Commit Graph

14 Commits

Author SHA1 Message Date
Tres Popp 9a52ea5cf9 Create a gpu.module operation for the GPU Dialect.
Summary:
This is based on the use of code constantly checking for an attribute on
a model and instead represents the distinct operaion with a different
op. Instead, this op can be used to provide better filtering.

Reverts "Revert "[mlir] Create a gpu.module operation for the GPU Dialect.""

This reverts commit ac446302ca4145cdc89f377c0c364c29ee303be5 after
fixing internal Google issues.

This additionally updates ROCDL lowering to use the new gpu.module.

Reviewers: herhut, mravishankar, antiagainst, nicolasvasilache

Subscribers: jholewinski, mgorny, mehdi_amini, jpienaar, burmako, shauheen, csigg, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, llvm-commits, mravishankar, rriddle, antiagainst, bkramer

Tags: #llvm

Differential Revision: https://reviews.llvm.org/D72921
2020-01-21 14:05:03 +01:00
Benjamin Kramer 0133cc60e4 Revert "[mlir] Create a gpu.module operation for the GPU Dialect."
This reverts commit 4624a1e8ac. Causing
problems downstream.
2020-01-15 17:52:17 +01:00
Tres Popp 4624a1e8ac [mlir] Create a gpu.module operation for the GPU Dialect.
Summary:
This is based on the use of code constantly checking for an attribute on
a model and instead represents the distinct operaion with a different
op. Instead, this op can be used to provide better filtering.

Reviewers: herhut, mravishankar, antiagainst, rriddle

Reviewed By: herhut, antiagainst, rriddle

Subscribers: liufengdb, aartbik, jholewinski, mgorny, mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, nicolasvasilache, csigg, arpith-jacob, mgester, lucyrfox, llvm-commits

Tags: #llvm

Differential Revision: https://reviews.llvm.org/D72336
2020-01-14 12:05:47 +01:00
Alex Zinenko 6273fa0c6a Plug gpu.func into the GPU lowering pipelines
This updates the lowering pipelines from the GPU dialect to lower-level
dialects (NVVM, SPIRV) to use the recently introduced gpu.func operation
instead of a standard function annotated with an attribute. In particular, the
kernel outlining is updated to produce gpu.func instead of std.func and the
individual conversions are updated to consume gpu.funcs and disallow standard
funcs after legalization, if necessary. The attribute "gpu.kernel" is preserved
in the generic syntax, but can also be used with the custom syntax on
gpu.funcs. The special kind of function for GPU allows one to use additional
features such as memory attribution.

PiperOrigin-RevId: 285822272
2019-12-16 12:12:48 -08:00
Alex Zinenko d5e627f84b Introduce Linkage attribute to the LLVM dialect
LLVM IR supports linkage on global objects such as global variables and
functions. Introduce the Linkage attribute into the LLVM dialect, backed by an
integer storage. Use this attribute on LLVM::GlobalOp and make it mandatory.
Implement parsing/printing of the attribute and conversion to LLVM IR.

See tensorflow/mlir#277.

PiperOrigin-RevId: 283309328
2019-12-02 03:28:10 -08:00
Alex Zinenko 5f867d26b4 Use LLVM_Type instead of AnyType in the definition of LLVM_CallOp
The type constraint had to be relaxed due to the order of lowering passes in
the examples, that since has been fixed. The relaxed version was still used by
the CUDA lowering for launch sizes of `index` type. This is not necessary since
the GPU dialect does not restrict the type of the launch size operands. Use an
LLVM type instead and restore the check in the LLVM_CallOp definition.

PiperOrigin-RevId: 275920109
2019-10-21 14:12:19 -07:00
Alex Zinenko 5e7959a353 Use llvm.func to define functions with wrapped LLVM IR function type
This function-like operation allows one to define functions that have wrapped
LLVM IR function type, in particular variadic functions. The operation was
added in parallel to the existing lowering flow, this commit only switches the
flow to use it.

Using a custom function type makes the LLVM IR dialect type system more
consistent and avoids complex conversion rules for functions that previously
had to use the built-in function type instead of a wrapped LLVM IR dialect type
and perform conversions during the analysis.

