[llvm] [mlir] [mlir] Implement Mesh's ShardingInterface for Linalg ops (PR #82284)
Lei Zhang via llvm-commits
llvm-commits at lists.llvm.org
Mon Mar 4 22:41:09 PST 2024
================
@@ -0,0 +1,165 @@
+// RUN: mlir-opt \
+// RUN: --mesh-spmdization \
+// RUN: --test-constant-fold \
+// RUN: --split-input-file \
+// RUN: %s | FileCheck %s
+
+// CHECK: #[[$MAP_IDENTITY_1D:.*]] = affine_map<(d0) -> (d0)>
+#map_identity_1d = affine_map<(d0) -> (d0)>
+
+mesh.mesh @mesh_1d(shape = 2)
+
+// CHECK-LABEL: func @elementwise_static_1d_mesh_static_1d_tensor
+func.func @elementwise_static_1d_mesh_static_1d_tensor(
+ // CHECK-SAME: %[[IN1:[A-Za-z0-9_]+]]: tensor<1xi8>,
+ %in1: tensor<2xi8>,
+ // CHECK-SAME: %[[IN2:[A-Za-z0-9_]+]]: tensor<1xi8>,
+ %in2: tensor<2xi8>,
+ // CHECK-SAME: %[[DPS_OUT:[A-Za-z0-9_]+]]: tensor<1xi8>
+ %dps_out: tensor<2xi8>
+// CHECK-SAME: -> tensor<1xi8> {
+) -> tensor<2xi8> {
+ %in1_shared1 = mesh.shard %in1 to <@mesh_1d, [[0]]> : tensor<2xi8>
+ %in1_shared2 = mesh.shard %in1_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<2xi8>
+ %in2_shared1 = mesh.shard %in2 to <@mesh_1d, [[0]]> : tensor<2xi8>
+ %in2_shared2 = mesh.shard %in2_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<2xi8>
+ %dps_out_shared1 = mesh.shard %dps_out to <@mesh_1d, [[0]]> : tensor<2xi8>
+ %dps_out_shared2 = mesh.shard %dps_out_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<2xi8>
+ // CHECK: %[[RES:.*]] = linalg.generic {
+ // CHECK-SAME: indexing_maps = [#[[$MAP_IDENTITY_1D]], #[[$MAP_IDENTITY_1D]], #[[$MAP_IDENTITY_1D]]],
+ // CHECK-SAME: iterator_types = ["parallel"]}
+ // CHECK-SAME: ins(%[[IN1]], %[[IN2]] : tensor<1xi8>, tensor<1xi8>)
+ // CHECK-SAME: outs(%[[DPS_OUT]] : tensor<1xi8>) {
+ %res = linalg.generic {
+ indexing_maps = [#map_identity_1d, #map_identity_1d, #map_identity_1d],
+ iterator_types = ["parallel"]
+ } ins(%in1_shared2, %in2_shared2 : tensor<2xi8>, tensor<2xi8>)
+ outs(%dps_out_shared2 : tensor<2xi8>) {
+ ^bb0(%in1_scalar: i8, %in2_scalar: i8, %out: i8):
+ %res_scalar = arith.muli %in1_scalar, %in2_scalar : i8
+ linalg.yield %res_scalar : i8
+ } -> tensor<2xi8>
+ %res_shared1 = mesh.shard %res to <@mesh_1d, [[0]]> : tensor<2xi8>
+ %res_shared2 = mesh.shard %res_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<2xi8>
+ // CHECK: return %[[RES]] : tensor<1xi8>
+ return %res_shared2 : tensor<2xi8>
+}
+
+// -----
+
+mesh.mesh @mesh_1d(shape = 4)
+
+// CHECK-LABEL: func @matmul_1d_mesh_static_tensors_parallel_iterator_sharding
+func.func @matmul_1d_mesh_static_tensors_parallel_iterator_sharding(
+ // CHECK-SAME: %[[IN1:[A-Za-z0-9_]+]]: tensor<1x3xi8>,
+ %in1: tensor<4x3xi8>,
+// CHECK-SAME: %[[IN2:[A-Za-z0-9_]+]]: tensor<3x8xi8>,
+ %in2: tensor<3x8xi8>,
+// CHECK-SAME: %[[DPS_OUT:[A-Za-z0-9_]+]]: tensor<1x8xi8>
+ %dps_out: tensor<4x8xi8>
+// CHECK-SAME: -> tensor<1x8xi8> {
+) -> tensor<4x8xi8> {
+ %in1_shared1 = mesh.shard %in1 to <@mesh_1d, [[0]]> : tensor<4x3xi8>
+ %in1_shared2 = mesh.shard %in1_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<4x3xi8>
+ %in2_shared1 = mesh.shard %in2 to <@mesh_1d, [[]]> : tensor<3x8xi8>
+ %in2_shared2 = mesh.shard %in2_shared1 to <@mesh_1d, [[]]> annotate_for_users: tensor<3x8xi8>
