| Commit message (Collapse) | Author | Age | Files | Lines |
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Extend spv.array with Layoutinfo to support (de)serialization.
Closes tensorflow/mlir#80
PiperOrigin-RevId: 263795304
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Generate the EnumAttr to represent BuiltIns in SPIR-V dialect. The
builtIn can be specified as a StringAttr with value being the
name of the builtin. Extend Decoration (de)serialization to handle
BuiltIns.
Also fix an error in the SPIR-V dialect generator script.
PiperOrigin-RevId: 263596624
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All 'getValue' variants now require that the index is valid, queryable via 'isValidIndex'. 'getSplatValue' now requires that the attribute is a proper splat. This allows for querying these methods on DenseElementAttr with all possible value types; e.g. float, int, APInt, etc. This also allows for removing unnecessary conversions to Attribute that really want the underlying value.
PiperOrigin-RevId: 263437337
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Since raw pointers are always passed around for IR construct without
implying any ownership transfer, it can be error prone to have implicit
ownership transferred the same way.
For example this code can seem harmless:
Pass *pass = ....
pm.addPass(pass);
pm.addPass(pass);
pm.run(module);
PiperOrigin-RevId: 263053082
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The current implementation only returns one element for the splat case, which often comes as a surprise; leading to subtle/confusing bugs. The new behavior will include an iterate over the full range of elements, as defined by the shaped type, by providing the splat value for each iterator index.
PiperOrigin-RevId: 262756780
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There are currently several different terms used to refer to a parent IR unit in 'get' methods: getParent/getEnclosing/getContaining. This cl standardizes all of these methods to use 'getParent*'.
PiperOrigin-RevId: 262680287
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This will allow for reusing the same pattern list, which may be costly to continually reconstruct, on multiple invocations.
PiperOrigin-RevId: 262664599
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Building the symbol table upfront from module op allows for O(1)
lookup of the function while verifying duplicate EntryPointOp within
the module.
PiperOrigin-RevId: 262435697
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Adding the SymbolTable trait allows looking up the name of the
functions using the symbol table while verifying EntryPointOps instead
of manually tracking the function names.
PiperOrigin-RevId: 262431220
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The entry block is often used recently after insertion. This removes the need to perform an additional lookup in such cases.
PiperOrigin-RevId: 262265671
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PiperOrigin-RevId: 262225919
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This allows for proper forward declaration, as opposed to leaking the internal implementation via a using directive. This also allows for all pattern building to go through 'insert' methods on the OwningRewritePatternList, replacing uses of 'push_back' and 'RewriteListBuilder'.
PiperOrigin-RevId: 261816316
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This trait provides the ensureTerminator() utility function and
the checks to make sure a spv.module is indeed terminated with
spv._module_end.
PiperOrigin-RevId: 261664153
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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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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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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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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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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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Per tacit agreement, individual dialects should now live in lib/Dialect/Name
with headers in include/mlir/Dialect/Name and tests in test/Dialect/Name.
PiperOrigin-RevId: 259896851
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This CL covers the case of composite spv.constant. We encode/decode
them into/from OpConstantComposite/OpConstantNull.
PiperOrigin-RevId: 259394700
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This CL adds support for float scalar spv.constant in (de)serialization.
PiperOrigin-RevId: 259311776
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SPIR-V has multiple constant instructions covering different
constant types:
* `OpConstantTrue` and `OpConstantFalse` for boolean constants
* `OpConstant` for scalar constants
* `OpConstantComposite` for composite constants
* `OpConstantNull` for null constants
* ...
We model them all with a single spv.constant op for uniformity
and friendliness to transformations. This does mean that when
doing (de)serialization, we need to poke spv.constant's type
to determine which SPIR-V binary instruction to use.
This CL only covers the case of bool and integer spv.constant.
The rest will follow.
PiperOrigin-RevId: 259311698
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* Let them return `LogicalResult` so we can chain them together
with other functions returning `LogicalResult`.
* Added "Into" as the suffix to the function name and made the
`binary` as the first parameter so that it reads more naturally.
PiperOrigin-RevId: 259311636
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We already have two levels of controls in SPIRVBase.td: hasOpcode and
autogenSerialization. The former controls whether to add an entry to
the dispatch table, while the latter controls whether to autogenerate
the op's (de)serialization method specialization. This is enough for
our cases. Remove the indirection from processOp to processOpImpl
to simplify the picture.
PiperOrigin-RevId: 259308711
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Since the serialization of EntryPointOp contains the name of the
function as well, the function serialization emits the function name
using OpName instruction, which is used during deserialization to get
the correct function name.
PiperOrigin-RevId: 259158784
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When printing the value attribute in spv.constant, OpAsmPrinter
already attaches a trailing type. So we don't need to duplicate
it again unless it's an array attribute, which does not have
type by default but we use it for spirv::ArrayType.
PiperOrigin-RevId: 258994197
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PiperOrigin-RevId: 258986485
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It's a known bug that older GCC is not happy with method specialization in
the enclosing (global) namespace:
https://gcc.gnu.org/bugzilla/show_bug.cgi?id=56480
This CL wraps the generated specialization methods in the anonymous namespace
to make sure the specialization is in the same namespace as the class.
