[mlir][docs] Bring bufferization docs up to date.

[silvas]

Summary

This spilts out BufferDeallocationInternals.md, since buffer
deallocation is not part of bufferization per se.

[silvas]

Remove old stray stuff and add a TOC

[dfki-jugr]

mlir/docs/Bufferization.md

6–50

the the -> the, or: converting tensor type to memref type.

[silvas]

Fix dfki-jugr@'s comments.

[herhut]

Could you wordwrap this to make it easier to read?

mlir/docs/Bufferization.md

6

Nit: Could you wordwrap this?

Also, straightforward is a big word.

147

to do as a single sounds wrong.

[silvas]

Address Stephan's comments.

mlir/docs/BufferDeallocationInternals.md

-# Bufferization on MLIR
-
-The general task of bufferization is to move SSA values (like tensors) into
-allocated memory buffers that have to be freed when they are no longer needed.
-This also involves the placement of copies to clone contents of allocated
-memory buffers at specific locations (similar to register allocation). On the
-one hand, these copies are needed to ensure the right behavior of a program, on
-the other hand, introducing several aliases for a certain buffer could lead to
-a wrong freeing of buffers. Therefore, we have to take care of them and the
-program structure. The introduction of copies solves this problem. Several
-unnecessary introduced copies during this process can be eliminated afterwards.
-
-```mlir
-func @func_on_tensors(%source: tensor<4xf32>) -> tensor<4xf32> {
-  %0 = mhlo.exp %source : (tensor<4xf32>) -> (tensor<4xf32>)
-  return %0 : tensor<4xf32>
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @func_on_buffers(%source: memref<4xf32>, %target: memref<4xf32>) {
-  %0 = alloc() : memref<4xf32>
-  lmhlo.exp %source, %0 : (memref<4xf32>, memref<4xf32>) -> ()
-  lmhlo.copy %0, %target : (memref<4xf32>, memref<4xf32>) -> ()
-  dealloc %0 : memref<4xf32>
-  return
-}
-```
-
-In general, Bufferization is split into three separate phases: a preparation
-phase, a bufferization phase and a post-processing phase. The assignment
-process happens during dialect conversion and allocates buffers for each value
-that should be moved into a memory buffer. This has to be implemented by each
-dialect using the following tools and patterns. Thereby, all  operations on
-memory buffers have to be changed to `memref<T>` types (see Preparation Phase).
-Afterwards, the placement transformation (see BufferDeallocation) adds all
-required deallocation operations, temporary buffers and copy operations
-automatically.
-
-## Preparation Phase
-
-In order to apply the BufferDeallocation code transformation, the input MLIR
-program needs to leverage allocations (buffers in general) and `memref<T>`
-types(as outlined above). If your input program does not work on buffers, you
-need to perform this preparation step in order to port it to the “buffer
-world”. This is a user-defined preparation step that is intended to be applied
-during dialect conversion. The user has to take care for the right conversion
-by providing conversion patterns relying on a type converter to assign buffers.
-
-A necessary step is to apply type and function signature conversion.
-Furthermore, after changing all function signatures, the associated return and
-call operations must comply with the new corresponding function signatures. For
-this purpose, three operation conversion patterns are introduced:
-
-* BufferizeFuncOpConverter
-* BufferizeReturnOpConverter
-* BufferizeCallOpConverter
-
-In order to use these conversion patterns, the user needs to define a custom
-BufferizeTypeConverter implementation.
-
-### BufferizeTypeConverter
-
-The BufferizeTypeConverter is an extension to the TypeConverter class that
-provides additional functionality for dialect developers to decide on the
-signature of a function. The extra features include:
-
-* `setResultConversionKind` to decide on a function result after the conversion
-with a specific type to be appended to the function argument list as an output
-argument or remains as a function result.
-* define their own callback function for type or value unwrapping.
-
-### ResultConversionKind
-
-ResultConversionKind is an enum with two values
-
-* AppendToArgumentList
-* KeepAsFunctionResult
-
-that defines how BufferizeFuncOpConverter should handle function results in
-order to convert the signature of the function. The other two operation
-conversion patterns also use ResultConversionKind to adapt themselves with the
-new function signature.
-
-`ResultConversionKind` can be set using `setResultConversionKind`, which needs
-two template parameters that correspond to the types before and after type
-conversion. This mapping specifies whether the resulting type should stay as a
-function result or should be appended to the arguments list after the
-conversion is done. Note that the default value for unspecified mappings is
-`KeepAsFunctionResult`. For instance, the following command updates the
-`BufferizeTypeConverter` instance that defines all MemRefType function results
-(converted from `RankedTensorTypes`). These results should be appended to the
-function argument list in BufferizeFuncOpConverter:
-
-```mlir
-converter.setResultConversionKind<RankedTensorType, MemRefType>(
-        BufferizeTypeConverter::AppendToArgumentsList);
-```
-
-## Callbacks for Unpacking Types
-
-```mlir
-func @func_on_tensors(%arg0: tuple<i1,f32>) -> (tuple<tensor<10xf32>, tensor<5xf32>>) {
-  ...
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @func_on_buffers(%arg0: i1, %arg1: f32, %target0: memref<10xf32>, %target1: memref<5xf32>) {
-  ...
-}
-```
-
-BufferizeFuncOpConverter is also able to unpack the types of arguments and
-results of a function during function signature conversion. In the example
-above, it unwraps the tuple type and converts the type of each constituent.
-
-For this purpose, users can provide custom callbacks by using
-`addDecomposeTypeConversion` for the `BufferizeTypeConverter` instance to show
-how a specific type (i.e. TupleType) can be unpacked. However, when a type
-decomposition is provided, there are two additional callbacks that have to be
-defined as well. They specify how to pack or unpack values of that particular
-type. These two callbacks can be provided by the `addArgumentMaterialization`
-(packing) **and** `addDecomposeValueConversion` (unpacking) functions:
-
-The following piece of code demonstrates this functionality by flattening a
-`TupleType`.
-
-```mlir
-converter.addDecomposeTypeConversion(
-        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
-          tupleType.getFlattenedTypes(types);
-          return success();
-        });
-
-converter.addArgumentMaterialization(
-        [](OpBuilder &builder, TupleType resultType, ValueRange inputs,
-           Location loc) -> Optional<Value> {
-          if (inputs.size() == 1)
-            return llvm::None;
-          TypeRange TypeRange = inputs.getTypes();
-          SmallVector<Type, 2> types(TypeRange.begin(), TypeRange.end());
-          TupleType tuple = TupleType::get(types, builder.getContext());
-          mlir::Value value = builder.create<MakeTupleOp>(loc, tuple, inputs);
-          return value;
-        });
-
-converter.addDecomposeValueConversion([](OpBuilder &builder, Location loc,
-                                         TupleType resultType, Value value,
-                                         SmallVectorImpl<Value> &values) {
-      for (unsigned i = 0, e = resultType.size(); i < e; ++i) {
-        Value res = builder.create<GetTupleElementOp>(
-            loc, resultType.getType(i), value, builder.getI32IntegerAttr(i));
-        values.push_back(res);
-      }
-      return success();
- });
-```
-
-In the scope of these callback functions, the elements of a tuple value can be
-decomposed using `GetTupleElementOp`. Conversely, `MakeTupleOp` is used to pack
-a list of values as a single tuple type.
-
-### Bufferization Operation Conversion Patterns
-
-The following conversion patterns can be used to conveniently transform the
-signature of a function, the return and call operations:
-
-* `BufferizeFuncOpConverter`
-* `BufferizeReturnOpConverter`
-* `BufferizeCallOpConverter`
-
-Any combination of these conversion patterns can be specified by the user. If
-you need to apply  all of these operation converters, you can use
-`populateWithBufferizeOpConversionPatterns` which sets up all converters.
-
-### BufferizeFuncOpConverter
-
-The BufferizeFuncOpConverter is the actual function operation converter that
-applies signature conversion by using a previously defined
-`BufferizeTypeConverter`.
-
-In the following example, we configure a `BufferizeTypeConverter` instance such
-that
-
-* all RankedTensorTypes should be converted to MemRefTypes.
-* all function results that are results of type conversion from
-RankedTensorTypes to MemRefTypes should be appended to the function argument
-list.
-* all TupleTypes should be flattened and decomposed to its constituents.
-
-```mlir
-converter.addConversion([](RankedTensorType type) {
-    return (Type)MemRefType::get(type.getShape(), type.getElementType());
-  });
-converter.setResultConversionKind<RankedTensorType, MemRefType>(
-        BufferizeTypeConverter::AppendToArgumentsList);
-converter.addDecomposeTypeConversion(
-        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
-          tupleType.getFlattenedTypes(types);
-          return success();
-        });
-```
-
-Consider the following signature conversion:
-
-```mlir
-func @on_tensors(%arg1: tuple<i1,f32>) -> (tuple<memref<10xf32>, tensor<5xf32>>){
- ...
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @on_buffers(%arg0: i1, %arg1: f32, %out: memref<5xf32>) -> memref<10xf32> {
- ...
-}
-```
-
-Using the presented converter setup, all TupleType arguments and results are
-decomposed first. The tensor<5xf32> result is converted to a memref<5xf32> type
-and appended to the argument list. There is no conversion for the types memref,
-i1, and f32. Therefore, the memref<10xf32> result is kept as it is and will
-also be kept as a function result since there is no ResultConversionKind
-mapping from a MemRefType to a MemRefType. However, if we change the
-result-conversion behavior via
-
-```mlir
-converter.setResultConversionKind<RankedTensorType, MemRefType>(
-        BufferizeTypeConverter::KeepAsFunctionResult);
-```
-
-the output will be:
-
-```mlir
-func @on_buffers(%arg0: i1, %arg1: f32) -> (memref<10xf32>, memref<5xf32>) {
- ...
-}
-```
-
-### BufferizeReturnOpConverter
-
-When changing the signature of a function, the return operands must match with
-the results of the corresponding function if buffer-typed-results have been
-configured to be appended to the function arguments list. This matching
-consists of two separate steps. First, we have to remove the operands that have
-been appended to the argument list as output arguments. Second, we have to
-introduce additional copies for each operand. However, since each dialect has
-its own dialect-dependent return and copy operations, this conversion pattern
-comes with three template parameters which are the original return operation,
-target return operation, and copy operation kinds.
-
-In the following example, two conversion patterns are inserted into the pattern
-list. The `BufferizeReturnOpConverter` is set to replace a standard return
-operation with the same operation type.
-
-```mlir
-patterns->insert<
-  BufferizeFuncOpConverter,
-  BufferizeReturnOpConverter
-    <mlir::ReturnOp, mlir::ReturnOp, linalg::CopyOp>
-                >(...)
-```
-
-Consider the following input/output program using a single return:
-
-```mlir
-func @on_tensors(%arg0: tensor<5xf32>, %arg1: i1) -> (tensor<5xf32>, i1) {
-  return %arg0, %arg1 : tensor<5xf32>, i1
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @on_buffers(%arg0: memref<5xf32>, %arg1: i1, %out: memref<5xf32>) -> i1 {
-  linalg.copy(%arg0, %out) : memref<5xf32>, memref<5xf32>
-  return %arg1 : i1
-}
-```
-
-Based on our previously configured `BufferizeTypeConverter` instance which
-requires buffer-typed-function-results to be appended to the function argument
-list, the new `on_buffers` function signature is created. The operands of the
-return operation must be adapted with the new function signature. Therefore,
-the buffer-typed operand is removed from the operand list of the new return
-operation. Instead, a copy operation is inserted right before the return
-operation to copy the content of the operand buffer to the target buffer and
-yields the output as shown above.
-
-### BufferizeCallOpConverter
-
-The BufferizeCallOpConverter is a call operation converter that transforms and
-matches the operands and results of a call operation with the arguments and
-results of the callee. Besides converting operand and result types, it
-allocates a buffer for each buffer-based result of the called function that is
-appended to the argument list (if buffer typed results have been configured to
-be appended to the function arguments list).
-
-The following piece of code shows a sample call site, based on our previously
-configured `BufferizeTypeConversion`:
-
-```mlir
-func @callee(%arg0: tensor<5xf32>) -> (tensor<5xf32>) {
-  return %arg0 : tensor<5xf32>
-}
-
-func @caller(%arg0: tensor<5xf32>) -> tensor<5xf32> {
-  %x = call @callee(%arg0) : (tensor<5xf32>) -> tensor<5xf32>
-  return %x : tensor<5xf32>
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @callee(%arg0: memref<5xf32>, %out: memref<5xf32>) {
-  linalg.copy(%arg0, %out) : memref<5xf32>, memref<5xf32>
-  return
-}
-
-func @caller(%arg0: memref<5xf32>, %out: memref<5xf32>) {
-  %0 = alloc() : memref<5xf32>
-  call @callee(%arg0, %0) : (memref<5xf32>, memref<5xf32>) -> ()
-  linalg.copy(%0, %out) : memref<5xf32>, memref<5xf32>
-  return
-}
-```
-
-### Summarizing Example
-
-To summarize all preparation converters, the following sample is a complete
-listing of an input IR program and its output after applying all converters:
-
-```mlir
-func @callee(%arg0: tuple<tensor<5xf32>,i1>) -> tuple<tensor<5xf32>,i1> {
-  return %arg0 : tuple<tensor<5xf32>,i1>
-}
-
-func @caller(%arg0: tuple<tensor<5xf32>,i1>) -> tuple<tensor<5xf32>,i1> {
-  %x = call @callee(%arg0) : (tuple<tensor<5xf32>,i1>) -> tuple<tensor<5xf32>,i1>
-  return %x : tuple<tensor<5xf32>,i1>
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @callee(%arg0: memref<5xf32>, %arg1: i1, %arg2: memref<5xf32>) -> i1 {
-  %0 = "test.make_tuple"(%arg0, %arg1) : (memref<5xf32>, i1) -> tuple<memref<5xf32>, i1>
-  %1 = "test.get_tuple_element"(%0) {index = 0 : i32} : (tuple<memref<5xf32>, i1>) -> memref<5xf32>
-  %2 = "test.get_tuple_element"(%0) {index = 1 : i32} : (tuple<memref<5xf32>, i1>) -> i1
-  linalg.copy(%1, %arg2) : memref<5xf32>, memref<5xf32>
-  return %2 : i1
-}
-func @caller(%arg0: memref<5xf32>, %arg1: i1, %arg2: memref<5xf32>) -> i1 {
-  %0 = "test.make_tuple"(%arg0, %arg1) : (memref<5xf32>, i1) -> tuple<memref<5xf32>, i1>
-  %1 = "test.get_tuple_element"(%0) {index = 0 : i32} : (tuple<memref<5xf32>, i1>) -> memref<5xf32>
-  %2 = "test.get_tuple_element"(%0) {index = 1 : i32} : (tuple<memref<5xf32>, i1>) -> i1
-  %3 = alloc() : memref<5xf32>
-  %4 = call @callee(%1, %2, %3) : (memref<5xf32>, i1, memref<5xf32>) -> i1
-  %5 = "test.make_tuple"(%3, %4) : (memref<5xf32>, i1) -> tuple<memref<5xf32>, i1>
-  %6 = "test.get_tuple_element"(%5) {index = 0 : i32} : (tuple<memref<5xf32>, i1>) -> memref<5xf32>
-  %7 = "test.get_tuple_element"(%5) {index = 1 : i32} : (tuple<memref<5xf32>, i1>) -> i1
-  linalg.copy(%6, %arg2) : memref<5xf32>, memref<5xf32>
-  return %7 : i1
-}
-```
-
-## Buffer Deallocation - Internal Functionality
+# Buffer Deallocation - Internals
 
