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Similar to all LLVM dialect operations, llvm.func needs to have the custom
syntax. Use the generic FunctionLike printer and parser to implement it.
PiperOrigin-RevId: 261641755
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llvm ir printer was changed at LLVM r367755.
Prints value numbers for unnamed functions argument.
Closes tensorflow/mlir#67
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/67 from denis0x0D:sandbox/fix_mlir_translate ae46844e66f34a02e0cf86782ddadc5bce58b30d
PiperOrigin-RevId: 261640048
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The includes related to the LLVM dialect are not used in this file and
introduce an implicit dependencies between the two libraries which isn't
reflected in the CMakeLists.txt, causing non-deterministic build failures.
PiperOrigin-RevId: 261576935
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LLVM r367686 changed the locking scheme to avoid potential deadlocks and the
related llvm::orc::ThreadSafeModule APIs ExecutionEngine was relying upon,
breaking the MLIR build. Update our use of ThreadSafeModule to unbreak the
build.
PiperOrigin-RevId: 261566571
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This CL added a new NonNegativeIntAttrBase class and two instantiations,
one for I32 and the other for I64.
PiperOrigin-RevId: 261513292
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When inlining the declaration for llvm::DenseSet/DenseMap in the mlir
namespace from a forward declaration, clang does not take the default
for the template parameters if their are declared later.
namespace llvm {
template<typename Foo>
class DenseMap;
}
namespace mlir {
using llvm::DenseMap;
}
namespace llvm {
template<typename Foo = int>
class DenseMap {};
}
namespace mlir {
DenseMap<> map;
}
PiperOrigin-RevId: 261495612
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This CL introduces a linalg.generic op to represent generic tensor contraction operations on views.
A linalg.generic operation requires a numbers of attributes that are sufficient to emit the computation in scalar form as well as compute the appropriate subviews to enable tiling and fusion.
These attributes are very similar to the attributes for existing operations such as linalg.matmul etc and existing operations can be implemented with the generic form.
In the future, most existing operations can be implemented using the generic form.
This CL starts by splitting out most of the functionality of the linalg::NInputsAndOutputs trait into a ViewTrait that queries the per-instance properties of the op. This allows using the attribute informations.
This exposes an ordering of verifiers issue where ViewTrait::verify uses attributes but the verifiers for those attributes have not been run. The desired behavior would be for the verifiers of the attributes specified in the builder to execute first but it is not the case atm. As a consequence, to emit proper error messages and avoid crashing, some of the
linalg.generic methods are defensive as such:
```
unsigned getNumInputs() {
// This is redundant with the `n_views` attribute verifier but ordering of verifiers
// may exhibit cases where we crash instead of emitting an error message.
if (!getAttr("n_views") || n_views().getValue().size() != 2)
return 0;
```
In pretty-printed form, the specific attributes required for linalg.generic are factored out in an independent dictionary named "_". When parsing its content is flattened and the "_name" is dropped. This allows using aliasing for reducing boilerplate at each linalg.generic invocation while benefiting from the Tablegen'd verifier form for each named attribute in the dictionary.
For instance, implementing linalg.matmul in terms of linalg.generic resembles:
```
func @mac(%a: f32, %b: f32, %c: f32) -> f32 {
%d = mulf %a, %b: f32
%e = addf %c, %d: f32
return %e: f32
}
#matmul_accesses = [
(m, n, k) -> (m, k),
(m, n, k) -> (k, n),
(m, n, k) -> (m, n)
]
#matmul_trait = {
doc = "C(m, n) += A(m, k) * B(k, n)",
fun = @mac,
indexing_maps = #matmul_accesses,
library_call = "linalg_matmul",
n_views = [2, 1],
n_loop_types = [2, 1, 0]
}
```
And can be used in multiple places as:
```
linalg.generic #matmul_trait %A, %B, %C [other-attributes] :
!linalg.view<?x?xf32>, !linalg.view<?x?xf32>, !linalg.view<?x?xf32>
```
In the future it would be great to have a mechanism to alias / register a new
linalg.op as a pair of linalg.generic, #trait.
Also, note that with one could theoretically only specify the `doc` string and parse all the attributes from it.
PiperOrigin-RevId: 261338740
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PiperOrigin-RevId: 261325481
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Add StdIndexedValue to EDSC helper so that we can use it
to generated std.load and std.store in EDSC.
