Regenerate MLIR Bindings (#644)

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

Author: github-actions[bot]

src/mlir/Dialects/Nvvm.jl

@@ -688,6 +688,132 @@ function cp_async_bulk_commit_group(; location=Location())
     )
 end
 
+"""
+`cp_async_bulk_shared_cluster_global`
+
+Initiates an asynchronous copy operation from global memory to cluster\'s
+shared memory.
+
+The `multicastMask` operand is optional. When it is present, the Op copies
+data from global memory to shared memory of multiple CTAs in the cluster.
+Operand `multicastMask` specifies the destination CTAs in the cluster such
+that each bit position in the 16-bit `multicastMask` operand corresponds to
+the `nvvm.read.ptx.sreg.ctaid` of the destination CTA.
+
+The `l2CacheHint` operand is optional, and it is used to specify cache
+eviction policy that may be used during the memory access.
+[For more information, see PTX ISA]
+(https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk)
+"""
+function cp_async_bulk_shared_cluster_global(
+    dstMem::Value,
+    srcMem::Value,
+    mbar::Value,
+    size::Value,
+    multicastMask=nothing::Union{Nothing,Value};
+    l2CacheHint=nothing::Union{Nothing,Value},
+    location=Location(),
+)
+    op_ty_results = IR.Type[]
+    operands = Value[dstMem, srcMem, mbar, size]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[]
+    !isnothing(multicastMask) && push!(operands, multicastMask)
+    !isnothing(l2CacheHint) && push!(operands, l2CacheHint)
+    push!(attributes, operandsegmentsizes([
+        1,
+        1,
+        1,
+        1,
+        if (multicastMask == nothing)
+            0
+        elseif 1(l2CacheHint == nothing)
+            0
+        else
+            1
+        end,
+    ]))
+
+    return create_operation(
+        "nvvm.cp.async.bulk.shared.cluster.global",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
+"""
+`cp_async_bulk_global_shared_cta`
+
+Initiates an asynchronous copy operation from Shared CTA memory to
+global memory.
+
+The `l2CacheHint` operand is optional, and it is used to specify cache
+eviction policy that may be used during the memory access.
+[For more information, see PTX ISA]
+(https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk)
+"""
+function cp_async_bulk_global_shared_cta(
+    dstMem::Value,
+    srcMem::Value,
+    size::Value,
+    l2CacheHint=nothing::Union{Nothing,Value};
+    location=Location(),
+)
+    op_ty_results = IR.Type[]
+    operands = Value[dstMem, srcMem, size]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[]
+    !isnothing(l2CacheHint) && push!(operands, l2CacheHint)
+
+    return create_operation(
+        "nvvm.cp.async.bulk.global.shared.cta",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
+"""
+`cp_async_bulk_shared_cluster_shared_cta`
+
+Initiates an asynchronous copy operation from Shared CTA memory to Shared
+cluster memory.
+
+[For more information, see PTX ISA]
+(https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk)
+"""
+function cp_async_bulk_shared_cluster_shared_cta(
+    dstMem::Value, srcMem::Value, mbar::Value, size::Value; location=Location()
+)
+    op_ty_results = IR.Type[]
+    operands = Value[dstMem, srcMem, mbar, size]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[]
+
+    return create_operation(
+        "nvvm.cp.async.bulk.shared.cluster.shared.cta",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
 """
 `cp_async_bulk_tensor_shared_cluster_global`
 
@@ -1063,6 +1189,41 @@ function cp_async_wait_group(; n, location=Location())
     )
 end
 
+"""
+`cvt_float_to_tf32`
+
+This Op converts the given f32 input to tf32.
+The result `res` is represented as an i32 type.
+The `relu` attribute, when set, lowers to the \'.relu\' variant of
+the cvt instruction. The `rnd` and `sat` attributes specify the
+the rounding and saturation modes respectively.
+[For more information, see PTX ISA]
+(https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cvt)
+"""
+function cvt_float_to_tf32(
+    src::Value; res::IR.Type, rnd=nothing, sat=nothing, relu=nothing, location=Location()
+)
+    op_ty_results = IR.Type[res,]
+    operands = Value[src,]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[]
+    !isnothing(rnd) && push!(attributes, namedattribute("rnd", rnd))
+    !isnothing(sat) && push!(attributes, namedattribute("sat", sat))
+    !isnothing(relu) && push!(attributes, namedattribute("relu", relu))
+
+    return create_operation(
+        "nvvm.cvt.float.to.tf32",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
 """
 `elect_sync`
 

src/mlir/Dialects/Shardy.jl

@@ -18,10 +18,11 @@ import ...API
 
 Gathers chunks of a tensor along axes specified in `gathering_axes`.
 
