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78 lines
3.8 KiB
Python
78 lines
3.8 KiB
Python
import unittest
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from tinygrad import Tensor, Device, dtypes, Context
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from tinygrad.device import is_dtype_supported
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from tinygrad.helpers import getenv
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from extra.gemm.asm.cdna.gemm import asm_gemm
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from test.helpers import needs_second_gpu
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# On non CDNA4 it will only validate the Tensor.custom_kernel integration
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# Use NULL=1 EMULATE=AMD_CDNA4 to also test the assembly
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def is_cdna4(): return getattr(Device[Device.DEFAULT].renderer, "arch", "").startswith("gfx950")
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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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with Context(DEBUG=0):
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Tensor.realize(a_rand, b_rand)
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devs = tuple(f"{Device.DEFAULT}:{i}" for i in range(gpus)) if (multi:=gpus>1) else None
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a, b = Tensor(a_rand.numpy(), requires_grad=True).cast(dtype), Tensor(b_rand.numpy(), requires_grad=True).cast(dtype)
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if multi: a, b = a.shard(devs, axis=0), b.shard(devs, axis=None)
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with Context(ASM_GEMM=1):
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tst = asm_gemm(a, b)
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tst.sum().backward()
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Tensor.realize(tst, a.grad, b.grad)
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a_ref, b_ref = Tensor(a_rand.numpy(), requires_grad=True).cast(dtype), Tensor(b_rand.numpy(), requires_grad=True).cast(dtype)
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if multi: a_ref, b_ref = a_ref.shard(devs, axis=0), b_ref.shard(devs, axis=None)
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with Context(ASM_GEMM=0):
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ref = asm_gemm(a_ref, b_ref)
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ref.sum().backward()
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Tensor.realize(ref, a_ref.grad, b_ref.grad)
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# no validation on the NULL device
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if a_rand.device.startswith("NULL"): return None
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with Context(DEBUG=0):
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assert (tst - ref).square().max().float().item() < 1e-6, "forward mismatch"
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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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# 128x smaller than usual
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# uses the UOp GEMM, runs on non CDNA4 and CI
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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 setUp(self):
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if is_cdna4(): self.skipTest("shapes are too small for the assembly GEMM")
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def test_simple(self): verify_asm_gemm(1, N:=getenv("N", 32), N, N, dtype=dtypes.half)
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def test_gemm(self): verify_asm_gemm(1, 64, 32, 112)
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def test_gemm_batched(self): verify_asm_gemm(2, 64, 32, 32)
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@needs_second_gpu
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def test_gemm_multi(self): verify_asm_gemm(2, 64, 32, 32, gpus=2)
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# uses the Asm GEMM on CDNA4 only for speed reasons
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class TestGemmLarge(unittest.TestCase):
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def setUp(self):
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if not is_cdna4():
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self.skipTest("very slow on non mi350x")
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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_batched(self): verify_asm_gemm(2, 8192, 4096, 4096)
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def test_gemm1(self): verify_asm_gemm(8, 8192, 4096, 14336, dtype=dtypes.bfloat16, gpus=8)
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@unittest.skip("disabled, asm in this shape is slower than tinygrad")
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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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@unittest.skip("disabled, asm in this shape is slower than tinygrad")
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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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