Add a configurable parameter for the number of threads per warp for
other GPU. Like: Intel GPU.
Make it default to be 32 not change code logic on the CUDA/AMD GPU.
Note: The Intel GPU GenX ISA is explicit SIMD and can support variant
number of threads lane per HW execution unit.
clearly differentiate between standard fp8e4 (which we'll stop
supporting on SM <= 89 because conversions are too expensive if we want
to handle the single NaN and clipping properly) and a software-optimized
fp8e4b15 format.
Since the kWidth optimization was happening during software pipelining
it was skipped in case pipelining wasn't applied.
This also improve separation of concerns.
`tl.cat(tensor<64>, tensor<64>) -> tensor(128)`, because it concatenates
elements into a single thread, if number of threads is 128, each thread
should own at least 2 elements.
With this PR, we also disable remat of the cat op in some cases.
Add a configurable parameter for the number of threads per warp for
other GPU. Like: Intel GPU.
Make it default to be 32 not change code logic on the CUDA/AMD GPU.
Note: The Intel GPU GenX ISA is explicit SIMD and can support variant
number of threads lane per HW execution unit.
Also catch out-of-bounds indices at constructio and throw a proper error
in the frontend.
Finally, let's make the IR a bit prettier:
%0 = tt.get_program_id {axis = 0 : i32} : i32
becomes:
%0 = tt.get_program_id x : i32
Fixes#1718
* [MFMA] Implementation of MFMA DotOp pipeline
* Added MFMA test_dot unit tests
* Added missing ifdefs
* Update offline tests
* Removing duplicate parts
* fix build after rebase
* remove redundant stuff
* simplify MMAv3.cpp
* move reps function into operand attr description,
remove coreMatrixType type from layout conversion,
refactored type conversion
* remove duplication of mfma intruction shape computation
* move all MFMA instruction shape details into layout attribute
* fix formatting
* reenable matmul acceleration
* fix dot operator type conversion
* add offline test for dotop
* add missing ifdef wrappers
* run clang format on changes
* review and rebase fix
* add switch for MFMA instructions
* change check precision for float16 test
* disable redundant check for allowTF32
* - skip unsupported block size in matmul autotuner
- support transposed inputs of dot
* reenable matmul acceleration
* Add first part to FMA for dot operation on HW without MFMA support.
* Fix offline tests.
* Fix lit tests
* refactor mmav3 to mfma
* fix rebase issues
* fix detection of mfma support and wrong assert
* remove unnecessary macros
* Add documentation for MFMA layout.
* fix line size computation for B argument
* Fix getElemsPerThread() and getSizePerThread() functions for MFMA layout.
---------
Co-authored-by: Alexander Efimov <efimov.alexander@gmail.com>
Co-authored-by: dfukalov <1671137+dfukalov@users.noreply.github.com>
Co-authored-by: weihan13 <weihan13@amd.com>
Co-authored-by: Ognjen Plavsic <ognjen.plavsic@dxc.com>
Also try to switch APIs access to the new upstream APIs that separate
explicitly the access to "discardable" and "inherent" attributes (the
latter being stored in properties now).
Generic accessors like `getAttr()` `setAttr()` `setAttrs()` are much
more expensive and to be avoided.
This does not change the compiler behavior, it is purely an internal
change: it'll store inherent attributes (the one defined in ODS) within
the operations themselves instead of in a DictionaryAttr in the
MLIRContext.
The use of generic accessors like getAttribute("axis") should be avoided
and direct access to the properties is prefered, like
reduceOp.getProperties().axis
Also `getAttrs()` or `getAttrDictionary()` are gonna be deprecated and
users should move to `getDiscardableAttrDictionary()` instead.
Conflicts:
lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMPass.cpp
lib/Target/LLVMIR/LLVMIRTranslation.cpp
python/test/unit/language/assert_helper.py
python/triton/third_party/cuda/bin/ptxas
test/Conversion/tritongpu_to_llvm.mlir
It looks like you may be committing a merge.
If this is not correct, please remove the file
.git/MERGE_HEAD
and try again.
This depends on a [pending LLVM
release](https://github.com/ptillet/triton-llvm-releases/pull/10).
* Implement setCalleeFromCallable in CallOp.
* Cast type to ShapedType for various getters.
* Improve TritonDialect::materializeConstant due to breaking change in
constructor of arith::ConstantOp.
* Add OpaqueProperties argument in inferReturnTypes.
Co-authored-by: Philippe Tillet <phil@openai.com>
Re-enabled reduce test after fixing the %cst stride in the ttgir, and
modifying the sweep parameters to make sure the shape per CTA to be less
than or equal to the tensor shape.
# Introducing the `noinline` Parameter for Triton JIT Decorator
We're excited to introduce a new parameter, `noinline`, that can be
added to the `jit` decorator in Triton. This parameter allows developers
to specify that a particular Triton function should not be inlined into
its callers. In this post, we'll dive into the syntax, purpose, and
implementation details of this new feature.
## Syntax
To use the `noinline` parameter, simply add `noinline=True` to the `jit`
decorator for the function that you don't want to be inlined. Here's an
example:
```python
@triton.jit(noinline=True)
def device_fn(x, y, Z):
z = x + y
tl.store(Z, z)
def test_noinline():
@triton.jit
def kernel(X, Y, Z):
x = tl.load(X)
y = tl.load(Y)
device_fn(x, y, Z)
```
In this example, the `device_fn` function is decorated with
`@triton.jit(noinline=True)`, indicating that it should not be inlined
into its caller, `kernel`.
