| /llvm-project-15.0.7/mlir/lib/ExecutionEngine/ |
| H A D | CRunnerUtils.cpp | 86 for (int64_t axis = rank - 1; axis >= 0; --axis) { in memrefCopy() local 88 auto newIndex = ++indices[axis]; in memrefCopy() 89 readIndex += srcStrides[axis]; in memrefCopy() 90 writeIndex += dstStrides[axis]; in memrefCopy() 92 if (src.sizes[axis] != newIndex) in memrefCopy() 95 if (axis == 0) in memrefCopy() 99 indices[axis] = 0; in memrefCopy() 100 readIndex -= src.sizes[axis] * srcStrides[axis]; in memrefCopy() 101 writeIndex -= dst.sizes[axis] * dstStrides[axis]; in memrefCopy()
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| /llvm-project-15.0.7/mlir/test/Dialect/Quant/ |
| H A D | parse-ops-invalid.mlir | 43 ]> : tensor<3x2xi8>, axis = 3 : i64 51 …ected-error@+1 {{axisStats must have shape [N,2] where N = the slice size defined by the axis dim}} 59 ]> : tensor<4x2xf32>, axis = 3 : i64 67 …ected-error@+1 {{axisStats must have shape [N,2] where N = the slice size defined by the axis dim}} 74 ]> : tensor<3x3xf32>, axis = 3 : i64 81 // expected-error@+1 {{axis must be specified for axisStats}}
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| H A D | parse-ops.mlir | 22 …min = [0.0 : f32, 1.0 : f32], max = [2.0 : f32, 3.0 : f32], axis = 2, num_bits = 8, narrow_range =… 25 …min = [0.0 : f32, 1.0 : f32], max = [2.0 : f32, 3.0 : f32], axis = 2, num_bits = 8, narrow_range =… 28 min = [0.0 : f32, 1.0 : f32], max = [2.0 : f32, 3.0 : f32], axis = 2, num_bits = 8 53 ]> : tensor<3x2xf32>, axis = 2 : i64
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| H A D | parse-uniform.mlir | 132 // Per-axis scales and zero points (affine) 141 // Per-axis scales and no zero points (fixedpoint) 150 // Per-axis scales and zero points (mixed affine and fixedpoint)
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| H A D | convert-fakequant.mlir | 217 // Verifies a qint8 per axis 230 num_bits = 8, narrow_range = false, is_signed = true, axis = 2
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| /llvm-project-15.0.7/mlir/test/Dialect/Tosa/ |
| H A D | tosa-decompose-transpose-conv.mlir | 5 // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64} 6 // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64} 17 // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64} 18 // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64} 28 // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64} 29 // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64} 46 // CHECK-DAG: %[[REV1:.+]] = "tosa.reverse"(%[[RESW2]]) {axis = 1 : i64} 47 // CHECK-DAG: %[[NEWWEIGHT:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64} 78 // CHECK-DAG: %[[REV1:.+]] = "tosa.reverse"(%[[RESW2]]) {axis = 1 : i64} 79 // CHECK-DAG: %[[NEWWEIGHT:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
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| H A D | tosa-infer-shapes.mlir | 57 // CHECK: "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<4xf32>) -> tensor<4xf32> 58 %9 = "tosa.reverse"(%arg0) { axis = 0 : i64 } : (tensor<4xf32>) -> tensor<?xf32> 96 // CHECK: "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<4xi32>) -> tensor<4xi32> 97 %6 = "tosa.reverse"(%arg0) { axis = 0 : i64 } : (tensor<4xi32>) -> tensor<?xi32> 263 // CHECK: "tosa.argmax"(%arg0) {axis = 0 : i64} : (tensor<2x3xi32>) -> tensor<3xi32> 264 %0 = "tosa.argmax"(%arg0) {axis = 0 : i64} : (tensor<2x3xi32>) -> tensor<?xi32> 266 // CHECK: "tosa.argmax"(%arg0) {axis = 1 : i64} : (tensor<2x3xi32>) -> tensor<2xi32> 267 %1 = "tosa.argmax"(%arg0) {axis = 1 : i64} : (tensor<2x3xi32>) -> tensor<?xi32> 275 // CHECK: "tosa.argmax"(%arg0) {axis = 0 : i64} : (tensor<2x?xi32>) -> tensor<?xi32> 276 %0 = "tosa.argmax"(%arg0) {axis = 0 : i64} : (tensor<2x?xi32>) -> tensor<?xi32> [all …]
