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Results 11 - 20 of 120 for output1 (0.18 sec)
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tensorflow/compiler/mlir/tf2xla/internal/passes/extract_outside_compilation.cc
shard_output_types.reserve(outputs.size()); full_output_types.reserve(outputs.size()); for (const auto& output : outputs) { Type shard_type; if (failed(GetShardShapedType(original_op, num_cores_per_replica, output.getType(), shard_type))) return mlir::failure(); shard_output_types.push_back(shard_type); full_output_types.push_back(output.getType()); }
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/incremental_build.adoc
WARNING: If the task type is already using the incremental build annotations, registering inputs or outputs with the same property names will result in an error. [[sec:task_input_output_side_effects]] == Benefits of declaring task inputs and outputs
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Wed Jan 24 23:14:04 UTC 2024 - 63.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test.py
Args: input_tensor: Input tensor to matmul with the filter. Returns: A 'output' -> output tensor mapping """ out = math_ops.matmul(input_tensor, random_tensor_gen_fn((2, 3))) out = math_ops.matmul(out, random_tensor_gen_fn((3, 4))) return {'output': out} model = TwoMatmulModel() input_shape = (1, 2) save.save( model,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 51.4K bytes - Viewed (0) -
src/cmd/compile/internal/ssa/_gen/S390XOps.go
var ( gp01 = regInfo{inputs: []regMask{}, outputs: gponly} gp11 = regInfo{inputs: []regMask{gp}, outputs: gponly} gp11sp = regInfo{inputs: []regMask{gpsp}, outputs: gponly} gp21 = regInfo{inputs: []regMask{gp, gp}, outputs: gponly} gp21sp = regInfo{inputs: []regMask{gpsp, gp}, outputs: gponly} gp21tmp = regInfo{inputs: []regMask{gp &^ tmp, gp &^ tmp}, outputs: []regMask{gp &^ tmp}, clobbers: tmp}
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Feb 24 00:21:13 UTC 2023 - 52.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/sparsecore/embedding_pipelining.cc
results.insert(result); break; } } } } llvm::SetVector<Value> outputs; for (auto output : results) outputs.insert(output); auto tf_caller = EncapsulateOpsInFunc(builder, symbol_table, ops, inputs, outputs, parent_func, module, func_name, flag_for_inlining);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 92.9K bytes - Viewed (0) -
src/cmd/compile/internal/ssa/_gen/ARM64Ops.go
var ( gp01 = regInfo{inputs: nil, outputs: []regMask{gp}} gp0flags1 = regInfo{inputs: []regMask{0}, outputs: []regMask{gp}} gp11 = regInfo{inputs: []regMask{gpg}, outputs: []regMask{gp}} gp11sp = regInfo{inputs: []regMask{gpspg}, outputs: []regMask{gp}} gp1flags = regInfo{inputs: []regMask{gpg}} gp1flags1 = regInfo{inputs: []regMask{gpg}, outputs: []regMask{gp}}
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu May 23 15:49:20 UTC 2024 - 58.8K bytes - Viewed (0) -
tensorflow/compiler/jit/deadness_analysis_test.cc
Output live0 = ops::Add(root.WithOpName("live0"), m0.output, m1.output); Output live1 = ops::Add(root.WithOpName("live1"), m2.output, m3.output); Output halfdead0 = ops::Add(root.WithOpName("halfdead0"), m0.output, m2.output); Output halfdead1 = ops::Add(root.WithOpName("halfdead1"), m1.output, m3.output); std::unique_ptr<DeadnessAnalysis> result;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 06:59:07 UTC 2024 - 51.6K bytes - Viewed (0) -
platforms/software/dependency-management/src/integTest/groovy/org/gradle/integtests/resolve/transform/ArtifactTransformWithDependenciesIntegrationTest.groovy
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue Oct 24 06:54:47 UTC 2023 - 54.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
as intermediate ops for control dependency grouping. ### `-tf-executor-convert-control-to-data-outputs` _Chain control outputs of while loop body_ This pass converts the control outputs of a while loop body function to data outputs. Thus, inter iteration control dependencies are transformed to data dependencies. Since data dependencies can express which particular
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/while_to_map_fn.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 23 06:40:22 UTC 2024 - 68.6K bytes - Viewed (0)