test: remove unnecessary Enzyme runtime API

docs/src/introduction/updating_to_v1.md

@@ -140,3 +140,5 @@ abstraction.
  align with Pytorch. Both are controlled using `init_bias` and `use_bias`.
 - [`ConvTranspose`](@ref) allows `flipkernel=true` via `cross_correlation=true`. This makes
  it efficient for MIOpen.
+- Pooling Layers based on lpnorm have been added -- [`LPPool`](@ref),
+ [`GlobalLPPool`](@ref), and [`AdaptiveLPPool`](@ref).

test/layers/recurrent_tests.jl

@@ -1,5 +1,4 @@
 @testitem "RNNCell" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
- Enzyme.API.runtimeActivity!(true)
  rng = StableRNG(12345)
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
@@ -8,7 +7,7 @@
  RNNCell(3 => 5, identity; use_bias=false),
  RNNCell(3 => 5, identity; use_bias=false, train_state=false))
  display(rnncell)
- ps, st = Lux.setup(rng, rnncell) .|> dev
+ ps, st = Lux.setup(rng, rnncell) |> dev
  for x_size in ((3, 2), (3,))
  x = randn(rng, Float32, x_size...) |> aType
  (y, carry), st_ = Lux.apply(rnncell, x, ps, st)
@@ -36,10 +35,10 @@
  RNNCell(3 => 5, identity; use_bias=true, train_state=true))
  rnn_no_trainable_state = RNNCell(
  3 => 5, identity; use_bias=false, train_state=false)
- _ps, _st = Lux.setup(rng, rnn_no_trainable_state) .|> dev
+ _ps, _st = Lux.setup(rng, rnn_no_trainable_state) |> dev
 
  rnncell = RNNCell(3 => 5, identity; use_bias=false, train_state=true)
- ps, st = Lux.setup(rng, rnncell) .|> dev
+ ps, st = Lux.setup(rng, rnncell) |> dev
  ps = merge(_ps, (hidden_state=ps.hidden_state,))
 
  for x_size in ((3, 2), (3,))
@@ -60,14 +59,13 @@
 end
 
 @testitem "LSTMCell" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
- Enzyme.API.runtimeActivity!(true)
  rng = StableRNG(12345)
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
  for lstmcell in (LSTMCell(3 => 5), LSTMCell(3 => 5; use_bias=true),
  LSTMCell(3 => 5; use_bias=false))
  display(lstmcell)
- ps, st = Lux.setup(rng, lstmcell) .|> dev
+ ps, st = Lux.setup(rng, lstmcell) |> dev
 
  for x_size in ((3, 2), (3,))
  x = randn(rng, Float32, x_size...) |> aType
@@ -97,12 +95,12 @@ end
  x = randn(rng, Float32, x_size...) |> aType
  _lstm = LSTMCell(
  3 => 5; use_bias=false, train_state=false, train_memory=false)
- _ps, _st = Lux.setup(rng, _lstm) .|> dev
+ _ps, _st = Lux.setup(rng, _lstm) |> dev
  (_y, _carry), _ = Lux.apply(_lstm, x, _ps, _st)
 
  lstm = LSTMCell(
  3 => 5; use_bias=false, train_state=false, train_memory=false)
- ps, st = Lux.setup(rng, lstm) .|> dev
+ ps, st = Lux.setup(rng, lstm) |> dev
  ps = _ps
  (y, carry), _ = Lux.apply(lstm, x, ps, st)
  @test carry == _carry
@@ -116,7 +114,7 @@ end
 
  lstm = LSTMCell(
  3 => 5; use_bias=false, train_state=true, train_memory=false)
- ps, st = Lux.setup(rng, lstm) .|> dev
+ ps, st = Lux.setup(rng, lstm) |> dev
  ps = merge(_ps, (hidden_state=ps.hidden_state,))
  (y, carry), _ = Lux.apply(lstm, x, ps, st)
  @test carry == _carry
@@ -130,7 +128,7 @@ end
 
  lstm = LSTMCell(
  3 => 5; use_bias=false, train_state=false, train_memory=true)
- ps, st = Lux.setup(rng, lstm) .|> dev
+ ps, st = Lux.setup(rng, lstm) |> dev
  ps = merge(_ps, (memory=ps.memory,))
  (y, carry), _ = Lux.apply(lstm, x, ps, st)
  @test carry == _carry
@@ -143,7 +141,7 @@ end
  @test !isnothing(gs.memory)
 
  lstm = LSTMCell(3 => 5; use_bias=false, train_state=true, train_memory=true)
- ps, st = Lux.setup(rng, lstm) .|> dev
+ ps, st = Lux.setup(rng, lstm) |> dev
  ps = merge(_ps, (hidden_state=ps.hidden_state, memory=ps.memory))
  (y, carry), _ = Lux.apply(lstm, x, ps, st)
  @test carry == _carry
@@ -156,7 +154,7 @@ end
  @test !isnothing(gs.memory)
 
  lstm = LSTMCell(3 => 5; use_bias=true, train_state=true, train_memory=true)
- ps, st = Lux.setup(rng, lstm) .|> dev
+ ps, st = Lux.setup(rng, lstm) |> dev
  ps = merge(_ps, (; ps.bias_ih, ps.bias_hh, ps.hidden_state, ps.memory))
  (y, carry), _ = Lux.apply(lstm, x, ps, st)
  l, back = Zygote.pullback(
@@ -172,14 +170,13 @@ end
 end
 
