wehub-resource-sync
169 lines
6.7 KiB
C++
169 lines
6.7 KiB
C++
/* Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/phi/infermeta/spmd_rules/roll.h"
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#include "glog/logging.h"
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#include "paddle/common/enforce.h"
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#include "paddle/phi/infermeta/spmd_rules/spmd_rule_macro_define.h"
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#include "paddle/phi/infermeta/spmd_rules/utils.h"
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namespace phi {
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namespace distributed {
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SpmdInfo RollInferSpmd(const DistMetaTensor& x,
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const std::vector<int64_t>& shifts,
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const std::vector<int64_t>& axis) {
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EXTRACT_SHAPE_AND_DIST_ATTR(x);
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std::vector<int64_t> x_dims_mapping_dst = x_dims_mapping_src;
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if (axis.empty()) {
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PADDLE_ENFORCE_EQ(
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shifts.size(),
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1,
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common::errors::InvalidArgument("When dims.size() == 0, shifts.size() "
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"should be equal to 1, But received "
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"shifts.size() = %d",
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shifts.size()));
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for (int i = 0; i < x_ndim; i++) {
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x_dims_mapping_dst[i] = -1;
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}
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} else {
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PADDLE_ENFORCE_EQ(
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axis.size(),
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shifts.size(),
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common::errors::InvalidArgument("When dims.size() != 0, dims.size() "
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"should be equal to "
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"shifts.size(). But received "
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"dims.size() = %d, shifts.size() = %d",
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axis.size(),
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shifts.size()));
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for (const auto& i : axis) {
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int64_t axis_i = i < 0 ? x_ndim + i : i;
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PADDLE_ENFORCE_EQ(
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0 <= axis_i && axis_i < x_ndim,
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true,
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common::errors::InvalidArgument("The axis of roll should "
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"be in range [0, %d), but got %d.",
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x_ndim,
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axis_i));
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x_dims_mapping_dst[axis_i] = -1;
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}
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}
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TensorDistAttr x_dist_attr_dst = CopyTensorDistAttrForOutput(x_dist_attr_src);
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TensorDistAttr out_dist_attr_dst =
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CopyTensorDistAttrForOutput(x_dist_attr_src);
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x_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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out_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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VLOG(4) << "RollInferSpmd: Done.";
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LOG_SPMD_INPUT(x);
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LOG_SPMD_OUTPUT(out_dist_attr_dst);
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return {{x_dist_attr_dst}, {out_dist_attr_dst}};
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}
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SpmdInfo RollGradInferSpmd(const DistMetaTensor& x,
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const DistMetaTensor& out_grad,
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const std::vector<int64_t>& shifts,
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const std::vector<int64_t>& axis) {
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EXTRACT_SHAPE_AND_DIST_ATTR(x);
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EXTRACT_SHAPE_AND_DIST_ATTR(out_grad);
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PADDLE_ENFORCE_EQ(x_ndim,
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out_grad_ndim,
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common::errors::InvalidArgument(
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"RollGrad: The rank of X [%d] and OutGrad [%d] "
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"must be the same.",
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x_ndim,
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out_grad_ndim));
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// Build einsum notation
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std::string alphabet = "abcdefghijlopqrstuvwxyz";
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std::string x_axes = alphabet.substr(0, x_ndim);
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std::string out_grad_axes = x_axes;
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std::vector<int64_t> x_dims_mapping(x_dims_mapping_src);
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std::vector<int64_t> out_grad_dims_mapping(out_grad_dims_mapping_src);
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if (axis.empty()) {
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PADDLE_ENFORCE_EQ(
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shifts.size(),
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1,
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common::errors::InvalidArgument("When dims.size() == 0, shifts.size() "
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"should be equal to 1, But received "
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"shifts.size() = %d",
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shifts.size()));
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for (int i = 0; i < x_ndim; ++i) {
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x_dims_mapping[i] = -1;
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out_grad_dims_mapping[i] = -1;
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}
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} else {
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PADDLE_ENFORCE_EQ(
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axis.size(),
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shifts.size(),
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common::errors::InvalidArgument("When dims.size() != 0, dims.size() "
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"should be equal to "
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"shifts.size(). But received "
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"dims.size() = %d, shifts.size() = %d",
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axis.size(),
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shifts.size()));
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for (const auto& i : axis) {
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int64_t axis_i = i < 0 ? x_ndim + i : i;
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PADDLE_ENFORCE_EQ(
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0 <= axis_i && axis_i < x_ndim,
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true,
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common::errors::InvalidArgument("The axis of roll should "
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"be in range [0, %d), but got %d.",
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x_ndim,
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axis_i));
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x_dims_mapping[axis_i] = -1;
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out_grad_dims_mapping[axis_i] = -1;
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}
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}
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auto axis_to_dim_map = ShardingMergeForTensors(
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{{x_axes, x_dims_mapping}, {out_grad_axes, out_grad_dims_mapping}});
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std::vector<int64_t> x_dims_mapping_dst =
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GetDimsMappingForAxes(x_axes, axis_to_dim_map);
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std::vector<int64_t> out_grad_dims_mapping_dst = x_dims_mapping_dst;
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TensorDistAttr x_dist_attr_dst = CopyTensorDistAttrForOutput(x_dist_attr_src);
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TensorDistAttr out_grad_dist_attr_dst =
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CopyTensorDistAttrForOutput(out_grad_dist_attr_src);
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TensorDistAttr x_grad_dist_attr_dst =
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CopyTensorDistAttrForOutput(x_dist_attr_src);
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x_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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out_grad_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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x_grad_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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VLOG(4) << "RollGradInferSpmd: Done.";
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LOG_SPMD_INPUT(x);
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LOG_SPMD_INPUT(out_grad);
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LOG_SPMD_OUTPUT(x_grad_dist_attr_dst);
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return {{x_dist_attr_dst, out_grad_dist_attr_dst}, {x_grad_dist_attr_dst}};
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}
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SpmdInfo RollInferSpmdDynamic(const DistMetaTensor& x,
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const IntArray& shifts,
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const std::vector<int64_t>& axis) {
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return RollInferSpmd(x, shifts.GetData(), axis);
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}
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SpmdInfo RollGradInferSpmdDynamic(const DistMetaTensor& x,
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const DistMetaTensor& out_grad,
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const IntArray& shifts,
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const std::vector<int64_t>& axis) {
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return RollGradInferSpmd(x, out_grad, shifts.GetData(), axis);
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}
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} // namespace distributed
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} // namespace phi
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