PiperOrigin-RevId: 258983181
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Version of std::equal used required C++14, switching to for-loop for now. Just a direct change from std::equal to the equivalent using for loop.
PiperOrigin-RevId: 258970366
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This CL changes the Op definition of spirv::EntryPointOp and
spirv::ExecutionModeOp to be consistent with the SPIR-V spec.
1) The EntryPointOp doesn't return a value
2) The ExecutionModeOp takes as argument, the SymbolRefAttr to refer
to the function, instead of the result of the EntryPointOp.
Following this, the spirv::EntryPointType is no longer necessary, and
is removed.
PiperOrigin-RevId: 258964027
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Several groups of operations in different dialects (e.g. AffineForOp,
AffineIfOp; loop::ForOp, loop::IfOp) share the requirement for their regions to
contain 0 or 1 block, and for blocks to always have a specific terminator type.
Furthermore, this terminator may be omitted from the custom syntax. Generalize
this behavior into OpTrait::SingleBlockImplicitTerminator, parameterized by the
terminator operation type. This trait provides the verifier that checks the
presence of the terminator, and utility functions adding the terminator in case
of absence.
PiperOrigin-RevId: 258957180
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This CL groups (de)serialization methods logically and improves comments
at various places. It also sorted method implementations to follow the
order of their declarations. There is NFC.
PiperOrigin-RevId: 258843490
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Some TensorFlow simulated quantize ops such as QuantizeAndDequantizeV2Op have
attribute for the sign of the quantization, so quant_ConstFakeQuant should be
able to represent it with the new attribute is added.
The method for converting these attributes to an QuantizedType is updated to
handle this new argument.
PiperOrigin-RevId: 258810290
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For ops in SPIR-V dialect that are a direct mirror of SPIR-V
operations, the serialization/deserialization methods can be
automatically generated from the Op specification. To enable this an
'autogenSerialization' field is added to SPV_Ops. When set to
non-zero, this will enable the automatic (de)serialization function
generation
Also adding tests that verify the spv.Load, spv.Store and spv.Variable
ops are serialized and deserialized correctly. To fully support these
tests also add serialization and deserialization of float types and
spv.ptr types
PiperOrigin-RevId: 258684764
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incompatible
Currently, Broadcastable trait also rejects instances when the op result has shape other than what can be statically inferred based on the operand shapes even if the result shape is compatible with the inferred broadcasted shape.
For example,
(tensor<3x2xi32>, tensor<*xi32>) -> tensor<4x3x2xi32>
(tensor<2xi32>, tensor<2xi32>) -> tensor<*xi32>
PiperOrigin-RevId: 258647493
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As the move to ODS is made, body and region names across affine and loop dialects are uniformized.
PiperOrigin-RevId: 258416590
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This patch added a new argument to the fakeQuantAttrsToType utility method, so
it can be used to convert min/max to quantized type with different signed
storage types.
PiperOrigin-RevId: 258382538
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LoopOps needs the definition ConstantIndexOp in the verifier of loop::ForOp.
PiperOrigin-RevId: 258355329
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PiperOrigin-RevId: 258345603
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These ops should not belong to the std dialect.
This CL extracts them in their own dialect and updates the corresponding conversions and tests.
PiperOrigin-RevId: 258123853
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Move the data members out of Function and into a new impl storage class 'FunctionStorage'. This allows for Function to become value typed, which will greatly simplify the transition of Function to FuncOp(given that FuncOp is also value typed).
PiperOrigin-RevId: 255983022
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into the mlir namespace.
Now that Locations are attributes, they have direct access to the MLIR context. This allows for simplifying error emission by removing unnecessary context lookups.
PiperOrigin-RevId: 255112791
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DenseElementsAttr.
PiperOrigin-RevId: 253910543
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being a separate Attribute type. DenseElementsAttr provides a better internal representation for splat values as well as better API for accessing elements.
PiperOrigin-RevId: 253138287
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construction. This essentially means that we always auto-detect splat data and only store the minimum amount of data necessary. Support for parsing dense splats, and removing SplatElementsAttr(now that it is redundant) will come in followup cls
PiperOrigin-RevId: 252720561
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match/rewrite methods with an instance of the source op instead of a raw Operation*.
--
PiperOrigin-RevId: 250003405
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notify the PatternRewriter that the operation is being replaced.
--
PiperOrigin-RevId: 248965082
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kernel utils
MemRef may soon be a subclass of ShapedType.
--
PiperOrigin-RevId: 248788950
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This is in preparation for making it also support/be a parent class of MemRefType. MemRefs have similar shape/rank/element semantics and it would be useful to be able to use these same utilities for them.
This CL should not change any semantics and only change variables, types, string literals, and comments. In follow-up CLs I will prepare all callers to handle MemRef types or remove their dependence on ShapedType.
Discussion/Rationale in https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/cHLoyfGu8y8
--
PiperOrigin-RevId: 248476449
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