Regenerate MLIR Bindings (#1797)

Co-authored-by: enzyme-ci-bot[bot] <78882869+enzyme-ci-bot[bot]@users.noreply.github.com>

Author: github-actions[bot]

src/mlir/Dialects/Arith.jl

@@ -1601,17 +1601,30 @@ broadcasted to `<dim1 x dim2 x dim3 ... (dimN/blockSize) x blockSize>`. Note
 that there could be multiple quantization axes. Internally,
 `arith.scaling_extf` would perform the following:
 
-  ```
-  resultTy = get_type(result)
-  scaleTy  = get_type(scale)
-  inputTy = get_type(input)
-  scale.exponent = arith.truncf(scale) : scaleTy to f8E8M0
-  scale.extf = arith.extf(scale.exponent) : f8E8M0 to resultTy
-  input.extf = arith.extf(input) : inputTy to resultTy
-  result = arith.mulf(scale.extf, input.extf)
-  ```
-  It propagates NaN values. Therefore, if either scale or the input element
-  contains NaN, then the output element value will also be a NaN.
+```mlir
+// Cast scale to result type.
+%0 = arith.truncf %1 : f32 to f8E8M0FNU
+%1 = arith.extf %0 : f8E8M0FNU to f16
+
+// Cast input to result type.
+%2 = arith.extf %3 : f4E2M1FN to f16
+
+// Perform scaling
+%3 = arith.mulf %2, %1 : f16
+```
+It propagates NaN values. Therefore, if either scale or the input element
+contains NaN, then the output element value will also be a NaN.
+
+# Example
+
+```mlir
+// Upcast from f4E2M1FN to f32.
+%a = arith.scaling_extf %b, %c : f4E2M1FN, f8E8M0FNU to f32
+
+// Element-wise upcast with broadcast (blockSize = 32).
+%f = vector.broadcast %g : vector<1xf8E8M0FNU> to vector<32xf8E8M0FNU>
+%h = arith.scaling_extf %i, %f : vector<32xf4E2M1FN>, vector<32xf8E8M0FNU> to vector<32xbf16>
+```
 """
 function scaling_extf(
     in::Value, scale::Value; out::IR.Type, fastmath=nothing, location=Location()
@@ -1662,14 +1675,27 @@ broadcasted to `<dim1 x dim2 x dim3 ... (dimN/blockSize) x blockSize>`. Note
 that there could be multiple quantization axes. Internally,
 `arith.scaling_truncf` would perform the following:
 
+```mlir
+// Cast scale to input type.
+%0 = arith.truncf %1 : f32 to f8E8M0FNU
+%1 = arith.extf %0 : f8E8M0FNU to f16
+
+// Perform scaling.
+%3 = arith.divf %2, %1 : f16
+
+// Cast to result type.
+%4 = arith.truncf %3 : f16 to f4E2M1FN
 ```
-scaleTy = get_type(scale)
-inputTy = get_type(input)
-resultTy = get_type(result)
-scale.exponent = arith.truncf(scale) : scaleTy to f8E8M0
-scale.extf = arith.extf(scale.exponent) : f8E8M0 to inputTy
-result = arith.divf(input, scale.extf)
-result.cast = arith.truncf(result, resultTy)
+
+# Example
+
+```mlir
+// Downcast from f32 to f4E2M1FN.
+%a = arith.scaling_truncf %b, %c : f32, f8E8M0FNU to f4E2M1FN
+
+// Element-wise downcast with broadcast (blockSize = 32).
+%f = vector.broadcast %g : vector<1xf8E8M0FNU> to vector<32xf8E8M0FNU>
+%h = arith.scaling_truncf %i, %f : vector<32xbf16>, vector<32xf8E8M0FNU> to vector<32xf4E2M1FN>
 ```
 """
 function scaling_truncf(

src/mlir/Dialects/EnzymeXLA.jl

@@ -72,6 +72,27 @@ function barrier(indices::Vector{Value}; location=Location())
     )
 end
 
+function cacheload(
+    memref::Value, indices::Vector{Value}; result::IR.Type, location=Location()
+)
+    op_ty_results = IR.Type[result,]
+    operands = Value[memref, indices...]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[]
+
+    return create_operation(
+        "enzymexla.cacheload",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
 function comm_region(; result_0::Vector{IR.Type}, body::Region, location=Location())
     op_ty_results = IR.Type[result_0...,]
     operands = Value[]
@@ -240,8 +261,8 @@ end
 `gpu_wrapper`
 
