mirror of https://github.com/openXC7/prjxray.git
timfuz: timfuz_solve main()
Signed-off-by: John McMaster <johndmcmaster@gmail.com>
This commit is contained in:
John McMaster
parent
97ea34d4cd
commit
aeebb45b34
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@ -1,7 +1,5 @@
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#!/usr/bin/env python
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# https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.linprog.html
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from scipy.optimize import linprog
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import math
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import numpy as np
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from collections import OrderedDict
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@ -14,6 +12,7 @@ import copy
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import sys
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import random
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import glob
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from fractions import Fraction
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from benchmark import Benchmark
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@ -229,36 +228,33 @@ def simplify_cols(names, A_ubd, b_ub):
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nr = list(names_ret.keys())
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return nr, A_ub_ret, b_ub
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def A_ubr_np2d(row, sf=1):
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def A_ubr_np2d(row):
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'''Convert a single row'''
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#d = {}
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d = OrderedDict()
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for coli, val in enumerate(row):
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if val:
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d[coli] = sf * val
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d[coli] = val
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return d
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def A_ub_np2d(A_ub, sf=1):
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def A_ub_np2d(A_ub):
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'''Convert A_ub entries in numpy matrix to dictionary / sparse form'''
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A_ubd = [None] * len(A_ub)
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for i, row in enumerate(A_ub):
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A_ubd[i] = A_ubr_np2d(row, sf=sf)
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A_ubd[i] = A_ubr_np2d(row)
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return A_ubd
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# def Ar_ds2np(row_ds, names):
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# Ar_di2np(row_di, cols, sf=1)
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def Ar_di2np(row_di, cols, sf=1):
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def Ar_di2np(row_di, cols):
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rownp = np.zeros(cols)
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for coli, val in row_di.items():
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# Sign inversion due to way solver works
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rownp[coli] = sf * val
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rownp[coli] = val
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return rownp
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# NOTE: sign inversion
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def A_di2np(Adi, cols, sf=1):
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def A_di2np(Adi, cols):
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'''Convert A_ub entries in dictionary / sparse to numpy matrix form'''
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return [Ar_di2np(row_di, cols, sf=sf) for row_di in Adi]
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return [Ar_di2np(row_di, cols) for row_di in Adi]
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def Ar_ds2t(rowd):
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'''Convert a dictionary row into a tuple with (column number, value) tuples'''
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@ -817,3 +813,102 @@ def index_names(Ads):
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for k1 in row_ds.keys():
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names.add(k1)
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return names
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def load_sub(fn):
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j = json.load(open(fn, 'r'))
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for name, vals in sorted(j['subs'].items()):
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for k, v in vals.items():
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vals[k] = Fraction(v[0], v[1])
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return j
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def row_sub_syms(row, sub_json, verbose=False):
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if 0 and verbose:
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print("")
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print(row.items())
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delsyms = 0
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for k in sub_json['drop_names']:
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try:
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del row[k]
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delsyms += 1
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except KeyError:
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pass
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if verbose:
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print("Deleted %u symbols" % delsyms)
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if verbose:
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print('Checking pivots')
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print(sorted(row.items()))
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for group, pivot in sorted(sub_json['pivots'].items()):
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if pivot not in row:
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continue
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n = row[pivot]
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print(' pivot %u %s' % (n, pivot))
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for group, pivot in sorted(sub_json['pivots'].items()):
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if pivot not in row:
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continue
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# take the sub out n times
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# note constants may be negative
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n = row[pivot]
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if verbose:
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print('pivot %i %s' % (n, pivot))
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for subk, subv in sorted(sub_json['subs'][group].items()):
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oldn = row.get(subk, Fraction(0))
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rown = oldn - n * subv
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if verbose:
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print(" %s: %d => %d" % (subk, oldn, rown))
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if rown == 0:
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# only becomes zero if didn't previously exist
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del row[subk]
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if verbose:
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print(" del")
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else:
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row[subk] = rown
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row[group] = n
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assert pivot not in row
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# after all constants are applied, the row should end up positive?
