[Mlir-commits] [mlir] [mlir][linalg] Add e2e test for linalg.mmt4d (PR #84964)
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llvmlistbot at llvm.org
Tue Mar 12 10:57:37 PDT 2024
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir
@llvm/pr-subscribers-mlir-linalg
Author: Andrzej Warzyński (banach-space)
<details>
<summary>Changes</summary>
This is a follow-up for #<!-- -->81790. This patch basically extends:
* test/Integration/Dialect/Linalg/CPU/mmt4d.mlir
with pack/unpack ops so that to overall computation is a matrix
multiplication (as opposed to linalg.mmt4d). For comparison (and to make
it easier to verify correctness), linalg.matmul is also included in the
test.
---
Full diff: https://github.com/llvm/llvm-project/pull/84964.diff
1 Files Affected:
- (added) mlir/test/Integration/Dialect/Linalg/CPU/pack-unpack-mmt4d.mlir (+174)
``````````diff
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/pack-unpack-mmt4d.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/pack-unpack-mmt4d.mlir
new file mode 100644
index 00000000000000..bf7a98fc07f086
--- /dev/null
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/pack-unpack-mmt4d.mlir
@@ -0,0 +1,174 @@
+// DEFINE: %{compile} = mlir-opt %s \
+// DEFINE: -transform-interpreter -test-transform-dialect-erase-schedule \
+// DEFINE: -one-shot-bufferize -func-bufferize -cse -canonicalize -convert-vector-to-scf -test-lower-to-llvm -o %t
+// DEFINE: %{entry_point} = main
+// DEFINE: %{run} = mlir-cpu-runner %t -e %{entry_point} -entry-point-result=void \
+// DEFINE: -shared-libs=%mlir_runner_utils,%mlir_c_runner_utils
+
+// RUN: %{compile}
+
+// RUN: %{run} | FileCheck %s
+
+/// End-to-end test for computing matrix-multiplicatin using linalg.mmt4d. In
+/// particular, demonstrates how the following MLIR sequence (implemented in @mmt4d):
+///
+/// A_pack = tensor.pack A
+/// B_pack = tensor.pack B
+/// C_pack = tensor.pack C
+/// out_pack = linalg.mmt4d(A_pack, B_ pack, C_pack)
+///
+/// is equivalent to:
+///
+/// linalg.matmul(A, B, C)
+///
+/// (implemented in @matmul).
+
+func.func @main() {
+ // Allocate and initialise the inputs
+ %A_alloc = tensor.empty() : tensor<7x16xi32>
+ %B_alloc = tensor.empty() : tensor<16x13xi32>
+
+ %three = arith.constant 3 : i32
+ %four = arith.constant 4 : i32
+ %A = linalg.fill ins(%three : i32) outs(%A_alloc : tensor<7x16xi32>) -> tensor<7x16xi32>
+ %B = linalg.fill ins(%four : i32) outs(%B_alloc : tensor<16x13xi32>) -> tensor<16x13xi32>
+ %C = arith.constant dense<[
+ [ 1, 8, 15, 22, 29, 36, 43, 50, 57, 64, 71, 78, 85],
+ [ 2, 9, 16, 23, 30, 37, 44, 51, 58, 65, 72, 79, 86],
+ [ 3, 10, 17, 24, 31, 38, 45, 52, 59, 66, 73, 80, 87],
+ [ 4, 11, 18, 25, 32, 39, 46, 53, 60, 67, 74, 81, 88],
+ [ 5, 12, 19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89],
+ [ 6, 13, 20, 27, 34, 41, 48, 55, 62, 69, 76, 83, 90],
+ [ 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 91]
+ ]> : tensor<7x13xi32>
+
+ // Matrix multiplication via linalg.mmt4d
+ // CHECK: Unranked Memref
+ // CHECK: [193, 200, 207, 214, 221, 228, 235, 242, 249, 256, 263, 270, 277]
+ // CHECK: [194, 201, 208, 215, 222, 229, 236, 243, 250, 257, 264, 271, 278]
+ // CHECK: [195, 202, 209, 216, 223, 230, 237, 244, 251, 258, 265, 272, 279]
+ // CHECK: [196, 203, 210, 217, 224, 231, 238, 245, 252, 259, 266, 273, 280]
