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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2022/06/24 12:56:09 UTC
[GitHub] [incubator-mxnet] bgawrych commented on a diff in pull request #20976: [FEATURE] Add _npi_power_scalar and _npi_multiply_scalar fuse
bgawrych commented on code in PR #20976:
URL: https://github.com/apache/incubator-mxnet/pull/20976#discussion_r906009360
##########
src/operator/subgraph/dnnl/dnnl_pow_mul_scalar.cc:
##########
@@ -0,0 +1,151 @@
+/*
+ * 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_pow_mul_scalar.cc
+ * \brief DNNL pow_mul_scalar operator based on subgraph
+ */
+
+#if MXNET_USE_ONEDNN == 1
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "operator/nn/dnnl/dnnl_base-inl.h"
+#include "operator/nn/dnnl/dnnl_pow_mul_scalar-inl.h"
+#include "operator/subgraph/common.h"
+
+namespace mxnet {
+namespace op {
+bool DNNLPowMulScalarType(const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ const DNNLPowMulScalarParam& param = nnvm::get<DNNLPowMulScalarParam>(attrs.parsed);
+ bool scalar_is_int = param.exp_is_int && param.mul_is_int;
+ if (common::is_int(in_attrs->at(0)) && !scalar_is_int) {
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat64);
+ } else if (in_attrs->at(0) == mshadow::kBool) {
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, scalar_is_int ? mshadow::kInt64 : mshadow::kFloat64);
+ } else {
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+ }
+ return out_attrs->at(0) != -1;
+}
+
+inline static bool DNNLPowMulScalarStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+}
+
+template <typename OP>
+static void ComputeOP(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ mshadow::Stream<cpu>* s,
+ const TBlob& input,
+ const TBlob& output,
+ const double scalar) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ MSHADOW_TYPE_SWITCH(output.type_flag_, DType, {
+ auto temp_req = input.dptr_ == output.dptr_ ? kWriteInplace : kWriteTo;
+ TBlob temp_tblob = input;
+ if (input.type_flag_ != output.type_flag_) {
+ temp_tblob = TBlob(ctx.requested[0].get_space_typed<cpu, 1, DType>(Shape1(output.Size()), s));
+ CastCompute<cpu>(attrs, ctx, {input}, {kWriteTo}, {temp_tblob});
+ }
+ MXNET_ASSIGN_REQ_SWITCH(temp_req, Req, {
+ mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, cpu>::Launch(
+ s, input.Size(), output.dptr<DType>(), temp_tblob.dptr<DType>(), DType(scalar));
+ });
+ });
+}
+
+static void PowMulScalarCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+ DCHECK_EQ(inputs.size(), 1);
+ DCHECK_EQ(outputs.size(), 1);
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ const DNNLPowMulScalarParam& param = nnvm::get<DNNLPowMulScalarParam>(attrs.parsed);
+ // temp_mid_tblob is output of power operation and input of multiplication.
+ // Its dtype depends on input dtype and scalar type.
+ TBlob temp_mid_tblob =
+ ((common::is_int(inputs[0].type_flag_) || inputs[0].type_flag_ == kBool) &&
+ !param.exp_is_int) ?
+ outputs[0] :
+ inputs[0].type_flag_ == kBool ?
+ TBlob(ctx.requested[0].get_space_typed<cpu, 1, int64_t>(Shape1(inputs[0].Size()), s)) :
+ inputs[0];
Review Comment:
please use regular if else statements, it is not readable at all
##########
src/operator/nn/dnnl/dnnl_pow_mul_scalar.cc:
##########
@@ -18,49 +18,59 @@
*/
/*!
