Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Bump llvm to 29b92d07746fac26cd64c914bc9c5c3833974f6d #2962

Merged
merged 4 commits into from
Oct 2, 2024
Merged

Bump llvm to 29b92d07746fac26cd64c914bc9c5c3833974f6d #2962

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
7a28cc9
bump llvm to 29b92d07746fac26cd64c914bc9c5c3833974f6d
Oct 2, 2024
6b88ff6
Revert changes on refs
Oct 2, 2024
b1a4dc1
Merge branch 'main' into ferdinand.update_llvm_october_2024
hamptonm1 Oct 2, 2024
35486f3
Merge branch 'main' into ferdinand.update_llvm_october_2024
hamptonm1 Oct 2, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/BuildOnLinuxOSX.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ Firstly, install MLIR (as a part of LLVM-Project):
``` bash
git clone -n https://github.com/llvm/llvm-project.git
# Check out a specific branch that is known to work with ONNX-MLIR.
cd llvm-project && git checkout eaa95a1c2bd38332c1a4e634595f29d22b28ffea && cd ..
cd llvm-project && git checkout 29b92d07746fac26cd64c914bc9c5c3833974f6d && cd ..
```

[same-as-file]: <> (utils/build-mlir.sh)
Expand Down
2 changes: 1 addition & 1 deletion docs/BuildOnWindows.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ Install MLIR (as a part of LLVM-Project):
```shell
git clone -n https://github.com/llvm/llvm-project.git
# Check out a specific branch that is known to work with ONNX-MLIR.
cd llvm-project && git checkout eaa95a1c2bd38332c1a4e634595f29d22b28ffea && cd ..
cd llvm-project && git checkout 29b92d07746fac26cd64c914bc9c5c3833974f6d && cd ..
```

[same-as-file]: <> (utils/build-mlir.cmd)
Expand Down
31 changes: 15 additions & 16 deletions test/mlir/conversion/onnx_to_krnl/ControlFlow/Loop.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,19 +41,19 @@ func.func private @test_loop_simple_main_graph(%arg0: tensor<i64>, %arg1: tensor
// CHECK-DAG: [[CST_1_2_:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[CST_1_3_:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[RES_3_:%.+]] = memref.alloc() {{.*}}: memref<1xi64>
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_8_:%.+]] = builtin.unrealized_conversion_cast [[RES_3_]] : memref<1xi64> to tensor<1xi64>
// CHECK-DAG: [[CST_0_2_:%.+]] = arith.constant 0 : index
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[LOAD_RES_MEM_:%.+]] = krnl.load [[RES_]]{{.}}[[CST_0_2_]]{{.}} : memref<1xi64>
// CHECK-DAG: [[LOAD_RES_2_MEM_:%.+]] = krnl.load [[RES_2_]][] : memref<i64>
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_10_:%.+]] = arith.addi [[LOAD_RES_MEM_]], [[LOAD_RES_2_MEM_]] : i64
