[QNN] Use Int16 upcast in Fallback Conv2D. Fix test names. (#4329)

src/relay/qnn/op/convolution.cc

@@ -106,8 +106,6 @@ WorkloadType GetWorkload(const Array<tvm::relay::Type>& arg_types, const QnnConv
  * \brief Fallback to simpler lowering for dilation or depthwise conv.
  * \param data The input expr.
  * \param weight The weight expr.
- * \param zp_data The data zero point expr.
- * \param zp_kernel The kernel zero point expr.
  * \param param The qnn conv2d attributes.
  * \return The fallback lowered sequence of Relay expr.
  * \note In case of dilation, normal lowering would require a dilated pool.
@@ -115,16 +113,19 @@ WorkloadType GetWorkload(const Array<tvm::relay::Type>& arg_types, const QnnConv
  *       Relay operations. This will potentially lead to performance degradation
  *       as the convolution is called on int32 tensors instead of int8 tensors.
  */
-Expr Conv2DFallBack(const Expr& data, const Expr& weight, const Expr& zp_data,
-                    const Expr& zp_kernel, const QnnConv2DAttrs* param) {
-  auto shifted_data = data;
+Expr Conv2DFallBack(const Expr& data, const Expr& weight, const QnnConv2DAttrs* param) {
+  // Upcast the zero point to Int16.
+  auto zp_data = MakeConstantScalar(Int(16), param->input_zero_point);
+  auto zp_kernel = MakeConstantScalar(Int(16), param->kernel_zero_point);
+
+  auto shifted_data = Cast(data, Int(16));
   if (param->input_zero_point != 0) {
-    shifted_data = Subtract(Cast(data, Int(32)), zp_data);
+    shifted_data = Subtract(Cast(data, Int(16)), zp_data);
   }
 
-  auto shifted_kernel = weight;
+  auto shifted_kernel = Cast(weight, Int(16));
   if (param->kernel_zero_point != 0) {
-    shifted_kernel = Subtract(Cast(weight, Int(32)), zp_kernel);
+    shifted_kernel = Subtract(Cast(weight, Int(16)), zp_kernel);
   }
 
   return Conv2D(shifted_data, shifted_kernel, param->strides, param->padding, param->dilation,
@@ -186,7 +187,6 @@ Expr Conv2DFirstTerm(const Expr& padded_data, const Expr& weight, const QnnConv2
 /*
  * \brief Calculates the second term in the qnn.conv2d lowering sequence.
  * \param padded_data The padded data expr.
- * \param zp_kernel The kernel zero point expr.
  * \param param The qnn conv2d attributes.
  * \param kernel_h The height of kernel.
  * \param kernel_w The width of kernel.
@@ -200,8 +200,11 @@ Expr Conv2DFirstTerm(const Expr& padded_data, const Expr& weight, const QnnConv2
  *       followed by a reduce on the C axis. Using avg_pool2d also gives an
  *       opportunity to reuse alter_op_layout infrastructure.
  */
-Expr Conv2DSecondTerm(const Expr& padded_data, const Expr& zp_kernel, const QnnConv2DAttrs* param,
-                      int kernel_h, int kernel_w, int out_channels) {
+Expr Conv2DSecondTerm(const Expr& padded_data, const QnnConv2DAttrs* param, int kernel_h,
+                      int kernel_w, int out_channels) {
+  // Constant Expr for the kernel zero point.
+  auto zp_kernel = MakeConstantScalar(Int(32), param->kernel_zero_point);
+
   auto casted_t2 = Cast(padded_data, Int(32));
 