Closes tensorflow/mlir#59
PiperOrigin-RevId: 261324965
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AffineDataCopyGeneration pass relied on command line flags for internal logic
in several places, which makes it unusable in a library context (i.e. outside a
standalone mlir-opt binary that does the command line parsing). Define
configuration flags in the constructor instead, and set them up to command
line-based defaults to maintain the original behavior.
PiperOrigin-RevId: 261322364
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This functionality was added recently and is intended to ensure that parametric
passes can be configured programmatically and not only from command-line flags,
which are mostly useless outside of standalone mlir-opt biary.
PiperOrigin-RevId: 261320932
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This is fixing the build of MLIR on MacOS when built within TensorFlow
PiperOrigin-RevId: 261223250
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Explicit copying to contiguous buffers is a standard technique to avoid
conflict misses and TLB misses, and improve hardware prefetching
performance. When done in conjunction with cache tiling, it nearly
eliminates all cache conflict and TLB misses, and a single hardware
prefetch stream is needed per data tile.
- generalize/extend DMA generation pass (renamed data copying pass) to
perform either point-wise explicit copies to fast memory buffers or
DMAs (depending on a cmd line option). All logic is the same as
erstwhile -dma-generate.
- -affine-dma-generate is now renamed -affine-data-copy; when -dma flag is
provided, DMAs are generated, or else explicit copy loops are generated
(point-wise) by default.
- point-wise copying could be used for CPUs (or GPUs); some indicative
performance numbers with a "C" version of the MLIR when compiled with
and without this optimization (about 2x improvement here).
With a matmul on 4096^2 matrices on a single core of an Intel Core i7
Skylake i7-8700K with clang 8.0.0:
clang -O3: 518s
clang -O3 with MLIR tiling (128x128): 24.5s
clang -O3 with MLIR tiling + data copying 12.4s
(code equivalent to test/Transforms/data-copy.mlir func @matmul)
- fix some misleading comments.
- change default fast-mem space to 0 (more intuitive now with the
default copy generation using point-wise copies instead of DMAs)
On a simple 3-d matmul loop nest, code generated with -affine-data-copy:
```
affine.for %arg3 = 0 to 4096 step 128 {
affine.for %arg4 = 0 to 4096 step 128 {
%0 = affine.apply #map0(%arg3, %arg4)
%1 = affine.apply #map1(%arg3, %arg4)
%2 = alloc() : memref<128x128xf32, 2>
// Copy-in Out matrix.
affine.for %arg5 = 0 to 128 {
%5 = affine.apply #map2(%arg3, %arg5)
affine.for %arg6 = 0 to 128 {
%6 = affine.apply #map2(%arg4, %arg6)
%7 = load %arg2[%5, %6] : memref<4096x4096xf32>
affine.store %7, %2[%arg5, %arg6] : memref<128x128xf32, 2>
}
}
affine.for %arg5 = 0 to 4096 step 128 {
%5 = affine.apply #map0(%arg3, %arg5)
%6 = affine.apply #map1(%arg3, %arg5)
%7 = alloc() : memref<128x128xf32, 2>
// Copy-in LHS.
affine.for %arg6 = 0 to 128 {
%11 = affine.apply #map2(%arg3, %arg6)
affine.for %arg7 = 0 to 128 {
%12 = affine.apply #map2(%arg5, %arg7)
%13 = load %arg0[%11, %12] : memref<4096x4096xf32>
affine.store %13, %7[%arg6, %arg7] : memref<128x128xf32, 2>
}
}
%8 = affine.apply #map0(%arg5, %arg4)
%9 = affine.apply #map1(%arg5, %arg4)
%10 = alloc() : memref<128x128xf32, 2>
// Copy-in RHS.
affine.for %arg6 = 0 to 128 {
%11 = affine.apply #map2(%arg5, %arg6)
affine.for %arg7 = 0 to 128 {
%12 = affine.apply #map2(%arg4, %arg7)
%13 = load %arg1[%11, %12] : memref<4096x4096xf32>
affine.store %13, %10[%arg6, %arg7] : memref<128x128xf32, 2>
}
}
// Compute.
