import pytest import torch import bitsandbytes as bnb from itertools import product n = 1 k = 25 dim1 = torch.randint(16,64, size=(n,)).tolist() dim2 = torch.randint(32,96, size=(n,)).tolist() dim3 = torch.randint(32,96, size=(n,)).tolist() dim4 = torch.randint(32,96, size=(n,)).tolist() funcs = [(torch.bmm, bnb.bmm_cublas), (torch.matmul, bnb.matmul_cublas)] str_funcs = ['bmm', 'matmul'] req_grad = [(False, False), (True, False), (True, True), (False, True)] req_grad_str = ['FF', 'TF', 'TT', 'FT'] transpose = [(False, False), (False, True), (True, True), (True, False)] str_transpose = ['FF', 'FT', 'TT', 'TF'] dtype = [torch.float32, torch.float16] values = list(product(dim1,dim2,dim3,dim4,funcs, dtype, req_grad, transpose)) str_values = list(product(dim1,dim2,dim3,dim4,str_funcs, dtype, req_grad_str, str_transpose)) names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}'.format(*vals) for vals in str_values] @pytest.mark.parametrize("dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose", values, ids=names) def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose): if dim2 > 0: dim2 = dim2 - (dim2 % 16) dim3 = dim3 - (dim3 % 16) dim4 = dim4 - (dim4 % 16) for i in range(k): # normal multiply if funcs[0] in [torch.mm, torch.matmul]: dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2) dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3) A = torch.randn(size=dimA, device='cuda', requires_grad=req_grad[0]) B = torch.randn(size=dimB, device='cuda', requires_grad=req_grad[1]) target = torch.randn(size=(dim2, dim4), device='cuda', requires_grad=req_grad[1]) torch.nn.init.xavier_uniform_(B) if not transpose[0] and not transpose[1]: out_torch = funcs[0](A, B) out_bnb = funcs[1](A, B) elif not transpose[0] and transpose[1]: out_torch = funcs[0](A, B.t()) out_bnb = funcs[1](A, B.t()) elif transpose[0] and not transpose[1]: out_torch = funcs[0](A.t(), B) out_bnb = funcs[1](A.t(), B) elif transpose[0] and transpose[1]: out_torch = funcs[0](A.t(), B.t()) out_bnb = funcs[1](A.t(), B.t()) n = out_bnb.numel() idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1) assert (idx==0).sum().item() < n*0.0175 idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2) assert (idx==0).sum().item() < n*0.001 if any(req_grad): out_bnb.data.copy_(out_torch) torch.cuda.synchronize() loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean() loss_bnb.backward() gradA1 = A.grad gradB1 = B.grad A.grad = None B.grad = None loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean() loss_torch.backward() gradA2 = A.grad gradB2 = B.grad A.grad = None B.grad = None if req_grad[0]: torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1) if req_grad[1]: n = gradB1.numel() idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3) assert (idx==0).sum().item() < n*0.1 idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3) assert (idx==0).sum().item() < n*0.02 torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3) # batched matrix multiply if funcs[0] in [torch.bmm, torch.matmul]: A = torch.randn(size=(dim1, dim2, dim3), device='cuda', requires_grad=req_grad[0]) B = torch.randn(size=(dim1, dim3, dim4), device='cuda', requires_grad=req_grad[1]) target = torch.randn(size=(dim1, dim2, dim4), device='cuda', requires_grad=req_grad[1]) torch.nn.init.xavier_uniform_(B) out_torch = funcs[0](A, B) out_bnb = funcs[1](A, B) n = out_bnb.numel() idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1) assert (idx==0).sum().item() < n*0.01 torch.testing.assert_allclose(out_bnb, out_torch, atol=0.027, rtol=0.2) if any(req_grad): out_bnb.data.copy_(out_torch) torch.cuda.synchronize() loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean() loss_bnb.backward() gradA1 = A.grad gradB1 = B.grad A.grad = None B.grad = None loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean() loss_torch.backward() gradA2 = A.grad gradB2 = B.grad A.grad = None B.grad = None if req_grad[0]: torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1) if req_grad[1]: n = gradB1.numel() idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3) assert (idx==0).sum().item() < n*0.1 idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3) assert (idx==0).sum().item() < n*0.02 if funcs[0] in [torch.matmul]: dim1 = dim1 - (dim1 % 16) A = torch.randn(size=(dim1, dim2, dim3), device='cuda', requires_grad=req_grad[0]) dimB = (dim4, dim3) if transpose[1] else (dim3, dim4) B = torch.randn(size=dimB, device='cuda', requires_grad=req_grad[1]) target = torch.randn(size=(dim1, dim2, dim4), device='cuda', requires_grad=req_grad[1]) torch.nn.init.xavier_uniform_(B) if transpose[1]: out_torch = funcs[0](A, B.t()) out_bnb = funcs[1](A, B.t()) else: out_torch = funcs[0](A, B) out_bnb = funcs[1](A, B) n = out_bnb.numel() idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1) assert (idx==0).sum().item() < n*0.0175 idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2) assert (idx==0).sum().item() < n*0.001 if any(req_grad): out_bnb.data.copy_(out_torch) torch.cuda.synchronize() loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean() loss_bnb.backward() gradA1 = A.grad gradB1 = B.grad A.grad = None B.grad = None loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean() loss_torch.backward() gradA2 = A.grad gradB2 = B.grad A.grad = None B.grad = None if req_grad[0]: torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1) if req_grad[1]: n = gradB1.numel() idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3) assert (idx==0).sum().item() < n*0.1 idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3) assert (idx==0).sum().item() < n*0.02 n = 1 k = 3 dim1 = torch.randint(16,64, size=(n,)).tolist() dim2 = torch.randint(32,96, size=(n,)).tolist() dim3 = torch.randint(32,96, size=(n,)).tolist() dim4 = torch.randint(32,96, size=(n,)).tolist() dim2.append(0) #dim1 = (17,) #dim2 = (7,) #dim3 = (37,) #dim4 = (23,) decomp = [0.0, 6.0] funcs = [(torch.matmul, bnb.matmul)] str_funcs = ['matmul'] req_grad = [(False, False), (True, False), (True, True), (False, True)] req_grad_str = ['FF', 'TF', 'TT', 'FT'] transpose = [(False, True), (False, False)] str_transpose = ['NT', 'NN'] dtype = [torch.float16] has_fp16_weights = [True, False] values = list(product(dim1,dim2,dim3,dim4,funcs, dtype, req_grad, transpose, decomp, has_fp16_weights)) str_values = list(product(dim1,dim2,dim3,dim4,str_funcs, dtype, req_grad_str, str_transpose, decomp, has_fp16_weights)) names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}'.format(*vals) for vals in str_values] @pytest.mark.parametrize("dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights", values, ids=names) def test_matmullt(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights): dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2) dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3) outlier_dim = torch.randint(0, dimA[1], size=(dimA[1]//8,), device='cuda') for i in range(k): # normal multiply if funcs[0] in [torch.mm, torch.matmul]: A = torch.randn(size=dimA, device='cuda', requires_grad=req_grad[0], dtype=dtype) if decomp == 6.0: with torch.no_grad(): A[:, outlier_dim] = 6.0 B = torch.randn(size=dimB, device='cuda', requires_grad=req_grad[1], dtype=dtype) target = torch.randn(size=(dim2, dim4), device='cuda', requires_grad=req_grad[1], dtype=dtype) torch.nn.init.xavier_uniform_(B) B2 = B.clone() state = bnb.MatmulLtState() state.threshold = decomp state.has_fp16_weights = has_fp16_weights if not has_fp16_weights: if not transpose[0] and not transpose[1]: B2 = B2.t().contiguous() state.CB, CBt, state.SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B2) B2 = state.CB if not transpose[0] and transpose[1]: out_torch = funcs[0](A, B.t()) out_bnb = funcs[1](A, B2, state=state) elif not transpose[0] and not transpose[1]: out_torch = funcs[0](A, B) out_bnb = funcs[1](A, B2.t(), state=state) n = out_bnb.numel() err = torch.abs(out_bnb-out_torch).mean().item() #print(f'abs error {err:.4f}') idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1) assert (idx==0).sum().item() <= n*0.0175 idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2) assert (idx==0).sum().item() <= n*0.001 if has_fp16_weights: if any(req_grad): out_bnb.data.copy_(out_torch) torch.cuda.synchronize() loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean() loss_bnb.backward() gradA1 = A.grad gradB1 = B.grad A.grad = None B.grad = None loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean() loss_torch.backward() gradA2 = A.grad gradB2 = B.grad A.grad = None B.grad = None if req_grad[0]: torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1) if req_grad[1]: n = gradB1.numel() if dim2 > 0: assert torch.abs(gradB1).sum() > 0.0 assert torch.abs(gradB2).sum() > 0.0 else: assert torch.abs(gradB1).sum() == 0.0 assert torch.abs(gradB2).sum() == 0.0 idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3) assert (idx==0).sum().item() <= n*0.1 idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3) assert (idx==0).sum().item() <= n*0.02 torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3)