+ %dps_out_shared1 = mesh.shard %dps_out to <@mesh_1d, [[0]]> : tensor<4x8xi8>
+ %dps_out_shared2 = mesh.shard %dps_out_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<4x8xi8>
+ // CHECK: %[[RES:.*]] = linalg.matmul
+ // CHECK-SAME: ins(%[[IN1]], %[[IN2]] : tensor<1x3xi8>, tensor<3x8xi8>)
+ // CHECK-SAME: outs(%[[DPS_OUT]] : tensor<1x8xi8>)
+ // CHECK-SAME: -> tensor<1x8xi8>
+ %res = linalg.matmul ins(%in1_shared2, %in2_shared2 : tensor<4x3xi8>, tensor<3x8xi8>)
+ outs(%dps_out_shared2 : tensor<4x8xi8>) -> tensor<4x8xi8>
+ %res_shared1 = mesh.shard %res to <@mesh_1d, [[0]]> : tensor<4x8xi8>
+ %res_shared2 = mesh.shard %res_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<4x8xi8>
+ // CHECK: return %[[RES]] : tensor<1x8xi8>
+ return %res_shared2 : tensor<4x8xi8>
+}
+
+// -----
+
+mesh.mesh @mesh_1d(shape = 3)
+
+// CHECK-LABEL: func @matmul_1d_mesh_static_tensors_reduction_iterator_sharding
+func.func @matmul_1d_mesh_static_tensors_reduction_iterator_sharding(
+ // CHECK-SAME: %[[IN1:[A-Za-z0-9_]+]]: tensor<4x2xi8>,
+ %in1: tensor<4x6xi8>,
+// CHECK-SAME: %[[IN2:[A-Za-z0-9_]+]]: tensor<2x8xi8>,
+ %in2: tensor<6x8xi8>,
+// CHECK-SAME: %[[DPS_OUT:[A-Za-z0-9_]+]]: tensor<4x8xi8>
+ %dps_out: tensor<4x8xi8>
+// CHECK-SAME: -> tensor<4x8xi8> {
+) -> tensor<4x8xi8> {
+ %in1_shared1 = mesh.shard %in1 to <@mesh_1d, [[], [0]]> : tensor<4x6xi8>
+ %in1_shared2 = mesh.shard %in1_shared1 to <@mesh_1d, [[], [0]]> annotate_for_users: tensor<4x6xi8>
+ %in2_shared1 = mesh.shard %in2 to <@mesh_1d, [[0]]> : tensor<6x8xi8>
+ %in2_shared2 = mesh.shard %in2_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<6x8xi8>
+ %dps_out_shared1 = mesh.shard %dps_out to <@mesh_1d, [[]]> : tensor<4x8xi8>
+ %dps_out_shared2 = mesh.shard %dps_out_shared1 to <@mesh_1d, [[]]> annotate_for_users: tensor<4x8xi8>
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ // CHECK-DAG: %[[C0_I8:.*]] = arith.constant 0 : i8
+ // CHECK-DAG: %[[PROCESS_IDX:.*]] = mesh.process_multi_index on @mesh_1d axes = [0] : index
+ // CHECK-DAG: %[[MESH_SIZE:.*]] = mesh.mesh_shape @mesh_1d axes = [0] : index
+ // CHECK: %[[DPS_INIT_OPERAND_CONDITION:.*]] = arith.cmpi eq, %[[PROCESS_IDX]], %[[C0]] : index
+ // CHECK: %[[DPS_INIT_OPERAND:.*]] = scf.if %[[DPS_INIT_OPERAND_CONDITION]] -> (tensor<4x8xi8>) {
+ // CHECK: scf.yield %[[DPS_OUT]] : tensor<4x8xi8>
+ // CHECK: } else {
+ // CHECK-DAG: %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<4x8xi8>
+ // CHECK: %[[NEUTRAL_ELEMENT_FILLED_TENSOR:.*]] = linalg.fill ins(%[[C0_I8]] : i8)
+ // CHECK-SAME: outs(%[[EMPTY_TENSOR]] : tensor<4x8xi8>) -> tensor<4x8xi8>
+ // CHECK: scf.yield %[[NEUTRAL_ELEMENT_FILLED_TENSOR]] : tensor<4x8xi8>
+ // CHECK: }
+ // CHECK: %[[SHARDED_MATMUL:.*]] = linalg.matmul ins(%[[IN1]], %[[IN2]] : tensor<4x2xi8>, tensor<2x8xi8>)
+ // CHECK-SAME: outs(%[[DPS_INIT_OPERAND]] : tensor<4x8xi8>) -> tensor<4x8xi8>
+ // CHECK: %[[ALL_REDUCED:.*]] = mesh.all_reduce %[[SHARDED_MATMUL]] on @mesh_1d mesh_axes = [0] : tensor<4x8xi8> -> tensor<4x8xi8>
+ %res = linalg.matmul ins(%in1_shared2, %in2_shared2 : tensor<4x6xi8>, tensor<6x8xi8>)
+ outs(%dps_out_shared2 : tensor<4x8xi8>) -> tensor<4x8xi8>
