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Don't copy sharding constraints to sdy.data_flow_edge since they hold the sharding of multiple targets, not just the root. #26

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Jul 20, 2024
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Don't copy sharding constraints to sdy.data_flow_edge since they hold the sharding of multiple targets, not just the root. #26

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5 changes: 3 additions & 2 deletions shardy/dialect/sdy/transforms/import/apply_sharding_constraints.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ limitations under the License.
#include "mlir/Support/LLVM.h"
#include "shardy/dialect/sdy/ir/dialect.h"
#include "shardy/dialect/sdy/ir/utils.h"
#include "third_party/openxla/shardy/src/shardy/dialect/sdy/ir/dialect.h"

namespace mlir {
namespace sdy {
Expand All @@ -34,8 +35,8 @@ namespace sdy {
namespace {

bool shouldApply(Value input, Operation* op) {
if (getSharding(input)) {
// `input` already has a sharding.
if (getSharding(input) || input.getDefiningOp<DataFlowEdgeOp>()) {
// `input` already has a sharding or is produced by a `DataFlowEdgeOp`.
return false;
}

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2 changes: 2 additions & 0 deletions shardy/dialect/sdy/transforms/import/passes.td
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Expand Up @@ -37,6 +37,8 @@ def ApplyShardingConstraintsPass : Pass<"sdy-apply-sharding-constraints", "func:
all of the following:

* The input doesn't have an existing sharding.
* The input isn't produced by a `DataFlowEdgeOp`, which holds the sharding
of all targets of the edge.
* The input is either only used by the `ShardingConstraintOp` or the latter
doesn't have any uses (dangling) and the input doesn't have any other
users of type `ShardingConstraintOp` or `ManualComputationOp`.
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10 changes: 10 additions & 0 deletions shardy/dialect/sdy/transforms/import/test/apply_sharding_constraints.mlir
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Expand Up @@ -18,6 +18,16 @@ func.func @input_has_one_use(%arg0: tensor<8x8xf32>) -> tensor<8x8xf32> {
return %1 : tensor<8x8xf32>
}

// CHECK-LABEL: func @input_produced_by_data_flow_edge
func.func @input_produced_by_data_flow_edge(%arg0: tensor<8x8xf32>) -> tensor<8x8xf32> {
// CHECK-NEXT: sdy.data_flow_edge %arg0 : tensor<8x8xf32>
// CHECK-NOT: sdy.sharding
// CHECK-NEXT: sdy.sharding_constraint
%0 = sdy.data_flow_edge %arg0 : tensor<8x8xf32>
%1 = sdy.sharding_constraint %0 <@mesh, [{}, {"b"}]> : tensor<8x8xf32>
return %1 : tensor<8x8xf32>
}

// CHECK-LABEL: func @input_is_func_input_with_one_use(
// CHECK-SAMEL %arg0: tensor<8x8xf32> {sdy.sharding = #sdy.sharding<@mesh, [{}, {"b"}]>})
func.func @input_is_func_input_with_one_use(%arg0: tensor<8x8xf32>) -> tensor<8x8xf32> {
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20 changes: 19 additions & 1 deletion shardy/dialect/sdy/transforms/import/test/import_pipeline.mlir
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@@ -1,5 +1,6 @@
// RUN: sdy_opt %s -sdy-import-pipeline 2>&1 | FileCheck %s
// RUN: sdy_opt %s -split-input-file -sdy-import-pipeline 2>&1 | FileCheck %s

// Verifies that function `-inliner` pass is applied
// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<16x16xf32>) -> (tensor<8x16xf32>, tensor<8x16xf32>) {
// CHECK-NEXT: %[[CONST_0:.*]] = sdy.constant dense<1.000000e+00>
Expand All @@ -19,3 +20,20 @@ func.func private @add_matmul_to_lhs(%arg0: tensor<8x16xf32>, %arg1: tensor<16x1
%1 = stablehlo.add %arg0, %0 : tensor<8x16xf32>
return %1 : tensor<8x16xf32>
}

// -----

sdy.mesh @mesh = <"a"=2>

// Verifies that `-apply-sharding-constraints` pass is applied after
// `-add-data_flow_edges` pass
// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<32x96xf32>) -> tensor<32x96xf32> {
// CHECK-NEXT: %[[OPT_BARRIER:.*]] = stablehlo.optimization_barrier %arg0
// CHECK-NEXT: sdy.data_flow_edge %[[OPT_BARRIER]] : tensor<32x96xf32>
// CHECK-NOT: sdy.sharding
// CHECK-NEXT: sdy.sharding_constraint
%0 = stablehlo.optimization_barrier %arg0 : tensor<32x96xf32>
%1 = sdy.sharding_constraint %0 <@mesh, [{}, {"a"}]> : tensor<32x96xf32>
return %1 : tensor<32x96xf32>
}
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