wehub-resource-sync
181 lines
6.9 KiB
C++
181 lines
6.9 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/topk.h"
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#include "glog/logging.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 TopkInferSpmdBase(const DistMetaTensor& x, int axis) {
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// Verify input args
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EXTRACT_SHAPE_AND_DIST_ATTR(x);
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axis = axis < 0 ? x_ndim + axis : axis;
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PADDLE_ENFORCE_EQ(
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0 <= axis && axis < x_ndim,
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true,
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common::errors::InvalidArgument(
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"The axis of topk should be in range [0, %d), but got %d.",
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x_ndim,
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axis));
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// Create destination dist attrs
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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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TensorDistAttr indices_dist_attr_dst =
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CopyTensorDistAttrForOutput(x_dist_attr_src);
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// Infer dims_mapping
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std::vector<int64_t> x_dims_mapping_dst = x_dims_mapping_src;
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x_dims_mapping_dst[axis] = -1;
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std::vector<int64_t> out_dims_mapping_dst = x_dims_mapping_dst;
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std::vector<int64_t> indices_dims_mapping_dst = x_dims_mapping_dst;
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// Set the dims mapping for outputs
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out_dist_attr_dst.set_dims_mapping(out_dims_mapping_dst);
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indices_dist_attr_dst.set_dims_mapping(indices_dims_mapping_dst);
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// Update the dims mapping for inputs
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x_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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VLOG(4) << "TopkInferSpmd: Done.";
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LOG_SPMD_INPUT(x);
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LOG_SPMD_OUTPUT(out_dist_attr_dst);
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LOG_SPMD_OUTPUT(indices_dist_attr_dst);
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return {{x_dist_attr_dst}, {out_dist_attr_dst, indices_dist_attr_dst}};
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}
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SpmdInfo TopkGradInferSpmdBase(const DistMetaTensor& x,
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const DistMetaTensor& indices,
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const DistMetaTensor& out_grad,
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int axis) {
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// Verify input args
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EXTRACT_SHAPE_AND_DIST_ATTR(x);
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EXTRACT_SHAPE_AND_DIST_ATTR(indices);
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EXTRACT_SHAPE_AND_DIST_ATTR(out_grad);
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PADDLE_ENFORCE_EQ(indices_ndim,
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out_grad_ndim,
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common::errors::InvalidArgument(
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"TopKGrad: The rank of Indices [%d] and OutGrad [%d] "
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"must be the same.",
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indices_ndim,
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out_grad_ndim));
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PADDLE_ENFORCE_EQ(x_ndim,
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indices_ndim,
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common::errors::InvalidArgument(
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"TopKGrad: The rank of Input [%d] and Indices [%d] "
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"must be the same.",
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x_ndim,
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indices_ndim));
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axis = axis < 0 ? x_ndim + axis : axis;
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PADDLE_ENFORCE_EQ(
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0 <= axis && axis < x_ndim,
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true,
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common::errors::InvalidArgument(
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"The axis of topk_grad should be in range [0, %d), but got %d.",
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x_ndim,
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axis));
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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 indices_axes = x_axes;
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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> indices_dims_mapping(indices_dims_mapping_src);
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std::vector<int64_t> out_grad_dims_mapping(out_grad_dims_mapping_src);
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x_dims_mapping[axis] = -1;
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indices_dims_mapping[axis] = -1;
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out_grad_dims_mapping[axis] = -1;
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// Merge sharding
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std::pair<std::string, std::vector<int64_t>> indices_pair(
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indices_axes, indices_dims_mapping);
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std::pair<std::string, std::vector<int64_t>> out_grad_pair(
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out_grad_axes, out_grad_dims_mapping);
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std::pair<std::string, std::vector<int64_t>> x_pair(x_axes, x_dims_mapping);
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auto axis_to_dim_map =
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ShardingMergeForTensors({x_pair, indices_pair, out_grad_pair});
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// Infer dims mapping
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std::vector<int64_t> x_grad_dims_mapping_dst =
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GetDimsMappingForAxes(x_axes, axis_to_dim_map);
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std::vector<int64_t> x_dims_mapping_dst = x_grad_dims_mapping_dst;
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std::vector<int64_t> indices_dims_mapping_dst = x_grad_dims_mapping_dst;
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std::vector<int64_t> out_grad_dims_mapping_dst = x_grad_dims_mapping_dst;
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// Set the dims mapping
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TensorDistAttr x_grad_dist_attr_dst =
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CopyTensorDistAttrForOutput(out_grad_dist_attr_src);
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TensorDistAttr x_dist_attr_dst =
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CopyTensorDistAttrForOutput(out_grad_dist_attr_src);
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TensorDistAttr indices_dist_attr_dst =
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CopyTensorDistAttrForOutput(out_grad_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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x_grad_dist_attr_dst.set_dims_mapping(x_grad_dims_mapping_dst);
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x_dist_attr_dst.set_dims_mapping(x_dims_mapping_dst);
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indices_dist_attr_dst.set_dims_mapping(indices_dims_mapping_dst);
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out_grad_dist_attr_dst.set_dims_mapping(out_grad_dims_mapping_dst);
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VLOG(4) << "TopkGradInferSpmd: Done.";
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LOG_SPMD_INPUT(x);
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LOG_SPMD_INPUT(indices);
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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, indices_dist_attr_dst, out_grad_dist_attr_dst},
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{x_grad_dist_attr_dst}};
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}
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SpmdInfo TopkInferSpmd(
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const DistMetaTensor& x, int k, int axis, bool largest, bool sorted) {
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return TopkInferSpmdBase(x, axis);
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}
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SpmdInfo TopkGradInferSpmd(const DistMetaTensor& x,
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const DistMetaTensor& indices,
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const DistMetaTensor& out_grad,
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int k,
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int axis,
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bool largest,
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bool sorted) {
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return TopkGradInferSpmdBase(x, indices, out_grad, axis);
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}
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SpmdInfo TopkInferSpmdDynamic(const DistMetaTensor& x,
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const Scalar& k,
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int axis,
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bool largest,
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bool sorted) {
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return TopkInferSpmd(x, k.to<int>(), axis, largest, sorted);
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}
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SpmdInfo TopkGradInferSpmdDynamic(const DistMetaTensor& x,
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const DistMetaTensor& indices,
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const DistMetaTensor& out_grad,
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const Scalar& k,
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int axis,
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bool largest,
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bool sorted) {
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return TopkGradInferSpmd(
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x, indices, out_grad, k.to<int>(), axis, largest, sorted);
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}
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} // namespace distributed
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} // namespace phi
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