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Results 1 - 10 of 41 for TypeAttr (0.12 sec)
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tensorflow/compiler/mlir/lite/utils/convert_type.h
// Returns element type from attribute Type 'type_attr'. mlir::Type GetShapeStrippedType(mlir::TypeAttr type_attr); // Returns true if 'val' is not from Quantize op or // from Quantize Op with same quant type as 'qtype_attr' bool NotFromQuantOpOrSameQuantType(mlir::Value val, mlir::TypeAttr qtype_attr); } // namespace tflite
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 2.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/lstm_utils.cc
/*asymmetric_quantize_inputs=*/mlir::BoolAttr(), /*input_to_input_intermediate=*/mlir::TypeAttr(), /*input_to_forget_intermediate=*/mlir::TypeAttr(), /*input_to_cell_intermediate=*/mlir::TypeAttr(), /*input_to_output_intermediate=*/mlir::TypeAttr(), /*effective_hidden_scale_intermediate=*/mlir::TypeAttr()); // Cast the static shaped lstm result to FuncOp's signature -
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 36.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/quantization_context.cc
input_specs.push_back(original_input_specs[i]); } else if (requantize.pos == RequantizeState::ON_OUTPUT) { input_specs.push_back(TypeAttr::get(requantize.params)); } else { input_specs.push_back(TypeAttr::get(state.params)); } } op->setAttr("input_specs", ArrayAttr::get(context, input_specs)); llvm::SmallVector<Attribute, 4> output_specs;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 08 01:38:03 UTC 2024 - 13.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/ir/QuantOps.cc
return srcScastOp.getArg(); } /// The quantization specification should match the expressed type. static bool isValidQuantizationSpec(Attribute quantSpec, Type expressed) { if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) { Type spec = typeAttr.getValue(); if (mlir::isa<TensorType, VectorType>(spec)) return false; // The spec should be either a quantized type which is compatible to the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.cc
return srcScastOp.getArg(); } /// The quantization specification should match the expressed type. static bool isValidQuantizationSpec(Attribute quantSpec, Type expressed) { if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) { Type spec = typeAttr.getValue(); if (mlir::isa<TensorType, VectorType>(spec)) return false; // The spec should be either a quantized type which is compatible to the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/c/c_api_unified_experimental_mlir.cc
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 28.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_helper.h
// index. template <typename LstmOp> inline QuantizedType GetIntermediateElementType(LstmOp op, int tensor_index) { if (tensor_index < 0 || tensor_index > 4) return nullptr; TypeAttr attr = op->template getAttrOfType<TypeAttr>( intermediate_attributes[tensor_index]); if (!attr) { return nullptr; } return QuantizedType::getQuantizedElementType(attr.getValue()); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 28K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tf_to_quant.cc
// folding logic will use a "arith.constant" op to replace the // "tf.FakeQuantWithMinMaxVarsOp", the "tfl.quantize" op is used to preserve // the quantization parameters as a TypeAttr and "tfl.dequantize" op used to // convert the output type to the next op. Here are the transformations: // // input min cst max cst input min cst max cst
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/convert_attr.cc
case AttrValue::kB: return builder->getBoolAttr(value.b()); case AttrValue::kType: { mlir::Type type; TF_RETURN_IF_ERROR(ConvertDataType(value.type(), *builder, &type)); return mlir::TypeAttr::get(type); } case AttrValue::kShape: return ConvertTensorShapeProto(value.shape(), builder->getContext()); case AttrValue::kTensor: return ConvertTensorProto(value.tensor(), builder);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 4.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/utils/fake_quant_utils.h
// before the op being constant folded. Since the constant // folding logic will use a "arith.constant" op to replace the // "tf.FakeQuantWithMinMaxVarsOp", the "quant.qcast" op is used to preserve // the quantization parameters as a TypeAttr and "quant.dcast" op used to // convert the output type to the next op. Here are the transformations: // // input min cst max cst input // \ | | |
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.3K bytes - Viewed (0)