-The `gathering_axes` is a list of lists of axes. Each inner list specifies
-the axes along which a separate gather should be performed. The outer list
-is over the dimensions of the tensor. It will be applied to the sharding of
-the operand (`tensor`) to obtain the sharding of the result (`out_sharding`).
+The `gathering_axes` is a list of lists of axes. The outer list is over the
+dimensions of the tensor. Each inner list specifies the axes along which a
+separate gather should be performed on the respective dimension. It will be
+applied to the sharding of the operand (`tensor`) to obtain the sharding of
+the result (`out_sharding`).
 
 Note that `out_sharding` is not used to determine the sharding of the
 result. Instead, the sharding of the result is determined by the sharding of
@@ -35,7 +36,7 @@ inferred sharding.
 ```
 
 **Constraints:**
-- Elements in `gatheringAxes` must satisfy the constraints listed in
+- Elements in `gathering_axes` must satisfy the constraints listed in
   `AxisRefListAttr`.
 - `out_sharding` must satisfy the constraints listed in
   `TensorShardingAttr`.
@@ -72,6 +73,67 @@ function all_gather(
     )
 end
 
+"""
+`all_slice`
+
+Slices chunks of a tensor along axes specified in `slicing_axes`. There is
+an algebric duality between `sdy.all_slice` and `sdy.all_gather`.
+
+The `slicing_axes` is a list of lists of axes. The outer list is over the
+dimensions of the tensor. Each inner list specifies the axes along which a
+slice should be performed on the respective dimension. It will be applied to
+the sharding of the operand (`tensor`) to obtain the sharding of the result
+(`out_sharding`).
+
+Note that `out_sharding` is not used to determine the sharding of the
+result. Instead, the sharding of the result is determined by the sharding of
+the operand and the `slicing_axes`, and `out_sharding` must match this
+inferred sharding.
+
+# Example
+```mlir
+%1 = stablehlo.tanh(%0) {sdy.sharding = #sdy.sharding_per_value<[<@mesh, [{\"a\"}, {}, {}\\]>]>} : tensor<8x8xf32>
+%2 = sdy.all_slice [{\"b\", \"c\"}, {}, {\"d\"}\\] %1 to_sharding=<@mesh, [{\"a\", \"b\", \"c\"}, {}, {\"d\"}\\]> : tensor<8x8xf32>
+```
+
+**Constraints:**
+- Elements in `slicing_axes` must satisfy the constraints listed in
+  `AxisRefListAttr`.
+- `out_sharding` must satisfy the constraints listed in
+  `TensorShardingAttr`.
+- The operand must have a sharding.
+- Both operand and result shardings should be bound to the same `MeshAttr`.
+- Applying `slicing_axes` to the operand sharding gets `out_sharding`.
+"""
+function all_slice(
+    tensor::Value;
+    result=nothing::Union{Nothing,IR.Type},
+    slicing_axes,
+    out_sharding,
+    location=Location(),
+)
+    op_ty_results = IR.Type[]
+    operands = Value[tensor,]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[
+        namedattribute("slicing_axes", slicing_axes),
+        namedattribute("out_sharding", out_sharding),
+    ]
+    !isnothing(result) && push!(op_ty_results, result)
+
+    return create_operation(
+        "sdy.all_slice",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=(length(op_ty_results) == 0 ? nothing : op_ty_results),
+        result_inference=(length(op_ty_results) == 0 ? true : false),
+    )
+end
+
 """
 `constant`
 
@@ -136,7 +198,7 @@ This while op has n data flow edges, the i-th data flow edges is between
 sources `x_i`, `return_value_i` and targets `y_i`, `pred_arg_i`,
 `body_arg_i`.
 
-An `sdy.data_flow_edge` takes as input the root target of an edge (can be
+An `sdy.data_flow_edge` takes as input the owner of an edge (can be
 any of the targets, but preferably an op result rather than a block
 argument), which shouldn\'t have any other uses. This op isn\'t pure because
 it can take an input that originally didn\'t have any uses.
@@ -163,8 +225,8 @@ We don\'t allow the input of a `sdy.data_flow_edge` to be defined by an
 unregistered `sdy.sharding` attribute.
 
 NOTE: it\'s NOT the responsibility of the `sdy.data_flow_edge` to link
-between sources and targets, it\'s simply attached to the root target of the
-edge. The op that this edge is bound to (while in the example above) is
+between sources and targets, it\'s simply attached to the owner of the edge.
+The op that this edge is bound to (while in the example above) is
 responsible for providing this information.
 """
 function data_flow_edge(

src/mlir/Dialects/StableHLO.jl

@@ -1994,14 +1994,19 @@ https://github.com/openxla/stablehlo/blob/main/docs/spec.md#exponential
 ```
 """
 function exponential(
-    operand::Value; result=nothing::Union{Nothing,IR.Type}, location=Location()
+    operand::Value;
+    result=nothing::Union{Nothing,IR.Type},
+    result_accuracy=nothing,
+    location=Location(),
 )
     op_ty_results = IR.Type[]
     operands = Value[operand,]
     owned_regions = Region[]
     successors = Block[]
     attributes = NamedAttribute[]
     !isnothing(result) && push!(op_ty_results, result)
+    !isnothing(result_accuracy) &&
+        push!(attributes, namedattribute("result_accuracy", result_accuracy))
 