   // We can reduce the H and W axis by using avg_pool2d. However, avg_pool2d averages the sum.
@@ -241,7 +244,6 @@ Expr Conv2DSecondTerm(const Expr& padded_data, const Expr& zp_kernel, const QnnC
 /*
  * \brief Calculates the third term in the qnn.conv2d lowering sequence.
  * \param weight The weight expr.
- * \param zp_data The data zero point expr.
  * \param param The qnn conv2d attributes.
  * \param batch_size The batch size.
  * \param out_channels The number of output channels.
@@ -254,8 +256,11 @@ Expr Conv2DSecondTerm(const Expr& padded_data, const Expr& zp_kernel, const QnnC
  *       a 1D tensor. The tensor is then reshaped to conform to NHWC/NCHW
  *       format.
  */
-Expr Conv2DThirdTerm(const Expr& weight, const Expr& zp_data, const QnnConv2DAttrs* param,
-                     int batch_size, int out_channels) {
+Expr Conv2DThirdTerm(const Expr& weight, const QnnConv2DAttrs* param, int batch_size,
+                     int out_channels) {
+  // Constant expr for input zero point.
+  auto zp_data = MakeConstantScalar(Int(32), param->input_zero_point);
+
   // Find which dimensions are C, R, S.
   Array<Integer> axes_t3;
   if (param->kernel_layout == "OIHW") {
@@ -415,21 +420,19 @@ Expr QnnConv2DCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
   int batch_size, in_channels, out_channels, kernel_h, kernel_w;
   std::tie(batch_size, in_channels, out_channels, kernel_h, kernel_w) =
       GetWorkload(arg_types, param);
-  auto zp_data = MakeConstantScalar(Int(32), param->input_zero_point);
-  auto zp_kernel = MakeConstantScalar(Int(32), param->kernel_zero_point);
 
   // Fallback to int32 conv if there is dilation or depthwise conv2d
   CHECK_EQ(param->dilation.size(), 2) << "qnn.conv2d only supports 2D dilation";
   auto dilation_h = get_const_int(param->dilation[0]);
   auto dilation_w = get_const_int(param->dilation[1]);
   if (dilation_h != 1 || dilation_w != 1 || param->groups != 1) {
-    return Conv2DFallBack(data, weight, zp_data, zp_kernel, param);
+    return Conv2DFallBack(data, weight, param);
   }
 
   auto padded_data = Conv2DPadInput(data, param);
   auto term1 = Conv2DFirstTerm(padded_data, weight, param);
-  auto term2 = Conv2DSecondTerm(padded_data, zp_kernel, param, kernel_h, kernel_w, out_channels);
-  auto term3 = Conv2DThirdTerm(weight, zp_data, param, batch_size, out_channels);
+  auto term2 = Conv2DSecondTerm(padded_data, param, kernel_h, kernel_w, out_channels);
+  auto term3 = Conv2DThirdTerm(weight, param, batch_size, out_channels);
   auto term4 = Conv2DFourthTerm(param, batch_size, in_channels, kernel_h, kernel_w);
   return Conv2DCombineTerms(term1, term2, term3, term4, param);
 }

tests/python/relay/test_op_qnn_conv2d.py

@@ -160,7 +160,7 @@ def verify(ref_func, qnn_func, data_shape, data_dtype, kernel_shape,
     qnn_output = get_output(qnn_func, golden_inputs)
     np.testing.assert_equal(qnn_output, golden_output)
 
-def no_zero_point_test():
+def test_no_zero_point():
     # uint8 input
     data_shape = (2, 1, 2, 4)
     data_dtype = 'uint8'
@@ -203,7 +203,7 @@ def no_zero_point_test():
     verify(ref_func, qnn_func, data_shape, data_dtype,
             kernel_shape, kernel_dtype)
 
-def kernel_zero_point_test():
+def test_kernel_zero_point():
     # uint8 input
     data_shape = (2, 4, 2, 4)
     data_dtype = 'uint8'
@@ -247,7 +247,7 @@ def kernel_zero_point_test():
             kernel_shape, kernel_dtype)
 
 
-def input_zero_point_test():
+def test_input_zero_point():
     # uint8 input
     data_shape = (2, 4, 2, 4)
     data_dtype = 'uint8'
@@ -290,7 +290,7 @@ def input_zero_point_test():
     verify(ref_func, qnn_func, data_shape, data_dtype,
             kernel_shape, kernel_dtype)
 
-def both_zero_point_test():
+def test_both_zero_point():
     # uint8 input
     data_shape = (2, 4, 2, 4)
     data_dtype = 'uint8'
@@ -333,7 +333,7 @@ def both_zero_point_test():
     verify(ref_func, qnn_func, data_shape, data_dtype,
             kernel_shape, kernel_dtype)
 
