[Mlir-commits] [mlir] 689f978 - [ml_program] fix bufferizesToMemoryRead for ml_program.global_store (#177387)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Fri Jan 23 04:20:35 PST 2026
Author: Nathan Malimban
Date: 2026-01-23T13:20:30+01:00
New Revision: 689f9788d3e482791d45e1e631a1bacf78f318ba
URL: https://github.com/llvm/llvm-project/commit/689f9788d3e482791d45e1e631a1bacf78f318ba
DIFF: https://github.com/llvm/llvm-project/commit/689f9788d3e482791d45e1e631a1bacf78f318ba.diff
LOG: [ml_program] fix bufferizesToMemoryRead for ml_program.global_store (#177387)
This is a fix for the `BufferizableOpInterface` implementation for
`ml_program.global_store`.
`bufferizesToMemoryRead` currently returns false for
`GlobalStoreOpInterface`, but I believe it should return true as
`ml_program.global_store` needs to read its input buffer to know what
value to store to global.
This manifested in a bug where `one-shot-bufferize` would produce MLIR
that copies uninitialized data to the global var instead of the intended
value to be stored.
For the following MLIR:
```
module {
ml_program.global private mutable @"state_tensor"(dense<0.0> : tensor<4x75xf32>) : tensor<4x75xf32>
func.func @main() -> tensor<4x75xf32> {
%c0 = arith.constant 0 : index
%cst_val = arith.constant 1.0 : f32
%initial_state = ml_program.global_load @"state_tensor" : tensor<4x75xf32>
%val = tensor.extract %initial_state[%c0, %c0] : tensor<4x75xf32>
%next_val = arith.addf %val, %cst_val : f32
%updated_tensor = tensor.insert %next_val into %initial_state[%c0, %c0] : tensor<4x75xf32>
ml_program.global_store @"state_tensor" = %updated_tensor : tensor<4x75xf32>
return %updated_tensor : tensor<4x75xf32>
}
}
```
`one-shot-bufferize` produces this incorrect MLIR
```
module {
memref.global "private" @state_tensor : memref<4x75xf32> = dense<0.000000e+00>
func.func @main() -> tensor<4x75xf32> {
%c0 = arith.constant 0 : index
%cst = arith.constant 1.000000e+00 : f32
%0 = memref.get_global @state_tensor : memref<4x75xf32>
%1 = memref.load %0[%c0, %c0] : memref<4x75xf32>
%2 = arith.addf %1, %cst : f32
%alloc = memref.alloc() {alignment = 64 : i64} : memref<4x75xf32>
memref.copy %0, %alloc : memref<4x75xf32> to memref<4x75xf32>
memref.store %2, %alloc[%c0, %c0] : memref<4x75xf32>
%3 = bufferization.to_tensor %alloc : memref<4x75xf32> to tensor<4x75xf32>
%alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<4x75xf32>
%4 = memref.get_global @state_tensor : memref<4x75xf32>
memref.copy %alloc_0, %4 : memref<4x75xf32> to memref<4x75xf32>
return %3 : tensor<4x75xf32>
}
}
```
Note that `memref.copy` at the end copies an uninitialized `alloc_0` to
the global variable.
But after the change we see the following MLIR:
```
module {
memref.global "private" @state_tensor : memref<4x75xf32> = dense<0.000000e+00>
func.func @main() -> tensor<4x75xf32> {
%c0 = arith.constant 0 : index
%cst = arith.constant 1.000000e+00 : f32
%0 = memref.get_global @state_tensor : memref<4x75xf32>
%1 = memref.load %0[%c0, %c0] : memref<4x75xf32>
%2 = arith.addf %1, %cst : f32
%alloc = memref.alloc() {alignment = 64 : i64} : memref<4x75xf32>
memref.copy %0, %alloc : memref<4x75xf32> to memref<4x75xf32>
memref.store %2, %alloc[%c0, %c0] : memref<4x75xf32>
%3 = bufferization.to_tensor %alloc : memref<4x75xf32> to tensor<4x75xf32>
%alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<4x75xf32>
memref.copy %alloc, %alloc_0 : memref<4x75xf32> to memref<4x75xf32>
%4 = memref.get_global @state_tensor : memref<4x75xf32>
memref.copy %alloc_0, %4 : memref<4x75xf32> to memref<4x75xf32>
return %3 : tensor<4x75xf32>
}
}
```
We now see that the relevant data is copied to `alloc_0` before it is
stored in global.
