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Extend mlir-gen to emit linalg named Ops (libxsmm#933).
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Adds support to generate linalg named Ops for matmul, bias, relu.
This feature can be controlled using a new flag '--output'.

For example:
To generate generic linalg Ops use '--output=generic"
To generate named linalg Ops use '--output=named"

The default behaviour is to generate linalg generic Ops.

Adds named op test which pass out of the box.
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@shahidact
shahidact committed Jul 12, 2024
1 parent 385bbc4 commit 52f0331
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7 changes: 7 additions & 0 deletions test/BF16/Integration/mlir-gen-bf16.mlir
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// MLP without softmax (can't print packed version for now)
// RUN: mlir-gen --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10,10 --float-type=bf16 | tpp-run -e entry -entry-point-result=void
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10,10 --float-type=bf16 | tpp-run -e entry -entry-point-result=void

// Matmul only
// RUN: mlir-gen --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10 --float-type=bf16 | tpp-run -e entry -entry-point-result=void
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10 --float-type=bf16 | tpp-run -e entry -entry-point-result=void

// Kernel - matmul
// RUN: mlir-gen --kernel=args --seed=123 --float-type=bf16 --batch=10 --layers=10,10 | tpp-run -e entry -entry-point-result=void -print | FileCheck %s --check-prefix=GEN-MATMUL-BF16
// RUN: mlir-gen --output=named --kernel=args --seed=123 --float-type=bf16 --batch=10 --layers=10,10 | tpp-run -e entry -entry-point-result=void -print | FileCheck %s --check-prefix=GEN-MATMUL-BF16

// Kernel - fc
// RUN: mlir-gen --kernel=args --bias --relu --seed=123 --float-type=bf16 --batch=10 --layers=10,10 | tpp-run -e entry -entry-point-result=void -print | FileCheck %s --check-prefix=GEN-FC-BF16
// RUN: mlir-gen --output=named --kernel=args --bias --relu --seed=123 --float-type=bf16 --batch=10 --layers=10,10 | tpp-run -e entry -entry-point-result=void -print | FileCheck %s --check-prefix=GEN-FC-BF16

// BF16/VNNI execution
// RUN: mlir-gen --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10 --tiles=2,2,2 --float-type=bf16 | tpp-run -e entry -entry-point-result=void -n 10 | FileCheck %s --check-prefix=PERF
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10 --tiles=2,2,2 --float-type=bf16 | tpp-run -e entry -entry-point-result=void -n 10 | FileCheck %s --check-prefix=PERF
// RUN: mlir-gen --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10 --tiles=2,2,2 --float-type=bf16 | tpp-opt --pack-vnni | tpp-run -e entry -entry-point-result=void -n 10 | FileCheck %s --check-prefix=PERF
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=123 --batch=10 --layers=10,10 --tiles=2,2,2 --float-type=bf16 | tpp-opt --pack-vnni | tpp-run -e entry -entry-point-result=void -n 10 | FileCheck %s --check-prefix=PERF


// GEN-MATMUL-BF16: ( 11, 11, 11, 11, 11, 11, 11, 11, 11, 11 )