 This section covers the internal functionality of the BufferDeallocation
 transformation. The transformation consists of several passes. The main pass
 called BufferDeallocation can be applied via “-buffer-deallocation” on MLIR
-programs. Currently, there are three optimization passes, that move allocs and
-convert AllocOps to AllocaOps, if possible. The first and second pass can be
-applied using “-buffer-hoisting” or “-buffer-loop-hoisting”, the third one
-using “-promote-buffers-to-stack”. However, these optimizations must be applied
-before using the BufferDeallocation pass.
+programs.
 
-### Requirements
+## Requirements
 
 In order to use BufferDeallocation on an arbitrary dialect, several
 control-flow interfaces have to be implemented when using custom operations.
 This is particularly important to understand the implicit control-flow
 dependencies between different parts of the input program. Without implementing
 the following interfaces, control-flow relations cannot be discovered properly
 and the resulting program can become invalid:
 
 * Branch-like terminators should implement the `BranchOpInterface` to query and
 manipulate associated operands.
 * Operations involving structured control flow have to implement the
 `RegionBranchOpInterface` to model inter-region control flow.
 * Terminators yielding values to their parent operation (in particular in the
 scope of nested regions within `RegionBranchOpInterface`-based operations),
 should implement the `ReturnLike` trait to represent logical “value returns”.
 
 Example dialects that are fully compatible are the “std” and “scf” dialects
 with respect to all implemented interfaces.
 
-### Detection of Buffer Allocations
+## Detection of Buffer Allocations
 
 The first step of the BufferDeallocation transformation is to identify
 manageable allocation operations that implement the `SideEffects` interface.
 Furthermore, these ops need to apply the effect `MemoryEffects::Allocate` to a
 particular result value while not using the resource
 `SideEffects::AutomaticAllocationScopeResource` (since it is currently reserved
 for allocations, like `Alloca` that will be automatically deallocated by a
 parent scope). Allocations that have not been detected in this phase will not

 ```
 
 Example of using a conditional branch with alloc and alloca. BufferDeallocation
 can detect and handle the different allocation types that might be intermixed.
 
 Note: the current version does not support allocation operations returning
 multiple result buffers.
 
-### Conversion from AllocOp to AllocaOp
+## Conversion from AllocOp to AllocaOp
 
 The PromoteBuffersToStack-pass converts AllocOps to AllocaOps, if possible. In
 some cases, it can be useful to use such stack-based buffers instead of
 heap-based buffers. The conversion is restricted to several constraints like:
 
 * Control flow
 * Buffer Size
 * Dynamic Size
 
 If a buffer is leaving a block, we are not allowed to convert it into an
 alloca. If the size of the buffer is large, we could convert it, but regarding
 stack overflow, it makes sense to limit the size of these buffers and only
 convert small ones. The size can be set via a pass option. The current default
 value is 1KB. Furthermore, we can not convert buffers with dynamic size, since
 the dimension is not known a priori.
 
-### Movement and Placement of Allocations
+## Movement and Placement of Allocations
 
 Using the buffer hoisting pass, all buffer allocations are moved as far upwards
 as possible in order to group them and make upcoming optimizations easier by
 limiting the search space. Such a movement is shown in the following graphs.
 In addition, we are able to statically free an alloc, if we move it into a
 dominator of all of its uses. This simplifies further optimizations (e.g.
 buffer fusion) in the future. However, movement of allocations is limited by
 external data dependencies (in particular in the case of allocations of
^bb2(%0: index):
   use(%1)
   br ^bb3(%1 : memref<?xf32>)
 ^bb3(%2: memref<?xf32>):
   "linalg.copy"(%2, %arg2) : (memref<?xf32>, memref<?xf32>) -> ()
   return
 }
 ```
 
-### Introduction of Copies
+## Introduction of Copies
 
 In order to guarantee that all allocated buffers are freed properly, we have to
 pay attention to the control flow and all potential aliases a buffer allocation
 can have. Since not all allocations can be safely freed with respect to their
 aliases (see the following code snippet), it is often required to introduce
 copies to eliminate them. Consider the following example in which the
 allocations have already been placed:
 
then(%arg2 : memref<?x?xf32>) {
     dealloc %arg4 : memref<?x?xf32>
     custom.region_if_yield %4 : memref<?x?xf32>
    }
   dealloc %0 : memref<?x?xf32>  // %0 can be safely freed here
   return %1 : memref<?x?xf32>
 }
 ```
 
-### Placement of Deallocs
+## Placement of Deallocs
 
 After introducing allocs and copies, deallocs have to be placed to free
 allocated memory and avoid memory leaks. The deallocation needs to take place
 after the last use of the given value. The position can be determined by
 calculating the common post-dominator of all values using their remaining
 non-critical aliases. A special-case is the presence of back edges: since such
 edges can cause memory leaks when a newly allocated buffer flows back to
 another part of the program. In these cases, we need to free the associated
func @loop_nested_if(
   return
 }
 ```
 
 Example for loop-like control flow. The CFG contains back edges that have to be
 handled to avoid memory leaks. The bufferization is able to free the backedge
 iteration variable %iterBuf.
 
-### Private Analyses Implementations
+## Private Analyses Implementations
 
 The BufferDeallocation transformation relies on one primary control-flow
 analysis: BufferPlacementAliasAnalysis. Furthermore, we also use dominance and
 liveness to place and move nodes. The liveness analysis determines the live
 range of a given value. Within this range, a value is alive and can or will be
 used in the course of the program. After this range, the value is dead and can
 be discarded - in our case, the buffer can be freed. To place the allocs, we
 need to know from which position a value will be alive. The allocs have to be
 placed in front of this position. However, the most important analysis is the
 alias analysis that is needed to introduce copies and to place all
 deallocations.
 
-## Post Phase
+# Post Phase
 
 In order to limit the complexity of the BufferDeallocation transformation, some
 tiny code-polishing/optimization transformations are not applied on-the-fly
 during placement. Currently, there is only the CopyRemoval transformation to
 remove unnecessary copy and allocation operations.
 
