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Results 11 - 20 of 26 for qtype_attr (0.19 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/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/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) -
tensorflow/compiler/mlir/lite/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 "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 - 6.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize_variables.cc
builder.setInsertionPoint(assign_variable_op); auto new_q_op = builder.create<QuantizeOp>( assign_variable_op.getLoc(), ref_qtype, dq_op.getInput(), TypeAttr::get(ref_qtype)); auto new_assign_variable_op = builder.create<AssignVariableOp>( assign_variable_op.getLoc(), assign_variable_op.getResourceId(), new_q_op.getResult());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.5K bytes - Viewed (0)