+ // CHECK: [197, 204, 211, 218, 225, 232, 239, 246, 253, 260, 267, 274, 281]
+ // CHECK: [198, 205, 212, 219, 226, 233, 240, 247, 254, 261, 268, 275, 282]
+ // CHECK: [199, 206, 213, 220, 227, 234, 241, 248, 255, 262, 269, 276, 283]
+ %C_mmt4d = func.call @mmt4d(%A, %B, %C) : (tensor<7x16xi32>, tensor<16x13xi32>, tensor<7x13xi32>) -> tensor<7x13xi32>
+ %xf = tensor.cast %C_mmt4d : tensor<7x13xi32> to tensor<*xi32>
+ call @printMemrefI32(%xf) : (tensor<*xi32>) -> ()
+
+ // Matrix multiplicaiton with linalg.matmul
+ // CHECK: Unranked Memref
+ // CHECK: [193, 200, 207, 214, 221, 228, 235, 242, 249, 256, 263, 270, 277]
+ // CHECK: [194, 201, 208, 215, 222, 229, 236, 243, 250, 257, 264, 271, 278]
+ // CHECK: [195, 202, 209, 216, 223, 230, 237, 244, 251, 258, 265, 272, 279]
+ // CHECK: [196, 203, 210, 217, 224, 231, 238, 245, 252, 259, 266, 273, 280]
+ // CHECK: [197, 204, 211, 218, 225, 232, 239, 246, 253, 260, 267, 274, 281]
+ // CHECK: [198, 205, 212, 219, 226, 233, 240, 247, 254, 261, 268, 275, 282]
+ // CHECK: [199, 206, 213, 220, 227, 234, 241, 248, 255, 262, 269, 276, 283]
+ %C_matmul = func.call @matmul(%A, %B, %C) : (tensor<7x16xi32>, tensor<16x13xi32>, tensor<7x13xi32>) -> tensor<7x13xi32>
+ %xf_2 = tensor.cast %C_matmul : tensor<7x13xi32> to tensor<*xi32>
+ call @printMemrefI32(%xf_2) : (tensor<*xi32>) -> ()
+
+ return
+}
+
+func.func @matmul(%A: tensor<7x16xi32>, %B: tensor<16x13xi32>, %C: tensor<7x13xi32>) -> tensor<7x13xi32> {
+ %C_matmul = linalg.matmul ins(%A, %B: tensor<7x16xi32>, tensor<16x13xi32>)
+ outs(%C: tensor<7x13xi32>) -> tensor<7x13xi32>
+
+ return %C_matmul : tensor<7x13xi32>
+}
+
+func.func @mmt4d(%A: tensor<7x16xi32>, %B: tensor<16x13xi32>, %C: tensor<7x13xi32>) -> tensor<7x13xi32> {
+ %zero = arith.constant 0 : i32
+
+ %cst = arith.constant 0 : i32
+ %A_pack_empty = tensor.empty() : tensor<2x16x8x1xi32>
+ %B_pack_empty = tensor.empty() : tensor<2x16x8x1xi32>
+ %C_pack_empty = tensor.empty() : tensor<2x2x8x8xi32>
+
+ // Pack matrices
+ %A_pack = tensor.pack %A padding_value(%zero : i32) outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [8, 1] into %A_pack_empty : tensor<7x16xi32> -> tensor<2x16x8x1xi32>
+ %B_pack = tensor.pack %B padding_value(%zero : i32) outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [8, 1] into %B_pack_empty : tensor<16x13xi32> -> tensor<2x16x8x1xi32>
+ %C_pack = tensor.pack %C padding_value(%zero : i32) outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %C_pack_empty : tensor<7x13xi32> -> tensor<2x2x8x8xi32>
+
+ // MMT4D
+ %mmt4d = linalg.mmt4d ins(%A_pack, %B_pack : tensor<2x16x8x1xi32>, tensor<2x16x8x1xi32>) outs(%C_pack : tensor<2x2x8x8xi32>) -> tensor<2x2x8x8xi32>
+
+ // Unpack output
+ %C_out_empty = tensor.empty() : tensor<7x13xi32>
+ %C_out_unpack = tensor.unpack %mmt4d outer_dims_perm = [0, 1] inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %C_out_empty : tensor<2x2x8x8xi32> -> tensor<7x13xi32>
+
+ return %C_out_unpack : tensor<7x13xi32>
+}
+
+module @transforms attributes { transform.with_named_sequence } {
+ transform.named_sequence @__transform_main(%module: !transform.any_op {transform.readonly}) {
+ %mmt4d = transform.collect_matching @match_mmt4d in %module : (!transform.any_op) -> (!transform.any_op)
