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[Auto Parallel]Add spmd_rule for min and min_grad ops. #72269

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Merged
merged 8 commits into from
Apr 23, 2025
Merged

[Auto Parallel]Add spmd_rule for min and min_grad ops. #72269

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8dfce3a
add unary ops which have spmd_rule but not add in yaml file.
Glencsa Apr 10, 2025
1d129c2
Merge branch 'spmd_test' into develop
Glencsa Apr 15, 2025
b9c9e6a
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
Glencsa Apr 15, 2025
0fe12e4
Add spmd_rule for min operator
Glencsa Apr 15, 2025
d6ae448
add head file.
Glencsa Apr 16, 2025
4d81465
add CI to test min ops.
Glencsa Apr 17, 2025
7d07765
add spmd rules for min_grad ops.
Glencsa Apr 17, 2025
8565511
Add cpp unit test for spmd_rule of min and min_grad ops.
Glencsa Apr 21, 2025
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7 changes: 6 additions & 1 deletion paddle/phi/infermeta/spmd_rules/reduction.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -159,7 +159,12 @@ SpmdInfo ReductionMaxInferSpmdDynamic(const DistMetaTensor& x,
return ReductionInferSpmdBase(
x, axis.GetData(), keep_dim, static_cast<int>(ReduceType::kRedMax));
}

SpmdInfo ReductionMinInferSpmdDynamic(const DistMetaTensor& x,
const IntArray& axis,
bool keep_dim) {
return ReductionInferSpmdBase(
x, axis.GetData(), keep_dim, static_cast<int>(ReduceType::kRedMin));
}
SpmdInfo ReductionAllInferSpmdDynamic(const DistMetaTensor& x,
const IntArray& axis,
bool keep_dim) {
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3 changes: 3 additions & 0 deletions paddle/phi/infermeta/spmd_rules/reduction.h
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Expand Up @@ -49,6 +49,9 @@ SpmdInfo ReductionMaxInferSpmdDynamic(const DistMetaTensor& x,
const IntArray& axis,
bool keep_dim);

SpmdInfo ReductionMinInferSpmdDynamic(const DistMetaTensor& x,
const IntArray& axis,
bool keep_dim);
SpmdInfo ReductionAllInferSpmdDynamic(const DistMetaTensor& x,
const IntArray& axis,
bool keep_dim);
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1 change: 1 addition & 0 deletions paddle/phi/ops/yaml/inconsistent/dygraph_backward.yaml
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Expand Up @@ -212,6 +212,7 @@
infer_meta :
func : UnchangedInferMeta
param: [x]
spmd_rule : ReductionGradInferSpmd
kernel :
func : min_grad
composite : min_grad(x, out, out_grad, axis, keepdim, reduce_all, x_grad)
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1 change: 1 addition & 0 deletions paddle/phi/ops/yaml/inconsistent/dygraph_ops.yaml
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Expand Up @@ -258,6 +258,7 @@
output : Tensor(out)
infer_meta :
func : ReduceIntArrayAxisInferMeta
spmd_rule : ReductionMinInferSpmdDynamic
kernel :
func : min
backward : min_grad
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1 change: 1 addition & 0 deletions paddle/phi/ops/yaml/inconsistent/static_backward.yaml
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Expand Up @@ -399,6 +399,7 @@
infer_meta :
func : UnchangedInferMeta
param: [x]
spmd_rule : ReductionGradInferSpmd
kernel :
func : min_grad

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1 change: 1 addition & 0 deletions paddle/phi/ops/yaml/inconsistent/static_ops.yaml
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Expand Up @@ -539,6 +539,7 @@
output : Tensor(out)
infer_meta :
func : ReduceIntArrayAxisInferMeta
spmd_rule : ReductionMinInferSpmdDynamic
kernel :
func : min
backward : min_grad
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21 changes: 21 additions & 0 deletions test/cpp/auto_parallel/spmd_rule_test.cc
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Expand Up @@ -957,6 +957,27 @@ TEST(ReduceMaxRule, Ctor) {
check_partial_dims(backward_info.second[0], {});
}

TEST(ReduceMinRule, Ctor) {
std::vector<int64_t> mesh_shape = {2};
std::vector<int64_t> process_ids = {0, 1};
std::vector<std::string> dim_names = {"x"};
ProcessMesh process_mesh(mesh_shape, process_ids, dim_names);

// test forward
auto t_dist_attr = TensorDistAttr();
t_dist_attr.set_process_mesh(process_mesh);
t_dist_attr.set_dims_mapping({-1, 0, -1});
t_dist_attr.set_dynamic_dims({false, false, false});
phi::distributed::DistMetaTensor x = phi::distributed::DistMetaTensor(
common::make_ddim({4, 6, 8}), t_dist_attr);
phi::IntArray axis = {1};
bool keep_dim = false;
phi::distributed::SpmdInfo forward_info =
phi::distributed::ReductionMinInferSpmdDynamic(x, axis, keep_dim);
check_dim_mapping(forward_info.second[0], {-1, -1});
check_partial_dims(forward_info.second[0], {0});
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shall we test backward?

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The spmd_rule function of min_grad reuses ReductionGradInferSpmd, which has already been validated through previous CI tests.

}

TEST(ReduceAllRule, Ctor) {
std::vector<int64_t> mesh_shape = {2};
std::vector<int64_t> process_ids = {0, 1};
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