[QNN] Optimize requantize for power of 2 and fix dequantize for per-c…

…hannel quantized input (#6675)

* [QNN] Optimize requantize for power of 2 and bug in dequantize

* Comments

* Docs

* Comments

* Ethos

src/relay/qnn/op/dequantize.cc

@@ -96,8 +96,8 @@ Expr DequantizeLower(const Expr& input_tensor, const Expr& input_scale,
     expanded_input_zero_point = ExpandBiasToMatchAxis(input_zero_point, n_dim, {axis});
   }
 
-  auto shift = Subtract(Cast(input_tensor, DataType::Int(32)), input_zero_point);
-  auto scaled_output = Multiply(Cast(shift, DataType::Float(32)), input_scale);
+  auto shift = Subtract(Cast(input_tensor, DataType::Int(32)), expanded_input_zero_point);
+  auto scaled_output = Multiply(Cast(shift, DataType::Float(32)), expanded_input_scale);
   return scaled_output;
 }
 

src/target/intrin_rule.cc

@@ -128,37 +128,68 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.q_multiply_shift")
       PrimExpr q = call->args[2];
       PrimExpr s = call->args[3];
 
-      // Only int32 types are supported (any number of lanes is allowed)
-      ICHECK(y.dtype().code() == DLDataTypeCode::kDLInt && y.dtype().bits() == 32);
-      ICHECK(s.dtype().code() == DLDataTypeCode::kDLInt && s.dtype().bits() == 32);
-
-      DataType hp_dtype = DataType::Int(64, x.dtype().lanes());
-      DataType lp_dtype = DataType::Int(32, x.dtype().lanes());
-
-      // 1) Calculating the integer multiplier and integer shift
-      PrimExpr zero = make_const(s.dtype(), 0);
-      PrimExpr left_shift = tir::Select(s > zero, s, zero);
-      PrimExpr right_shift = tir::Select(s > zero, zero, -s);
-
-      // 2) Cast and Multiply the integer multiplier
-      PrimExpr one = make_const(hp_dtype, 1);
-      x = cast(hp_dtype, x);
-      y = cast(hp_dtype, y);
-      x = tir::Select(left_shift != zero, x << left_shift, x);
-
-      // 3) Perform the multiplication in higher precision.
-      x = x * y;
-
-      // 4) Find the rounding scalar
-      PrimExpr total_right_shift = right_shift + q;
-      PrimExpr pos_rounding_value = (one << (total_right_shift - 1));
-      x = x + pos_rounding_value;
-
-      // 5) Simply right shift the result to get the final output.
-      x = x >> total_right_shift;
-
-      // 6) The fixed point multiplication keeps the value in int32 range. Casting back to int32.
-      *rv = cast(lp_dtype, x);
+      // Lambda function to extract the int value from PrimExpr
+      auto get_int_value = [](const PrimExpr node) {
+        if (auto int_node = node.as<IntImmNode>()) {
+          return int_node->value;
+        }
+        auto broadcast_node = node.as<BroadcastNode>();
+        CHECK(broadcast_node != nullptr);
+        auto int_node = broadcast_node->value.as<IntImmNode>();
+        CHECK(int_node != nullptr);
+        return int_node->value;
+      };
+      // Power of 2 is determined by the fixed_point_multiplier == 1 << 30. In case of power of 2,
+      // fixed point multiplier will represent a float value of 0.5. In fixed point, this is
+      // represented by 1 << 30.
+      if (get_int_value(y) == (1 << 30)) {
+        PrimExpr exp = s - 1;
+        int exp_val = get_int_value(s) - 1;
+        if (exp_val > 0) {
+          // power of 2 is greater than 0, apply left shift.
+          *rv = x << exp;
+        } else {
+          // power of 2 is less than 0, round and then apply right shift.
+          DataType lp_dtype = DataType::Int(32, x.dtype().lanes());
+          PrimExpr one = make_const(lp_dtype, 1);
+          exp = -exp;
+          PrimExpr rounding_factor = one << (exp - 1);
+          PrimExpr rounded_t = x + rounding_factor;
+          *rv = rounded_t >> exp;
+        }
+      } else {
+        // Only int32 types are supported (any number of lanes is allowed)
+        ICHECK(y.dtype().code() == DLDataTypeCode::kDLInt && y.dtype().bits() == 32);
+        ICHECK(s.dtype().code() == DLDataTypeCode::kDLInt && s.dtype().bits() == 32);
+
+        DataType hp_dtype = DataType::Int(64, x.dtype().lanes());
+        DataType lp_dtype = DataType::Int(32, x.dtype().lanes());
+
+        // 1) Calculating the integer multiplier and integer shift
+        PrimExpr zero = make_const(s.dtype(), 0);
+        PrimExpr left_shift = tir::Select(s > zero, s, zero);
+        PrimExpr right_shift = tir::Select(s > zero, zero, -s);
+
+        // 2) Cast and Multiply the integer multiplier
+        PrimExpr one = make_const(hp_dtype, 1);
+        x = cast(hp_dtype, x);
+        y = cast(hp_dtype, y);
+        x = tir::Select(left_shift != zero, x << left_shift, x);
+
+        // 3) Perform the multiplication in higher precision.
+        x = x * y;
+
+        // 4) Find the rounding scalar
+        PrimExpr total_right_shift = right_shift + q;
+        PrimExpr pos_rounding_value = (one << (total_right_shift - 1));
+        x = x + pos_rounding_value;
+
+        // 5) Simply right shift the result to get the final output.
+        x = x >> total_right_shift;
+
+        // 6) The fixed point multiplication keeps the value in int32 range. Casting back to int32.
+        *rv = cast(lp_dtype, x);
+      }
     });
 
