[Mlir-commits] [mlir] [mlir] [memref] Compile-time memref.alloc Scheduling/Merging optimization (PR #95882)

[llvmlistbot at llvm.org]

================
@@ -0,0 +1,464 @@
+# Compile-time memref.alloc Scheduling and Merging
+
+This document describes a compile-time optimization on `memref.alloc` to reduce
+memory usage and improve memory locality.
+
+## Current status of bufferization and memref pass pipeline
+Bufferization is a process in the current MLIR of converting ops with tensor
+semantics to ops with memref semantics. One-Shot Bufferize is a new tensor
+bufferization pass designed for IR in destination-passing style, and with
+aggressive in-place bufferization. The goal of
+bufferization is to use as little memory as possible and copy as little memory
+as possible, as a result, the existing focus is to determine in-place or
+out-of-place among the OpOperand and OpResult of individual ops, while not
+considering much about the overall memory reuse across Operators within a
+sub-graph (or partition).
+
+The current implementation of Bufferization and memref pass pipeline focuses on
+copy-avoidance and in-place reusing of the memory. Consider a computation graph
+of 4 layers of matmul sharing the same weight:
+```mlir
+func.func @mlp(%x: tensor<128x128xf32>, %y: tensor<128x128xf32>) -> tensor<128x128xf32> {
+   %a0 = tensor.empty() : tensor<128x128xf32>
+   %a = linalg.matmul ins(%x, %y: tensor<128x128xf32>, tensor<128x128xf32>) outs(%a0: tensor<128x128xf32>) -> tensor<128x128xf32>
+   %b0 = tensor.empty() : tensor<128x128xf32>
+   %b = linalg.matmul ins(%a, %y: tensor<128x128xf32>, tensor<128x128xf32>) outs(%b0: tensor<128x128xf32>) -> tensor<128x128xf32>
+   %c0 = tensor.empty() : tensor<128x128xf32>
+   %c = linalg.matmul ins(%b, %y: tensor<128x128xf32>, tensor<128x128xf32>) outs(%c0: tensor<128x128xf32>) -> tensor<128x128xf32>
+   %d0 = tensor.empty() : tensor<128x128xf32>
+   %d = linalg.matmul ins(%c, %y: tensor<128x128xf32>, tensor<128x128xf32>) outs(%d0: tensor<128x128xf32>) -> tensor<128x128xf32>
+   return %d : tensor<128x128xf32>
+}
+```
+
+The bufferization pass will create an `memref.alloc` for each of the tensor
+`a0`, `b0` and `c0`. The bufferization result is like:
+
+```mlir
+func.func @mlp(%x: memref<128x128xf32>, %y: memref<128x128xf32>) -> memref<128x128xf32> {
+   %a0 = memref.alloc() : memref<128x128xf32>
+   linalg.matmul ins(%x, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%a0: memref<128x128xf32>)
+   %b0 = memref.alloc() : memref<128x128xf32>
+   linalg.matmul ins(%a0, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%b0: memref<128x128xf32>)
+   %c0 = memref.alloc() : memref<128x128xf32>
+   linalg.matmul ins(%b0, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%c0: memref<128x128xf32>)
+   %d0 = memref.alloc() : memref<128x128xf32>
+   linalg.matmul ins(%c0, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%d0: memref<128x128xf32>)
+   return %d0 : memref<128x128xf32>
+}
+```
+
+Without further optimizations, 3 temp buffers will be allocated at the runtime
+for these tensors. However, as we can see in the IR, the buffer `a0` is no
+longer used when buffer `c0` is allocated. So buffer `c0` can reuse the memory
+buffer of buffer `a0`, to reduce the memory size footprint and improve the
+locality.
+
+An observation of the current bufferization and memref passes is that they do
+not consider the memory buffer planning - to reuse the buffer/memref for less
+total size and better locality.
+
+## Merge-alloc pass
+An optimization pass has been introduced to consolidate multiple allocations
+(`memref.alloc` ops) into a single `memref.alloc` op and each "mergeable"
+`memref.alloc` op will be transformed into a "slice" from the "single allocated
+buffer" with `memref.view` and some compile-time decided `offsets`. This
+optimization works on `memref` instead of `tensor` ops, so it should be executed
+after bufferization pass, and before adding buffer deallocation ops.
+
+While merging the memory allocations, the transform should consider the lifetime
+of each allocated `memref`s. By lifetime, we mean the range of time when the
+memory allocated from `memref.alloc` is actively used. Views (aliases) into a
+"base" memref should contribute to the lifetime of the "base". A later
+`memref.alloc` should consider to reuse the memory of a previously allocated
+memref, if the lifetime of these two does not overlap. The transform will
+perform the "reusing" of memory by setting the `offset` of the later
+`memref.view` to a position within the memory range of a previous allocation's
+`memref.alloc` from the `single allocated buffer`.
+
+Below is the expected transformation result of the example IR in the above
+section:
+
+```mlir
+func.func @mlp(%x: memref<256x128xf32>, %y: memref<128x128xf32>) -> memref<128x128xf32> {
+   %single_buffer = memref.alloc() : memref<131072xi8> // 128*128*sizeof(f32)*2
+   %a0 = memref.view %single_buffer[0][] : memref<131072xi8> to memref<128x128xf32> // a0 takes the memory from byte offset 0
+   linalg.matmul ins(%x, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%a0: memref<128x128xf32>)
+   %b0 = memref.view %single_buffer[65536][] : memref<131072xi8> to memref<128x128xf32> // b0 takes the memory from byte offset 128*128*sizeof(f32)
+   linalg.matmul ins(%a0, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%b0: memref<128x128xf32>) 
+   %c0 = memref.view %single_buffer[0][] : memref<131072xi8> to memref<128x128xf32> // c0 takes the memory from byte offset 0
+   linalg.matmul ins(%b0, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%c0: memref<128x128xf32>)
+   %d0 = memref.alloc() : memref<128x128xf32> // d0 is returned, do not merge
+   linalg.matmul ins(%c0, %y: memref<128x128xf32>, memref<128x128xf32>) outs(%d0: memref<128x128xf32>)
+   return %d0 : memref<128x128xf32>
+}
+```
+
+There is one single allocation `single_buffer` for all temp buffers and `alloc`
+ops for `a0`, `b0` and `c0` are removed. The returned memref `d0` is untouched.
----------------
Menooker wrote:

OK. I added the code to handle ExtractAlignedPointerAsIndexOp and ExtractStridedMetadataOp, and added todo.

https://github.com/llvm/llvm-project/pull/95882