feat: sharding with non-divisible dimensions [alternate approach]

src/Compiler.jl

@@ -636,6 +636,7 @@ function compile_mlir!(
     backend="gpu",
     fn_kwargs=(),
     raise::Union{Bool,String}=false,
+    input_shardings=nothing,
 )
     # Explicitly don't use block! to avoid creating a closure, which creates
     # both compile-time and relocatability issues
@@ -652,7 +653,7 @@ function compile_mlir!(
     activate_raising!(is_raising)
 
     mlir_fn_res = try
-        Reactant.TracedUtils.make_mlir_fn(f, args, fn_kwargs, "main", true)
+        Reactant.TracedUtils.make_mlir_fn(f, args, fn_kwargs, "main", true; input_shardings)
     finally
         deactivate_raising!(is_raising)
         deactivate_sdycache!(sdycache)
@@ -1167,14 +1168,16 @@ function codegen_flatten!(
 
         if is_sharded
             carg = inv_seen_args[arg]
+            condensed_op_sharding = convert(
+                Reactant.Sharding.XLA.CondensedOpSharding, linear_parameter_shardings[i]
+            )
             if Reactant.Sharding.is_sharded(carg)
-                # Currently disabling the error since we roundtrip from MHLO to generate
-                # the shardings
-                # # Check if the sharding provided is same as the one we have
-                # arg_condensed_op_sharding = Reactant.Sharding.XLA.CondensedOpSharding(
-                #     Reactant.Sharding.ShardingWithShape(carg.sharding, size(carg))
-                # )
-                # @assert arg_condensed_op_sharding == condensed_op_sharding "Sharding provided by the user ($arg_condensed_op_sharding) does not match the sharding computed by XLA ($condensed_op_sharding). This generally means that Reactant.jl made an error in generating the executable. Please open an issue with the error message and an MWE."
+                arg_condensed_op_sharding = convert(
+                    Reactant.Sharding.XLA.CondensedOpSharding,
+                    carg.sharding.sharding.hlo_sharding,
+                )
+                # Check if the sharding provided is same as the one we have
+                @assert arg_condensed_op_sharding == condensed_op_sharding "Sharding provided by the user ($arg_condensed_op_sharding) does not match the sharding computed by XLA ($condensed_op_sharding). This generally means that Reactant.jl made an error in generating the executable. Please open an issue with the error message and an MWE."
 
                 push!(flatten_code, :($usbuf = $flatcode.data))
                 for j in 1:length(mesh)
@@ -1183,11 +1186,8 @@ function codegen_flatten!(
                     push!(flatten_code, :($sbuf = XLA.synced_buffer(getindex($usbuf, $j))))
                 end
             else
-                condensed_op_sharding = convert(
-                    Reactant.Sharding.XLA.CondensedOpSharding, linear_parameter_shardings[i]
-                )
                 push!(flatten_code, :($usbuf = $flatcode))
-                device_to_array_slices = XLA.sharding_to_concrete_array_indices(
+                device_to_array_slices, _ = XLA.sharding_to_concrete_array_indices(
                     condensed_op_sharding, size(carg), mesh.logical_device_ids
                 )
                 for j in 1:length(mesh)

src/Sharding.jl

@@ -200,7 +200,7 @@ function (sharding::NamedSharding)(
     client::XLA.PJRT.Client, device::Nothing, x::Union{AbstractArray,Number}
 )
     @assert length(sharding.partition_spec) == ndims(x)
-    return (convert(HloSharding, sharding))(client, device, x)
+    return HloSharding(sharding, client, device, x)
 end
 
 function get_shardy_tensor_sharding_attribute(
@@ -339,7 +339,9 @@ end
     return ShardInfo{HloSharding{D1,D2},Vector{NTuple{N,UnitRange{Int64}}}}
 end
 
-function Base.convert(::Type{HloSharding}, sharding::NamedSharding)
+# This doesn't account for the size of the input so in-presence of padding this will be
+# incorrect. Hence always use the HloSharding constructor.
+function generate_hlo_sharding_from_tensor_attribute(sharding::NamedSharding)
     if MLIR.IR._has_context()
         ctx = MLIR.IR.context()
     else
@@ -370,14 +372,62 @@ function Base.convert(::Type{HloSharding}, sharding::NamedSharding)
     end
 end
 
+function HloSharding(sharding::NamedSharding, client::XLA.PJRT.Client, _, x)
+    hlo_sharding = generate_hlo_sharding_from_tensor_attribute(sharding)
+
+    # Check if the input needs to be padded. If so this sharding is not valid and we
+    # need to request the tensor sharding from XLA
+    condensed_op_sharding = convert(XLA.CondensedOpSharding, hlo_sharding.hlo_sharding)
+    device_to_array_slices, needs_padding = XLA.sharding_to_concrete_array_indices(
+        condensed_op_sharding, size(x), hlo_sharding.mesh.logical_device_ids
+    )
+
+    if needs_padding
+        # Compile a dummy function to get the tensor sharding
+        tmp = if x isa Number
+            Reactant.ConcretePJRTNumber(zero(eltype(x)))
+        else
+            Reactant.ConcretePJRTArray(ones(eltype(x), size(x)...))
+        end
+        _, exec, _, _, _ = Reactant.Compiler.compile_xla(
+            Reactant.Ops.negate, (tmp,); input_shardings=IdDict(tmp => sharding)
+        )
+        xla_hlo_sharding = convert(
+            Reactant.XLA.HloSharding, only(Reactant.XLA.get_parameter_shardings(exec))
+        )
+        hlo_sharding = HloSharding(
+            xla_hlo_sharding,
+            hlo_sharding.mesh,
+            hlo_sharding.is_closed,
+            hlo_sharding.priority,
+        )
+
+        condensed_op_sharding = convert(XLA.CondensedOpSharding, hlo_sharding.hlo_sharding)
+        device_to_array_slices, needs_padding = XLA.sharding_to_concrete_array_indices(
+            condensed_op_sharding, size(x), hlo_sharding.mesh.logical_device_ids
+        )
+    end
+
+    data = ntuple(length(hlo_sharding.mesh)) do i
+        XLA.PJRT.AsyncBuffer(
+            client,
+            x[device_to_array_slices[i]...],
+            XLA.get_device(client, hlo_sharding.mesh.device_ids[i]),
+        )
+    end
+
+    return data, ShardInfo(hlo_sharding, device_to_array_slices)
+end
+
 function (sharding::HloSharding)(
     client::XLA.PJRT.Client, ::Nothing, x::Union{AbstractArray,Number}
 )
     condensed_op_sharding = convert(XLA.CondensedOpSharding, sharding.hlo_sharding)
 
-    device_to_array_slices = XLA.sharding_to_concrete_array_indices(
+    device_to_array_slices, needs_padding = XLA.sharding_to_concrete_array_indices(
         condensed_op_sharding, size(x), sharding.mesh.logical_device_ids
     )
+    @assert !needs_padding "This shouldn't happen. Open an issue on Reactant.jl"
 
     data = ntuple(length(sharding.mesh)) do i
         XLA.PJRT.AsyncBuffer(

src/TracedUtils.jl

@@ -161,6 +161,7 @@ function make_mlir_fn(
     args_in_result::Symbol=:all,
     construct_function_without_args::Bool=false,
     do_transpose=true,
+    input_shardings=nothing, # This is not meant to be used by the user.
 )
     if sizeof(typeof(f)) != 0 || f isa Base.BroadcastFunction
         mlir_fn_res = make_mlir_fn(
@@ -173,6 +174,7 @@ function make_mlir_fn(
             return_dialect,
             do_transpose,
             args_in_result,
+            input_shardings,
         )
         mlir_fn_res.fnwrapped = true
         return mlir_fn_res
@@ -221,10 +223,14 @@ function make_mlir_fn(
     # Insert meshes for the sharded arguments
     traced_args_to_shardings = OrderedIdDict()
     for (k, v) in seen_args
-        if (k isa Reactant.ConcretePJRTArray || k isa Reactant.ConcretePJRTNumber) &&
-            Reactant.Sharding.is_sharded(k)
-            Reactant.Ops.mesh(k.sharding.mesh)
-            traced_args_to_shardings[v] = k.sharding
+        if (k isa Reactant.ConcretePJRTArray || k isa Reactant.ConcretePJRTNumber)
+            if Reactant.Sharding.is_sharded(k)
+                Reactant.Ops.mesh(k.sharding.mesh)
+                traced_args_to_shardings[v] = k.sharding
+            elseif input_shardings !== nothing && haskey(input_shardings, k)
+                Reactant.Ops.mesh(input_shardings[k].mesh)
+                traced_args_to_shardings[v] = input_shardings[k]
+            end
         end
     end
 

src/xla/IFRT/Array.jl

@@ -48,7 +48,7 @@ function Array(client::Client, array::Base.Array{T,N}, sharding::Sharding) where
     end
 
     all_devices = XLA.devices(sharding)
-    array_slices = XLA.sharding_to_concrete_array_indices(
+    array_slices, _ = XLA.sharding_to_concrete_array_indices(
         convert(XLA.HloSharding, sharding),
         size(array),
         collect(Int64, 0:(length(all_devices) - 1)),
@@ -159,7 +159,7 @@ function XLA.to_host(buffer::Array, data)
     # avoid the complexity of supporting that for now.
     single_device_arrays = disassemble_into_single_device_arrays(buffer, true)
 
-    array_slices = XLA.sharding_to_concrete_array_indices(
+    array_slices, _ = XLA.sharding_to_concrete_array_indices(
         convert(XLA.HloSharding, sharding),
         size(data),
         collect(Int64, 0:(length(all_devices) - 1)),

src/xla/Sharding.jl

@@ -251,31 +251,36 @@ function sharding_to_concrete_array_indices(
     sharding::CondensedOpSharding, shape::Dims{N}, device_ids
 ) where {N}
     if sharding.type == OpShardingType.Replicated || sharding.type == OpShardingType.Maximal
-        return ntuple(Returns(ntuple(i -> 1:shape[i], N)), length(device_ids))
+        return map(Returns(UnitRange.(1, shape)), device_ids), false
     elseif sharding.type == OpShardingType.Other
         partitions, num_replicas = get_number_of_ways_dim_sharded(sharding)
         @assert length(partitions) == length(shape)
         shape = reverse(shape)
 
+        # XLA will automatically pad the inputs that don't match the final shape
+        partitionable_shape = map(zip(shape, partitions)) do (dim, n_shards)
+            dim % n_shards == 0 && return dim
+            res = dim + n_shards ÷ 2
+            return res - res % n_shards
+        end
+        partitionable_shape = Tuple(partitionable_shape)
+
+        needs_padding = any(partitionable_shape .!= shape)
+
         # Calculate indices for each dimension
-        axis_indices = map(zip(shape, partitions)) do (dim, n_shards)
-            @assert dim > 0 "Invalid dimension: $dim"
-            @assert n_shards > 0 "Invalid number of shards: $n_shards"
-            n_shards == 1 && return [1:dim]
-            shard_size, remainder = divrem(dim, n_shards)
-
-            if remainder != 0
-                throw(
-                    DimensionMismatch(
-                        "Dimension of Size $(dim) cannot be partitioned into $(n_shards) \
-                         shards each of size $(shard_size) (remainder = $(remainder)).",
-                    ),
-                )
+        axis_indices =
+            map(zip(partitionable_shape, shape, partitions)) do (dim_padded, dim, n_shards)
+                @assert dim > 0 "Invalid dimension: $dim"
+                @assert n_shards > 0 "Invalid number of shards: $n_shards"
+                n_shards == 1 && return [1:dim]
+                shard_size = dim_padded ÷ n_shards
+
+                return [
+                    (i * shard_size + 1):min((i + 1) * shard_size, dim) for
+                    i in 0:(n_shards - 1)
+                ]
             end
 
-            return [(i * shard_size + 1):((i + 1) * shard_size) for i in 0:(n_shards - 1)]
-        end
-
         indices = Dict{Int,NTuple{N,UnitRange{Int}}}()
         device_idx = 1
         for _ in 1:num_replicas
@@ -285,7 +290,7 @@ function sharding_to_concrete_array_indices(
             end
         end
 
-        return map(Base.Fix1(getindex, indices), device_ids)
+        return map(Base.Fix1(getindex, indices), device_ids), needs_padding
     else
         error("Unsupported sharding type: $(sharding.type)")
     end
@@ -295,7 +300,7 @@ function compute_array_indices_and_hlo_sharding(
     sharding::CondensedOpSharding, array_size, device_ids
 )
     return (
-        sharding_to_concrete_array_indices(sharding, array_size, device_ids),
+        first(sharding_to_concrete_array_indices(sharding, array_size, device_ids)),
         convert(HloSharding, sharding.opsharding),
     )
 end

test/sharding.jl

@@ -221,6 +221,27 @@ end
     end
 end
 
+@testset "Sharding with non-divisible axes sizes" begin
+    if length(Reactant.addressable_devices()) ≥ 8
+        mesh = Sharding.Mesh(reshape(collect(Int64, 0:7), 2, 4), ("data", "model"))
+        x = reshape(collect(Float32, 1:14), 2, 7)
+        x_ra = Reactant.to_rarray(
+            x; sharding=Sharding.NamedSharding(mesh, ("data", "model"))
+        )
+
+        @test Array(@jit sum(x_ra; dims=2)) ≈ sum(x; dims=2)
+
+        x = reshape(collect(Float32, 1:25), 5, 5)
+        x_ra = Reactant.to_rarray(
+            x; sharding=Sharding.NamedSharding(mesh, ("data", "model"))
+        )
+
+        @test Array(@jit fn_test2(x_ra)) ≈ fn_test2(x)
+    else
+        @warn "Not enough addressable devices to run sharding tests"
+    end
+end
+
 # Tests from the examples in
 # https://github.com/openxla/xla/blob/96d6678053d867099a42be9001c49b2ed7111afd/xla/hlo/ir/tile_assignment.h#L53-L68
 @testset "Device List from Iota Tile" begin