 Note: further transformations might be added to the post-pass phase in the
 future.
 
-### CopyRemoval Pass
+## CopyRemoval Pass
 
 A common pattern that arises during placement is the introduction of
 unnecessary temporary copies that are used instead of the original source
 buffer. For this reason, there is a post-pass transformation that removes these
 allocations and copies via `-copy-removal`. This pass, besides removing
 unnecessary copy operations, will also remove the dead allocations and their
 corresponding deallocation operations. The CopyRemoval pass can currently be
 applied to operations that implement the `CopyOpInterface` in any of these two
 situations which are
 
 * reusing the source buffer of the copy operation.
 * reusing the target buffer of the copy operation.
 
-### Reusing the Source Buffer of the Copy Operation
+## Reusing the Source Buffer of the Copy Operation
 
 In this case, the source of the copy operation can be used instead of target.
 The unused allocation and deallocation operations that are defined for this
 copy operation are also removed. Here is a working example generated by the
 BufferDeallocation pass that allocates a buffer with dynamic size. A deeper
 analysis of this sample reveals that the highlighted operations are redundant
 and can be removed.
 

 
 * The copy operation, the defining operation for the target value, and the
 deallocation of the source value lie in the same block.
 * There are no users/aliases of the target value between the defining operation
 of the target value and its copy operation.
 * There are no users/aliases of the source value between its associated copy
 operation and the deallocation of the source value.
 
-### Reusing the Target Buffer of the Copy Operation
+## Reusing the Target Buffer of the Copy Operation
 
 In this case, the target buffer of the copy operation can be used instead of
 its source. The unused allocation and deallocation operations that are defined
 for this copy operation are also removed.
 
 Consider the following example where a generic linalg operation writes the
 result to %temp and then copies %temp to %result. However, these two operations
 can be merged into a single step. Copy removal removes the copy operation and

mlir/docs/Bufferization.md

-# Bufferization on MLIR
+# Bufferization
 
-The general task of bufferization is to move SSA values (like tensors) into
-allocated memory buffers that have to be freed when they are no longer needed.
-This also involves the placement of copies to clone contents of allocated
-memory buffers at specific locations (similar to register allocation). On the
-one hand, these copies are needed to ensure the right behavior of a program, on
-the other hand, introducing several aliases for a certain buffer could lead to
-a wrong freeing of buffers. Therefore, we have to take care of them and the
-program structure. The introduction of copies solves this problem. Several
-unnecessary introduced copies during this process can be eliminated afterwards.
-
-```mlir
-func @func_on_tensors(%source: tensor<4xf32>) -> tensor<4xf32> {
-  %0 = mhlo.exp %source : (tensor<4xf32>) -> (tensor<4xf32>)
-  return %0 : tensor<4xf32>
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @func_on_buffers(%source: memref<4xf32>, %target: memref<4xf32>) {
-  %0 = alloc() : memref<4xf32>
-  lmhlo.exp %source, %0 : (memref<4xf32>, memref<4xf32>) -> ()
-  lmhlo.copy %0, %target : (memref<4xf32>, memref<4xf32>) -> ()
-  dealloc %0 : memref<4xf32>
-  return
-}
-```
-
-In general, Bufferization is split into three separate phases: a preparation
-phase, a bufferization phase and a post-processing phase. The assignment
-process happens during dialect conversion and allocates buffers for each value
-that should be moved into a memory buffer. This has to be implemented by each
-dialect using the following tools and patterns. Thereby, all  operations on
-memory buffers have to be changed to `memref<T>` types (see Preparation Phase).
-Afterwards, the placement transformation (see BufferDeallocation) adds all
-required deallocation operations, temporary buffers and copy operations
-automatically.
-
-## Preparation Phase
-
-In order to apply the BufferDeallocation code transformation, the input MLIR
-program needs to leverage allocations (buffers in general) and `memref<T>`
-types(as outlined above). If your input program does not work on buffers, you
-need to perform this preparation step in order to port it to the “buffer
-world”. This is a user-defined preparation step that is intended to be applied
-during dialect conversion. The user has to take care for the right conversion
-by providing conversion patterns relying on a type converter to assign buffers.
-
-A necessary step is to apply type and function signature conversion.
-Furthermore, after changing all function signatures, the associated return and
-call operations must comply with the new corresponding function signatures. For
-this purpose, three operation conversion patterns are introduced:
-
-* BufferizeFuncOpConverter
-* BufferizeReturnOpConverter
-* BufferizeCallOpConverter
-
-In order to use these conversion patterns, the user needs to define a custom
-BufferizeTypeConverter implementation.
-
-### BufferizeTypeConverter
-
-The BufferizeTypeConverter is an extension to the TypeConverter class that
-provides additional functionality for dialect developers to decide on the
-signature of a function. The extra features include:
-
-* `setResultConversionKind` to decide on a function result after the conversion
-with a specific type to be appended to the function argument list as an output
-argument or remains as a function result.
-* define their own callback function for type or value unwrapping.
-
-### ResultConversionKind
-
-ResultConversionKind is an enum with two values
-
-* AppendToArgumentList
-* KeepAsFunctionResult
-
-that defines how BufferizeFuncOpConverter should handle function results in
-order to convert the signature of the function. The other two operation
-conversion patterns also use ResultConversionKind to adapt themselves with the
-new function signature.
-
-`ResultConversionKind` can be set using `setResultConversionKind`, which needs
-two template parameters that correspond to the types before and after type
-conversion. This mapping specifies whether the resulting type should stay as a
-function result or should be appended to the arguments list after the
-conversion is done. Note that the default value for unspecified mappings is
-`KeepAsFunctionResult`. For instance, the following command updates the
-`BufferizeTypeConverter` instance that defines all MemRefType function results
-(converted from `RankedTensorTypes`). These results should be appended to the
-function argument list in BufferizeFuncOpConverter:
-
-```mlir
-converter.setResultConversionKind<RankedTensorType, MemRefType>(
-        BufferizeTypeConverter::AppendToArgumentsList);
-```
-
-## Callbacks for Unpacking Types
-
-```mlir
-func @func_on_tensors(%arg0: tuple<i1,f32>) -> (tuple<tensor<10xf32>, tensor<5xf32>>) {
-  ...
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @func_on_buffers(%arg0: i1, %arg1: f32, %target0: memref<10xf32>, %target1: memref<5xf32>) {
-  ...
-}
-```
-
-BufferizeFuncOpConverter is also able to unpack the types of arguments and
-results of a function during function signature conversion. In the example
-above, it unwraps the tuple type and converts the type of each constituent.
-
-For this purpose, users can provide custom callbacks by using
-`addDecomposeTypeConversion` for the `BufferizeTypeConverter` instance to show
-how a specific type (i.e. TupleType) can be unpacked. However, when a type
-decomposition is provided, there are two additional callbacks that have to be
-defined as well. They specify how to pack or unpack values of that particular
-type. These two callbacks can be provided by the `addArgumentMaterialization`
-(packing) **and** `addDecomposeValueConversion` (unpacking) functions:
-
-The following piece of code demonstrates this functionality by flattening a
-`TupleType`.
-
-```mlir
-converter.addDecomposeTypeConversion(
-        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
-          tupleType.getFlattenedTypes(types);
-          return success();
-        });
-
-converter.addArgumentMaterialization(
-        [](OpBuilder &builder, TupleType resultType, ValueRange inputs,
-           Location loc) -> Optional<Value> {
-          if (inputs.size() == 1)
-            return llvm::None;
-          TypeRange TypeRange = inputs.getTypes();
-          SmallVector<Type, 2> types(TypeRange.begin(), TypeRange.end());
-          TupleType tuple = TupleType::get(types, builder.getContext());
-          mlir::Value value = builder.create<MakeTupleOp>(loc, tuple, inputs);
-          return value;
-        });
-
-converter.addDecomposeValueConversion([](OpBuilder &builder, Location loc,
-                                         TupleType resultType, Value value,
-                                         SmallVectorImpl<Value> &values) {
-      for (unsigned i = 0, e = resultType.size(); i < e; ++i) {
-        Value res = builder.create<GetTupleElementOp>(
-            loc, resultType.getType(i), value, builder.getI32IntegerAttr(i));
-        values.push_back(res);
-      }
-      return success();
- });
-```
+[TOC]
+## Overview
 
-In the scope of these callback functions, the elements of a tuple value can be
-decomposed using `GetTupleElementOp`. Conversely, `MakeTupleOp` is used to pack
-a list of values as a single tuple type.
-
-### Bufferization Operation Conversion Patterns
-
-The following conversion patterns can be used to conveniently transform the
-signature of a function, the return and call operations:
-
-* `BufferizeFuncOpConverter`
-* `BufferizeReturnOpConverter`
-* `BufferizeCallOpConverter`
-
-Any combination of these conversion patterns can be specified by the user. If
-you need to apply  all of these operation converters, you can use
-`populateWithBufferizeOpConversionPatterns` which sets up all converters.
-
-### BufferizeFuncOpConverter
-
-The BufferizeFuncOpConverter is the actual function operation converter that
-applies signature conversion by using a previously defined
-`BufferizeTypeConverter`.
-
-In the following example, we configure a `BufferizeTypeConverter` instance such
-that
-
-* all RankedTensorTypes should be converted to MemRefTypes.
-* all function results that are results of type conversion from
-RankedTensorTypes to MemRefTypes should be appended to the function argument
-list.
-* all TupleTypes should be flattened and decomposed to its constituents.
-
-```mlir
-converter.addConversion([](RankedTensorType type) {
-    return (Type)MemRefType::get(type.getShape(), type.getElementType());
-  });
-converter.setResultConversionKind<RankedTensorType, MemRefType>(
-        BufferizeTypeConverter::AppendToArgumentsList);
-converter.addDecomposeTypeConversion(
-        [](TupleType tupleType, SmallVectorImpl<Type> &types) {
-          tupleType.getFlattenedTypes(types);
-          return success();
-        });
-```
+Bufferization in MLIR is the process of converting the `tensor` type to the `memref` type. MLIR provides a composable system that allows dialects to systematically bufferize a program. This system is a straightforward application of MLIR's [dialect conversion](DialectConversion.md) infrastructure. The bulk of the code related to bufferization is a set of ordinary `ConversionPattern`'s that dialect authors write for converting ops that operate on `tensor`'s to ops that operate on `memref`'s. A set of conventions and best practices are followed that allow these patterns to be run across multiple independent passes (rather than requiring a single huge atomic conversion pass), which makes the compilation pipelines scalable, robust, and easy to debug.
 
-Consider the following signature conversion:
+This document is targeted at people looking to utilize MLIR's bufferization functionality, along with people who want to extend it to cover their own ops.
 