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| H A D | canonicalize.mlir | 6 %0 = "tosa.argmax"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 92 %0 = "tosa.concat"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 100 %0 = "tosa.concat"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x?xf32> 242 %0 = "tosa.reduce_all"(%arg0) {axis = 1 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 249 %0 = "tosa.reduce_all"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 256 %0 = "tosa.reduce_any"(%arg0) {axis = 1 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 263 %0 = "tosa.reduce_any"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 270 %0 = "tosa.reduce_max"(%arg0) {axis = 1 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 277 %0 = "tosa.reduce_max"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> 284 %0 = "tosa.reduce_min"(%arg0) {axis = 1 : i64}: (tensor<?x1xf32>) -> tensor<?x1xf32> [all …]
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| H A D | ops.mlir | 8 %0 = "tosa.argmax"(%arg0) {axis = 1 : i64} : (tensor<14x19xf32>) -> tensor<14xi32> 339 %0 = "tosa.reduce_all"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1> 347 %0 = "tosa.reduce_any"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xi1>) -> tensor<1x21x3xi1> 355 %0 = "tosa.reduce_max"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32> 363 %0 = "tosa.reduce_min"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32> 371 %0 = "tosa.reduce_prod"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32> 379 %0 = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32> 387 …%0 = "tosa.concat"(%arg0, %arg1) {axis = 0 : i64} : (tensor<13x21x3xf32>, tensor<13x21x3xf32>) -> … 416 %0 = "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<13x21x3xf32>) -> tensor<13x21x3xf32>
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| H A D | constrained_shapes.mlir | 9 %0 = "tosa.argmax"(%arg0) {axis = 1 : i64} : (tensor<?xf32>) -> tensor<?xi32>
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| /llvm-project-15.0.7/mlir/lib/Dialect/Quant/Utils/ |
| H A D | FakeQuantSupport.cpp | 164 for (size_t axis = 0; axis != axisSize; ++axis) { in fakeQuantAttrsToType() local 165 double rmin = rmins[axis]; in fakeQuantAttrsToType() 166 double rmax = rmaxs[axis]; in fakeQuantAttrsToType()
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| /llvm-project-15.0.7/mlir/include/mlir/Dialect/Quant/ |
| H A D | QuantOps.td | 154 Simulates the effect of per axis uniform quantization with const range. 159 same per axis uniform quantization simulation as is done by the TensorFlow 170 I64Attr:$axis, 210 and those for each axis, in the (optional) `axisStats` attribute. The 216 splitted by the `axis` dimension. For example: 219 <?x?x3x2>, axis=3 => N=2 220 <?x?x3x2>, axis=2 => N=6 228 OptionalAttr<I64Attr>:$axis);
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| /llvm-project-15.0.7/mlir/include/mlir/Dialect/Tosa/IR/ |
| H A D | TosaOps.td | 48 I64Attr: $axis 1228 I64Attr:$axis 1253 I64Attr:$axis 1278 I64Attr:$axis 1303 I64Attr:$axis 1323 Reduce a tensor along the given axis by computing the product of the axis. 1328 I64Attr:$axis 1348 Reduce a tensor along the given axis by computing the sum of the axis. 1353 I64Attr:$axis 1384 I64Attr:$axis [all …]
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| /llvm-project-15.0.7/mlir/test/Conversion/TosaToLinalg/ |
| H A D | tosa-to-linalg.mlir | 714 %0 = "tosa.reduce_sum"(%arg0) {axis = 0 : i64} : (tensor<5x4xf32>) -> tensor<1x4xf32> 881 %0 = "tosa.reduce_all"(%arg0) {axis = 0 : i64} : (tensor<5x4xi1>) -> tensor<1x4xi1> 887 %1 = "tosa.reduce_any"(%arg0) {axis = 0 : i64} : (tensor<5x4xi1>) -> tensor<1x4xi1> 1139 %0 = "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<5x4xi32>) -> tensor<5x4xi32> 1152 %1 = "tosa.reverse"(%arg0) {axis = 1 : i64} : (tensor<5x4xi32>) -> tensor<5x4xi32> 1174 %0 = "tosa.reverse"(%arg0) {axis = 0 : i64} : (tensor<?xi32>) -> tensor<?xi32> 1355 %0 = "tosa.argmax"(%arg0) { axis = 0 : i64} : (tensor<3x2xi32>) -> (tensor<2xi32>) 1370 %1 = "tosa.argmax"(%arg0) { axis = 1 : i64} : (tensor<3x2xi32>) -> (tensor<3xi32>) 1379 %2 = "tosa.argmax"(%arg1) { axis = 0 : i64} : (tensor<6xf32>) -> (tensor<i32>) 1405 %0 = "tosa.argmax"(%arg0) { axis = 0 : i64} : (tensor<3x?xi32>) -> (tensor<?xi32>) [all …]
