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|
// RUN: mlir-opt %s -one-shot-bufferize="test-analysis-only bufferize-function-boundaries" -split-input-file | FileCheck %s
/// All combinations of matmul(fill(extract(alloc_tensor)), fill(extract(%alloc_tensor)), %arg2)
/// These should all be inplaceable except the first op.
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_1234(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_1243(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_1324(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_1342(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_1423(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_1432(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_2134(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_2143(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_2314(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_2341(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_2413(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_2431(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "false"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_3124(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_3142(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_3214(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true}) -> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_3241(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %2[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %4 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_3412(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_3421(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_4123(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_4132(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_4213(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %1[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%3, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_4231(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_4312(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
// -----
// CHECK-LABEL: func @fill_extract_matmul_
func.func @fill_extract_matmul_4321(
%arg0: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg1: tensor<518x518xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = false},
%arg2: tensor<256x256xf32> {bufferization.buffer_layout = affine_map<(d0, d1) -> (d0, d1)>, bufferization.writable = true})
-> tensor<256x256xf32>
{
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 1.000000e+00 : f32
%0 = bufferization.alloc_tensor() : tensor<256x256xf32>
// CHECK: {__inplace_operands_attr__ = ["false"]}
%4 = tensor.extract_slice %0[0, 0] [16, 256] [1, 1] : tensor<256x256xf32> to tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["true"]}
%3 = tensor.extract_slice %0[0, 0] [256, 16] [1, 1] : tensor<256x256xf32> to tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%2 = linalg.fill ins(%cst_0 : f32) outs(%4 : tensor<16x256xf32>) -> tensor<16x256xf32>
// CHECK: {__inplace_operands_attr__ = ["none", "true"]}
%1 = linalg.fill ins(%cst : f32) outs(%3 : tensor<256x16xf32>) -> tensor<256x16xf32>
// CHECK: {__inplace_operands_attr__ = ["true", "true", "true"]}
%5 = linalg.matmul ins(%1, %2 : tensor<256x16xf32>, tensor<16x256xf32>) outs(%arg2 : tensor<256x256xf32>) -> tensor<256x256xf32>
return %5 : tensor<256x256xf32>
}
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