-```mlir
-func @on_tensors(%arg1: tuple<i1,f32>) -> (tuple<memref<10xf32>, tensor<5xf32>>){
- ...
-}
-```
-
-Will be transformed to:
+<a name="the-talk">**NOTE:**</a> Before reading this document, please watch the talk "Type Conversions the Not-So-Hard-Way: MLIR's New Bufferization Infrastructure" ([slides](https://drive.google.com/file/d/1FVbzCXxZzS9LBLuvpPNLWJD-XDkt54ky/view?usp=sharing), [recording](https://drive.google.com/file/d/1VfVajitgf8ZPnd-HRkJvaJiFLhBsluXN/view?usp=sharing)). That talk gives a high-level overview of the bufferization infrastructure and important conceptual details related to using the MLIR dialect conversion infrastructure.
 
-```mlir
-func @on_buffers(%arg0: i1, %arg1: f32, %out: memref<5xf32>) -> memref<10xf32> {
- ...
-}
-```
 
-Using the presented converter setup, all TupleType arguments and results are
-decomposed first. The tensor<5xf32> result is converted to a memref<5xf32> type
-and appended to the argument list. There is no conversion for the types memref,
-i1, and f32. Therefore, the memref<10xf32> result is kept as it is and will
-also be kept as a function result since there is no ResultConversionKind
-mapping from a MemRefType to a MemRefType. However, if we change the
-result-conversion behavior via
-
-```mlir
-converter.setResultConversionKind<RankedTensorType, MemRefType>(
-        BufferizeTypeConverter::KeepAsFunctionResult);
-```
+## Bufferization's place in a compilation pipeline
 
-the output will be:
+Bufferization itself does not free any of the buffers that have been allocated, nor does it do anything particularly intelligent with the placement of buffers w.r.t. control flow. Thus, a realistic compilation pipeline will usually consist of:
 
-```mlir
-func @on_buffers(%arg0: i1, %arg1: f32) -> (memref<10xf32>, memref<5xf32>) {
- ...
-}
-```
+1. Bufferization
+1. Buffer optimizations such as `buffer-hoisting`, `buffer-loop-hoisting`, and `promote-buffers-to-stack`, which do optimizations that are only exposed after bufferization.
+1. Finally, running the [buffer deallocation](BufferDeallocation.md) pass.
 
-### BufferizeReturnOpConverter
+After buffer deallocation has been completed, the program will be quite difficult to transform due to the presence of the deallocation ops. Thus, other optimizations such as linalg fusion on memrefs should be done before that stage.
 
-When changing the signature of a function, the return operands must match with
-the results of the corresponding function if buffer-typed-results have been
-configured to be appended to the function arguments list. This matching
-consists of two separate steps. First, we have to remove the operands that have
-been appended to the argument list as output arguments. Second, we have to
-introduce additional copies for each operand. However, since each dialect has
-its own dialect-dependent return and copy operations, this conversion pattern
-comes with three template parameters which are the original return operation,
-target return operation, and copy operation kinds.
-
-In the following example, two conversion patterns are inserted into the pattern
-list. The `BufferizeReturnOpConverter` is set to replace a standard return
-operation with the same operation type.
-
-```mlir
-patterns->insert<
-  BufferizeFuncOpConverter,
-  BufferizeReturnOpConverter
-    <mlir::ReturnOp, mlir::ReturnOp, linalg::CopyOp>
-                >(...)
-```
+## General structure of the bufferization process
 
-Consider the following input/output program using a single return:
+Bufferization consists of running multiple *partial* bufferization passes, followed by one *finalizing* bufferization pass.
 
-```mlir
-func @on_tensors(%arg0: tensor<5xf32>, %arg1: i1) -> (tensor<5xf32>, i1) {
-  return %arg0, %arg1 : tensor<5xf32>, i1
-}
-```
+There is typically one partial bufferization pass per dialect (though other subdivisions are possible). For example, for a dialect `X` there will typically be a pass `X-bufferize` that knows how to bufferize all the ops in that dialect. By running pass `X-bufferize` for each dialect `X` in the program, all the ops in the program are incrementally bufferized.
 
-Will be transformed to:
+Partial bufferization passes create programs where only some ops have been bufferized. These passes will create *materializations* (also sometimes called "casts") that convert between the `tensor` and `memref` type, which allows bridging between ops that have been bufferized and ops that have not yet been bufferized.
 
-```mlir
-func @on_buffers(%arg0: memref<5xf32>, %arg1: i1, %out: memref<5xf32>) -> i1 {
-  linalg.copy(%arg0, %out) : memref<5xf32>, memref<5xf32>
-  return %arg1 : i1
-}
-```
+Finalizing bufferizations complete the bufferization process, and guarantee that there are no tensors remaining in the program. This involves eliminating the materializations. The pass `finalizing-bufferize` provides a minimal pass that only eliminates materializations and issues an error if any unbufferized ops exist in the program.
 
-Based on our previously configured `BufferizeTypeConverter` instance which
-requires buffer-typed-function-results to be appended to the function argument
-list, the new `on_buffers` function signature is created. The operands of the
-return operation must be adapted with the new function signature. Therefore,
-the buffer-typed operand is removed from the operand list of the new return
-operation. Instead, a copy operation is inserted right before the return
-operation to copy the content of the operand buffer to the target buffer and
-yields the output as shown above.
-
-### BufferizeCallOpConverter
-
-The BufferizeCallOpConverter is a call operation converter that transforms and
-matches the operands and results of a call operation with the arguments and
-results of the callee. Besides converting operand and result types, it
-allocates a buffer for each buffer-based result of the called function that is
-appended to the argument list (if buffer typed results have been configured to
-be appended to the function arguments list).
-
-The following piece of code shows a sample call site, based on our previously
-configured `BufferizeTypeConversion`:
-
-```mlir
-func @callee(%arg0: tensor<5xf32>) -> (tensor<5xf32>) {
-  return %arg0 : tensor<5xf32>
-}
+However, it is possible for a finalizing bufferization to do more than just eliminate materializations. By adding patterns (just as a partial bufferization would), it is possible for a finalizing bufferization pass to simultaneously bufferize ops and eliminate materializations. This has a number of disadvantages discussed in the talk and should generally be avoided.
 
-func @caller(%arg0: tensor<5xf32>) -> tensor<5xf32> {
-  %x = call @callee(%arg0) : (tensor<5xf32>) -> tensor<5xf32>
-  return %x : tensor<5xf32>
-}
-```
+### Example
 
-Will be transformed to:
+As a concrete example, we will look at the bufferization pipeline from the `mlir-npcomp` reference backend ([code](https://github.com/llvm/mlir-npcomp/blob/97d6d04d41216e73d40b89ffd79620973fc14ce3/lib/RefBackend/RefBackend.cpp#L232)). The code, slightly simplified and annotated, is reproduced here:
 
-```mlir
-func @callee(%arg0: memref<5xf32>, %out: memref<5xf32>) {
-  linalg.copy(%arg0, %out) : memref<5xf32>, memref<5xf32>
-  return
-}
+```c++
+  // Partial bufferization passes.
+  pm.addPass(createTensorConstantBufferizePass());
+  pm.addNestedPass<FuncOp>(createTCPBufferizePass()); // Bufferizes the downstream `tcp` dialect.
+  pm.addNestedPass<FuncOp>(createSCFBufferizePass());
+  pm.addNestedPass<FuncOp>(createLinalgBufferizePass());
+  pm.addNestedPass<FuncOp>(createStdBufferizePass());
+  pm.addNestedPass<FuncOp>(createTensorBufferizePass());
+  pm.addPass(createFuncBufferizePass());
 