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21 changes: 21 additions & 0 deletions test/Integration/mlir-gen-flops.mlir
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// Unit sizes
// RUN: mlir-gen --kernel=args --seed=0 --float-type=f32 --batch=1 --layers=1,1 2>&1 | FileCheck %s --check-prefix=MATMUL-UNIT
// RUN: mlir-gen --output=named --kernel=args --seed=0 --float-type=f32 --batch=1 --layers=1,1 2>&1 | FileCheck %s --check-prefix=MATMUL-UNIT-NAMED
// RUN: mlir-gen --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=1 --layers=1,1 2>&1 | FileCheck %s --check-prefix=FC-UNIT
// RUN: mlir-gen --output=named --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=1 --layers=1,1 2>&1 | FileCheck %s --check-prefix=FC-UNIT-NAMED
// RUN: mlir-gen --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=1 --layers=1,1 2>&1 | FileCheck %s --check-prefix=MLP-UNIT
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=1 --layers=1,1 2>&1 | FileCheck %s --check-prefix=MLP-UNIT-NAMED
// Small sizes
// RUN: mlir-gen --kernel=args --seed=0 --float-type=f32 --batch=8 --layers=4,16 2>&1 | FileCheck %s --check-prefix=MATMUL-SMALL
// RUN: mlir-gen --output=named --kernel=args --seed=0 --float-type=f32 --batch=8 --layers=4,16 2>&1 | FileCheck %s --check-prefix=MATMUL-SMALL-NAMED
// RUN: mlir-gen --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=8 --layers=4,16 2>&1 | FileCheck %s --check-prefix=FC-SMALL
// RUN: mlir-gen --output=named --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=8 --layers=4,16 2>&1 | FileCheck %s --check-prefix=FC-SMALL-NAMED
// RUN: mlir-gen --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=8 --layers=4,8,16 2>&1 | FileCheck %s --check-prefix=MLP-SMALL
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=8 --layers=4,8,16 2>&1 | FileCheck %s --check-prefix=MLP-SMALL-NAMED
// Large sizes + no tiling
// RUN: mlir-gen --kernel=args --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 2>&1 | FileCheck %s --check-prefix=MATMUL-LARGE
// RUN: mlir-gen --output=named --kernel=args --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 2>&1 | FileCheck %s --check-prefix=MATMUL-LARGE-NAMED
// RUN: mlir-gen --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 2>&1 | FileCheck %s --check-prefix=FC-LARGE
// RUN: mlir-gen --output=named --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 2>&1 | FileCheck %s --check-prefix=FC-LARGE-NAMED
// RUN: mlir-gen --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,1024,1024 2>&1 | FileCheck %s --check-prefix=MLP-LARGE
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,1024,1024 2>&1 | FileCheck %s --check-prefix=MLP-LARGE-NAMED
// Large sizes + tiling
// RUN: mlir-gen --kernel=args --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 --tiles=64,64,64 2>&1 | FileCheck %s --check-prefix=MATMUL-LARGE
// RUN: mlir-gen --output=named --kernel=args --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 --tiles=64,64,64 2>&1 | FileCheck %s --check-prefix=MATMUL-LARGE-NAMED
// RUN: mlir-gen --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 --tiles=64,64,64 2>&1 | FileCheck %s --check-prefix=FC-LARGE
// RUN: mlir-gen --output=named --kernel=args --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,4096 --tiles=64,64,64 2>&1 | FileCheck %s --check-prefix=FC-LARGE-NAMED
// RUN: mlir-gen --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,1024,1024 --tiles=64,64,64 2>&1 | FileCheck %s --check-prefix=MLP-LARGE
// RUN: mlir-gen --output=named --kernel=const --bias --relu --seed=0 --float-type=f32 --batch=128 --layers=1024,1024,1024 --tiles=64,64,64 2>&1 | FileCheck %s --check-prefix=MLP-LARGE-NAMED

// Validate that flops are computed correctly
// MATMUL-UNIT: // BENCH_TOTAL_FLOPS: 2
// MATMUL-UNIT-NAMED: // BENCH_TOTAL_FLOPS: 2
// FC-UNIT: // BENCH_TOTAL_FLOPS: 4
// FC-UNIT-NAMED: // BENCH_TOTAL_FLOPS: 4
// MLP-UNIT: // BENCH_TOTAL_FLOPS: 4
// MLP-UNIT-NAMED: // BENCH_TOTAL_FLOPS: 4

// MATMUL-SMALL: // BENCH_TOTAL_FLOPS: 1024
// MATMUL-SMALL-NAMED: // BENCH_TOTAL_FLOPS: 1024
// FC-SMALL: // BENCH_TOTAL_FLOPS: 1280
// FC-SMALL-NAMED: // BENCH_TOTAL_FLOPS: 1280
// MLP-SMALL: // BENCH_TOTAL_FLOPS: 2944
// MLP-SMALL-NAMED: // BENCH_TOTAL_FLOPS: 2944