-def layout_test():
+def test_layout():
     # uint8 input
     data_shape = (2, 2, 4, 4) # NHWC
     data_dtype = 'uint8'
@@ -378,7 +378,7 @@ def layout_test():
 
 
 
-def padding_test():
+def test_padding():
     # uint8 input
     data_shape = (1, 4, 2, 2)
     data_dtype = 'uint8'
@@ -421,7 +421,7 @@ def padding_test():
     verify(ref_func, qnn_func, data_shape, data_dtype,
             kernel_shape, kernel_dtype)
 
-def dilation_test():
+def test_dilation():
     # uint8 input
     data_shape = (2, 4, 4, 4)
     data_dtype = 'uint8'
@@ -444,7 +444,7 @@ def dilation_test():
             kernel_shape, kernel_dtype)
 
 
-def const_folding_test():
+def test_const_folding():
     data_shape = (2, 4, 2, 4)
     data_dtype = 'uint8'
     kernel_shape = (3, 4, 2, 2)
@@ -470,7 +470,7 @@ def const_folding_test():
     folded_func = folded_mod["main"]
     assert "reshape" not in folded_func.astext()
 
-def kernel_size_1x1_test():
+def test_kernel_size_1x1():
     # uint8 input
     data_shape = (2, 4, 2, 4)
     data_dtype = 'uint8'
@@ -493,7 +493,7 @@ def kernel_size_1x1_test():
     verify(ref_func, qnn_func, data_shape, data_dtype,
             kernel_shape, kernel_dtype)
 
-def tflite_large_irregular_test():
+def test_tflite_large_irregular():
     # uint8 input
     data_shape = (1, 1024, 1, 1)
     data_dtype = 'uint8'
@@ -526,7 +526,7 @@ def tflite_large_irregular_test():
     golden_output = np.full((1, 1001, 1, 1), 0).astype('uint8')
     np.testing.assert_equal(qnn_output, golden_output)
 
-def tflite_output_multiplier_greater_than_one():
+def test_tflite_output_multiplier_greater_than_one():
     # uint8 input
     data_shape = (2, 1, 2, 4)
     data_dtype = 'uint8'
@@ -570,7 +570,7 @@ def tflite_output_multiplier_greater_than_one():
                               0, 0)).reshape(2, 3, 1, 2)
     np.testing.assert_equal(qnn_output, golden_output)
 
-def tflite_anistropic_strides():
+def test_tflite_anistropic_strides():
     # uint8 input
     data_shape = (1, 1, 3, 6)
     data_dtype = 'uint8'
@@ -607,7 +607,7 @@ def tflite_anistropic_strides():
     golden_output = np.array((124, -92, 164, -132)).reshape(1, 1, 2, 2)
     np.testing.assert_equal(qnn_output, golden_output)
 
-def broadcast_layout_test():
+def test_broadcast_layout():
     # Test broadcast support for NHWC layout.
     data_shape = (1, 229, 229, 3) # NHWC
     data_dtype = 'uint8'
@@ -641,16 +641,16 @@ def broadcast_layout_test():
         graph, lib, params = relay.build(mod, "llvm -mcpu=skylake-avx512")
 
 if __name__ == "__main__":
-    no_zero_point_test()
-    input_zero_point_test()
-    kernel_zero_point_test()
-    both_zero_point_test()
-    layout_test()
-    padding_test()
-    dilation_test()
-    const_folding_test()
-    kernel_size_1x1_test()
-    tflite_large_irregular_test()
-    tflite_output_multiplier_greater_than_one()
-    tflite_anistropic_strides()
-    broadcast_layout_test()
+    test_no_zero_point()
+    test_input_zero_point()
+    test_kernel_zero_point()
+    test_both_zero_point()
+    test_layout()
+    test_padding()
+    test_dilation()
+    test_const_folding()
+    test_kernel_size_1x1()
+    test_tflite_large_irregular()
+    test_broadcast_layout()
+    test_tflite_output_multiplier_greater_than_one()
+    test_tflite_anistropic_strides()

tests/python/relay/test_op_qnn_requantize.py

@@ -22,230 +22,227 @@ from tvm.contrib import graph_runtime
 
 roundings = ["UPWARD", "TONEAREST"]
 