// CHECK-DAG: [[VAR_11_:%.+]] = arith.addi [[LOAD_RES_MEM_]], [[LOAD_RES_2_MEM_]] : i64
// CHECK-DAG: [[CST_0_3_:%.+]] = arith.constant 0 : index
// CHECK: krnl.store [[VAR_10_]], [[RES_3_]]{{.}}[[CST_0_3_]]{{.}} : memref<1xi64>
// CHECK-DAG: [[VAR_11_:%.+]] = builtin.unrealized_conversion_cast [[RES_3_]] : memref<1xi64> to tensor<1xi64>
// CHECK: krnl.store [[VAR_11_]], [[RES_3_]]{{.}}[[CST_0_3_]]{{.}} : memref<1xi64>
// CHECK-DAG: [[VAR_12_:%.+]] = builtin.unrealized_conversion_cast [[PARAM_1_]] : memref<i1> to memref<i1>
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_13_:%.+]] = builtin.unrealized_conversion_cast [[VAR_11_]] : tensor<1xi64> to memref<1xi64>
// CHECK-DAG: [[LOAD_VAR_12_MEM_:%.+]] = krnl.load [[VAR_12_]][] : memref<i1>
// CHECK-DAG: [[VAR_13_:%.+]] = builtin.unrealized_conversion_cast [[VAR_8_]] : tensor<1xi64> to memref<1xi64>
// CHECK: [[LOAD_VAR_12_MEM_:%.+]] = krnl.load [[VAR_12_]][] : memref<i1>
// CHECK: krnl.store [[LOAD_VAR_12_MEM_]], [[RES_1_]][] : memref<i1>
// CHECK-DAG: [[LOOP_2_:%.+]] = krnl.define_loops 1
// CHECK-DAG: [[CST_0_4_:%.+]] = arith.constant 0 : index
Expand Down Expand Up @@ -111,8 +111,10 @@ func.func @test_loop(%arg0: tensor<i64>, %arg1: tensor<i1>, %arg2: tensor<?xf32>
// CHECK-DAG: [[VAR_dim_7_:%.+]] = memref.dim [[PARAM_2_]], [[CST_0_1_]] : memref<?xf32>
// CHECK-DAG: [[CST_0_2_:%.+]] = arith.constant 0 : index
// CHECK: [[VAR_dim_9_:%.+]] = memref.dim [[PARAM_2_]], [[CST_0_2_]] : memref<?xf32>
// CHECK: [[VAR_11_:%.+]] = affine.max [[MAP_0_]]([[VAR_dim_7_]], [[VAR_dim_9_]])
// CHECK-DAG: [[RES_3_:%.+]] = memref.alloc([[VAR_11_]]) {{.*}}: memref<?xf32>
// CHECK-DAG: [[VAR_11_:%.+]] = affine.max [[MAP_0_]]([[VAR_dim_7_]], [[VAR_dim_9_]])
// CHECK-DAG: [[CST_1_1_:%.+]] = arith.constant 1 : index
// CHECK: [[RES_3_:%.+]] = memref.alloc([[VAR_11_]]) {{.*}}: memref<?xf32>
// CHECK-DAG: [[VAR_12_:%.+]] = builtin.unrealized_conversion_cast [[RES_3_]] : memref<?xf32> to tensor<?xf32>
// CHECK-DAG: [[LOOP_1_:%.+]] = krnl.define_loops 1
// CHECK-DAG: [[CST_0_3_:%.+]] = arith.constant 0 : index
// CHECK-DAG: [[CST_0_4_:%.+]] = arith.constant 0 : index
Expand All @@ -123,11 +125,9 @@ func.func @test_loop(%arg0: tensor<i64>, %arg1: tensor<i1>, %arg2: tensor<?xf32>
// CHECK: [[VAR_20_:%.+]] = arith.addf [[LOAD_PARAM_2_MEM_]], [[LOAD_PARAM_2_MEM_1_]] : f32
// CHECK: krnl.store [[VAR_20_]], [[RES_3_]]{{.}}[[VAR_17_]]{{.}} : memref<?xf32>
// CHECK: }
// CHECK-DAG: [[VAR_13_:%.+]] = builtin.unrealized_conversion_cast [[RES_3_]] : memref<?xf32> to tensor<?xf32>
