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test asm_gemm in CI (#14551)
* test asm_gemm in CI * default float16 * use a smaller shape for multi * smaller size * smaller for CI * smaller for ci * need half
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@@ -35,9 +35,13 @@ def can_use_asm_gemm(a:Tensor, b:Tensor) -> bool:
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return todo(f"sharding mismatch a.ndim={a.ndim} a.uop.axis={a.uop.axis} b.uop.axis={b.uop.axis}")
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batch, M, K = (1, *a.shape) if a.ndim == 2 else a.shape
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N = b.shape[1]
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if isinstance(a.device, tuple): batch //= len(a.device)
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if isinstance(a.device, tuple):
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batch //= len(a.device)
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dname = a.device[0]
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else: dname = a.device
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arch = getattr(Device[dname].renderer, "arch", "")
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if batch not in {1, 2}: return todo(f"GEMM batch size {batch}")
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if (key:=(M, N, K)) not in GEMM_ARGS: return todo(f"GEMM shape not supported {key}")
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if (key:=(M, N, K)) not in GEMM_ARGS and arch == "gfx950": return todo(f"GEMM shape not supported {key} on {arch}")
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return True
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# ** UOp gemm to test Tensor.custom_kernel multi and backward correctness on non cdna4
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@@ -1,9 +1,10 @@
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import unittest
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from tinygrad import Tensor, Device, dtypes, Context
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from tinygrad.helpers import getenv
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from tinygrad.device import is_dtype_supported
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from tinygrad.helpers import getenv, CI
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from extra.gemm.asm.cdna.gemm import asm_gemm
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def verify_asm_gemm(batch:int, M:int, N:int, K:int, dtype=dtypes.bfloat16, gpus:int=1) -> None:
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def verify_asm_gemm(batch:int, M:int, N:int, K:int, dtype=dtypes.float16, gpus:int=1) -> None:
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Tensor.manual_seed(0)
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a_rand = Tensor.randn((batch, M, K), dtype=dtypes.float).sub(0.5).cast(dtype)
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b_rand = Tensor.randn((K, N), dtype=dtypes.float).sub(0.5).cast(dtype)
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@@ -29,26 +30,29 @@ def verify_asm_gemm(batch:int, M:int, N:int, K:int, dtype=dtypes.bfloat16, gpus:
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assert (a.grad - a_ref.grad).square().max().float().item() < 1e-3, "grad_a mismatch"
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assert (b.grad - b_ref.grad).square().max().float().item() < 1e-3, "grad_b mismatch"
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SCALE = 128 if CI else 1
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@unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
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class TestGemm(unittest.TestCase):
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def test_simple(self): verify_asm_gemm(1, N:=getenv("N", 4096), N, N, dtype=dtypes.half)
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def test_gemm(self): verify_asm_gemm(1, 8192, 4096, 14336)
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def test_gemm_multi(self): verify_asm_gemm(2, 8192, 4096, 14336, gpus=2)
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def test_gemm_unsupported(self):
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with self.assertRaisesRegex(AssertionError, "shape not supported"):
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verify_asm_gemm(8, 8192, 1024, 4096, gpus=8)
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def test_simple(self): verify_asm_gemm(1, N:=getenv("N", 4096)//SCALE, N//SCALE, N//SCALE, dtype=dtypes.half)
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def test_gemm(self): verify_asm_gemm(1, 8192//SCALE, 4096//SCALE, 14336//SCALE)
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def test_gemm_multi(self): verify_asm_gemm(2, 8192//SCALE, 4096//SCALE, 4096//SCALE, gpus=2)
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class TestGemmLarge(unittest.TestCase):
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def setUp(self):
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if getattr(Device[Device.DEFAULT].renderer, "arch", "") != "gfx950":
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self.skipTest("very slow on non mi350x")
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def test_gemm1(self): verify_asm_gemm(8, 8192, 4096, 14336, gpus=8)
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def test_gemm2(self): verify_asm_gemm(8, 8192, 128256, 4096, gpus=8)
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def test_gemm3(self): verify_asm_gemm(8, 8192, 14336, 4096, gpus=8)
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def test_gemm4(self): verify_asm_gemm(8, 4096, 14336, 4096, gpus=8)
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def test_gemm5(self): verify_asm_gemm(8, 4096, 4096, 14336, gpus=8)
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def test_gemm6(self): verify_asm_gemm(16, 4096, 4096, 14336, gpus=8)
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def test_gemm1(self): verify_asm_gemm(8, 8192, 4096, 14336, dtype=dtypes.bfloat16, gpus=8)
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def test_gemm2(self): verify_asm_gemm(8, 8192, 128256, 4096, dtype=dtypes.bfloat16, gpus=8)
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def test_gemm3(self): verify_asm_gemm(8, 8192, 14336, 4096, dtype=dtypes.bfloat16, gpus=8)
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def test_gemm4(self): verify_asm_gemm(8, 4096, 14336, 4096, dtype=dtypes.bfloat16, gpus=8)
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def test_gemm5(self): verify_asm_gemm(8, 4096, 4096, 14336, dtype=dtypes.bfloat16, gpus=8)
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def test_gemm6(self): verify_asm_gemm(16, 4096, 4096, 14336, dtype=dtypes.bfloat16, gpus=8)
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def test_gemm7(self): verify_asm_gemm(1, 8192, 128256, 4096)
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def test_gemm_unsupported(self):
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with self.assertRaisesRegex(AssertionError, "shape not supported"):
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verify_asm_gemm(8, 1024, 1024, 4096, gpus=8)
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if __name__ == "__main__":
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unittest.main()
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