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# numeric precision issues previously limited this
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# Ex: AssertionError: ('PIP_BSW_2ELSING0', -2.220446049250313e-16)
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for k, v in sorted(row.items()):
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assert v > 0, (k, v)
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def run_sub_json(Ads, sub_json, verbose=False):
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nrows = 0
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nsubs = 0
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ncols_old = 0
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ncols_new = 0
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print('Subbing %u rows' % len(Ads))
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prints = set()
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for rowi, row in enumerate(Ads):
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if 0 and verbose:
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print(row)
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if verbose:
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print('')
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print('Row %u w/ %u elements' % (rowi, len(row)))
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row_orig = dict(row)
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row_sub_syms(row, sub_json, verbose=verbose)
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nrows += 1
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if row_orig != row:
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nsubs += 1
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if verbose:
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rowt = Ar_ds2t(row)
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if rowt not in prints:
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print('row', row)
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prints.add(rowt)
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ncols_old += len(row_orig)
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ncols_new += len(row)
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if verbose:
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print('')
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print("Sub: %u / %u rows changed" % (nsubs, nrows))
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print("Sub: %u => %u cols" % (ncols_old, ncols_new))
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@ -1,6 +1,6 @@
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#!/usr/bin/env python3
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from timfuz import Benchmark, Ar_di2np, Ar_ds2t, A_di2ds, A_ds2di, simplify_rows, loadc_Ads_b, index_names, A_ds2np
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from timfuz import Benchmark, Ar_di2np, Ar_ds2t, A_di2ds, A_ds2di, simplify_rows, loadc_Ads_b, index_names, A_ds2np, load_sub, run_sub_json
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import numpy as np
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import glob
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import json
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@ -8,9 +8,6 @@ import math
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from collections import OrderedDict
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from fractions import Fraction
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# check for issues that may be due to round off error
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STRICT = 1
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def Adi2matrix_random(A_ubd, b_ub, names):
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# random assignment
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# was making some empty rows
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@ -44,97 +41,6 @@ def Ads2matrix_linear(Ads, b):
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dst_rowi = (dst_rowi + 1) % rows_out
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return A_ret, b_ret
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def row_sub_syms(row, sub_json, verbose=False):
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if 0 and verbose:
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print("")
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print(row.items())
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delsyms = 0
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for k in sub_json['drop_names']:
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try:
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del row[k]
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delsyms += 1
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except KeyError:
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pass
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if verbose:
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print("Deleted %u symbols" % delsyms)
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if verbose:
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print('Checking pivots')
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print(sorted(row.items()))
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for group, pivot in sorted(sub_json['pivots'].items()):
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if pivot not in row:
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continue
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n = row[pivot]
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print(' pivot %u %s' % (n, pivot))
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for group, pivot in sorted(sub_json['pivots'].items()):
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if pivot not in row:
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continue
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# take the sub out n times
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# note constants may be negative
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n = row[pivot]
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if verbose:
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print('pivot %i %s' % (n, pivot))
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for subk, subv in sorted(sub_json['subs'][group].items()):
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oldn = row.get(subk, Fraction(0))
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rown = oldn - n * subv
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if verbose:
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print(" %s: %d => %d" % (subk, oldn, rown))
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if rown == 0:
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# only becomes zero if didn't previously exist
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del row[subk]
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if verbose:
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print(" del")
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else:
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row[subk] = rown
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row[group] = n
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assert pivot not in row
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# after all constants are applied, the row should end up positive?
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# numeric precision issues may limit this
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# Ex: AssertionError: ('PIP_BSW_2ELSING0', -2.220446049250313e-16)
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if STRICT:
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for k, v in sorted(row.items()):
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assert v > 0, (k, v)
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def run_sub_json(Ads, sub_json, verbose=False):
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nrows = 0
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nsubs = 0
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ncols_old = 0
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ncols_new = 0
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print('Subbing %u rows' % len(Ads))
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prints = set()
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for rowi, row in enumerate(Ads):
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if 0 and verbose:
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print(row)
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if verbose:
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print('')
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print('Row %u w/ %u elements' % (rowi, len(row)))
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row_orig = dict(row)
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row_sub_syms(row, sub_json, verbose=verbose)
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nrows += 1
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if row_orig != row:
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nsubs += 1
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if verbose:
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rowt = Ar_ds2t(row)
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if rowt not in prints:
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print('row', row)
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prints.add(rowt)
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ncols_old += len(row_orig)
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ncols_new += len(row)
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if verbose:
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print('')
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print("Sub: %u / %u rows changed" % (nsubs, nrows))
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print("Sub: %u => %u cols" % (ncols_old, ncols_new))
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def pmatrix(Anp, s):
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import sympy
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msym = sympy.Matrix(Anp)
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@ -146,7 +52,10 @@ def pds(Ads, s):
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pmatrix(Anp, s)
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print('Names: %s' % (names,))
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def run(fns_in, sub_json=None, verbose=False, corner=None):
