Implement the lazytensor package (#331)

Implement the `lazytensor` python package for converting
lazy computations captured by the Lazy Tensor Core into MLIR.
This PR also fixes a few things with `torchfx` and its example

README.md

@@ -104,13 +104,40 @@ jupyter notebook
 
 ### TorchFX
 
-TODO
+The `examples` folder includes the Python package `torchfx`, which is a functional prototype of a TorchFX to MLIR pipeline. The main entry point into the `torchfx` package is the `torchfx.builder` module, which includes a function for converting the output of a TorchFX trace into MLIR. Currently, the number of PyTorch operations supported is very limited, but will be expanded in the future.
+
+#### Example usage of `torchfx`
+
+The `examples` folder includes scripts `torchfx_*.py` showing how to use the TorchFX to MLIR pipeline. In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-env) instructions, and add `/path/to/torch-mlir/examples` to your `PYTHONPATH`.
+
+Then, run
+
+```
+python torchfx_example_name.py
+```
+
+replacing `torchfx_example_name.py` with the actual `torchfx` example you want to run.
 
 
 ### Lazy Tensor Core
 
-TODO
+The `examples` folder includes the Python package `lazytensor`, which implements a Lazy Tensor Core (LTC) to MLIR pipeline. The main entry point into the `lazytensor` package is the `lazytensor.builder`, which includes the function `build_module` that takes a computation captured and converted to TorchScript IR by LTC, and converts it to MLIR.
 
+#### Example usage of `lazytensor`
+
+The `examples` folder includes scripts `lazytensor_*.py` showing how to use the Lazy Tensor to MLIR pipeline. The examples depend on the Lazy Tensor Core (LTC) of PyTorch. For information on how to obtain LTC, see [here](https://github.com/pytorch/pytorch/blob/lazy_tensor_staging/lazy_tensor_core/QUICKSTART.md). 
+
+In order to run the examples, make sure you've setup your `PYTHONPATH` by following [these](#setup-env) instructions, and also add the following to your `PYTHONPATH`:
+1. `/path/to/torch-mlir/examples`
+2. `/path/to/pytorch/lazy_tensor_core`
+
+Then, run
+
+```
+python lazytensor_example_name.py
+```
+
+replacing `lazytensor_example_name.py` with the actual `lazytensor` example you want to run.
 
 ## Repository Layout
 

examples/lazytensor/builder.py

@@ -0,0 +1,64 @@
+# 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
+"""
+Translator from torch.jit.ScriptFunction to MLIR.
+
+The following defines a function that take a torch.jit.ScriptFunction
+and converts it into an MLIR module.
+
+The expected use for this module is to use the function
+`build_module(jit_function: torch.jit.ScriptFunction
+              annotation: Annotation) -> ir.Module`
+to convert the TorchScript function into MLIR using the `torch`
+dialect.
+"""
+
+from typing import Optional
+
+from torch.jit import ScriptFunction
+
+from torch_mlir.dialects.torch.importer.jit_ir import ModuleBuilder
+from torch_mlir.dialects.builtin import FuncOp
+from torch_mlir import ir
+
+from utils.annotator import AnnotationConverter as ac
+from utils.annotator import Annotation
+
+def _get_func_op_with_name(module: ir.Module, name: str) -> Optional[FuncOp]:
+    with module.context:
+        name_attr = ir.StringAttr.get(name)
+    for op in module.body.operations:
+        if isinstance(op, FuncOp) and op.name == name_attr:
+            return op
+
+    return None
+
+def build_module(jit_function: ScriptFunction,
+                 annotation: Annotation) -> ir.Module:
+    """
+    Translate input function into an MLIR module in the `torch` dialect.
+
+    Parameters
+    ----------
+    jit_function: ScriptFunction
+        Function in TorchScript IR to turn into MLIR.
+    annotation: Annotation
+        Annotation object representing the types of
+        the operands of `jit_function`.
+
+    Returns
+    -------
+    ir.Module
+        Translation of the input module into an MLIR module
+    """
+    mb = ModuleBuilder()
+    mb.import_function(jit_function)
+
+    func_op = _get_func_op_with_name(mb.module, jit_function.name)
+    assert func_op is not None, 'Unable to find FuncOp in new module. Make sure function was imported correctly into ModuleBuilder'
+
+    arg_attrs = ac.to_mlir_array_attr(annotation, mb.context)
+    func_op.attributes['arg_attrs'] = arg_attrs
+
+    return mb.module