// MATMUL-LARGE: // BENCH_TOTAL_FLOPS: 1073741824
// MATMUL-LARGE-NAMED: // BENCH_TOTAL_FLOPS: 1073741824
// FC-LARGE: // BENCH_TOTAL_FLOPS: 1074790400
// FC-LARGE-NAMED: // BENCH_TOTAL_FLOPS: 1074790400
// MLP-LARGE: // BENCH_TOTAL_FLOPS: 537395200
// MLP-LARGE-NAMED: // BENCH_TOTAL_FLOPS: 537395200
141 changes: 141 additions & 0 deletions test/Integration/tiling-add-named-op.mlir
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// RUN: tpp-opt %s -default-tpp-passes | FileCheck -check-prefix=IR %s

// RUN: tpp-run %s -print \
// RUN: -e entry -entry-point-result=void | \
// RUN: FileCheck %s

// RUN: tpp-run %s -linalg-to-loops -print \
// RUN: -e entry -entry-point-result=void | \
// RUN: FileCheck %s

#map0 = affine_map<(d0, d1) -> (d0, d1)>

// IR-LABEL: bigadd
func.func @bigadd(%A: tensor<32x16xf32>,
%B: tensor<32x16xf32>) -> tensor<32x16xf32> {
// IR: xsmm_binary_invoke
%0 = tensor.empty() : tensor<32x16xf32>
%1 = linalg.add ins(%A, %B : tensor<32x16xf32>, tensor<32x16xf32>) outs(%0 : tensor<32x16xf32>) -> tensor<32x16xf32>
return %1 : tensor<32x16xf32>
}

func.func @entry() {
%c0 = arith.constant 0 : index
%d1 = arith.constant -1.0 : f32