Co-authored-by: Nathan Malimban <nmalimba at ah-nmalimba-l.dhcp.mathworks.com>
Added:
Modified:
mlir/lib/Dialect/MLProgram/Transforms/BufferizableOpInterfaceImpl.cpp
mlir/test/Dialect/MLProgram/one-shot-bufferize.mlir
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/MLProgram/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/MLProgram/Transforms/BufferizableOpInterfaceImpl.cpp
index 364e4d385fd62..c2cbc95e94343 100644
--- a/mlir/lib/Dialect/MLProgram/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/MLProgram/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -118,7 +118,7 @@ struct GlobalStoreOpInterface
bool bufferizesToMemoryRead(Operation *, OpOperand &,
const AnalysisState &) const {
- return false;
+ return true;
}
bool bufferizesToMemoryWrite(Operation *, OpOperand &,
diff --git a/mlir/test/Dialect/MLProgram/one-shot-bufferize.mlir b/mlir/test/Dialect/MLProgram/one-shot-bufferize.mlir
index 942247aef2603..d0eb2606ec7f9 100644
--- a/mlir/test/Dialect/MLProgram/one-shot-bufferize.mlir
+++ b/mlir/test/Dialect/MLProgram/one-shot-bufferize.mlir
@@ -50,3 +50,34 @@ func.func @raw_hazard() -> i64 {
return %extracted : i64
}
+// -----
+
+// CHECK-LABEL: memref.global "private" @state_tensor
+ml_program.global private mutable @"state_tensor"(dense<0.0> : tensor<4x75xf32>) : tensor<4x75xf32>
+
+// CHECK-LABEL: func.func @global_load_store_tensor
+func.func @global_load_store_tensor() -> tensor<4x75xf32> {
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0
+ // CHECK-DAG: %[[CST:.*]] = arith.constant 1.000000e+00
+ // CHECK-DAG: %[[GLOB:.*]] = memref.get_global @state_tensor
+ // CHECK: %[[VAL:.*]] = memref.load %[[GLOB]][%[[C0]], %[[C0]]]
+ // CHECK: %[[ADD:.*]] = arith.addf %[[VAL]], %[[CST]]
+ // CHECK: %[[ALLOC1:.*]] = memref.alloc() {alignment = 64 : i64}
+ // CHECK: memref.copy %[[GLOB]], %[[ALLOC1]]
+ // CHECK: memref.store %[[ADD]], %[[ALLOC1]][%[[C0]], %[[C0]]]
+ // CHECK: %[[TENSOR:.*]] = bufferization.to_tensor %[[ALLOC1]]
+ // CHECK: %[[ALLOC2:.*]] = memref.alloc() {alignment = 64 : i64}
+ // CHECK: memref.copy %[[ALLOC1]], %[[ALLOC2]]
+ // CHECK: %[[GLOB_REF:.*]] = memref.get_global @state_tensor
+ // CHECK: memref.copy %[[ALLOC2]], %[[GLOB_REF]]
+ // CHECK: return %[[TENSOR]]
+ %c0 = arith.constant 0 : index
+ %cst_val = arith.constant 1.0 : f32
+ %initial_state = ml_program.global_load @"state_tensor" : tensor<4x75xf32>
+ %val = tensor.extract %initial_state[%c0, %c0] : tensor<4x75xf32>
+ %next_val = arith.addf %val, %cst_val : f32
+ %updated_tensor = tensor.insert %next_val into %initial_state[%c0, %c0] : tensor<4x75xf32>
+ ml_program.global_store @"state_tensor" = %updated_tensor : tensor<4x75xf32>
+ return %updated_tensor : tensor<4x75xf32>
+}
+
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