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Subject [GitHub] [incubator-tvm] anijain2305 commented on a change in pull request #5153: Adding support for QNN subtract op
Date Fri, 27 Mar 2020 17:36:23 GMT
anijain2305 commented on a change in pull request #5153: Adding support for QNN subtract op
URL: https://github.com/apache/incubator-tvm/pull/5153#discussion_r399431735
 
 

 ##########
 File path: src/relay/qnn/op/subtract.cc
 ##########
 @@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/qnn/op/subtract.cc
+ * \brief QNN add operator.
+ */
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/op_attr_types.h>
+#include "op_common.h"
+
+namespace tvm {
+namespace relay {
+namespace qnn {
+
+/*
+ * \brief Canonicalizes the QNN subtract op.
+ * \param attrs The empty attribute.
+ * \param new_args The new mutated args to the call node.
+ * \param arg_types The types of input and output.
+ * \return The sequence of Relay ops for add op.
+ */
+Expr QnnSubtractCanonicalize(const Attrs &attrs,
+                             const Array<Expr> &new_args,
+                             const Array<tvm::relay::Type> &arg_types) {
+  // Get the args.
+  QnnBinaryOpArguments args(new_args);
+
+  // Get the input dtype and shape.
+  QnnBinaryOpTensorType input_type(arg_types, 0);
+
+  // TODO(shoubhik) - The lowering can be further optimized. Instead of inserting requantize
in
+  // the start, we can insert requantize at the end if both input tensors have same qnn params.
In
+  // that case, we can first subtract the tensors, add the zero point, and requantize at
the end.
+  // This can be done in future.
+
+  // Since the input qnn params can be different than output qnn params, we first requantize
the
+  // input tensors to the output qnn params. Then we call relay.subtract on the requantized
inputs.
+  // This subtraction results in extra subtraction of the output zero point. We further add
+  // the zero point. The whole process can be represented using following equations
+  //
+  //          scale_c * (Q_c - zp_c) = scale_a * (Q_a - zp_a) - scale_b * (Q_b - zp_b)
+  //
+  // After requantizing Q_a and Q_b, equation becomes,
+  //          scale_c * (Q_c - zp_c) = scale_c * (Q_a' - zp_c) - scale_c * (Q_b' - zp_c)
+  //          scale_c * (Q_c - zp_c) = scale_c * (Q_a' - Q_b')
+  //
+  // Comparing the LHS and RHS, it results in
+  //          Q_c = Q_a' - Q_b' + zp_c
+  // The subtract op is done in int32 precision.
+
+  // Requantize LHS if necessary. Computes Q_a'
+  auto requantized_lhs = RequantizeOrUpcast(args.lhs, args.lhs_scale,
+                                            args.lhs_zero_point,
+                                            args.output_scale,
+                                            args.output_zero_point,
+                                            input_type.shape);
+  // Requantize RHS if necessary. Computes Q_b'
+  auto requantized_rhs = RequantizeOrUpcast(args.rhs, args.rhs_scale,
+                                            args.rhs_zero_point,
+                                            args.output_scale,
+                                            args.output_zero_point,
+                                            input_type.shape);
+
+  // Computes Q_a' - Q_b'
+  auto output = Subtract(requantized_lhs, requantized_rhs);
+
+  // Add zero point. Computes (Q_a' - Q_b') + zp_c
+  auto zero_scalar = MakeConstantScalar(DataType::Int(32), 0);
+  if (!IsEqualScalar(args.output_zero_point, zero_scalar)) {
+    output = Add(output, args.output_zero_point);
+  }
+
+  // Go back to lower precision.
+  return ConvertDtype(output, input_type.dtype);
+}
+
+// QNN Addition operator.
+QNN_REGISTER_BINARY_OP("subtract")
+.describe("Elementwise subtract with with broadcasting for quantized tensors.")
+.set_support_level(11)
+.set_attr<FTVMLegalize>("FTVMQnnCanonicalize", QnnSubtractCanonicalize)
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout", QnnBinaryBroadcastLayout);
 
 Review comment:
   While you are here, this can be moved to op_common.h where we define MACRO `QNN_REGISTER_BINARY_OP`,
and remove it from here.

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