Improve SDFG work-depth analysis and add SDFG simulated operational i…

…ntensity analysis (#1607)

This PR is here to merge the reviewed PR #1495, which has remained
inactive for a long time with minor comments open. The comments have
been addressed here and merge conflicts have been resolved.

---------

Co-authored-by: Cliff Hodel <hodelcl@student.ethz.ch>
Co-authored-by: Cliff Hodel <111381329+hodelcl@users.noreply.github.com>
Co-authored-by: Cliff Hodel <hodelcl@ethz.ch>

dace/sdfg/work_depth_analysis/assumptions.py → dace/sdfg/performance_evaluation/assumptions.py

@@ -153,7 +153,7 @@ def propagate_assumptions_equal_symbols(condensed_assumptions):
                 equality_subs1.update({sym: sp.Symbol(uf.find(sym))})
 
     equality_subs2 = {}
-    # In a second step, each symbol gets replace with its equal number (if present)
+    # In a second step, each symbol gets replaced with its equal number (if present)
     # using equality_subs2.
     for sym, assum in condensed_assumptions.items():
         for e in assum.equal:
@@ -182,7 +182,7 @@ def parse_assumptions(assumptions, array_symbols):
     Parses a list of assumptions into substitution dictionaries. Firstly, it gathers all assumptions and
     keeps only the strongest ones. Afterwards it constructs two substitution dicts for the equality
     assumptions: First dict for symbol==symbol assumptions; second dict for symbol==number assumptions.
-    The other assumptions get handles by N tuples of substitution dicts (N = max number of concurrent
+    The other assumptions get handled by N tuples of substitution dicts (N = max number of concurrent
     assumptions for a single symbol). Each tuple is responsible for at most one assumption for each symbol. 
     First dict in the tuple substitutes the symbol with the assumption; second dict restores the initial symbol.
 

dace/sdfg/work_depth_analysis/helpers.py → dace/sdfg/performance_evaluation/helpers.py

@@ -214,6 +214,10 @@ def get_backedges(graph: nx.DiGraph,
         return backedges
 
 
+class LoopExtractionError(Exception):
+    pass
+
+
 def find_loop_guards_tails_exits(sdfg_nx: nx.DiGraph):
     """
     Detects loops in a SDFG. For each loop, it identifies (node, oNode, exit).
@@ -241,15 +245,15 @@ def find_loop_guards_tails_exits(sdfg_nx: nx.DiGraph):
 
     # sanity check:
     if sdfg_nx.in_degree(artificial_end_node) == 0:
-        raise ValueError('No end node could be determined in the SDFG')
+        raise LoopExtractionError('No end node could be determined in the SDFG')
 
     # compute dominators and backedges
     iDoms = nx.immediate_dominators(sdfg_nx, start)
-    allDom, domTree = get_domtree(sdfg_nx, start, iDoms)
+    allDom, _ = get_domtree(sdfg_nx, start, iDoms)
 
     reversed_sdfg_nx = sdfg_nx.reverse()
     iPostDoms = nx.immediate_dominators(reversed_sdfg_nx, artificial_end_node)
-    allPostDoms, postDomTree = get_domtree(reversed_sdfg_nx, artificial_end_node, iPostDoms)
+    _, postDomTree = get_domtree(reversed_sdfg_nx, artificial_end_node, iPostDoms)
 
     backedges = get_backedges(sdfg_nx, start)
     backedgesDstDict = {}
@@ -297,7 +301,7 @@ def find_loop_guards_tails_exits(sdfg_nx: nx.DiGraph):
                         exitCandidates.add(succ)
 
                 if len(exitCandidates) == 0:
-                    raise ValueError('failed to find any exit nodes')
+                    raise LoopExtractionError('failed to find any exit nodes')
                 elif len(exitCandidates) > 1:
                     # Find the exit candidate that sits highest up in the
                     # postdominator tree (i.e., has the lowest level).
@@ -323,7 +327,7 @@ def find_loop_guards_tails_exits(sdfg_nx: nx.DiGraph):
                     if len(minSet) > 0:
                         exitCandidates = minSet
                     else:
-                        raise ValueError('failed to find exit minSet')
+                        raise LoopExtractionError('failed to find exit minSet')
 