-def test_requantize():
-    def verify(mod, goldens):
-        with relay.build_config(opt_level=3):
-            graph, lib, params = relay.build(mod, "llvm", params=None)
-            golden_data, golden_output = goldens
-            rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
-            rt_mod.set_input("quantized_data",golden_data)
-            rt_mod.set_input(**params)
-            rt_mod.run()
-            res = rt_mod.get_output(0).asnumpy()
-            np.testing.assert_equal(res, golden_output)
-
-    def get_mod(data_shape, data_dtype, out_dtype, input_scale, output_scale,
-            input_zero_point=0, output_zero_point=0, rounding="TONEAREST"):
-        quantized_data = relay.var("quantized_data", shape=data_shape,
-                dtype=data_dtype)
-        mod = relay.qnn.op.requantize(
-                quantized_data,
-                input_scale=input_scale,
-                input_zero_point=input_zero_point,
-                output_scale=output_scale,
-                output_zero_point=output_zero_point,
-                rounding=rounding,
-                out_dtype=out_dtype)
-
-        mod = relay.Function(relay.analysis.free_vars(mod), mod)
-        mod = relay.Module.from_expr(mod)
-        return mod
-
-    def same_scale_test():
-        # Have same scales, everything within range
-        golden_data = np.arange(-100, 100, 1).astype('int32')
-        golden_output = golden_data
-
-        for rounding in roundings:
-            mod = get_mod(data_shape=(200, ),
-                          data_dtype='int32',
-                          out_dtype="int8",
-                          input_scale=0.5,
-                          output_scale=0.5,
-                          rounding=rounding)
-            assert 'right_shift' not in mod.astext()
-            verify(mod, (golden_data, golden_output))
-
-    def downscale_test():
-        for rounding in roundings:
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='int32',
-                          out_dtype='int8',
-                          input_scale=1,
-                          output_scale=16,
-                          rounding=rounding)
-
-            # Try positive values
-            # 8 corresponds to 0.5, resulting in 1
-            golden_data = np.arange(0, 32, 1).astype('int32')
-            golden_output = np.repeat([0, 1, 2], [8, 16, 8])
-            verify(mod, (golden_data, golden_output))
-
-            # Try negative values
-            # -8 corresponds to -0.5. For UPWARD, this is 0
-            golden_data = np.arange(0, -32, -1).astype('int32')
-            if rounding == "UPWARD":
-                golden_output = np.repeat([0, -1, -2], [9, 16, 7])
-            else:
-                golden_output = np.repeat([0, -1, -2], [8, 16, 8])
-            verify(mod, (golden_data, golden_output))
-
-            # Try a different scale
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='int32',
-                          out_dtype="int8",
-                          input_scale=1,
-                          output_scale=4,
-                          rounding=rounding)
-
-            # Try positive values
-            # 2I corresponds to 0.5, resulting in 1
-            golden_data = np.arange(0, 32, 1).astype('int32')
-            golden_output = np.repeat([0, 1, 2, 3, 4, 5, 6, 7, 8],
+def verify(mod, goldens):
+    with relay.build_config(opt_level=3):
+        graph, lib, params = relay.build(mod, "llvm", params=None)
+        golden_data, golden_output = goldens
+        rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
+        rt_mod.set_input("quantized_data",golden_data)
+        rt_mod.set_input(**params)
+        rt_mod.run()
+        res = rt_mod.get_output(0).asnumpy()
+        np.testing.assert_equal(res, golden_output)
+
+def get_mod(data_shape, data_dtype, out_dtype, input_scale, output_scale,
+        input_zero_point=0, output_zero_point=0, rounding="TONEAREST"):
+    quantized_data = relay.var("quantized_data", shape=data_shape,
+            dtype=data_dtype)
+    mod = relay.qnn.op.requantize(
+            quantized_data,
+            input_scale=input_scale,
+            input_zero_point=input_zero_point,
+            output_scale=output_scale,
+            output_zero_point=output_zero_point,
+            rounding=rounding,
+            out_dtype=out_dtype)
+
+    mod = relay.Function(relay.analysis.free_vars(mod), mod)
+    mod = relay.Module.from_expr(mod)
+    return mod
+
