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torch-mlir/include/npcomp/E2E/Passes.td

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Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
//===-- Passes.td - Pass definition file -------------------*- tablegen -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#ifndef NPCOMP_E2E_PASSES
#define NPCOMP_E2E_PASSES
include "mlir/Pass/PassBase.td"
def LowerLinalgOnTensorToLinalgOnMemref :
Pass<"lower-linalg-tensor-to-memref", "FuncOp"> {
let summary = "Lowers linalg on tensors to linalg on memrefs";
let constructor = "mlir::NPCOMP::createLowerLinalgOnTensorToLinalgOnMemrefPass()";
}
def LowerBroadcastToToLoops :
Pass<"lower-broadcast-to-to-loops", "FuncOp"> {
let summary = "Lower tcp::BroadcastTo to loops.";
let constructor = "mlir::NPCOMP::createLowerBroadcastToToLoopsPass()";
}
def ResolveShapeOfOps : Pass<"resolve-shape-of-ops", "FuncOp"> {
let summary = "Resolve shape.shape_of ops to other shapes.";
let constructor = "mlir::NPCOMP::createResolveShapeOfOpsPass()";
}
"Finish" tensor -> memref conversion. There's a lot of details to flesh out here, but the basic approach seems promising (see comments in createE2ELoweringPipeline). This approach will be put to the test when we try to do our first fusions since that tickles some of the nasty phase ordering issues involved here. But we're not there yet.
2020-05-12 05:24:57 +08:00
def ResolveTensorLoadStoreOps : Pass<"resolve-tensor-load-store-ops", "FuncOp"> {
let summary = "Resolve tensor_load/tensor_store ops";
let constructor = "mlir::NPCOMP::createResolveTensorLoadStoreOpsPass()";
}
Add lowering from linalg to loops. This also adds a small pass to clean up the `dim` ops that linalg introduces. For now, it only has a trivial pattern that looks for a `tcp.alloc_memref(%shape)` op to get the shape as we currently have an invariant that all memrefs are the result of such ops. But eventually this will need to look through view ops and any other shape-ish stuff that linalg introduces as it lowers to loops, along with any slicing ops introduced by buffer allocation.
2020-05-12 09:50:51 +08:00
def LowerLinalgLoopDimOps : Pass<"lower-linalg-loop-dim-ops", "FuncOp"> {
let summary = "Lower dim ops introduced by linalg to loops lowering";
let constructor = "mlir::NPCOMP::createLowerLinalgLoopDimOpsPass();";
}
Lower !shape.shape to SSA values. This uses an approach inspired by what is done in IREE. See comments on LowerRankedShapes.cpp for how it works. The basic gist is that we have an op that creates a !shape.shape from a set of SSA values representing the extents, and then iteratively replace any op producing a !shape.shape with instances of that op.
2020-05-12 12:22:40 +08:00
def LowerRankedShapes : Pass<"lower-ranked-shapes", "FuncOp"> {
let summary = "Lower ranked !shape.shape types to SSA values";
let constructor = "mlir::NPCOMP::createLowerRankedShapesPass()";
}
Lower to the upstream memref ABI. Specifically, we use unranked memrefs which get passed as a fixed-size set of arguments/returns. One big caveat about this is that returning results isn't going to work. See TODO in LowerTensorLoadOp. This is far from enough runtime-wise, but it starts to demarcate a plausible layering. Notice for example how this removes the runtime-dependence from LowerRankedShapes. Eventually, we want to have an `npcomp_rt` or `npcomp_hal` dialect with its own set of runtime types that will supercede this. See comments in LowerTensorLoadOp for more direction about where this is going to evolve.
2020-05-16 07:33:01 +08:00
def LowerToMemRefABI : Pass<"lower-to-memref-abi", "FuncOp"> {
let summary = "Lower tensors at ABI boundaries to memref";
let constructor = "mlir::NPCOMP::createLowerToMemRefABIPass()";
}
Lower tcp.alloc_memref ops to tcp.get_extent + std.alloc. - tcp.get_extent will be liminated while lowering shapes - std.alloc is supported by the upstream LLVM lowering.
2020-05-19 03:50:16 +08:00
def LowerAllocMemRefOps : Pass<"lower-alloc-memref-ops", "FuncOp"> {
let summary = "Lower AllocMemRefOp's";
let constructor = "mlir::NPCOMP::createLowerAllocMemRefOpsPass()";
}
Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
#endif // NPCOMP_E2E_PASSES