affine.for %arg6 = #map7(%arg3) to #map8(%arg3) {
affine.for %arg7 = #map7(%arg4) to #map8(%arg4) {
affine.for %arg8 = #map7(%arg5) to #map8(%arg5) {
%11 = affine.load %7[-%arg3 + %arg6, -%arg5 + %arg8] : memref<128x128xf32, 2>
%12 = affine.load %10[-%arg5 + %arg8, -%arg4 + %arg7] : memref<128x128xf32, 2>
%13 = affine.load %2[-%arg3 + %arg6, -%arg4 + %arg7] : memref<128x128xf32, 2>
%14 = mulf %11, %12 : f32
%15 = addf %13, %14 : f32
affine.store %15, %2[-%arg3 + %arg6, -%arg4 + %arg7] : memref<128x128xf32, 2>
}
}
}
dealloc %10 : memref<128x128xf32, 2>
dealloc %7 : memref<128x128xf32, 2>
}
%3 = affine.apply #map0(%arg3, %arg4)
%4 = affine.apply #map1(%arg3, %arg4)
// Copy out result matrix.
affine.for %arg5 = 0 to 128 {
%5 = affine.apply #map2(%arg3, %arg5)
affine.for %arg6 = 0 to 128 {
%6 = affine.apply #map2(%arg4, %arg6)
%7 = affine.load %2[%arg5, %arg6] : memref<128x128xf32, 2>
store %7, %arg2[%5, %6] : memref<4096x4096xf32>
}
}
dealloc %2 : memref<128x128xf32, 2>
}
}
```
With -affine-data-copy -dma:
```
affine.for %arg3 = 0 to 4096 step 128 {
%0 = affine.apply #map3(%arg3)
%1 = alloc() : memref<128xf32, 2>
%2 = alloc() : memref<1xi32>
affine.dma_start %arg2[%arg3], %1[%c0], %2[%c0], %c128_0 : memref<4096xf32>, memref<128xf32, 2>, memref<1xi32>
affine.dma_wait %2[%c0], %c128_0 : memref<1xi32>
%3 = alloc() : memref<1xi32>
affine.for %arg4 = 0 to 4096 step 128 {
%5 = affine.apply #map0(%arg3, %arg4)
%6 = affine.apply #map1(%arg3, %arg4)
%7 = alloc() : memref<128x128xf32, 2>
%8 = alloc() : memref<1xi32>
affine.dma_start %arg0[%arg3, %arg4], %7[%c0, %c0], %8[%c0], %c16384, %c4096, %c128_2 : memref<4096x4096xf32>, memref<128x128xf32, 2>, memref<1xi32>
affine.dma_wait %8[%c0], %c16384 : memref<1xi32>
%9 = affine.apply #map3(%arg4)
%10 = alloc() : memref<128xf32, 2>
%11 = alloc() : memref<1xi32>
affine.dma_start %arg1[%arg4], %10[%c0], %11[%c0], %c128_1 : memref<4096xf32>, memref<128xf32, 2>, memref<1xi32>
affine.dma_wait %11[%c0], %c128_1 : memref<1xi32>
affine.for %arg5 = #map3(%arg3) to #map5(%arg3) {
affine.for %arg6 = #map3(%arg4) to #map5(%arg4) {
%12 = affine.load %7[-%arg3 + %arg5, -%arg4 + %arg6] : memref<128x128xf32, 2>
%13 = affine.load %10[-%arg4 + %arg6] : memref<128xf32, 2>
%14 = affine.load %1[-%arg3 + %arg5] : memref<128xf32, 2>
%15 = mulf %12, %13 : f32
%16 = addf %14, %15 : f32
affine.store %16, %1[-%arg3 + %arg5] : memref<128xf32, 2>
}
}
dealloc %11 : memref<1xi32>
dealloc %10 : memref<128xf32, 2>
dealloc %8 : memref<1xi32>
dealloc %7 : memref<128x128xf32, 2>
}
%4 = affine.apply #map3(%arg3)
affine.dma_start %1[%c0], %arg2[%arg3], %3[%c0], %c128 : memref<128xf32, 2>, memref<4096xf32>, memref<1xi32>
affine.dma_wait %3[%c0], %c128 : memref<1xi32>
dealloc %3 : memref<1xi32>
dealloc %2 : memref<1xi32>
dealloc %1 : memref<128xf32, 2>
}
```
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closes tensorflow/mlir#50
PiperOrigin-RevId: 261221903
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PiperOrigin-RevId: 261195069
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This CL extends the existing spv.constant op to also support
specialization constant by adding an extra unit attribute
on it.
