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[tx] Enable MPS backend for Apple Silicon on Jax backend #1332

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[tx] Enable MPS backend for Apple Silicon on Jax backend #1332

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4 changes: 3 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,8 @@ jax = [
"jax>=0.8,<1.0",
"flax>=0.12.2",
"optax>=0.2.5",
"ml_dtypes>=0.5.0",
"jax-mps>=0.0.6; sys_platform == 'darwin' and platform_machine == 'arm64'",
]

skyrl-train = [
Expand Down Expand Up @@ -208,7 +210,7 @@ override-dependencies = [
"causal-conv1d; sys_platform == 'never'",
"transformer-engine[pytorch]==2.10.0; sys_platform == 'linux'",
"megatron-core==0.16.0; sys_platform == 'linux'",
"ml_dtypes>=0.5.0; sys_platform == 'linux'",
"ml_dtypes>=0.5.0",
]

[tool.uv.extra-build-dependencies]
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11 changes: 6 additions & 5 deletions skyrl/backends/jax.py
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Expand Up @@ -787,12 +787,13 @@ def optim_step(self, model_id: str, request_data: types.OptimStepInput) -> types
logger.warning(f"No accumulated gradients for model {model_id}; applying step with zero gradients")

# Update hyperparameters from the request
# Use direct .value assignment instead of [...] indexing to avoid MPS zero-sized tensor issues
hp = optimizer.opt_state.hyperparams
hp["learning_rate"][...] = learning_rate
hp["b1"][...] = request_data.adam_params.beta1
hp["b2"][...] = request_data.adam_params.beta2
hp["eps"][...] = request_data.adam_params.eps
hp["weight_decay"][...] = request_data.adam_params.weight_decay
hp["learning_rate"].value = jnp.asarray(learning_rate, dtype=hp["learning_rate"].value.dtype)
hp["b1"].value = jnp.asarray(request_data.adam_params.beta1, dtype=hp["b1"].value.dtype)
hp["b2"].value = jnp.asarray(request_data.adam_params.beta2, dtype=hp["b2"].value.dtype)
hp["eps"].value = jnp.asarray(request_data.adam_params.eps, dtype=hp["eps"].value.dtype)
hp["weight_decay"].value = jnp.asarray(request_data.adam_params.weight_decay, dtype=hp["weight_decay"].value.dtype)
Comment on lines +790 to +796
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@gemini-code-assist gemini-code-assist bot Mar 17, 2026

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medium

The code for updating hyperparameters is quite repetitive. To improve readability and maintainability, you could refactor this block to use a loop over a dictionary of parameters.

        # Use direct .value assignment instead of [...] indexing to avoid MPS zero-sized tensor issues
        hp = optimizer.opt_state.hyperparams
        params_to_update = {
            "learning_rate": learning_rate,
            "b1": request_data.adam_params.beta1,
            "b2": request_data.adam_params.beta2,
            "eps": request_data.adam_params.eps,
            "weight_decay": request_data.adam_params.weight_decay,
        }
        for name, value in params_to_update.items():
            hp[name].value = jnp.asarray(value, dtype=hp[name].value.dtype)


# JIT-compiled: compute full gradients, apply optimizer update, and reset accumulated grads
with jax.set_mesh(self.mesh):
Expand Down
5 changes: 4 additions & 1 deletion skyrl/tx/layers/lora.py
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Expand Up @@ -361,7 +361,10 @@ def init_adapter(path, value):
if key_name == "lora_A":
# Reinitialize with he_uniform, then zero columns beyond rank
new_A = nnx.initializers.he_uniform()(rngs.params(), value[idx].shape, value.dtype)
new_A = new_A.at[..., effective_rank:].set(0.0)
# Zero columns beyond rank using multiplication (avoids MPS zero-sized tensor issues)
if new_A.shape[-1] > 0:
mask = jnp.arange(new_A.shape[-1]) < effective_rank
new_A = new_A * mask.astype(new_A.dtype)
return value.at[idx].set(new_A)
if key_name == "lora_B":
# Explicitly zero lora_B
Expand Down
3 changes: 2 additions & 1 deletion skyrl/tx/utils/models.py
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Expand Up @@ -194,7 +194,8 @@ def load_safetensors(
tensor = tensor.reshape(param.shape)
assert param.shape == tensor.shape, f"shape mismatch for {key}"
# ArrayRef.set_raw_value writes through to the stacked parent variable
param.set_raw_value(jax.device_put(tensor.astype(param.dtype), param.sharding))
# Use np.ascontiguousarray to ensure data is contiguous (required by jax-mps backend)
param.set_raw_value(jax.device_put(np.ascontiguousarray(tensor.astype(param.dtype)), param.sharding))


def save_safetensors(
Expand Down
13 changes: 7 additions & 6 deletions tests/tx/models/test_qwen3.py
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Expand Up @@ -55,11 +55,11 @@ def test_qwen3(tp: int):

def load_moe_base_weights(jax_moe_layer: Qwen3MoeSparseMoeBlock, hf_moe_layer: HFQwen3MoeSparseMoeBlock) -> None:
"""Load base weights from HF MoE layer to JAX MoE layer."""
jax_moe_layer.gate.kernel[:] = hf_moe_layer.gate.weight.detach().numpy().T
jax_moe_layer.gate.kernel[:] = np.ascontiguousarray(hf_moe_layer.gate.weight.detach().numpy().T)
for i, expert in enumerate(hf_moe_layer.experts):
jax_moe_layer.experts.gate_proj.weight[i, :, :] = expert.gate_proj.weight.detach().numpy().T
jax_moe_layer.experts.up_proj.weight[i, :, :] = expert.up_proj.weight.detach().numpy().T
jax_moe_layer.experts.down_proj.weight[i, :, :] = expert.down_proj.weight.detach().numpy().T
jax_moe_layer.experts.gate_proj.weight[i, :, :] = np.ascontiguousarray(expert.gate_proj.weight.detach().numpy().T)
jax_moe_layer.experts.up_proj.weight[i, :, :] = np.ascontiguousarray(expert.up_proj.weight.detach().numpy().T)
jax_moe_layer.experts.down_proj.weight[i, :, :] = np.ascontiguousarray(expert.down_proj.weight.detach().numpy().T)


@pytest.mark.parametrize("ep,tp", [(1, 1), (1, 2), (2, 1)])
Expand Down Expand Up @@ -100,8 +100,9 @@ def load_lora_weights(
and jax_module.lora_scaling is not None
and jax_module.lora_ranks is not None
)
jax_module.lora_A[...] = jax_module.lora_A[...].at[adapter_idx].set(jnp.array(lora_A_weights))
jax_module.lora_B[...] = jax_module.lora_B[...].at[adapter_idx].set(jnp.array(lora_B_weights))
# Use np.ascontiguousarray to ensure data is contiguous (required by jax-mps backend)
jax_module.lora_A[...] = jax_module.lora_A[...].at[adapter_idx].set(jnp.array(np.ascontiguousarray(lora_A_weights)))
jax_module.lora_B[...] = jax_module.lora_B[...].at[adapter_idx].set(jnp.array(np.ascontiguousarray(lora_B_weights)))
jax_module.lora_scaling[...] = jax_module.lora_scaling[...].at[adapter_idx].set(scaling)
jax_module.lora_ranks[...] = jax_module.lora_ranks[...].at[adapter_idx].set(rank)

Expand Down
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