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/llvm-project-15.0.7/mlir/lib/ExecutionEngine/
H A DCRunnerUtils.cpp86 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()
/llvm-project-15.0.7/mlir/test/Dialect/Quant/
H A Dparse-ops-invalid.mlir43 ]> : 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}}
H A Dparse-ops.mlir22 …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
H A Dparse-uniform.mlir132 // 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)
H A Dconvert-fakequant.mlir217 // Verifies a qint8 per axis
230 num_bits = 8, narrow_range = false, is_signed = true, axis = 2
/llvm-project-15.0.7/mlir/test/Dialect/Tosa/
H A Dtosa-decompose-transpose-conv.mlir5 // 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}
H A Dtosa-infer-shapes.mlir57 // 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 …]
H A Dcanonicalize.mlir6 %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 …]
H A Dops.mlir8 %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>
H A Dconstrained_shapes.mlir9 %0 = "tosa.argmax"(%arg0) {axis = 1 : i64} : (tensor<?xf32>) -> tensor<?xi32>
/llvm-project-15.0.7/mlir/lib/Dialect/Quant/Utils/
H A DFakeQuantSupport.cpp164 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()
/llvm-project-15.0.7/mlir/include/mlir/Dialect/Quant/
H A DQuantOps.td154 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);
/llvm-project-15.0.7/mlir/include/mlir/Dialect/Tosa/IR/
H A DTosaOps.td48 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 …]
/llvm-project-15.0.7/mlir/test/Conversion/TosaToLinalg/
H A Dtosa-to-linalg.mlir714 %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 …]
/llvm-project-15.0.7/mlir/lib/Conversion/TosaToLinalg/
H A DTosaToLinalg.cpp800 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 …]
/llvm-project-15.0.7/clang/test/CodeGenObjCXX/
H A Dmangle.mm65 @property (assign) enum { T2x, T2y, T2z } axis;
77 Test2Template<decltype(t.axis)> t0;
/llvm-project-15.0.7/llvm/lib/Analysis/models/
H A Dgen-regalloc-eviction-test-model.py49 result = tf.math.argmax(inputs[0]['mask'], axis=-1) + module.var
/llvm-project-15.0.7/mlir/lib/Dialect/Tosa/IR/
H A DTosaOps.cpp347 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()
/llvm-project-15.0.7/openmp/runtime/tools/
H A DsummarizeStats.py247 plt.axis('equal')
264 plt.axis('equal')
/llvm-project-15.0.7/mlir/docs/
H A DQuantization.md26 * *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
H A DShapeInference.md5 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
/llvm-project-15.0.7/mlir/test/python/ir/
H A Darray_attributes.py148 array = np.packbits(bool_array, axis=None, bitorder="little")
/llvm-project-15.0.7/llvm/utils/llvm-locstats/
H A Dllvm-locstats.py31 plt.grid(color='grey', which='major', axis='y', linestyle='-', linewidth=0.3)
/llvm-project-15.0.7/libc/benchmarks/
H A DREADME.md105 The Y-axis unit can be changed via the `--mode` flag:
/llvm-project-15.0.7/llvm/cmake/
H A Dconfig.guess903 echo cris-axis-linux-gnu
906 echo crisv32-axis-linux-gnu

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