+def test_same_scale():
+    # Have same scales, everything within range
+    golden_data = np.arange(-100, 100, 1).astype('int32')
+    golden_output = golden_data
+
+    for rounding in roundings:
+        mod = get_mod(data_shape=(200, ),
+                      data_dtype='int32',
+                      out_dtype="int8",
+                      input_scale=0.5,
+                      output_scale=0.5,
+                      rounding=rounding)
+        assert 'right_shift' not in mod.astext()
+        verify(mod, (golden_data, golden_output))
+
+def test_downscale():
+    for rounding in roundings:
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='int32',
+                      out_dtype='int8',
+                      input_scale=1,
+                      output_scale=16,
+                      rounding=rounding)
+
+        # Try positive values
+        # 8 corresponds to 0.5, resulting in 1
+        golden_data = np.arange(0, 32, 1).astype('int32')
+        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        # -8 corresponds to -0.5. For UPWARD, this is 0
+        golden_data = np.arange(0, -32, -1).astype('int32')
+        if rounding == "UPWARD":
+            golden_output = np.repeat([0, -1, -2], [9, 16, 7])
+        else:
+            golden_output = np.repeat([0, -1, -2], [8, 16, 8])
+        verify(mod, (golden_data, golden_output))
+
+        # Try a different scale
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='int32',
+                      out_dtype="int8",
+                      input_scale=1,
+                      output_scale=4,
+                      rounding=rounding)
+
+        # Try positive values
+        # 2I corresponds to 0.5, resulting in 1
+        golden_data = np.arange(0, 32, 1).astype('int32')
+        golden_output = np.repeat([0, 1, 2, 3, 4, 5, 6, 7, 8],
+                                  [2, 4, 4, 4, 4, 4, 4, 4, 2])
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        # -8 corresponds to -0.5. For UPWARD, this is 0
+        golden_data = np.arange(0, -32, -1).astype('int32')
+        if rounding == "UPWARD":
+            golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8],
+                                      [3, 4, 4, 4, 4, 4, 4, 4, 1])
+        else:
+            golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8],
                                       [2, 4, 4, 4, 4, 4, 4, 4, 2])
-            verify(mod, (golden_data, golden_output))
-
-            # Try negative values
-            # -8 corresponds to -0.5. For UPWARD, this is 0
-            golden_data = np.arange(0, -32, -1).astype('int32')
-            if rounding == "UPWARD":
-                golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8],
-                                          [3, 4, 4, 4, 4, 4, 4, 4, 1])
-            else:
-                golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8],
-                                          [2, 4, 4, 4, 4, 4, 4, 4, 2])
-            verify(mod, (golden_data, golden_output))
-
-            # Try uint8 out_dtype
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='int32',
-                          out_dtype='uint8',
-                          input_scale=1,
-                          output_scale=16,
-                          rounding=rounding)
-
-            # Try positive values
-            # 8 corresponds to 0.5, resulting in 1
-            golden_data = np.arange(0, 32, 1).astype('int32')
-            golden_output = np.repeat([0, 1, 2], [8, 16, 8])
-            verify(mod, (golden_data, golden_output))
-
-            # Try uint8 in_dtyope and uint8 out_dtype
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='uint8',
-                          out_dtype='uint8',
-                          input_scale=1,
-                          output_scale=16,
-                          rounding=rounding)
-
-            # Try positive values
-            # 8 corresponds to 0.5, resulting in 1
-            golden_data = np.arange(0, 32, 1).astype('int32')
-            golden_output = np.repeat([0, 1, 2], [8, 16, 8])
-            verify(mod, (golden_data, golden_output))
-
-    def upscale_test():
-        for rounding in roundings:
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='int32',
-                          out_dtype="int8",
-                          input_scale=2,
-                          output_scale=1,
-                          rounding=rounding)
-
-            # Try positive values