 }  // namespace intrin

tests/python/relay/test_op_qnn_dequantize.py

@@ -101,8 +101,26 @@ def test_channelwise_axis_1():
     )
 
 
+def test_channelwise_axis_0():
+    data = np.array([0, 1, 2, 3, 4, 243, 247, 249, 250, 251]).astype("uint8").reshape((2, 5))
+    output = (
+        np.array([-63.5, -63, -62.5, -62, -61.5, 30, 31, 31.5, 31.75, 32])
+        .astype("float32")
+        .reshape((2, 5))
+    )
+    quant_args = {
+        "in_zero_point": np.array([127, 123]).astype("int32"),
+        "in_scale": np.array([0.5, 0.25]).astype("float32"),
+    }
+
+    dequantize_test_driver(
+        in_dtype="uint8", quant_args=quant_args, in_data=data, verify_output_data=output, axis=0
+    )
+
+
 if __name__ == "__main__":
     test_uint8_to_float32()
     test_int8_to_float32()
     test_int32_to_float32()
     test_channelwise_axis_1()
+    test_channelwise_axis_0()

tests/python/relay/test_op_qnn_requantize.py

@@ -204,6 +204,48 @@ def test_upscale():
         verify(mod, (golden_data, golden_output))
 
 
+def test_non_power_of_two():
+    for rounding in roundings:
+        mod = get_mod(
+            data_shape=(32,),
+            data_dtype="int32",
+            out_dtype="int8",
+            input_scale=1,
+            output_scale=3,
+            rounding=rounding,
+        )
+
+        # Try positive values
+        golden_data = np.multiply(np.arange(0, 32, 1).astype("int32"), 3)
+        golden_output = np.arange(0, 32, 1)
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        golden_data = np.multiply(np.arange(0, -32, -1).astype("int32"), 3)
+        golden_output = np.arange(0, -32, -1)
+        verify(mod, (golden_data, golden_output))
+
+        # Try a different scale
+        mod = get_mod(
+            data_shape=(32,),
+            data_dtype="int32",
+            out_dtype="int8",
+            input_scale=3,
+            output_scale=1,
+            rounding=rounding,
+        )
+
+        # Try positive values
+        golden_data = np.arange(0, 32, 1).astype("int32")
+        golden_output = np.multiply(golden_data, 3)
+        verify(mod, (golden_data, golden_output))
+
+        # Try negative values
+        golden_data = np.arange(0, -32, -1).astype("int32")
+        golden_output = np.multiply(golden_data, 3)
+        verify(mod, (golden_data, golden_output))
+
+
 def test_saturation():
     for rounding in roundings:
         mod = get_mod(
@@ -397,6 +439,7 @@ def test_per_channel_different_scale():
     test_same_scale()
     test_downscale()
     test_upscale()
+    test_non_power_of_two()
     test_saturation()
     test_zero_point()
     test_per_channel_same_scale()