mirror of https://github.com/openXC7/prjxray.git
timfuz: massage cleanup
Signed-off-by: John McMaster <johndmcmaster@gmail.com>
This commit is contained in:
John McMaster
parent
bf5b435a69
commit
43ccabe815
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@ -333,83 +333,12 @@ def derive_eq_by_col(Ads, b_ub, verbose=0):
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return Ads_ret, b_ret
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# keep derriving until solution is (probably) stable
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def massage_equations_old(Ads, b, verbose=False, derive_lim=3, corner=None):
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'''
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Equation pipeline
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Some operations may generate new equations
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Simplify after these to avoid unnecessary overhead on redundant constraints
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Similarly some operations may eliminate equations, potentially eliminating a column (ie variable)
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Remove these columns as necessary to speed up solving
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'''
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def debug(what):
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if verbose:
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print('')
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print_eqns(Ads, b, verbose=verbose, label=what, lim=20)
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col_dist(Ads, what)
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check_feasible_d(Ads, b)
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# Try to (intelligently) subtract equations to generate additional constraints
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# This helps avoid putting all delay in a single shared variable
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dstart = len(b)
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# Original simple
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for di in range(derive_lim):
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print
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assert len(Ads) == len(b)
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n_orig = len(b)
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# Meat of the operation
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# Focus on easy equations for first pass to get a lot of easy derrivations
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col_lim = 12 if di == 0 else None
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#col_lim = None
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Ads, b = derive_eq_by_row(Ads, b, col_lim=col_lim)
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debug("der_rows")
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# Run another simplify pass since new equations may have overlap with original
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Ads, b = simplify_rows(Ads, b, corner=corner)
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print(
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'Derive row %d / %d: %d => %d equations' %
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(di + 1, derive_lim, n_orig, len(b)))
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debug("der_rows simp")
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n_orig2 = len(b)
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# Meat of the operation
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Ads, b = derive_eq_by_col(Ads, b)
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debug("der_cols")
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# Run another simplify pass since new equations may have overlap with original
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Ads, b = simplify_rows(Ads, b, corner=corner)
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print(
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'Derive col %d / %d: %d => %d equations' %
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(di + 1, derive_lim, n_orig2, len(b)))
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debug("der_cols simp")
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if n_orig == len(b):
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break
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dend = len(b)
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print('')
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print('Derive net: %d => %d' % (dstart, dend))
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print('')
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# Was experimentting to see how much the higher order columns really help
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'''
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cols_min_post = opts.get('cols_min_post', None)
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cols_max_post = opts.get('cols_max_post', None)
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# Filter input based on number of columns
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if cols_min_post or cols_max_post:
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Ads, b = filter_ncols(Ads=Ads, b=b, cols_min=cols_min_post, cols_max=cols_max_post)
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debug("filter_ncals final")
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'''
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# Helps debug readability
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Ads, b = sort_equations(Ads, b)
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debug("final (sorted)")
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return Ads, b
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# iteratively increasing column limit until all columns are added
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def massage_equations_inc_col_lim(Ads, b, verbose=False, corner=None):
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def massage_equations(Ads, b, verbose=False, corner=None):
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'''
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Subtract equations from each other to generate additional constraints
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Helps provide additional guidance to solver for realistic delays
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Equation pipeline
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Some operations may generate new equations
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Simplify after these to avoid unnecessary overhead on redundant constraints
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@ -463,6 +392,7 @@ def massage_equations_inc_col_lim(Ads, b, verbose=False, corner=None):
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col_dist(Ads, 'derive done iter %d, lim %d' % (di, col_lim), lim=12)
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rows = len(Ads)
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# possible that a new equation was generated and taken away, but close enough
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if n_orig == len(b) and col_lim >= cols:
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break
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col_lim += col_lim / 5
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@ -480,72 +410,3 @@ def massage_equations_inc_col_lim(Ads, b, verbose=False, corner=None):
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print('')
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print('Massage final: %d => %d rows' % (dstart, dend))
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return Ads, b
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# only derive based on nearby equations
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# theory is they will be the best to diff
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def massage_equations_near(Ads, b, verbose=False, corner=None):
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'''
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Equation pipeline
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Some operations may generate new equations
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Simplify after these to avoid unnecessary overhead on redundant constraints
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Similarly some operations may eliminate equations, potentially eliminating a column (ie variable)
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Remove these columns as necessary to speed up solving
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'''
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def debug(what):
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if verbose:
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print('')
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print_eqns(Ads, b, verbose=verbose, label=what, lim=20)
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col_dist(Ads, what)
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check_feasible_d(Ads, b)
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# Try to (intelligently) subtract equations to generate additional constraints
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# This helps avoid putting all delay in a single shared variable
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dstart = len(b)
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cols = len(index_names(Ads))
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# Each iteration one more column is allowed until all columns are included
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# (and the system is stable)
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print
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n_orig = len(b)
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# Meat of the operation
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Ads, b = derive_eq_by_near_row(Ads, b, tweak=True)
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debug("der_rows")
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# Run another simplify pass since new equations may have overlap with original
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Ads, b = simplify_rows(Ads, b, corner=corner)
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print('Derive row: %d => %d equations' % (n_orig, len(b)))
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debug("der_rows simp")
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n_orig2 = len(b)
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# Meat of the operation
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Ads, b = derive_eq_by_col(Ads, b)
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debug("der_cols")
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# Run another simplify pass since new equations may have overlap with original
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Ads, b = simplify_rows(Ads, b, corner=corner)
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print('Derive col: %d => %d equations' % (n_orig2, len(b)))
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debug("der_cols simp")
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# Doesn't help computation, but helps debugging
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Ads, b = sort_equations(Ads, b)
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debug("loop done")
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col_dist(Ads, 'derive done', lim=12)
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rows = len(Ads)
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dend = len(b)
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print('')
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print('Derive net: %d => %d' % (dstart, dend))
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print('')
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# Was experimentting to see how much the higher order columns really help
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# Helps debug readability
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Ads, b = sort_equations(Ads, b)
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debug("final (sorted)")
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print('')
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print('Massage final: %d => %d rows' % (dstart, dend))
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return Ads, b
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massage_equations = massage_equations_inc_col_lim
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