-            # 8 corresponds to 0.5, resulting in 1
-            golden_data = np.arange(0, 32, 1).astype('int32')
-            golden_output = np.multiply(2, golden_data)
-            verify(mod, (golden_data, golden_output))
-
-            # Try negative values
-            # -8 corresponds to -0.5. For UPWARD, this is 0
-            golden_data = np.arange(0, -32, -1).astype('int32')
-            golden_output = np.multiply(2, golden_data)
-            verify(mod, (golden_data, golden_output))
-
-    def saturation_test():
-        for rounding in roundings:
-            mod = get_mod(data_shape=(16, ),
-                          data_dtype='int32',
-                          out_dtype="int8",
-                          input_scale=0.5,
-                          output_scale=0.5,
-                          rounding=rounding)
-            golden_data = np.arange(0, 16, 1).astype('int32')
-            golden_data = np.add(120, golden_data)
-            output = np.array([120, 121, 122, 123, 124, 125, 126, 127,
-                               127, 127, 127, 127, 127, 127, 127, 127])
-            golden_output = output
-            verify(mod, (golden_data, golden_output))
-
-            # Try negative numbers
-            golden_data = np.arange(0, -16, -1).astype('int32')
-            golden_data = np.add(-120, golden_data)
-            output = np.array([-120, -121, -122, -123, -124, -125, -126, -127,
-                               -128, -128, -128, -128, -128, -128, -128, -128])
-            golden_output = output
-            verify(mod, (golden_data, golden_output))
-
-    def zero_point_test():
-        # Output zero point
-        for rounding in roundings:
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='int32',
-                          out_dtype='int8',
-                          input_scale=1,
-                          output_scale=16,
-                          output_zero_point=1,
-                          rounding=rounding)
-
-            # Try positive values
-            # 8 corresponds to 0.5, resulting in 1
-            golden_data = np.arange(0, 32, 1).astype('int32')
-            golden_output = np.repeat([0, 1, 2], [8, 16, 8])
-            golden_output = np.add(1, golden_output)
-            verify(mod, (golden_data, golden_output))
-
-            # Try negative values
-            # -8 corresponds to -0.5. For UPWARD, this is 0
-            golden_data = np.arange(-32, -64, -1).astype('int32')
-            if rounding == "UPWARD":
-                golden_output = np.repeat([-2, -3, -4], [9, 16, 7])
-            else:
-                golden_output = np.repeat([-2, -3, -4], [8, 16, 8])
-            golden_output = np.add(1, golden_output)
-            verify(mod, (golden_data, golden_output))
-
-        # Input zero point
-        for rounding in roundings:
-            mod = get_mod(data_shape=(32, ),
-                          data_dtype='int32',
-                          out_dtype='int8',
-                          input_scale=1,
-                          output_scale=16,
-                          input_zero_point=16,
-                          rounding=rounding)
-
-            # Try positive values
-            golden_data = np.arange(32, 64, 1).astype('int32')
-            golden_output = np.repeat([2, 3, 4], [8, 16, 8])
-            golden_output = np.subtract(golden_output, 1)
-            verify(mod, (golden_data, golden_output))
-
-            # Try negative values
-            golden_data = np.arange(-32, -64, -1).astype('int32')
-            if rounding == "UPWARD":
-                golden_output = np.repeat([-2, -3, -4], [9, 16, 7])
-            else:
-                golden_output = np.repeat([-2, -3, -4], [8, 16, 8])
-            golden_output = np.subtract(golden_output, 1)
-            verify(mod, (golden_data, golden_output))
-
-    same_scale_test()
-    downscale_test()
-    upscale_test()
-    saturation_test()
-    zero_point_test()
+        verify(mod, (golden_data, golden_output))
+
+        # Try uint8 out_dtype
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='int32',
+                      out_dtype='uint8',
+                      input_scale=1,
+                      output_scale=16,
+                      rounding=rounding)
+
+        # Try positive values
+        # 8 corresponds to 0.5, resulting in 1
+        golden_data = np.arange(0, 32, 1).astype('int32')
+        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