// CHECK-DAG: [[VAR_14_:%.+]] = builtin.unrealized_conversion_cast [[PARAM_1_]] : memref<i1> to memref<i1>
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_15_:%.+]] = builtin.unrealized_conversion_cast [[VAR_13_]] : tensor<?xf32> to memref<?xf32>
// CHECK-DAG: [[LOAD_VAR_14_MEM_:%.+]] = krnl.load [[VAR_14_]][] : memref<i1>
// CHECK-DAG: [[VAR_15_:%.+]] = builtin.unrealized_conversion_cast [[VAR_12_]] : tensor<?xf32> to memref<?xf32>
// CHECK: [[LOAD_VAR_14_MEM_:%.+]] = krnl.load [[VAR_14_]][] : memref<i1>
// CHECK: krnl.store [[LOAD_VAR_14_MEM_]], [[RES_1_]][] : memref<i1>
// CHECK: "krnl.seqstore"([[VAR_15_]], [[RES_]], [[VAR_8_]]) : (memref<?xf32>, memref<?xmemref<?xf32>>, index) -> ()
// CHECK: }) : () -> ()
Expand All @@ -150,11 +150,10 @@ func.func @test_loop(%arg0: tensor<i64>, %arg1: tensor<i1>, %arg2: tensor<?xf32>
// CHECK: [[VAR_dim_7_1_:%.+]] = memref.dim [[LOAD_RES_1_MEM_1_]], [[CST_0_9_]] : memref<?xf32>
// CHECK: krnl.iterate([[LOOP_3_]]) with ([[LOOP_3_]] -> [[I_3_:%.+]] = 0 to [[MAP_2_]]([[VAR_dim_7_1_]])){
// CHECK: [[VAR_11_1_:%.+]] = krnl.get_induction_var_value([[LOOP_3_]]) : (!krnl.loop) -> index
// CHECK: [[LOOP_1_:%.+]] = krnl.load [[LOAD_RES_1_MEM_1_]]{{.}}[[VAR_11_1_]]{{.}} : memref<?xf32>
// CHECK: krnl.store [[LOOP_1_]], [[RES_4_]]{{.}}[[VAR_8_1_]], [[VAR_11_1_]]{{.}} : memref<?x?xf32>
// CHECK: [[VAR_12_1_:%.+]] = krnl.load [[LOAD_RES_1_MEM_1_]]{{.}}[[VAR_11_1_]]{{.}} : memref<?xf32>
// CHECK: krnl.store [[VAR_12_1_]], [[RES_4_]]{{.}}[[VAR_8_1_]], [[VAR_11_1_]]{{.}} : memref<?x?xf32>
// CHECK: }
// CHECK: }) : () -> ()
// CHECK: }
// CHECK: return [[RES_4_]] : memref<?x?xf32>
// CHECK: }
}
}
4 changes: 2 additions & 2 deletions test/mlir/conversion/onnx_to_krnl/NN/Normalization_O3_SIMD_canonicalize.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -433,6 +433,7 @@ func.func @layernorm_4D_with_scale_bias_no_SIMD(%arg0: tensor<2x64x31x3xf32>, %a
// CHECK: }
// CHECK: }
// CHECK-DAG: [[RES_41_:%.+]] = memref.alloc() {{.*}}: memref<2x64x31x3xf32>
// CHECK-DAG: [[VAR_6_:%.+]] = builtin.unrealized_conversion_cast [[RES_41_]] : memref<2x64x31x3xf32> to tensor<2x64x31x3xf32>
// CHECK-DAG: [[RES_42_:%.+]] = memref.alloc() {{.*}}: memref<3xindex>
// CHECK: affine.store [[CST_2_]], [[RES_42_]][0] : memref<3xindex>
// CHECK: affine.store [[CST_64_]], [[RES_42_]][1] : memref<3xindex>
Expand Down Expand Up @@ -467,8 +468,7 @@ func.func @layernorm_4D_with_scale_bias_no_SIMD(%arg0: tensor<2x64x31x3xf32>, %a