%da = arith.constant dense<[

[ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 13.1, 14.1, 15.1, 16.1 ],
[ 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 15.2, 16.2 ],
[ 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 13.3, 14.3, 15.3, 16.3 ],
[ 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 9.4, 10.4, 11.4, 12.4, 13.4, 14.4, 15.4, 16.4 ],
[ 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5 ],
[ 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 14.6, 15.6, 16.6 ],
[ 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 10.7, 11.7, 12.7, 13.7, 14.7, 15.7, 16.7 ],
[ 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8 ],
[ 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 12.9, 13.9, 14.9, 15.9, 16.9 ],
[ 1.10, 2.10, 3.10, 4.10, 5.10, 6.10, 7.10, 8.10, 9.10, 10.10, 11.10, 12.10, 13.10, 14.10, 15.10, 16.10 ],
[ 1.11, 2.11, 3.11, 4.11, 5.11, 6.11, 7.11, 8.11, 9.11, 10.11, 11.11, 12.11, 13.11, 14.11, 15.11, 16.11 ],
[ 1.12, 2.12, 3.12, 4.12, 5.12, 6.12, 7.12, 8.12, 9.12, 10.12, 11.12, 12.12, 13.12, 14.12, 15.12, 16.12 ],
[ 1.13, 2.13, 3.13, 4.13, 5.13, 6.13, 7.13, 8.13, 9.13, 10.13, 11.13, 12.13, 13.13, 14.13, 15.13, 16.13 ],
[ 1.14, 2.14, 3.14, 4.14, 5.14, 6.14, 7.14, 8.14, 9.14, 10.14, 11.14, 12.14, 13.14, 14.14, 15.14, 16.14 ],
[ 1.15, 2.15, 3.15, 4.15, 5.15, 6.15, 7.15, 8.15, 9.15, 10.15, 11.15, 12.15, 13.15, 14.15, 15.15, 16.15 ],
[ 1.16, 2.16, 3.16, 4.16, 5.16, 6.16, 7.16, 8.16, 9.16, 10.16, 11.16, 12.16, 13.16, 14.16, 15.16, 16.16 ],
[ 1.17, 2.17, 3.17, 4.17, 5.17, 6.17, 7.17, 8.17, 9.17, 10.17, 11.17, 12.17, 13.17, 14.17, 15.17, 16.17 ],
[ 1.18, 2.18, 3.18, 4.18, 5.18, 6.18, 7.18, 8.18, 9.18, 10.18, 11.18, 12.18, 13.18, 14.18, 15.18, 16.18 ],
[ 1.19, 2.19, 3.19, 4.19, 5.19, 6.19, 7.19, 8.19, 9.19, 10.19, 11.19, 12.19, 13.19, 14.19, 15.19, 16.19 ],
[ 1.20, 2.20, 3.20, 4.20, 5.20, 6.20, 7.20, 8.20, 9.20, 10.20, 11.20, 12.20, 13.20, 14.20, 15.20, 16.20 ],
[ 1.21, 2.21, 3.21, 4.21, 5.21, 6.21, 7.21, 8.21, 9.21, 10.21, 11.21, 12.21, 13.21, 14.21, 15.21, 16.21 ],
[ 1.22, 2.22, 3.22, 4.22, 5.22, 6.22, 7.22, 8.22, 9.22, 10.22, 11.22, 12.22, 13.22, 14.22, 15.22, 16.22 ],
[ 1.23, 2.23, 3.23, 4.23, 5.23, 6.23, 7.23, 8.23, 9.23, 10.23, 11.23, 12.23, 13.23, 14.23, 15.23, 16.23 ],
[ 1.24, 2.24, 3.24, 4.24, 5.24, 6.24, 7.24, 8.24, 9.24, 10.24, 11.24, 12.24, 13.24, 14.24, 15.24, 16.24 ],
[ 1.25, 2.25, 3.25, 4.25, 5.25, 6.25, 7.25, 8.25, 9.25, 10.25, 11.25, 12.25, 13.25, 14.25, 15.25, 16.25 ],
[ 1.26, 2.26, 3.26, 4.26, 5.26, 6.26, 7.26, 8.26, 9.26, 10.26, 11.26, 12.26, 13.26, 14.26, 15.26, 16.26 ],
[ 1.27, 2.27, 3.27, 4.27, 5.27, 6.27, 7.27, 8.27, 9.27, 10.27, 11.27, 12.27, 13.27, 14.27, 15.27, 16.27 ],
[ 1.28, 2.28, 3.28, 4.28, 5.28, 6.28, 7.28, 8.28, 9.28, 10.28, 11.28, 12.28, 13.28, 14.28, 15.28, 16.28 ],
[ 1.29, 2.29, 3.29, 4.29, 5.29, 6.29, 7.29, 8.29, 9.29, 10.29, 11.29, 12.29, 13.29, 14.29, 15.29, 16.29 ],
[ 1.30, 2.30, 3.30, 4.30, 5.30, 6.30, 7.30, 8.30, 9.30, 10.30, 11.30, 12.30, 13.30, 14.30, 15.30, 16.30 ],
[ 1.31, 2.31, 3.31, 4.31, 5.31, 6.31, 7.31, 8.31, 9.31, 10.31, 11.31, 12.31, 13.31, 14.31, 15.31, 16.31 ],
[ 1.32, 2.32, 3.32, 4.32, 5.32, 6.32, 7.32, 8.32, 9.32, 10.32, 11.32, 12.32, 13.32, 14.32, 15.32, 16.32 ]