+        verify(mod, (golden_data, golden_output))
+
+        # Try uint8 in_dtyope and uint8 out_dtype
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='uint8',
+                      out_dtype='uint8',
+                      input_scale=1,
+                      output_scale=16,
+                      rounding=rounding)
+
+        # Try positive values
+        # 8 corresponds to 0.5, resulting in 1
+        golden_data = np.arange(0, 32, 1).astype('int32')
+        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
+        verify(mod, (golden_data, golden_output))
+
+def test_upscale():
+    for rounding in roundings:
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='int32',
+                      out_dtype="int8",
+                      input_scale=2,
+                      output_scale=1,
+                      rounding=rounding)
+
+        # Try positive values
+        # 8 corresponds to 0.5, resulting in 1
+        golden_data = np.arange(0, 32, 1).astype('int32')
+        golden_output = np.multiply(2, golden_data)
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        # -8 corresponds to -0.5. For UPWARD, this is 0
+        golden_data = np.arange(0, -32, -1).astype('int32')
+        golden_output = np.multiply(2, golden_data)
+        verify(mod, (golden_data, golden_output))
+
+def test_saturation():
+    for rounding in roundings:
+        mod = get_mod(data_shape=(16, ),
+                      data_dtype='int32',
+                      out_dtype="int8",
+                      input_scale=0.5,
+                      output_scale=0.5,
+                      rounding=rounding)
+        golden_data = np.arange(0, 16, 1).astype('int32')
+        golden_data = np.add(120, golden_data)
+        output = np.array([120, 121, 122, 123, 124, 125, 126, 127,
+                           127, 127, 127, 127, 127, 127, 127, 127])
+        golden_output = output
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative numbers
+        golden_data = np.arange(0, -16, -1).astype('int32')
+        golden_data = np.add(-120, golden_data)
+        output = np.array([-120, -121, -122, -123, -124, -125, -126, -127,
+                           -128, -128, -128, -128, -128, -128, -128, -128])
+        golden_output = output
+        verify(mod, (golden_data, golden_output))
+
+def test_zero_point():
+    # Output zero point
+    for rounding in roundings:
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='int32',
+                      out_dtype='int8',
+                      input_scale=1,
+                      output_scale=16,
+                      output_zero_point=1,
+                      rounding=rounding)
+
+        # Try positive values
+        # 8 corresponds to 0.5, resulting in 1
+        golden_data = np.arange(0, 32, 1).astype('int32')
+        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
+        golden_output = np.add(1, golden_output)
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        # -8 corresponds to -0.5. For UPWARD, this is 0
+        golden_data = np.arange(-32, -64, -1).astype('int32')
+        if rounding == "UPWARD":
+            golden_output = np.repeat([-2, -3, -4], [9, 16, 7])
+        else:
+            golden_output = np.repeat([-2, -3, -4], [8, 16, 8])
+        golden_output = np.add(1, golden_output)
+        verify(mod, (golden_data, golden_output))
+
+    # Input zero point
+    for rounding in roundings:
+        mod = get_mod(data_shape=(32, ),
+                      data_dtype='int32',
+                      out_dtype='int8',
+                      input_scale=1,
+                      output_scale=16,
+                      input_zero_point=16,
+                      rounding=rounding)
+
+        # Try positive values
+        golden_data = np.arange(32, 64, 1).astype('int32')
+        golden_output = np.repeat([2, 3, 4], [8, 16, 8])
+        golden_output = np.subtract(golden_output, 1)
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        golden_data = np.arange(-32, -64, -1).astype('int32')
+        if rounding == "UPWARD":
+            golden_output = np.repeat([-2, -3, -4], [9, 16, 7])
+        else:
+            golden_output = np.repeat([-2, -3, -4], [8, 16, 8])
+        golden_output = np.subtract(golden_output, 1)
+        verify(mod, (golden_data, golden_output))
 
 if __name__ == "__main__":
-    test_requantize()
+    test_same_scale()
+    test_downscale()
+    test_upscale()
+    test_saturation()
+    test_zero_point()