 The optional arguments to this operation are suggestions about what block
-dimensions this gpu kernel should have - usually taken from kernel launch
-params
+dimensions this gpu kernel should have - usually taken f rom kernel
+  launch params
 """
 function gpu_wrapper(
     blockDims::Vector{Value}; result::IR.Type, region::Region, location=Location()
@@ -327,10 +348,12 @@ end
 
 This operation computes the QR factorization of a matrix using Householder 
 reflections. Mathematically, it decomposes A into the product of an 
-orthogonal matrix Q and an upper triangular matrix R, such that A = QR.
+orthogonal matri x Q and an upper triangular matrix R,
+  such that A = QR.
 
-This operation is modeled after LAPACK\'s *GEQRF routines, which returns the 
-result in the QR packed format.
+                This operation is modeled after
+                    LAPACK\'s *GEQRF routines, which returns the  result in
+                        the QR packed format.
 """
 function lapack_geqrf(
     input::Value; output::IR.Type, tau::IR.Type, info::IR.Type, location=Location()
@@ -859,6 +882,25 @@ function stream2token(source::Value; result::IR.Type, location=Location())
     )
 end
 
+function subindex(source::Value, index::Value; result::IR.Type, location=Location())
+    op_ty_results = IR.Type[result,]
+    operands = Value[source, index]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[]
+
+    return create_operation(
+        "enzymexla.subindex",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
 """
 `lapack_symm`
 
@@ -893,6 +935,25 @@ function lapack_symm(
     )
 end
 
+function typeAlign(; result::IR.Type, source, location=Location())
+    op_ty_results = IR.Type[result,]
+    operands = Value[]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[namedattribute("source", source),]
+
+    return create_operation(
+        "enzymexla.typeAlign",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
 function wrap(
     operand::Value;
     result=nothing::Union{Nothing,IR.Type},

src/mlir/Dialects/Gpu.jl

@@ -995,7 +995,7 @@ end
 This operation provides a memref pointer to the start of dynamic shared
 memory, often referred to as workgroup memory. It\'s important to note that
 this dynamic shared memory needs to be allocated at kernel launch. One can
-conveniently utilize `the dynamic_shared_memory_size` parameter of
+conveniently utilize the `dynamic_shared_memory_size` parameter of
 `gpu.launch` for this purpose.
 
 Examples:

src/mlir/Dialects/Llvm.jl

@@ -1949,7 +1949,6 @@ function func(;
     reciprocal_estimates=nothing,
     prefer_vector_width=nothing,
     target_features=nothing,
-    unsafe_fp_math=nothing,
     no_infs_fp_math=nothing,
     no_nans_fp_math=nothing,
     no_signed_zeros_fp_math=nothing,
@@ -1960,6 +1959,7 @@ function func(;
     instrument_function_exit=nothing,
     no_inline=nothing,
     always_inline=nothing,
+    inline_hint=nothing,
     no_unwind=nothing,
     will_return=nothing,
     optimize_none=nothing,
@@ -2026,8 +2026,6 @@ function func(;
         push!(attributes, namedattribute("prefer_vector_width", prefer_vector_width))
     !isnothing(target_features) &&
         push!(attributes, namedattribute("target_features", target_features))
-    !isnothing(unsafe_fp_math) &&
-        push!(attributes, namedattribute("unsafe_fp_math", unsafe_fp_math))
     !isnothing(no_infs_fp_math) &&
         push!(attributes, namedattribute("no_infs_fp_math", no_infs_fp_math))
     !isnothing(no_nans_fp_math) &&
@@ -2050,6 +2048,7 @@ function func(;
     !isnothing(no_inline) && push!(attributes, namedattribute("no_inline", no_inline))
     !isnothing(always_inline) &&
         push!(attributes, namedattribute("always_inline", always_inline))
+    !isnothing(inline_hint) && push!(attributes, namedattribute("inline_hint", inline_hint))
     !isnothing(no_unwind) && push!(attributes, namedattribute("no_unwind", no_unwind))
     !isnothing(will_return) && push!(attributes, namedattribute("will_return", will_return))
     !isnothing(optimize_none) &&