Adding support for dequantizing from int32 to float32. (#4130)

src/relay/qnn/op/dequantize.cc

@@ -43,8 +43,9 @@ bool DequantizeRel(const Array<Type>& types,
   CHECK_EQ(types.size(), 2);
   const auto* data = types[0].as<TensorTypeNode>();
   const auto input_dtype = data->dtype;
-  CHECK(input_dtype == Int(8) || input_dtype == UInt(8))
-    << "Input type should be one of the quantized types [unit8, int8] but was " <<  input_dtype;
+  CHECK(input_dtype == Int(8) || input_dtype == UInt(8) || input_dtype == Int(32))
+    << "Input type should be one of the quantized types [unit8, int8, int32] but was "
+    <<  input_dtype;
   const Array<tvm::Expr> oshape = data->shape;
   // assign output type, output will always be float 32.
   reporter->Assign(types[1], TensorTypeNode::make(oshape, Float(32)));

tests/python/relay/test_op_qnn_dequantize.py

@@ -44,10 +44,10 @@ def test_dequantize_op():
     def test_uint8_to_float32():
         data = np.array([0, 1, 2, 3, 4, 251, 252, 253, 254, 255]) \
             .astype('uint8') \
-            .reshape((2,5))
+            .reshape((2, 5))
         output = np.array([-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64]) \
             .astype('float32') \
-            .reshape((2,5))
+            .reshape((2, 5))
         quant_args = {"in_zero_point":127, "in_scale":0.5}
         quantize_test_driver(in_dtype='uint8', quant_args=quant_args, in_data=data,
                              verify_output_data=output)
@@ -55,16 +55,24 @@ def test_dequantize_op():
     def test_int8_to_float32():
         data = np.array([-128, -127, -126, -125, -124, 123, 124, 125, 126, 127]) \
             .astype('int8') \
-            .reshape((2,5))
+            .reshape((2, 5))
         output = np.array([-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64]) \
             .astype('float32') \
-            .reshape((2,5))
-        quant_args = {"in_zero_point":-1, "in_scale":0.5}
+            .reshape((2, 5))
+        quant_args = {"in_zero_point": -1, "in_scale": 0.5}
         quantize_test_driver(in_dtype='int8', quant_args=quant_args, in_data=data,
                              verify_output_data=output)
 
+    def test_int32_to_float32():
+        data = np.array([113, 29, -1052]).astype('int32')
+        output = np.array([0.6550452, 0.16810896, -6.098297]).astype('float32')
+        quant_args = {"in_zero_point": 0, "in_scale": 0.0057968604}
+        quantize_test_driver(in_dtype='int32', quant_args=quant_args, in_data=data,
+                             verify_output_data=output)
+
     test_uint8_to_float32()
     test_int8_to_float32()
+    test_int32_to_float32()
 
 
 if __name__ == "__main__":