bgawrych commented on a change in pull request #20227:
URL: https://github.com/apache/incubator-mxnet/pull/20227#discussion_r639705039
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File path: tests/python/quantization/test_quantization.py
##########
@@ -314,42 +341,46 @@ def check_quantized_elemwise_add(data_shape, qtype):
print('skipped testing quantized_elemwise_add for gpu since it is
not supported yet')
return
- dataA = mx.sym.Variable(name='dataA', shape=data_shape,
dtype='float32')
- dataB = mx.sym.Variable(name='dataB', shape=data_shape,
dtype='float32')
- elemwise_add_fp32 = mx.sym.elemwise_add(dataA, dataB)
- arg_names = elemwise_add_fp32.list_arguments()
- elemwise_add_fp32_exe =
elemwise_add_fp32._simple_bind(ctx=mx.current_context(), grad_req='null')
+ class ElemwiseSumBlock(mx.gluon.nn.HybridBlock):
+ def __init__(self, **kwargs):
+ super(ElemwiseSumBlock, self).__init__(**kwargs)
+
+ def hybrid_forward(self, F, dataA, dataB):
+ return F.elemwise_add(dataA, dataB)
+
+ class QuantElemwiseSumBlock(mx.gluon.nn.HybridBlock):
+ def __init__(self, **kwargs):
+ super(QuantElemwiseSumBlock, self).__init__(**kwargs)
+
+ def hybrid_forward(self, F, dataA, dataB, dataA_min, dataA_max,
dataB_min, dataB_max):
+ return F.contrib.quantized_elemwise_add(dataA, dataB,
dataA_min, dataA_max, dataB_min, dataB_max)
+
+ elemwise_add_fp32 = ElemwiseSumBlock()
+
if qtype == 'uint8':
data_low = 0.0
data_high = 255.0
else:
data_low = -127.0
data_high = 127.0
- dataA_val = mx.nd.random.uniform(low=data_low, high=data_high,
shape=data_shape).astype('int32')
- dataB_val = mx.nd.random.uniform(low=data_low, high=data_high,
shape=data_shape).astype('int32')
- elemwise_add_fp32_exe.arg_dict[arg_names[0]][:] = dataA_val
-
- elemwise_add_fp32_exe.arg_dict[arg_names[1]][:] = dataB_val
-
- output = elemwise_add_fp32_exe.forward()[0]
- qdataA = mx.sym.Variable(name='qdataA', shape=data_shape, dtype=qtype)
- qdataB = mx.sym.Variable(name='qdataB', shape=data_shape, dtype=qtype)
- min_dataA = mx.sym.Variable(name='min_dataA', dtype='float32')
- max_dataA = mx.sym.Variable(name='max_dataA', dtype='float32')
- min_dataB = mx.sym.Variable(name='min_dataB', dtype='float32')
- max_dataB = mx.sym.Variable(name='max_dataB', dtype='float32')
- quantized_elemwise_add = mx.sym.contrib.quantized_elemwise_add(qdataA,
qdataB, min_dataA, max_dataA, min_dataB, max_dataB)
- elemwise_add_int8_exe =
quantized_elemwise_add._simple_bind(ctx=mx.current_context(), grad_req='null')
- qarg_names = quantized_elemwise_add.list_arguments()
- elemwise_add_int8_exe.arg_dict[qarg_names[0]][:] =
elemwise_add_fp32_exe.arg_dict[arg_names[0]].astype(qtype)
- elemwise_add_int8_exe.arg_dict[qarg_names[1]][:] =
elemwise_add_fp32_exe.arg_dict[arg_names[1]].astype(qtype)
+ dataA_val = mx.nd.random.uniform(low=data_low, high=data_high,
shape=data_shape).astype('int32').astype('float32')
+ dataB_val = mx.nd.random.uniform(low=data_low, high=data_high,
shape=data_shape).astype('int32').astype('float32')
+
+ output = elemwise_add_fp32(dataA_val, dataB_val)
+
+ #run quantized
+ quantized_elemwise_add = QuantElemwiseSumBlock()
+ dataA_val_int8 = dataA_val.astype(qtype)
+ dataB_val_int8 = dataB_val.astype(qtype)
quantized_range = 127.0
- elemwise_add_int8_exe.arg_dict[qarg_names[2]][:] = data_low
- elemwise_add_int8_exe.arg_dict[qarg_names[3]][:] = data_high
- elemwise_add_int8_exe.arg_dict[qarg_names[4]][:] = data_low
- elemwise_add_int8_exe.arg_dict[qarg_names[5]][:] = data_high
- qoutput, min_range, max_range = elemwise_add_int8_exe.forward()
+ min_dataA = mx.nd.array([data_low])
+ max_dataA = mx.nd.array([data_high])
+ min_dataB = mx.nd.array([data_low])
+ max_dataB = mx.nd.array([data_high])
+ qoutput, min_range, max_range = quantized_elemwise_add(dataA_val_int8,
dataB_val_int8,
+ min_dataA,
max_dataA,
+ min_dataB,
max_dataB)
int8_rslt = qoutput.astype(output.dtype)*max_range/0x7fffffff
Review comment:
Added spaces but I left equation as it was (max range seems to be float)
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