examples/lazytensor_tanh.py

@@ -0,0 +1,81 @@
+# 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
+"""
+Example of taking a Lazy Tensor computation and compiling it using torch-mlir.
+
+This example depends on the Lazy Tensor Core (LTC) of PyTorch. For information
+on how to obtain LTC, see here:
+https://github.com/pytorch/pytorch/blob/lazy_tensor_staging/lazy_tensor_core/QUICKSTART.md
+
+To run the example, make sure the following are in your PYTHONPATH:
+    1. /path/to/torch-mlir/examples
+    2. /path/to/pytorch/lazy_tensor_core
+    3. /path/to/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir
+
+then, simply call `python lazytensor_tanh.py`.
+"""
+
+import numpy as np
+import torch
+import lazy_tensor_core as ltc
+from torch._C import CompilationUnit
+
+from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend \
+    import RefBackendLinalgOnTensorsBackend
+from torch_mlir.passmanager import PassManager
+
+from utils.annotator import Annotation
+from utils.torch_mlir_types import TorchTensorType
+from lazytensor.builder import build_module
+
+ltc._LAZYC._ltc_init_ts_backend()
+device = 'lazy'
+dtype = torch.float32
+shape = (2, 3)
+
+x = torch.randn(shape, device=device, dtype=dtype)
+y = torch.randn(shape, device=device, dtype=dtype)
+
+def computation(x, y):
+    return y * x.tanh()
+
+# Capture lazy computation and convert to TorchScript IR
+graph_str = ltc._LAZYC._get_ltc_tensors_backend([computation(x, y)])
+print("LAZY GRAPH")
+print(graph_str)
+graph = torch._C.parse_ir(graph_str)
+
+# Create a torch.jit.ScriptFunction out of the graph
+cu = CompilationUnit()
+func_name = 'my_method'
+script_function = cu.create_function(func_name, graph)
+
+# `build_module` takes he torch.jit.ScriptFunction and the
+# annotation on the operand types, and outputs an `ir.Module`
+# with a single function representing the ScriptFunction in
+# the torch MLIR dialect
+func_annotation = Annotation([TorchTensorType(shape=shape, dtype=torch.float),
+                              TorchTensorType(shape=shape, dtype=torch.float)])
+mlir_module = build_module(script_function, func_annotation)
+
+print("MLIR")
+mlir_module.dump()
+
+# Compile the torch MLIR and execute the compiled program
+with mlir_module.context:
+    pm = PassManager.parse('torchscript-function-to-linalg-on-tensors-backend-pipeline')
+pm.run(mlir_module)
+
+print("BEFORE LINALG-ON-TENSORS BACKEND PIPELINE")
+print(mlir_module)
+
+backend = RefBackendLinalgOnTensorsBackend()
+compiled = backend.compile(mlir_module)
+jit_module = backend.load(compiled)
+
+print("\n\nRunning Example Calculation")
+print("Compiled result:")
+print(jit_module.my_method(x.cpu().numpy(), y.cpu().numpy()))
+print("Expected result:")
+print(computation(x, y))

examples/torchfx/annotator.py

@@ -1,20 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-#
-# pylint: disable=no-member, no-name-in-module, invalid-name, missing-function-docstring, fixme
-
-from typing import Iterable, Union
-from torch.fx import GraphModule
-from .torch_mlir_types import TorchTensorType, PythonType
-
-def annotate_forward_args(module: GraphModule,
-             types: Iterable[Union[TorchTensorType, type]]
-             ) -> GraphModule:
-    operands = filter(lambda node: node.op == 'placeholder', module.graph.nodes)
-    for operand, type_ in zip(operands, types):
-        if isinstance(type_, type):
-            type_ = PythonType(type_)
-        operand.update_kwarg('torch_mlir_type', type_)
-
-    return module

examples/torchfx/builder.py

@@ -1,3 +1,6 @@
+# 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
 """
 Translator from Torch.FX to MLIR.
 
