[Relay][Frontend][TFlite] Add test for qnn_mul operator (#4395)

* Add a function to set the qnn output range wrt each elemwise operation.
* Add comments warning for nonsense clamped output in the tflite/tvm results comparison.

tests/python/frontend/tflite/test_forward.py

@@ -143,7 +143,8 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,
             converter.inference_type = tf.lite.constants.QUANTIZED_UINT8
             input_arrays = converter.get_input_arrays()
             input_stats = {}
-            # hardcode the mean_values and std_dev_values (m,s) to be the same for all inputs
+            # hardcode the mean_values and std_dev_values (m,s) to be the same
+            # if all inputs are in (float_min; float_max) == (-100, 100)
             # s = 255/(fmax-fmin);  m = -fmin*s (the zero point)
             for i in input_arrays:
                 input_stats[i] = (128., 1.275)
@@ -160,6 +161,10 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors,
 
             tvm_output = run_tvm_graph(tflite_model_buffer, in_data, in_node, target=device,
                                        num_output=len(out_names), out_names=out_names)
+
+            # WARNING: the results could well be random values clipped to 0 or 255 because of badly tuned output
+            # range for the specific operator. While adding test ensure that we aren't getting only clipped values
+            # in output tensors that still pass the assertion. For reference see _test_elemwise_qnn_out_range()
             if quantized:
                 for i in range(len(tflite_output)):
                     # allow absolute tolerance of 1 in the quantized results
@@ -562,7 +567,7 @@ def test_forward_concatenation():
 # Element-wise
 # ---
 
-def _test_elemwise(math_op, data, fused_activation_function=None, quantized=False):
+def _test_elemwise(math_op, data, fused_activation_function=None, quantized=False, qnn_op=None):
     """ One iteration of elemwise """
 
     assert len(data) == 2
@@ -578,7 +583,9 @@ def _test_elemwise(math_op, data, fused_activation_function=None, quantized=Fals
                         tf.quantization.fake_quant_with_min_max_args(in_data[1], min=-100, max=100, name="inq_1")]
             out = math_op(inq_data[0], inq_data[1])
             out = with_fused_activation_function(out, fused_activation_function)
-            out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
+            # set the quantized output range with respect to the operation
+            out_min, out_max = _test_elemwise_qnn_out_range(qnn_op)
+            out = tf.quantization.fake_quant_with_min_max_args(out, min=out_min, max=out_max, name="out")
             compare_tflite_with_tvm(data, ['inq_0:0', 'inq_1:0'], inq_data, [out], quantized=True)
         else:
             out = math_op(in_data[0], in_data[1])
@@ -595,7 +602,8 @@ def _test_elemwise(math_op, data, fused_activation_function=None, quantized=Fals
             # the 2nd tensor is treated as constant and directly added as part of the operation
             out = math_op(inq_data, ops.convert_to_tensor(inq_const, dtype='float32', name='inq_const'))
             out = with_fused_activation_function(out, fused_activation_function)
-            out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
+            out_min, out_max = _test_elemwise_qnn_out_range(qnn_op)
+            out = tf.quantization.fake_quant_with_min_max_args(out, min=out_min, max=out_max, name="out")
             compare_tflite_with_tvm(data[0], ['inq_0:0'], inq_data, [out], quantized=True)
         else:
             out = math_op(in_data[0], ops.convert_to_tensor(data[1], dtype=data[1].dtype))
@@ -606,9 +614,9 @@ def _test_elemwise(math_op, data, fused_activation_function=None, quantized=Fals
 # Add
 # ---
 
-def _test_add(data, fused_activation_function=None, quantized=False):
+def _test_add(data, fused_activation_function=None, quantized=False, qnn_op=None):
     """ One iteration of add """
-    return _test_elemwise(math_ops.add, data, fused_activation_function, quantized)
+    return _test_elemwise(math_ops.add, data, fused_activation_function, quantized, qnn_op)
 
 #######################################################################
 # Subtract
@@ -620,9 +628,10 @@ def _test_sub(data, fused_activation_function=None):
 #######################################################################
 # Mul
 # ---
-def _test_mul(data, fused_activation_function=None):
+
+def _test_mul(data, fused_activation_function=None, quantized=False, qnn_op=None):
     """ One iteration of mul """
-    return _test_elemwise(math_ops.multiply, data, fused_activation_function)
+    return _test_elemwise(math_ops.multiply, data, fused_activation_function, quantized, qnn_op)
 
 #######################################################################
 # Divide
@@ -671,7 +680,17 @@ def _test_forward_elemwise(testop):
 
 def _test_forward_elemwise_quantized(testop):
     testop([np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8),
-            np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8)], quantized=True)
+            np.array(np.random.uniform(0, 255, (3, 6)), dtype=np.uint8)], quantized=True, qnn_op=testop)
+
+def _test_elemwise_qnn_out_range(qnn_op):
+    # set the fake_quant output range if input tensors are in [-100, 100] float32
+    qnn_out_range = {
+        _test_add: (-200, 200),
+        _test_sub: (-200, 200),
+        _test_mul: (-1e+4, 1e+4),
+    }
+
+    return qnn_out_range[qnn_op]
 
 def test_all_elemwise():
     _test_forward_elemwise(_test_add)
@@ -682,6 +701,7 @@ def test_all_elemwise():
     _test_forward_elemwise(partial(_test_sub, fused_activation_function="RELU"))
     _test_forward_elemwise(partial(_test_sub, fused_activation_function="RELU6"))
     _test_forward_elemwise(_test_mul)
+    _test_forward_elemwise_quantized(_test_mul)
     _test_forward_elemwise(partial(_test_mul, fused_activation_function="RELU"))
     _test_forward_elemwise(partial(_test_mul, fused_activation_function="RELU6"))
     _test_forward_elemwise(_test_div)