]> : tensor<32x16xf32>

%db = arith.constant dense<[

[ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 13.1, 14.1, 15.1, 16.1 ],
[ 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 15.2, 16.2 ],
[ 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 13.3, 14.3, 15.3, 16.3 ],
[ 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 9.4, 10.4, 11.4, 12.4, 13.4, 14.4, 15.4, 16.4 ],
[ 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5, 14.5, 15.5, 16.5 ],
[ 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 14.6, 15.6, 16.6 ],
[ 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 10.7, 11.7, 12.7, 13.7, 14.7, 15.7, 16.7 ],
[ 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8 ],
[ 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 12.9, 13.9, 14.9, 15.9, 16.9 ],
[ 1.10, 2.10, 3.10, 4.10, 5.10, 6.10, 7.10, 8.10, 9.10, 10.10, 11.10, 12.10, 13.10, 14.10, 15.10, 16.10 ],
[ 1.11, 2.11, 3.11, 4.11, 5.11, 6.11, 7.11, 8.11, 9.11, 10.11, 11.11, 12.11, 13.11, 14.11, 15.11, 16.11 ],
[ 1.12, 2.12, 3.12, 4.12, 5.12, 6.12, 7.12, 8.12, 9.12, 10.12, 11.12, 12.12, 13.12, 14.12, 15.12, 16.12 ],
[ 1.13, 2.13, 3.13, 4.13, 5.13, 6.13, 7.13, 8.13, 9.13, 10.13, 11.13, 12.13, 13.13, 14.13, 15.13, 16.13 ],
[ 1.14, 2.14, 3.14, 4.14, 5.14, 6.14, 7.14, 8.14, 9.14, 10.14, 11.14, 12.14, 13.14, 14.14, 15.14, 16.14 ],
[ 1.15, 2.15, 3.15, 4.15, 5.15, 6.15, 7.15, 8.15, 9.15, 10.15, 11.15, 12.15, 13.15, 14.15, 15.15, 16.15 ],
[ 1.16, 2.16, 3.16, 4.16, 5.16, 6.16, 7.16, 8.16, 9.16, 10.16, 11.16, 12.16, 13.16, 14.16, 15.16, 16.16 ],
[ 1.17, 2.17, 3.17, 4.17, 5.17, 6.17, 7.17, 8.17, 9.17, 10.17, 11.17, 12.17, 13.17, 14.17, 15.17, 16.17 ],
[ 1.18, 2.18, 3.18, 4.18, 5.18, 6.18, 7.18, 8.18, 9.18, 10.18, 11.18, 12.18, 13.18, 14.18, 15.18, 16.18 ],
[ 1.19, 2.19, 3.19, 4.19, 5.19, 6.19, 7.19, 8.19, 9.19, 10.19, 11.19, 12.19, 13.19, 14.19, 15.19, 16.19 ],
[ 1.20, 2.20, 3.20, 4.20, 5.20, 6.20, 7.20, 8.20, 9.20, 10.20, 11.20, 12.20, 13.20, 14.20, 15.20, 16.20 ],
[ 1.21, 2.21, 3.21, 4.21, 5.21, 6.21, 7.21, 8.21, 9.21, 10.21, 11.21, 12.21, 13.21, 14.21, 15.21, 16.21 ],
[ 1.22, 2.22, 3.22, 4.22, 5.22, 6.22, 7.22, 8.22, 9.22, 10.22, 11.22, 12.22, 13.22, 14.22, 15.22, 16.22 ],
[ 1.23, 2.23, 3.23, 4.23, 5.23, 6.23, 7.23, 8.23, 9.23, 10.23, 11.23, 12.23, 13.23, 14.23, 15.23, 16.23 ],
[ 1.24, 2.24, 3.24, 4.24, 5.24, 6.24, 7.24, 8.24, 9.24, 10.24, 11.24, 12.24, 13.24, 14.24, 15.24, 16.24 ],
[ 1.25, 2.25, 3.25, 4.25, 5.25, 6.25, 7.25, 8.25, 9.25, 10.25, 11.25, 12.25, 13.25, 14.25, 15.25, 16.25 ],
[ 1.26, 2.26, 3.26, 4.26, 5.26, 6.26, 7.26, 8.26, 9.26, 10.26, 11.26, 12.26, 13.26, 14.26, 15.26, 16.26 ],
[ 1.27, 2.27, 3.27, 4.27, 5.27, 6.27, 7.27, 8.27, 9.27, 10.27, 11.27, 12.27, 13.27, 14.27, 15.27, 16.27 ],
[ 1.28, 2.28, 3.28, 4.28, 5.28, 6.28, 7.28, 8.28, 9.28, 10.28, 11.28, 12.28, 13.28, 14.28, 15.28, 16.28 ],
[ 1.29, 2.29, 3.29, 4.29, 5.29, 6.29, 7.29, 8.29, 9.29, 10.29, 11.29, 12.29, 13.29, 14.29, 15.29, 16.29 ],
[ 1.30, 2.30, 3.30, 4.30, 5.30, 6.30, 7.30, 8.30, 9.30, 10.30, 11.30, 12.30, 13.30, 14.30, 15.30, 16.30 ],
[ 1.31, 2.31, 3.31, 4.31, 5.31, 6.31, 7.31, 8.31, 9.31, 10.31, 11.31, 12.31, 13.31, 14.31, 15.31, 16.31 ],
[ 1.32, 2.32, 3.32, 4.32, 5.32, 6.32, 7.32, 8.32, 9.32, 10.32, 11.32, 12.32, 13.32, 14.32, 15.32, 16.32 ]