@@ -9,9 +12,6 @@ The expected use for this module is to use the function
 `build_module(py_module: torch.fx.GraphModule) -> ir.Module`
 to convert the output from the tracer into MLIR using the `torch`
 dialect.
-
-This file is licensed under a pytorch-style license
-See frontends/pytorch/LICENSE for license information.
 """
 
 # pylint: disable=no-member, no-name-in-module, invalid-name, fixme
@@ -27,7 +27,8 @@ from torch_mlir.dialects import builtin, std
 import torch.fx
 from torch.fx.experimental.fx_acc import acc_ops
 
-from .torch_mlir_types import TorchTensorType, PythonType, TorchNnModuleType
+from utils.torch_mlir_types import TorchTensorType, PythonType, \
+    TorchNnModuleType
 
 Environment = MutableMapping[torch.fx.Node, ir.Value]
 
@@ -388,7 +389,7 @@ an argument named `input`'
 
 
 @_add_handler(ACC_OP_HANDLERS, acc_ops.add)
-def _add_handler(func_builder: _ForwardFunctionBuilder,
+def _add_tensor_handler(func_builder: _ForwardFunctionBuilder,
                  args: Mapping[str, ir.Value]) -> ir.OpResult:
     input_arg = args.get('input')
     other_arg = args.get('other')
@@ -396,10 +397,10 @@ def _add_handler(func_builder: _ForwardFunctionBuilder,
         'A call to this handler must include an argument named `input` \
 and an argument named `other`'
     tensor_type = TorchTensorType().to_mlir(func_builder.context)
-    int_type = PythonType(int).to_mlir(func_builder.context)
+    torch_int_type = PythonType(int).to_mlir(func_builder.context)
-    attr_type = ir.Type.parse('i64', func_builder.context)
+    int_type = ir.Type.parse("i64", context=func_builder.context)
-    int_attr = ir.IntegerAttr.get(attr_type, 1)
+    int_attr = ir.IntegerAttr.get(int_type, 1)
-    alpha_arg = torch_d.ConstantIntOp(int_type,
+    alpha_arg = torch_d.ConstantIntOp(torch_int_type,
                                       int_attr,
                                       loc=func_builder.loc,
                                       ip=func_builder.func_ip).result

examples/torchfx/examples/example_add_tanh_sigmoid.py

@@ -1,59 +0,0 @@
-# -*- Python -*-
-# This file is licensed under a pytorch-style license
-# See frontends/pytorch/LICENSE for license information.
-
-# From the torch-mlir root, run with:
-# `python -m examples.torchfx.examples.example_add_tanh_sigmoid`
-# (after setting up python environment with write_env_file.sh)
-
-import torch
-from torch.fx.experimental.fx_acc import acc_tracer
-import torch_mlir
-from torch_mlir.dialects.torch import register_dialect
-from torch_mlir.passmanager import PassManager
-from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend import RefBackendLinalgOnTensorsBackend
-
-from ..builder import build_module
-from ..annotator import annotate_forward_args
-from ..torch_mlir_types import TorchTensorType
-
-
-class MyModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x, y):
-        # TODO: Debug issue with RefBackend
-        #return torch.tanh(x) + torch.sigmoid(y)
-        return torch.tanh(x)
-
-
-module = MyModule()
-traced_module = acc_tracer.trace(module, [torch.Tensor(2,2),
-                                          torch.Tensor(2,2)])
-
-print("TRACE")
-arg_type = TorchTensorType(shape=[None, None], dtype=torch.float)
-traced_module = annotate_forward_args(traced_module, [arg_type, arg_type])
-print(traced_module.graph)
-torch_mlir_module = build_module(traced_module)
-
-print("\n\nTORCH MLIR")
-torch_mlir_module.dump()
-print(torch_mlir_module.operation.verify())
-
-with torch_mlir_module.context:
-    pm = PassManager.parse('torchscript-to-linalg-on-tensors-backend-pipeline')
-    pm.run(torch_mlir_module)
-
-print("\n\nLOWERED MLIR")
-torch_mlir_module.dump()
-
-backend = RefBackendLinalgOnTensorsBackend()
-compiled = backend.compile(torch_mlir_module)
-jit_module = backend.load(compiled)
-
-print("\n\nRunning Forward Function")
-t = torch.rand((2, 2), dtype=torch.float)
-print("Compiled result:\n", jit_module.forward(t.numpy(), t.numpy()))
-print("\nExpected result:\n", module.forward(t, t))