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| /llvm-project-15.0.7/mlir/lib/Conversion/TosaToLinalg/ |
| H A D | TosaToLinalg.cpp | 800 if (axis != i) { in reduceMatchAndRewriteHelper() 834 if (axis != i) in reduceMatchAndRewriteHelper() 1651 int axis = op.getAxis(); in matchAndRewrite() local 1670 Value resultDimSize = sizes[axis]; in matchAndRewrite() 1676 sizes[axis] = resultDimSize; in matchAndRewrite() 1701 offsets[axis] = in matchAndRewrite() 1702 rewriter.createOrFold<arith::AddIOp>(loc, offsets[axis], sizes[axis]); in matchAndRewrite() 1719 auto axis = op.getAxis(); in matchAndRewrite() local 1747 if (i == axis) { in matchAndRewrite() 1934 int axis = argmaxOp.getAxis(); in matchAndRewrite() local [all …]
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| /llvm-project-15.0.7/clang/test/CodeGenObjCXX/ |
| H A D | mangle.mm | 65 @property (assign) enum { T2x, T2y, T2z } axis; 77 Test2Template<decltype(t.axis)> t0;
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| /llvm-project-15.0.7/llvm/lib/Analysis/models/ |
| H A D | gen-regalloc-eviction-test-model.py | 49 result = tf.math.argmax(inputs[0]['mask'], axis=-1) + module.var
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| /llvm-project-15.0.7/mlir/lib/Dialect/Tosa/IR/ |
| H A D | TosaOps.cpp | 347 IntegerAttr axis = attributes.get("axis").cast<IntegerAttr>(); in inferReturnTypeComponents() local 348 int32_t axisVal = axis.getValue().getSExtValue(); in inferReturnTypeComponents() 372 int32_t axis = in inferReturnTypeComponents() local 387 if (i == axis || operandShape.isDynamicDim(i)) in inferReturnTypeComponents() 410 if (!operandShape.hasRank() || operandShape.isDynamicDim(axis)) { in inferReturnTypeComponents() 415 concatDimSize += operandShape.getDimSize(axis); in inferReturnTypeComponents() 418 outputShape[axis] = concatDimSize; in inferReturnTypeComponents() 839 ShapeAdaptor operandShape, IntegerAttr axis, in ReduceInferReturnTypes() argument 848 int64_t axisVal = axis.getValue().getSExtValue(); in ReduceInferReturnTypes()
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| /llvm-project-15.0.7/openmp/runtime/tools/ |
| H A D | summarizeStats.py | 247 plt.axis('equal') 264 plt.axis('equal')
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| /llvm-project-15.0.7/mlir/docs/ |
| H A D | Quantization.md | 26 * *per-axis* (also called *per-channel*) : Applying individually to each index 27 along a specific axis of a tensor type. 220 * stats : Declares inline statistics (per layer and per axis) for the point in
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| H A D | ShapeInference.md | 5 three axis: 1) elemental type, 2) rank (including static or dynamic), 3) 284 would also be common (e.g., a concat of `[n,m]` and `[n,m]` matrix along axis 0
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| /llvm-project-15.0.7/mlir/test/python/ir/ |
| H A D | array_attributes.py | 148 array = np.packbits(bool_array, axis=None, bitorder="little")
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| /llvm-project-15.0.7/llvm/utils/llvm-locstats/ |
| H A D | llvm-locstats.py | 31 plt.grid(color='grey', which='major', axis='y', linestyle='-', linewidth=0.3)
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| /llvm-project-15.0.7/libc/benchmarks/ |
| H A D | README.md | 105 The Y-axis unit can be changed via the `--mode` flag:
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| /llvm-project-15.0.7/llvm/cmake/ |
| H A D | config.guess | 903 echo cris-axis-linux-gnu 906 echo crisv32-axis-linux-gnu
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