Reduce after quantization memory usage (#20894)

python/mxnet/contrib/quantization.py

@@ -921,6 +921,9 @@ def quantize_net(network, quantized_dtype='auto', quantize_mode='full', quantize
         if calib_mode in ['naive', 'entropy', 'custom']:
             inputs = [mx.sym.var(desc.name) for desc in data_descs]
             calib_net = SymbolBlock(symnet, inputs)
+            for k, v in calib_net.collect_params().items():
+               v.grad_req = 'null'
+
             calib_net.load_dict(params, cast_dtype=True, dtype_source='saved')
             calib_net.hybridize(static_alloc=False, static_shape=False)
             num_batches = _collect_layer_statistics(calib_net, calib_data, collector, num_inputs,
@@ -939,6 +942,9 @@ def quantize_net(network, quantized_dtype='auto', quantize_mode='full', quantize
         inputs = [mx.sym.var(desc.name) for desc in data_descs]
 
     net = SymbolBlock(qsym, inputs)
+    for k, v in net.collect_params().items():
+        v.grad_req = 'null'
+
     all_params = {('arg:%s' % k): v.as_in_context(cpu()) for k, v in qarg_params.items()}
     all_params.update({('aux:%s' % k): v.as_in_context(cpu()) for k, v in aux_params.items()})
     net.load_dict(all_params, cast_dtype=True, dtype_source='saved')