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tensorflow/compiler/mlir/lite/experimental/tac/README.md
3 Estimate the costs for each subgraph (and their alternative views) based on the hardware cost model. See the following diagram. ![Estimate costs](g3doc/images/compute_cost.png) 4 Pick the proper subgraphs from the alternative views for execution based on costs(computation costs, transfer costs, quant/dequant costs). As shown in the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/compute_cost.cc
namespace mlir { namespace TFL { namespace tac { namespace { // We will caculate the total compute cost for each Func Op. // // The compute cost is simply an add-up of the costs of all the operations // within the FuncOp. (Excluding const ops since they're just "data".) // We will ignore quant/dequant/requant costs within the Func Op as well, // intuition: //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 24 15:10:02 UTC 2022 - 4.2K bytes - Viewed (0) -
.github/ISSUE_TEMPLATE/11-language-change.yml
validations: required: false - type: input id: perf-costs attributes: label: Performance Costs description: "What is the compile time cost? What is the run time cost? " validations: required: false - type: textarea id: prototype attributes: label: "Prototype"
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed Nov 22 20:49:24 UTC 2023 - 4.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/runtime_metadata.fbs
// op1 = .... {tac.device = "CPU"} // op2 = .... {tac.device = "GPU"} // op3 = .... {tac.device = "GPU"} // op4 = .... {tac.device = "CPU"} // // We will run a separate cost estimation for each op with each different // hardwares. // // ======== IRs after cost-estimation ======== // op1 = .... {tac.device = "CPU"} {"CPU": 10, "GPU": -1.0} // op2 = .... {tac.device = "GPU"} {"CPU": 20, "GPU": 5.0}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jul 21 01:22:53 UTC 2021 - 2.5K bytes - Viewed (0) -
staging/src/k8s.io/apiserver/pkg/cel/library/cost_test.go
if err != nil { t.Fatalf("%v", err) } cost := details.ActualCost() if *cost != expectRuntimeCost { t.Errorf("Expected cost of %d but got %d", expectRuntimeCost, *cost) } } func TestSize(t *testing.T) { exactSize := func(size int) checker.SizeEstimate {
Registered: Sat Jun 15 01:39:40 UTC 2024 - Last Modified: Tue Apr 23 17:22:44 UTC 2024 - 40.8K bytes - Viewed (0) -
ci/official/utilities/setup_docker.sh
if [[ "$TFCI_DOCKER_PULL_ENABLE" == 1 ]]; then # Simple retry logic for docker-pull errors. Sleeps if a pull fails. # Pulling an already-pulled container image will finish instantly, so # repeating the command costs nothing. docker pull "$TFCI_DOCKER_IMAGE" || sleep 15 docker pull "$TFCI_DOCKER_IMAGE" || sleep 30 docker pull "$TFCI_DOCKER_IMAGE" || sleep 60 docker pull "$TFCI_DOCKER_IMAGE" fi
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 18:22:06 UTC 2024 - 1.9K bytes - Viewed (0) -
releasenotes/notes/sni-dnat-default.yaml
apiVersion: release-notes/v2 kind: feature area: networking issue: - 27749 releaseNotes: - | **Updated** the default installation of gateways to not configure clusters for `AUTO_PASSTHROUGH`, reducing memory costs. upgradeNotes: - title: "`AUTO_PASSTHROUGH` Gateway mode" content: | Previously, gateways were configured with multiple Envoy `cluster` configurations for each Service in the cluster, even those
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Thu Nov 19 09:47:40 UTC 2020 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/pick_subgraphs.cc
// Then the current_subgraph's aggregated optimal costs with regards to target // perspective is simply: // for target in current_subgraph.supported_targets: // total_cost = 0 // for input_subgraph in current_subgraph.input_subgraphs: // input_cost = kInfinity // for input_target in input_subgraphs.upported_targets: // # cost = aggregated cost for input_subgraph with transfer cost.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 24 15:10:02 UTC 2022 - 19.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/analysis/update_op_cost_in_tfrt_mlir_test.cc
ASSERT_TRUE(module); // Create a cost recorder with fake cost records. auto expected_op_cost_map = GetOpCostMap(module.get()); EXPECT_EQ(expected_op_cost_map.size(), 1); unsigned int seed = 23579; for (auto& [op_key, cost] : expected_op_cost_map) { cost = rand_r(&seed) % 1000; } tensorflow::tfrt_stub::CostRecorder cost_recorder; for (const auto& [op_key, cost] : expected_op_cost_map) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jul 21 22:52:12 UTC 2023 - 3.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/analysis/cost_analysis.h
namespace tensorflow { namespace tfrt_compiler { // Analyze costs for tensorflow operations. // // The current heuristic used is quite simple, which is to calculate the total // size of input tensors. The exception is that ops whose cost is irrelevant to // input sizes, such as tf.Shape and tf.Reshape, are whitelisted to have cheap // cost. This cost analysis is expected to be used conservatively (eg. use a low
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 06 03:08:33 UTC 2023 - 3.1K bytes - Viewed (0)