[QNN] Optimize lowering for requantize and FixedPointMultiply. (apach…

…e#4798)

* [QNN] Optimize lowering for requantize and FixedPointMultiply.

* Add check for requantize scale gt 1.

* Added test case.

src/relay/qnn/op/requantize.cc

@@ -67,6 +67,9 @@ Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
     tensor = Subtract(tensor, Cast(input_zero_point, hp_dtype));
   }
 
+  // Check if multiplier is greater than 1.
+  bool is_multiplier_gt_one = false;
+
   // 2) If the input and output scales are same, we can skip the fixed point multiplication. Check
   // if the input scale is per-tensor or per-channel. If it is per-tensor, there is single scale for
   // the whole tensor. For per-channel (aka per-axis), there is a vector of scales for the input
@@ -78,6 +81,9 @@ Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
     float input_scale_float = GetScalarFromConstant<float>(input_scale);
     double double_multiplier =
         static_cast<double>(input_scale_float) / static_cast<double>(output_scale_float);
+    if (double_multiplier > 1) {
+      is_multiplier_gt_one = true;
+    }
     // Skip if input and output scales are same.
     if (!IsEqualScalar(input_scale, output_scale)) {
       scaled_int64_t =
@@ -88,8 +94,12 @@ Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
     std::vector<double> double_multipliers;
     auto input_axis_scales = GetFloatVectorFromConstant(input_scale);
     for (auto input_axis_scale : input_axis_scales) {
-      double_multipliers.push_back(static_cast<double>(input_axis_scale) /
-                                   static_cast<double>(output_scale_float));
+      double multiplier =
+          static_cast<double>(input_axis_scale) / static_cast<double>(output_scale_float);
+      double_multipliers.push_back(multiplier);
+      if (multiplier > 1) {
+        is_multiplier_gt_one = true;
+      }
     }
     int axis = param->axis;
     axis = (axis == -1) ? input_shape.size() - 1 : axis;
@@ -103,7 +113,11 @@ Expr RequantizeLower(const Expr& input_tensor, const Expr& input_scale,
     shifted_int64_t = Add(Cast(output_zero_point, hp_dtype), scaled_int64_t);
   }
 
-  // 4) Clip to the out_dtype min/max.
+  // 4) Clip to the out_dtype min/max. Skip clipping if out_dtype is Int32. The fixed point
+  // multiplication keeps the value in int32 range if the requantize scale is less than 1.
+  if (out_dtype == DataType::Int(32) && !is_multiplier_gt_one) {
+    return Cast(shifted_int64_t, out_dtype);
+  }
   auto q_min = GetQmin(out_dtype);
   auto q_max = GetQmax(out_dtype);
   auto clipped_t = Clip(shifted_int64_t, q_min, q_max);

src/relay/qnn/util.cc

@@ -149,6 +149,7 @@ Expr FixedPointMultiplyPerChannel(Expr tensor, std::vector<double> multipliers,
   // 1) Calculating the integer multiplier and integer shift. These are calculated per axis/per
   // channel.
   std::vector<int32_t> fixed_pt_multipliers, lshifts, rshifts;
+  bool is_lshift_required = false;
   for (auto multiplier : multipliers) {
     int32_t fixed_pt_multiplier, shift;
     std::tie(fixed_pt_multiplier, shift) = GetFixedPointMultiplierShift(multiplier);
@@ -157,12 +158,15 @@ Expr FixedPointMultiplyPerChannel(Expr tensor, std::vector<double> multipliers,
     fixed_pt_multipliers.push_back(fixed_pt_multiplier);
     lshifts.push_back(lshift);
     rshifts.push_back(rshift);
+    is_lshift_required = is_lshift_required | (lshift != 0);
   }
 
   // 2) Multiply the integer multiplier. Convert lefts shifts into expr and multiply.
-  auto lshift_expr = MakeConstantTensor(hp_dtype, {n_channels}, lshifts);
-  auto exp_lshift_expr = ExpandBiasToMatchAxis(lshift_expr, n_dim, {channel_axis});
-  tensor = LeftShift(tensor, exp_lshift_expr);
+  if (is_lshift_required) {
+    auto lshift_expr = MakeConstantTensor(hp_dtype, {n_channels}, lshifts);
+    auto exp_lshift_expr = ExpandBiasToMatchAxis(lshift_expr, n_dim, {channel_axis});
+    tensor = LeftShift(tensor, exp_lshift_expr);
+  }
 
   // 3) Perform the multiplication in higher precision.
   // The scalar is a fixed point value of int32 where the decimal point is

tests/python/relay/test_op_qnn_requantize.py

@@ -311,6 +311,21 @@ def test_per_channel_different_scale():
                       rounding=rounding)
         verify(mod, (golden_data, golden_output))
 
+    # Have input scale > output scale
+    golden_data = np.arange(-5, 5, 1).astype('int32').reshape((5,2))
+    golden_output = np.array([-10, -2, -6, -1, -2, 0, 2, 1, 6, 2]).reshape((5, 2))
+
+    for rounding in roundings:
+        mod = get_mod(data_shape=(5, 2),
+                      data_dtype='int32',
+                      out_dtype="int8",
+                      input_scale=[1.0, 0.25],
+                      output_scale=0.5,
+                      axis=1,
+                      rounding=rounding)
+        verify(mod, (golden_data, golden_output))
+
+
 if __name__ == "__main__":
     test_same_scale()
     test_downscale()