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

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//===-- 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_REFBACKEND_PASSES
#define NPCOMP_REFBACKEND_PASSES
include "mlir/Pass/PassBase.td"
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
def BypassShapes : Pass<"bypass-shapes", "FuncOp"> {
let summary = "Bypass shape calculations around ops";
let constructor = "mlir::NPCOMP::createBypassShapesPass()";
}
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
def LowerShapedResultsToMemref : Pass<"lower-shaped-results-to-memref", "FuncOp"> {
let summary = "Lower refback.shaped_results regions";
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
let constructor = "mlir::NPCOMP::createLowerShapedResultsToMemrefPass()";
}
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
def LowerStdToMemref : Pass<"lower-std-to-memref", "FuncOp"> {
let summary = "Lower std ops to memref";
let constructor = "mlir::NPCOMP::createLowerStdToMemrefPass()";
}
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
def LowerConstantTensorsToMemref :
Pass<"lower-constant-tensors-to-memref", "ModuleOp"> {
let summary = "Lower std.constant of tensor type to memref";
let description = [{
This must be a module pass since it involves creating refback.global ops.
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
}];
let constructor = "mlir::NPCOMP::createLowerConstantTensorsToMemrefPass()";
}
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
def LowerStructuralToMemref :
Pass<"lower-structural-to-memref", "FuncOp"> {
let summary = "Lower structural IR constructs to memref";
let description = [{
Structural constructs include:
- control flow ops (both CFG and SCF)
- function signatures
- TODO: calls
An op is "structural" if it doesn't really care about the types it operates
on, but the types just have to converted to be consistent.
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
This pass also cleans up any previous memref<->tensor materializations,
finalizing the conversion from tensor to memref.
}];
let constructor = "mlir::NPCOMP::createLowerStructuralToMemrefPass()";
}
Rework e2e flow to use new "npcomprt" This ~totally reworks the existing "runtime" stuff to be more principled and usable, such as from Python. It's still not fully production-quality, mainly in the department of memory management (e.g. it currently leaks memory; we need to figure out "who frees memrefs" + the analysis and transformation needed to do that (maybe use upstream buffer allocation pass?)). The user API is in include/npcomp/runtime/UserAPI.h, though include/npcomp/JITRuntime/JITModule.h is a friendlier wrapper. The stuff under {include,lib}/runtime is totally firewalled from the compiler and tiny (<6kB, though no attention has gone into optimizing that size). For example, we don't link in libSupport into the runtime, instead having our own bare bones replacements for basics like ArrayRef (the JITRuntime helps with bridging that gap, since it *can* depend on all common LLVM utilities). The overall features of npcomprt is that it exposes a module that with multiple function entry points. Each function has arguments and results that are tensor-valued, and npcomprt::Tensor is the runtime type that is used to interact with that (and a npcomprt::Ref<T> reference-counting wrapper is provided to wrap npcomprt::Tensor in the common case). From an implementation perspective, an npcomprt module at the LLVM/object/binary level exposes a single module descriptor struct that has pointers to other metadata (currently just a list of function metadata descriptors). All interactions with the npcomp runtime are keyed off of that module descriptor, including function lookups and dispatching. This is done to dodge platform ABI issues and also allow enough reflection to e.g. verify provided arguments. Most of the compiler-side work here was in LowerToNpcomprtABI and LowerToLLVM. Also, - Rename npcomp_rt/NpcompRt to npcomprt/Npcomprt; it was getting annoying to type the underscores/caps. - misc improvements to bash_helpers.sh
2020-07-09 08:15:40 +08:00
def LowerToNpcomprtABI : Pass<"lower-to-npcomprt-abi", "ModuleOp"> {
Totally rework RefE2E tensor to memref flow. (#42) This now gets the overall "RefE2E" compilation stack to a point that I'm fairly happy with. We simplify it by mostly embracing the "descriptor" view of the world. The overall flow is best understood by reading through the createE2ELoweringPipeline function in lib/E2E/E2E.cpp That function creates a pass pipeline that lowers from "TCF" (which is ~numpy level of abstraction) down to LLVM IR. A brief high-level summary of what happens there: 1. TCF to TCP conversion. This involves reifying error handling in the form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir 2. Lowering shape constraints. This converts shape constraints into eager error-handling code. See test/E2E/lower-shape-constraints.mlir This pass will soon go upstream. Because this lowers to std.assert, some later passes like LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this through e2e. See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test that properly aborts in case of an error. 3. Lowering tensors to memrefs. This is done via a series of passes rather than an single mega conversion. Unlike the previous code that mixed in the npcomprt ABI stuff here, it's now a very clean "pure memref" conversion. See test/E2E/lower-*-to-memref.mlir and lib/E2E/TensorToMemref/ Most of the changes are concentrated here. 4. As part of the above, we use the upstream ConvertShapeToStandard for lowering shapes. 5. We lower linalg to loops and lower loops to CFG using upstream passes. 6. Rewrite the "ABI" boundaries of the program to npcomprt data structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and how global tensor constants are represented. One of the major improvements in this commit is that now it's a very clean rewrite that just replaces memrefs on ABI boundaries with !npcomprt.tensor (before there was a get_extent function that is not needed). See test/E2E/lower-to-npcomprt-abi.mlir 7. Lower to LLVM with upstream mlir patterns + some patterns for the npcomprt lowerings. One aspect here that is still a remnant of a non-descriptor-based tensor to memref flow is the BypassShapes + LowerShapedResultsToMemref. BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results (basically a "tie_shape" kind of op), and then LowerShapedResultsToMemref uses those annotations to allocate output buffers while lowering the "tensor compute ops". Note that there are very few "tensor compute" ops currently supported (tcp.add + tcp.broadcast_to), so we just hardcode them in both passes. Realistically, I expect this to go away as we fully embrace the descriptor-based approach for simplicity, so don't look too deep into it.
