sort with skiplist

KunQuant/Driver.py

@@ -106,6 +106,7 @@ def compileit(f: Function, module_name: str, partition_factor = 3, dtype = "floa
         blocking_len = suggested_len[dtype]
     if element_size[dtype] * blocking_len not in simd_len:
         raise RuntimeError(f"Blocking length {blocking_len} is not supported for {dtype} on {_cpu_arch}")
+    options['blocking_len'] = blocking_len
     if output_layout not in ["STs", "TS", "STREAM"]:
         raise RuntimeError("Bad output_layout name " + output_layout)
     if input_layout not in ["STs", "TS", "STREAM"]:

KunQuant/Op.py

@@ -74,6 +74,9 @@ def traverse_replace_map(op: 'OpBase', replace_map: Dict['OpBase', 'OpBase']) ->
     return traverse_replace_map(found, replace_map)
 
 class AcceptSingleValueInputTrait(ABC):
+    '''
+    The ops that accept a time_len=1 array as input
+    '''
     @abstractmethod
     def get_single_value_input_id() -> int:
         pass
@@ -479,6 +482,18 @@ class GloablStatefulOpTrait(StatefulOpTrait):
     '''
     pass
 
+class GlobalStatefulProducerTrait(GloablStatefulOpTrait):
+    '''
+    The ops that have an internal state, and the state is carried between different time steps, and the state must be consumed by a StateConsumerTrait
+    '''
+    pass
+
+class StateConsumerTrait:
+    '''
+    The ops that consume a state from a GlobalStatefulProducerTrait
+    '''
+    pass
+
 class ReductionOp(OpBase, StatefulOpTrait):
     '''
     Base class of all reduction ops. A reduction op takes inputs that is originated from a IterValue. The input must be in a loop (v.get_parent() is a loop). The data produced

KunQuant/ops/CompOp.py

@@ -1,14 +1,21 @@
 from .ReduceOp import ReduceAdd, ReduceMul, ReduceArgMax, ReduceRank, ReduceMin, ReduceMax, ReduceDecayLinear, ReduceArgMin
 from KunQuant.Op import ConstantOp, OpBase, CompositiveOp, WindowedTrait, ForeachBackWindow, WindowedTempOutput, Builder, IterValue, WindowLoopIndex
 from .ElewiseOp import And, DivConst, GreaterThan, LessThan, Or, Select, SetInfOrNanToValue, Sub, Mul, Sqrt, SubConst, Div, CmpOp, Exp, Log, Min, Max, Equals, Abs
-from .MiscOp import Accumulator, BackRef, SetAccumulator, WindowedLinearRegression, WindowedLinearRegressionResiImpl, WindowedLinearRegressionRSqaureImpl, WindowedLinearRegressionSlopeImpl, ReturnFirstValue
+from .MiscOp import Accumulator, BackRef, SetAccumulator, WindowedLinearRegression, WindowedLinearRegressionResiImpl,\
+    WindowedLinearRegressionRSqaureImpl, WindowedLinearRegressionSlopeImpl, ReturnFirstValue, SkipListState, SkipListQuantile, SkipListRank, SkipListMin, SkipListMax,\
+    SkipListArgMin
 from collections import OrderedDict
 from typing import Union, List, Tuple, Dict
 import math
 
 def _is_fast_stat(opt: dict, attrs: dict) -> bool:
     return not opt.get("no_fast_stat", True) and not attrs.get("no_fast_stat", False)
 
+def _decide_use_skip_list(window: int, blocking_len: int) -> bool:
+    naive_cost = window
+    skip_list_cost = math.log2(window) * blocking_len * 5
+    return skip_list_cost < naive_cost
+
 class WindowedCompositiveOp(CompositiveOp, WindowedTrait):
     def __init__(self, v: OpBase, window: int, v2 = None) -> None:
         inputs = [v]
@@ -17,7 +24,7 @@ def __init__(self, v: OpBase, window: int, v2 = None) -> None:
         super().__init__(inputs, [("window", window)])
 
 class WindowedReduce(WindowedCompositiveOp):
-    def make_reduce(self, v: OpBase) -> OpBase:
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
         raise RuntimeError("Not implemented")
 
     def decompose(self, options: dict) -> List[OpBase]:
@@ -26,7 +33,7 @@ def decompose(self, options: dict) -> List[OpBase]:
             v0 = WindowedTempOutput(self.inputs[0], self.attrs["window"])
             v1 = ForeachBackWindow(v0, self.attrs["window"])
             itr = IterValue(v1, v0)
-            v2 = self.make_reduce(itr)
+            v2 = self.make_reduce(itr, self.inputs[0])
         return b.ops
 
 class WindowedSum(WindowedReduce):
@@ -35,7 +42,7 @@ class WindowedSum(WindowedReduce):
     For indices < window-1, the output will be NaN
     similar to pandas.DataFrame.rolling(n).sum()
     '''
-    def make_reduce(self, v: OpBase) -> OpBase:
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
         return ReduceAdd(v)
 
 class WindowedProduct(WindowedReduce):
@@ -44,26 +51,55 @@ class WindowedProduct(WindowedReduce):
     For indices < window-1, the output will be NaN
     similar to pandas.DataFrame.rolling(n).product()
     '''
-    def make_reduce(self, v: OpBase) -> OpBase:
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
         return ReduceMul(v)
 
