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google-ai-edge · Jun 7, 2024 · c9493ea · c9493ea
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diff --git a/ai_edge_quantizer/algorithms/uniform_quantize/naive_min_max_quantize.py b/ai_edge_quantizer/algorithms/uniform_quantize/naive_min_max_quantize.py
@@ -30,7 +30,9 @@ def check_op_quantization_config(
   """
   if op_quant_config.weight_tensor_config.dtype != qtyping.TensorDataType.INT:
     raise ValueError(
-        "Weights need to have integer type for naive min/max quantization."
+        "Weights need to have integer type for min/max uniform quantization. If"
+        " you wish to perform float casting quantization (e.g., fp16 weight"
+        " only), please set algorithm key as 'float_casting'."
     )
   execution_mode = op_quant_config.execution_mode
   if execution_mode == _OpExecutionMode.WEIGHT_ONLY:

diff --git a/ai_edge_quantizer/algorithms/utils/min_max_quantize_utils.py b/ai_edge_quantizer/algorithms/utils/min_max_quantize_utils.py
@@ -65,6 +65,8 @@ def check_srq_config(
     )
   if act_config is None:
     raise ValueError("activation_tensor_config is required for SRQ.")
+  if act_config.dtype != qtyping.TensorDataType.INT:
+    raise ValueError("SRQ requires activation tensor to be int type.")
   if act_config.num_bits not in (8, 16):
     raise ValueError(
         f"Only int8/int16 activation SRQ is supported for op {op_name}."