-func @caller(%arg0: memref<5xf32>, %out: memref<5xf32>) {
-  %0 = alloc() : memref<5xf32>
-  call @callee(%arg0, %0) : (memref<5xf32>, memref<5xf32>) -> ()
-  linalg.copy(%0, %out) : memref<5xf32>, memref<5xf32>
-  return
-}
+  // Finalizing bufferization pass.
+  pm.addNestedPass<FuncOp>(createFinalizingBufferizePass());
 ```
 
-### Summarizing Example
+Looking first at the partial bufferization passes, we see that there are a sequence of `FuncOp` passes (which run in parallel on functions). These function passes are bracketed by `tensor-constant-bufferize` and `func-bufferize`, which are module passes (and thus serialize the parallel compilation process). These two passes must be module passes because they make changes to the top-level module.
 
-To summarize all preparation converters, the following sample is a complete
-listing of an input IR program and its output after applying all converters:
+The bulk of the bufferization work is done by the function passes. Most of these passes are provided as part of the upstream MLIR distribution and bufferize their respective dialects (e.g. `scf-bufferize` bufferizes the `scf` dialect). The `tcp-bufferize` pass is an exception -- it is a partial bufferization pass used to bufferize the downstream `tcp` dialect, and fits in perfectly with all the other passes provided upstream.
 
-```mlir
-func @callee(%arg0: tuple<tensor<5xf32>,i1>) -> tuple<tensor<5xf32>,i1> {
-  return %arg0 : tuple<tensor<5xf32>,i1>
-}
-
-func @caller(%arg0: tuple<tensor<5xf32>,i1>) -> tuple<tensor<5xf32>,i1> {
-  %x = call @callee(%arg0) : (tuple<tensor<5xf32>,i1>) -> tuple<tensor<5xf32>,i1>
-  return %x : tuple<tensor<5xf32>,i1>
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @callee(%arg0: memref<5xf32>, %arg1: i1, %arg2: memref<5xf32>) -> i1 {
-  %0 = "test.make_tuple"(%arg0, %arg1) : (memref<5xf32>, i1) -> tuple<memref<5xf32>, i1>
-  %1 = "test.get_tuple_element"(%0) {index = 0 : i32} : (tuple<memref<5xf32>, i1>) -> memref<5xf32>
-  %2 = "test.get_tuple_element"(%0) {index = 1 : i32} : (tuple<memref<5xf32>, i1>) -> i1
-  linalg.copy(%1, %arg2) : memref<5xf32>, memref<5xf32>
-  return %2 : i1
-}
-func @caller(%arg0: memref<5xf32>, %arg1: i1, %arg2: memref<5xf32>) -> i1 {
-  %0 = "test.make_tuple"(%arg0, %arg1) : (memref<5xf32>, i1) -> tuple<memref<5xf32>, i1>
-  %1 = "test.get_tuple_element"(%0) {index = 0 : i32} : (tuple<memref<5xf32>, i1>) -> memref<5xf32>
-  %2 = "test.get_tuple_element"(%0) {index = 1 : i32} : (tuple<memref<5xf32>, i1>) -> i1
-  %3 = alloc() : memref<5xf32>
-  %4 = call @callee(%1, %2, %3) : (memref<5xf32>, i1, memref<5xf32>) -> i1
-  %5 = "test.make_tuple"(%3, %4) : (memref<5xf32>, i1) -> tuple<memref<5xf32>, i1>
-  %6 = "test.get_tuple_element"(%5) {index = 0 : i32} : (tuple<memref<5xf32>, i1>) -> memref<5xf32>
-  %7 = "test.get_tuple_element"(%5) {index = 1 : i32} : (tuple<memref<5xf32>, i1>) -> i1
-  linalg.copy(%6, %arg2) : memref<5xf32>, memref<5xf32>
-  return %7 : i1
-}
-```
+The last pass is the finalizing bufferization pass. The `mlir-npcomp` reference backend has arranged that all ops are bufferized by partial bufferizations, so that the upstream `finalizing-bufferize` pass can be used as the finalizing bufferization pass. This gives excellent diagnostics when something goes wrong with the bufferization process, such as due to an op that wasn't handled by any pattern.
 
-## Buffer Deallocation - Internal Functionality
+## How to write a partial bufferization pass
 
-This section covers the internal functionality of the BufferDeallocation
-transformation. The transformation consists of several passes. The main pass
-called BufferDeallocation can be applied via “-buffer-deallocation” on MLIR
-programs. Currently, there are three optimization passes, that move allocs and
-convert AllocOps to AllocaOps, if possible. The first and second pass can be
-applied using “-buffer-hoisting” or “-buffer-loop-hoisting”, the third one
-using “-promote-buffers-to-stack”. However, these optimizations must be applied
-before using the BufferDeallocation pass.
-
-### Requirements
-
-In order to use BufferDeallocation on an arbitrary dialect, several
-control-flow interfaces have to be implemented when using custom operations.
-This is particularly important to understand the implicit control-flow
-dependencies between different parts of the input program. Without implementing
-the following interfaces, control-flow relations cannot be discovered properly
-and the resulting program can become invalid:
-
-* Branch-like terminators should implement the `BranchOpInterface` to query and
-manipulate associated operands.
-* Operations involving structured control flow have to implement the
-`RegionBranchOpInterface` to model inter-region control flow.
-* Terminators yielding values to their parent operation (in particular in the
-scope of nested regions within `RegionBranchOpInterface`-based operations),
-should implement the `ReturnLike` trait to represent logical “value returns”.
-
-Example dialects that are fully compatible are the “std” and “scf” dialects
-with respect to all implemented interfaces.
-
-### Detection of Buffer Allocations
-
-The first step of the BufferDeallocation transformation is to identify
-manageable allocation operations that implement the `SideEffects` interface.
-Furthermore, these ops need to apply the effect `MemoryEffects::Allocate` to a
-particular result value while not using the resource
-`SideEffects::AutomaticAllocationScopeResource` (since it is currently reserved
-for allocations, like `Alloca` that will be automatically deallocated by a
-parent scope). Allocations that have not been detected in this phase will not
-be tracked internally, and thus, not deallocated automatically. However,
-BufferDeallocation is fully compatible with “hybrid” setups in which tracked
-and untracked allocations are mixed:
-
-```mlir
-func @mixedAllocation(%arg0: i1) {
-   %0 = alloca() : memref<2xf32>  // aliases: %2
-   %1 = alloc() : memref<2xf32>  // aliases: %2
-   cond_br %arg0, ^bb1, ^bb2
-^bb1:
-  use(%0)
-  br ^bb3(%0 : memref<2xf32>)
-^bb2:
-  use(%1)
-  br ^bb3(%1 : memref<2xf32>)
-^bb3(%2: memref<2xf32>):
-  ...
-}
-```
+The contract of a partial bufferization pass is that a subset of ops (or kinds of ops, customizable by a ConversionTarget) get bufferized.
 
-Example of using a conditional branch with alloc and alloca. BufferDeallocation
-can detect and handle the different allocation types that might be intermixed.
+A partial bufferization pass is just a pass that uses the [dialect conversion](DialectConversion.md) framework to apply `ConversionPattern`s with a `tensor` to `memref` type conversion.
 
-Note: the current version does not support allocation operations returning
-multiple result buffers.
+To describe how to write such a pass, we will walk through an example, the `tensor-bufferize` pass ([code](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/lib/Dialect/Tensor/Transforms/Bufferize.cpp#L23), [test](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/test/Dialect/Tensor/bufferize.mlir#L1)) that bufferizes the `tensor` dialect.
 
-### Conversion from AllocOp to AllocaOp
+The bulk of the code in the pass will be a set of conversion patterns, with a simple example being [BufferizeCastOp](https://github.com/llvm/llvm-project/blob/2bf6e443e54604c7818c4d1a1837f3d091023270/mlir/lib/Dialect/Tensor/Transforms/Bufferize.cpp#L23)).
 
-The PromoteBuffersToStack-pass converts AllocOps to AllocaOps, if possible. In
-some cases, it can be useful to use such stack-based buffers instead of
-heap-based buffers. The conversion is restricted to several constraints like:
-
-* Control flow
-* Buffer Size
-* Dynamic Size
-
-If a buffer is leaving a block, we are not allowed to convert it into an
-alloca. If the size of the buffer is large, we could convert it, but regarding
-stack overflow, it makes sense to limit the size of these buffers and only
-convert small ones. The size can be set via a pass option. The current default
-value is 1KB. Furthermore, we can not convert buffers with dynamic size, since
-the dimension is not known a priori.
-
-### Movement and Placement of Allocations
-
-Using the buffer hoisting pass, all buffer allocations are moved as far upwards
-as possible in order to group them and make upcoming optimizations easier by
-limiting the search space. Such a movement is shown in the following graphs.
-In addition, we are able to statically free an alloc, if we move it into a
-dominator of all of its uses. This simplifies further optimizations (e.g.
-buffer fusion) in the future. However, movement of allocations is limited by
-external data dependencies (in particular in the case of allocations of
-dynamically shaped types). Furthermore, allocations can be moved out of nested
-regions, if necessary. In order to move allocations to valid locations with
-respect to their uses only, we leverage Liveness information.
-
-The following code snippets shows a conditional branch before running the
-BufferHoisting pass:
-
-![branch_example_pre_move](/includes/img/branch_example_pre_move.svg)
-
-```mlir
-func @condBranch(%arg0: i1, %arg1: memref<2xf32>, %arg2: memref<2xf32>) {
-  cond_br %arg0, ^bb1, ^bb2
-^bb1:
-  br ^bb3(%arg1 : memref<2xf32>)
-^bb2:
-  %0 = alloc() : memref<2xf32>  // aliases: %1
-  use(%0)
-  br ^bb3(%0 : memref<2xf32>)
-^bb3(%1: memref<2xf32>):  // %1 could be %0 or %arg1
-  "linalg.copy"(%1, %arg2) : (memref<2xf32>, memref<2xf32>) -> ()
-  return
-}
 ```
-
-Applying the BufferHoisting pass on this program results in the following piece
-of code:
-
-![branch_example_post_move](/includes/img/branch_example_post_move.svg)
-
-```mlir
-func @condBranch(%arg0: i1, %arg1: memref<2xf32>, %arg2: memref<2xf32>) {
-  %0 = alloc() : memref<2xf32>  // moved to bb0
-  cond_br %arg0, ^bb1, ^bb2
-^bb1:
-  br ^bb3(%arg1 : memref<2xf32>)
-^bb2:
-   use(%0)
-   br ^bb3(%0 : memref<2xf32>)
-^bb3(%1: memref<2xf32>):
-  "linalg.copy"(%1, %arg2) : (memref<2xf32>, memref<2xf32>) -> ()
-  return
+class BufferizeCastOp : public OpConversionPattern<tensor::CastOp> {
+public:
+  using OpConversionPattern::OpConversionPattern;
+  LogicalResult
+  matchAndRewrite(tensor::CastOp op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const override {
+    auto resultType = getTypeConverter()->convertType(op.getType());
+    rewriter.replaceOpWithNewOp<MemRefCastOp>(op, resultType, operands[0]);
+    return success();
   }
+};
 ```
 
-The alloc is moved from bb2 to the beginning and it is passed as an argument to
-bb3.
+See [the talk](#the-talk) for more details on how to write these patterns.
 
-The following example demonstrates an allocation using dynamically shaped
-types. Due to the data dependency of the allocation to %0, we cannot move the
-allocation out of bb2 in this case:
-
-```mlir
-func @condBranchDynamicType(
-  %arg0: i1,
-  %arg1: memref<?xf32>,
-  %arg2: memref<?xf32>,
-  %arg3: index) {
-  cond_br %arg0, ^bb1, ^bb2(%arg3: index)
-^bb1:
-  br ^bb3(%arg1 : memref<?xf32>)
-^bb2(%0: index):
-  %1 = alloc(%0) : memref<?xf32>   // cannot be moved upwards to the data
-                                   // dependency to %0
-  use(%1)
-  br ^bb3(%1 : memref<?xf32>)
-^bb3(%2: memref<?xf32>):
-  "linalg.copy"(%2, %arg2) : (memref<?xf32>, memref<?xf32>) -> ()
-  return
-}
-```
+The [pass itself](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/lib/Dialect/Tensor/Transforms/Bufferize.cpp#L57) is very small, and follows the basic pattern of any dialect conversion pass.
 
-### Introduction of Copies
-
-In order to guarantee that all allocated buffers are freed properly, we have to
-pay attention to the control flow and all potential aliases a buffer allocation
-can have. Since not all allocations can be safely freed with respect to their
-aliases (see the following code snippet), it is often required to introduce
-copies to eliminate them. Consider the following example in which the
-allocations have already been placed:
-
-```mlir
-func @branch(%arg0: i1) {
-  %0 = alloc() : memref<2xf32>  // aliases: %2
-  cond_br %arg0, ^bb1, ^bb2
-^bb1:
-  %1 = alloc() : memref<2xf32>  // resides here for demonstration purposes
-                                // aliases: %2
-  br ^bb3(%1 : memref<2xf32>)
-^bb2:
-  use(%0)
-  br ^bb3(%0 : memref<2xf32>)
-^bb3(%2: memref<2xf32>):
-  …
-  return
-}
 ```
-
-The first alloc can be safely freed after the live range of its post-dominator
-block (bb3). The alloc in bb1 has an alias %2 in bb3 that also keeps this
-buffer alive until the end of bb3. Since we cannot determine the actual
-branches that will be taken at runtime, we have to ensure that all buffers are
-freed correctly in bb3 regardless of the branches we will take to reach the
-exit block. This makes it necessary to introduce a copy for %2, which allows us
-to free %alloc0 in bb0 and %alloc1 in bb1. Afterwards, we can continue
-processing all aliases of %2 (none in this case) and we can safely free %2 at
-the end of the sample program. This sample demonstrates that not all
-allocations can be safely freed in their associated post-dominator blocks.
-Instead, we have to pay attention to all of their aliases.
-
-Applying the BufferDeallocation pass to the program above yields the following
-result:
-
-```mlir
-func @branch(%arg0: i1) {
-  %0 = alloc() : memref<2xf32>
-  cond_br %arg0, ^bb1, ^bb2
-^bb1:
-  %1 = alloc() : memref<2xf32>
-  %3 = alloc() : memref<2xf32>  // temp copy for %1
-  "linalg.copy"(%1, %3) : (memref<2xf32>, memref<2xf32>) -> ()
-  dealloc %1 : memref<2xf32> // %1 can be safely freed here
-  br ^bb3(%3 : memref<2xf32>)
-^bb2:
-  use(%0)
-  %4 = alloc() : memref<2xf32>  // temp copy for %0
-  "linalg.copy"(%0, %4) : (memref<2xf32>, memref<2xf32>) -> ()
-  br ^bb3(%4 : memref<2xf32>)
-^bb3(%2: memref<2xf32>):
-  …
-  dealloc %2 : memref<2xf32> // free temp buffer %2
-  dealloc %0 : memref<2xf32> // %0 can be safely freed here
-  return
+void mlir::populateTensorBufferizePatterns(
+    MLIRContext *context, BufferizeTypeConverter &typeConverter,
+    OwningRewritePatternList &patterns) {
+  patterns.insert<BufferizeCastOp, BufferizeExtractOp>(typeConverter, context);
 }
-```
 