-class WindowedMin(WindowedReduce):
+class _WindowedMinMaxBase(WindowedReduce):
+    '''
+    Base class for windowed min/max ops that can use skip list. If the window is small enough, use naive linear scan. Otherwise, use skip list
+    with Log(window) complexity.
+    '''
+    def on_skip_list(self, skplist: SkipListState, cur: OpBase) -> OpBase:
+        raise RuntimeError("Not implemented")
+
+    def decompose(self, options: dict) -> List[OpBase]:
+        window = self.attrs["window"]
+        blocking_len = options["blocking_len"]
+        if _decide_use_skip_list(window, blocking_len):
+            b = Builder(self.get_parent())
+            with b:
+                newv = self.inputs[0]
+                oldv = BackRef(newv, window)
+                v2 = SkipListState(oldv, newv, window)
+                self.on_skip_list(v2, newv)
+            return b.ops
+        else:
+            return super().decompose(options)
+
+class WindowedMin(_WindowedMinMaxBase):
     '''
     Min of a rolling look back window, including the current newest data.
     For indices < window-1, the output will be NaN
     similar to pandas.DataFrame.rolling(n).min()
     '''
-    def make_reduce(self, v: OpBase) -> OpBase:
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
         return ReduceMin(v)
 
-class WindowedMax(WindowedReduce):
+    def on_skip_list(self, skplist: SkipListState, cur: OpBase) -> OpBase:
+        return SkipListMin(skplist)
+
+
+class WindowedMax(_WindowedMinMaxBase):
     '''
     Max of a rolling look back window, including the current newest data.
     For indices < window-1, the output will be NaN
     similar to pandas.DataFrame.rolling(n).max()
     '''
-    def make_reduce(self, v: OpBase) -> OpBase:
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
         return ReduceMax(v)
+
+    def on_skip_list(self, skplist: SkipListState, cur: OpBase) -> OpBase:
+        return SkipListMax(skplist)
 
 # small - sum
 def _kahan_sub(mask: OpBase, sum: OpBase, small: OpBase, compensation: OpBase) -> Union[OpBase, OpBase]:
@@ -424,51 +460,54 @@ def decompose(self, options: dict) -> List[OpBase]:
         return b.ops
 
 
-class TsArgMax(WindowedCompositiveOp):
+class TsArgMax(WindowedReduce):
     '''
     ArgMax in a rolling look back window, including the current newest data.
     The result should be the index of the max element in the rolling window. The index of the oldest element of the rolling window is 1.
     Similar to df.rolling(window).apply(np.argmax) + 1 
     '''
     def decompose(self, options: dict) -> List[OpBase]:
-        b = Builder(self.get_parent())
-        with b:
-            v0 = WindowedTempOutput(self.inputs[0], self.attrs["window"])
-            v1 = ForeachBackWindow(v0, self.attrs["window"])
-            v2 = ReduceArgMax(IterValue(v1, v0))
-            v3 = SubConst(v2, self.attrs["window"], True)
-        return b.ops
+        window = self.attrs["window"]
+        blocking_len = options["blocking_len"]
+        if _decide_use_skip_list(window, blocking_len):
+            b = Builder(self.get_parent())
+            with b:
+                TsArgMin(0-self.inputs[0], window)
+            return b.ops
+        else:
+            return super().decompose(options)
+
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
+        v2 = ReduceArgMax(v)
+        return self.attrs["window"] - v2
 
-class TsArgMin(WindowedCompositiveOp):
+class TsArgMin(_WindowedMinMaxBase):
     '''
     ArgMin in a rolling look back window, including the current newest data.
     The result should be the index of the min element in the rolling window. The index of the oldest element of the rolling window is 1.
     Similar to df.rolling(window).apply(np.argmin) + 1 
     '''
-    def decompose(self, options: dict) -> List[OpBase]:
-        b = Builder(self.get_parent())
-        with b:
-            v0 = WindowedTempOutput(self.inputs[0], self.attrs["window"])
-            v1 = ForeachBackWindow(v0, self.attrs["window"])
-            v2 = ReduceArgMin(IterValue(v1, v0))
-            v3 = SubConst(v2, self.attrs["window"], True)
-        return b.ops
+    def on_skip_list(self, skplist: SkipListState, cur: OpBase) -> OpBase:
+        return SkipListArgMin([skplist], [])
+
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
+        v2 = ReduceArgMin(v)
+        return self.attrs["window"] - v2
+
 
