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tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
node { name: "Placeholder" op: "Placeholder" attr { key: "dtype" value { type: DT_FLOAT } } attr { key: "shape" value { shape { dim { size: 2 } dim { size: 5 } dim { size: 3 } } } } } node {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td
// tail of the other operand and the intermediate result isn't used by other // ops. // $rhs is required to be the tail shape of $lhs, so after transformation the // shape of the binary op result is valid. For example, assume the shapes of // $input, $lhs and $rhs are [1600], [1,40,40] and [40x1]. After the // transformation, the shape of the binary op result is [40x1600], which
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 66.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/export_utils.h
// Fill in the contents of TensorShapeProto for the given shape. // ShapeContainerT is any type with the following methods: // bool hasRank() // ArrayRef<int64_t> getShape() // This includes mlir::TF::ShapeAttr and mlir::ShapedType. template <typename ShapeContainerT> void SetTensorShapeProto(ShapeContainerT shape, TensorShapeProto* proto) { if (shape.hasRank()) { for (int64_t dim : shape.getShape()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/c/c_api_experimental.cc
ShapeHandle shape_handle = c.output(i); TF_ShapeAndType& shape = output_shapes_result->items[i]; shape.num_dims = c.Rank(shape_handle); if (shape.num_dims == InferenceContext::kUnknownRank) { shape.dims = nullptr; continue; } shape.dims = new int64_t[shape.num_dims]; for (size_t j = 0; j < shape.num_dims; ++j) { shape.dims[j] = c.Value(c.Dim(shape_handle, j)); } }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 29.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/fold_constant_transpose.cc
// in a `shape` shaped tensor. Assumes row-major order. `indices` and `shape` // should have the same size. // Example: Index (2, 3) of a (4, 5)-shaped tensor has the contiguous offset of // 2 * 5 + 3 = 13. int64_t GetContiguousOffset(const ArrayRef<int64_t> indices, const ArrayRef<int64_t> shape) { int64_t contiguous_offset = 0; int64_t base_offset = 1;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.7K bytes - Viewed (0) -
tensorflow/c/eager/c_api_unified_experimental_graph.cc
TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype)); if (!shape.unknown_rank()) { TF_RETURN_IF_ERROR(operation->SetAttrShape( "shape", reinterpret_cast<int64_t*>(shape.dim_sizes().data()), shape.dims())); } int num_outputs = 1; std::vector<AbstractTensorHandle*> outputs(num_outputs); TF_RETURN_IF_ERROR(operation->Execute(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 12 20:00:09 UTC 2024 - 15.4K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_device_context.cc
xla_tensor->WaitForDefinitionEventOnStream(device_to_host_stream.get()); // Transfer manager requires the shape of the shaped buffer to be the same as // literal shape except for the layout. Set the literal to use xla_tensor's // shape as it is derived from the cpu_tensor's shape using // shape_representation_fn_. xla::MutableBorrowingLiteral literal; TF_CHECK_OK(HostTensorToMutableBorrowingLiteral(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 00:36:08 UTC 2024 - 12.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
} // This assumes that the bias is of shape NxCx1x1 and doesn't require transpose // Its corresponding constraint is optimize_patterns.td:IsBiasShape() ElementsAttr ReshapeNCHWBiasToNHWC(Value v, Attribute a) { auto elements = mlir::cast<DenseElementsAttr>(a); auto shape = mlir::cast<ShapedType>(v.getType()).getShape(); if (shape.size() != 4 || shape[2] != 1 || shape[3] != 1) return elements;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/tests/raise_to_tf.mlir
func.return %2 : tensor<1x2x3x4x!tf_type.string> // CHECK: %[[id:.*]] = "tf.RiscSame"(%arg0) : (tensor<1x2x3x4x!tf_type.string>) -> tensor<*x!tf_type.string> // CHECK: %[[es:.*]] = "tf.EnsureShape"(%[[id]]) <{shape = #tf_type.shape<1x2x3x4>}> : (tensor<*x!tf_type.string>) -> tensor<1x2x3x4x!tf_type.string> // CHECK: return %[[es]] : tensor<1x2x3x4x!tf_type.string> } // CHECK-LABEL: decompose_tf_consecutive
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/graphdef2mlir/graph-function-resource-args.pbtxt
node { name: "x" op: "VarHandleOp" device: "/CPU:0" attr { key: "container" value { s: "a" } } attr { key: "dtype" value { type: DT_INT64 } } attr { key: "shape" value { shape { } } } attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 1.8K bytes - Viewed (0)