[Mlir-commits] [mlir] 6876fe5 - [mlir][linalg] Add a test to demonstrate peeling + vectorisation (#77590)

[llvmlistbot at llvm.org]

Author: Andrzej Warzyński
Date: 2024-01-10T15:19:16Z
New Revision: 6876fe53afabfc6f0c3b5e7c838f32a282da6f77

URL: https://github.com/llvm/llvm-project/commit/6876fe53afabfc6f0c3b5e7c838f32a282da6f77
DIFF: https://github.com/llvm/llvm-project/commit/6876fe53afabfc6f0c3b5e7c838f32a282da6f77.diff

LOG: [mlir][linalg] Add a test to demonstrate peeling + vectorisation (#77590)

Following on from #75842, we can demonstrate that loop peeling combined
with masked vectorisation and existing canonicalization for vector.mask
operations leads to the following loop structure:

```
// M dimension
scf.for 1:M
  // N dimension (contains vector ops _without_ masking)
  scf.for 1:UB
    // K dimension
    scf.for 1:K
      vector.add

  // N dimension (contains vector ops _with_ masking)
  scf.for UB:N
    // K dimension
    scf.for 1:K
      vector.mask { vector.add }
```

This is particularly beneficial for scalable vectors which normally
require masking. This example demonstrates how to avoid them.

Added: 
    mlir/test/Dialect/Linalg/transform-op-peel-and-vectorize.mlir

Modified: 
    

Removed: 
    