]> : tensor<32x16xf32>

%0 = call @bigadd(%da, %db) : (tensor<32x16xf32>, tensor<32x16xf32>) -> tensor<32x16xf32>

//
// CHECK: ( ( 2.2, 4.2, 6.2, 8.2, 10.2, 12.2, 14.2, 16.2, 18.2, 20.2, 22.2, 24.2, 26.2, 28.2, 30.2, 32.2 ),
// CHECK-SAME: ( 2.4, 4.4, 6.4, 8.4, 10.4, 12.4, 14.4, 16.4, 18.4, 20.4, 22.4, 24.4, 26.4, 28.4, 30.4, 32.4 ),
// CHECK-SAME: ( 2.6, 4.6, 6.6, 8.6, 10.6, 12.6, 14.6, 16.6, 18.6, 20.6, 22.6, 24.6, 26.6, 28.6, 30.6, 32.6 ),
// CHECK-SAME: ( 2.8, 4.8, 6.8, 8.8, 10.8, 12.8, 14.8, 16.8, 18.8, 20.8, 22.8, 24.8, 26.8, 28.8, 30.8, 32.8 ),
// CHECK-SAME: ( 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 ),
// CHECK-SAME: ( 3.2, 5.2, 7.2, 9.2, 11.2, 13.2, 15.2, 17.2, 19.2, 21.2, 23.2, 25.2, 27.2, 29.2, 31.2, 33.2 ),
// CHECK-SAME: ( 3.4, 5.4, 7.4, 9.4, 11.4, 13.4, 15.4, 17.4, 19.4, 21.4, 23.4, 25.4, 27.4, 29.4, 31.4, 33.4 ),
// CHECK-SAME: ( 3.6, 5.6, 7.6, 9.6, 11.6, 13.6, 15.6, 17.6, 19.6, 21.6, 23.6, 25.6, 27.6, 29.6, 31.6, 33.6 ),
// CHECK-SAME: ( 3.8, 5.8, 7.8, 9.8, 11.8, 13.8, 15.8, 17.8, 19.8, 21.8, 23.8, 25.8, 27.8, 29.8, 31.8, 33.8 ),
// CHECK-SAME: ( 2.2, 4.2, 6.2, 8.2, 10.2, 12.2, 14.2, 16.2, 18.2, 20.2, 22.2, 24.2, 26.2, 28.2, 30.2, 32.2 ),
// CHECK-SAME: ( 2.22, 4.22, 6.22, 8.22, 10.22, 12.22, 14.22, 16.22, 18.22, 20.22, 22.22, 24.22, 26.22, 28.22, 30.22, 32.22 ),
// CHECK-SAME: ( 2.24, 4.24, 6.24, 8.24, 10.24, 12.24, 14.24, 16.24, 18.24, 20.24, 22.24, 24.24, 26.24, 28.24, 30.24, 32.24 ),
// CHECK-SAME: ( 2.26, 4.26, 6.26, 8.26, 10.26, 12.26, 14.26, 16.26, 18.26, 20.26, 22.26, 24.26, 26.26, 28.26, 30.26, 32.26 ),
// CHECK-SAME: ( 2.28, 4.28, 6.28, 8.28, 10.28, 12.28, 14.28, 16.28, 18.28, 20.28, 22.28, 24.28, 26.28, 28.28, 30.28, 32.28 ),
// CHECK-SAME: ( 2.3, 4.3, 6.3, 8.3, 10.3, 12.3, 14.3, 16.3, 18.3, 20.3, 22.3, 24.3, 26.3, 28.3, 30.3, 32.3 ),
// CHECK-SAME: ( 2.32, 4.32, 6.32, 8.32, 10.32, 12.32, 14.32, 16.32, 18.32, 20.32, 22.32, 24.32, 26.32, 28.32, 30.32, 32.32 ),
// CHECK-SAME: ( 2.34, 4.34, 6.34, 8.34, 10.34, 12.34, 14.34, 16.34, 18.34, 20.34, 22.34, 24.34, 26.34, 28.34, 30.34, 32.34 ),