examples/torchfx/loc.py

@@ -1,6 +1,7 @@
 # -*- Python -*-
-# This file is licensed under a pytorch-style license
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
-# See frontends/pytorch/LICENSE for license information.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 #
 # pylint: disable=no-member, no-name-in-module, invalid-name, missing-function-docstring, fixme
 

examples/torchfx_add_tanh_sigmoid.py

@@ -0,0 +1,67 @@
+# -*- Python -*-
+# 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
+"""
+Example of taking a moduled traced by TorchFX and compiling it using torch-mlir.
+
+To run the example, make sure the following are in your PYTHONPATH:
+    1. /path/to/torch-mlir/examples
+    2. /path/to/torch-mlir/build/tools/torch-mlir/python_packages/torch_mlir
+
+then, simply call `python torchfx_add_tanh_sigmoid.py`.
+"""
+
+import torch
+import numpy as np
+from torch.fx.experimental.fx_acc import acc_tracer
+
+from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend \
+    import RefBackendLinalgOnTensorsBackend
+from torch_mlir.passmanager import PassManager
+
+from torchfx.builder import build_module
+from utils.annotator import annotate_forward_args
+from utils.torch_mlir_types import TorchTensorType
+
+
+class MyModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, y):
+        # TODO: Debug issue with RefBackend
+        #return torch.tanh(x) + torch.sigmoid(y)
+        return torch.tanh(x)
+
+
+module = MyModule()
+traced_module = acc_tracer.trace(module, [torch.Tensor(2,2),
+                                          torch.Tensor(2,2)])
+
+print("TRACE")
+arg_type = TorchTensorType(shape=[None, None], dtype=torch.float)
+traced_module = annotate_forward_args(traced_module, [arg_type, arg_type])
+print(traced_module.graph)
+mlir_module = build_module(traced_module)
+
+print("\n\nTORCH MLIR")
+mlir_module.dump()
+print(mlir_module.operation.verify())
+
+with mlir_module.context:
+    pm = PassManager.parse('torchscript-module-to-linalg-on-tensors-backend-pipeline')
+pm.run(mlir_module)
+
+print("\n\nLOWERED MLIR")
+mlir_module.dump()
+
+backend = RefBackendLinalgOnTensorsBackend()
+compiled = backend.compile(mlir_module)
+jit_module = backend.load(compiled)
+
+print("\n\nRunning Forward Function")
+np_t = np.random.rand(2, 2).astype(dtype=np.float32)
+t = torch.tensor(np_t, dtype=torch.float)
+print("Compiled result:\n", jit_module.forward(np_t, np_t))
+print("\nExpected result:\n", module.forward(t, t))

examples/utils/annotator.py

@@ -0,0 +1,58 @@
+# -*- Python -*-
+# 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
+#
+# pylint: disable=no-member, no-name-in-module, invalid-name, missing-function-docstring, fixme
+
+from typing import Iterable, Union
+from torch.fx import GraphModule
+from torch_mlir import ir
+from torch_mlir.dialects import builtin
+from .torch_mlir_types import TorchTensorType, PythonType
+
+class Annotation:
+    def __init__(self, types: Iterable[Union[TorchTensorType, type]]):
+        self.types = list(map(lambda t:
+                              PythonType(t) if isinstance(t, type) else t,
+                              types))
+
+    def __str__(self):
+        result = f'Annotation instance with {len(self.types)} types\n'
+        for e, type_ in enumerate(self.types):
+            result += f'    Type of argument {e + 1}: {str(type_)}\n'
+        return result
+
+    def __iter__(self):
+        return iter(self.types)
+
+
+class AnnotationConverter:
+    @staticmethod
+    def to_mlir_array_attr(annotation: Annotation,
+                           context: ir.Context) -> ir.ArrayAttr:
+        dict_attrs = []
+        for type_ in annotation:
+            if not isinstance(type_, TorchTensorType):
+                dict_attrs.append(ir.DictAttr.get({}, context=context))
+                continue
+
+            ir_type = type_.to_mlir(context)
+            with context:
+                type_attr = ir.TypeAttr.get(ir_type)
+                dict_attr = ir.DictAttr.get({'torch.type_bound': type_attr})
+                dict_attrs.append(dict_attr)
+
+        return ir.ArrayAttr.get(dict_attrs, context=context)
+
+
+def annotate_forward_args(module: GraphModule,
+             types: Iterable[Union[TorchTensorType, type]]
+             ) -> GraphModule:
+    operands = filter(lambda node: node.op == 'placeholder', module.graph.nodes)
+    for operand, type_ in zip(operands, types):
+        if isinstance(type_, type):
+            type_ = PythonType(type_)
+        operand.update_kwarg('torch_mlir_type', type_)
+
+    return module