// CHECK: krnl.store [[LOAD_VAR_reshape_MEM_6_1_1_1_1_]], [[VAR_reshape_75_]]{{.}}[[VAR_8_5_]]#0, [[VAR_8_5_]]#1, [[VAR_11_12_]]{{.}} : memref<2x64x93xf32>
// CHECK: }
// CHECK: }
// CHECK: [[VAR_7_:%.+]] = builtin.unrealized_conversion_cast [[RES_41_]] : memref<2x64x31x3xf32> to tensor<2x64x31x3xf32>
// CHECK: onnx.Return [[VAR_7_]] : tensor<2x64x31x3xf32>
// CHECK: onnx.Return [[VAR_6_]] : tensor<2x64x31x3xf32>
// CHECK: }
}

Expand Down
75 changes: 37 additions & 38 deletions test/mlir/conversion/onnx_to_krnl/Tensor/onnx_lowering_depth_to_space_op.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,47 +7,46 @@ func.func private @test_depth_to_space_dynamic_dims(%arg0 : tensor<1x?x8x?xf32>)
%0 = "onnx.DepthToSpace"(%arg0) {blocksize = 4 : si64} : (tensor<1x?x8x?xf32>) -> tensor<1x?x32x?xf32>
"func.return"(%0) : (tensor<1x?x32x?xf32>) -> ()

// CHECK-DAG: [[MAP_1_:#.+]] = affine_map<()[s0] -> (s0 floordiv 16)>
// CHECK-DAG: [[MAP_2_:#.+]] = affine_map<()[s0] -> (s0 * 4)>
// CHECK-LABEL: func private @test_depth_to_space_dynamic_dims
// CHECK-DAG: [[MAP_0_:#.+]] = affine_map<()[s0] -> (s0 floordiv 16)>
// CHECK-DAG: [[MAP_1_:#.+]] = affine_map<()[s0] -> (s0 * 4)>
// CHECK-LABEL: func.func private @test_depth_to_space_dynamic_dims
// CHECK-SAME: ([[PARAM_0_:%.+]]: memref<1x?x8x?xf32>) -> memref<1x?x32x?xf32> {
// CHECK-DAG: [[VAR_c3_:%.+]] = arith.constant 3 : index
// CHECK-DAG: [[VAR_c2_:%.+]] = arith.constant 2 : index
// CHECK-DAG: [[VAR_c1_:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[VAR_c0_:%.+]] = arith.constant 0 : index
// CHECK-DAG: [[VAR_c32_:%.+]] = arith.constant 32 : index
// CHECK-DAG: [[VAR_c5_:%.+]] = arith.constant 5 : index
// CHECK-DAG: [[VAR_c4_:%.+]] = arith.constant 4 : index
// CHECK-DAG: [[VAR_c8_:%.+]] = arith.constant 8 : index
// CHECK-DAG: [[VAR_5_:%.+]] = builtin.unrealized_conversion_cast [[PARAM_0_]] : memref<1x?x8x?xf32> to tensor<1x?x8x?xf32>
// CHECK-DAG: [[CST_5_:%.+]] = arith.constant 5 : index
// CHECK-DAG: [[CST_2_:%.+]] = arith.constant 2 : index
// CHECK-DAG: [[CST_0_:%.+]] = arith.constant 0 : index
// CHECK-DAG: [[CST_32_:%.+]] = arith.constant 32 : index
// CHECK-DAG: [[CST_4_:%.+]] = arith.constant 4 : index
// CHECK-DAG: [[CST_3_:%.+]] = arith.constant 3 : index
// CHECK-DAG: [[CST_8_:%.+]] = arith.constant 8 : index
// CHECK-DAG: [[CST_1_:%.+]] = arith.constant 1 : index
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_0_:%.+]] = memref.dim [[PARAM_0_]], [[VAR_c1_]] : memref<1x?x8x?xf32>
// CHECK-DAG: [[VAR_1_:%.+]] = memref.dim [[PARAM_0_]], [[VAR_c3_]] : memref<1x?x8x?xf32>
