| /llvm-project/mlir/test/Dialect/Linalg/ |
| H A D | rank-reduce-contraction-ops.mlir | 14 %1 = linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x128x512xf32>, tensor<1x512x256xf32>)
31 linalg.batch_matmul ins(%arg0, %arg1 : memref<1x?x?xf32>, memref<1x?x?xf32>)
160 linalg.batch_matmul ins(%arg0, %arg1: memref<1x1x?xf32>, memref<1x?x?xf32>) outs(%arg2: memref<1x1x?xf32>)
254 // CHECK: linalg.batch_matmul
256 %1 = linalg.batch_matmul ins(%arg0, %arg1 : tensor<2x?x?xf32>, tensor<2x?x?xf32>)
266 // CHECK: linalg.batch_matmul
268 %1 = linalg.batch_matmul ins(%arg0, %arg1 : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
|
| H A D | reshape_control_fusion.mlir | 52 %1 = linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x?x?xf32>, tensor<1x?x?xf32>)
61 // CHECK: linalg.batch_matmul
|
| H A D | transform-op-specialize-matmul.mlir | 96 func.func @batch_matmul(%arg0: tensor<2x16x8xf32>, %arg1: tensor<2x8x16xf32>, %arg2: tensor<2x16x16…
108 // CHECK-LABEL: @batch_matmul
111 // CHECK: linalg.batch_matmul ins(%[[ARG0]], %[[ARG1]] : tensor<2x16x8xf32>, tensor<2x8x16xf32>) ou…
|
| H A D | transpose-matmul-a.mlir | 5 …%matmul = transform.structured.match ops{["linalg.matmul", "linalg.batch_matmul"]} in %arg1 : (!tr…
|
| H A D | transpose-matmul-b.mlir | 5 …%matmul = transform.structured.match ops{["linalg.matmul", "linalg.batch_matmul"]} in %arg1 : (!tr…
|
| H A D | roundtrip.mlir | 287 linalg.batch_matmul ins(%a3, %b3: memref<?x?x?xf32>, memref<?x?x?xf32>)
293 %res1 = linalg.batch_matmul
304 // CHECK: linalg.batch_matmul
306 // CHECK: linalg.batch_matmul
639 %5 = linalg.batch_matmul ins(%collapsed_0, %collapsed : tensor<36x2x5xf32>, tensor<36x5x2xf32>) outs(%4 : tensor<36x2x2xf32>) -> tensor<36x2x2xf32>
|
| H A D | transpose-matmul.mlir | 109 …%0 = linalg.batch_matmul ins(%A, %B : tensor<2x16x8xf32>, tensor<2x8x16xf32>) outs(%C : tensor<2x1…
148 …%0 = linalg.batch_matmul ins(%A, %B : tensor<?x?x?xf32>, tensor<?x?x?xf32>) outs(%C : tensor<?x?x?…
177 …%0 = linalg.batch_matmul ins(%A, %B : tensor<2x?x8xf32>, tensor<2x8x16xf32>) outs(%C : tensor<2x?x…
|
| H A D | affine.mlir | 21 linalg.batch_matmul ins(%A, %B: memref<?x?x?xf32>, memref<?x?x?xf32>)
|
| H A D | specialize-generic-ops.mlir | 81 // CHECK: linalg.batch_matmul ins(%[[A]], %[[B]] : tensor<2x16x8xf32>, tensor<2x8x16xf32>) outs(%[[…
|
| H A D | invalid.mlir | 300 linalg.batch_matmul ins(%a3, %b3: memref<?x?x?xf32>, memref<?x?xf32>)
|
| H A D | block-pack-matmul.mlir | 166 %0 = linalg.batch_matmul ins(%A, %B : tensor<512x64x128xf32>, tensor<512x128x64xf32>)
|
| H A D | vectorization-with-patterns.mlir | 68 linalg.batch_matmul
76 %0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
|
| H A D | loops.mlir | 678 linalg.batch_matmul ins(%A, %B : memref<?x?x?xf32>, memref<?x?x?xf32>)
|
| /llvm-project/mlir/test/Integration/Dialect/Transform/ |
| H A D | match_batch_matmul.mlir | 14 …transform.match.operation_name %entry ["linalg.batch_matmul", "linalg.generic"] : !transform.any_op
64 …%result = linalg.batch_matmul ins(%lhs, %rhs: tensor<40x10x20xf16>, tensor<40x20x15xf32>) outs(%fi…
|
| /llvm-project/mlir/utils/tree-sitter-mlir/dialect/ |
| H A D | linalg.js | 6 'linalg.batch_matmul',
|
| /llvm-project/mlir/utils/tree-sitter-mlir/queries/ |
| H A D | highlights.scm | 215 "linalg.batch_matmul"
|
| /llvm-project/mlir/lib/Dialect/Linalg/Transforms/ |
| H A D | TransposeMatmul.cpp | 79 /// linalg.batch_matmul(a, b)
|
| H A D | ConvertConv2DToImg2Col.cpp | 181 // we cannot use existing linalg named ops like linalg.batch_matmul. in rewriteInIm2Col()
466 // we cannot use existing linalg named ops like linalg.batch_matmul. in rewriteInIm2Col()
|
| H A D | Specialize.cpp | 94 // e.g. linalg.batch_matmul(%A, %B : tensor<20x20x20xf32>, ...) is
|
| /llvm-project/mlir/test/Conversion/TosaToLinalg/ |
| H A D | tosa-to-linalg-named.mlir | 10 // CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x3xf32>, tensor<1x3x6xf32>) outs([[FILLED]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32>
39 // CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<?x5x3xf32>, tensor<?x3x6xf32>) outs(%[[FILLED]] : tensor<?x5x6xf32>) -> tensor<?x5x6xf32>
53 // CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x3xf32>, tensor<1x3x?xf32>) outs(%[[FILLED]] : tensor<1x5x?xf32>) -> tensor<1x5x?xf32>
65 // CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x5x?xf32>, tensor<1x?x6xf32>) outs(%[[FILLED]] : tensor<1x5x6xf32>) -> tensor<1x5x6xf32>
79 // CHECK: linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x1x8xf32>, tensor<1x8x1xf32>) outs(%[[FILLED]] : tensor<?x1x1xf32>) -> tensor<?x1x1xf32>
|
| /llvm-project/mlir/python/mlir/dialects/linalg/opdsl/ops/ |
| H A D | core_named_ops.py | 476 linalg.batch_matmul, the differences from linalg.batch_matmul in the
488 def batch_matmul(
511 def batch_matmul( global() function
|
| /llvm-project/mlir/include/mlir/Dialect/Linalg/IR/ |
| H A D | LinalgNamedStructuredOps.yaml | 1405 linalg.batch_matmul, the differences from linalg.batch_matmul in the
1476 name: batch_matmul
1616 name: batch_matmul global() play
|
| /llvm-project/mlir/include/mlir/Dialect/Linalg/Transforms/ |
| H A D | Transforms.h | 1883 /// example a `linalg.batch_matmul` with unit batch size will convert to
|
| /llvm-project/mlir/include/mlir/Dialect/Linalg/TransformOps/ |
| H A D | LinalgTransformOps.td | 2593 `linalg.matmul` or `linalg.batch_matmul`. Otherwise, the operation succeeds
|