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107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
import unittest
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from tinygrad import Tensor
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class TestRangeify(unittest.TestCase):
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def test_double_gemm(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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(A@B@C).realize()
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def test_double_gemm_exp(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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(((A@B).exp()@C).exp()).realize()
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def test_double_gemm_relu(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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(((A@B).relu()@C).relu()).realize()
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def test_double_gemm_relu_half_contig(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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(((A@B).relu().contiguous(arg=(1,))@C).relu()).realize()
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def test_double_gemm_half_contig(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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((A@B).contiguous(arg=(1,))@C).realize()
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def test_double_gemm_contig(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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((A@B).contiguous()@C).realize()
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def test_many_gemm(self):
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N = 1024
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A = Tensor.empty(N, N)
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B = Tensor.empty(N, N)
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C = Tensor.empty(N, N)
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D = Tensor.empty(N, N)
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E = Tensor.empty(N, N)
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F = Tensor.empty(N, N)
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(A@B@C@D@E@F).realize()
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def test_conv2d(self):
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x = Tensor.empty(1, 4, 32, 32)
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w1 = Tensor.empty(8, 4, 3, 3)
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x.conv2d(w1).realize()
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def test_conv2d_t(self):
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x = Tensor.empty(1, 4, 32, 32)
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w1 = Tensor.empty(8, 4, 3, 3)
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(x*2).conv2d(w1).realize()
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def test_double_conv2d(self):
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x = Tensor.empty(1, 4, 32, 32)
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w1 = Tensor.empty(8, 4, 3, 3)
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w2 = Tensor.empty(12, 8, 3, 3)
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x.conv2d(w1).conv2d(w2).realize()
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def test_double_conv2d_half_contig(self):
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x = Tensor.empty(1, 4, 32, 32)
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w1 = Tensor.empty(8, 4, 3, 3)
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w2 = Tensor.empty(12, 8, 3, 3)
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# NOTE: this contiguous doesn't help
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x.conv2d(w1).contiguous(arg=(1,)).conv2d(w2).permute(0,2,3,1).contiguous().realize()
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def test_double_conv2d_contig(self):
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x = Tensor.empty(1, 4, 32, 32)
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w1 = Tensor.empty(8, 4, 3, 3)
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w2 = Tensor.empty(12, 8, 3, 3)
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x.conv2d(w1).contiguous().conv2d(w2).realize()
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def test_transformer_ffn(self):
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from tinygrad.apps.llm import TransformerBlock
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from tinygrad import nn
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blk = TransformerBlock(1024, 4096, 1, 1, 1e-5)
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for p in nn.state.get_parameters(blk): p.replace(Tensor.empty(p.shape))
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x = Tensor.empty(128, 1024)
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out = blk._feed_forward(x)
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out.realize()
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def test_flash_attention(self):
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BS = 4
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HEADS = 2
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MATDIM = 16
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EMB = 8
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q = Tensor.empty(BS, HEADS, MATDIM, EMB)
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k = Tensor.empty(BS, HEADS, MATDIM, EMB)
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v = Tensor.empty(BS, HEADS, MATDIM, EMB)
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q.scaled_dot_product_attention(k, v).realize()
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if __name__ == '__main__':
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unittest.main()
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