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tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
int64_t value_rank = value_type.getRank(); int64_t axis = op.getAxis(); if (axis < -value_rank || axis >= value_rank) return op.emitOpError("axis attribute must be in the range of [-") << value_rank << ", " << value_rank << ')'; axis = GetDimForAxis(axis, value_rank); int64_t dim_size = value_type.getDimSize(axis); if (ShapedType::isDynamic(dim_size)) return success();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
pkg/scheduler/framework/plugins/helper/shape_score.go
Score int64 } // BuildBrokenLinearFunction creates a function which is built using linear segments. Segments are defined via shape array. // Shape[i].Utilization slice represents points on "Utilization" axis where different segments meet. // Shape[i].Score represents function values at meeting points. // // function f(p) is defined as: // // shape[0].Score for p < shape[0].Utilization
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Tue Jul 26 17:14:05 UTC 2022 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.cc
Type packed_type = RankedTensorType::get( {bcast.output_batch_size(), rows, cols}, element_type); const auto axis = rewriter.getI64IntegerAttr(0); auto pack_op = rewriter.create<TF::PackOp>(loc, packed_type, /*values=*/matmuls, axis); // Reshape the rank-3 tensor into the correct output shape. const auto& result_batch_shape = bcast.output_batch_shape().dim_sizes();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/dot_general.cc
{static_cast<int>( dot_dimensions_info.batch_dimensions().AxesArray().size())}, builder.getIntegerType(32)), operand_shape, batch_axes_tensor, /*axis*/ 0, /*batch_dims*/ 0); flattend_shape_values.push_back(batch_dims); } else { llvm::SmallVector<int32_t> batch_i32_vec; for (int64_t element :
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 19.2K bytes - Viewed (0) -
tensorflow/cc/gradients/nn_grad.cc
if (!IsZero(scope, grad_grad)) { std::vector<int> axis; auto logits_softmax = Softmax(scope, logits); auto grad_grad_expand = ExpandDims(scope, grad_grad, 1); auto logits_softmax_expand = ExpandDims(scope, logits_softmax, 2); auto matmul_result = BatchMatMul(scope, grad_grad_expand, logits_softmax_expand); axis.push_back(1);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 27 23:34:33 UTC 2022 - 24.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
} func.func private @func_2_CPU_FLOAT(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> attributes {tac.device = "CPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} { %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", tac.inference_type = "FLOAT", values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_xla_weight_only.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 03 15:43:38 UTC 2023 - 7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
// CHECK: %17 = "tfl.gather"(%arg13, %14) <{axis = 0 : i32, batch_dims = 0 : i32}> {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<5xi32>, tensor<?xi32>) -> tensor<?xi32> // CHECK: %18 = tfl.add %arg14, %17 {fused_activation_function = "NONE", tac.device = "DARWINN", tac.inference_type = "FLOAT"} : tensor<?xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/canonicalize.mlir
// ----- // CHECK-LABEL: @RemoveRedundantUnpackPack func.func @RemoveRedundantUnpackPack(%arg0: tensor<2x5xf32>) -> tensor<2x5xf32> { %0:2 = "tfl.unpack"(%arg0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x5xf32>) -> (tensor<5xf32>, tensor<5xf32>) %1 = "tfl.pack"(%0#0, %0#1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<5xf32>, tensor<5xf32>) -> (tensor<2x5xf32>) func.return %1: tensor<2x5xf32> // CHECK-NOT: pack
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/canonicalize.mlir
func.return } // CHECK-LABEL: testGatherToV2 // Ensures that axis param and batch_dims attr use their default values of 0. func.func @testGatherToV2(%params: tensor<4x3xf32>, %indices: tensor<1x2xi32>) -> tensor<2x3xf32> { // CHECK: %[[AXIS:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 132.1K bytes - Viewed (0)