-Note that a temporary buffer for %2 was introduced to free all allocations
-properly. Note further that the unnecessary allocation of %3 can be easily
-removed using one of the post-pass transformations.
-
-Reconsider the previously introduced sample demonstrating dynamically shaped
-types:
-
-```mlir
-func @condBranchDynamicType(
-  %arg0: i1,
-  %arg1: memref<?xf32>,
-  %arg2: memref<?xf32>,
-  %arg3: index) {
-  cond_br %arg0, ^bb1, ^bb2(%arg3: index)
-^bb1:
-  br ^bb3(%arg1 : memref<?xf32>)
-^bb2(%0: index):
-  %1 = alloc(%0) : memref<?xf32>  // aliases: %2
-  use(%1)
-  br ^bb3(%1 : memref<?xf32>)
-^bb3(%2: memref<?xf32>):
-  "linalg.copy"(%2, %arg2) : (memref<?xf32>, memref<?xf32>) -> ()
-  return
-}
-```
+struct TensorBufferizePass : public TensorBufferizeBase<TensorBufferizePass> {
+  void runOnFunction() override {
+    auto *context = &getContext();
+    BufferizeTypeConverter typeConverter;
+    OwningRewritePatternList patterns;
+    ConversionTarget target(*context);
 
-In the presence of DSTs, we have to parameterize the allocations with
-additional dimension information of the source buffers, we want to copy from.
-BufferDeallocation automatically introduces all required operations to extract
-dimension specifications and wires them with the associated allocations:
-
-```mlir
-func @condBranchDynamicType(
-  %arg0: i1,
-  %arg1: memref<?xf32>,
-  %arg2: memref<?xf32>,
-  %arg3: index) {
-  cond_br %arg0, ^bb1, ^bb2(%arg3 : index)
-^bb1:
-  %c0 = constant 0 : index
-  %0 = dim %arg1, %c0 : memref<?xf32>   // dimension operation to parameterize
-                                        // the following temp allocation
-  %1 = alloc(%0) : memref<?xf32>
-  "linalg.copy"(%arg1, %1) : (memref<?xf32>, memref<?xf32>) -> ()
-  br ^bb3(%1 : memref<?xf32>)
-^bb2(%2: index):
-  %3 = alloc(%2) : memref<?xf32>
-  use(%3)
-  %c0_0 = constant 0 : index
-  %4 = dim %3, %c0_0 : memref<?xf32>  // dimension operation to parameterize
-                                      // the following temp allocation
-  %5 = alloc(%4) : memref<?xf32>
-  "linalg.copy"(%3, %5) : (memref<?xf32>, memref<?xf32>) -> ()
-  dealloc %3 : memref<?xf32>  // %3 can be safely freed here
-  br ^bb3(%5 : memref<?xf32>)
-^bb3(%6: memref<?xf32>):
-  "linalg.copy"(%6, %arg2) : (memref<?xf32>, memref<?xf32>) -> ()
-  dealloc %6 : memref<?xf32>  // %6 can be safely freed here
-  return
-}
-```
+    populateTensorBufferizePatterns(context, typeConverter, patterns);
+    target.addIllegalOp<tensor::CastOp, tensor::ExtractOp>();
+    target.addLegalDialect<StandardOpsDialect>();
 