                 # now we have a triple (node, oNode, exitCandidates)
                 nodes_oNodes_exits.append((node, oNode, exitCandidates))

dace/sdfg/performance_evaluation/op_in_helpers.py

@@ -0,0 +1,283 @@
+# Copyright 2019-2023 ETH Zurich and the DaCe authors. All rights reserved.
+""" Contains class CacheLineTracker which keeps track of all arrays of an SDFG and their cache line position
+and class AccessStack which which corresponds to the stack used to compute the stack distance. 
+Further, provides a curve fitting method and plotting function. """
+
+import warnings
+from dace.data import Array
+import sympy as sp
+from collections import deque
+from scipy.optimize import curve_fit
+import numpy as np
+from dace import symbol
+
+
+class CacheLineTracker:
+    """ A CacheLineTracker maps data container accesses to the corresponding accessed cache line. """
+
+    def __init__(self, L) -> None:
+        self.array_info = {}
+        self.start_lines = {}
+        self.next_free_line = 0
+        self.L = L
+
+    def add_array(self, name: str, a: Array, mapping):
+        if name not in self.start_lines:
+            # new array encountered
+            self.array_info[name] = a
+            self.start_lines[name] = self.next_free_line
+            # increase next_free_line
+            self.next_free_line += (a.total_size.subs(mapping) * a.dtype.bytes + self.L - 1) // self.L  # ceil division
+
+    def cache_line_id(self, name: str, access: [int], mapping):
+        arr = self.array_info[name]
+        one_d_index = 0
+        for dim in range(len(access)):
+            i = access[dim]
+            one_d_index += (i + sp.sympify(arr.offset[dim]).subs(mapping)) * sp.sympify(arr.strides[dim]).subs(mapping)
+
+        # divide by L to get the cache line id
+        return self.start_lines[name] + (one_d_index * arr.dtype.bytes) // self.L
+
+    def copy(self):
+        new_clt = CacheLineTracker(self.L)
+        new_clt.array_info = dict(self.array_info)
+        new_clt.start_lines = dict(self.start_lines)
+        new_clt.next_free_line = self.next_free_line
+        return new_clt
+
+
+class Node:
+
+    def __init__(self, val: int, n=None) -> None:
+        self.v = val
+        self.next = n
+
+
+class AccessStack:
+    """ A stack of cache line ids. For each memory access, we search the corresponding cache line id
+    in the stack, report its distance and move it to the top of the stack. If the id was not found,
+    we report a distance of -1. """
+
+    def __init__(self, C) -> None:
+        self.top = None
+        self.num_calls = 0
+        self.length = 0
+        self.C = C
+
+    def touch(self, id):
+        self.num_calls += 1
+        curr = self.top
+        prev = None
+        found = False
+        distance = 0
+        while curr is not None:
+            # check if we found id
+            if curr.v == id:
+                # take curr node out
+                if prev is not None:
+                    prev.next = curr.next
+                    curr.next = self.top
+                    self.top = curr
+
+                found = True
+                break
+
+            # iterate further
+            prev = curr
+            curr = curr.next
+            distance += 1
+
+        if not found:
+            # we accessed this cache line for the first time ever
+            self.top = Node(id, self.top)
+            self.length += 1
+            distance = -1
+
+        return distance
+
+    def in_cache_as_list(self):
+        """
+        Returns a list of cache ids currently in cache. Index 0 is the most recently used.
+        """
+        res = deque()
+        curr = self.top
+        dist = 0
+        while curr is not None and dist < self.C:
+            res.append(curr.v)
+            curr = curr.next
+            dist += 1
+        return res
+
+    def debug_print(self):
+        # prints the whole stack
+        print('\n')
+        curr = self.top
+        while curr is not None:
+            print(curr.v, end=', ')
+            curr = curr.next
+        print('\n')
+
+    def copy(self):
+        new_stack = AccessStack(self.C)
+        cache_content = self.in_cache_as_list()
+        if len(cache_content) > 0:
+            new_top_value = cache_content.popleft()
+            new_stack.top = Node(new_top_value)
+            curr = new_stack.top
+            for x in cache_content:
+                curr.next = Node(x)
+                curr = curr.next
+        return new_stack
+
+
+def plot(x, work_map, cache_misses, op_in_map, symbol_name, C, L, sympy_f, element, name):
+    plt = None
+    try:
+        import matplotlib.pyplot as plt_import
