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tx.Dropout does internally rng, _ = tx.iter_split(flax_key, 2) - y_flax = flax_module.apply(variables, x, rng=rng) + y_flax = flax_module.apply(variables, x, rngs={"dropout": rng}) y_treex = treex_module(x) assert np.allclose(y_flax, y_treex) diff --git a/tests/nn/test_flax_module.py b/tests/nn/test_flax_module.py index ebdd0f47..8a80a1f8 100644 --- a/tests/nn/test_flax_module.py +++ b/tests/nn/test_flax_module.py @@ -29,10 +29,10 @@ def __call__(self, x, training): def test_pretrained_flax_module(self): class SomeModule(flax.linen.Module): @flax.linen.compact - def __call__(self, x, rng, training): + def __call__(self, x, training): x = flax.linen.Dense(16)(x) x = flax.linen.BatchNorm()(x, use_running_average=not training) - x = flax.linen.Dropout(0.5)(x, deterministic=not training, rng=rng) + x = flax.linen.Dropout(0.5)(x, deterministic=not training) x = flax.linen.Conv(16, [3])(x) return x @@ -48,13 +48,12 @@ def __call__(self, x, rng, training): variables = flax_module.init( {"params": params_key, "dropout": dropout_key}, x, - rng, False, ) treex_module = tx.FlaxModule(SomeModule(), variables=variables,).init( 42, - inputs=tx.Inputs(x, rng), + inputs=tx.Inputs(x), ) assert all( @@ -72,23 +71,26 @@ def __call__(self, x, rng, training): ) ) - y_treex = treex_module(x, rng) - y_treex = treex_module(x, rng) + flax_next_key = treex_module.next_key.copy() + y_treex = treex_module(x) + y_treex = treex_module(x) + rng, next_rng = tx.iter_split(dropout_key) y_flax, updates = flax_module.apply( variables, x, - rng, training, mutable=["batch_stats"], + rngs={"dropout": flax_next_key()}, ) + variables = variables.copy(updates) y_flax, updates = flax_module.apply( variables, x, - rng, training, mutable=["batch_stats"], + rngs={"dropout": flax_next_key()}, ) variables = variables.copy(updates) diff --git a/treex/nn/dropout.py b/treex/nn/dropout.py index 471a8259..8e2d89b3 100644 --- a/treex/nn/dropout.py +++ b/treex/nn/dropout.py @@ -89,7 +89,7 @@ def __call__( variables, x, deterministic=not training, - rng=rng, + rngs={"dropout": rng}, ) return tp.cast(jnp.ndarray, output)