Commit Graph

22 Commits (68338eafb7ff72612221fa0ec796656ba27ddfe5)

Author SHA1 Message Date
Sean Silva c424c24ed8 Bump llvm-project to c68d2895a1f4019b387c69d1e5eec31b0eb5e7b0
- dialect registration
- StringAttr::get: order of context arg
- math dialect
- LogicalResult nodiscard
- error message for invalid broadcast
2021-02-22 12:23:24 -08:00
Aaron Arthurs 85898aaf10
Add TCF convolutional op with bias addition (#137) 2020-12-15 12:53:12 -08:00
Sean Silva b2077738ca Bump llvm-project to 444822d77a7fea28aa49edf24533c987efa1b2ee
Fixes:
- renames StandardTypes -> BuiltinTypes
- std.extract_element -> tensor.extract
2020-12-11 14:43:38 -08:00
Sean Silva 251aa6e435 Bump llvm-project to 774f1d3ffd458d6cb82d5039758ef1cf6370957f
Date:   Mon Nov 30 15:20:30 2020 -0800

Changes:
- finalizing-bufferize is stricter now, and we need to pull in a DimOp
  bufferization which was previously working by happenstance. The
  offending DimOp's are actually created by the linalg bufferization
  (which creates dim ops on the original tensor values, not the
  converted memrefs), so the fix is moving std-bufferize after
  linalg-bufferize.
2020-11-30 18:40:13 -08:00
Sean Silva f9b32a99fc Bump llvm-project to 164410324d8bf3b5a99e39f7dfe3c6d6972dab30
Date:   Mon Nov 30 12:44:35 2020 -0800

Fixes:
- func-bufferize is no longer finalizing, so we need to add
  finalizing-bufferize.
2020-11-30 13:58:13 -08:00
Sean Silva 64a7e83184 [RefBackend] Add refback-tcf-to-tcp-pipeline
This allows invoking TCF to TCP-level conversion more easily, and starts
us towards a path of factoring it out of the RefBackend.
2020-11-17 12:33:37 -08:00
Sean Silva 358159a6eb [RefBackend] Open-code shape.get_extent as extract_element
It was annoying that we were creating shape.get_extent in the middle of
the bufferization pipeline, as it required running convert-shape-to-std
at an awkward place. To make that cleaner, just open-code the
extract_element ops that shape.get_extent expands into.

This is a little gross, but it helps with the macroscopic pipeline
ordering issues. Anyway, the train is long-gone of trying to treat
shapes as some special data type that should only be operated on with
shape ops.

Also,
- reorder tensor constant bufferize (which is a module pass) to bracket
all the bufferization function passes, to make the parallelism
opportunities there clearer. Now we have a very clean little
bufferization segment of our pipeline construction.
2020-11-17 11:00:38 -08:00
Sean Silva 5227d52c26 [RefBackend] Use std.global_memref instead of homegrown thing
This vastly simplifies our code, allowing deleting multiple ops,
simplifying multiple passes, and removing a whole pass.

Now `refback` dialect is down to one op (refback.alloc_memref, which
simplifies allocations to just take a shape instead of individual
extents).
2020-11-13 18:43:50 -08:00
Sean Silva 32388d938b Make some passes run on FuncOp so they can run in parallel. 2020-11-13 16:12:18 -08:00
Stella Laurenzo b4c7ae1e0c Repurpose numpy-compiler compiler/runtime flow for PyTorch.
* A bit gross because I took the chance to upgrade all of the backend bits to the new MLIR Python bindings and we still co-mingle the old and new for now.
* Since the Python created PassManagers are configured for explicit nesting, I had to upgrade some of the pass pipelines to be explicit.
* The demo in mul_maximum_e2e.py now compiles, runs through PyTorch and through the JIT, prints and asserts the same results.
* I am not claiming that this is the prettiest API in this patch: consider that this is just directly using low-level APIs and there should be an intervening high level API.
2020-11-11 10:38:13 -08:00
Sean Silva 1c7c362e29 [TCP] Replace tcp.matmul with linalg.matmul.
This involved adding a `tcp.splatted` op to splat a dynamically sized
init tensor. See rationale in TCPOps.td docs.

