mirror of https://github.com/llvm/torch-mlir
update llvm tag to a2620e00. (#1567)
- also update MHLO to 57ba12a2(branch greencommit/2022-11-07-a2620e00) - change -pass-pipeline format to make tests pass.pull/1571/head snapshot-20221110.653
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4f173c6e0f
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@ -1 +1 @@
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Subproject commit 74fb770de9399d7258a8eda974c93610cfde698e
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Subproject commit a2620e00ffa232a406de3a1d8634beeda86956fd
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@ -1 +1 @@
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Subproject commit 36238f16441cd1a884af988d4400d2ebb0c75bbc
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Subproject commit 57ba12a2a1934c3c9fc3cd1580f28f0c233f41d4
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@ -346,7 +346,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
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option_string = "{backend-legal-ops=" + ",".join(backend_legal_ops) + "}"
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run_pipeline_with_repro_report(
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mb.module,
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f"torchscript-module-to-torch-backend-pipeline{option_string}",
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f"builtin.module(torchscript-module-to-torch-backend-pipeline{option_string})",
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"Lowering TorchScript IR -> Torch Backend IR",
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)
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@ -361,7 +361,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
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if output_type == OutputType.TOSA:
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run_pipeline_with_repro_report(
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mb.module,
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"torch-backend-to-tosa-backend-pipeline",
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"builtin.module(torch-backend-to-tosa-backend-pipeline)",
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"Lowering Torch Backend IR -> TOSA Backend IR")
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if verbose:
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print("\n====================")
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@ -372,7 +372,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
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if output_type == OutputType.LINALG_ON_TENSORS:
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run_pipeline_with_repro_report(
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mb.module,
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"torch-backend-to-linalg-on-tensors-backend-pipeline",
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"builtin.module(torch-backend-to-linalg-on-tensors-backend-pipeline)",
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"Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR")
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if verbose:
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print("\n====================")
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@ -383,7 +383,7 @@ PyTorch TorchScript module -> torch-mlir Object Graph IR import failed with:
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elif output_type == OutputType.MHLO:
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run_pipeline_with_repro_report(
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mb.module,
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"torch-backend-to-mhlo-backend-pipeline",
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"builtin.module(torch-backend-to-mhlo-backend-pipeline)",
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"Lowering Torch Backend IR -> MHLO Backend IR")
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if verbose:
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print("\n====================")
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@ -1261,7 +1261,7 @@ def main(args):
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for function in torch.jit._state._python_cu.get_functions():
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mb.import_function(function)
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# Clean up the IR a bit before writing it out.
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pm = PassManager.parse("canonicalize", context=mb.module.context)
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pm = PassManager.parse("builtin.module(canonicalize)", context=mb.module.context)
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pm.run(mb.module)
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# Munge the IR a bit to make it more systematically accessible.
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asm = mb.module.operation.get_asm()
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@ -67,7 +67,7 @@ class EagerModeRefBackend(TorchMLIREagerBackend):
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if module_hash not in self.module_to_refbackend_invoker:
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run_pipeline_with_repro_report(
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imported_module,
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"torch-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline",
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"builtin.module(torch-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline)",
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"EagerMode",
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)
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self.module_to_refbackend_invoker[module_hash] = _ref_backend.load(
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@ -114,7 +114,7 @@ class RefBackendInvoker:
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return invoke
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LOWERING_PIPELINE = ",".join([
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LOWERING_PIPELINE = "builtin.module(" + ",".join([
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"func.func(refback-generalize-tensor-pad)",
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# Bufferize.
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"func.func(scf-bufferize)",
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@ -152,7 +152,7 @@ LOWERING_PIPELINE = ",".join([
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"convert-func-to-llvm",
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"convert-cf-to-llvm",
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"reconcile-unrealized-casts",
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])
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]) + ")"
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class RefBackendLinalgOnTensorsBackend(LinalgOnTensorsBackend):
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@ -36,7 +36,7 @@ class LinalgOnTensorsMhloBackend(MhloBackend):
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"""
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run_pipeline_with_repro_report(
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imported_module,
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"func.func(symbolic-shape-optimization),func.func(hlo-legalize-to-linalg),func.func(canonicalize)",
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"builtin.module(func.func(symbolic-shape-optimization),func.func(hlo-legalize-to-linalg),func.func(canonicalize))",
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"Lowering MLIR-HLO to Linalg-on-Tensors")
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return self.refbackend.compile(imported_module)
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@ -43,20 +43,20 @@ class LinalgOnTensorsTosaBackend(TosaBackend):
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# that depend on TOSA as well as TOSA-to-Standard.
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run_pipeline_with_repro_report(
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imported_module,
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"func.func(tosa-to-arith)",
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"builtin.module(func.func(tosa-to-arith))",
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"Lowering TOSA to Arith")
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# Named ops must be legalized prior to general tosa-to-linalg
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run_pipeline_with_repro_report(
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imported_module,
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"func.func(tosa-to-linalg-named)",
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"builtin.module(func.func(tosa-to-linalg-named))",
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"Lowering TOSA to Linalg-on-Tensors for Named Ops")
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# TOSA-to-LinAlg may generate tosa.const() ops, so we want to lower them
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# to arith.constants here before proceeding further.
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run_pipeline_with_repro_report(
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imported_module,
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"func.func(tosa-to-tensor),func.func(tosa-to-linalg),func.func(tosa-to-arith)",
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"builtin.module(func.func(tosa-to-tensor),func.func(tosa-to-linalg),func.func(tosa-to-arith))",
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"Lowering TOSA to Linalg-on-Tensors")
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return self.refbackend.compile(imported_module)
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@ -1,4 +1,4 @@
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// RUN: torch-mlir-opt -pass-pipeline='torch-function-to-torch-backend-pipeline{backend-legal-ops=torch.aten.square,torch.aten.argmax}' -split-input-file %s | FileCheck %s
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// RUN: torch-mlir-opt -pass-pipeline='builtin.module(torch-function-to-torch-backend-pipeline{backend-legal-ops=torch.aten.square,torch.aten.argmax})' -split-input-file %s | FileCheck %s
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// CHECK-LABEL: func.func @torch.aten.square
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func.func @torch.aten.square(%arg0: !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
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