## Purpose
The `noinline` parameter serves several key purposes:
- Reducing code size: By preventing inlining, we can reduce the size of
the compiled code.
- Facilitating debugging: Keeping functions separate can make it easier
to debug the code.
- Avoiding common subexpression elimination (CSE) in certain cases: CSE
can sometimes be avoided by using the `noinline` parameter to reduce
register pressure.
- Enabling dynamic linking: This parameter makes it possible to
dynamically link Triton functions.
## Implementation
The implementation of the `noinline` parameter involves significant
changes to three analysis modules in Triton: *Allocation*, *Membar*, and
*AxisInfo*. Prior to this update, these modules assumed that all Triton
functions had been inlined into the root kernel function. With the
introduction of non-inlined functions, we've had to rework these
assumptions and make corresponding changes to the analyses.
### Call Graph and Limitations
<div style="text-align: center;">
<img
src="https://user-images.githubusercontent.com/2306281/234663904-12864247-3412-4405-987b-6991cdf053bb.png"
alt="figure 1" width="200" height="auto">
</div>
To address the changes, we build a call graph and perform all the
analyses on the call graph instead of a single function. The call graph
is constructed by traversing the call edges and storing them in an edge
map. Roots are extracted by checking nodes with no incoming edges.
The call graph has certain limitations:
- It does not support recursive function calls, although this could be
implemented in the future.
- It does not support dynamic function calls, where the function name is
unknown at compilation time.
### Allocation
<div style="text-align: center;">
<img
src="https://user-images.githubusercontent.com/2306281/234665110-bf6a2660-06fb-4648-85dc-16429439e72d.png"
alt="figure 2" width="400" height="auto">
</div>
In Triton, shared memory allocation is achieved through two operations:
`triton_gpu.convert_layout` and `triton_gpu.alloc_tensor`. The
`convert_layout` operation allocates an internal tensor, which we refer
to as a *scratch* buffer, while the `alloc_tensor` operation returns an
allocated tensor and is thus known as an *explicit* buffer.
To accommodate the introduction of function calls, we are introducing a
third type of buffer called a *virtual* buffer. Similar to scratch
buffers, virtual buffers are allocated internally within the scope of a
function call, and the buffers allocated by the called functions remain
invisible to subsequent operations in the calling function. However,
virtual buffers are distinct from scratch buffers in that the call
operation itself does not allocate memory—instead, it specifies the
total amount of memory required by all the child functions being called.
The actual allocation of buffers is performed by individual operations
within these child functions. For example, when invoking edge e1, no
memory is allocated, but the total amount of memory needed by function B
is reserved. Notably, the amount of shared memory used by function B
remains fixed across its call sites due to the consideration of dynamic
control flows within each function.
An additional challenge to address is the calculation of shared memory
offsets for functions within a call graph. While we can assume a shared
memory offset starting at 0 for a single root function, this is not the
case with a call graph, where we must determine each function's starting
offset based on the call path. Although each function has a fixed memory
consumption, the starting offset may vary. For instance, in Figure 2,
the starting offset of function C through edges e1->e2 differs from that
through edges e2->e4. To handle this, we accumulate the starting offset
at each call site and pass it as an argument to the called function.
Additionally, we amend both the function declaration and call sites by
appending an offset variable.
### Membar
<div style="text-align: center;">
<img
src="https://user-images.githubusercontent.com/2306281/234665157-844dd66f-5028-4ef3-bca2-4ca74b8f969d.png"
alt="figure 3" width="300" height="auto">
</div>
The membar pass is dependent on the allocation analysis. Once the offset
and size of each buffer are known, we conduct a post-order traversal of
the call graph and analyze each function on an individual basis. Unlike
previous analyses, we now return buffers that remain unsynchronized at
the end of functions, allowing the calling function to perform
synchronization in cases of overlap.
### AxisInfo
<div style="text-align: center;">
<img
src="https://user-images.githubusercontent.com/2306281/234665183-790a11ac-0ba1-47e1-98b1-e356220405a3.png"
alt="figure 4" width="400" height="auto">
</div>
The AxisInfo analysis operates differently from both membar and
allocation, as it traverses the call graph in topological order. This is
necessary because function arguments may contain axis information that
will be utilized by callee functions. As we do not implement
optimizations like function cloning, each function has a single code
base, and the axis information for an argument is determined as a
conservative result of all axis information passed by the calling
functions.
---------
Co-authored-by: Philippe Tillet <phil@openai.com>
MLIR current only supports a custom inlining interface per dialect, so
we cannot change the inlining decision of `func.func`.
https://discourse.llvm.org/t/avoid-inlining-some-functions-using-the-func-dialect/69830/3
Could revert it back once they've designed a better inliner interface.
Inlining attributes will be implemented in the next PR since this PR is
already huge.
This is a combination of 5 commits.
look up triple and warpsize with HSA
This is a combination of 6 commits.
add scripts
create basic stub
Add HSA
This is a combination of 3 commits.
add hsa
move has file
add hsa include and lib
functional name string
simplify gfx look up
return warpsize
clean up unnecssary imports
remove scripts
use tuple
remove prints
The purpose of this PR is to remove some circular dependencies and
separate concerns better in the frontend. It's still not perfect --
`triton.compile` still includes a few runtime architecture-specific
component, but at least much better than before.
This PR still assumes that AMD only supports empty kernels right now.
Other PRs will follow to make the frontend supports multiple devices in
a more modular way.
This PR address the remaing issues from #1312. It does the following
* LLVM String Join
* adds comment to GCNBuilder Class
---------
Co-authored-by: Rahul Batra <rahbatra@amd.com>
