mirror of
https://github.com/tinygrad/tinygrad.git
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df0f9d6860
* add olmoe support to llm * cleanups * simpler * clean * fix mypy * lil * remove dumb assert
54 lines
2.5 KiB
Python
54 lines
2.5 KiB
Python
import unittest
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import numpy as np
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from tinygrad import Tensor
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class TestMoEFeedForward(unittest.TestCase):
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def test_moe_feed_forward(self):
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from tinygrad.apps.llm import TransformerBlock
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dim, hidden, n_heads = 8, 16, 2
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num_experts, k = 4, 2
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block = TransformerBlock(dim, hidden, n_heads, n_heads, norm_eps=1e-5, head_dim=dim//n_heads,
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rope_theta=10000, max_context=16, num_experts=num_experts, num_experts_per_tok=k)
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# set up weights: gate scales by (expert_id+1), up/down are identity-ish, router picks experts 0,2
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block.ffn_gate_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) * (i + 1) for i in range(num_experts)])
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block.ffn_up_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) for _ in range(num_experts)])
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block.ffn_down_exps.weight = Tensor.stack(*[Tensor.eye(dim, hidden) for _ in range(num_experts)])
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block.ffn_gate_inp.weight = Tensor([[1, 0, 1, 0]] * dim).T # router strongly prefers experts 0 and 2
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block.ffn_norm.weight = Tensor.ones(dim) # identity norm
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# input of ones -> after norm still ~ones -> experts 0,2 selected -> weighted sum of silu outputs
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h = Tensor.ones(1, 1, dim)
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out = block._feed_forward(h)
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# expected: residual + moe_output ≈ 1 + avg(silu(1), silu(3))
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expected = 1 + (Tensor([1.0]).silu().item() + Tensor([3.0]).silu().item()) / 2
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np.testing.assert_allclose(out.numpy()[0, 0, 0], expected, rtol=1e-2)
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def test_moe_feed_forward_batched(self):
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from tinygrad.apps.llm import TransformerBlock
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dim, hidden, n_heads = 8, 16, 2
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num_experts, k = 4, 2
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block = TransformerBlock(dim, hidden, n_heads, n_heads, norm_eps=1e-5, head_dim=dim//n_heads,
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rope_theta=10000, max_context=16, num_experts=num_experts, num_experts_per_tok=k)
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# same setup as BS=1 test
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block.ffn_gate_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) * (i + 1) for i in range(num_experts)])
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block.ffn_up_exps.weight = Tensor.stack(*[Tensor.eye(hidden, dim) for _ in range(num_experts)])
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block.ffn_down_exps.weight = Tensor.stack(*[Tensor.eye(dim, hidden) for _ in range(num_experts)])
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block.ffn_gate_inp.weight = Tensor([[1, 0, 1, 0]] * dim).T
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block.ffn_norm.weight = Tensor.ones(dim)
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# test with BS=2, T=3
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h = Tensor.ones(2, 3, dim)
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out = block._feed_forward(h)
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# all outputs should match the BS=1 expected value
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expected = 1 + (Tensor([1.0]).silu().item() + Tensor([3.0]).silu().item()) / 2
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np.testing.assert_allclose(out.numpy(), expected, rtol=1e-2)
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if __name__ == '__main__':
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unittest.main()
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