- * \file dnnl_power_scalar.cc
- * \author: Adam Grabowski, adam.grabowski@intel.com
+ * \file dnnl_pow_mul_scalar.cc
*/
#if MXNET_USE_ONEDNN == 1
-#include "dnnl_power_scalar-inl.h"
+#include "dnnl_pow_mul_scalar-inl.h"
namespace mxnet {
namespace op {
-DNNLPowerFwd& DNNLPowerFwd::GetPowerForward(const nnvm::NodeAttrs& attrs,
- const NDArray& input,
- const NDArray& output) {
- const NumpyBinaryScalarParam& param = nnvm::get<NumpyBinaryScalarParam>(attrs.parsed);
+bool SupportDNNLPower(const NDArray& input) {
+ return input.shape().Size() != 0 && input.shape().ndim() > 0 && input.shape().ndim() <= 6 &&
+ input.dtype() == mshadow::kFloat32;
+}
Review Comment:
rebase and apply refactored SupportDNNL functions
##########
src/operator/subgraph/dnnl/dnnl_pow_mul_scalar.cc:
##########
@@ -0,0 +1,151 @@
+/*
+ * 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_pow_mul_scalar.cc
+ * \brief DNNL pow_mul_scalar operator based on subgraph
+ */
+
+#if MXNET_USE_ONEDNN == 1
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "operator/nn/dnnl/dnnl_base-inl.h"
+#include "operator/nn/dnnl/dnnl_pow_mul_scalar-inl.h"
+#include "operator/subgraph/common.h"
+
+namespace mxnet {
+namespace op {
+bool DNNLPowMulScalarType(const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ const DNNLPowMulScalarParam& param = nnvm::get<DNNLPowMulScalarParam>(attrs.parsed);
+ bool scalar_is_int = param.exp_is_int && param.mul_is_int;
+ if (common::is_int(in_attrs->at(0)) && !scalar_is_int) {
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat64);
+ } else if (in_attrs->at(0) == mshadow::kBool) {
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, scalar_is_int ? mshadow::kInt64 : mshadow::kFloat64);
+ } else {
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+ }
+ return out_attrs->at(0) != -1;
+}
+
+inline static bool DNNLPowMulScalarStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
+}
+
+template <typename OP>
+static void ComputeOP(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ mshadow::Stream<cpu>* s,
+ const TBlob& input,
+ const TBlob& output,
+ const double scalar) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ MSHADOW_TYPE_SWITCH(output.type_flag_, DType, {
+ auto temp_req = input.dptr_ == output.dptr_ ? kWriteInplace : kWriteTo;
+ TBlob temp_tblob = input;
+ if (input.type_flag_ != output.type_flag_) {
+ temp_tblob = TBlob(ctx.requested[0].get_space_typed<cpu, 1, DType>(Shape1(output.Size()), s));
+ CastCompute<cpu>(attrs, ctx, {input}, {kWriteTo}, {temp_tblob});
+ }
+ MXNET_ASSIGN_REQ_SWITCH(temp_req, Req, {
+ mxnet_op::Kernel<mxnet_op::op_with_req<OP, Req>, cpu>::Launch(
+ s, input.Size(), output.dptr<DType>(), temp_tblob.dptr<DType>(), DType(scalar));
+ });
+ });
+}
+
+static void PowMulScalarCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ mshadow::Stream<cpu>* s = ctx.get_stream<cpu>();
+ DCHECK_EQ(inputs.size(), 1);
+ DCHECK_EQ(outputs.size(), 1);
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ const DNNLPowMulScalarParam& param = nnvm::get<DNNLPowMulScalarParam>(attrs.parsed);
+ // temp_mid_tblob is output of power operation and input of multiplication.
+ // Its dtype depends on input dtype and scalar type.
+ TBlob temp_mid_tblob =
+ ((common::is_int(inputs[0].type_flag_) || inputs[0].type_flag_ == kBool) &&
+ !param.exp_is_int) ?
+ outputs[0] :
+ inputs[0].type_flag_ == kBool ?
+ TBlob(ctx.requested[0].get_space_typed<cpu, 1, int64_t>(Shape1(inputs[0].Size()), s)) :
+ inputs[0];
Review Comment:
also are you sure that inputs[0] should be here as temp_mid_tblob?
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