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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/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/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/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/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/get-op-cost.mlir
// ----- func.func @pack_CPU(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.device = "CPU", tac.interface_name = "func_2"} { // CHECK: tac.cost = 1.000000e+02 %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", values_count = 2 : i32} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32> func.return %0 : tensor<2x100xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:29:10 UTC 2022 - 5.7K bytes - Viewed (0) -
tensorflow/cc/framework/gradient_checker_test.cc
xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(shape))); xs.push_back(Placeholder(scope, DT_DOUBLE, Placeholder::Shape(shape))); auto tmp = Stack(scope, xs, Stack::Axis(0)); auto y = Unstack(scope, tmp, 2, Unstack::Axis(0)); double max_error; TF_ASSERT_OK((ComputeGradientError<double, double, double>( scope, xs, {shape, shape}, y.output, {shape, shape}, &max_error))); EXPECT_LT(max_error, 1e-10);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Aug 06 15:54:08 UTC 2018 - 6.7K bytes - Viewed (0) -
tensorflow/c/experimental/ops/array_ops.cc
// // Description: // Given a tensor `input`, this operation inserts a dimension of 1 at the // dimension index `axis` of `input`'s shape. The dimension index `axis` // starts at zero; if you specify a negative number for `axis` it is counted // backward from the end. // // This operation is useful if you want to add a batch dimension to a single
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 10 19:11:36 UTC 2022 - 6.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions_drq.td
[(IsNotInLiftedFunc $res), (IsConstTensor $b)], [], (addBenefit 1)>; def LiftGather : Pat< (TF_GatherV2Op:$res $params, $indices, $axis, $batch_dims), (LiftAsTFPartitionedCall<"composite_gather_fn"> (ArgumentList $params, $indices, $axis), (ResultList $res), (NamedAttributeList (NamedAttr<"batch_dims"> $batch_dims))), [(IsNotInLiftedFunc $res), (IsConstTensor $params)], [], (addBenefit 1)>;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Dec 10 05:52:02 UTC 2023 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/convert_ref_variables.mlir
// CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2xi32> %axis = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32> %0 = "tf.VariableV2"() {container = "", shape = #tf_type.shape<>, shared_name = "x"} : () -> tensor<!tf_type.int32ref> %1 = "tf.ConcatV2"(%0, %0, %axis) : (tensor<!tf_type.int32ref>, tensor<!tf_type.int32ref>, tensor<i32>) -> tensor<2xi32> func.return %1 : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 28 12:06:33 UTC 2022 - 4.6K bytes - Viewed (0)