One interesting observation is that when lowering tcf.matmul to
linalg.matmul, we need to both 1) create the error checks and 2)
calculate a shape transfer function to create the init tensors.
Previously, 2) was deferred to bufferizing tcp.matmul later. I'm not
sure if this is a conflation of concerns or not. For now, it's not a big
burden.
2020-11-10 18:58:28 -08:00
Sean Silva 0427aacb0b [TCP] Replace elementwise ops with std elementwise ops. 2020-11-10 18:58:28 -08:00
Sean Silva 57e58b9272 [RefBackend] Use upstream func-bufferize pass.
Now, the only bufferization we have left is lowering tensor constants to
memref, which will hopefully proceed soon after Rahul's new
std.global_memref lands + the lowering to LLVM IR. Then I'll port
LowerConstantTensorsToMemref to upstream and we'll be 100% upstream
bufferization, except for our local TCP dialect (which will probably go
away and be replaced by std elementwise + linalg named ops on tensors :)
).
2020-11-02 17:38:33 -08:00
Sean Silva f9c2f8eb0d [RefBackend] Use upstream SCF bufferization pass. 2020-10-30 18:12:41 -07:00
Stella Laurenzo a3f4db9fe8 Bump llvm-project to c8c07b76b2cf2ada8e7ec132f7f57b97d76743cf.
* Several NFC changes to signatures/includes.
2020-10-29 15:25:55 -07:00
Sean Silva 14470f9ff6 [RefBackend] Use upstream std bufferization.
It now subsumes the one we had.
2020-10-21 16:46:56 -07:00
Sean Silva 06a8ba6900 [RefBackend] Use more idiomatic bufferize pattern for TCP.
The time has come for BypassShapes/LowerShapedResultsToMemref to go away :(
For the reference backend, being consistent with upstream conventions is
the name of the game now.

This is a step down in a number of ways, e.g. test clarity and
separation of concerns. But it is fewer files and fewer tests, and
*does* address the "TODO: This is really fragile". It also eliminates two
more ops from the refback dialect (sadly, they are the
shaped_results/yield that we were getting kind of fond of, but alas).
2020-10-15 20:15:53 -07:00
Sean Silva b6bdc8cc4f [RefBackend] Use upstream BufferizeTypeConverter
Now that it has grown source/target materialization capabilities
(spelled with ops tensor_load/tensor_to_memref), we can use it. We can
also now delete refback.memref_to_tensor/refback.tensor_to_memref.

This is also a first step to reducing the downstream functionality
needed in the refback dialect.
2020-10-15 15:58:51 -07:00
Sean Silva 93fc21dad0 [RefBackend] Split out TCF->TCP conversion.
Now the reference backend is cleanly accepts "TCP"+scalar ops.

We introduce tcf-refback-lowering-pipeline which also does TCF->TCP
conversion for convenience until we have a "target interface".
2020-10-12 11:56:39 -07:00
Sean Silva 7edb5f3641 [RefBackend] Rename RefBackend dialect to Refback
I now realize that VerboseCamelCase is not the best choice for dialect
directory/file names and C++ identifiers (take e.g. "Linalg", "Basicpy",
etc. as prior art here; not LinearAlgebra or BasicPython). If I had to
name the convention it seems to be "Shortword" (or of course just
acronym dialects like LLVM, SCF, etc.).

This rename also has the side benefit of differentiating RefBackend
directories, which now refer to the actual backend itself, from
Refback/Refbackrt, which are the dialects which happen to be used by
that backend.
2020-10-08 09:07:00 -07:00
Sean Silva bf99a82832 [RefBackend] Rename Npcomprt dialect to Refbackrt. 2020-10-08 09:07:00 -07:00
Sean Silva 21255d5f8e [RefBackend] Rename "E2E" to RefBackend. 2020-10-07 10:29:48 -07:00