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nir_opt_algebraic is currently one of the most expensive NIR passes,
because of the many different patterns we've added over the years. Even
though patterns are already sorted by opcode, there are still way too
many patterns for common opcodes like bcsel and fadd, which means that
many patterns are tried but only a few actually match. One way to fix
this is to add a pre-pass over the code that scans it using an automaton
constructed beforehand, similar to the automatons produced by lex and
yacc for parsing source code. This automaton has to walk the SSA graph
and recognize possible pattern matches.
It turns out that the theory to do this is quite mature already, having
been developed for instruction selection as well as other non-compiler
things. I followed the presentation in the dissertation cited in the
code, "Tree algorithms: Two Taxonomies and a Toolkit," trying to keep
the naming similar. To create the automaton, we have to perform
something like the classical NFA to DFA subset construction used by lex,
but it turns out that actually computing the transition table for all
possible states would be way too expensive, with the dissertation
reporting times of almost half an hour for an example of size similar to
nir_opt_algebraic. Instead, we adopt one of the "filter" approaches
explained in the dissertation, which trade much faster table generation
and table size for a few more table lookups per instruction at runtime.
I chose the filter which resulted the fastest table generation time,
with medium table size. Right now, the table generation takes around .5
seconds, despite being implemented in pure Python, which I think is good
enough. Based on the numbers in the dissertation, the other choice might
make table compilation time 25x slower to get 4x smaller table size, but
I don't think that's worth it. As of now, we get the following binary
size before and after this patch:
text data bss dec hex filename
11979455 464720 730864 13175039 c908ff before i965_dri.so
text data bss dec hex filename
12037835 616244 791792 13445871 cd2aef after i965_dri.so
There are a number of places where I've simplified the automaton by
getting rid of details in the LHS patterns rather than complicate things
to deal with them. For example, right now the automaton doesn't
distinguish between constants with different values. This means that it
isn't as precise as it could be, but the decrease in compile time is
still worth it -- these are the compilation time numbers for a shader-db
run with my (admittedly old) database on Intel skylake:
Difference at 95.0% confidence
-42.3485 +/- 1.375
-7.20383% +/- 0.229926%
(Student's t, pooled s = 1.69843)
We can always experiment with making it more precise later.
Reviewed-by: Jason Ekstrand <[email protected]>
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Consider the following search expression and NIR sequence:
('iadd', ('imul', a, b), b)
ssa_2 = imul ssa_0, ssa_1
ssa_3 = iadd ssa_2, ssa_0
The current algorithm is greedy and, the moment the imul finds a match,
it commits those variable names and returns success. In the above
example, it maps a -> ssa_0 and b -> ssa_1. When we then try to match
the iadd, it sees that ssa_0 is not b and fails to match. The iadd
match will attempt to flip itself and try again (which won't work) but
it cannot ask the imul to try a flipped match.
This commit instead counts the number of commutative ops in each
expression and assigns an index to each. It then does a loop and loops
over the full combinatorial matrix of commutative operations. In order
to keep things sane, we limit it to at most 4 commutative operations (16
combinations). There is only one optimization in opt_algebraic that
goes over this limit and it's the bitfieldReverse detection for some UE4
demo.
Shader-db results on Kaby Lake:
total instructions in shared programs: 15310125 -> 15302469 (-0.05%)
instructions in affected programs: 1797123 -> 1789467 (-0.43%)
helped: 6751
HURT: 2264
total cycles in shared programs: 357346617 -> 357202526 (-0.04%)
cycles in affected programs: 15931005 -> 15786914 (-0.90%)
helped: 6024
HURT: 3436
total loops in shared programs: 4360 -> 4360 (0.00%)
loops in affected programs: 0 -> 0
helped: 0
HURT: 0
total spills in shared programs: 23675 -> 23666 (-0.04%)
spills in affected programs: 235 -> 226 (-3.83%)
helped: 5
HURT: 1
total fills in shared programs: 32040 -> 32032 (-0.02%)
fills in affected programs: 190 -> 182 (-4.21%)
helped: 6
HURT: 2
LOST: 18
GAINED: 5
Reviewed-by: Thomas Helland <[email protected]>
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Instead of a single i2b and b2i, we now have i2b32 and b2iN where N is
one if 8, 16, 32, or 64. This leads to having a few more opcodes but
now everything is consistent and booleans aren't a weird special case
anymore.
