-
Notifications
You must be signed in to change notification settings - Fork 3.5k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
e99def2
commit 5679038
Showing
6 changed files
with
205 additions
and
45 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,134 @@ | ||
/* | ||
* 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. | ||
*/ | ||
|
||
/*! | ||
* Copyright (c) 2019 by Contributors | ||
* \file src/relay/qnn/op/concatenate.cc | ||
* \brief QNN concatenate operator. It concatenates quantized input tensors along a given axis. | ||
*/ | ||
|
||
#include <tvm/ir.h> | ||
#include <tvm/relay/analysis.h> | ||
#include <tvm/relay/op_attr_types.h> | ||
#include <tvm/relay/qnn/attrs.h> | ||
#include "../../op/tensor/transform.h" | ||
#include "../../pass/pattern_util.h" | ||
#include "../util.h" | ||
|
||
namespace tvm { | ||
namespace relay { | ||
namespace qnn { | ||
|
||
TVM_REGISTER_NODE_TYPE(QnnConcatenateAttrs); | ||
|
||
Expr MakeQnnConcatenate(Expr data, Array<tvm::Expr> input_scales, | ||
Array<tvm::Expr> input_zero_points, double output_scale, | ||
int32_t output_zero_point, int axis) { | ||
auto attrs = make_node<QnnConcatenateAttrs>(); | ||
attrs->input_scales = input_scales; | ||
attrs->input_zero_points = input_zero_points; | ||
attrs->output_scale = output_scale; | ||
attrs->output_zero_point = output_zero_point; | ||
attrs->axis = axis; | ||
static const Op& op = Op::Get("qnn.concatenate"); | ||
return CallNode::make(op, {data}, Attrs(attrs), {}); | ||
} | ||
|
||
/* | ||
* \brief Canonicalizes the QNN concatenate op. | ||
* \param ref_call The original call that will be lowered. | ||
* \param new_args The new mutated args to the call node. | ||
* \param ctx The node context. | ||
* \return The sequence of Relay ops for concatenate op. | ||
*/ | ||
Expr ConcatenateQnnCanonicalize(const Attrs& attrs, const Array<Expr>& new_args, | ||
const Array<tvm::relay::Type>& arg_types) { | ||
// Get the attrs. | ||
CHECK_EQ(new_args.size(), 1); | ||
auto& data = new_args[0]; | ||
const auto* concatenate_attrs = attrs.as<QnnConcatenateAttrs>(); | ||
CHECK(concatenate_attrs != nullptr); | ||
auto input_scales = concatenate_attrs->input_scales; | ||
auto input_zero_points = concatenate_attrs->input_zero_points; | ||
auto output_scale = concatenate_attrs->output_scale; | ||
auto output_zero_point = concatenate_attrs->output_zero_point; | ||
|
||
// Get the input dtype and shape. | ||
CHECK_GE(arg_types.size(), 1); | ||
auto tuple_type = arg_types[0].as<TupleTypeNode>(); | ||
CHECK(tuple_type != nullptr); | ||
|
||
// FIXME (anijain2305) - The lowering can be further optimized. Instead of inserting requantize in | ||
// the start, we can insert requantize at the end if and only if all the input tensors have same | ||
// qnn params. This can be done in future. | ||
|
||
// If the output qnn params do not match the input qnn params, we can call requantize on the input | ||
// expr first, followed by a concatenate on the requantized input exprs. | ||
|
||
auto tuple_data = data.as<TupleNode>(); | ||
CHECK(tuple_data != nullptr); | ||
|
||
int idx = 0; | ||
Array<Expr> requantized_exprs; | ||
for (auto quantized_expr : tuple_data->fields) { | ||
// Get the input scale for the idx quantized input tensor. | ||
auto input_scale_expr = input_scales[idx].as<tvm::ir::FloatImm>(); | ||
CHECK(input_scale_expr != nullptr); | ||
auto input_scale = input_scale_expr->value; | ||
|
||
// Get the zero point for the idx quantized input tensor. | ||
auto input_zero_point_expr = input_zero_points[idx].as<tvm::ir::IntImm>(); | ||
CHECK(input_zero_point_expr != nullptr); | ||
auto input_zero_point = input_zero_point_expr->value; | ||
|
||
// Check if output and input qnn params are same. If not, requantize. | ||
if (input_scale != output_scale || input_zero_point != output_zero_point) { | ||
// Get the input shape and dtype. | ||
auto tensor_type = tuple_type->fields[idx].as<TensorTypeNode>(); | ||
auto input_dtype = tensor_type->dtype; | ||
auto input_shape = tensor_type->shape; | ||
|
||
// Requantize the input. | ||
auto requantized_expr = Requantize(quantized_expr, input_shape, input_scale, input_zero_point, | ||
output_scale, output_zero_point, input_dtype); | ||
requantized_exprs.push_back(requantized_expr); | ||
} else { | ||
requantized_exprs.push_back(quantized_expr); | ||
} | ||
idx++; | ||
} | ||
return Concatenate(TupleNode::make(requantized_exprs), concatenate_attrs->axis); | ||
} | ||
|
||
RELAY_REGISTER_OP("qnn.concatenate") | ||
.describe(R"code(Concatenate the quantized input tensors along the given axis. | ||
)code" TVM_ADD_FILELINE) | ||
.set_attrs_type_key("relay.attrs.QnnConcatenateAttrs") | ||
.set_num_inputs(1) | ||
.add_argument("data", "Tensor", "The tensor to concatenate.") | ||
.set_support_level(11) | ||
.add_type_rel("QnnConcatenate", ConcatenateRel<QnnConcatenateAttrs>) | ||
.set_attr<FTVMLegalize>("FTVMQnnCanonicalize", ConcatenateQnnCanonicalize); | ||
|
||
TVM_REGISTER_API("relay.qnn.op._make.concatenate") | ||
.set_body_typed(MakeQnnConcatenate); | ||
|
||
} // namespace qnn | ||
} // namespace relay | ||
} // namespace tvm |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters