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tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/device-transform-nnapi.mlir
%0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> // CHECK: %[[VAL_0:.*]] = arith.constant dense<[2, 1]> : tensor<2xi32> // CHECK: %[[CONCAT:.*]] = "tfl.concatenation"(%arg0, %arg1) <{axis = 0 : i32, fused_activation_function = "NONE"}> : (tensor<1xf32>, tensor<1xf32>) -> tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf_patterns.td
: Constraint<CPred<"$0.getType().isa<RankedTensorType>()">>; // This pattern converts TensorFlow axis format to HLO axis format which // doesn't wrap around like TensorFlow and is always positive. For this // conversion, use the first input to get inputs rank. Other inputs need not be // ranked. // Defining op for `axis` is TensorFlow constant op in the pattern as during
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 06 18:46:23 UTC 2024 - 34.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/FakeQuantSupport.cc
} SmallVector<double, 4> scales; SmallVector<int64_t, 4> zeroPoints; scales.reserve(axisSize); zeroPoints.reserve(axisSize); for (size_t axis = 0; axis != axisSize; ++axis) { double rmin = rmins[axis]; double rmax = rmaxs[axis]; if (std::fabs(rmax - rmin) < std::numeric_limits<double>::epsilon()) { scales.push_back(1.0); zeroPoints.push_back(qmin); continue; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 11:52:27 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/cc/gradients/manip_grad.cc
const std::vector<Output>& grad_inputs, std::vector<Output>* grad_outputs) { auto shift = op.input(1); auto axis = op.input(2); auto grad_op = Roll(scope, grad_inputs[0], Neg(scope, shift), axis); grad_outputs->push_back(grad_op); grad_outputs->push_back(NoGradient()); grad_outputs->push_back(NoGradient()); return scope.status(); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 19 12:19:42 UTC 2020 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.td
<?x?x3x2>, axis=2 => N=6 ``` }]; let arguments = (ins quant_RealValueType:$arg, ElementsAttr:$layerStats, OptionalAttr<ElementsAttr>:$axisStats, OptionalAttr<I64Attr>:$axis); let results = (outs quant_RealValueType); let hasVerifier = 1; } def quantfork_CoupledRefOp : quantfork_Op<"coupled_ref", [SameOperandsAndResultType]> { let summary = [{
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Oct 13 12:46:08 UTC 2022 - 10.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/convert_tf_quant_types_test.cc
func.func @main(%arg0: tensor<3x3x!tf_type.qint8>, %arg1: tensor<3x3x!tf_type.qint8>) -> tensor<6x3x!tf_type.qint8> { %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64> %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3x!tf_type.qint8>, tensor<3x3x!tf_type.qint8>, tensor<i64>) -> tensor<6x3x!tf_type.qint8> func.return %1 : tensor<6x3x!tf_type.qint8> } })";
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 09:05:02 UTC 2024 - 4.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/simple-graph.mlir
%2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %3 : tensor<2x1xf32> } // CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/fuse-tftext.mlir
%178 = "tf.Pack"(%7, %177) {axis = 0 : i64, device = ""} : (tensor<i32>, tensor<i32>) -> tensor<2xi32> %179 = "tf.Tile"(%115, %178) {device = ""} : (tensor<?x1xi64>, tensor<2xi32>) -> tensor<?x?xi64> %180 = "tf.Mul"(%177, %118) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32> %181 = "tf.Pack"(%180) {axis = 0 : i64, device = ""} : (tensor<i32>) -> tensor<1xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 460.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
int64_t axis = axis_int.getSExtValue(); if (axis < 0) { axis += input_type.getRank(); } if (axis < 0 || (input_type.hasRank() && axis >= input_type.getRank())) { return op.emitOpError( llvm::formatv("perm[{0}] must be in [-rank, rank)", index)); } if (std::count(axes.begin(), axes.end(), axis) > 0) { return op.emitOpError(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/device-transform-gpu.mlir
// RUN: tac-opt-all-backends -tfl-device-transform-gpu %s -split-input-file -verify-diagnostics | FileCheck %s func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> { %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> } // CHECK: func @pack(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 15.6K bytes - Viewed (0)