-class TsRank(WindowedCompositiveOp):
+class TsRank(_WindowedMinMaxBase):
     '''
     Time series rank of the newest data in a rolling look back window, including the current newest data.
     Let num_values_less = the number of values in rolling window that is less than the current newest data.
     Let num_values_eq = the number of values in rolling window that is equal to the current newest data.
     rank = num_values_less + (num_values_eq + 1) / 2
     Similar to df.rolling(window).rank()
     '''
-    def decompose(self, options: dict) -> List[OpBase]:
-        b = Builder(self.get_parent())
-        with b:
-            v0 = WindowedTempOutput(self.inputs[0], self.attrs["window"])
-            v1 = ForeachBackWindow(v0, self.attrs["window"])
-            v2 = ReduceRank(IterValue(v1, v0), self.inputs[0])
-        return b.ops
+    def on_skip_list(self, skplist: SkipListState, cur: OpBase) -> OpBase:
+        return SkipListRank(skplist, cur)
+
+    def make_reduce(self, v: OpBase, newest: OpBase) -> OpBase:
+        return ReduceRank(v, newest)
 
 class Clip(CompositiveOp):
     '''
@@ -636,4 +675,27 @@ def decompose(self, options: dict) -> List[OpBase]:
                 filtered = Select(index >= max_bar_index, IterValue(each, v), inf)
             trough = ReduceMin(filtered)
             (peak - trough) / peak
+        return b.ops
+
+
+class WindowedQuantile(CompositiveOp, WindowedTrait):
+    '''
+    Quantile in `window` rows ago.
+    Similar to pd.rolling(window).quantile(q, interpolation='linear')
+    '''
+    def __init__(self, v: OpBase, window: int, q: float) -> None:
+        super().__init__([v], [("window", window), ("q", q)])
+
+    def required_input_window(self) -> int:
+        return self.attrs["window"] + 1
+
+    def decompose(self, options: dict) -> List[OpBase]:
+        b = Builder(self.get_parent())
+        window = self.attrs["window"]
+        v = self.inputs[0]
+        q = self.attrs["q"]
+        with b:
+            old = BackRef(v, window)
+            v2 = SkipListState(old, v, window)
+            v3 = SkipListQuantile(v2, q)
         return b.ops

KunQuant/ops/MiscOp.py

@@ -1,5 +1,5 @@
 import KunQuant
-from KunQuant.Op import AcceptSingleValueInputTrait, Input, OpBase, WindowedTrait, SinkOpTrait, CrossSectionalOp, GloablStatefulOpTrait, UnaryElementwiseOp, BinaryElementwiseOp
+from KunQuant.Op import AcceptSingleValueInputTrait, Input, OpBase, WindowedTrait, SinkOpTrait, CrossSectionalOp, GlobalStatefulProducerTrait, GloablStatefulOpTrait, StateConsumerTrait, UnaryElementwiseOp, BinaryElementwiseOp
 from typing import List, Union
 
 class BackRef(OpBase, WindowedTrait):
@@ -12,16 +12,6 @@ def __init__(self, v: OpBase, window: int) -> None:
     def required_input_window(self) -> int:
         return self.attrs["window"] + 1
 
-class WindowedQuantile(OpBase, WindowedTrait):
-    '''
-    Quantile in `window` rows ago.
-    Similar to pd.rolling(window).quantile(q, interpolation='linear')
-    '''
-    def __init__(self, v: OpBase, window: int, q: float) -> None:
-        super().__init__([v], [("window", window), ("q", q)])
-
-    def required_input_window(self) -> int:
-        return self.attrs["window"] + 1
 
 class FastWindowedSum(OpBase, WindowedTrait, GloablStatefulOpTrait):
     '''
@@ -39,7 +29,7 @@ def get_state_variable_name_prefix(self) -> str:
     def generate_step_code(self, idx: str, time_idx: str, inputs: List[str], buf_name: str) -> str:
         return f"auto v{idx} = sum_{idx}.step({buf_name}, {inputs[0]}, {time_idx});"
 
-class Accumulator(OpBase, GloablStatefulOpTrait):
+class Accumulator(OpBase, GlobalStatefulProducerTrait):
     '''
     Accumulator is a stateful op that accumulates the input value over time.
     It can be used to compute running totals, moving averages, etc.'''
@@ -64,7 +54,7 @@ def verify(self, func) -> None:
             raise RuntimeError(f"Accumulator {self.attrs['name']} is not used with any SetAccumulator")
         return super().verify(func)
 
