[v2.0] Split Large Source Files

src/operator/numpy/np_broadcast_reduce_op_value.h

@@ -0,0 +1,193 @@
+/*
+ * 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 np_broadcast_reduce_op_value.h
+ * \brief Definition of broadcast and reduce functions based on value.
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_VALUE_H_
+#define MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_VALUE_H_
+
+#include <string>
+#include <vector>
+
+#if MXNET_USE_TVM_OP
+#include "../tvmop/op_module.h"
+#endif  // MXNET_USE_TVM_OP
+
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpySumType(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 NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+
+  if (param.dtype.has_value()) {
+    if (in_attrs->at(0) == mshadow::kBool) {
+      CHECK(param.dtype.value() == mshadow::kInt32 || param.dtype.value() == mshadow::kInt64 ||
+            param.dtype.value() == mshadow::kFloat32 || param.dtype.value() == mshadow::kFloat64)
+          << "Only support the following output dtypes when input dtype is bool: "
+             "int32, int64, float32, float64.";
+    }
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else if (in_attrs->at(0) == mshadow::kBool) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
+  } else {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+    TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+  }
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+#if MXNET_USE_TVM_OP
+static constexpr int max_reduce_ndim = 5;
+TBlob PrependAxes(const TBlob& src, const int dst_ndim);
+#endif  // MXNET_USE_TVM_OP
+
+inline void TVMOpReduce(const OpContext& ctx,
+                        const TBlob& input,
+                        const dmlc::optional<mxnet::Tuple<int>>& axis,
+                        const TBlob& output,
+                        const OpReqType req,
+                        const std::string& reducer_name) {
+#if MXNET_USE_TVM_OP
+  CHECK_GE(input.ndim(), output.ndim());
+  CHECK_LE(input.ndim(), max_reduce_ndim)
+      << "TVMOpReduce only supports ndim <= " << max_reduce_ndim;
+
+  const TBlob expanded_output =
+      (input.ndim() == output.ndim()
+           ? output
+           : output.reshape(NumpyReduceAxesShapeImpl(input.shape_, axis, true)));
+  CHECK_EQ(input.ndim(), expanded_output.ndim());
+  int reduce1st_dim = 0;
+  if (input.ndim() > 0 && input.size(0) != expanded_output.size(0)) {
+    reduce1st_dim = 1;
+  }
+  // collapse consecutive dimensions where reduction are performed or not performed
+  std::vector<index_t> ishape_vec;
+  for (int i = 0; i < input.ndim(); ++i) {
+    if (i == 0 || ((input.size(i) != expanded_output.size(i)) !=
+                   (input.size(i - 1) != expanded_output.size(i - 1)))) {
+      ishape_vec.push_back(input.size(i));
+    } else {
+      ishape_vec.back() *= input.size(i);
+    }
+  }
+  // append axes after collapsed ishape to reach the max ndim allowed
+  for (int i = ishape_vec.size(); i < max_reduce_ndim; ++i) {
+    ishape_vec.push_back(1);
+  }
+  std::vector<index_t> oshape_vec;
+  for (size_t i = reduce1st_dim; i < ishape_vec.size(); i += 2) {
+    oshape_vec.push_back(ishape_vec[i]);
+  }
+  TShape ishape(ishape_vec.begin(), ishape_vec.end()), oshape(oshape_vec.begin(), oshape_vec.end());
+  TBlob input_tvm  = input.reshape(ishape);
+  TBlob output_tvm = output.reshape(oshape);
+  const std::string ctx_name =
+      (ctx.run_ctx.ctx.dev_type == mxnet::Context::DeviceType::kCPU) ? "cpu" : "gpu";
+  std::ostringstream func_name;
+  func_name << reducer_name << "_"
+            << (ctx.run_ctx.ctx.dev_type == mxnet::Context::DeviceType::kCPU ? "cpu" : "gpu")
+            << "reduce1st_dim_" << reduce1st_dim << "req_"
+            << (req == kWriteTo ? "kWriteTo" : "kAddTo");
+  tvm::runtime::TVMOpModule::Get()->Call(func_name.str(), ctx, {input_tvm, output_tvm, output_tvm});
+#else
+  LOG(FATAL) << "Please add USE_TVM_OP=1 as a compile flag to enable TVM-generated kernels.";
+#endif  // MXNET_USE_TVM_OP
+}
+
+inline bool NumpyReduceAxesNoDTypeType(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);
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+  TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+inline bool IsIntType(const int dtype) {
+  return (dtype == mshadow::kUint8 || dtype == mshadow::kInt32 || dtype == mshadow::kInt8 ||
+          dtype == mshadow::kInt64);
+}
+
+inline bool NumpyMeanType(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 NumpyReduceAxesParam& param = nnvm::get<NumpyReduceAxesParam>(attrs.parsed);
+
+  if (param.dtype.has_value()) {
+    TYPE_ASSIGN_CHECK(*out_attrs, 0, param.dtype.value());
+  } else {
+    if (common::is_float(in_attrs->at(0))) {
+      TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0));
+      TYPE_ASSIGN_CHECK(*in_attrs, 0, out_attrs->at(0));
+    } else {
+      TYPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::common::GetDefaultDtype());
+    }
+  }
+
+  return out_attrs->at(0) != -1 && in_attrs->at(0) != -1;
+}
+
+inline bool NumpyBroadcastToShape(const nnvm::NodeAttrs& attrs,
+                                  mxnet::ShapeVector* in_attrs,
+                                  mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& ishape = (*in_attrs)[0];
+  if (!mxnet::shape_is_known(ishape))
+    return false;
+  const BroadcastToParam& param = nnvm::get<BroadcastToParam>(attrs.parsed);
+  CHECK_LE(ishape.ndim(), param.shape.ndim())
+      << "shape " << ishape << " is not broadcastable to " << param.shape;
+  TShape pshape = param.shape;
+  for (int i = param.shape.ndim() - 1; i >= 0; --i) {
+    int j = i - param.shape.ndim() + ishape.ndim();
+    if (j < 0)
+      break;
+    if (pshape[i] == -2) {
+      pshape[i] = ishape[j];
+    }
+    CHECK(ishape[j] == pshape[i] || ishape[j] == 1)
+        << "shape " << ishape << " is not broadcastable to " << pshape;
+  }
+  CHECK(mxnet::shape_is_known(pshape))
+      << "the objective shape for broadcasting array must be known";
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, pshape);
+  return true;
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_BROADCAST_REDUCE_OP_VALUE_H_