-BufferDeallocation performs a fix-point iteration taking all aliases of all
-tracked allocations into account. We initialize the general iteration process
-using all tracked allocations and their associated aliases. As soon as we
-encounter an alias that is not properly dominated by our allocation, we mark
-this alias as _critical_ (needs to be freed and tracked by the internal
-fix-point iteration). The following sample demonstrates the presence of
-critical and non-critical aliases:
-
-![nested_branch_example_pre_move](/includes/img/nested_branch_example_pre_move.svg)
-
-```mlir
-func @condBranchDynamicTypeNested(
-  %arg0: i1,
-  %arg1: memref<?xf32>,  // aliases: %3, %4
-  %arg2: memref<?xf32>,
-  %arg3: index) {
-  cond_br %arg0, ^bb1, ^bb2(%arg3: index)
-^bb1:
-  br ^bb6(%arg1 : memref<?xf32>)
-^bb2(%0: index):
-  %1 = alloc(%0) : memref<?xf32>   // cannot be moved upwards due to the data
-                                   // dependency to %0
-                                   // aliases: %2, %3, %4
-  use(%1)
-  cond_br %arg0, ^bb3, ^bb4
-^bb3:
-  br ^bb5(%1 : memref<?xf32>)
-^bb4:
-  br ^bb5(%1 : memref<?xf32>)
-^bb5(%2: memref<?xf32>):  // non-crit. alias of %1, since %1 dominates %2
-  br ^bb6(%2 : memref<?xf32>)
-^bb6(%3: memref<?xf32>):  // crit. alias of %arg1 and %2 (in other words %1)
-  br ^bb7(%3 : memref<?xf32>)
-^bb7(%4: memref<?xf32>):  // non-crit. alias of %3, since %3 dominates %4
-  "linalg.copy"(%4, %arg2) : (memref<?xf32>, memref<?xf32>) -> ()
-  return
+    if (failed(
+            applyPartialConversion(getFunction(), target, std::move(patterns))))
+      signalPassFailure();
   }
+};
 ```
 
-Applying BufferDeallocation yields the following output:
+The pass has all the hallmarks of a dialect conversion pass that does type conversions: a `TypeConverter`, a `OwningRewritePatternList`, and a `ConversionTarget`, and a call to `applyPartialConversion`. Note that a function `populateTensorBufferizePatterns` is separated, so that power users can use the patterns independently, if necessary (such as to combine multiple sets of conversion patterns into a single conversion call, for performance).
 
-![nested_branch_example_post_move](/includes/img/nested_branch_example_post_move.svg)
+One convenient utility provided by the MLIR bufferization infrastructure is the `BufferizeTypeConverter`, which comes pre-loaded with the necessary conversions and materializations between `tensor` and `memref`.
 
-```mlir
-func @condBranchDynamicTypeNested(
-  %arg0: i1,
-  %arg1: memref<?xf32>,
-  %arg2: memref<?xf32>,
-  %arg3: index) {
-  cond_br %arg0, ^bb1, ^bb2(%arg3 : index)
-^bb1:
-  %c0 = constant 0 : index
-  %d0 = dim %arg1, %c0 : memref<?xf32>
-  %5 = alloc(%d0) : memref<?xf32>  // temp buffer required due to alias %3
-  "linalg.copy"(%arg1, %5) : (memref<?xf32>, memref<?xf32>) -> ()
-  br ^bb6(%5 : memref<?xf32>)
-^bb2(%0: index):
-  %1 = alloc(%0) : memref<?xf32>
-  use(%1)
-  cond_br %arg0, ^bb3, ^bb4
-^bb3:
-  br ^bb5(%1 : memref<?xf32>)
-^bb4:
-  br ^bb5(%1 : memref<?xf32>)
-^bb5(%2: memref<?xf32>):
-  %c0_0 = constant 0 : index
-  %d1 = dim %2, %c0_0 : memref<?xf32>
-  %6 = alloc(%d1) : memref<?xf32>  // temp buffer required due to alias %3
-  "linalg.copy"(%1, %6) : (memref<?xf32>, memref<?xf32>) -> ()
-  dealloc %1 : memref<?xf32>
-  br ^bb6(%6 : memref<?xf32>)
-^bb6(%3: memref<?xf32>):
-  br ^bb7(%3 : memref<?xf32>)
-^bb7(%4: memref<?xf32>):
-  "linalg.copy"(%4, %arg2) : (memref<?xf32>, memref<?xf32>) -> ()
-  dealloc %3 : memref<?xf32>  // free %3, since %4 is a non-crit. alias of %3
-  return
-}
-```
+In this case, the `StandardOpsDialect` is marked as legal, so the `tensor_load` and `tensor_to_memref` ops, which are inserted automatically by the dialect conversion framework as materializations, are legal. There is a helper `populateBufferizeMaterializationLegality` ([code](https://github.com/llvm/llvm-project/blob/a0b65a7bcd6065688189b3d678c42ed6af9603db/mlir/include/mlir/Transforms/Bufferize.h#L53)) which helps with this in general.
 
-Since %3 is a critical alias, BufferDeallocation introduces an additional
-temporary copy in all predecessor blocks. %3 has an additional (non-critical)
-alias %4 that extends the live range until the end of bb7. Therefore, we can
-free %3 after its last use, while taking all aliases into account. Note that %4
- does not need to be freed, since we did not introduce a copy for it.
-
-The actual introduction of buffer copies is done after the fix-point iteration
-has been terminated and all critical aliases have been detected. A critical
-alias can be either a block argument or another value that is returned by an
-operation. Copies for block arguments are handled by analyzing all predecessor
-blocks. This is primarily done by querying the `BranchOpInterface` of the
-associated branch terminators that can jump to the current block. Consider the
-following example which involves a simple branch and the critical block
-argument %2:
-
-```mlir
-  custom.br ^bb1(..., %0, : ...)
-  ...
-  custom.br ^bb1(..., %1, : ...)
-  ...
-^bb1(%2: memref<2xf32>):
-  ...
-```
+### Other partial bufferization examples
 
-The `BranchOpInterface` allows us to determine the actual values that will be
-passed to block bb1 and its argument %2 by analyzing its predecessor blocks.
-Once we have resolved the values %0 and %1 (that are associated with %2 in this
-sample), we can introduce a temporary buffer and clone its contents into the
-new buffer. Afterwards, we rewire the branch operands to use the newly
-allocated buffer instead. However, blocks can have implicitly defined
-predecessors by parent ops that implement the `RegionBranchOpInterface`. This
-can be the case if this block argument belongs to the entry block of a region.
-In this setting, we have to identify all predecessor regions defined by the
-parent operation. For every region, we need to get all terminator operations
-implementing the `ReturnLike` trait, indicating that they can branch to our
-current block. Finally, we can use a similar functionality as described above
-to add the temporary copy. This time, we can modify the terminator operands
-directly without touching a high-level interface.
-
-Consider the following inner-region control-flow sample that uses an imaginary
-“custom.region_if” operation. It either executes the “then” or “else” region
-and always continues to the “join” region. The “custom.region_if_yield”
-operation returns a result to the parent operation. This sample demonstrates
-the use of the `RegionBranchOpInterface` to determine predecessors in order to
-infer the high-level control flow:
-
-```mlir
-func @inner_region_control_flow(
-  %arg0 : index,
-  %arg1 : index) -> memref<?x?xf32> {
-  %0 = alloc(%arg0, %arg0) : memref<?x?xf32>
-  %1 = custom.region_if %0 : memref<?x?xf32> -> (memref<?x?xf32>)
-   then(%arg2 : memref<?x?xf32>) {  // aliases: %arg4, %1
-    custom.region_if_yield %arg2 : memref<?x?xf32>
-   } else(%arg3 : memref<?x?xf32>) {  // aliases: %arg4, %1
-    custom.region_if_yield %arg3 : memref<?x?xf32>
-   } join(%arg4 : memref<?x?xf32>) {  // aliases: %1
-    custom.region_if_yield %arg4 : memref<?x?xf32>
-   }
-  return %1 : memref<?x?xf32>
-}
-```
+- `linalg-bufferize` ([code](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/lib/Dialect/Linalg/Transforms/Bufferize.cpp#L1), [test](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/test/Dialect/Linalg/bufferize.mlir#L1))
+  - Bufferizes the `linalg` dialect.
+  - This is an example of how to simultaneously bufferize all the ops that satisfy a certain OpInterface with a single pattern. Specifically, `BufferizeAnyLinalgOp` ([code](https://github.com/llvm/llvm-project/blob/daaaed6bb89044ac58a23f1bb1ccdd12342a5a58/mlir/lib/Dialect/Linalg/Transforms/Bufferize.cpp#L170)) bufferizes any ops that implements the `LinalgOp` interface.
 
-![region_branch_example_pre_move](/includes/img/region_branch_example_pre_move.svg)
+- `scf-bufferize` ([code](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/lib/Dialect/SCF/Transforms/Bufferize.cpp#L1), [test](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/test/Dialect/SCF/bufferize.mlir#L1))
+  - Bufferizes ops from the `scf` dialect.
+  - This is an example of how to bufferize ops that implement `RegionBranchOpInterface` (that is, they use regions to represent control flow).
+  - The bulk of the work is done by `lib/Dialect/SCF/Transforms/StructuralTypeConversions.cpp` ([code](https://github.com/llvm/llvm-project/blob/daaaed6bb89044ac58a23f1bb1ccdd12342a5a58/mlir/lib/Dialect/SCF/Transforms/StructuralTypeConversions.cpp#L1)), which is well-commented and covers how to correctly convert ops that contain regions.
 
-Non-block arguments (other values) can become aliases when they are returned by
-dialect-specific operations. BufferDeallocation supports this behavior via the
-`RegionBranchOpInterface`. Consider the following example that uses an “scf.if”
-operation to determine the value of %2 at runtime which creates an alias:
-
-```mlir
-func @nested_region_control_flow(%arg0 : index, %arg1 : index) -> memref<?x?xf32> {
-  %0 = cmpi "eq", %arg0, %arg1 : index
-  %1 = alloc(%arg0, %arg0) : memref<?x?xf32>
-  %2 = scf.if %0 -> (memref<?x?xf32>) {
-    scf.yield %1 : memref<?x?xf32>   // %2 will be an alias of %1
-  } else {
-    %3 = alloc(%arg0, %arg1) : memref<?x?xf32>  // nested allocation in a div.
-                                                // branch
-    use(%3)
-    scf.yield %1 : memref<?x?xf32>   // %2 will be an alias of %1
-  }
-  return %2 : memref<?x?xf32>
-}
-```
+- `func-bufferize` ([code](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/lib/Dialect/StandardOps/Transforms/FuncBufferize.cpp#L1), [test](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/test/Dialect/Standard/func-bufferize.mlir#L1))
+  - Bufferizes `func`, `call`, and `BranchOpInterface` ops.
+  - This is an example of how to bufferize ops that have multi-block regions.
+  - This is an example of a pass that is not split along dialect subdivisions.
 
-In this example, a dealloc is inserted to release the buffer within the else
-block since it cannot be accessed by the remainder of the program. Accessing
-the `RegionBranchOpInterface`, allows us to infer that %2 is a non-critical
-alias of %1 which does not need to be tracked.
-
-```mlir
-func @nested_region_control_flow(%arg0: index, %arg1: index) -> memref<?x?xf32> {
-    %0 = cmpi "eq", %arg0, %arg1 : index
-    %1 = alloc(%arg0, %arg0) : memref<?x?xf32>
-    %2 = scf.if %0 -> (memref<?x?xf32>) {
-      scf.yield %1 : memref<?x?xf32>
-    } else {
-      %3 = alloc(%arg0, %arg1) : memref<?x?xf32>
-      use(%3)
-      dealloc %3 : memref<?x?xf32>  // %3 can be safely freed here
-      scf.yield %1 : memref<?x?xf32>
-    }
-    return %2 : memref<?x?xf32>
-}
-```
+- `tensor-constant-bufferize` ([code](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/lib/Dialect/StandardOps/Transforms/TensorConstantBufferize.cpp#L1), [test](https://github.com/llvm/llvm-project/blob/bc8acf2ce8ad6e8c9b1d97b2e02d3f4ad26e1d9d/mlir/test/Dialect/Standard/tensor-constant-bufferize.mlir#L1))
+  - Bufferizes only `std.constant` ops of `tensor` type.
+  - This is an example of setting up the legality so that only a subset of `std.constant` ops get bufferized.
+  - This is an example of a pass that is not split along dialect subdivisions.
 
-Analogous to the previous case, we have to detect all terminator operations in
-all attached regions of “scf.if” that provides a value to its parent operation
-(in this sample via scf.yield). Querying the `RegionBranchOpInterface` allows
-us to determine the regions that “return” a result to their parent operation.
-Like before, we have to update all `ReturnLike` terminators as described above.
-Reconsider a slightly adapted version of the “custom.region_if” example from
-above that uses a nested allocation:
-
-```mlir
-func @inner_region_control_flow_div(
-  %arg0 : index,
-  %arg1 : index) -> memref<?x?xf32> {
-  %0 = alloc(%arg0, %arg0) : memref<?x?xf32>
-  %1 = custom.region_if %0 : memref<?x?xf32> -> (memref<?x?xf32>)
-   then(%arg2 : memref<?x?xf32>) {  // aliases: %arg4, %1
-    custom.region_if_yield %arg2 : memref<?x?xf32>
-   } else(%arg3 : memref<?x?xf32>) {
-    %2 = alloc(%arg0, %arg1) : memref<?x?xf32>  // aliases: %arg4, %1
-    custom.region_if_yield %2 : memref<?x?xf32>
-   } join(%arg4 : memref<?x?xf32>) {  // aliases: %1
-    custom.region_if_yield %arg4 : memref<?x?xf32>
-   }
-  return %1 : memref<?x?xf32>
-}
-```
+## How to write a finalizing bufferization pass
 
-Since the allocation %2 happens in a divergent branch and cannot be safely
-deallocated in a post-dominator, %arg4 will be considered a critical alias.
-Furthermore, %arg4 is returned to its parent operation and has an alias %1.
-This causes BufferDeallocation to introduce additional copies:
-
-```mlir
-func @inner_region_control_flow_div(
-  %arg0 : index,
-  %arg1 : index) -> memref<?x?xf32> {
-  %0 = alloc(%arg0, %arg0) : memref<?x?xf32>
-  %1 = custom.region_if %0 : memref<?x?xf32> -> (memref<?x?xf32>)
-   then(%arg2 : memref<?x?xf32>) {