examples/utils/torch_mlir_types.py

@@ -1,8 +1,9 @@
+# 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
+#
+# pylint: disable=no-member, no-name-in-module, invalid-name, missing-function-docstring, fixme
 """
--*- Python -*-
-This file is licensed under a pytorch-style license
-See frontends/pytorch/LICENSE for license information.
-
 The following defines a set of classes for converting
 types used by Python and PyTorch into MLIR types from the
 `torch` dialect.
@@ -16,8 +17,6 @@ Information about what types are supported by each class
 can be found in docstrings of each of the classes.
 """
 
-# pylint: disable=no-member, no-name-in-module, invalid-name, missing-function-docstring, fixme
-
 import abc
 from typing import Any, Optional, Iterable
 
@@ -62,6 +61,9 @@ class TorchTensorType(TorchMlirType):
             err = "If shape is specified, dtype must also be specified"
             raise TorchTensorTypeError(err)
 
+    def __str__(self):
+        return f'Torch Tensor (shape={self.shape}, dtype={self.dtype})'
+
     def to_mlir(self, context: ir.Context) -> ir.Type:
         if self.dtype is None:
             return ir.Type.parse('!torch.tensor', context=context)
@@ -90,6 +92,9 @@ class TorchNnModuleType(TorchMlirType):
     def __init__(self, module_name: str):
         self.module_name = module_name
 
+    def __str__(self):
+        return "torch.nn.Module"
+
     def to_mlir(self, context: ir.Context) -> ir.Type:
         return ir.Type.parse(f'!torch.nn.Module<"{self.module_name}">',
                              context=context)
@@ -111,6 +116,9 @@ class PythonType(TorchMlirType):
     def __init__(self, type_: Any):
         self.type_ = type_
 
+    def __str__(self):
+        return str(self.type_)
+
     def to_mlir(self, context: ir.Context) -> ir.Type:
         asm = self._type_to_asm_dict.get(self.type_)
         if asm is None:

include/torch-mlir/Dialect/TorchConversion/Transforms/Passes.h

@@ -18,10 +18,17 @@ namespace mlir {
 namespace torch {
 namespace TorchConversion {
 
-/// Creates a pipeline that lowers the object graph IR that is produced by
+/// Creates a pipeline that lowers the object graph IR that is given by a
-/// TorchScript import into the form expected by
+/// TorchScript jit.ScriptModule into the form expected by
 /// torch-verify-linalg-on-tensors-verify-backend-contract.
-void createTorchScriptToLinalgOnTensorsBackendPipeline(
+void createTorchScriptModuleToLinalgOnTensorsBackendPipeline(
+    OpPassManager &pm,
+    const torch::Torch::TorchLoweringPipelineOptions &options);
+
+/// Creates a pipeline that lowers the object graph IR that is given by a
+/// TorchScript jit.ScriptFunction into the form expected by
+/// torch-verify-linalg-on-tensors-verify-backend-contract.
+void createTorchScriptFunctionToLinalgOnTensorsBackendPipeline(
     OpPassManager &pm,
     const torch::Torch::TorchLoweringPipelineOptions &options);
 

lib/Dialect/Torch/Transforms/Passes.cpp

@@ -56,8 +56,6 @@ void mlir::torch::Torch::createTorchScriptToTorchBackendPipeline(
   // calls, so inline everything.
   // TODO: Improve shape inference.
   pm.addPass(createInlinerPass());
-  // Incorporate user annotations and remove signature Python-isms.
-  pm.addPass(createAdjustCallingConventionsPass());
 
   createGlobalizedModuleToTorchBackendPipeline(pm, options);
 }
@@ -86,6 +84,9 @@ void mlir::torch::Torch::createGlobalizedModuleToTorchBackendPipeline(
   // Please try to keep this list somewhat up to date when adding
   // "optimize hard enough that it works" transformations.
 
