Add export_to_enzymejax for automated JAX/EnzymeAD integration

CondaPkg.toml

@@ -1,7 +1,7 @@
 [deps]
-python = "<=3.13,>=3.9,<4"
+python = "<=3.12,>=3.9,<4"
 
 [pip.deps]
-jax = ">= 0.6"
+jax = ">= 0.5"
 tensorflow = ">= 2.17"
-numpy = ">= 2"
+numpy = ">= 1, >= 2"

Project.toml

@@ -39,6 +39,7 @@ GPUCompiler = "61eb1bfa-7361-4325-ad38-22787b887f55"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+NPZ = "15e1cf62-19b3-5cfa-8e77-841668bca605"
 OffsetArrays = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
 OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
@@ -62,6 +63,7 @@ ReactantFloat8sExt = "Float8s"
 ReactantKernelAbstractionsExt = "KernelAbstractions"
 ReactantMPIExt = "MPI"
 ReactantNNlibExt = ["NNlib", "Statistics"]
+ReactantNPZExt = "NPZ"
 ReactantOffsetArraysExt = "OffsetArrays"
 ReactantOneHotArraysExt = "OneHotArrays"
 ReactantPythonCallExt = "PythonCall"
@@ -96,6 +98,7 @@ Libdl = "1.10"
 LinearAlgebra = "1.10"
 MPI = "0.20"
 NNlib = "0.9.26"
+NPZ = "0.4"
 OffsetArrays = "1"
 OneHotArrays = "0.2.10"
 OrderedCollections = "1"

docs/src/api/serialization.md

@@ -27,3 +27,22 @@ or [TensorFlow Hub](https://tensorflow.org/hub). Refer to the
 ```@docs
 Reactant.Serialization.export_as_tf_saved_model
 ```
+
+## Exporting to JAX via EnzymeAD
+
+!!! note "Load NPZ"
+
+    This export functionality requires the `NPZ` package to be loaded.
+
+This export functionality generates:
+
+1. A `.mlir` file containing the StableHLO representation of your Julia function
+2. Input `.npz` files containing the input arrays for the function
+3. A Python script that wraps the function for use with `enzyme_ad.jax.hlo_call`
+
+The generated Python script can be immediately used with JAX and EnzymeAD without any
+additional Julia dependencies.
+
+```@docs
+Reactant.Serialization.EnzymeJAX.export_to_enzymejax
+```

ext/ReactantNPZExt.jl

@@ -0,0 +1,24 @@
+module ReactantNPZExt
+
+using NPZ: npzwrite
+using Reactant.Serialization: Serialization, EnzymeJAX
+
+Serialization.serialization_supported(::Val{:NPZ}) = true
+
+# Helper function to save all input data to a single NPZ file
+function EnzymeJAX.save_inputs_npz_impl(
+    output_path::String, inputs::Dict{String,<:Union{AbstractArray,Number}}
+)
+    # Transpose arrays for Python/NumPy (row-major vs column-major)
+    transposed_inputs = Dict{String,Union{AbstractArray,Number}}()
+    for (name, arr) in inputs
+        transposed_inputs[name] =
+            arr isa Number ? arr : permutedims(arr, reverse(1:ndims(arr)))
+    end
+
+    # Save all inputs to a single NPZ file with compression
+    npzwrite(output_path, transposed_inputs)
+    return output_path
+end
+
+end  # module

ext/ReactantPythonCallExt/ReactantPythonCallExt.jl

@@ -3,6 +3,7 @@ module ReactantPythonCallExt
 using PythonCall: PythonCall, Py, pyconvert, pydict, pyfunc, pyimport, pylist
 using Reactant: Reactant, TracedRArray, TracedRNumber, @reactant_overlay
 using Reactant.Ops: @opcall
+using Reactant.Serialization: NUMPY_SIMPLE_TYPES
 
 const jaxptr = Ref{Py}()
 const jnpptr = Ref{Py}()
@@ -15,24 +16,6 @@ const npptr = Ref{Py}()
 
 const SAVED_MODEL_EXPORT_SUPPORTED = Ref{Bool}(false)
 
-const NUMPY_SIMPLE_TYPES = Dict(
-    Bool => :bool,
-    Int8 => :int8,
-    Int16 => :int16,
-    Int32 => :int32,
-    Int64 => :int64,
-    UInt8 => :uint8,
-    UInt16 => :uint16,
-    UInt32 => :uint32,
-    UInt64 => :uint64,
-    Float16 => :float16,
-    Float32 => :float32,
-    Float64 => :float64,
-    ComplexF16 => :complex16,
-    ComplexF32 => :complex32,
-    ComplexF64 => :complex64,
-)
-
 function __init__()
     try
         jaxptr[] = pyimport("jax")

src/Compiler.jl

@@ -1411,7 +1411,7 @@ function __get_compile_options_and_kwargs(;
     )
 end
 
-function compile_mlir(f, args; client=nothing, kwargs...)
+function compile_mlir(f, args; client=nothing, drop_unsupported_attributes=false, kwargs...)
     client = client !== nothing ? client : XLA.default_backend()
     backend = XLA.platform_name(client)
 
@@ -1441,6 +1441,11 @@ function compile_mlir(f, args; client=nothing, kwargs...)
             mod, f; mlir_fn_res.num_partitions, mlir_fn_res.num_replicas
         )
 
+        if drop_unsupported_attributes
+            # Drop some of our attributes
+            run_pass_pipeline!(mod, "drop-unsupported-attributes", "drop_enzymexla_attributes")
+        end
+
         return mod, mlir_fn_res
     end
 
@@ -3571,6 +3576,9 @@ function compile_xla(
             mod, f; mlir_fn_res.num_partitions, mlir_fn_res.num_replicas
         )
 
+        # Drop some of our attributes
+        run_pass_pipeline!(mod, "drop-unsupported-attributes", "drop_enzymexla_attributes")
+
         # compile MLIR module to XLA executable
         global_device_ids = collect(Int64, mlir_fn_res.global_device_ids)
         mlir_fn_res.is_sharded && (device = nothing)
@@ -3584,9 +3592,6 @@ function compile_xla(
             module_string = ""
         end
 
-        # Drop some of our attributes
-        run_pass_pipeline!(mod, "drop-unsupported-attributes", "drop_enzymexla_attributes")
-
         if before_xla_optimizations
             exec = nothing
             hlo_modules = XLA.HloModule(mod)

src/Sharding.jl

@@ -949,7 +949,7 @@ function HloSharding(sharding::NamedSharding, client::XLA.IFRT.Client, _, x)
     data = XLA.IFRT.AsyncArray(client, x, ifrt_sharding)
 
     # XXX: Can we auto-pad this case too? Will think about it later, for now use
-    #      NamedSharidng
+    #      NamedSharding
     return data, ShardInfo(hlo_sharding, device_to_array_slices), nothing
 end
 
@@ -997,7 +997,7 @@ function (sharding::HloSharding)(
     data = XLA.IFRT.AsyncArray(client, x, ifrt_sharding)
 
     # XXX: Can we auto-pad this case too? Will think about it later, for now use
-    #      NamedSharidng
+    #      NamedSharding
     return data, ShardInfo(sharding, device_to_array_slices), nothing
 end