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Re: [Patch 0,1a] Improving effectiveness and generality of autovectorization using unified representation.

On Wednesday 15 June 2016 05:52 PM, Richard Biener wrote:

On Mon, Jun 13, 2016 at 12:56 PM, Sameera Deshpande
<sameera.deshpande@imgtec.com> wrote:

On Thursday 09 June 2016 05:45 PM, Richard Biener wrote:


On Thu, Jun 9, 2016 at 10:54 AM, Richard Biener
<richard.guenther@gmail.com> wrote:


On Tue, Jun 7, 2016 at 3:59 PM, Sameera Deshpande
<Sameera.Deshpande@imgtec.com> wrote:


Hi Richard,

This is with reference to our discussion at GNU Tools Cauldron 2015
regarding my talk titled "Improving the effectiveness and generality of GCC
auto-vectorization." Further to our prototype implementation of the concept,
we have started implementing this concept in GCC.

We are following incremental model to add language support in our
front-end, and corresponding back-end (for auto-vectorizer) will be added
for feature completion.

Looking at the complexity and scale of the project, we have divided this
project into subtasks listed below, for ease of implementation, testing and
review.

0. Add new pass to perform autovectorization using unified
representation - Current GCC framework does not give complete overview of
the loop to be vectorized : it either breaks the loop across body, or across
iterations. Because of which these data structures can not be reused for our
approach which gathers all the information of loop body at one place using
primitive permute operations. Hence, define new data structures and populate
them.

1. Add support for vectorization of LOAD/STORE instructions
      a. Create permute order tree for the loop with LOAD and STORE
instructions for single or multi-dimensional arrays, aggregates within
nested loops.
      b. Basic transformation phase to generate vectorized code for the
primitive reorder tree generated at stage 1a using tree tiling algorithm.
This phase handles code generation for SCATTER, GATHER, stridded memory
accesses etc. along with permute instruction generation.

2. Implementation of k-arity promotion/reduction : The permute nodes
within primitive reorder tree generated from input program can have any
arity. However, the target can support maximum of arity = 2 in most of the
cases. Hence, we need to promote or reduce the arity of permute order tree
to enable successful tree tiling.

3. Vector size reduction : Depending upon the vector size for target,
reduce vector size per statement and adjust the loop count for vectorized
loop accordingly.

4. Support simple arithmetic operations :
      a. Add support for analyzing statements with simple arithmetic
operations like +, -, *, / for vectorization, and create primitive reorder
tree with compute_op.
      b. Generate vector code for primitive reorder tree generated at
stage 4a using tree tiling algorithm - here support for complex patterns
like multiply-add should be checked and appropriate instruction to be
generated.

5. Support reduction operation :
      a. Add support for reduction operation analysis and primitive
reorder tree generation. The reduction operation needs special handling, as
the finish statement should COLLAPSE the temporary reduction vector TEMP_VAR
into original reduction variable.
      b. The code generation for primitive reorder tree does not need any
handling - as reduction tree is same as tree generated in 4a, with only
difference that in 4a, the destination is MEMREF (because of STORE
operation) and for reduction it is TEMP_VAR. At this stage, generate code
for COLLAPSE node in finish statements.

6. Support other vectorizable statements like complex arithmetic
operations, bitwise operations, type conversions etc.
      a. Add support for analysis and primitive reorder tree generation.
      b. Vector code generation.

7. Cost effective tree tiling algorithm : Till now, the tree tiling is
happening without considering cost of computation. However, there can be
multiple target instructions covering the tree - hence, instead of picking
first matched largest instruction cover, select the instruction cover based
on cost of instruction given in .md for the target.

8. Optimizations on created primitive reorder tree : This stage is open
ended, and depending upon perf analysis, the scope of it can be defined.

The current patch I have attached herewith handles stage 0 and 1a : Adds
new pass to perform autovectorization using unified representation, defines
new data structures to cater to this requirement and creates primitive
reorder tree for LOAD/STORE instructions within the loop.

The whole loop is represented using the ITER_NODE, which have
information about
- The preparatory statements for vectorization to be executed before
entering the loop (like initialization of vectors, prepping for reduction
operations, peeling etc.)
- Vectorizable loop body represented as PRIMOP_TREE (primitive
reordering tree)
- Final statements (For peeling, variable loop bound, COLLAPSE operation
for reduction etc.)
- Other loop attributes (loop bound, peeling needed, dependences, etc.)

Memory accesses within a loop have definite repetitive pattern which can
be captured using primitive permute operators which can be used to
determine desired permute order for the vector computations.  The
PRIMOP_TREE is AST which records all computations and permutations required
to store  destination vector into continuous memory at the end of all
iterations of the  loop. It can have INTERLEAVE, CONCAT, EXTRACT, SPLIT,
ITER or any compute operation as intermediate node. Leaf nodes can either be
memory reference, constant or vector of loop invariants. Depending upon the
operation, PRIMOP_TREE holds appropriate information about the statement
within the loop which is necessary for vectorization.