+  // Incorporate user annotations and remove signature Python-isms.
+  pm.addPass(createAdjustCallingConventionsPass());
+
   if (options.optimize) {
     // Inline global slots, which for most inference scenarios deletes them.
     // This also exposes more information to intraprocedural transformations

lib/Dialect/TorchConversion/Transforms/Passes.cpp

@@ -33,18 +33,17 @@ namespace {
 void mlir::torch::registerTorchConversionPasses() {
   ::registerPasses();
   mlir::PassPipelineRegistration<Torch::TorchLoweringPipelineOptions>(
-      "torchscript-to-linalg-on-tensors-backend-pipeline",
+      "torchscript-module-to-linalg-on-tensors-backend-pipeline",
-      "Pipeline lowering torch object graph to linalg-on-tensors backend format.",
+      "Pipeline lowering torch object graph representing a torch.jit.ScriptModule to linalg-on-tensors backend format.",
-      mlir::torch::TorchConversion::createTorchScriptToLinalgOnTensorsBackendPipeline);
+      TorchConversion::createTorchScriptModuleToLinalgOnTensorsBackendPipeline);
+  mlir::PassPipelineRegistration<Torch::TorchLoweringPipelineOptions>(
+      "torchscript-function-to-linalg-on-tensors-backend-pipeline",
+      "Pipeline lowering a flat list of functions representing a torch.jit.ScriptFunction to linalg-on-tensors backend format.",
+      TorchConversion::createTorchScriptFunctionToLinalgOnTensorsBackendPipeline);
 }
 
-void mlir::torch::TorchConversion::createTorchScriptToLinalgOnTensorsBackendPipeline(
+static void createTorchBackendToLinalgOnTensorsBackendPipeline(
     OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
-
-  // Conversion to the linalg-on-tensors backend contract starts from the Torch
-  // backend contract.
-  Torch::createTorchScriptToTorchBackendPipeline(pm, options);
-
   // Check some invariants to catch errors in a clear way.
   pm.addPass(
       TorchConversion::createVerifyInvariantsBeforeBackendLoweringPass());
@@ -79,3 +78,21 @@ void mlir::torch::TorchConversion::createTorchScriptToLinalgOnTensorsBackendPipe
   // correct form.
   pm.addPass(TorchConversion::createVerifyLinalgOnTensorsBackendContractPass());
 }
+
+void TorchConversion::createTorchScriptModuleToLinalgOnTensorsBackendPipeline(
+    OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
+
+  // Conversion to the linalg-on-tensors backend contract starts from the Torch
+  // backend contract.
+  Torch::createTorchScriptToTorchBackendPipeline(pm, options);
+  createTorchBackendToLinalgOnTensorsBackendPipeline(pm, options);
+}
+
+void TorchConversion::createTorchScriptFunctionToLinalgOnTensorsBackendPipeline(
+    OpPassManager &pm, const Torch::TorchLoweringPipelineOptions &options) {
+
+  // Conversion to the linalg-on-tensors backend contract starts from the Torch
+  // backend contract.
+  Torch::createGlobalizedModuleToTorchBackendPipeline(pm, options);
+  createTorchBackendToLinalgOnTensorsBackendPipeline(pm, options);
+}

python/torch_mlir_e2e_test/torchscript/configs/linalg_on_tensors_backend.py

@@ -91,7 +91,7 @@ Diagnostics:
             sys.stderr = StringIO()
             asm_for_error_report = mb.module.operation.get_asm(
                 large_elements_limit=10, enable_debug_info=True)
-            pipeline_str = "torchscript-to-linalg-on-tensors-backend-pipeline"
+            pipeline_str = "torchscript-module-to-linalg-on-tensors-backend-pipeline"
             # Lower module in place to make it ready for compiler backends.
             with mb.module.context:
                 pm = PassManager.parse(pipeline_str)