Merge branch 'improve-combine-per-channel-data-speed' of github.com:d…

…uanshengliu/onnxruntime into improve-combine-per-channel-data-speed

onnxruntime/python/tools/quantization/base_quantizer.py

@@ -418,6 +418,9 @@ def quantize_weight_per_channel_impl(
         zero_point_list = []
         scale_list = []
         quantized_per_channel_data_list = []
+        weights_shape = list(weights.shape)
+        reshape_dims = list(weights_shape)  # deep copy
+        reshape_dims[channel_axis] = 1  # only one per channel for reshape
         for i in range(channel_count):
             per_channel_data = weights.take(i, channel_axis)
             channel_override_index = i if i < num_channel_overrides else 0
@@ -460,17 +463,10 @@ def quantize_weight_per_channel_impl(
 
             zero_point_list.append(zero_point)
             scale_list.append(scale)
-            quantized_per_channel_data_list.append(quantized_per_channel_data)
+            quantized_per_channel_data_list.append(np.asarray(quantized_per_channel_data).reshape(reshape_dims))
 
         # combine per_channel_data into one
-        weights_shape = list(weights.shape)
-        reshape_dims = list(weights_shape)  # deep copy
-        reshape_dims[channel_axis] = 1  # only one per channel for reshape
-        quantized_weights = np.asarray(quantized_per_channel_data_list[0]).reshape(reshape_dims)
-        for i in range(1, len(quantized_per_channel_data_list)):
-            channel_weights = np.asarray(quantized_per_channel_data_list[i]).reshape(reshape_dims)
-            quantized_weights = np.concatenate((quantized_weights, channel_weights), channel_axis)
-
+        quantized_weights = np.concatenate(quantized_per_channel_data_list, channel_axis)
         q_weight_name = weight_name + TENSOR_NAME_QUANT_SUFFIX
         zp_name = weight_name + "_zero_point"
         scale_name = weight_name + "_scale"