PiperOrigin-RevId: 261194869
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Closes tensorflow/mlir#62
PiperOrigin-RevId: 261187850
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Add binary logical operations regarding to the spec section 3.32.15:
OpIEqual, OpINotEqual, OpUGreaterThan, OpSGreaterThan,
OpUGreaterThanEqual, OpSGreaterThanEqual, OpULessThan, OpSLessThan,
OpULessThanEqual, OpSLessThanEqual.
Closes tensorflow/mlir#61
PiperOrigin-RevId: 261181281
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verifyUnusedValue is a bit strange given that it is specified in a
result pattern but used to generate match statements. Now we are
able to support multi-result ops better, we can retire it and replace
it with a HasNoUseOf constraint. This reduces the number of mechanisms.
PiperOrigin-RevId: 261166863
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PiperOrigin-RevId: 261045611
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We allow to generate more ops than what are needed for replacing
the matched root op. Only the last N static values generated are
used as replacement; the others serve as auxiliary ops/values for
building the replacement.
With the introduction of multi-result op support, an op, if used
as a whole, may be used to replace multiple static values of
the matched root op. We need to consider this when calculating
the result range an generated op is to replace.
For example, we can have the following pattern:
```tblgen
def : Pattern<(ThreeResultOp ...),
[(OneResultOp ...), (OneResultOp ...), (OneResultOp ...)]>;
// Two op to replace all three results
def : Pattern<(ThreeResultOp ...),
[(TwoResultOp ...), (OneResultOp ...)]>;
// One op to replace all three results
def : Pat<(ThreeResultOp ...), (ThreeResultOp ...)>;
def : Pattern<(ThreeResultOp ...),
[(AuxiliaryOp ...), (ThreeResultOp ...)]>;
```
PiperOrigin-RevId: 261017235
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Previously we use one single method with lots of branches to
generate multiple builders. This makes the method difficult
to follow and modify. This CL splits the method into multiple
dedicated ones, by extracting common logic into helper methods
while leaving logic specific to each builder in their own
methods.
PiperOrigin-RevId: 261011082
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During serialization, the operand number must be used to get the
values assocaited with an operand. Using the argument number in Op
specification was wrong since some of the elements in the arguments
list might be attributes on the operation. This resulted in a segfault
during serialization.
Add a test that exercise that path.
PiperOrigin-RevId: 260977758
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Add binary operations such as: OpUdiv, OpSDiv, OpUMod, OpSRem, OpSMod.
Closes tensorflow/mlir#56
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/56 from denis0x0D:sandbox/bin_ops_int 4959325a693b4658b978a8b97f79b8237eb39764
PiperOrigin-RevId: 260961681
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Use of std::isupper and std::islower need <cctype> header file. Fix
that and also fix the header of a file to match the file name.
PiperOrigin-RevId: 260816852
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Extend the recently introduced support for hexadecimal float literals to tensor
literals, which may also contain special floating point values such as
infinities and NaNs.
Modify TensorLiteralParser to store the list of tokens representing values
until the type is parsed instead of trying to guess the tensor element type
from the token kinds (hexadecimal values can be either integers or floats, and
can be mixed with both). Maintain the error reports as close as possible to
the existing implementation to avoid disturbing the tests. They can be
improved in a separate clean-up if deemed necessary.
PiperOrigin-RevId: 260794716
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All non-argument attributes specified for an operation are treated as
decorations on the result value and (de)serialized using OpDecorate
instruction. An error is generated if an attribute is not an argument,
and the name doesn't correspond to a Decoration enum. Name of the
attributes that represent decoerations are to be the snake-case-ified
version of the Decoration name.
Add utility methods to convert to snake-case and camel-case.
PiperOrigin-RevId: 260792638
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MLIR does not have support for parsing special floating point values such as
infinities and NaNs. If programmatically constructed, these values are printed
as NaN and (+-)Inf and cannot be parsed back. Add parser support for
hexadecimal literals in float attributes, following LLVM IR. The literal
corresponds to the in-memory representation of the floating point value.
IEEE 754 defines a range of possible values for NaNs, storing the bitwise
representation allows MLIR to properly roundtrip NaNs with different bit values
of significands.
The initial version of this commit was missing support for float literals that
used to be printed in decimal notation as a fallback, but ended up being
printed in hexadecimal format which became the fallback for special values.
The decimal fallback behavior was not exercised by tests. It is currently
reinstated and tested by the newly added test @f32_potential_precision_loss in
parser.mlir.
PiperOrigin-RevId: 260790900
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Add a missed library that needs to be linked with mlir-opt. This
results in a test failure in the MLIR due to the pass
`-convert-gpu-to-spirv` not being found.