// CHECK-SAME: ( 2.36, 4.36, 6.36, 8.36, 10.36, 12.36, 14.36, 16.36, 18.36, 20.36, 22.36, 24.36, 26.36, 28.36, 30.36, 32.36 ),
// CHECK-SAME: ( 2.38, 4.38, 6.38, 8.38, 10.38, 12.38, 14.38, 16.38, 18.38, 20.38, 22.38, 24.38, 26.38, 28.38, 30.38, 32.38 ),
// CHECK-SAME: ( 2.4, 4.4, 6.4, 8.4, 10.4, 12.4, 14.4, 16.4, 18.4, 20.4, 22.4, 24.4, 26.4, 28.4, 30.4, 32.4 ),
// CHECK-SAME: ( 2.42, 4.42, 6.42, 8.42, 10.42, 12.42, 14.42, 16.42, 18.42, 20.42, 22.42, 24.42, 26.42, 28.42, 30.42, 32.42 ),
// CHECK-SAME: ( 2.44, 4.44, 6.44, 8.44, 10.44, 12.44, 14.44, 16.44, 18.44, 20.44, 22.44, 24.44, 26.44, 28.44, 30.44, 32.44 ),
// CHECK-SAME: ( 2.46, 4.46, 6.46, 8.46, 10.46, 12.46, 14.46, 16.46, 18.46, 20.46, 22.46, 24.46, 26.46, 28.46, 30.46, 32.46 ),
// CHECK-SAME: ( 2.48, 4.48, 6.48, 8.48, 10.48, 12.48, 14.48, 16.48, 18.48, 20.48, 22.48, 24.48, 26.48, 28.48, 30.48, 32.48 ),
// CHECK-SAME: ( 2.5, 4.5, 6.5, 8.5, 10.5, 12.5, 14.5, 16.5, 18.5, 20.5, 22.5, 24.5, 26.5, 28.5, 30.5, 32.5 ),
// CHECK-SAME: ( 2.52, 4.52, 6.52, 8.52, 10.52, 12.52, 14.52, 16.52, 18.52, 20.52, 22.52, 24.52, 26.52, 28.52, 30.52, 32.52 ),
// CHECK-SAME: ( 2.54, 4.54, 6.54, 8.54, 10.54, 12.54, 14.54, 16.54, 18.54, 20.54, 22.54, 24.54, 26.54, 28.54, 30.54, 32.54 ),
// CHECK-SAME: ( 2.56, 4.56, 6.56, 8.56, 10.56, 12.56, 14.56, 16.56, 18.56, 20.56, 22.56, 24.56, 26.56, 28.56, 30.56, 32.56 ),
// CHECK-SAME: ( 2.58, 4.58, 6.58, 8.58, 10.58, 12.58, 14.58, 16.58, 18.58, 20.58, 22.58, 24.58, 26.58, 28.58, 30.58, 32.58 ),
// CHECK-SAME: ( 2.6, 4.6, 6.6, 8.6, 10.6, 12.6, 14.6, 16.6, 18.6, 20.6, 22.6, 24.6, 26.6, 28.6, 30.6, 32.6 ),
// CHECK-SAME: ( 2.62, 4.62, 6.62, 8.62, 10.62, 12.62, 14.62, 16.62, 18.62, 20.62, 22.62, 24.62, 26.62, 28.62, 30.62, 32.62 ),
// CHECK-SAME: ( 2.64, 4.64, 6.64, 8.64, 10.64, 12.64, 14.64, 16.64, 18.64, 20.64, 22.64, 24.64, 26.64, 28.64, 30.64, 32.64 ) )
//

%v0 = vector.transfer_read %0[%c0, %c0], %d1 : tensor<32x16xf32>, vector<32x16xf32>
vector.print %v0 : vector<32x16xf32>

return
}
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