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Results 31 - 40 of 69 for getElementDtype (0.19 sec)

  1. tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h

      LogicalResult matchAndRewrite(quantfork::StatisticsOp op,
                                    PatternRewriter& rewriter) const override {
        Type expressed = op.getType().cast<ShapedType>().getElementType();
        quant::QuantizedType quant_type;
        SmallVector<double, 4> mins, maxs;
    
        if (op.getAxisStats().has_value()) {
          // Per axis quantization (or per channel quantization)
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed Apr 24 20:30:06 UTC 2024
    - 41.7K bytes
    - Viewed (0)
  2. tensorflow/compiler/mlir/tensorflow/translate/export_graphdef.cc

              mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {
        llvm::ArrayRef<mlir::TensorType> subtypes = resource_type.getSubtypes();
        if (!subtypes.empty()) {
          AttrValue handle_dtypes_attr;
          AttrValue handle_shapes_attr;
          for (mlir::TensorType subtype : subtypes) {
            DataType dtype;
            TF_RETURN_IF_ERROR(ConvertToDataType(subtype.getElementType(), &dtype));
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed May 01 11:17:36 UTC 2024
    - 35.2K bytes
    - Viewed (0)
  3. tensorflow/compiler/mlir/tf2xla/api/v2/tf_executor_to_graph.cc

              mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {
        llvm::ArrayRef<mlir::TensorType> subtypes = resource_type.getSubtypes();
        if (!subtypes.empty()) {
          AttrValue handle_dtypes_attr;
          AttrValue handle_shapes_attr;
          for (mlir::TensorType subtype : subtypes) {
            DataType dtype;
            TF_RETURN_IF_ERROR(ConvertToDataType(subtype.getElementType(), &dtype));
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Jun 06 23:04:51 UTC 2024
    - 35.2K bytes
    - Viewed (0)
  4. platforms/core-configuration/model-core/src/main/java/org/gradle/api/internal/provider/AbstractCollectionProperty.java

                if (!elementType.isAssignableFrom(collectionProp.getElementType())) {
                    throw new IllegalArgumentException(String.format("Cannot set the value of a property of type %s with element type %s using a provider with element type %s.", collectionType.getName(), elementType.getName(), collectionProp.getElementType().getName()));
                }
            }
    Registered: Wed Jun 12 18:38:38 UTC 2024
    - Last Modified: Fri May 17 11:41:55 UTC 2024
    - 29.8K bytes
    - Viewed (0)
  5. platforms/ide/tooling-api/src/main/java/org/gradle/tooling/internal/adapter/ProtocolToModelAdapter.java

                        Type targetElementType = getElementType(parameterizedTargetType, 0);
                        return convertCollectionInternal(rawClass, targetElementType, (Iterable<?>) sourceObject, decoration, graphDetails);
                    }
                    if (Map.class.isAssignableFrom(rawClass)) {
                        Type targetKeyType = getElementType(parameterizedTargetType, 0);
    Registered: Wed Jun 12 18:38:38 UTC 2024
    - Last Modified: Tue May 21 04:42:54 UTC 2024
    - 45.4K bytes
    - Viewed (0)
  6. tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_driver.cc

        if (auto dq_op = dyn_cast_or_null<quantfork::DequantizeCastOp>(
                op->getOperand(i).getDefiningOp())) {
          auto type =
              mlir::cast<TensorType>(dq_op.getArg().getType()).getElementType();
          if (auto per_axis_qtype =
                  mlir::dyn_cast_or_null<quant::UniformQuantizedPerAxisType>(
                      QuantizedType::getQuantizedElementType(type))) {
            return true;
          }
        }
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 16:01:03 UTC 2024
    - 38.1K bytes
    - Viewed (0)
  7. tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize.cc

      bool need_to_set_input_nodes_quantization_params = false;
      for (const BlockArgument arg : func.getArguments()) {
        auto shaped = mlir::dyn_cast<ShapedType>(arg.getType());
        if (shaped && mlir::isa<FloatType>(shaped.getElementType()) &&
            !has_quantize_op(arg)) {
          need_to_set_input_nodes_quantization_params = true;
          break;
        }
      }
    
      if (!need_to_set_input_nodes_quantization_params) {
        return false;
      }
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 16:01:03 UTC 2024
    - 17.2K bytes
    - Viewed (0)
  8. tensorflow/compiler/mlir/lite/transforms/prepare_quantize.cc

      bool need_to_set_input_nodes_quantization_params = false;
      for (const BlockArgument arg : func.getArguments()) {
        auto shaped = mlir::dyn_cast<ShapedType>(arg.getType());
        if (shaped && mlir::isa<FloatType>(shaped.getElementType()) &&
            !has_quantize_op(arg)) {
          need_to_set_input_nodes_quantization_params = true;
          break;
        }
      }
    
      if (!need_to_set_input_nodes_quantization_params) {
        return false;
      }
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 16:01:03 UTC 2024
    - 17.6K bytes
    - Viewed (0)
  9. tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc

                             QuantizationUnits& quantizable_ops) const {
        // Non-float tensors do not need quantization.
        auto type = mlir::dyn_cast<ShapedType>(op.getType());
        if (!type || !type.getElementType().isF32()) return false;
    
        Value value = op.getResult();
    
        // Check whether dynamic range quantization can be applied.
        for (auto& use : value.getUses()) {
          Operation* user = use.getOwner();
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 16:01:03 UTC 2024
    - 20.8K bytes
    - Viewed (0)
  10. tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.cc

                    split_dimension, num_split));
          }
    
          shape[split_dimension] = shape[split_dimension] / num_split;
          output_type =
              mlir::RankedTensorType::get(shape, input_type.getElementType());
        }
      } else {
        output_type = input_type;
      }
    
      // Creates a split op that splits |src_input| along |split_dimension|.
      llvm::SmallVector<mlir::Type, 4> output_types(num_split, output_type);
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Wed May 22 21:28:13 UTC 2024
    - 34K bytes
    - Viewed (0)
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