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Results 121 - 130 of 193 for Quantile (0.3 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_weight_only.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize | FileCheck %s // Test that hybrid quantized dot_general is produced when q/dq pair only exists // for weight. module attributes {tf_saved_model.semantics} { func.func private @quantize_dot_general_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} { %cst = stablehlo.constant dense<3.000000e-01> : tensor<2x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 4.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
%2 = "tfl.reshape"(%arg0, %0) : (tensor<4x384x32x!quant.uniform<i8:f32, 0.2:-3>>, tensor<4xi32>) -> tensor<1x4x384x32x!quant.uniform<i8:f32, 0.2:-3>> // CHECK-NOT: tac.device tac.inference_type %3 = "tfl.quantize"(%2) {qtype = tensor<1x4x384x32x!quant.uniform<i8:f32, 0.19:1>>} : (tensor<1x4x384x32x!quant.uniform<i8:f32, 0.2:-3>>) -> tensor<1x4x384x32x!quant.uniform<i8:f32, 0.19:1>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/common/tfl_pass_config.h
// have side effects e.g. reduced flatbuffer size. Only certain type // conversions are supported. bool reduce_type_precision = false; // Whether to consider this model a quantized model with quantize/dequantize // ops and to convert kernels to quantized kernels wherever appropriate. quant::QDQConversionMode qdq_conversion_mode = quant::QDQConversionMode::kQDQNone;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:05:30 UTC 2024 - 6.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/cc/calibration/component.h
const std::unordered_set<std::string> tags_; const absl::flat_hash_map<std::string, tensorflow::SignatureDef> signature_def_map_; // Signature keys to identify the functions to load & quantize. const std::vector<std::string> signature_keys_; }; // Runs passes to prepare the calibration model. absl::Status RunCalibrationPasses(mlir::ModuleOp module_op, MLIRContext& ctx,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 5.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/passes/decompose.cc
// The pass to decompose unregistered TF ops with the TFR compose function. // namespace mlir { namespace TFR { namespace { // Quantize the float value based on given scale and zero point attributes. IntegerAttr Quantize(float value, Attribute scale_attr, Attribute zp_attr, OpBuilder builder) { double scale = mlir::cast<FloatAttr>(scale_attr).getValueAsDouble();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.h
// whose storage type is 32-bit integer and expressed type is f32. bool IsI32F32UniformQuantizedPerAxisType(Type type); // Determines whether the storage type of a quantized type is supported by // `tfl.quantize` or `tfl.dequantize` ops. ui8, i8 and i16 are supported. bool IsSupportedByTfliteQuantizeOrDequantizeOps(IntegerType storage_type); // Returns true if a type is quantized tensor type. bool IsQuantizedTensorType(Type type);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_model.cc
pm.addPass(TFL::CreatePostQuantizeRemoveQDQPass()); if (failed(pm.run(module.get()))) { const std::string err(statusHandler.ConsumeStatus().message()); LOG(ERROR) << "Failed to quantize: " << err; return kTfLiteError; } // Export the results. tflite::FlatbufferExportOptions options; options.toco_flags.set_force_select_tf_ops(false);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq_per_channel.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op -quant-prepare-quantize-drq='enable-per-channel-quantization=true' | FileCheck %s module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.cc
if (dot_general_op == nullptr) return std::nullopt; const int64_t filter_rank = mlir::dyn_cast<ShapedType>(dot_general_op.getOperand(1).getType()) .getRank(); // To quantize rhs per-channel, we currently only consider the case where // `stablehlo.dot_general` is legalizable to `tfl.fully_connected`. const bool is_per_axis_quantizable = IsDotGeneralFullyConnected(dot_general_op).value();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_quantize_drq.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op -quant-prepare-quantize-drq | FileCheck %s module { func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) { %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.7K bytes - Viewed (0)