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def run(fns_in, sub_json=None, verbose=False):
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# arbitrary...data is thrown away
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corner = "slow_max"
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Ads, b = loadc_Ads_b(fns_in, corner, ico=True)
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# Remove duplicate rows
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@ -190,15 +99,6 @@ def run(fns_in, sub_json=None, verbose=False, corner=None):
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else:
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print('slogdet :) : %s, %s' % (sign, logdet))
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def load_sub(fn):
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j = json.load(open(fn, 'r'))
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for name, vals in sorted(j['subs'].items()):
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for k, v in vals.items():
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vals[k] = Fraction(v[0], v[1])
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return j
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def main():
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import argparse
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@ -209,18 +109,17 @@ def main():
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parser.add_argument('--verbose', action='store_true', help='')
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parser.add_argument('--sub-json', help='')
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parser.add_argument('--corner', default="slow_max", help='')
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parser.add_argument(
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'fns_in',
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nargs='*',
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help='timing3.txt input files')
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help='timing3.csv input files')
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args = parser.parse_args()
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# Store options in dict to ease passing through functions
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bench = Benchmark()
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fns_in = args.fns_in
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if not fns_in:
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fns_in = glob.glob('specimen_*/timing3.txt')
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fns_in = glob.glob('specimen_*/timing3.csv')
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sub_json = None
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if args.sub_json:
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@ -228,7 +127,7 @@ def main():
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try:
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run(sub_json=sub_json,
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fns_in=fns_in, verbose=args.verbose, corner=args.corner)
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fns_in=fns_in, verbose=args.verbose)
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finally:
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print('Exiting after %s' % bench)
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@ -1,24 +1,77 @@
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#!/usr/bin/env python3
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def run_corner(A_ubd, b_ub, names, verbose=0, opts={}, meta={}):
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# https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.linprog.html
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from scipy.optimize import linprog
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from timfuz import Benchmark, Ar_di2np, Ar_ds2t, A_di2ds, A_ds2di, simplify_rows, loadc_Ads_b, index_names, A_ds2np, load_sub, run_sub_json
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import numpy as np
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import glob
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import json
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import math
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from collections import OrderedDict
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from fractions import Fraction
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import sys
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import datetime
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import os
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import time
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def check_feasible(A_ub, b_ub):
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'''
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Put large timing constants into the equations
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See if that would solve it
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Its having trouble giving me solutions as this gets bigger
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Make a terrible baseline guess to confirm we aren't doing something bad
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'''
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sys.stdout.write('Check feasible ')
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sys.stdout.flush()
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rows = len(b_ub)
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cols = len(A_ub[0])
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progress = max(1, rows / 100)
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# Chose a high arbitrary value for x
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# Delays should be in order of ns, so a 10 ns delay should be way above what anything should be
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xs = [10e3 for _i in range(cols)]
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# FIXME: use the correct np function to do this for me
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# Verify bounds
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#b_res = np.matmul(A_ub, xs)
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#print(type(A_ub), type(xs)
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#A_ub = np.array(A_ub)
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#xs = np.array(xs)
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#b_res = np.matmul(A_ub, xs)
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def my_mul(A_ub, xs):
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#print('cols', cols
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#print('rows', rows
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ret = [None] * rows
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for row in range(rows):
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this = 0
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for col in range(cols):
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this += A_ub[row][col] * xs[col]
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ret[row] = this
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return ret
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b_res = my_mul(A_ub, xs)
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# Verify bound was respected
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for rowi, (this_b, this_b_ub) in enumerate(zip(b_res, b_ub)):
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if rowi % progress == 0:
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sys.stdout.write('.')
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sys.stdout.flush()
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if this_b >= this_b_ub or this_b > 0:
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print('% 4d Want res % 10.1f <= % 10.1f <= 0' % (rowi, this_b, this_b_ub))
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raise Exception("Bad ")
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print(' done')
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def run_corner(Anp, b, names, verbose=False, opts={}, meta={}):
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assert type(Anp[0]) is np.ndarray, type(Anp[0])
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assert type(b) is np.ndarray, type(b)
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# Given timing scores for above delays (-ps)
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names_orig = names
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#print_eqns(A_ub, b_ub, verbose=verbose)
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names, A_ubd, b_ub = massage_equations(A_ubd, b_ub, opts, names, verbose=verbose)
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print
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print_eqns(A_ubd, b_ub, verbose=verbose)
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print
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col_dist(A_ubd, 'final', names)
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A_ub, b_ub = Ab_d2np(A_ubd, b_ub, names)
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# Its having trouble giving me solutions as this gets bigger
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# Make a terrible baseline guess to confirm we aren't doing something bad
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#print_names(names, verbose=verbose)
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check_feasible(A_ub=A_ub, b_ub=b_ub)
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#check_feasible(Anp, b)
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'''
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Be mindful of signs
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@ -31,8 +84,14 @@ def run_corner(A_ubd, b_ub, names, verbose=0, opts={}, meta={}):
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-delay1 + -delay2 + -delay4 <= -timing1
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-delay2 + -delay3 <= -timing2
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'''
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rows = len(A_ub)
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cols = len(A_ub[0])
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rows = len(Anp)
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cols = len(Anp[0])
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print('Scaling to solution form...')