PiperOrigin-RevId: 260773067
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Fixes build warnings on clang-8, no warnings on redundant moves on
gcc-(6.5,7.4,8.3).
Closes tensorflow/mlir#41
PiperOrigin-RevId: 260764269
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This CL adds an initial implementation for translation of kernel
function in GPU Dialect (used with a gpu.launch_kernel) op to a
spv.Module. The original function is translated into an entry
function.
Most of the heavy lifting is done by adding TypeConversion and other
utility functions/classes that provide most of the functionality to
translate from Standard Dialect to SPIR-V Dialect. These are intended
to be reusable in implementation of different dialect conversion
pipelines.
Note : Some of the files for have been renamed to be consistent with
the norm used by the other Conversion frameworks.
PiperOrigin-RevId: 260759165
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We are relying on serializer to construct positive cases to drive
the test for deserializer. This leaves negative cases untested.
This CL adds a basic test fixture for covering the negative
corner cases to enforce a more robust deserializer.
Refactored common SPIR-V building methods out of serializer to
share it with the deserialization test.
PiperOrigin-RevId: 260742733
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Add binary operations such as: OpIAdd, OpFAdd, OpISub, OpFSub, OpIMul,
OpFDiv, OpFRem, OpFMod.
Closes tensorflow/mlir#54
PiperOrigin-RevId: 260734166
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PiperOrigin-RevId: 260585594
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RewriterGen was emitting invalid C++ code if the pattern required to create a
zero-result operation due to the absence of a special case that would avoid
generating a spurious comma. Handle this case. Also add rewriter tests for
zero-argument operations.
PiperOrigin-RevId: 260576998
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The code was written with the assumption that on failure an error would be
issued by another verifier. However verification is stopping on the first
failure which lead to an empty output. Instead we make sure an error is
displayed.
Also add tests in the test dialect for this trait.
PiperOrigin-RevId: 260541290
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There is a wrapper for SingleBlockImplicitTerminator in ODS, this is nicer to
read than using `NativeOpTrait`.
PiperOrigin-RevId: 260539473
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Automatic generation of spirv::AccessChainOp (de)serialization needs
the (de)serialization emitters to handle argument specified as
Variadic<...>. To handle this correctly, this argument can only be
the last entry in the arguments list.
Add a test to (de)serialize spirv::AccessChainOp
PiperOrigin-RevId: 260532598
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operation (NFC)
PiperOrigin-RevId: 260532592
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definitions
This allows classes to refer to each other in the ODS file, for instance for traits.
PiperOrigin-RevId: 260532419
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Reported by clang-6.
PiperOrigin-RevId: 260311814
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dimension or symbol identifiers.
PiperOrigin-RevId: 260197567
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PiperOrigin-RevId: 260136255
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This CL adds a few specializations for sgemm.
A minor change to alpha is made in cblas_interface.cpp to be compatible with actual BLAS calls.
For now this is for internal testing purposes only.
PiperOrigin-RevId: 260129027
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It's quite common that we want to put further constraints on the matched
multi-result op's specific results. This CL enables referencing symbols
bound to source op with the `__N` syntax.
PiperOrigin-RevId: 260122401
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In the backward slice computation, BlockArgument coming from function arguments represent a natural boundary for the traversal and should not trigger llvm_unreachable.
This CL also improves the error message and adds a relevant test.
PiperOrigin-RevId: 260118630
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This CL provides a fix that makes linal_matmul_impl compliant with the BLAS interface. Before this CL it would compute either C += A * B when called with cblas.cpp:cblas_sgemm implementation and C = A * B with other implementations.
PiperOrigin-RevId: 260117367
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Clipping creates non-affine memory accesses, use std_load and std_store instead of affine_load and affine_store.
In the future we may also want a fill with the neutral element rather than clip, this would make the accesses affine if we wanted more analyses and transformations to happen post lowering to pointwise copies.
PiperOrigin-RevId: 260110503
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This API is needed for the TFLite flatbuffer importer as the importer constructs
arrays of NamedAttributes based on the flatbuffer that need to be added to an
operation.
PiperOrigin-RevId: 260041403
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PiperOrigin-RevId: 260037115
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AccessChainOp creates a pointer into a composite object that can be used with
OpLoad and OpStore.
Closes tensorflow/mlir#52
PiperOrigin-RevId: 260035676
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