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Results 41 - 50 of 79 for Quantile (0.26 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform.h
RewritePatternSet GetHardwareRewritePatterns(MLIRContext* context, const std::string& hardware); // Convert quantized ops to float, this will essentially insert dequantize & // quantize pair around the op. void ConvertQuantizedOpToFloat(func::FuncOp func, OpBuilder* builder); // This will optimize the quantized ops -> float graph. void OptimizeQuantizedOpToFloat(func::FuncOp func, MLIRContext* context);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 07 18:43:51 UTC 2022 - 2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-prepare-quantize | FileCheck %s module { func.func @same_scale_test(%arg0: tensor<*xf32>) -> tensor<*xf32> { %cst = arith.constant dense<[-1, 144]> : tensor<2xi32> %cst_1 = arith.constant dense<1.0> : tensor<144x10xf32> %cst_2 = arith.constant dense<0.1> : tensor<10xf32> %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.05:-10>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Dec 29 02:42:57 UTC 2022 - 2.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.cc
// `tfl.quantize` or `tfl.dequantize` ops. ui8, i8 and i16 are supported. bool IsSupportedByTfliteQuantizeOrDequantizeOps(IntegerType storage_type) { if (storage_type.getWidth() == 8 || (storage_type.isSigned() && storage_type.getWidth() == 16)) { return true; } LLVM_DEBUG(llvm::dbgs() << "Uniform quantize / dequantize op only supports ui8, i8 or "
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/wrap_converter.py
enable_whole_model_verify, denylisted_ops, denylisted_nodes, enable_variable_quantization, disable_per_channel_for_dense_layers, debug_options_str, ): """Wraps experimental mlir quantize model.""" return _pywrap_converter_api.ExperimentalMlirQuantizeModel( input_data_str, disable_per_channel, fully_quantize, inference_type, input_data_type, output_data_type,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 31 18:18:30 UTC 2024 - 3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/concurrency_test.py
), tags={tag_constants.SERVING}, signature_keys=['serving_default'], ) model = quantize_model.quantize( temp_path, quantization_options=quantization_options, representative_dataset=data_gen(), ) return model @test_util.run_in_graph_and_eager_modes
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Sep 11 00:47:05 UTC 2023 - 3.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform.cc
if (devce_hardware == nullptr) return {}; return devce_hardware->IsOpSupported(op); } // ================== Convert Quantized Op ============================ // Walk through the func and convert the quantize ops to their float version. void ConvertQuantizedOpToFloat(mlir::func::FuncOp func, OpBuilder* builder) { func.walk([&](Operation* op) { // TODO(renjieliu): Find a generic way to deal with const ops.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_tfl_translate_cl.cc
// going forward. // NOLINTNEXTLINE llvm::cl::list<std::string> custom_opdefs( "tf-custom-opdefs", llvm::cl::desc("List of custom opdefs when importing " "graphdef")); // Quantize and Dequantize ops pair can be optionally emitted before and after // the quantized model as the adaptors to receive and produce floating point // type data with the quantized model. Set this to `false` if the model input is
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 20:53:17 UTC 2024 - 7.9K bytes - Viewed (0) -
guava-tests/benchmark/com/google/common/math/QuantilesBenchmark.java
import com.google.common.collect.ImmutableSet; import com.google.common.collect.Range; import java.util.Random; /** Benchmarks some algorithms providing the same functionality as {@link Quantiles}. */ public class QuantilesBenchmark { private static final ContiguousSet<Integer> ALL_DECILE_INDEXES = ContiguousSet.create(Range.closed(0, 10), DiscreteDomain.integers());
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Mon Oct 10 19:45:10 UTC 2022 - 3.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/instrumentations/save_report.cc
// It is known that `op` is `ModuleOp` when `pass` is // `QuantizeCompositeFunctionPass`, but the check is still performed to be // defensive. return pass != nullptr && pass->getArgument() == "stablehlo-quantize-composite-functions" && isa_and_nonnull<ModuleOp>(op); } // Report is saved only when: // * After running `QuantizeCompositeFunctionPass`. // * The pass is run on `ModuleOp`.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 02:59:01 UTC 2024 - 3.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_ptq.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-prepare-quantize='post-training-quantize=true' | FileCheck %s // ----- module { func.func @same_scale_ptq_test(%arg0: tensor<*xf32>) -> tensor<*xf32> { %cst = arith.constant dense<[-1, 144]> : tensor<2xi32> %cst_1 = arith.constant dense<1.0> : tensor<144x10xf32> %cst_2 = arith.constant dense<0.1> : tensor<10xf32> %0 = "quantfork.stats"(%arg0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 01 10:21:29 UTC 2023 - 9.1K bytes - Viewed (0)