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platforms/core-configuration/model-core/src/test/groovy/org/gradle/model/internal/registry/DefaultModelRegistryTest.groovy
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Thu Sep 28 09:51:04 UTC 2023 - 56K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/extract_outside_compilation.cc
full_output_types.push_back(output.getType()); } // Convert split sharded inputs to MANUAL sharded inputs. // common_split_sharding is the split sharding that is common to all inputs // and outputs. llvm::SmallVector<Value, 4> manual_inputs; manual_inputs.reserve(inputs.size()); for (Value in : inputs) { Type shard_type; if (failed(GetShardShapedType(original_op, num_cores_per_replica,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 21:25:12 UTC 2024 - 68.3K bytes - Viewed (0) -
platforms/documentation/docs/src/docs/userguide/optimizing-performance/configuration_cache.adoc
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Fri Mar 29 16:24:12 UTC 2024 - 71.1K bytes - Viewed (0) -
tensorflow/c/kernels_test.cc
p.device = &dummy_device; p.step_id = 43; Tensor t(tensorflow::uint8(123)); gtl::InlinedVector<TensorValue, 4> inputs; // Simulate 2 inputs inputs.emplace_back(&t); inputs.emplace_back(); p.inputs = inputs; Status status; std::unique_ptr<OpKernel> kernel = GetFakeKernel(device_name, op_name, node_name, &status); TF_EXPECT_OK(status);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 50.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/sparsecore/embedding_pipelining.cc
} if (TF::TPUReplicatedInputOp input = llvm::dyn_cast<TF::TPUReplicatedInputOp>(user)) { if (!input.getIsPacked()) { input.emitOpError() << "unexpected variable input, not packed"; return LogicalResult::failure(); } if (is_variable) { input.emitOpError() << "unexpected multiple TPUReplicatedInputOp "
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 92.9K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs, ninputs, grad_inputs, s_, grad_outputs); } else { TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs, ninputs, nullptr, s_, grad_outputs); } } void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 15 03:35:10 UTC 2024 - 96.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/flatbuffer_import.cc
} else if (sparse_index_vector.type == tflite::SparseIndexVector_Uint16Vector) { const auto& inputs = sparse_index_vector.AsUint16Vector()->values; std::vector<int32_t> outputs(inputs.size()); std::transform(inputs.begin(), inputs.end(), outputs.begin(), [](auto x) { return static_cast<int32_t>(x); }); return outputs; } else if (sparse_index_vector.type ==
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 21 18:21:50 UTC 2024 - 66.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td
let summary = [{ output = input; While (Cond(output)) { output = Body(output) } }]; let description = [{ output = input; While (Cond(output)) { output = Body(output) } input: A list of input tensors whose types are T. output: A list of output tensors whose types are T. cond: A function that takes 'input' and returns a tensor. If the tensor is
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 04:08:35 UTC 2024 - 90.5K bytes - Viewed (0) -
tensorflow/compiler/jit/deadness_analysis.cc
// Predicate denote logical formulas and mapping a node `n` to a predicate // `pred` implies that `n` is live whenever `pred` is true. Then we can deduce // mismatching liveness in the inputs to node by comparing the predicate those // inputs are mapped to. The core logic of this pass resides in creating the // map from TensorFlow nodes to predicates. // // // MAPPING NODES TO PREDICATES, MODULO CYCLES
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 12 06:33:33 UTC 2024 - 60.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode. ### `-tf-tpu-validate-inputs` _Validates inputs to the TPU TF/XLA bridge_ This pass checks that the IR has valid input to TPU TF/XLA bridge. It checks the relations of multiple ops. Properties of single ops are checked by the 'verify' method of ops.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0)