+        plt = plt_import
+    except ModuleNotFoundError:
+        pass
+
+    if plt is None:
+        warnings.warn('Plotting only possible with matplotlib installed')
+        return
+
+    work_map = work_map[element]
+    cache_misses = cache_misses[element]
+    op_in_map = op_in_map[element]
+    sympy_f = sympy_f[element]
+
+    a = np.linspace(1, max(x) + 5, max(x) * 4)
+
+    fig, ax = plt.subplots(1, 2, figsize=(12, 5))
+    ax[0].scatter(x, cache_misses, label=f'C={C*L}, L={L}')
+    b = []
+    for curr in a:
+        b.append(sp.N(sp.sympify(sympy_f).subs(symbol_name, curr)))
+    ax[0].plot(a, b)
+
+    c = []
+    for i, curr in enumerate(x):
+        if work_map[0].subs(symbol_name, curr) == 0:
+            c.append(0)
+        elif (cache_misses[i] * L) == 0:
+            c.append(9999)
+        else:
+            c.append(work_map[0].subs(symbol_name, curr) / (cache_misses[i] * L))
+    c = np.array(c).astype(np.float64)
+
+    ax[1].scatter(x, c, label=f'C={C*L}, L={L}')
+    b = []
+    for curr in a:
+        b.append(sp.N(sp.sympify(op_in_map).subs(symbol_name, curr)))
+    ax[1].plot(a, b)
+
+    ax[0].set_ylim(bottom=0, top=max(cache_misses) + max(cache_misses) / 10)
+    ax[0].set_xlim(left=0, right=max(x) + 1)
+    ax[0].set_xlabel(symbol_name)
+    ax[0].set_ylabel('Number of Cache Misses')
+    ax[0].set_title(name)
+    ax[0].legend(fancybox=True, framealpha=0.5)
+
+    ax[1].set_ylim(bottom=0, top=max(c) + max(c) / 10)
+    ax[1].set_xlim(left=0, right=max(x) + 1)
+    ax[1].set_xlabel(symbol_name)
+    ax[1].set_ylabel('Operational Intensity')
+    ax[1].set_title(name)
+
+    fig.show()
+
+
+def compute_mape(f, test_x, test_y, test_set_size):
+    total_error = 0
+    for i in range(test_set_size):
+        pred = f(test_x[i])
+        err = abs(test_y[i] - pred)
+        total_error += err / test_y[i]
+    return total_error / test_set_size
+
+
+def r_squared(pred, y):
+    if np.sum(np.square(y - y.mean())) <= 0.0001:
+        return 1
+    return 1 - np.sum(np.square(y - pred)) / np.sum(np.square(y - y.mean()))
+
+
+def find_best_model(x, y, I, J, symbol_name):
+    """ Find the best model out of all combinations of (i, j) from I and J via leave-one-out cross validation. """
+    min_error = None
+    for i in I:
+        for j in J:
+            # current model
+            if i == 0 and j == 0:
+
+                def f(x, b):
+                    return b * np.ones_like(x)
+            else:
+
+                def f(x, c, b):
+                    return c * np.power(x, i) * np.power(np.log2(x), j) + b
+
+            error_sum = 0
+            for left_out in range(len(x)):
+                xx = np.delete(x, left_out)
+                yy = np.delete(y, left_out)
+                try:
+                    param, _ = curve_fit(f, xx, yy)
+
+                    # predict on left out sample
+                    pred = f(x[left_out], *param)
+                    squared_error = np.square(pred - y[left_out])
+                    error_sum += squared_error
+                except RuntimeError:
+                    # triggered if no fit was found --> give huge error
+                    error_sum += 999999
+
+            mean_error = error_sum / len(x)
+            if min_error is None or mean_error < min_error:
+                # new best model found
+                min_error = mean_error
+                best_i_j = (i, j)
+    if best_i_j[0] == 0 and best_i_j[1] == 0:
+
+        def f_best(x, b):
+            return b * np.ones_like(x)
+    else:
+
+        def f_best(x, c, b):
+            return c * np.power(x, best_i_j[0]) * np.power(np.log2(x), best_i_j[1]) + b
+
+    # fit best model to all data points
+    final_p, _ = curve_fit(f_best, x, y)
+
+    def final_f(x):
+        return f_best(x, *final_p)
+
+    if best_i_j[0] == 0 and best_i_j[1] == 0:
+        sympy_f = final_p[0]
+    else:
+        sympy_f = sp.simplify(final_p[0] * symbol(symbol_name)**best_i_j[0] *
+                              sp.log(symbol(symbol_name), 2)**best_i_j[1] + final_p[1])
+    # compute r^2
+    r_s = r_squared(final_f(x), y)
+    return final_f, sympy_f, r_s
+
+
+def fit_curve(x, y, symbol_name):
+    """
+    Fits a function throught the data set.
+
+    :param x: The independent values.
+    :param y: The dependent values.
+    :param symbol_name: The name of the SDFG symbol.
+    """
+    x = np.array(x).astype(np.int32)
+    y = np.array(y).astype(np.float64)
+
+    # model search space
+    I = [x / 4 for x in range(13)]
+    J = [0, 1, 2]
+    final_f, sympy_final_f, r_s = find_best_model(x, y, I, J, symbol_name)
+
+    return final_f, sympy_final_f, r_s