This logic can be shared with the tiled code generation.
Reviewers: anemet, Gerolf, hfinkel, andrew.w.kaylor, LuoYuanke
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D75565
This patch slightly generalizes the code to emit loads and stores of a
matrix and adds helpers to load/store a tile of a larger matrix.
This will be used in a follow-up patch introducing initial tiling.
Reviewers: anemet, Gerolf, hfinkel, andrew.w.kaylor, LuoYuanke
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D75564
This patch adds support for propagating matrix expressions along the
inlined-at chain and emitting remarks at the traversed function scopes.
To motivate this new behavior, consider the example below. Without the
remark 'up-leveling', we would only get remarks in load.h and store.h,
but we cannot generate a remark describing the full expression in
toplevel.cpp, which is the place where the user has the best chance of
spotting/fixing potential problems.
With this patch, we generate a remark for the load in load.h, one for
the store in store.h and one for the complete expression in
toplevel.cpp. For a bigger example, please see remarks-inlining.ll.
load.h:
template <typename Ty, unsigned R, unsigned C> Matrix<Ty, R, C> load(Ty *Ptr) {
Matrix<Ty, R, C> Result;
Result.value = *reinterpret_cast <typename Matrix<Ty, R, C>::matrix_t *>(Ptr);
return Result;
}
store.h:
template <typename Ty, unsigned R, unsigned C> void store(Matrix<Ty, R, C> M1, Ty *Ptr) {
*reinterpret_cast<typename decltype(M1)::matrix_t *>(Ptr) = M1.value;
}
toplevel.cpp
void test(double *A, double *B, double *C) {
store(add(load<double, 3, 5>(A), load<double, 3, 5>(B)), C);
}
For a given function, we traverse the inlined-at chain for each
matrix instruction (= instructions with shape information). We collect
the matrix instructions in each DISubprogram we visit. This produces a
mapping of DISubprogram -> (List of matrix instructions visible in the
subpogram). We then generate remarks using the list of instructions for
each subprogram in the inlined-at chain. Note that the list of instructions
for a subprogram includes the instructions from its own subprograms
recursively. For example using the example above, for the subprogram
'test' this includes inline functions 'load' and 'store'. This allows
surfacing the remarks at a level useful to users.
Please note that the current approach may create a lot of extra remarks.
Additional heuristics to cut-off the traversal can be implemented in the
future. For example, it might make sense to stop 'up-leveling' once all
matrix instructions are at the same debug location.
Reviewers: anemet, Gerolf, thegameg, hfinkel, andrew.w.kaylor, LuoYuanke
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D73600
This is how it should've been and brings it more in line with
std::string_view. There should be no functional change here.
This is mostly mechanical from a custom clang-tidy check, with a lot of
manual fixups. It uncovers a lot of minor inefficiencies.
This doesn't actually modify StringRef yet, I'll do that in a follow-up.
This patch adds support for explicitly highlighting sub-expressions
shared by multiple leaf nodes. For example consider the following
code
%shared.load = tail call <8 x double> @llvm.matrix.columnwise.load.v8f64.p0f64(double* %arg1, i32 %stride, i32 2, i32 4), !dbg !10, !noalias !10
%trans = tail call <8 x double> @llvm.matrix.transpose.v8f64(<8 x double> %shared.load, i32 2, i32 4), !dbg !10
tail call void @llvm.matrix.columnwise.store.v8f64.p0f64(<8 x double> %trans, double* %arg3, i32 10, i32 4, i32 2), !dbg !10
%load.2 = tail call <30 x double> @llvm.matrix.columnwise.load.v30f64.p0f64(double* %arg3, i32 %stride, i32 2, i32 15), !dbg !10, !noalias !10
%mult = tail call <60 x double> @llvm.matrix.multiply.v60f64.v8f64.v30f64(<8 x double> %trans, <30 x double> %load.2, i32 4, i32 2, i32 15), !dbg !11
tail call void @llvm.matrix.columnwise.store.v60f64.p0f64(<60 x double> %mult, double* %arg2, i32 10, i32 4, i32 15), !dbg !11
We have two leaf nodes (the 2 stores) and the first store stores %trans
which is also used by the matrix multiply %mult. We generate separate
remarks for each leaf (stores). To denote that parts are shared, the
shared expressions are marked as shared (), with a reference to the
other remark that shares it. The operation summary also denotes the
shared operations separately.
Reviewers: anemet, Gerolf, thegameg, hfinkel, andrew.w.kaylor, LuoYuanke
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D72526
This patch updates the remark to also include a summary of the number of
vector operations generated for each matrix expression.
Reviewers: anemet, Gerolf, thegameg, hfinkel, andrew.w.kaylor, LuoYuanke
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D72480
Generate remarks for matrix operations in a function. To generate remarks
for matrix expressions, the following approach is used:
1. Collect leafs of matrix expressions (done in
RemarkGenerator::getExpressionLeafs). Leafs are lowered matrix
instructions without other matrix users (like stores).
2. For each leaf, create a remark containing a linearizied version of the
matrix expression.
The following improvements will be submitted as follow-ups:
* Summarize number of vector instructions generated for each expression.
* Account for shared sub-expressions.
* Propagate matrix remarks up the inlining chain.
The information provided by the matrix remarks helps users to spot cases
where matrix expression got split up, e.g. due to inlining not
happening. The remarks allow users to address those issues, ensuring
best performance.
Reviewers: anemet, Gerolf, thegameg, hfinkel, andrew.w.kaylor, LuoYuanke
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D72453
This patch updates the shape propagation to iterate until no new shape
information is discovered.
As initial seed for the forward propagation, we use the matrix intrinsic
instructions. Both propagateShapeForward and propagateShapeBackward
return new work lists, with the instructions to be used for the next
iteration. When propagating forward, we record all instructions we added
new shape information for. When propagating backward, we record all
users of instructions we added new shape information for.
Reviewers: anemet, Gerolf, reames, hfinkel, andrew.w.kaylor
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70901
This patch extends to shape propagation to also include load
instructions and implements shape aware lowering for vector loads.
Reviewers: anemet, Gerolf, reames, hfinkel, andrew.w.kaylor
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70900
This patch extends the shape propagation for matrix operations to also
propagate the shape of instructions to their operands.
Reviewers: anemet, Gerolf, reames, hfinkel, andrew.w.kaylor
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70899
This patch extends the current shape propagation and shape aware
lowering to also support binary operators. Those operators are uniform
with respect to their shape (shape of the input operands is the same as
the shape of their result).
Reviewers: anemet, Gerolf, reames, hfinkel, andrew.w.kaylor
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70898
If the matrix.multiply calls have the contract fast math flag, we can
use fmuladd. This als adds a command line option to force fmuladd
generation. We can retire this option once there is a clang-level
option.
Reviewers: anemet, Gerolf, hfinkel, andrew.w.kaylor
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70951
This patch adds infrastructure for forward shape propagation to
LowerMatrixIntrinsics. It also updates the pass to make use of
the shape information to break up larger vector operations and to
eliminate unnecessary conversion operations between columnwise matrixes
and flattened vectors: if shape information is available for an
instruction, lower the operation to a set of instructions operating on
columns. For example, a store of a matrix is broken down into separate
stores for each column. For users that do not have shape
information (e.g. because they do not yet support shape information
aware lowering), we pack the result columns into a flat vector and
update those users.
It also adds shape aware lowering for the first non-intrinsic
instruction: vector stores.
Example:
For
%c = call <4 x double> @llvm.matrix.transpose(<4 x double> %a, i32 2, i32 2)
store <4 x double> %c, <4 x double>* %Ptr
We generate the code below without shape propagation. Note %9 which
combines the columns of the transposed matrix into a flat vector.
%split = shufflevector <4 x double> %a, <4 x double> undef, <2 x i32> <i32 0, i32 1>
%split1 = shufflevector <4 x double> %a, <4 x double> undef, <2 x i32> <i32 2, i32 3>
%1 = extractelement <2 x double> %split, i64 0
%2 = insertelement <2 x double> undef, double %1, i64 0
%3 = extractelement <2 x double> %split1, i64 0
%4 = insertelement <2 x double> %2, double %3, i64 1
%5 = extractelement <2 x double> %split, i64 1
%6 = insertelement <2 x double> undef, double %5, i64 0
%7 = extractelement <2 x double> %split1, i64 1
%8 = insertelement <2 x double> %6, double %7, i64 1
%9 = shufflevector <2 x double> %4, <2 x double> %8, <4 x i32> <i32 0, i32 1, i32 2, i32 3>
store <4 x double> %9, <4 x double>* %Ptr
With this patch, we propagate the 2x2 shape information from the
transpose to the store and we generate the code below. Note that we
store the columns directly and do not need an extra shuffle.
%9 = bitcast <4 x double>* %Ptr to double*
%10 = bitcast double* %9 to <2 x double>*
store <2 x double> %4, <2 x double>* %10, align 8
%11 = getelementptr double, double* %9, i32 2
%12 = bitcast double* %11 to <2 x double>*
store <2 x double> %8, <2 x double>* %12, align 8
Reviewers: anemet, Gerolf, reames, hfinkel, andrew.w.kaylor
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70897
This is the first patch adding an initial set of matrix intrinsics and a
corresponding lowering pass. This has been discussed on llvm-dev:
http://lists.llvm.org/pipermail/llvm-dev/2019-October/136240.html
The first patch introduces four new intrinsics (transpose, multiply,
columnwise load and store) and a LowerMatrixIntrinsics pass, that
lowers those intrinsics to vector operations.
Matrixes are embedded in a 'flat' vector (e.g. a 4 x 4 float matrix
embedded in a <16 x float> vector) and the intrinsics take the dimension
information as parameters. Those parameters need to be ConstantInt.
For the memory layout, we initially assume column-major, but in the RFC
we also described how to extend the intrinsics to support row-major as
well.
For the initial lowering, we split the input of the intrinsics into a
set of column vectors, transform those column vectors and concatenate
the result columns to a flat result vector.
This allows us to lower the intrinsics without any shape propagation, as
mentioned in the RFC. In follow-up patches, we plan to submit the
following improvements:
* Shape propagation to eliminate the embedding/splitting for each
intrinsic.
* Fused & tiled lowering of multiply and other operations.
* Optimization remarks highlighting matrix expressions and costs.
* Generate loops for operations on large matrixes.
* More general block processing for operation on large vectors,
exploiting shape information.
We would like to add dedicated transpose, columnwise load and store
intrinsics, even though they are not strictly necessary. For example, we
could instead emit a large shufflevector instruction instead of the
transpose. But we expect that to
(1) become unwieldy for larger matrixes (even for 16x16 matrixes,
the resulting shufflevector masks would be huge),
(2) risk instcombine making small changes, causing us to fail to
detect the transpose, preventing better lowerings
For the load/store, we are additionally planning on exploiting the
intrinsics for better alias analysis.
Reviewers: anemet, Gerolf, reames, hfinkel, andrew.w.kaylor, efriedma, rengolin
Reviewed By: anemet
Differential Revision: https://reviews.llvm.org/D70456
