[mlir][sparse] added some clarification on sparse tensors ABI

[aartbik]

No description provided.

[llvmbot]

@llvm/pr-subscribers-mlir-sparse

Author: Aart Bik (aartbik)

Changes

---

Full diff: https://github.com/llvm/llvm-project/pull/96331.diff

1 Files Affected:

• (modified) mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td (+13-1)

diff --git a/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td b/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td
index 75a479b898040..58c9b58352651 100644
--- a/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td
+++ b/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td
@@ -14,7 +14,17 @@ include "mlir/Pass/PassBase.td"
 def SparseAssembler : Pass<"sparse-assembler", "ModuleOp"> {
   let summary = "Add [dis]assemble operations on external sparse tensors";
   let description = [{
-    A pass that converts public entry methods that use sparse tensors as
+    Unlike dense tensors, MLIR does **not** provide a direct `_mlir_ciface_`
+    ABI for passing sparse tensors as arguments from and to external methods
+    (within MLIR-generated methods, sparse tensors can be freely passed
+    around, but this eventually uses a bespoke parameter passing format
+    that is subject to change; like opaque pointers when the sparse runtime
+    support library is used or the constituent arrays and structs for
+    direct IR codegen). The sparse assembler pass, however, can be used
+    to obtain a stable `_mlir_ciface_` API for passing sparse tensors
+    in and from an external environment, such as Python, PyTorch, or JAX.
+
+    The pass  converts public entry methods that use sparse tensors as
     input parameters and/or output return values into wrapper methods
     that [dis]assemble the individual tensors that constitute the actual
     storage used externally into MLIR sparse tensors. This pass can be used
@@ -27,6 +37,8 @@ def SparseAssembler : Pass<"sparse-assembler", "ModuleOp"> {
     By default, the pass uses the [dis]assemble operations to input and output
     sparse tensors. When the direct-out option is set, however, the output
     directly returns the MLIR allocated buffers to the external runtime.
+
+    The pass should always run before the actual sparsification passes.
   }];
   let constructor = "mlir::createSparseAssembler()";
   let dependentDialects = [

[llvmbot]

@llvm/pr-subscribers-mlir

Author: Aart Bik (aartbik)

Changes

---

Full diff: https://github.com/llvm/llvm-project/pull/96331.diff

1 Files Affected:

• (modified) mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td (+13-1)

diff --git a/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td b/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td
index 75a479b898040..58c9b58352651 100644
--- a/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td
+++ b/mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td
@@ -14,7 +14,17 @@ include "mlir/Pass/PassBase.td"
 def SparseAssembler : Pass<"sparse-assembler", "ModuleOp"> {
   let summary = "Add [dis]assemble operations on external sparse tensors";
   let description = [{
-    A pass that converts public entry methods that use sparse tensors as
+    Unlike dense tensors, MLIR does **not** provide a direct `_mlir_ciface_`
+    ABI for passing sparse tensors as arguments from and to external methods
+    (within MLIR-generated methods, sparse tensors can be freely passed
+    around, but this eventually uses a bespoke parameter passing format
+    that is subject to change; like opaque pointers when the sparse runtime
+    support library is used or the constituent arrays and structs for
+    direct IR codegen). The sparse assembler pass, however, can be used
+    to obtain a stable `_mlir_ciface_` API for passing sparse tensors
+    in and from an external environment, such as Python, PyTorch, or JAX.
+
+    The pass  converts public entry methods that use sparse tensors as
     input parameters and/or output return values into wrapper methods
     that [dis]assemble the individual tensors that constitute the actual
     storage used externally into MLIR sparse tensors. This pass can be used
@@ -27,6 +37,8 @@ def SparseAssembler : Pass<"sparse-assembler", "ModuleOp"> {
     By default, the pass uses the [dis]assemble operations to input and output
     sparse tensors. When the direct-out option is set, however, the output
     directly returns the MLIR allocated buffers to the external runtime.
+
+    The pass should always run before the actual sparsification passes.
   }];
   let constructor = "mlir::createSparseAssembler()";
   let dependentDialects = [