Commit Graph

16 Commits

Author SHA1 Message Date
Nicolai Hähnle 58297e4d8f LowerMatrixIntrinsics: Avoid use of deprecated CreateCall methods
Reviewers: t.p.northover

Subscribers: hiraditya, llvm-commits

Tags: #llvm

Differential Revision: https://reviews.llvm.org/D74675
2020-02-18 00:24:09 +01:00
Nikita Popov 7c362b25d7 [IRBuilder] Fix unnecessary IRBuilder copies; NFC
Fix a few cases where an IRBuilder is passed to a helper function
by value, while a by reference pass was intended.
2020-02-16 17:57:18 +01:00
Benjamin Kramer adcd026838 Make llvm::StringRef to std::string conversions explicit.
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.
2020-01-28 23:25:25 +01:00
Florian Hahn 5d0ffbeb4d [Matrix] Mark expressions shared between multiple remarks.
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
2020-01-28 09:27:55 -08:00
Florian Hahn 62e228f8fd [Matrix] Add info about number of operations to remarks.
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
2020-01-27 17:43:39 -08:00
Florian Hahn 949294f396 [Matrix] Add optimization remarks for matrix expression.
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
2020-01-27 16:39:29 -08:00
Guillaume Chatelet 07c9d53266 [Alignment][NFC] Use Align with CreateAlignedLoad
Summary:
This is patch is part of a series to introduce an Alignment type.
See this thread for context: http://lists.llvm.org/pipermail/llvm-dev/2019-July/133851.html
See this patch for the introduction of the type: https://reviews.llvm.org/D64790

Reviewers: courbet, bollu

Subscribers: hiraditya, cfe-commits, llvm-commits

Tags: #clang, #llvm

Differential Revision: https://reviews.llvm.org/D73449
2020-01-27 10:58:36 +01:00
Guillaume Chatelet 59f95222d4 [Alignment][NFC] Use Align with CreateAlignedStore
Summary:
This is patch is part of a series to introduce an Alignment type.
See this thread for context: http://lists.llvm.org/pipermail/llvm-dev/2019-July/133851.html
See this patch for the introduction of the type: https://reviews.llvm.org/D64790

Reviewers: courbet, bollu

Subscribers: arsenm, jvesely, nhaehnle, hiraditya, kerbowa, cfe-commits, llvm-commits

Tags: #clang, #llvm

Differential Revision: https://reviews.llvm.org/D73274
2020-01-23 17:34:32 +01:00
Florian Hahn f42994f228 [Matrix] Hide and describe matrix-propagate-shape option. 2020-01-21 14:28:47 -08:00
Florian Hahn ccf24225e3 [Matrix] Update shape propagation to iterate until done.
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
2020-01-09 10:52:52 +00:00
Florian Hahn 7adf6644f5 [Matrix] Propagate and use shape information for loads.
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
2020-01-09 10:21:20 +00:00
Florian Hahn 459ad8e97e [Matrix] Implement back-propagation of shape information.
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
2020-01-09 09:48:07 +00:00
Florian Hahn dc2c9b0fcf [Matrix] Propagate and use shape info for binary operators.
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
2019-12-27 15:50:47 +00:00
Florian Hahn 8d6f59b78a [Matrix] Use fmuladd for matrix.multiply if allowed.
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
2019-12-23 14:49:14 +01:00
Florian Hahn 109e4e3851 [Matrix] Add forward shape propagation and first shape aware lowerings.
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
2019-12-23 13:51:56 +01:00
Florian Hahn 526244b187 [Matrix] Add first set of matrix intrinsics and initial lowering pass.
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
2019-12-12 15:42:18 +00:00