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b_ub = -1.0 * b
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#A_ub = -1.0 * Anp
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A_ub = [-1.0 * x for x in Anp]
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print('Creating misc constants...')
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# Minimization function scalars
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# Treat all logic elements as equally important
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c = [1 for _i in range(len(names))]
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@ -50,12 +109,12 @@ def run_corner(A_ubd, b_ub, names, verbose=0, opts={}, meta={}):
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maxiter = 1000000
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if verbose >= 2:
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print('b_ub', b_ub)
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print('b_ub', b)
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print('Unique delay elements: %d' % len(names))
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print(' # delay minimization weights: %d' % len(c))
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print(' # delay constraints: %d' % len(bounds))
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print('Input paths')
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print(' # timing scores: %d' % len(b_ub))
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||||
print(' # timing scores: %d' % len(b))
|
||||
print(' Rows: %d' % rows)
|
||||
|
||||
tlast = [time.time()]
|
||||
|
|
@ -96,3 +155,69 @@ def run_corner(A_ubd, b_ub, names, verbose=0, opts={}, meta={}):
|
|||
print('Writing %s' % fn_out)
|
||||
np.save(fn_out, (3, c, A_ub, b_ub, bounds, names, res, meta))
|
||||
|
||||
def run(fns_in, corner, sub_json=None, dedup=True, verbose=False):
|
||||
Ads, b = loadc_Ads_b(fns_in, corner, ico=True)
|
||||
|
||||
# Remove duplicate rows
|
||||
# is this necessary?
|
||||
# maybe better to just add them into the matrix directly
|
||||
if dedup:
|
||||
oldn = len(Ads)
|
||||
Ads, b = simplify_rows(Ads, b)
|
||||
print('Simplify %u => %u rows' % (oldn, len(Ads)))
|
||||
|
||||
if sub_json:
|
||||
print('Sub: %u rows' % len(Ads))
|
||||
names_old = index_names(Ads)
|
||||
run_sub_json(Ads, sub_json, verbose=verbose)
|
||||
names = index_names(Ads)
|
||||
print("Sub: %u => %u names" % (len(names_old), len(names)))
|
||||
else:
|
||||
names = index_names(Ads)
|
||||
|
||||
if 0:
|
||||
print
|
||||
print_eqns(A_ubd, b_ub, verbose=verbose)
|
||||
|
||||
print
|
||||
col_dist(A_ubd, 'final', names)
|
||||
|
||||
print('Converting to numpy...')
|
||||
names, Anp = A_ds2np(Ads)
|
||||
run_corner(Anp, np.asarray(b), names, verbose=verbose)
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description=
|
||||
'Solve timing solution'
|
||||
)
|
||||
|
||||
parser.add_argument('--verbose', action='store_true', help='')
|
||||
parser.add_argument('--sub-json', help='Group substitutions to make fully ranked')
|
||||
parser.add_argument('--corner', default="slow_max", help='')
|
||||
parser.add_argument(
|
||||
'fns_in',
|
||||
nargs='*',
|
||||
help='timing3.csv input files')
|
||||
args = parser.parse_args()
|
||||
# Store options in dict to ease passing through functions
|
||||
bench = Benchmark()
|
||||
|
||||
fns_in = args.fns_in
|
||||
if not fns_in:
|
||||
fns_in = glob.glob('specimen_*/timing3.csv')
|
||||
|
||||
sub_json = None
|
||||
if args.sub_json:
|
||||
sub_json = load_sub(args.sub_json)
|
||||
|
||||
try:
|
||||
run(sub_json=sub_json,
|
||||
fns_in=fns_in, verbose=args.verbose, corner=args.corner)
|
||||
finally:
|
||||
print('Exiting after %s' % bench)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue