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/llvm-project-15.0.7/mlir/test/Dialect/Tosa/
H A Dbroadcast.mlir6 // CHECK-NOT: reshape
14 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1]}
23 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1]}
32 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
41 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
50 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
59 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
68 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
77 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 1]}
104 // CHECK-DAG: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 1, 1]}
[all …]
H A Dtosa-decompose-conv2d.mlir8 // CHECK: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [400, 2]}
10 // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%arg1) {new_shape = [3, 2]}
14 // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 3]}
26 // CHECK: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [400, 2]}
28 // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%arg1) {new_shape = [3, 2]}
33 // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 3]}
47 // CHECK: %[[VAL_3:.*]] = "tosa.reshape"(%[[VAL_0]]) {new_shape = [-1, 64]} : (tensor<?x1…
48 // CHECK: %[[VAL_4:.*]] = "tosa.reshape"(%[[VAL_1]]) {new_shape = [384, 64]} : (tensor<38…
50 // CHECK: %[[VAL_6:.*]] = "tosa.reshape"(%[[VAL_5]]) {new_shape = [-1, 14, 14, 384]} : (t…
H A Dtosa-decompose-transpose-conv.mlir43 // CHECK-DAG: %[[RESW1:.+]] = "tosa.reshape"(%[[PADW]]) {new_shape = [5, 2, 2, 2, 3, 3]}
45 // CHECK-DAG: %[[RESW2:.+]] = "tosa.reshape"(%[[TRANS]]) {new_shape = [30, 2, 2, 3]}
57 // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = "tosa.reshape"(%[[CONV]]) {new_shape = [2, 18, 16, 2, 3, 5]}
59 // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = "tosa.reshape"(%[[TRANS_OUT]]) {new_shape = [2, 36, 48, 5]}
75 // CHECK-DAG: %[[RESW1:.+]] = "tosa.reshape"(%[[PADW]]) {new_shape = [5, 2, 2, 2, 3, 3]}
77 // CHECK-DAG: %[[RESW2:.+]] = "tosa.reshape"(%[[TRANS]]) {new_shape = [30, 2, 2, 3]}
89 // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = "tosa.reshape"(%[[CONV]]) {new_shape = [2, 18, 16, 2, 3, 5]}
91 // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = "tosa.reshape"(%[[TRANS_OUT]]) {new_shape = [2, 36, 48, 5]}
H A Dtosa-decompose-depthwise.mlir8 // CHECK: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [4, 10, 10, 2, 1]}
10 // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%arg1) {new_shape = [1, 1, 1, 2, 3]}
14 // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 6]}
H A Dcanonicalize.mlir326 %0 = "tosa.reshape"(%arg0) {new_shape = [-1, 10]}: (tensor<?x10xf32>) -> tensor<?x10xf32>
332 // CHECK: %[[VAR0:.+]] = "tosa.reshape"(%arg0) {new_shape = [-1, 5]}
334 %0 = "tosa.reshape"(%arg0) {new_shape = [5, -1]}: (tensor<?x10xf32>) -> tensor<5x?xf32>
335 %1 = "tosa.reshape"(%0) {new_shape = [-1, 5]}: (tensor<5x?xf32>) -> tensor<?x5xf32>
344 %1 = "tosa.reshape"(%0) {new_shape = [1, 10]} : (tensor<10xi32>) -> tensor<1x10xi32>
354 %1 = "tosa.reshape"(%0) {new_shape = [1, 10]} : (tensor<10xi32>) -> tensor<1x10xi32>
360 //CHECK: "tosa.reshape"
362 %1 = "tosa.reshape"(%0) {new_shape = [1, 3]} : (tensor<3xi32>) -> tensor<1x3xi32>
408 %1 = "tosa.reshape"(%0) {new_shape = [1]} : (tensor<1x1xi1>) -> tensor<1xi1>
H A Dops.mlir340 %1 = "tosa.reshape"(%0) {new_shape = [21, 3]} : (tensor<1x21x3xi1>) -> tensor<21x3xi1>
348 %1 = "tosa.reshape"(%0) {new_shape = [21, 3]} : (tensor<1x21x3xi1>) -> tensor<21x3xi1>
356 %1 = "tosa.reshape"(%0) {new_shape = [21, 3]} : (tensor<1x21x3xf32>) -> tensor<21x3xf32>
364 %1 = "tosa.reshape"(%0) {new_shape = [21, 3]} : (tensor<1x21x3xf32>) -> tensor<21x3xf32>
372 %1 = "tosa.reshape"(%0) {new_shape = [21, 3]} : (tensor<1x21x3xf32>) -> tensor<21x3xf32>
380 %1 = "tosa.reshape"(%0) {new_shape = [21, 3]} : (tensor<1x21x3xf32>) -> tensor<21x3xf32>
407 // CHECK-LABEL: reshape
409 %0 = "tosa.reshape"(%arg0) {new_shape = [1, 819]} : (tensor<13x21x3xf32>) -> tensor<1x819xf32>
533 %4 = "tosa.reshape"(%2) {new_shape = [1]} : (tensor<i32>) -> tensor<1xi32>
/llvm-project-15.0.7/clang/test/Analysis/
H A Dmalloc-interprocedural.c77 static char *reshape(char *in) { in reshape() function
83 v = reshape(v); in testThatRemoveDeadBindingsRunBeforeEachCall()
84 v = reshape(v);// expected-warning {{Potential leak of memory pointed to by 'v'}} in testThatRemoveDeadBindingsRunBeforeEachCall()
/llvm-project-15.0.7/mlir/test/mlir-cpu-runner/
H A Dmemref-reshape.mlir53 %output = memref.reshape %input(%shape)
68 %output = memref.reshape %input(%shape)
83 %output = memref.reshape %input(%dyn_size_shape)
98 %output = memref.reshape %input(%dyn_size_shape)
/llvm-project-15.0.7/mlir/docs/Tutorials/Toy/
H A DCh-3.md179 A redundant reshape optimization similar to SimplifyRedundantTranspose can be
208 eliminating the reshape operation.
211 def ReshapeConstant : NativeCodeCall<"$0.reshape(($1.getType()).cast<ShapedType>())">;
217 We demonstrate these reshape optimizations using the following
233 %1 = toy.reshape(%0 : tensor<2xf64>) to tensor<2x1xf64>
234 %2 = toy.reshape(%1 : tensor<2x1xf64>) to tensor<2x1xf64>
235 %3 = toy.reshape(%2 : tensor<2x1xf64>) to tensor<2x1xf64>
255 As expected, no reshape operations remain after canonicalization.
/llvm-project-15.0.7/mlir/test/Examples/Toy/Ch4/
H A Dshape_inference.mlir13 %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64>
15 %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64>
H A Dcodegen.toy25 # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
27 # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
/llvm-project-15.0.7/mlir/test/Examples/Toy/Ch6/
H A Dshape_inference.mlir13 %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64>
15 %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64>
H A Dcodegen.toy25 # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
27 # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
/llvm-project-15.0.7/mlir/test/Examples/Toy/Ch5/
H A Dshape_inference.mlir13 %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64>
15 %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64>
H A Dcodegen.toy25 # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
27 # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
/llvm-project-15.0.7/mlir/test/Examples/Toy/Ch7/
H A Dshape_inference.mlir13 %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64>
15 %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64>
H A Dcodegen.toy25 # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
27 # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
/llvm-project-15.0.7/mlir/test/Examples/Toy/Ch3/
H A Dcodegen.toy25 # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
27 # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
/llvm-project-15.0.7/mlir/test/Examples/Toy/Ch2/
H A Dcodegen.toy25 # CHECK-NEXT: [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
27 # CHECK-NEXT: [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
/llvm-project-15.0.7/mlir/examples/toy/Ch3/include/toy/
H A DOps.td128 %1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
243 def ReshapeOp : Toy_Op<"reshape", [NoSideEffect]> {
244 let summary = "tensor reshape operation";
250 %0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
256 // We expect that the reshape operation returns a statically shaped tensor.
/llvm-project-15.0.7/mlir/examples/toy/Ch2/include/toy/
H A DOps.td129 %1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
244 def ReshapeOp : Toy_Op<"reshape"> {
245 let summary = "tensor reshape operation";
251 %0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
257 // We expect that the reshape operation returns a statically shaped tensor.
/llvm-project-15.0.7/mlir/test/Dialect/Linalg/
H A Dfusion-push-reshape.mlir1 // RUN: mlir-opt %s -test-linalg-elementwise-fusion-patterns=fuse-with-reshape-by-collapsing -split…
6 // CHECK-LABEL: func @reshape
14 func.func @reshape(%A: tensor<?x16xf32>, %B: tensor<16xf32>, %init: tensor<?x112x16xf32>) -> tensor…
/llvm-project-15.0.7/mlir/examples/toy/Ch4/include/toy/
H A DOps.td158 %1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
275 def ReshapeOp : Toy_Op<"reshape", [NoSideEffect]> {
276 let summary = "tensor reshape operation";
282 %0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
288 // We expect that the reshape operation returns a statically shaped tensor.
/llvm-project-15.0.7/mlir/examples/toy/Ch6/include/toy/
H A DOps.td158 %1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
276 def ReshapeOp : Toy_Op<"reshape", [NoSideEffect]> {
277 let summary = "tensor reshape operation";
283 %0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
296 // We expect that the reshape operation returns a statically shaped tensor.
/llvm-project-15.0.7/mlir/examples/toy/Ch5/include/toy/
H A DOps.td158 %1 = toy.reshape(%0 : tensor<f64>) to tensor<2x2xf64>
276 def ReshapeOp : Toy_Op<"reshape", [NoSideEffect]> {
277 let summary = "tensor reshape operation";
283 %0 = toy.reshape (%arg1 : tensor<10xf64>) to tensor<5x2xf64>
289 // We expect that the reshape operation returns a statically shaped tensor.

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