+ %func = transform.get_parent_op %mmt4d {isolated_from_above} : (!transform.any_op) -> !transform.op<"func.func">
+
+ // Step 1: Tile
+ // Tile parallel dims
+ %tiled_linalg_op_p, %loops:4 = transform.structured.tile_using_for %mmt4d[1, 1, 0, 8, 8, 0]
+ : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+ // Tile reduction dims
+ %tiled_linalg_op_r, %loops2:2 = transform.structured.tile_using_for %tiled_linalg_op_p[0, 0, 1, 0, 0, 1]
+ : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+
+ // Step 2: Vectorize
+ transform.structured.vectorize %tiled_linalg_op_r : !transform.any_op
+
+ // Step 3: Simplify
+ // vector.multi_reduction --> vector.contract
+ // Generates a 6-dim vector.contract with the dim matching the original MMT4D Op
+ // and with the following split into parallel and reduction dims:
+ // * parallel, parallel, reduction, parallel, parallel, reduction
+ transform.apply_patterns to %func {
+ transform.apply_patterns.vector.reduction_to_contract
+ // Reduce the rank of xfer ops. This transforms vector.contract to be
+ // more matmul-like and to enable the lowering to outer product Ops.
+ transform.apply_patterns.vector.transfer_permutation_patterns
+ } : !transform.op<"func.func">
+
+ // Hoisting and LICM - not strictly required
+ %func_h = transform.structured.hoist_redundant_vector_transfers %func
+ : (!transform.op<"func.func">) -> !transform.op<"func.func">
+ %all_loops = transform.structured.match interface{LoopLikeInterface} in %func_h
+ : (!transform.op<"func.func">) -> !transform.any_op
+ transform.apply_licm to %all_loops : !transform.any_op
+ transform.loop.hoist_loop_invariant_subsets %all_loops : !transform.any_op
+
+ // Simplify the 6-dim vector.contract into a 3-dim matmul-like
+ // vector.contract with the following split into parallel and reduction
+ // dims:
+ // * parallel, parallel, reduction
+ transform.apply_patterns to %func_h {
+ transform.apply_patterns.vector.reduction_to_contract
+ transform.apply_patterns.vector.cast_away_vector_leading_one_dim
+ transform.apply_patterns.canonicalization
+ } : !transform.op<"func.func">
+
+ // Step 4. Lower tensor.pack
+ %pack = transform.structured.match ops{["tensor.pack"]} in %func_h
+ : (!transform.op<"func.func">) -> !transform.op<"tensor.pack">
+ transform.structured.lower_pack %pack : (!transform.op<"tensor.pack">)
+ -> (!transform.op<"tensor.pad">, !transform.op<"tensor.expand_shape">, !transform.op<"linalg.transpose">)
+
+ // Step 5. Lower tensor.unpack
+ %unpack = transform.structured.match ops{["tensor.unpack"]} in %func_h
+ : (!transform.op<"func.func">) -> !transform.op<"tensor.unpack">
+ transform.structured.lower_unpack %unpack : (!transform.op<"tensor.unpack">)
+ -> (!transform.op<"tensor.empty">,
+ !transform.op<"linalg.transpose">,
+ !transform.op<"tensor.collapse_shape">,
+ !transform.op<"tensor.extract_slice">)
+ transform.yield
+ }
+
+ transform.named_sequence @match_mmt4d(
+ %entry: !transform.any_op {transform.readonly}) -> !transform.any_op {
+ transform.match.operation_name %entry ["linalg.mmt4d"] : !transform.any_op
+ transform.yield %entry : !transform.any_op
+ }
+}
+
+func.func private @printMemrefI32(%ptr : tensor<*xi32>)
``````````
</details>
https://github.com/llvm/llvm-project/pull/84964
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