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Results 111 - 120 of 213 for se_shape (0.17 sec)
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tensorflow/compiler/mlir/lite/stablehlo/tests/optimize.mlir
%0 = "mhlo.reshape"(%arg0) : (tensor<1x1x512xf32>) -> tensor<1x512xf32> %1 = "mhlo.dot"(%0, %arg1) : (tensor<1x512xf32>, tensor<512x13x!quant.uniform<i8:f32, 0.00285>>) -> tensor<1x13xf32> %2 = "mhlo.reshape"(%1) : (tensor<1x13xf32>) -> tensor<1x1x13xf32> func.return %2 : tensor<1x1x13xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 22.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/hlo_matchers.h
#include "mlir/IR/Value.h" // from @llvm-project namespace mlir { namespace odml { // The following 5 different forms of mhlo::iota will be matched: // 1. IotaOp. // 2. IotaOp + BroadCastInDim. // 3. IotaOp + Reshape. // 4. Constant (folded Iota) + BroadCastInDim. // 5. Constant (folded result). // Moreover, the dimensions has to match the iota_dimension. bool MatchIota(DenseIntElementsAttr dimensions, Value iota); } // namespace odml
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 20:53:17 UTC 2024 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-op-cost.mlir
%0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE", tac.device = "GPU"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32> // CHECK: tac.cost = 4.040000e+01 %1 = "tfl.reshape"(%0, %cst) {tac.device = "GPU"} : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32> func.return %1 : 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/compiler/mlir/lite/tests/fuse-tftext.mlir
%21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64> %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64> %23 = "tf.Reshape"(%arg0, %5) {device = ""} : (tensor<1x!tf_type.string>, tensor<1xi32>) -> tensor<1x!tf_type.string>
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/utils/const_tensor_utils.cc
type, builder.getFloatAttr(element_ty, unique_index)); if (auto qtype = mlir::dyn_cast<QuantizedType>(element_ty)) { mlir::RankedTensorType new_type = tensorflow::GetTypeFromTFTensorShape( type.getShape(), qtype.getStorageType()); return DenseElementsAttr::get( new_type, builder.getIntegerAttr(qtype.getStorageType(), unique_index)); } llvm_unreachable("unhandled element type"); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 07 23:04:40 UTC 2024 - 16.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/reduce_type_precision.cc
if (v_int > 7 || v_int < -8) { return failure(); } } Builder builder(op.getContext()); auto shaped_type = mlir::RankedTensorType::get(const_type.getShape(), builder.getI4Type()); auto newAttr = DenseElementsAttr::getFromRawBuffer( shaped_type, mlir::cast<DenseElementsAttr>(op.getValue()).getRawData()); rewriter.replaceOpWithNewOp<arith::ConstantOp>(op, newAttr);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass_test.cc
Output reshape_input = ops::Placeholder(root.WithOpName("reshape_input"), DT_FLOAT, ops::Placeholder::Shape(TensorShape({500, 500}))); Output reshape = ops::Reshape(root.WithOpName("reshape"), reshape_input, shape); std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global())); TF_ASSERT_OK(root.ToGraph(graph.get()));
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 10:11:10 UTC 2024 - 79.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
func.return %0 : tensor<1x519xf32> } // CHECK-LABEL: func @reshape( // CHECK-SAME: %[[VAL_0:.*]]: tensor<4x6xf32>) -> tensor<2x2x6xf32> { // CHECK: %[[VAL_1:.*]] = arith.constant dense<[2, 2, 6]> : tensor<3xi64> // CHECK: %[[VAL_2:.*]] = "tf.Reshape"(%[[VAL_0]], %[[VAL_1]]) : (tensor<4x6xf32>, tensor<3xi64>) -> tensor<2x2x6xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/verify_tfxla_legalization_test.cc
%0 = mhlo.constant dense<1.000000e+00> : tensor<f64> %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64> %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64> %3 = "mhlo.dynamic_iota"(%2) {iota_dimension = 0 : i64} : (tensor<1xi64>) -> tensor<?xi32> %4 = mhlo.multiply %3, %3 : tensor<?xi32> return %4 : tensor<?xi32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 7.5K bytes - Viewed (0)