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Results 11 - 20 of 57 for qtype_attr (0.46 sec)
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tensorflow/compiler/mlir/lite/utils/validators.h
} /// Returns whether the given `a` and `b` have broadcast-compatible /// types. bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b); // Returns true if every dimension of the attribute is 1 except the last one. bool IsDimensionsDegenerateExceptLastOne(mlir::TypedAttr val); // Returns true if every element is 1 except the last one. bool IsDimensionsDegenerateExceptLastOne(ArrayRef<int64_t> elements_shape);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 4.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/validators.cc
return !std::any_of(elements.begin(), elements.end(), [](Attribute e) { return mlir::cast<IntegerAttr>(e).getValue() != 1; }); } bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b) { // This would return false if we had unranked tensors (where they should // probably be considered as broadcastable), but given we are working with // attributes here that shouldn't be an issue,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 5.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/device_target.cc
if (!rop) return failure(); llvm::SmallVector<Type, 4> input_specs, out_specs; for (auto spec : rop.getInputSpecs()) { input_specs.push_back(spec.cast<TypeAttr>().getValue()); } for (auto spec : rop.getOutputSpecs()) { out_specs.push_back(spec.cast<TypeAttr>().getValue()); } auto in_spec = input_specs[0].dyn_cast<UniformQuantizedType>(); // TODO(fengliuai): handles the PerAxis QuantizedType.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 08 10:41:08 UTC 2024 - 7.3K 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/translate/export_graphdef.cc
DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype)); AttrValue type_attr; type_attr.set_type(dtype); (*node_def->mutable_attr())["T"] = type_attr; AttrValue index_attr; index_attr.set_i(index); (*node_def->mutable_attr())["index"] = index_attr; if (auto device_attr =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 35.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/api/v2/tf_executor_to_graph.cc
DataType dtype; TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype)); AttrValue type_attr; type_attr.set_type(dtype); (*node_def->mutable_attr())["T"] = type_attr; AttrValue index_attr; index_attr.set_i(index); (*node_def->mutable_attr())["index"] = index_attr; if (auto device_attr =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 23:04:51 UTC 2024 - 35.2K 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)