+ %res_shared1 = mesh.shard %res to <@mesh_1d, [[]]> : tensor<4x8xi8>
+ %res_shared2 = mesh.shard %res_shared1 to <@mesh_1d, [[]]> annotate_for_users: tensor<4x8xi8>
+ // CHECK: return %[[ALL_REDUCED]] : tensor<4x8xi8>
+ return %res_shared2 : tensor<4x8xi8>
+}
+
+// -----
+
+mesh.mesh @mesh_1d(shape = 3)
+
+// CHECK-LABEL: func @matmul_1d_mesh_static_tensors_reduction_iterator_sharding_with_partial_result
+func.func @matmul_1d_mesh_static_tensors_reduction_iterator_sharding_with_partial_result(
+ // CHECK-SAME: %[[IN1:[A-Za-z0-9_]+]]: tensor<4x2xi8>,
+ %in1: tensor<4x6xi8>,
+// CHECK-SAME: %[[IN2:[A-Za-z0-9_]+]]: tensor<2x8xi8>,
+ %in2: tensor<6x8xi8>,
+// CHECK-SAME: %[[DPS_OUT:[A-Za-z0-9_]+]]: tensor<4x8xi8>
+ %dps_out: tensor<4x8xi8>
+// CHECK-SAME: -> tensor<4x8xi8> {
+) -> tensor<4x8xi8> {
+ %in1_shared1 = mesh.shard %in1 to <@mesh_1d, [[], [0]]> : tensor<4x6xi8>
+ %in1_shared2 = mesh.shard %in1_shared1 to <@mesh_1d, [[], [0]]> annotate_for_users: tensor<4x6xi8>
+ %in2_shared1 = mesh.shard %in2 to <@mesh_1d, [[0]]> : tensor<6x8xi8>
+ %in2_shared2 = mesh.shard %in2_shared1 to <@mesh_1d, [[0]]> annotate_for_users: tensor<6x8xi8>
+ %dps_out_shared1 = mesh.shard %dps_out to <@mesh_1d, [[]]> : tensor<4x8xi8>
+ %dps_out_shared2 = mesh.shard %dps_out_shared1 to <@mesh_1d, [[]]> annotate_for_users: tensor<4x8xi8>
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ // CHECK-DAG: %[[C0_I8:.*]] = arith.constant 0 : i8
+ // CHECK-DAG: %[[PROCESS_IDX:.*]] = mesh.process_multi_index on @mesh_1d axes = [0] : index
+ // CHECK-DAG: %[[MESH_SIZE:.*]] = mesh.mesh_shape @mesh_1d axes = [0] : index
+ // CHECK: %[[DPS_INIT_OPERAND_CONDITION:.*]] = arith.cmpi eq, %[[PROCESS_IDX]], %[[C0]] : index
+ // CHECK: %[[DPS_INIT_OPERAND:.*]] = scf.if %[[DPS_INIT_OPERAND_CONDITION]] -> (tensor<4x8xi8>) {
+ // CHECK: scf.yield %[[DPS_OUT]] : tensor<4x8xi8>
+ // CHECK: } else {
+ // CHECK-DAG: %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<4x8xi8>
+ // CHECK: %[[NEUTRAL_ELEMENT_FILLED_TENSOR:.*]] = linalg.fill ins(%[[C0_I8]] : i8)
+ // CHECK-SAME: outs(%[[EMPTY_TENSOR]] : tensor<4x8xi8>) -> tensor<4x8xi8>
+ // CHECK: scf.yield %[[NEUTRAL_ELEMENT_FILLED_TENSOR]] : tensor<4x8xi8>
+ // CHECK: }
+ // CHECK: %[[SHARDED_MATMUL:.*]] = linalg.matmul ins(%[[IN1]], %[[IN2]] : tensor<4x2xi8>, tensor<2x8xi8>)
+ // CHECK-SAME: outs(%[[DPS_INIT_OPERAND]] : tensor<4x8xi8>) -> tensor<4x8xi8>
+ %res = linalg.matmul ins(%in1_shared2, %in2_shared2 : tensor<4x6xi8>, tensor<6x8xi8>)
+ outs(%dps_out_shared2 : tensor<4x8xi8>) -> tensor<4x8xi8>
+ %res_shared1 = mesh.shard %res to <@mesh_1d, [[]], partial = sum[0]> : tensor<4x8xi8>
+ %res_shared2 = mesh.shard %res_shared1 to <@mesh_1d, [[]], partial = sum[0]> annotate_for_users: tensor<4x8xi8>
+ // CHECK: return %[[SHARDED_MATMUL]] : tensor<4x8xi8>
+ return %res_shared2 : tensor<4x8xi8>
+}
----------------
antiagainst wrote:
Nice tests! We have coverage in the above for parallel iterator, reduction iterator, multiple parallel ones. Can we also test 1) multiple reductions, and also 2) mesh shape rank > 1?
https://github.com/llvm/llvm-project/pull/82284
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