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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2022/01/19 11:39:09 UTC
[GitHub] [incubator-mxnet] agrabows commented on a change in pull request #20817: quantized transpose operator
agrabows commented on a change in pull request #20817:
URL: https://github.com/apache/incubator-mxnet/pull/20817#discussion_r787634366
##########
File path: src/operator/quantization/dnnl/dnnl_quantized_transpose.cc
##########
@@ -0,0 +1,70 @@
+
+/*
+ * 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 dnnl_quantized_transpose.cc
+ */
+#if MXNET_USE_ONEDNN == 1
+#include "../../nn/dnnl/dnnl_transpose-inl.h"
+#include "../../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline static bool QuantizedTransposeStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 3U);
+ CHECK_EQ(out_attrs->size(), 3U);
+ return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+}
+
+static void DNNLQuantizedTransposeForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ CHECK(inputs[0].dtype() == mshadow::kUint8 || inputs[0].dtype() == mshadow::kInt8)
+ << "dnnl_quantized_pooling op only supports uint8 and int8 as input type";
Review comment:
This comment (dnnl_quantized_pooling) does not fit in here.
##########
File path: tests/python/quantization/test_quantization.py
##########
@@ -713,6 +713,32 @@ def forward(self, x):
check_quantized_fc((256, 2048, 2, 2), 800, False, qdtype)
check_quantized_fc((256, 111, 2, 2), 800, False, qdtype)
+@use_np
+def test_quantized_transpose():
Review comment:
Maybye add some test for quantized transpose after some other quantizable operator e.g. conv, fc.
##########
File path: src/operator/quantization/quantized_transpose.cc
##########
@@ -0,0 +1,109 @@
+/*
+ * 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 quantized_transpose.cc
+ */
+#include <mxnet/op_attr_types.h>
+#include "../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool QuantizedTransposeType(const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 3U);
+ CHECK_EQ(out_attrs->size(), 3U);
+ TYPE_ASSIGN_CHECK(*in_attrs, 1, mshadow::kFloat32);
+ TYPE_ASSIGN_CHECK(*in_attrs, 2, mshadow::kFloat32);
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
+ TYPE_ASSIGN_CHECK(*out_attrs, 1, mshadow::kFloat32);
+ TYPE_ASSIGN_CHECK(*out_attrs, 2, mshadow::kFloat32);
+ return (*in_attrs)[0] != -1;
+}
+
+inline bool QuantizedTransposeShape(const nnvm::NodeAttrs& attrs,
+ mxnet::ShapeVector* in_attrs,
+ mxnet::ShapeVector* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 3U);
+ CHECK_EQ(out_attrs->size(), 3U);
+ mxnet::ShapeVector qin_attrs(1);
+ mxnet::ShapeVector qout_attrs(1);
+ SHAPE_ASSIGN_CHECK(qin_attrs, 0, (*in_attrs)[0]);
+ SHAPE_ASSIGN_CHECK(qout_attrs, 0, (*out_attrs)[0]);
+ TransposeShape(attrs, &qin_attrs, &qout_attrs);
+ SHAPE_ASSIGN_CHECK(*in_attrs, 0, qin_attrs[0]);
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, qout_attrs[0]);
+ SHAPE_ASSIGN_CHECK(*in_attrs, 1, mxnet::TShape{1});
+ SHAPE_ASSIGN_CHECK(*in_attrs, 2, mxnet::TShape{1});
+ SHAPE_ASSIGN_CHECK(*out_attrs, 1, mxnet::TShape{1});
+ SHAPE_ASSIGN_CHECK(*out_attrs, 2, mxnet::TShape{1});
+ return shape_is_known(qout_attrs[0]);
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_transpose)
+ .add_alias("_npx_quantized_transpose")
+ .set_num_inputs(3)
+ .set_num_outputs(3)
+ .set_attr_parser(ParamParser<TransposeParam>)
+ .set_attr<mxnet::FInferShape>("FInferShape", QuantizedTransposeShape)
+ .set_attr<nnvm::FInferType>("FInferType", QuantizedTransposeType)
+ // TODO(Xinyu): a temp solution to enable GluonCV INT8 flow,
+ // will be reverted after the improvement of CachedOP is done.