// CHECK-DAG: [[VAR_dim_:%.+]] = memref.dim [[PARAM_0_]], [[CST_1_]] : memref<1x?x8x?xf32>
// CHECK-DAG: [[VAR_dim_0_:%.+]] = memref.dim [[PARAM_0_]], [[CST_3_]] : memref<1x?x8x?xf32>
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_2_:%.+]] = affine.apply [[MAP_1_]](){{.}}[[VAR_0_]]{{.}}
// CHECK-DAG: [[VAR_3_:%.+]] = affine.apply [[MAP_2_]](){{.}}[[VAR_1_]]{{.}}
// CHECK-DAG: [[VAR_0_:%.+]] = affine.apply [[MAP_0_]](){{.}}[[VAR_dim_]]{{.}}
// CHECK-DAG: [[VAR_1_:%.+]] = affine.apply [[MAP_1_]](){{.}}[[VAR_dim_0_]]{{.}}
// CHECK-DAG: [[RES_:%.+]] = memref.alloc() {{.*}}: memref<6xindex>
// CHECK: krnl.store [[VAR_c1_]], [[RES_]]{{.}}[[VAR_c0_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_c4_]], [[RES_]]{{.}}[[VAR_c1_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_c4_]], [[RES_]]{{.}}[[VAR_c2_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_2_]], [[RES_]]{{.}}[[VAR_c3_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_c8_]], [[RES_]]{{.}}[[VAR_c4_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_1_]], [[RES_]]{{.}}[[VAR_c5_]]{{.}} : memref<6xindex>
// CHECK-DAG: [[VAR_6_:%.+]] = builtin.unrealized_conversion_cast [[RES_]] : memref<6xindex> to tensor<6xi64>
// CHECK: [[VAR_7_:%.+]] = "onnx.Reshape"([[VAR_5_]], [[VAR_6_]]) {allowzero = 0 : si64} : (tensor<1x?x8x?xf32>, tensor<6xi64>) -> tensor<?x?x?x?x?x?xf32>
// CHECK: [[VAR_8_:%.+]] = builtin.unrealized_conversion_cast [[VAR_7_]] : tensor<?x?x?x?x?x?xf32> to memref<?x?x?x?x?x?xf32>
// CHECK: [[VAR_9_:%.+]] = memref.cast [[VAR_8_]] : memref<?x?x?x?x?x?xf32> to memref<1x4x4x?x8x?xf32>
// CHECK: [[VAR_10_:%.+]] = builtin.unrealized_conversion_cast [[VAR_9_]] : memref<1x4x4x?x8x?xf32> to tensor<1x4x4x?x8x?xf32>
// CHECK-DAG: [[VAR_11_:%.+]] = "onnx.Transpose"([[VAR_10_]]) {perm = [0, 3, 4, 1, 5, 2]} : (tensor<1x4x4x?x8x?xf32>) -> tensor<1x?x8x4x?x4xf32>
// CHECK: krnl.store [[CST_1_]], [[RES_]]{{.}}[[CST_0_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[CST_4_]], [[RES_]]{{.}}[[CST_1_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[CST_4_]], [[RES_]]{{.}}[[CST_2_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_0_]], [[RES_]]{{.}}[[CST_3_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[CST_8_]], [[RES_]]{{.}}[[CST_4_]]{{.}} : memref<6xindex>
// CHECK: krnl.store [[VAR_dim_0_]], [[RES_]]{{.}}[[CST_5_]]{{.}} : memref<6xindex>
// CHECK-DAG: [[VAR_2_:%.+]] = builtin.unrealized_conversion_cast [[RES_]] : memref<6xindex> to tensor<6xi64>
// CHECK-DAG: [[VAR_3_:%.+]] = builtin.unrealized_conversion_cast [[PARAM_0_]] : memref<1x?x8x?xf32> to tensor<1x?x8x?xf32>
