73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
import unittest
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import CI, dtypes
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from tinygrad import Device
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# similar to test/external/external_test_gpu_ast.py, but universal
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@unittest.skipIf(Device.DEFAULT == "CUDA" and CI, "slow on CUDA CI")
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class TestSpecific(unittest.TestCase):
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# from openpilot
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# 1x1 6 <- 24
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def test_1x1_6_24(self):
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x = Tensor.randn(1, 24 * 4, 32, 64)
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w = Tensor.randn(6 * 4, 24 * 4, 1, 1)
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x.conv2d(w).permute(0, 2, 3, 1).reshape(32, 384, 4).contiguous().realize()
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def test_vec_mul(self):
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# this forces it to be an image...
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x = Tensor.ones(1, 512, 4).contiguous().reshape(1, 2048)
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w = Tensor.randn(2048, 512)
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(x @ w).reshape(1, 128, 4).contiguous().realize()
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@unittest.skipIf(
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Device.DEFAULT in ["LLVM", "WEBGPU", "GPU", "CUDA"],
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"Broken on LLVM and webgpu, GPU requires cl_khr_fp16",
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)
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def test_big_vec_mul(self):
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# from LLaMA
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# 0 buffer<4096, dtypes.float> [View((1024, 1, 1, 4), (4, 0, 0, 1), 0, None)]
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# 1 buffer<4096, dtypes.float> [View((1024, 1024, 4, 4), (0, 4, 1, 0), 0, None)]
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# 2 buffer<16777216, dtypes.half> [View((1024, 1024, 4, 4), (16384, 4, 1, 4096), 0, None)]
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x = Tensor.randn(4096).realize()
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w = (
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Tensor.randn(4096, 4096, device="cpu")
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.cast(dtypes.float16)
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.to(Device.DEFAULT)
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.realize()
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)
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(x @ w.T).realize()
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# from https://dl.acm.org/doi/pdf/10.1145/3495243.3517020
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# ~260 GFLOPS on Adreno 640, should be 260*(720/890)*(596/710) = 176.5 on downclocked 630
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# we get 170
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def test_1x1_28_28(self):
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x = Tensor.randn(1, 256, 28, 28)
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w = Tensor.randn(256, 256, 1, 1)
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x.conv2d(w).permute(0, 2, 3, 1).reshape(
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28, 28 * 256 // 4, 4
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).contiguous().realize()
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# 132 GFLOPS on Adreno 640, should be 132*(720/890)*(596/710) = 90 on downclocked 630
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# gets 54 with broken opt, 74 without opt, and 146 if we pad and opt 3!
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def test_3x3_28_28_stride_2(self):
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x = Tensor.randn(1, 288, 36, 36)
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w = Tensor.randn(384, 288, 3, 3)
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x.conv2d(w, stride=2).permute(0, 2, 3, 1).reshape(
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17, 17 * 384 // 4, 4
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).contiguous().realize()
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def test_3x3_28_28_stride_2_padded(self):
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x = Tensor.randn(1, 288, 36, 36)
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w = Tensor.randn(384, 288, 3, 3)
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x.conv2d(w, stride=2, padding=1).permute(0, 2, 3, 1).reshape(
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18, 18 * 384 // 4, 4
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).contiguous().realize()
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if __name__ == "__main__":
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unittest.main()
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