+ .set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+ .set_attr<nnvm::FListInputNames>(
+ "FListInputNames",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::string>{"data", "min_data", "max_data"};
+ })
+ .set_attr<nnvm::FListOutputNames>(
+ "FListOutputNames",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::string>{"output", "min_output", "max_output"};
+ })
+ .set_attr<nnvm::FInplaceOption>(
+ "FInplaceOption",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::pair<int, int> >{{0, 0}, {1, 1}, {2, 2}};
+ })
+ .add_argument("data", "NDArray-or-Symbol", "A ndarray/symbol of type `float32`")
+ .add_argument("min_data",
+ "NDArray-or-Symbol",
+ "The minimum scalar value "
+ "possibly produced for the data")
+ .add_argument("max_data",
+ "NDArray-or-Symbol",
+ "The maximum scalar value "
+ "possibly produced for the data")
+ .add_arguments(TransposeParam::__FIELDS__());
Review comment:
Did you consider adding FQuantizable attribute with _return QuantizeType::kSupport;_ ?
##########
File path: src/operator/quantization/dnnl/dnnl_quantized_transpose.cc
##########
@@ -0,0 +1,70 @@
+
+/*
+ * 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 dnnl_quantized_transpose.cc
+ */
+#if MXNET_USE_ONEDNN == 1
+#include "../../nn/dnnl/dnnl_transpose-inl.h"
+#include "../../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline static bool QuantizedTransposeStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 3U);
+ CHECK_EQ(out_attrs->size(), 3U);
+ return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+}
+
+static void DNNLQuantizedTransposeForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ CHECK(inputs[0].dtype() == mshadow::kUint8 || inputs[0].dtype() == mshadow::kInt8)
+ << "dnnl_quantized_pooling op only supports uint8 and int8 as input type";
+ if (req[0] == kNullOp) {
+ return;
+ }
+ CHECK_EQ(inputs.size(), 3U);
+ CHECK_EQ(outputs.size(), 3U);
+ DNNLRun(DNNLTransposeForward<TransposeParam>, attrs, ctx, inputs[0], req[0], outputs[0]);
Review comment:
Shouldn't we check if inputs pass through _SupportDNNLTranspose_ functions' conditions before calling _DNNLTransposeForward_?
##########
File path: src/operator/quantization/quantized_transpose.cc
##########
@@ -0,0 +1,109 @@
+/*
+ * 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 quantized_transpose.cc
+ */
+#include <mxnet/op_attr_types.h>
+#include "../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool QuantizedTransposeType(const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 3U);
+ CHECK_EQ(out_attrs->size(), 3U);
+ TYPE_ASSIGN_CHECK(*in_attrs, 1, mshadow::kFloat32);
+ TYPE_ASSIGN_CHECK(*in_attrs, 2, mshadow::kFloat32);
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, (*in_attrs)[0]);
+ TYPE_ASSIGN_CHECK(*out_attrs, 1, mshadow::kFloat32);
+ TYPE_ASSIGN_CHECK(*out_attrs, 2, mshadow::kFloat32);
+ return (*in_attrs)[0] != -1;
+}
+
+inline bool QuantizedTransposeShape(const nnvm::NodeAttrs& attrs,
+ mxnet::ShapeVector* in_attrs,
+ mxnet::ShapeVector* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 3U);
+ CHECK_EQ(out_attrs->size(), 3U);
+ mxnet::ShapeVector qin_attrs(1);
+ mxnet::ShapeVector qout_attrs(1);
+ SHAPE_ASSIGN_CHECK(qin_attrs, 0, (*in_attrs)[0]);
+ SHAPE_ASSIGN_CHECK(qout_attrs, 0, (*out_attrs)[0]);
+ TransposeShape(attrs, &qin_attrs, &qout_attrs);
+ SHAPE_ASSIGN_CHECK(*in_attrs, 0, qin_attrs[0]);
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, qout_attrs[0]);
+ SHAPE_ASSIGN_CHECK(*in_attrs, 1, mxnet::TShape{1});
+ SHAPE_ASSIGN_CHECK(*in_attrs, 2, mxnet::TShape{1});
+ SHAPE_ASSIGN_CHECK(*out_attrs, 1, mxnet::TShape{1});
+ SHAPE_ASSIGN_CHECK(*out_attrs, 2, mxnet::TShape{1});
+ return shape_is_known(qout_attrs[0]);
+}
+
+NNVM_REGISTER_OP(_contrib_quantized_transpose)
+ .add_alias("_npx_quantized_transpose")
+ .set_num_inputs(3)
+ .set_num_outputs(3)
+ .set_attr_parser(ParamParser<TransposeParam>)
+ .set_attr<mxnet::FInferShape>("FInferShape", QuantizedTransposeShape)
+ .set_attr<nnvm::FInferType>("FInferType", QuantizedTransposeType)
+ // TODO(Xinyu): a temp solution to enable GluonCV INT8 flow,
Review comment:
Is this comment valid for this change?
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