// CHECK: [[VAR_4_:%.+]] = "onnx.Reshape"([[VAR_3_]], [[VAR_2_]]) {allowzero = 0 : si64} : (tensor<1x?x8x?xf32>, tensor<6xi64>) -> tensor<?x?x?x?x?x?xf32>
// CHECK: [[VAR_5_:%.+]] = builtin.unrealized_conversion_cast [[VAR_4_]] : tensor<?x?x?x?x?x?xf32> to memref<?x?x?x?x?x?xf32>
// CHECK: [[VAR_cast_:%.+]] = memref.cast [[VAR_5_]] : memref<?x?x?x?x?x?xf32> to memref<1x4x4x?x8x?xf32>
// CHECK: [[VAR_6_:%.+]] = builtin.unrealized_conversion_cast [[VAR_cast_]] : memref<1x4x4x?x8x?xf32> to tensor<1x4x4x?x8x?xf32>
// CHECK-DAG: [[VAR_7_:%.+]] = "onnx.Transpose"([[VAR_6_]]) {perm = [0, 3, 4, 1, 5, 2]} : (tensor<1x4x4x?x8x?xf32>) -> tensor<1x?x8x4x?x4xf32>
// CHECK-DAG: [[RES_1_:%.+]] = memref.alloc() {{.*}}: memref<4xindex>
// CHECK: krnl.store [[VAR_c1_]], [[RES_1_]]{{.}}[[VAR_c0_]]{{.}} : memref<4xindex>
// CHECK: krnl.store [[VAR_2_]], [[RES_1_]]{{.}}[[VAR_c1_]]{{.}} : memref<4xindex>
// CHECK: krnl.store [[VAR_c32_]], [[RES_1_]]{{.}}[[VAR_c2_]]{{.}} : memref<4xindex>
// CHECK: krnl.store [[VAR_3_]], [[RES_1_]]{{.}}[[VAR_c3_]]{{.}} : memref<4xindex>
// CHECK: [[VAR_13_:%.+]] = builtin.unrealized_conversion_cast [[RES_1_]] : memref<4xindex> to tensor<4xi64>
// CHECK: [[VAR_14_:%.+]] = "onnx.Reshape"([[VAR_11_]], [[VAR_13_]]) {allowzero = 0 : si64} : (tensor<1x?x8x4x?x4xf32>, tensor<4xi64>) -> tensor<?x?x?x?xf32>
// CHECK: [[VAR_15_:%.+]] = builtin.unrealized_conversion_cast [[VAR_14_]] : tensor<?x?x?x?xf32> to memref<?x?x?x?xf32>
// CHECK: [[VAR_16_:%.+]] = memref.cast [[VAR_15_]] : memref<?x?x?x?xf32> to memref<1x?x32x?xf32>
// CHECK: return [[VAR_16_]] : memref<1x?x32x?xf32>
// CHECK: }
// CHECK: krnl.store [[CST_1_]], [[RES_1_]]{{.}}[[CST_0_]]{{.}} : memref<4xindex>
// CHECK: krnl.store [[VAR_0_]], [[RES_1_]]{{.}}[[CST_1_]]{{.}} : memref<4xindex>
// CHECK: krnl.store [[CST_32_]], [[RES_1_]]{{.}}[[CST_2_]]{{.}} : memref<4xindex>
// CHECK: krnl.store [[VAR_1_]], [[RES_1_]]{{.}}[[CST_3_]]{{.}} : memref<4xindex>
// CHECK: [[VAR_8_:%.+]] = builtin.unrealized_conversion_cast [[RES_1_]] : memref<4xindex> to tensor<4xi64>
// CHECK: [[VAR_9_:%.+]] = "onnx.Reshape"([[VAR_7_]], [[VAR_8_]]) {allowzero = 0 : si64} : (tensor<1x?x8x4x?x4xf32>, tensor<4xi64>) -> tensor<?x?x?x?xf32>
// CHECK: [[VAR_10_:%.+]] = builtin.unrealized_conversion_cast [[VAR_9_]] : tensor<?x?x?x?xf32> to memref<?x?x?x?xf32>
// CHECK: [[VAR_cast_2_:%.+]] = memref.cast [[VAR_10_]] : memref<?x?x?x?xf32> to memref<1x?x32x?xf32>
// CHECK: return [[VAR_cast_2_]] : memref<1x?x32x?xf32>
}
Loading
Loading