-    %c0 = constant 0 : index  // determine dimension extents for temp allocation
-    %2 = dim %arg2, %c0 : memref<?x?xf32>
-    %c1 = constant 1 : index
-    %3 = dim %arg2, %c1 : memref<?x?xf32>
-    %4 = alloc(%2, %3) : memref<?x?xf32>  // temp buffer required due to critic.
-                                          // alias %arg4
-    linalg.copy(%arg2, %4) : memref<?x?xf32>, memref<?x?xf32>
-    custom.region_if_yield %4 : memref<?x?xf32>
-   } else(%arg3 : memref<?x?xf32>) {
-    %2 = alloc(%arg0, %arg1) : memref<?x?xf32>
-    %c0 = constant 0 : index  // determine dimension extents for temp allocation
-    %3 = dim %2, %c0 : memref<?x?xf32>
-    %c1 = constant 1 : index
-    %4 = dim %2, %c1 : memref<?x?xf32>
-    %5 = alloc(%3, %4) : memref<?x?xf32>  // temp buffer required due to critic.
-                                          // alias %arg4
-    linalg.copy(%2, %5) : memref<?x?xf32>, memref<?x?xf32>
-    dealloc %2 : memref<?x?xf32>
-    custom.region_if_yield %5 : memref<?x?xf32>
-   } join(%arg4: memref<?x?xf32>) {
-    %c0 = constant 0 : index  // determine dimension extents for temp allocation
-    %2 = dim %arg4, %c0 : memref<?x?xf32>
-    %c1 = constant 1 : index
-    %3 = dim %arg4, %c1 : memref<?x?xf32>
-    %4 = alloc(%2, %3) : memref<?x?xf32>  // this allocation will be removed by
-                                          // applying the copy removal pass
-    linalg.copy(%arg4, %4) : memref<?x?xf32>, memref<?x?xf32>
-    dealloc %arg4 : memref<?x?xf32>
-    custom.region_if_yield %4 : memref<?x?xf32>
-   }
-  dealloc %0 : memref<?x?xf32>  // %0 can be safely freed here
-  return %1 : memref<?x?xf32>
-}
-```
-
-### Placement of Deallocs
-
-After introducing allocs and copies, deallocs have to be placed to free
-allocated memory and avoid memory leaks. The deallocation needs to take place
-after the last use of the given value. The position can be determined by
-calculating the common post-dominator of all values using their remaining
-non-critical aliases. A special-case is the presence of back edges: since such
-edges can cause memory leaks when a newly allocated buffer flows back to
-another part of the program. In these cases, we need to free the associated
-buffer instances from the previous iteration by inserting additional deallocs.
-
-Consider the following “scf.for” use case containing a nested structured
-control-flow if:
-
-```mlir
-func @loop_nested_if(
-  %lb: index,
-  %ub: index,
-  %step: index,
-  %buf: memref<2xf32>,
-  %res: memref<2xf32>) {
-  %0 = scf.for %i = %lb to %ub step %step
-    iter_args(%iterBuf = %buf) -> memref<2xf32> {
-    %1 = cmpi "eq", %i, %ub : index
-    %2 = scf.if %1 -> (memref<2xf32>) {
-      %3 = alloc() : memref<2xf32>  // makes %2 a critical alias due to a
-                                    // divergent allocation
-      use(%3)
-      scf.yield %3 : memref<2xf32>
-    } else {
-      scf.yield %iterBuf : memref<2xf32>
-    }
-    scf.yield %2 : memref<2xf32>
-  }
-  "linalg.copy"(%0, %res) : (memref<2xf32>, memref<2xf32>) -> ()
-  return
-}
-```
+The contract of a finalizing bufferization pass is that all tensors are gone from the program.
 
-In this example, the _then_ branch of the nested “scf.if” operation returns a
-newly allocated buffer.
+The easiest way to write a finalizing bufferize pass is to not write one at all! MLIR provides a pass `finalizing-bufferize` which eliminates the `tensor_load` / `tensor_to_memref` materialization ops inserted by partial bufferization passes and emits an error if that is not sufficient to remove all tensors from the program.
 
-Since this allocation happens in the scope of a divergent branch, %2 becomes a
-critical alias that needs to be handled. As before, we have to insert
-additional copies to eliminate this alias using copies of %3 and %iterBuf. This
-guarantees that %2 will be a newly allocated buffer that is returned in each
-iteration. However, “returning” %2 to its alias %iterBuf turns %iterBuf into a
-critical alias as well. In other words, we have to create a copy of %2 to pass
-it to %iterBuf. Since this jump represents a back edge, and %2 will always be a
-new buffer, we have to free the buffer from the previous iteration to avoid
-memory leaks:
-
-```mlir
-func @loop_nested_if(
-  %lb: index,
-  %ub: index,
-  %step: index,
-  %buf: memref<2xf32>,
-  %res: memref<2xf32>) {
-  %4 = alloc() : memref<2xf32>
-  "linalg.copy"(%buf, %4) : (memref<2xf32>, memref<2xf32>) -> ()
-  %0 = scf.for %i = %lb to %ub step %step
-    iter_args(%iterBuf = %4) -> memref<2xf32> {
-    %1 = cmpi "eq", %i, %ub : index
-    %2 = scf.if %1 -> (memref<2xf32>) {
-      %3 = alloc() : memref<2xf32> // makes %2 a critical alias
-      use(%3)
-      %5 = alloc() : memref<2xf32> // temp copy due to crit. alias %2
-      "linalg.copy"(%3, %5) : memref<2xf32>, memref<2xf32>
-      dealloc %3 : memref<2xf32>
-      scf.yield %5 : memref<2xf32>
-    } else {
-      %6 = alloc() : memref<2xf32> // temp copy due to crit. alias %2
-      "linalg.copy"(%iterBuf, %6) : memref<2xf32>, memref<2xf32>
-      scf.yield %6 : memref<2xf32>
-    }
-    %7 = alloc() : memref<2xf32> // temp copy due to crit. alias %iterBuf
-    "linalg.copy"(%2, %7) : memref<2xf32>, memref<2xf32>
-    dealloc %2 : memref<2xf32>
-    dealloc %iterBuf : memref<2xf32> // free backedge iteration variable
-    scf.yield %7 : memref<2xf32>
-  }
-  "linalg.copy"(%0, %res) : (memref<2xf32>, memref<2xf32>) -> ()
-  dealloc %0 : memref<2xf32> // free temp copy %0
-  return
-}
-```
+This pass is sufficient when partial bufferization passes have bufferized all the ops in the program, leaving behind only the materializations. When possible, it is recommended to structure your pass pipeline this way, as this has the significant advantage that if an op does not get bufferized (due to a missing pattern, bug in the code, etc.), `finalizing-bufferize` will emit a nice clean error, and the IR seen by `finalizing-bufferize` will only contain only one unbufferized op.
 
-Example for loop-like control flow. The CFG contains back edges that have to be
-handled to avoid memory leaks. The bufferization is able to free the backedge
-iteration variable %iterBuf.
-
-### Private Analyses Implementations
-
-The BufferDeallocation transformation relies on one primary control-flow
-analysis: BufferPlacementAliasAnalysis. Furthermore, we also use dominance and
-liveness to place and move nodes. The liveness analysis determines the live
-range of a given value. Within this range, a value is alive and can or will be
-used in the course of the program. After this range, the value is dead and can
-be discarded - in our case, the buffer can be freed. To place the allocs, we
-need to know from which position a value will be alive. The allocs have to be
-placed in front of this position. However, the most important analysis is the
-alias analysis that is needed to introduce copies and to place all
-deallocations.
-
-## Post Phase
-
-In order to limit the complexity of the BufferDeallocation transformation, some
-tiny code-polishing/optimization transformations are not applied on-the-fly
-during placement. Currently, there is only the CopyRemoval transformation to
-remove unnecessary copy and allocation operations.
-
-Note: further transformations might be added to the post-pass phase in the
-future.
-
-### CopyRemoval Pass
-
-A common pattern that arises during placement is the introduction of
-unnecessary temporary copies that are used instead of the original source
-buffer. For this reason, there is a post-pass transformation that removes these
-allocations and copies via `-copy-removal`. This pass, besides removing
-unnecessary copy operations, will also remove the dead allocations and their
-corresponding deallocation operations. The CopyRemoval pass can currently be
-applied to operations that implement the `CopyOpInterface` in any of these two
-situations which are
-
-* reusing the source buffer of the copy operation.
-* reusing the target buffer of the copy operation.
-
-### Reusing the Source Buffer of the Copy Operation
-
-In this case, the source of the copy operation can be used instead of target.
-The unused allocation and deallocation operations that are defined for this
-copy operation are also removed. Here is a working example generated by the
-BufferDeallocation pass that allocates a buffer with dynamic size. A deeper
-analysis of this sample reveals that the highlighted operations are redundant
-and can be removed.
-
-```mlir
-func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
-  %7 = alloc(%arg0, %arg1) : memref<?x?xf32>
-  %c0_0 = constant 0 : index
-  %8 = dim %7, %c0_0 : memref<?x?xf32>
-  %c1_1 = constant 1 : index
-  %9 = dim %7, %c1_1 : memref<?x?xf32>
-  %10 = alloc(%8, %9) : memref<?x?xf32>
-  linalg.copy(%7, %10) : memref<?x?xf32>, memref<?x?xf32>
-  dealloc %7 : memref<?x?xf32>
-  return %10 : memref<?x?xf32>
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @dynamic_allocation(%arg0: index, %arg1: index) -> memref<?x?xf32> {
-  %7 = alloc(%arg0, %arg1) : memref<?x?xf32>
-  %c0_0 = constant 0 : index
-  %8 = dim %7, %c0_0 : memref<?x?xf32>
-  %c1_1 = constant 1 : index
-  %9 = dim %7, %c1_1 : memref<?x?xf32>
-  return %7 : memref<?x?xf32>
-}
-```
-
-In this case, the additional copy %10 can be replaced with its original source
-buffer %7. This also applies to the associated dealloc operation of %7.
-
-To limit the complexity of this transformation, it only removes copy operations
-when the following constraints are met:
-
-* The copy operation, the defining operation for the target value, and the
-deallocation of the source value lie in the same block.
-* There are no users/aliases of the target value between the defining operation
-of the target value and its copy operation.
-* There are no users/aliases of the source value between its associated copy
-operation and the deallocation of the source value.
-
-### Reusing the Target Buffer of the Copy Operation
-
-In this case, the target buffer of the copy operation can be used instead of
-its source. The unused allocation and deallocation operations that are defined
-for this copy operation are also removed.
-
-Consider the following example where a generic linalg operation writes the
-result to %temp and then copies %temp to %result. However, these two operations
-can be merged into a single step. Copy removal removes the copy operation and
-%temp, and replaces the uses of %temp with %result:
-
-```mlir
-func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
-  %temp = alloc() : memref<2xf32>
-  linalg.generic {
-    args_in = 1 : i64,
-    args_out = 1 : i64,
-    indexing_maps = [#map0, #map0],
-    iterator_types = ["parallel"]} %arg0, %temp {
-  ^bb0(%gen2_arg0: f32, %gen2_arg1: f32):
-    %tmp2 = exp %gen2_arg0 : f32
-    linalg.yield %tmp2 : f32
-  }: memref<2xf32>, memref<2xf32>
-  "linalg.copy"(%temp, %result) : (memref<2xf32>, memref<2xf32>) -> ()
-  dealloc %temp : memref<2xf32>
-  return
-}
-```
-
-Will be transformed to:
-
-```mlir
-func @reuseTarget(%arg0: memref<2xf32>, %result: memref<2xf32>){
-  linalg.generic {
-    args_in = 1 : i64,
-    args_out = 1 : i64,
-    indexing_maps = [#map0, #map0],
-    iterator_types = ["parallel"]} %arg0, %result {
-  ^bb0(%gen2_arg0: f32, %gen2_arg1: f32):
-    %tmp2 = exp %gen2_arg0 : f32
-    linalg.yield %tmp2 : f32
-  }: memref<2xf32>, memref<2xf32>
-  return
-}
-```
+However, before the current bufferization infrastructure was put in place, bufferization was only possible to do as a single finalizing bufferization mega-pass that used the `populate*BufferizePatterns` functions from multiple dialects to simultaneously bufferize everything at once. Thus, one might see code in downstream projects structured this way. This structure is not recommended in new code. A helper, `populateEliminateBufferizeMaterializationsPatterns` ([code](https://github.com/llvm/llvm-project/blob/a0b65a7bcd6065688189b3d678c42ed6af9603db/mlir/include/mlir/Transforms/Bufferize.h#L58)) is available for such passes to provide patterns that eliminate `tensor_load` and `tensor_to_memref`.
 
-Like before, several constraints to use the transformation apply:
+## Changes since [the talk](#the-talk)
 
-* The copy operation, the defining operation of the source value, and the
-deallocation of the source value lie in the same block.
-* There are no users/aliases of the target value between the defining operation
-of the source value and the copy operation.
-* There are no users/aliases of the source value between the copy operation and
-the deallocation of the source value.
-
-## Known Limitations
-
-BufferDeallocation introduces additional copies using allocations from the
-“std” dialect (“std.alloc”). Analogous, all deallocations use the “std”
-dialect-free operation “std.dealloc”. The actual copy process is realized using
-“linalg.copy”. Furthermore, buffers are essentially immutable after their
-creation in a block. Another limitations are known in the case using
-unstructered control flow.
+- `func-bufferize` was changed to be a partial conversion pass, and there is a new `finalizing-bufferize` which serves as a general finalizing bufferization pass.