[GitHub] [tvm] mbrookhart commented on a change in pull request #9982: Add qnn.rsqrt op

[GitBox <gi...@apache.org>]

mbrookhart commented on a change in pull request #9982:
URL: https://github.com/apache/tvm/pull/9982#discussion_r788050306



##########
File path: src/relay/qnn/op/rsqrt.cc
##########
@@ -0,0 +1,121 @@
+/*
+ * 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/rsqrt.cc
+ * \brief QNN rsqrt operator.
+ */
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/op_attr_types.h>
+
+#include "op_common.h"
+
+namespace tvm {
+namespace relay {
+namespace qnn {
+
+bool QnnRsqrtRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                 const TypeReporter& reporter) {
+  // Expected Types: data, scale, zero_point, output_scale, output_zero_point
+  ICHECK_EQ(types.size(), 6);
+  const auto* x = types[0].as<TensorTypeNode>();
+  if (x == nullptr) return false;
+  ICHECK(x->dtype == DataType::Int(8) || x->dtype == DataType::UInt(8))
+      << "Expected quantized rsqrt type(int8, uint8) for input but was " << x->dtype;
+
+  // Check the types of scale and zero points.
+  for (size_t i = 1; i < 5; ++i) {
+    if (types[i].as<IncompleteTypeNode>()) {
+      return false;
+    }
+  }
+  ICHECK(IsScalarType(types[1], DataType::Float(32)));  // scale
+  ICHECK(IsScalarType(types[2], DataType::Int(32)));    // zero_point
+  ICHECK(IsScalarType(types[3], DataType::Float(32)));  // output_scale
+  ICHECK(IsScalarType(types[4], DataType::Int(32)));    // output_zero_point
+
+  // Assign types for scale and zero points.
+  reporter->Assign(types[1], TensorType({}, DataType::Float(32)));  // scale
+  reporter->Assign(types[2], TensorType({}, DataType::Int(32)));    // zero_point
+  reporter->Assign(types[3], TensorType({}, DataType::Float(32)));  // output_scale
+  reporter->Assign(types[4], TensorType({}, DataType::Int(32)));    // output_zero_point
+
+  // Collect the input tensor and output tensor devoid of scale and zero points to reuse Relay
+  // IdentityRel infer type function.
+  Array<Type> tensor_types = {types[0], types[5]};
+  return IdentityRel(tensor_types, 2, attrs, reporter);
+}
+
+// Positional relay function to create quantized rsqrt operator used by frontend FFI.
+Expr MakeQuantizedRsqrt(Expr x, Expr scale, Expr zero_point, Expr output_scale,
+                        Expr output_zero_point) {
+  static const Op& op = Op::Get("qnn.rsqrt");
+  return Call(op, {x, scale, zero_point, output_scale, output_zero_point}, Attrs(), {});
+}
+
+/*
+ * \brief Canonicalizes the QNN rsqrt 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 QnnRsqrtCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
+                          const Array<tvm::relay::Type>& arg_types) {

Review comment:
       Would you mind leaving a comment on the approach here and possible future methods to keep this in Integer?




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