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Results 1 - 8 of 8 for weight_quantization (0.29 sec)
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tensorflow/compiler/mlir/lite/tf_tfl_translate.cc
llvm::errs() << "Failed to get input quant spec."; return kTrFailure; } if (weight_quantization != "NONE") { quant_specs.weight_quantization = true; if (weight_quantization == "INT8") { quant_specs.inference_type = tensorflow::DT_QINT8; } else if (weight_quantization == "FLOAT16") { quant_specs.inference_type = tensorflow::DT_HALF; } else {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 14K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize.cc
enable_whole_model_verify_ = quant_specs.whole_model_verify; enable_legacy_quantize_ = quant_specs.legacy_float_scale; enable_dynamic_range_quantization_ = quant_specs.weight_quantization; enable_weight_only_quantization_ = quant_specs.weight_only_quantization; } void runOnOperation() override; private: quant::QuantizationSpecs quant_specs; };
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 13.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_weights.cc
PassManager pm((*module)->getName(), OpPassManager::Nesting::Implicit); quant::QuantizationSpecs quant_specs; quant_specs.inference_type = tflite::TflTypeToTfType(inference_type); quant_specs.weight_quantization = true; quant_specs.weight_only_quantization = weight_only_quantization; quant_specs.minimum_elements_for_weights = minimum_elements_for_weights; quant_specs.disable_per_channel = disable_per_channel;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc
// quantization is enabled, `inference_type` and `inference_input_type` are // not used by MLIR passes. if (toco_flags.post_training_quantize()) { quant_specs->weight_quantization = true; quant_specs->disable_per_channel = toco_flags.disable_per_channel_quantization(); if (toco_flags.quantize_to_float16()) { quant_specs->inference_type = DT_HALF;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun May 12 12:39:37 UTC 2024 - 17.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc
// Constructor used by the PassRegistration. This is only used by test. explicit PrepareDynamicRangeQuantizePass() { quant_specs_.inference_type = tensorflow::DT_QINT8; quant_specs_.weight_quantization = true; quant_specs_.enable_mlir_dynamic_range_quantizer = true; } // Constructor used by manually creating the pass. explicit PrepareDynamicRangeQuantizePass(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.cc
return status_handler.Combine( absl::InternalError("Could not translate MLIR to FlatBuffer.")); } // TODO: b/176267167 - Quantize flex fallback in the MLIR pipeline if (quant_specs.weight_quantization && (!quant_specs.RunAndRewriteDynamicRangeQuantizationPasses() || !pass_config.emit_builtin_tflite_ops)) { // Apply post-training dynamic range quantization from the old TOCO
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 23.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h
return success(); } // Blocklist op is checked in advance for non-dynamic range quantization // case. if (!quant_params_.quant_spec.weight_quantization && (ops_blocklist.find(quantizing_op->getName().getStringRef().str()) != ops_blocklist.end())) { return failure(); } if (!nodes_blocklist.empty()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize_composite_functions.cc
pm.addNestedPass<func::FuncOp>(CreatePostQuantizePass()); } else { // Apply weight quantization. quant_specs.minimum_elements_for_weights = min_num_elements_for_weights_; quant_specs.weight_quantization = true; quant_specs.weight_only_quantization = enable_legacy_weight_only_; pm.addPass(CreatePrepareQuantizeDRQPass(quant_specs, target_opset_)); pm.addNestedPass<func::FuncOp>(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 54.5K bytes - Viewed (0)