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Results 11 - 20 of 43 for opset (0.07 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions.cc
bool test_mode_; Option<OpSet> op_set_{ *this, "target-opset", llvm::cl::init(OpSet::TF), llvm::cl::desc("Choose target opset."), llvm::cl::values( clEnumValN(OpSet::TF, "TF", "Uses TF ops that mimic quantization behavior"), clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"), clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 16.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/passes.h
const QuantizationSpecs& quant_specs, tensorflow::quantization::OpSet op_set); // Creates an instance of the PreprocessOp pass, which will perform op // preprocessing to allow multi-axis quantization, prior to quantization. std::unique_ptr<OperationPass<ModuleOp>> CreatePreprocessOpPass( tensorflow::quantization::OpSet op_set, tensorflow::quantization::QuantizationMethod::PresetMethod quantization_method,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 12.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/quantize_model.py
( quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED or quantization_options.quantization_method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8 ) or ( quantization_options.op_set in (quant_opts_pb2.OpSet.XLA, quant_opts_pb2.OpSet.STABLEHLO) and quantization_options.quantization_method.preset_method
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 34.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/quantize_model.cc
const absl::flat_hash_map<std::string, std::string> &function_aliases, absl::string_view calibration_data_dir) { const bool is_stablehlo = quantization_options.op_set() == OpSet::STABLEHLO; // Use StableHLO Quantizer option if opset is specified. if (is_stablehlo) { const QuantizationConfig quantization_config = GetQuantizationConfigForStaticRangePtq(quantization_options);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 23.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
), tags=tags, signature_keys=['serving_default'], op_set=target_opset, ) if target_opset != quant_opts_pb2.XLA: # Uniform quantized opset is not supported for weight-only with self.assertRaisesRegex( ValueError, 'TF/Uniform quantized opset does not support weight-only.' ): converted_model = quantize_model.quantize(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_quantize_op.cc
QuantizedType CalculateUniformQuantParams( PatternRewriter& rewriter, TF::ConstOp op, tensorflow::quantization::QuantizationComponentSpec& weight_spec) { // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset // Currently, support symmetric, per-tensor, signed int8 const bool kIsNarrowRange = true; const bool kIsSigned = true; const int kBitWidth = 8; DenseFPElementsAttr attr;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11K bytes - Viewed (0) -
src/cmd/internal/obj/util.go
spcs := &spcSpace[i] if spcs.lo <= spc && spc < spcs.hi { return spcs.SPCconv(spc) } } return fmt.Sprintf("SPC???%d", spc) } type opSet struct { lo As names []string } // Not even worth sorting var aSpace []opSet // RegisterOpcode binds a list of instruction names // to a given instruction number range. func RegisterOpcode(lo As, Anames []string) {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 15 15:44:14 UTC 2024 - 17.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_xla.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-quantize='target-opset=XLA' -verify-each=false | FileCheck %s func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} { %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/utils/tf_to_uniform_attribute_utils.cc
ShapedType input_shape = mlir::dyn_cast<ShapedType>(op->getOperand(0).getType()); if (!input_shape) { return op->emitError( "Only input with known shape is supported for Uniform Quantized " "opset."); } if (op->getParentOfType<func::FuncOp>().getName().contains("depthwise_")) { feature_group_cnt = input_shape.getDimSize(3); } attrs.push_back(rewriter.getNamedAttr(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 18.7K bytes - Viewed (0) -
cni/pkg/ipset/ipset.go
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. package ipset import ( "errors" "fmt" "net/netip" ) type IPSet struct { V4Name string V6Name string Prefix string Deps NetlinkIpsetDeps } const ( V4Name = "%s-v4" V6Name = "%s-v6" )
Registered: Fri Jun 14 15:00:06 UTC 2024 - Last Modified: Tue Apr 30 22:24:38 UTC 2024 - 3.9K bytes - Viewed (0)