# Owner(s): ["module: linear algebra"] import unittest from functools import partial import torch from torch.testing import make_tensor from torch.testing._internal.common_cuda import SM53OrLater from torch.testing._internal.common_device_type import ( dtypes, instantiate_device_type_tests, onlyCUDA, tol as xtol, toleranceOverride, ) from torch.testing._internal.common_utils import ( IS_ARM64, IS_JETSON, parametrize, run_tests, skipIfRocmVersionLessThan, TEST_WITH_ROCM, TestCase, ) # Protects against includes accidentally setting the default dtype # NOTE: jit_metaprogramming_utils sets the default dtype to double! torch.set_default_dtype(torch.float32) assert torch.get_default_dtype() is torch.float32 @unittest.skipIf(IS_ARM64, "Issue with numpy version on arm") class TestMatmulCuda(TestCase): def setUp(self): super(self.__class__, self).setUp() torch.backends.cuda.matmul.allow_tf32 = False def tearDown(self): torch.backends.cuda.matmul.allow_tf32 = True super(self.__class__, self).tearDown() @onlyCUDA @skipIfRocmVersionLessThan((5, 2)) # imported 'tol' as 'xtol' to avoid aliasing in code above @toleranceOverride({torch.float16: xtol(atol=1e-1, rtol=1e-1), torch.bfloat16: xtol(atol=1e-1, rtol=1e-1), torch.float32: xtol(atol=1e-1, rtol=1e-1)}) @dtypes(torch.float16, torch.bfloat16, torch.float32) @parametrize("size", [100, 1000, 10000]) def test_cublas_addmm(self, size: int, dtype: torch.dtype): # # Check for catastrophic cuBLAS inaccuracy by measuring the deviation between # results from the CUDA invocation of torch.addmm and the CPU invocation # (which does not use CUDA backend). # # Get dims n, m, p = (size + 1, size, size + 2) # Disable reduced precision reductions in BFloat16 to bypass some kernels # which fail the threshold check orig = torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction if dtype == torch.bfloat16 and torch.cuda.get_device_capability() >= (8, 6): torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False # Make random tensors on CPU (seed set on common_utils.py import) # (Not using numpy because it does not support bfloat16) make_arg = partial(make_tensor, dtype=dtype, device="cpu") m_beta = make_arg(1) m_input = make_arg((n, p)) m_1 = make_arg((n, m)) m_2 = make_arg((m, p)) # *(B)FLOAT16 Special Handling* # Backend does not tensorize float16 on CPU, # and bloat16 may present accuracy issues, # so convert to float32 for these cases # (but keep same for other types, e.g. float32 and int*) if dtype == torch.float16 or dtype == torch.bfloat16: m_beta = m_beta.to(dtype=torch.float32) m_input = m_input.to(dtype=torch.float32) m_1 = m_1.to(dtype=torch.float32) m_2 = m_2.to(dtype=torch.float32) # Get CPU result res_cpu = torch.addmm(m_input, m_1, m_2, beta=m_beta.item()) # *(B)FLOAT16 Special Handling*`` # Convert back to (b)float16 if dtype == torch.float16 or dtype == torch.bfloat16: m_beta = m_beta.to(dtype=dtype) m_input = m_input.to(dtype=dtype) m_1 = m_1.to(dtype=dtype) m_2 = m_2.to(dtype=dtype) res_cpu = res_cpu.to(dtype=dtype) # Move arg tensors to CUDA m_beta = m_beta.to("cuda") m_input = m_input.to("cuda") m_1 = m_1.to("cuda") m_2 = m_2.to("cuda") # Get CUDA result res_cuda = torch.addmm(m_input, m_1, m_2, beta=m_beta.item()) # Move to CPU for comparison res_cuda = res_cuda.to("cpu") # Compare self.assertEqual(res_cpu, res_cuda) torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = orig @onlyCUDA def test_cublas_addmm_alignment(self): dtype = torch.half device = 'cuda' # perturb X, A, or B alignment for idx in range(0, 3): for offset in range(1, 3): offsets = [0, 0, 0] offsets[idx] = offset x_offset, a_offset, b_offset = offsets A = torch.rand((5120 * 2560 + a_offset), requires_grad=True, dtype=dtype, device=device) A = A[a_offset:].reshape(5120, 2560) X = torch.rand((26 * 2560 + x_offset), requires_grad=True, dtype=dtype, device=device) X = X[x_offset:].reshape(26, 1, 2560) B = torch.rand((5120 + b_offset), requires_grad=True, dtype=dtype, device=device) B = B[b_offset:].reshape(5120) out = torch.nn.functional.linear(X, A, B) self.assertEqual(out, torch.matmul(X, A.transpose(1, 0)) + B) @onlyCUDA @unittest.skipIf(TEST_WITH_ROCM, "Only CUDA 11+ is supported") @unittest.skipIf(IS_JETSON, "Too large for Jetson") @toleranceOverride({torch.float32: xtol(atol=1e-5, rtol=1e-5)}) @dtypes(*([torch.float32, torch.float16] + [torch.bfloat16] if TEST_WITH_ROCM or SM53OrLater else [])) @parametrize( "batch_size, N, M, P", [(2, 100, 100, 100), (2, 1000, 1000, 1000), (1, 10000, 1000, 10000), (1, 10000, 10000, 10000)], name_fn=lambda batch_size, N, M, P: f"{batch_size}_{N}_{M}_{P}", ) def test_cublas_baddbmm_large_input(self, device, batch_size, N, M, P, dtype): cpu_dtype = dtype if dtype == torch.float16 or dtype == torch.bfloat16: cpu_dtype = torch.float32 M1 = torch.rand((N, M), device=device, dtype=dtype) M2 = torch.rand((M, P), device=device, dtype=dtype) A = torch.rand((N, P), device=device, dtype=dtype) def _convert_to_cpu(t): return t.to(device='cpu', dtype=cpu_dtype) M1_cpu, M2_cpu, A_cpu = map(_convert_to_cpu, [M1, M2, A]) # linear out1_cpu = torch.nn.functional.linear(M1_cpu, M2_cpu.t(), A_cpu).to(dtype=dtype) out1_gpu = torch.nn.functional.linear(M1, M2.t(), A).cpu() self.assertEqual(out1_cpu, out1_gpu) # test multiply the identity matrix if N == M and M == P: M2_eye = torch.eye(N, device=device, dtype=dtype) out1_eye_gpu = torch.nn.functional.linear(M1, M2_eye.t(), torch.zeros_like(A)) self.assertEqual(M1_cpu.to(dtype=dtype), out1_eye_gpu.cpu()) # baddbmm def _expand_to_batch(t: torch.Tensor): return t.expand((batch_size, ) + t.size()) alpha, beta = 1.0, 1.0 M1, M2, A, M1_cpu, M2_cpu, A_cpu = map(_expand_to_batch, [M1, M2, A, M1_cpu, M2_cpu, A_cpu]) out2_cpu = torch.baddbmm(A_cpu, M1_cpu, M2_cpu, beta=beta, alpha=alpha).to(dtype=dtype) out2_gpu = torch.baddbmm(A, M1, M2, beta=beta, alpha=alpha).cpu() self.assertEqual(out2_cpu, out2_gpu) # test multiply the identity matrix if N == M and M == P: M2_eye = torch.eye(N, device=device, dtype=dtype).expand(batch_size, N, N) out2_eye_gpu = torch.baddbmm(torch.zeros_like(A), M1, M2_eye, beta=beta, alpha=alpha) self.assertEqual(M1_cpu.to(dtype=dtype), out2_eye_gpu.cpu()) # cross comparison self.assertEqual(out1_gpu, out2_gpu[0]) instantiate_device_type_tests(TestMatmulCuda, globals(), except_for="cpu") if __name__ == '__main__': run_tests()