2020-09-17 08:31:40 +08:00
let summary = "Lower constructs requiring runtime support to `npcomprt`";
let description = [{
We have a specialized dialect `npcomprt` which models our runtime's data
structures, and function signatures (and presumably eventually, other
ABI boundaries like external calls if we ever support it) will be
converted.
The constructs requiring runtime support are:
- function signatures / module metadata
- globals
- error handling
}];
Rework e2e flow to use new "npcomprt" This ~totally reworks the existing "runtime" stuff to be more principled and usable, such as from Python. It's still not fully production-quality, mainly in the department of memory management (e.g. it currently leaks memory; we need to figure out "who frees memrefs" + the analysis and transformation needed to do that (maybe use upstream buffer allocation pass?)). The user API is in include/npcomp/runtime/UserAPI.h, though include/npcomp/JITRuntime/JITModule.h is a friendlier wrapper. The stuff under {include,lib}/runtime is totally firewalled from the compiler and tiny (<6kB, though no attention has gone into optimizing that size). For example, we don't link in libSupport into the runtime, instead having our own bare bones replacements for basics like ArrayRef (the JITRuntime helps with bridging that gap, since it *can* depend on all common LLVM utilities). The overall features of npcomprt is that it exposes a module that with multiple function entry points. Each function has arguments and results that are tensor-valued, and npcomprt::Tensor is the runtime type that is used to interact with that (and a npcomprt::Ref<T> reference-counting wrapper is provided to wrap npcomprt::Tensor in the common case). From an implementation perspective, an npcomprt module at the LLVM/object/binary level exposes a single module descriptor struct that has pointers to other metadata (currently just a list of function metadata descriptors). All interactions with the npcomp runtime are keyed off of that module descriptor, including function lookups and dispatching. This is done to dodge platform ABI issues and also allow enough reflection to e.g. verify provided arguments. Most of the compiler-side work here was in LowerToNpcomprtABI and LowerToLLVM. Also, - Rename npcomp_rt/NpcompRt to npcomprt/Npcomprt; it was getting annoying to type the underscores/caps. - misc improvements to bash_helpers.sh
2020-07-09 08:15:40 +08:00
let constructor = "mlir::NPCOMP::createLowerToNpcomprtABIPass()";
}
def LowerAllocMemRefOps : Pass<"lower-alloc-memref-ops", "FuncOp"> {
let summary = "Lower AllocMemRefOp's";
let constructor = "mlir::NPCOMP::createLowerAllocMemRefOpsPass()";
}
def LowerToLLVM : Pass<"refback-lower-to-llvm", "ModuleOp"> {
let summary = "Lower everything to LLVM";
let constructor = "mlir::NPCOMP::createLowerToLLVMPass();";
}
// TODO: Move this pass to upstream.
// TODO: This pass will still do "folding" on all ops.
// The applyPatternsAndFoldGreedily driver will need to be changed to restrict
// folding to the specified dialects as well.
// Perhaps a better design is having a pass that uses the conversion framework.
// The the pass constructor would take a set of op names, and it would
// set up a conversion target that makes all those ops illegal, and uses
// the canonicalization patterns from those ops to legalize them.
def RestrictedCanonicalizer : Pass<"restricted-canonicalize"> {
let summary = "Canonicalize operations";
let description = [{
This pass is the same as the regular `canonicalize` pass, but it only
applies a restricted set of patterns.
This is useful when a particular canonicalization is actually needed for
correctness of a lowering flow. For such cases, running a restricted set of
canonicalizations makes it clearer which passes are needed for correctness
and which passes are "just optimizations". This helps when debugging
miscompiles and other situations where the compiler is not behaving as
expected.
}];
let constructor = "mlir::NPCOMP::createRestrictedCanonicalizerPass()";
let options = [
ListOption<"includedDialects", "included-dialects", "std::string",
"Which dialects should be canonicalized",
"llvm::cl::MiscFlags::CommaSeparated">
];
}
#endif // NPCOMP_REFBACKEND_PASSES