfused_module_in_checkpoint = (weight_scale[-1] > torch.finfo(
-        torch.float8_e4m3fn).min)
-    q_weight = torch.empty_like(weight).to(torch.float8_e4m3fn)
-    # If unfused checkpoint, need quantize with the single scale.
-    if unfused_module_in_checkpoint:
-        start = 0
-        for idx, logical_width in enumerate(logical_widths):
-            end = start + logical_width
-            q_weight[start:end, :], _ = ops.scaled_fp8_quant(
-                weight[start:end, :], max_w_scale)
-            start = end    
-    return max_w_scale, q_weight
 
 def check_24(tensor):
     new_tensor = tensor.view(-1, 4)