################################################################################
diff  --git a/mlir/test/Dialect/Linalg/transform-op-peel-and-vectorize.mlir b/mlir/test/Dialect/Linalg/transform-op-peel-and-vectorize.mlir
new file mode 100644
index 00000000000000..762648050fdfdf
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/transform-op-peel-and-vectorize.mlir
@@ -0,0 +1,86 @@
+// RUN: mlir-opt %s --transform-interpreter --split-input-file -canonicalize | FileCheck %s
+
+// Demonstrates what happens when peeling the middle loop (2nd parallel
+// dimension) followed by vectorization in the presence of _scalable_ vectors
+// (these are introduced through scalable tiling). The main goal is to verify
+// that canonicalizations fold away the masks in the main loop.
+
+func.func @matmul(%A: tensor<1024x512xf32>,
+                  %B: tensor<512x2000xf32>,
+                  %C: tensor<1024x2000xf32>) -> tensor<1024x2000xf32> {
+
+// CHECK:      #[[MAP:.*]] = affine_map<()[s0] -> (-(2000 mod s0) + 2000)>
+// CHECK-DAG:  %[[C1:.*]] = arith.constant 1 : index
+// CHECK-DAG:  %[[C2000:.*]] = arith.constant 2000 : index
+// CHECK-DAG:  %[[C8:.*]] = arith.constant 8 : index
+// CHECK-DAG:  %[[C1024:.*]] = arith.constant 1024 : index
+// CHECK-DAG:  %[[C512:.*]] = arith.constant 512 : index
+// CHECK-DAG:  %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG:  %[[C16:.*]] = arith.constant 16 : index
+// CHECK:      %[[VSCALE:.*]] = vector.vscale
+// CHECK:      %[[STEP:.*]] = arith.muli %[[VSCALE]], %[[C16]] : index
+// CHECK:      %2 = scf.for {{.*}} %[[C0]] to %[[C1024]] step %[[C8]] iter_args(%arg4 = %arg2) -> (tensor<1024x2000xf32>) {
+
+// Main loop after vectorisation (without masking)
+
+// CHECK:         %[[UB_MAIN:.*]] = affine.apply #[[MAP]]()[%[[STEP]]]
+// CHECK:         scf.for {{.*}} %[[C0]] to %[[UB_MAIN]] step %[[STEP]] {{.*}} -> (tensor<1024x2000xf32>) {
+// CHECK:           scf.for %arg7 = %[[C0]] to %[[C512]] step %[[C1]] {{.*}} -> (tensor<1024x2000xf32>) {
+// CHECK-NOT:         vector.mask
+// CHECK:             arith.mulf {{.*}} : vector<8x[16]x1xf32>
+// CHECK-NEXT:        vector.shape_cast {{.*}} : vector<8x[16]x1xf32> to vector<8x[16]xf32>
+// CHECK-NEXT:        arith.addf {{.*}} : vector<8x[16]xf32>
+// CHECK-NOT:         vector.mask
+// CHECK:             scf.yield {{.*}} : tensor<1024x2000xf32>
+// CHECK-NEXT:      }
+// CHECK-NEXT:      scf.yield {{.*}} : tensor<1024x2000xf32>
+// CHECK-NEXT:    }
+
+// Remainder loop after vectorisation (with masking)
+
+// CHECK:       scf.for {{.*}} %[[UB_MAIN]] to %[[C2000]] step %[[STEP]] {{.*}} -> (tensor<1024x2000xf32>) {
+// CHECK:         scf.for {{.*}} %[[C0]] to %[[C512]] step %[[C1]] {{.*}} -> (tensor<1024x2000xf32>) {
+// CHECK:           %[[MASK_1:.*]] = vector.create_mask {{.*}} : vector<1x[16]xi1>
+// CHECK:           %[[RHS:.*]] = vector.mask %[[MASK_1]] { vector.transfer_read {{.*}} } : vector<1x[16]xi1> -> vector<8x[16]x1xf32>
+// CHECK:           %[[MASK_2:.*]] = vector.create_mask {{.*}} : vector<8x[16]xi1>
+// CHECK:           %[[LHS:.*]] = vector.mask %[[MASK_2]] { vector.transfer_read {{.*}} } : vector<8x[16]xi1> -> vector<8x[16]xf32>
+// CHECK:           %[[MUL:.*]] = arith.mulf %{{.*}}, %[[RHS]] : vector<8x[16]x1xf32>
+// CHECK:           %[[MASK_3:.*]] = vector.create_mask {{.*}} : vector<8x[16]xi1>
+// CHECK:           vector.shape_cast %[[MUL]] : vector<8x[16]x1xf32> to vector<8x[16]xf32>
+// CHECK:           arith.addf %[[LHS]], %{{.*}} : vector<8x[16]xf32>
+// CHECK:           arith.select %[[MASK_3]], {{.*}} : vector<8x[16]xi1>, vector<8x[16]xf32>
+// CHECK:           vector.mask %[[MASK_2]] { vector.transfer_write {{.*}} } : vector<8x[16]xi1> -> tensor<8x?xf32>
+// CHECK:           scf.yield %inserted_slice : tensor<1024x2000xf32>
+// CHECK:         }
+// CHECK:         scf.yield %7 : tensor<1024x2000xf32>
+// CHECK:       }
+// CHECK:       scf.yield %5 : tensor<1024x2000xf32>
+// CHECK-NEXT:    }
+
+  %res = linalg.matmul ins(%A, %B: tensor<1024x512xf32>, tensor<512x2000xf32>)
+            outs(%C: tensor<1024x2000xf32>) -> tensor<1024x2000xf32>
+  return %res : tensor<1024x2000xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%root: !transform.any_op {transform.readonly}) {
+    %matmul = transform.structured.match ops{["linalg.matmul"]} in %root : (!transform.any_op) -> !transform.any_op
+    // 1. Scalable tiling
+    %_, %loop_1, %loop_2, %loop_3 =
+      transform.structured.tile_using_for %matmul [8, [16], 1] : (!transform.any_op)
+      -> (!transform.any_op, !transform.op<"scf.for">, !transform.op<"scf.for">,!transform.op<"scf.for">)
+
+    // 2. Loop peeling (only the middle dimension)
+    %main_loop, %remainder_loop = transform.loop.peel %loop_2 : (!transform.op<"scf.for">) -> (!transform.op<"scf.for">, !transform.op<"scf.for">)
+
+    // 3. Vectorize the main loop
+    %matmul_main = transform.structured.match ops{["linalg.matmul"]} in %main_loop : (!transform.op<"scf.for">) -> !transform.any_op
+    transform.structured.vectorize %matmul_main vector_sizes [8, [16], 1] : !transform.any_op
+
+    // 4. Vectorize the remainder loop
+    %matmul_remainder = transform.structured.match ops{["linalg.matmul"]} in %remainder_loop : (!transform.op<"scf.for">) -> !transform.any_op
+    transform.structured.vectorize %matmul_remainder vector_sizes [8, [16], 1] : !transform.any_op
+
+    transform.yield
+  }
+}