Reviewed-by: Connor Abbott <[email protected]>
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All conversion opcodes require a destination size but this makes
constructing certain algebraic expressions rather cumbersome. This
commit adds support to nir_search and nir_algebraic for writing
conversion opcodes without a size. These meta-opcodes match any
conversion of that type regardless of destination size and the size gets
inferred from the sizes of the things being matched or from other
opcodes in the expression.
Reviewed-by: Connor Abbott <[email protected]>
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Before this commit, there were two copies of the algorithm: one in C,
that we would use to figure out what bit-size to give the replacement
expression, and one in Python, that emulated the C one and tried to
prove that the C algorithm would never fail to correctly assign
bit-sizes. That seemed pretty fragile, and likely to fall over if we
make any changes. Furthermore, the C code was really just recomputing
more-or-less the same thing as the Python code every time. Instead, we
can just store the results of the Python algorithm in the C
datastructure, and consult it to compute the bitsize of each value,
moving the "brains" entirely into Python. Since the Python algorithm no
longer has to match C, it's also a lot easier to change it to something
more closely approximating an actual type-inference algorithm. The
algorithm used is based on Hindley-Milner, although deliberately
weakened a little. It's a few more lines than the old one, judging by
the diffstat, but I think it's easier to verify that it's correct while
being as general as possible.
We could split this up into two changes, first making the C code use the
results of the Python code and then rewriting the Python algorithm, but
since the old algorithm never tracked which variable each equivalence
class, it would mean we'd have to add some non-trivial code which would
then get thrown away. I think it's better to see the final state all at
once, although I could also try splitting it up.
v2:
- Replace instances of "== None" and "!= None" with "is None" and
"is not None".
- Rename first_src to first_unsized_src
- Only merge the destination with the first unsized source, since the
sources have already been merged.
- Add a comment explaining what nir_search_value::bit_size now means.
v3:
- Fix one last instance to use "is not" instead of !=
- Don't try to be so clever when choosing which error message to print
based on whether we're in the search or replace expression.
- Fix trailing whitespace.
Reviewed-by: Jason Ekstrand <[email protected]>
Reviewed-by: Dylan Baker <[email protected]>
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This requires that we rework the interface a bit to use nir_builder but
that's a nice little modernization anyway.
Reviewed-by: Ian Romanick <[email protected]>
Reviewed-by: Eric Anholt <[email protected]>
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Reviewed-by: Jason Ekstrand <[email protected]>
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This makes calling nir_foo_as_bar a bit safer because we're no longer 100%
trusting in the caller to ensure that it's safe. The caller still needs to
do the right thing but this ensures that we catch invalid casts with an
assert rather than by reading garbage data. The one downside is that we do
use the casts a bit in nir_validate and it's not a validate_assert.
Signed-off-by: Jason Ekstrand <[email protected]>
Reviewed-by: Connor Abbott <[email protected]>
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Some optimizations, like converting integer multiply/divide into left/
right shifts, have additional constraints on the search expression.
Like requiring that a variable is a constant power of two. Support
these cases by allowing a fxn name to be appended to the search var
expression (ie. "a#32(is_power_of_two)").
Signed-off-by: Rob Clark <[email protected]>
Reviewed-by: Kenneth Graunke <[email protected]>
Reviewed-by: Jason Ekstrand <[email protected]>
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Signed-off-by: Rob Clark <[email protected]>
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Reviewed-by: Iago Toral Quiroga <[email protected]>
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Reviewed-by: Francisco Jerez <[email protected]>
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When we replace an expresion we have to compute bitsize information for the
replacement. We do this in two passes to validate that bitsize information
is consistent and correct: first we propagate bitsize from child nodes to
parent, then we do it the other way around, starting from the original's
instruction destination bitsize.
v2 (Iago):
- Always use nir_type_bool32 instead of nir_type_bool when generating
algebraic optimizations. Before we used nir_type_bool32 with constants
and nir_type_bool with variables.
- Fix bool comparisons in nir_search.c to account for bitsized types.
v3 (Sam):
- Unpack the double constant value as unsigned long long (8 bytes) in
nir_algrebraic.py.
v4 (Sam):
- Use helpers to get type size and base type from nir_alu_type.
Signed-off-by: Iago Toral Quiroga <[email protected]>
Signed-off-by: Samuel Iglesias Gonsálvez <[email protected]>
Reviewed-by: Jason Ekstrand <[email protected]>
Reviewed-by: Samuel Iglesias Gonsálvez <[email protected]>
Reviewed-by: Iago Toral Quiroga <[email protected]>
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Signed-off-by: Emil Velikov <[email protected]>
Acked-by: Matt Turner <[email protected]>
Acked-by: Jose Fonseca <[email protected]>
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