     return create_operation(
         "stablehlo.exponential",

src/mlir/Dialects/Triton.jl

@@ -471,14 +471,17 @@ function dot_scaled(
     d::IR.Type,
     lhs_type,
     rhs_type,
+    fastMath,
     location=Location(),
 )
     op_ty_results = IR.Type[d,]
     operands = Value[lhs, rhs, c]
     owned_regions = Region[]
     successors = Block[]
     attributes = NamedAttribute[
-        namedattribute("lhs_type", lhs_type), namedattribute("rhs_type", rhs_type)
+        namedattribute("lhs_type", lhs_type),
+        namedattribute("rhs_type", rhs_type),
+        namedattribute("fastMath", fastMath),
     ]
     !isnothing(lhs_scale) && push!(operands, lhs_scale)
     !isnothing(rhs_scale) && push!(operands, rhs_scale)
@@ -785,12 +788,17 @@ tensor. The input and indices tensors must have the same number of
 dimension, and each dimension of the indices tensor that is not the gather
 dimension cannot be greater than the corresponding dimension in the input
 tensor.
+
+The `efficient_layout` attribute is set when the compiler has determined an
+optimized layout for the operation, indicating that it should not be
+changed.
 """
 function gather(
     src::Value,
     indices::Value;
     result=nothing::Union{Nothing,IR.Type},
     axis,
+    efficient_layout=nothing,
     location=Location(),
 )
     op_ty_results = IR.Type[]
@@ -799,6 +807,8 @@ function gather(
     successors = Block[]
     attributes = NamedAttribute[namedattribute("axis", axis),]
     !isnothing(result) && push!(op_ty_results, result)
+    !isnothing(efficient_layout) &&
+        push!(attributes, namedattribute("efficient_layout", efficient_layout))
 
     return create_operation(
         "tt.gather",

src/mlir/Dialects/VHLO.jl

@@ -1452,6 +1452,27 @@ function exponential_v1(operand::Value; result::IR.Type, location=Location())
     )
 end
 
+function exponential_v2(
+    operand::Value; result::IR.Type, result_accuracy, location=Location()
+)
+    op_ty_results = IR.Type[result,]
+    operands = Value[operand,]
+    owned_regions = Region[]
+    successors = Block[]
+    attributes = NamedAttribute[namedattribute("result_accuracy", result_accuracy),]
+
+    return create_operation(
+        "vhlo.exponential_v2",
+        location;
+        operands,
+        owned_regions,
+        successors,
+        attributes,
+        results=op_ty_results,
+        result_inference=false,
+    )
+end
+
 function exponential_minus_one_v1(operand::Value; result::IR.Type, location=Location())
     op_ty_results = IR.Type[result,]
     operands = Value[operand,]