-class SetAccumulator(OpBase):
+class SetAccumulator(OpBase, StateConsumerTrait):
     '''
     Set the value of an Accumulator to a value, if mask is set. Otherwise, it does nothing.
     '''
@@ -120,7 +110,7 @@ def generate_init_code(self, idx: str, elem_type: str, simd_lanes: int, inputs:
     def generate_step_code(self, idx: str, time_idx: str, inputs: List[str]) -> str:
         return f"auto v{idx} = ema_{idx}.step({inputs[0]}, {time_idx});"
 
-class WindowedLinearRegression(OpBase, WindowedTrait, GloablStatefulOpTrait):
+class WindowedLinearRegression(OpBase, WindowedTrait, GlobalStatefulProducerTrait):
     '''
     Compute states of Windowed Linear Regression
     '''
@@ -135,37 +125,74 @@ def get_state_variable_name_prefix(self) -> str:
 
     def generate_step_code(self, idx: str, time_idx: str, inputs: List[str], buf_name: str) -> str:
         return f"const auto& v{idx} = linear_{idx}.step({buf_name}, {inputs[0]}, {time_idx});"
-
-class WindowedLinearRegressionImplBase(OpBase):
-    def __init__(self, v: OpBase) -> None:
-        super().__init__([v])
-
+
+
+class WindowedLinearRegressionConsumerTrait(StateConsumerTrait):
     def verify(self, func: 'KunQuant.Stage.Function') -> None:
         if len(self.inputs) < 1 or not isinstance(self.inputs[0], WindowedLinearRegression):
             raise RuntimeError("WindowedLinearRegressionImpl expects WindowedLinearRegression Op as input")
-        return super().verify(func)
+        return OpBase.verify(self, func)
+
+class WindowedLinearRegressionImplUnaryBase(WindowedLinearRegressionConsumerTrait, UnaryElementwiseOp):
+    pass
 
-class WindowedLinearRegressionConsumerTrait:
+class WindowedLinearRegressionImplBinaryBase(WindowedLinearRegressionConsumerTrait, BinaryElementwiseOp):
     pass
 
-class WindowedLinearRegressionRSqaureImpl(UnaryElementwiseOp, WindowedLinearRegressionConsumerTrait):
+class WindowedLinearRegressionRSqaureImpl(WindowedLinearRegressionImplUnaryBase):
     '''
     Compute RSqaure of Windowed Linear Regression
     '''
     pass
 
-class WindowedLinearRegressionSlopeImpl(UnaryElementwiseOp, WindowedLinearRegressionConsumerTrait):
+class WindowedLinearRegressionSlopeImpl(WindowedLinearRegressionImplUnaryBase):
     '''
     Compute RSqaure of Windowed Linear Regression
     '''
     pass
 
-class WindowedLinearRegressionResiImpl(BinaryElementwiseOp, WindowedLinearRegressionConsumerTrait):
+class WindowedLinearRegressionResiImpl(WindowedLinearRegressionImplBinaryBase):
     '''
     Compute RSqaure of Windowed Linear Regression
     '''
     pass
 
+class SkipListState(OpBase, GlobalStatefulProducerTrait):
+    '''
+    SkipListState is a stateful op that maintains a skip list of the input values.
+    '''
+    def __init__(self, oldvalue: OpBase, value: OpBase, window: int) -> None:
+        super().__init__([oldvalue, value], [("window", window)])
+
+    def get_state_variable_name_prefix(self) -> str:
+        return "skip_list_"
+
+    def generate_step_code(self, idx: str, time_idx: str, inputs: List[str]) -> str:
+        return f"auto& v{idx} = skip_list_{idx}.step({inputs[0]}, {inputs[1]}, {time_idx});"
+
+class SkipListConsumerOp(StateConsumerTrait):    
+    def verify(self, func: 'KunQuant.Stage.Function') -> None:
+        if len(self.inputs) < 1 or not isinstance(self.inputs[0], SkipListState):
+            raise RuntimeError("SkipListConsumerOp expects SkipListState Op as input")
+        return super().verify(func)
+
+class SkipListQuantile(SkipListConsumerOp, OpBase):
+    def __init__(self, v: OpBase, q: float) -> None:
+        super().__init__([v], [("q", q)])
+
+class SkipListRank(SkipListConsumerOp, BinaryElementwiseOp):
+    pass
+
+class SkipListMin(SkipListConsumerOp, UnaryElementwiseOp):
+    pass
+
+class SkipListMax(SkipListConsumerOp, UnaryElementwiseOp):
+    pass
+
+class SkipListArgMin(SkipListConsumerOp, OpBase):
+    pass
+
+
 class GenericCrossSectionalOp(CrossSectionalOp):
     '''
     Cross sectional op with customized C++ implementation.