PiperOrigin-RevId: 273910855
2019-10-10 01:34:06 -07:00
Alex Zinenko 11d12670da GPUToCUDA: attach CUBIN to the nested module rather than to the function
Originally, we were attaching attributes containing CUBIN blobs to the kernel
function called by `gpu.launch_func`. This kernel is now contained in a nested
module that is used as a compilation unit. Attach compiled CUBIN blobs to the
module rather than to the function since we were compiling the module. This
also avoids duplication of the attribute on multiple kernels within the same
module.

PiperOrigin-RevId: 273497303
2019-10-08 05:11:26 -07:00
Alex Zinenko 52e082b6ed GPUToCUDA: emit addressof directly instead of wrapping it into a getter function
Originally, the CUBIN getter function was introduced as a mechanism to
circumvent the absence of globals in the LLVM dialect. It would allocate memory
and populate it with the CUBIN data. LLVM dialect now supports globals and they
are already used to store CUBIN data, making the getter function a trivial
address computation of a global. Emit the address computation directly at the
place of `gpu.launch_func` instead of putting it in a function and calling it.
This simplifies the conversion flow and prepares it for using the
DialectConversion infrastructure.

PiperOrigin-RevId: 273496221
2019-10-08 05:03:42 -07:00
Alex Zinenko 16af5924cb Fuse GenerateCubinAccessors pass into LaunchFunctToCuda
Now that the accessor function is a trivial getter of the global variable, it
makes less sense to have the getter generation as a separate pass. Move the
getter generation into the lowering of `gpu.launch_func` to CUDA calls. This
change is mostly code motion, but the process can be simplified further by
generating the addressof inplace instead of using a call. This is will be done
in a follow-up.

PiperOrigin-RevId: 273492517
2019-10-08 04:35:33 -07:00
Alex Zinenko 90d65d32d6 Use named modules for gpu.launch_func
The kernel function called by gpu.launch_func is now placed into an isolated
nested module during the outlining stage to simplify separate compilation.
Until recently, modules did not have names and could not be referenced. This
limitation was circumvented by introducing a stub kernel at the same name at
the same nesting level as the module containing the actual kernel. This
relation is only effective in one direction: from actual kernel function to its
launch_func "caller".

Leverage the recently introduced symbol name attributes on modules to refer to
a specific nested module from `gpu.launch_func`. This removes the implicit
connection between the identically named stub and kernel functions. It also
enables support for `gpu.launch_func`s to call different kernels located in the
same module.

PiperOrigin-RevId: 273491891
2019-10-08 04:30:32 -07:00
Alex Zinenko c335d9d313 LLVM dialect: prefix auxiliary operations with "mlir."
Some of the operations in the LLVM dialect are required to model the LLVM IR in
MLIR, for example "constant" operations are needed to declare a constant value
since MLIR, unlike LLVM, does not support immediate values as operands.  To
avoid confusion with actual LLVM operations, we prefix such axuiliary
operations with "mlir.".

PiperOrigin-RevId: 266942838
2019-09-03 09:10:56 -07:00
Alex Zinenko 006fcce44a ConvertLaunchFuncToCudaCalls: use LLVM dialect globals
This conversion has been using a stack-allocated array of i8 to store the
null-terminated kernel name in order to pass it to the CUDA wrappers expecting
a C string because the LLVM dialect was missing support for globals.  Now that
the suport is introduced, use a global instead.

Refactor global string construction from GenerateCubinAccessors into a common
utility function living in the LLVM namespace.

PiperOrigin-RevId: 264382489
2019-08-20 07:52:01 -07:00
Stephan Herhut c72c6c3907 Make GPU to CUDA transformations independent of CUDA runtime.
The actual transformation from PTX source to a CUDA binary is now factored out,
enabling compiling and testing the transformations independently of a CUDA
runtime.

MLIR has still to be built with NVPTX target support for the conversions to be
built and tested.

PiperOrigin-RevId: 255167139
2019-06-26 05:16:37 -07:00