At this stage, these data structures are populated by gathering all the
information of the loop, statements within the loop and correlation of the
statements within the loop. Moreover the loop body is analyzed to check if
vectorization of each statement is possible. One has to note however that
this analysis phase will give worst-case estimate of instruction selection,
as it checks if specific named pattern is defined in .md for the target. It
not necessarily  give optimal cover which is aim of the transformation phase
using tree tiling algorithm - and can be invoked only once the loop body is
represented using primitive reoder tree.

At this stage, the focus is to create permute order tree for the loop
with LOAD and STORE instructions only. The code we intend to compile is of
the form
FOR(i = 0; i < N; i + +)
{
    stmt 1 : D[k â i + d 1 ] =S 1 [k â i + c 11 ]
    stmt 2 : D[k â i + d 2 ] =S 1 [k â i + c 21 ]
    ...
    stmt k : D[k â i + d k ] =S 1 [k â i + c k 1 ]
}
Here we are assuming that any data reference can be represented using
base + k * index + offset (The data structure struct data_reference from GCC
is used currently for this purpose). If not, the address is normalized to
convert to such representation.

We are looking forward to your suggestions and insight in this regard
for better execution of this project.



I will look at the patch in detail this afternoon and will write up
some comments.



Ok, so here we go.



Hi Richard,

Thanks for your detailed review. Please find my comments inlined.


I see you copy quite some code from the existing vectorizer - rather than
doing this and adding a "new" pass I'd make the
flag_tree_loop_vectorize_unified
flag guard code inside the existing vectorizer - thus share the pass.


I agree with you that I have copied lot of code from current implementation
of GCC, and some data structures seem redundant as they create same
information as that is already available. However, as per our discussion at
cauldron, I tried to generate ptrees after all the analysis phases were
done, using the information generated there. However, because of
overwhelming and scattered information in various statements, loops and
their respective info nodes, it did not yield much. Moreover, it was seen
that many functions or parts of the functions were not very useful, and some
stmt or loop related information needed different way of computation for our
scheme than GCC's framework. So, instead of tweaking GCC's codebase, and
corrupting the information, thereby making it unusable if our optimization
fails to vectorize by default vectorizer, we created new pass for the same.

I agree, that currently it looks like we are reusing most of the checks and
conditions for vectorization as in GCC - as for first phase, our aim is to
meet performance of GCC before adding different optimizations. However, we
plan to increase the scope further, thereby need to change the checks and
data generated accordingly.

e.g.: Peeling information won't be generated till transformation phase is
started, where it will be added in the ITER_node depending upon alignment
requirements, ITER_count and VEC_SIZE of target.

Or, scatter/gather information is not needed as tree tiling for vector
load/store has different tiles for those instructions if target supports
them.

Also, the way reduction operations are to be implemented in this scheme
makes all the categorization of reduction operation in current GCC
redundant.

However, if you still think it is good idea to reuse same data structures
and functions, I have older patch available, which I can clean up and update
to add updated ptree generation part in it, and share it.


My comment was mostly about making patches smaller and thus easier to review.
I do expect that little of the copied code will remain as-is.
Richard, the way we have phased out the project, the first patch is bigger one as we had to do all the initializations and checks relevant to auto-vectorization in this patch. However, we expect following patches to be smaller, and dedicated to single functionality. It is difficult to factor this patch, as the changes that we have added are needed for the functionality - to vectorize load/store operations. 



Similarly I'd re-use loop_vinfo and simply add fields to it for the
new vectorizer
so you can dispatch to existing vectorizer functions "easily".  Otherwise
it's going to be hard to keep things in-sync.  Likewise for your stmt_attr
and the existing stmt_vec_info.


The main reason behind creating new ITER_node instead of reusing loop_vinfo
is to capture whole loop as single entity, thereby allowing optimizations on
nested loops as well. The ITER_node, when implemented completely, can handle
vectorization of multiple nested loops, as similar to all permute nodes,
even ITER_node can be distributed over each element in ITER_node.stmts - and
can be propagated across compute_tree in permute order tree. So, for future
expansion, we are creating ITER_node with copy of some of loop_vinfo fields,
and do not compute the fields which are not needed for this scheme.

Similar is the case with stmt_vinfo - the information gathered in stmt_vinfo
is mostly usable for SLP optimizations, which we cannot use as is. So,
instead of generating redundant information, or alter generated information,
we chose to create new data structure.


Why isn't the existing data_reference structure good enough and you need
to invent a struct mem_ref?  Even if that might be leaner adding new
concepts
makes the code harder to understand.  You can always use dr->aux to
add auxiliar data you need (like the existing vectorizer does).



We can use data_reference structure as is, however, each of it's component
has very specific meaning associated with it - whereas the memref has only 3
components which are important - stride, offset - which is less than stride,
and remaining base - for address of first element. So, again we thought
instead of overriding current semantics of components of data_ref, we
created new data structure.


I was worried at the point you made an existing GCC function take data-ref
pieces to make your mem-ref look like a data-ref.  If the data-ref doesn't
give you the information you want and that is important it seems to me it
is better to fix that.  It sounds to me you want to look at innermost
behavior, DR_STEP and DR_BASE_ADDRESS + DR_OFFSET + DR_INIT
where a part of DR_INIT is your 'offset' (I think it is what the
current vectorizer
computes as GROUPs and offset would be the offset inside the group,
relative to the first element?)
I agree that the data structure mem-ref is redundant. Will eliminate it to use altered dataref. We can say that the computations to find mem-ref are similar to those to form GROUPS - however, GROUP is collection of all the statements which access elements of aggregate with definite interval, whereas mem-ref looks at small window of the aggregate with reference to stride, and the offset addressing is always relative to stride. Also, mem-ref is not collection of the statements, but the place-holder to identify the continuous piece of aggregate within the memory of size = stride. 
However, I can use dataref instead of mem-ref as the place holder.



It helps if you follow the GCC coding-conventions from the start - a lot
of the new functions miss comments.


Sorry about that. I will tidy up the patch.



I realize the patch is probably still a "hack" and nowhere a final step
0/1a,
but for example you copied the iteration scheme over vector sizes.  I hope
the new vectorizer can do without that and instead decide on the vector
size as part of the cost effective tiling algorithm.


I didn't get this point clearly. However, for my understanding, is the
objection with use of ITER_COUNT instead of VEC_SIZE for each statement?
Because, if that is the case, there is basic difference between current
GCC's implementation and this scheme - in case of GCC, we always look for
the standard pattern name to be matched for scalar instruction - for which
VEC_TYPE becomes crucial. Whereas, this scheme represents each statement
within the loop as a vector with element of type SCALAR_TYPE and
initial_vec_size = ITER_COUNT (as those many instances of the statement will
be executed). Then, depending upon the VEC_SIZE for target, VEC_SIZE
reduction will be applied to it, on top of which tiling algorithm functions.
So, for tiling, we will have to use VEC_SIZE. However, for all other
optimizations and transformations, we use each node of permute order tree to
have vec_size = ITER_COUNT to allow free movement of permute-nodes across
compute-nodes, and various optimizations on them.


The comment was about you copying the loop iterating over target vector sizes
and repeating vectorization attempts with that single vector size.  I hope
the cost effective tiling will simply choose a single vector size based on
cost and iteration count.  Your description above suggests so thus the
copying of the vector size iteration seems spurious and if then should
be done _after_ building the ptree (and sharing the ptree).

Yes, as I have mentioned in previous mail

>> (I have kept the loop checking for
>> different vector types in vect_analyze_loop_with_prim_tree() for now as I am
>> still not very clear at what point in transformation phase will it play any
>> role... Once, the end-to-end solution for it is developed for load/store
>> chains, there will be some clarity - and the loop can be moved there or
>> eliminated completely.)

So, this loop will be eliminated once the backend for tree tiling of load/store is implemented.






As the main feature of the patch is creation of the ptree (no code
generation
seems to be done?) the biggest missing part is dumping the ptree in
some nice form (like in DOT format and/or to the dump file).


Yes, the ptree dump was put on back burner because I was focusing on the
functionality. However, I will share the patch for dumping ptree in DOT
format shortly.


Thanks!




You do seem to check for target capabilities at ptree build time (looking
at
vectorizable_store).  I think that is a mistake as we'd ideally have the
same
ptree when considering different vector sizes (or even generic vectors).
Target capabilities will differ between vector sizes.


That's right. However as long as ITER_COUNT > VEC_SIZE of target, there is
at least a cover available for the ptree that we are creating. Hence, this
check is just to check if certain instruction can be represented anyhow on
target architecture. This will actually be handled at transform phase after
vec_size reduction is performed. (I have kept the loop checking for
different vector types in vect_analyze_loop_with_prim_tree() for now as I am
still not very clear at what point in transformation phase will it play any
role... Once, the end-to-end solution for it is developed for load/store
chains, there will be some clarity - and the loop can be moved there or
eliminated completely.)


Ok.

One limit of the current vectorizer is that it wants to use a single
vector size.

In principle vectorizable_* could compute a vector size mask for each
scalar statement (ptree node) noting the sizes the target has support for
encoding the stmt.  Similar to permute nodes the ptree can have
size-change nodes in case supported input/output size have no match.
We can use the macro TARGET_VECTORIZE_AUTOVECTORIZE_VECTOR_SIZES to get the mask, and perform cost-effective instruction selection for optimal vector size, than considering availability of single vector size in get_vectype_for_scalar_type_and_size(). However, I will check if it is needed to be done as early as vectorizable_*, or can be postponed till tree tiling algorithm hits. 

Thanks,
Richard.




That's all for now.

Last but not least - at the Cauldron I suggested to incrementally rewrite
the existing vectorizer by building the ptree at the point it is "ready"
for code generation and perform the permute optimizations on it and then
re-write the code generation routines to work off the ptree.





Thanks,
Richard.



- Thanks and regards,
   Sameera D.


I will create new branch in GCC as you directed in other thread, and create incremental patches for the changes suggested here. Will that be fine?

- Thanks and regards,
  Sameera D.
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