[Torch, QNN] Remove FP32 piggy back and use QNN add/mul/concatenate (#…

…5061)

* use qnn add/mul/concatenate

* remove logging

python/tvm/relay/frontend/qnn_torch.py

@@ -16,6 +16,7 @@
 # under the License.
 # pylint: disable=invalid-name, import-outside-toplevel
 """ Functions to convert quantized torch models to QNN """
+import logging
 
 import numpy as np
 
@@ -536,21 +537,23 @@ def _impl(inputs, _):
     return _impl
 
 
-def _binop(relay_op, with_relu=False):
+def _binop(relay_op, with_relu=False, fp32_piggy_back=False):
+    def qnn_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
+                 input_scale_rhs, input_zero_point_rhs,
+                 output_scale, output_zero_point):
+        qnn_out = relay_op(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
+                           input_scale_rhs, input_zero_point_rhs,
+                           output_scale, output_zero_point)
+        if with_relu:
+            clip_min = _get_scalar(output_zero_point)
+            return _op.tensor.clip(qnn_out, clip_min, 255)
+        return qnn_out
+
     # refer to aten/src/ATen/native/quantized/cpu/{qadd, qmul}.cpp
     # they piggy backs to fp32 math by dequantize -> fp32 math -> quantize
-    def _impl(inputs, _):
-        output_scale = _expr.const(inputs[2])
-        output_zero_point = _expr.const(inputs[3])
-        assert len(inputs) == 8, "Input quant params not found in op inputs"
-        # Manually added by add_input_quant_params_to_op_inputs above
-        input_scale_lhs = _expr.const(inputs[4])
-        input_zero_point_lhs = _expr.const(inputs[5])
-        input_scale_rhs = _expr.const(inputs[6])
-        input_zero_point_rhs = _expr.const(inputs[7])
-        lhs = inputs[0]
-        rhs = inputs[1]
-
+    def torch_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
+                   input_scale_rhs, input_zero_point_rhs,
+                   output_scale, output_zero_point):
         if isinstance(lhs, _expr.Call) and lhs.op.name == 'qnn.quantize':
             lhs = lhs.args[0]
         else:
@@ -574,30 +577,68 @@ def _impl(inputs, _):
                                      output_zero_point,
                                      axis=-1,
                                      out_dtype="uint8")
+
+    def _impl(inputs, _):
+        lhs = inputs[0]
+        rhs = inputs[1]
+        output_scale = _expr.const(inputs[2])
+        output_zero_point = _expr.const(inputs[3])
+        assert len(inputs) == 8, "Input quant params not found in op inputs"
+        # Manually added by add_input_quant_params_to_op_inputs above
+        input_scale_lhs = _expr.const(inputs[4])
+        input_zero_point_lhs = _expr.const(inputs[5])
+        input_scale_rhs = _expr.const(inputs[6])
+        input_zero_point_rhs = _expr.const(inputs[7])
+
+        if fp32_piggy_back:
+            logging.info("Piggy backing to FP32 op (PyTorch way)")
+            return torch_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
+                              input_scale_rhs, input_zero_point_rhs,
+                              output_scale, output_zero_point)
+
+        return qnn_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
+                        input_scale_rhs, input_zero_point_rhs,
+                        output_scale, output_zero_point)
+
     return _impl
 
 
-def _cat():
+def _cat(fp32_piggy_back=False):
     # refer to aten/src/ATen/native/quantized/cpu/qconcat.cpp
     # for concat they also piggy backs to fp32(!)
     # dequantize -> fp32 math -> quantize
-    # we can also use QNN concat op. we observed no change in accuracy
+    def torch_impl(inputs, input_scales, input_zero_points,
+                   output_scale, output_zero_point, axis):
+        dequantized = []
+        for inp, inp_scale, inp_zp in zip(inputs, input_scales,
+                                          input_zero_points):
+            dequantized.append(relay.qnn.op.dequantize(inp, inp_scale, inp_zp))
+
+        concat = _op.tensor.concatenate(dequantized, axis=axis)
+        return relay.qnn.op.quantize(concat, output_scale, output_zero_point,
+                                     axis=axis, out_dtype="uint8")
+
     def _impl(inputs, _):
         axis = inputs[1]
         output_scale = _expr.const(inputs[2])
         output_zero_point = _expr.const(inputs[3])
         num_inputs = (len(inputs) - 4) // 2
-        dequantized = []
+
+        input_scales = []
+        input_zero_points = []
 
         for i in range(0, num_inputs):
-            inp_scale = _expr.const(inputs[4+i*2])
-            inp_zp = _expr.const(inputs[4+i*2+1])
-            dequantized.append(relay.qnn.op.dequantize(inputs[0][i],
-                                                       inp_scale, inp_zp))
+            input_scales.append(_expr.const(inputs[4+i*2]))
+            input_zero_points.append(_expr.const(inputs[4+i*2+1]))
 
-        concat = _op.tensor.concatenate(dequantized, axis=axis)
-        return relay.qnn.op.quantize(concat, output_scale, output_zero_point,
-                                     axis=1, out_dtype="uint8")
+        if fp32_piggy_back:
+            return torch_impl(inputs[0], input_scales, input_zero_points,
+                              output_scale, output_zero_point, axis)
+
+        return relay.qnn.op.concatenate(inputs[0],
+                                        input_scales, input_zero_points,
+                                        output_scale, output_zero_point,
+                                        axis)
 
     return _impl
 
@@ -676,15 +717,15 @@ def _impl(inputs, _):
 
 convert_map = {
     'aten::quantize_per_tensor': _quantize_per_tensor(),
-    'quantized::conv2d_relu': _quantized_conv2d(True),
+    'quantized::conv2d_relu': _quantized_conv2d(with_relu=True),
     'aten::dequantize': _dequantize(),
     'quantized::conv2d': _quantized_conv2d(),
-    'quantized::add_relu': _binop(relay.add, True),
-    'quantized::add': _binop(relay.add),
-    'quantized::mul_relu': _binop(relay.multiply, True),
-    'quantized::mul': _binop(relay.multiply),
+    'quantized::add_relu': _binop(relay.qnn.op.add, with_relu=True),
+    'quantized::add': _binop(relay.qnn.op.add),
+    'quantized::mul_relu': _binop(relay.qnn.op.mul, with_relu=True),
+    'quantized::mul': _binop(relay.qnn.op.mul),
     'quantized::linear': _linear(),
-    'quantized::linear_relu': _linear(True),
+    'quantized::linear_relu': _linear(with_relu=True),
     'quantized::cat': _cat(),
     'quantized::add_scalar': _add_scalar(),
     'quantized::mul_scalar': _mul_scalar(),