feat: updates for new JLL

Project.toml

@@ -105,7 +105,7 @@ PythonCall = "0.9.25"
 Random = "1.10"
 Random123 = "1.7"
 ReactantCore = "0.1.16"
-Reactant_jll = "0.0.246"
+Reactant_jll = "0.0.247"
 ScopedValues = "1.3.0"
 Scratch = "1.2"
 Sockets = "1.10"

docs/src/tutorials/partial-evaluation.md

@@ -57,7 +57,7 @@ The StableHLO IR code generated here is:
 # output
 
 module @reactant_add attributes {mhlo.num_partitions = 1 : i64, mhlo.num_replicas = 1 : i64} {
-  func.func @main(%arg0: tensor<i64>, %arg1: tensor<i64>) -> tensor<i64> attributes {enzymexla.memory_effects = []} {
+  func.func @main(%arg0: tensor<i64> {enzymexla.memory_effects = []}, %arg1: tensor<i64> {enzymexla.memory_effects = []}) -> tensor<i64> attributes {enzymexla.memory_effects = []} {
     %0 = stablehlo.add %arg0, %arg1 : tensor<i64>
     return %0 : tensor<i64>
   }
@@ -101,7 +101,7 @@ variable input `%arg0`:
 # output
 
 module @reactant_add attributes {mhlo.num_partitions = 1 : i64, mhlo.num_replicas = 1 : i64} {
-  func.func @main(%arg0: tensor<i64>) -> tensor<i64> attributes {enzymexla.memory_effects = []} {
+  func.func @main(%arg0: tensor<i64> {enzymexla.memory_effects = []}) -> tensor<i64> attributes {enzymexla.memory_effects = []} {
     %c = stablehlo.constant dense<4> : tensor<i64>
     %0 = stablehlo.add %arg0, %c : tensor<i64>
     return %0 : tensor<i64>

ext/ReactantCUDAExt.jl

@@ -1460,14 +1460,6 @@ function Reactant.make_tracer(
     return newa
 end
 
-function __init__()
-    if CUDA.functional() && !Reactant.precompiling()
-        cap = CUDA.capability(CUDA.device())
-        Reactant.Compiler.cubinChip[] = "sm_$(cap.major)$(cap.minor)"
-    end
-    return nothing
-end
-
 # In Julia v1.11.3 precompiling this module caches bad code:
 # <https://github.com/EnzymeAD/Reactant.jl/issues/614>.
 @static if !Sys.isapple()

src/Compiler.jl

@@ -906,6 +906,8 @@ function optimization_passes(
         "trivial_reduce_window_to_reduce_op",
         "dot_general_add_distributive_simplify",
         "dot_general_subtract_distributive_simplify",
+        "dus_to_dynamic_pad",
+        "dynamic_pad_to_pad",
     ]
 
     if !compile_options.disable_auto_batching_passes
@@ -922,6 +924,7 @@ function optimization_passes(
                 "concat_insert_dim_reduce",
                 "concat_insert_dim_sort",
                 "concat_insert_dim_reduce_window",
+                "concat_insert_dim_elementwise",
                 "dot_general_slice_to_batch",
                 "gather_slice_to_batch",
                 "iota_slice_to_batch",
@@ -1071,6 +1074,7 @@ function optimization_passes(
             "const_prop_through_barrier<16>",
             "concat_const_prop<1>($max_constant_threshold)",
             "dynamic_update_slice_const_prop($max_constant_threshold)",
+            "clamp_const_prop",
         ],
     )
 
@@ -1105,7 +1109,8 @@ function optimization_passes(
                 "reshape_dus",
                 "dot_reshape_pad<1>",
                 "pad_dot_general<1>(0)",
-                "pad_dot_general<1>(1)",
+                # XXX: see https://github.com/EnzymeAD/Enzyme-JAX/issues/1445
+                # "pad_dot_general<1>(1)",
                 "reshape_pad",
                 "reshape_wrap",
                 "reshape_rotate",
@@ -1425,6 +1430,8 @@ const cubinChip = Ref{String}("sm_60")
 const cubinFormat = Ref{String}("bin")
 const cuindexBitWidth = Ref{Int}(32)
 const cuOptLevel = Ref{Int}(2)
+const cuWarpSize = Ref{Int}(32)
+
 # Wgatever the relevant highest version from our LLVM is within NVPTX.td
 # Or more specifically looking at clang/lib/Driver/ToolChains/Cuda.cpp:684
 #  We see relevant ptx version is CUDA 12.6 -> 85
@@ -3516,6 +3523,9 @@ 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/xla/XLA.jl

@@ -234,6 +234,15 @@ for runtime in (:PJRT, :IFRT)
                         )
                         state.clients["cuda"] = gpu
                         state.default_client = gpu
+
+                        # set values for cuda. This is being done here since we need cuda
+                        # to be initialized before we can use it. initializing the devices
+                        # implicitly initializes cuda.
+                        cc_major = @ccall MLIR.API.mlir_c.ReactantCudaDeviceGetComputeCapalilityMajor()::Int32
+                        cc_minor = @ccall MLIR.API.mlir_c.ReactantCudaDeviceGetComputeCapalilityMinor()::Int32
+                        Reactant.Compiler.cubinChip[] = "sm_$(cc_major)$(cc_minor)"
+
+                        Reactant.Compiler.cuWarpSize[] = @ccall MLIR.API.mlir_c.ReactantCudaDeviceGetWarpSizeInThreads()::Int32
                     catch e
                         println(stdout, e)
                     end

test/buffer_donation.jl

@@ -50,7 +50,7 @@ end
     hlo = @code_hlo(multiple_donated_args(a, b, c))
     @test contains(
         repr(hlo),
-        "@main(%arg0: tensor<2x2xf64> {tf.aliasing_output = 0 : i32}, %arg1: tensor<4x3xf64> {tf.aliasing_output = 2 : i32}, %arg2: tensor<2x2xf64> {tf.aliasing_output = 1 : i32})",
+        "@main(%arg0: tensor<2x2xf64> {enzymexla.memory_effects = [], tf.aliasing_output = 0 : i32}, %arg1: tensor<4x3xf64> {enzymexla.memory_effects = [], tf.aliasing_output = 2 : i32}, %arg2: tensor<2x2xf64> {enzymexla.memory_effects = [], tf.aliasing_output = 1 : i32}) -> (tensor<2x2xf64>, tensor<2x2xf64>, tensor<4x3xf64>) attributes {enzymexla.memory_effects = []} {",
     )
 end