add qwen3 moe support to tinygrad.apps.llm (#13775)

* qwen moe works

* simple moe

* one test

* integration

test/unit/test_llm_moe.py

@@ -0,0 +1,30 @@
+import unittest
+import numpy as np
+from tinygrad import Tensor
+
+class TestMoEFeedForward(unittest.TestCase):
+  def test_moe_feed_forward(self):
+    from tinygrad.apps.llm import TransformerBlock
+    dim, hidden, n_heads = 8, 16, 2
+    num_experts, k = 4, 2
+
+    block = TransformerBlock(dim, hidden, n_heads, n_heads, norm_eps=1e-5, head_dim=dim//n_heads,
+                             rope_theta=10000, max_context=16, num_experts=num_experts, num_experts_per_tok=k)
+
+    # set up weights: gate scales by (expert_id+1), up/down are identity-ish, router picks experts 0,2
+    block.ffn_gate_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) * (i + 1) for i in range(num_experts)])
+    block.ffn_up_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) for _ in range(num_experts)])
+    block.ffn_down_exps.weight = Tensor.stack(*[Tensor.eye(dim, hidden) for _ in range(num_experts)])
+    block.ffn_gate_inp.weight = Tensor([[1, 0, 1, 0]] * dim).T  # router strongly prefers experts 0 and 2
+    block.ffn_norm.weight = Tensor.ones(dim)  # identity norm
+
+    # input of ones -> after norm still ~ones -> experts 0,2 selected -> weighted sum of silu outputs
+    h = Tensor.ones(1, 1, dim)
+    out = block._feed_forward(h)
+
+    # expected: residual + moe_output ≈ 1 + avg(silu(1), silu(3))
+    expected = 1 + (Tensor([1.0]).silu().item() + Tensor([3.0]).silu().item()) / 2
+    np.testing.assert_allclose(out.numpy()[0, 0, 0], expected, rtol=1e-2)
+
+if __name__ == '__main__':
+  unittest.main()