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Results 131 - 140 of 192 for dequantize (0.23 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/common/utils.cc
#include "tensorflow/compiler/mlir/lite/utils/utils.h" namespace mlir { namespace TFL { namespace tac { bool NotTFLQuantDequantizeOp(Operation* op) { if (!op) return false; if (llvm::isa<TFL::QuantizeOp, TFL::DequantizeOp>(op)) return false; return true; } bool IsTerminatorOp(Operation* op) { if (!op) return false; return op->hasTrait<OpTrait::IsTerminator>(); } // Try to guess the inference type of the op.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 06 05:37:07 UTC 2024 - 2.3K 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/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/aot/BUILD
) filegroup( name = "quantize_header", srcs = ["quantize.h"], visibility = ["//visibility:public"], ) cc_library( name = "tfcompile_lib", srcs = [ "codegen.cc", "compile.cc", "flags.cc", ], hdrs = [ "codegen.h", "compile.h", "flags.h", "quantize.h", ], compatible_with = [],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 11 16:13:05 UTC 2024 - 11.7K 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) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_weight_only.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=weight_only target-opset=XLA' -quant-quantize-composite-functions='quantization-method=weight_only target-opset=XLA enable-per-channel-quantization=true' -symbol-dce...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 11.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/decompose_hybrid_quantization.cc
if (QuantizedType::getQuantizedElementType(operand.getType())) { auto newTy = QuantizedType::castToExpressedType(operand.getType()); newOperands.push_back( rewriter.create<TFL::DequantizeOp>(loc, newTy, operand)); continue; } newOperands.push_back(operand); } SmallVector<Type> newResultTys; for (auto result : op->getResults()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/UniformSupport.cc
converters.reserve(dim_size); for (int i = 0, e = dim_size; i != e; ++i) { converters.push_back(getPerChunkConverter(i)); } // Scan the elements of the dense elements attributes and quantize them by // using the right quantization parameters. int64_t flatten_index = 0; auto shape = type.getShape(); int64_t chunk_size = std::accumulate(std::next(shape.begin(), quantization_dim_ + 1),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 4.3K bytes - Viewed (0)