src/operator/numpy/np_broadcast_reduce_op_value_broadcast_to.cc

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+/*
+ * 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 np_broadcast_reduce_op_value_broadcast_to.cc
+ * \brief CPU Implementation of broadcast and reduce functions based on value.
+ */
+
+#if MXNET_USE_TVM_OP
+#include "../tvmop/op_module.h"
+#endif  // MXNET_USE_TVM_OP
+
+#include "np_broadcast_reduce_op_value.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_broadcast_to)
+    .set_num_inputs(1)
+    .set_num_outputs(1)
+    .set_attr<nnvm::FListInputNames>("FListInputNames",
+                                     [](const NodeAttrs& attrs) {
+                                       return std::vector<std::string>{"array"};
+                                     })
+    .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+    .set_attr<nnvm::FGradient>("FGradient",
+                               [](const nnvm::ObjectPtr& n,
+                                  const std::vector<nnvm::NodeEntry>& ograds) {
+                                 return MakeNonlossGradNode(
+                                     "_backward_np_broadcast_to", n, ograds, {}, n->attrs.dict);
+                               })
+    .add_argument("array", "NDArray-or-Symbol", "The input")
+    .set_attr_parser(ParamParser<BroadcastToParam>)
+    .add_arguments(BroadcastToParam::__FIELDS__())
+    .set_attr<mxnet::FInferShape>("FInferShape", NumpyBroadcastToShape)
+    .set_attr<FCompute>("FCompute<cpu>", NumpyBroadcastToForward<cpu>);
+
+NNVM_REGISTER_OP(_backward_np_broadcast_to)
+    .set_attr_parser(ParamParser<BroadcastToParam>)
+    .set_attr<nnvm::TIsBackward>("TIsBackward", true)
+    .set_attr<FCompute>("FCompute<cpu>", NumpyBroadcastToBackward<cpu>)
+    .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+    });
+
+}  // namespace op
+}  // namespace mxnet

src/operator/numpy/np_broadcast_reduce_op_value_broadcast_to.cu

@@ -0,0 +1,37 @@
+/*
+ * 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 np_broadcast_reduce_op_value_broadcast_to.cu
+ * \brief GPU Implementation of reduce functions based on value.
+ */
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_broadcast_to)
+    .set_attr<FCompute>("FCompute<gpu>", NumpyBroadcastToForward<gpu>);
+
+NNVM_REGISTER_OP(_backward_np_broadcast_to)
+    .set_attr<FCompute>("FCompute<gpu>", NumpyBroadcastToBackward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet