AMDGPU ci

.buildkite/pipeline.yml

@@ -48,47 +48,47 @@ steps:
         if: build.message !~ /\[skip tests\]/
         timeout_in_minutes: 120
 
-  # - label: ":julia: :linux: AMDGPU Julia v{{matrix.version}} -- {{matrix.group}} -- {{matrix.runtime}}"
-  #   matrix:
-  #     setup:
-  #       version:
-  #         - "1.10"
-  #       group:
-  #         - core
-  #         - neural_networks
-  #         - integration
-  #       runtime:
-  #         - "IFRT"
-  #   plugins:
-  #     - JuliaCI/julia#v1:
-  #         version: "{{matrix.version}}"
-  #     - JuliaCI/julia-coverage#v1:
-  #         codecov: true
-  #         dirs:
-  #           - src
-  #           - ext
-  #           - lib/ReactantCore/src
-  #   agents:
-  #     queue: "juliagpu"
-  #     rocm: "*"
-  #   commands: |
-  #     touch LocalPreferences.toml
+  - label: ":julia: :linux: AMDGPU Julia v{{matrix.version}} -- {{matrix.group}} -- {{matrix.runtime}}"
+    matrix:
+      setup:
+        version:
+          - "1.10"
+        group:
+          - core
+          - neural_networks
+          - integration
+        runtime:
+          - "IFRT"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "{{matrix.version}}"
+      - JuliaCI/julia-coverage#v1:
+          codecov: true
+          dirs:
+            - src
+            - ext
+            - lib/ReactantCore/src
+    agents:
+      queue: "juliagpu"
+      rocm: "*"
+    commands: |
+      touch LocalPreferences.toml
 
-  #     echo "[Reactant]" >> LocalPreferences.toml
-  #     echo "xla_runtime = \"{{matrix.runtime}}\"" >> LocalPreferences.toml
+      echo "[Reactant]" >> LocalPreferences.toml
+      echo "xla_runtime = \"{{matrix.runtime}}\"" >> LocalPreferences.toml
 
-  #     cat LocalPreferences.toml
+      cat LocalPreferences.toml
 
-  #     julia --project=. -e 'println("--- :julia: Instantiating project")
-  #         using Pkg
-  #         Pkg.develop([PackageSpec(path="lib/ReactantCore")])'
+      julia --project=. -e 'println("--- :julia: Instantiating project")
+          using Pkg
+          Pkg.develop([PackageSpec(path="lib/ReactantCore")])'
 
-  #     julia --project=. -e 'println("--- :julia: Run Tests")
-  #         using Pkg
-  #         Pkg.test(; coverage="user")'
-  #   env:
-  #     REACTANT_TEST_GROUP: "{{matrix.group}}"
-  #     JULIA_DEBUG: "Reactant,Reactant_jll"
-  #     CUDA_VISIBLE_DEVICES: 0
+      julia --project=. -e 'println("--- :julia: Run Tests")
+          using Pkg
+          Pkg.test(; coverage="user")'
+    env:
+      REACTANT_TEST_GROUP: "{{matrix.group}}"
+      JULIA_DEBUG: "Reactant,Reactant_jll"
+      CUDA_VISIBLE_DEVICES: 0
     # if: build.message !~ /\[skip tests\]/
     # timeout_in_minutes: 120

test/integration/kernelabstractions.jl

@@ -28,7 +28,7 @@ if !Reactant.Accelerators.TPU.has_tpu()
         A = Reactant.to_rarray(ones(100, 100))
         out = Reactant.to_rarray(ones(100, 100))
         platform_name = Reactant.XLA.platform_name(Reactant.XLA.default_backend())
-        raise = platform_name ∉ ("cpu", "cuda")
+        raise = platform_name ∉ ("cpu", "cuda", "rocm")
         @jit raise = raise matmul!(out, A)
         out_c = Array(out)
         A_c = Array(A)

test/nn/luxlib.jl

@@ -1,5 +1,7 @@
 using LuxLib, Reactant, Enzyme, NNlib, Test
 
+const MightUseMIOpen = contains(lowercase(string(Reactant.devices()[1])), "rocm")
+
 @testset "Fused Dense" begin
     sumabs2fuseddense(act, weight, x, bias) =
         sum(abs2, fused_dense_bias_activation(act, weight, x, bias))
@@ -182,15 +184,21 @@ end
             padding in ((0, 0), (2, 2), (2, 0)),
             dilation in ((1, 1), (1, 2))
 
-            conv_dims = DenseConvDims(x, weight; stride, padding, dilation, groups)
+            if any(!=(1), dilation) && MightUseMIOpen
+                continue # MIOpen doesn't support dilation != 1 yet
+            end
 
-            reactant_res = @jit fused_conv_bias_activation(
-                act, weight_reactant, x_reactant, bias_reactant, conv_dims
-            )
+            might_fail = MightUseMIOpen && any(!=(1), dilation)
 
-            luxlib_res = fused_conv_bias_activation(act, weight, x, bias, conv_dims)
+            conv_dims = DenseConvDims(x, weight; stride, padding, dilation, groups)
 
-            @test reactant_res ≈ luxlib_res atol = 1e-5 rtol = 1e-2
+            @test begin
+                reactant_res = @jit fused_conv_bias_activation(
+                    act, weight_reactant, x_reactant, bias_reactant, conv_dims
+                )
+                luxlib_res = fused_conv_bias_activation(act, weight, x, bias, conv_dims)
+                isapprox(reactant_res, luxlib_res; atol=1e-5, rtol=1e-2)
+            end broken = might_fail
         end
 
         # TODO: test for gradients