 @testitem "GRUCell" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
- Enzyme.API.runtimeActivity!(true)
  rng = StableRNG(12345)
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
  for grucell in (GRUCell(3 => 5), GRUCell(3 => 5; use_bias=true),
  GRUCell(3 => 5; use_bias=false))
  display(grucell)
- ps, st = Lux.setup(rng, grucell) .|> dev
+ ps, st = Lux.setup(rng, grucell) |> dev
 
  for x_size in ((3, 2), (3,))
  x = randn(rng, Float32, x_size...) |> aType
@@ -207,11 +204,11 @@ end
  for x_size in ((3, 2), (3,))
  x = randn(rng, Float32, x_size...) |> aType
  _gru = GRUCell(3 => 5; use_bias=false, train_state=false)
- _ps, _st = Lux.setup(rng, _gru) .|> dev
+ _ps, _st = Lux.setup(rng, _gru) |> dev
  (_y, _carry), _ = Lux.apply(_gru, x, _ps, _st)
 
  gru = GRUCell(3 => 5; use_bias=false, train_state=false)
- ps, st = Lux.setup(rng, gru) .|> dev
+ ps, st = Lux.setup(rng, gru) |> dev
  ps = _ps
  (y, carry), _ = Lux.apply(gru, x, ps, st)
  @test carry == _carry
@@ -222,7 +219,7 @@ end
  @test_throws ErrorException gs.hidden_state
 
  gru = GRUCell(3 => 5; use_bias=false, train_state=true)
- ps, st = Lux.setup(rng, gru) .|> dev
+ ps, st = Lux.setup(rng, gru) |> dev
  ps = merge(_ps, (hidden_state=ps.hidden_state,))
  (y, carry), _ = Lux.apply(gru, x, ps, st)
  @test carry == _carry
@@ -231,7 +228,7 @@ end
  @test !isnothing(gs.hidden_state)
 
  gru = GRUCell(3 => 5; use_bias=true, train_state=true)
- ps, st = Lux.setup(rng, gru) .|> dev
+ ps, st = Lux.setup(rng, gru) |> dev
  ps = merge(_ps, (; ps.bias_ih, ps.bias_hh, ps.hidden_state))
  (y, carry), _ = Lux.apply(gru, x, ps, st)
  @test carry == _carry
@@ -244,7 +241,6 @@ end
 end
 
 @testitem "StatefulRecurrentCell" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
- Enzyme.API.runtimeActivity!(true)
  rng = StableRNG(12345)
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
@@ -258,7 +254,7 @@ end
 
  for x_size in ((3, 2), (3,))
  x = randn(rng, Float32, x_size...) |> aType
- ps, st = Lux.setup(rng, rnn) .|> dev
+ ps, st = Lux.setup(rng, rnn) |> dev
 
  y, st_ = rnn(x, ps, st)
 
@@ -292,7 +288,6 @@ end
 end
 
 @testitem "Recurrence" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
- Enzyme.API.runtimeActivity!(true)
  rng = StableRNG(12345)
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
@@ -318,7 +313,7 @@ end
  (ntuple(identity, ndims(x) - 2)..., ndims(x), ndims(x) - 1))
  end
 
- ps, st = Lux.setup(rng, rnn) .|> dev
+ ps, st = Lux.setup(rng, rnn) |> dev
  y, st_ = rnn(x, ps, st)
  y_, st__ = rnn_seq(x, ps, st)
 
@@ -343,7 +338,7 @@ end
  randn(rng, Float32, 3, 4) |> aType,
  Tuple(randn(rng, Float32, 3) for _ in 1:4) .|> aType,
  [randn(rng, Float32, 3) for _ in 1:4] .|> aType)
- ps, st = Lux.setup(rng, rnn) .|> dev
+ ps, st = Lux.setup(rng, rnn) |> dev
  y, st_ = rnn(x, ps, st)
  y_, st__ = rnn_seq(x, ps, st)
 
@@ -383,7 +378,7 @@ end
  init_state=(rng, args...; kwargs...) -> zeros(args...; kwargs...),
  init_bias=(rng, args...; kwargs...) -> zeros(args...; kwargs...));
  return_sequence=true)
- ps, st = Lux.setup(rng, encoder) .|> dev
+ ps, st = Lux.setup(rng, encoder) |> dev
  m2 = reshape([0.5, 0.0, 0.7, 0.8], 1, :, 1) |> aType
  res, _ = encoder(m2, ps, st)
 
@@ -392,7 +387,6 @@ end
 end
 
 @testitem "Bidirectional" setup=[SharedTestSetup] tags=[:recurrent_layers] begin
- Enzyme.API.runtimeActivity!(true)
  rng = StableRNG(12345)
 
  @testset "$mode" for (mode, aType, dev, ongpu) in MODES
@@ -404,7 +398,7 @@ end
 
  # Batched Time Series
  x = randn(rng, Float32, 3, 4, 2) |> aType
- ps, st = Lux.setup(rng, bi_rnn) .|> dev
+ ps, st = Lux.setup(rng, bi_rnn) |> dev
  y, st_ = bi_rnn(x, ps, st)
  y_, st__ = bi_rnn_no_merge(x, ps, st)
 
@@ -440,7 +434,7 @@ end
 
  # Batched Time Series
  x = randn(rng, Float32, 3, 4, 2) |> aType
- ps, st = Lux.setup(rng, bi_rnn) .|> dev
+ ps, st = Lux.setup(rng, bi_rnn) |> dev
  y, st_ = bi_rnn(x, ps, st)
  y_, st__ = bi_rnn_no_merge(x, ps, st)