# Copyright (C) 2023, Tri Dao. import math import torch import torch.nn.functional as F from torch.autograd import gradcheck import pytest from einops import rearrange from mamba_ssm.ops.selective_scan_interface import selective_scan_fn, selective_scan_ref from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, mamba_inner_ref from mamba_ssm.ops.selective_scan_interface import bimamba_inner_fn, bimamba_inner_ref # @pytest.mark.parametrize('wtype', [torch.float32, torch.complex64]) @pytest.mark.parametrize('wtype', [torch.float32]) # @pytest.mark.parametrize('itype', [torch.float32, torch.float16, torch.bfloat16]) @pytest.mark.parametrize('itype', [torch.float32]) # @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 372, 512, 784, 1024, 1134, 2048, 4096]) @pytest.mark.parametrize('seqlen', [128, 256, 512, 1024, 2048, 4096]) # @pytest.mark.parametrize('seqlen', [128]) # @pytest.mark.parametrize("return_last_state", [False, True]) @pytest.mark.parametrize("return_last_state", [True]) # @pytest.mark.parametrize('has_delta_bias', [False, True]) @pytest.mark.parametrize('has_delta_bias', [True]) # @pytest.mark.parametrize('delta_softplus', [False, True]) @pytest.mark.parametrize('delta_softplus', [True]) # @pytest.mark.parametrize('has_z', [False, True]) @pytest.mark.parametrize('has_z', [True]) # @pytest.mark.parametrize('has_D', [False, True]) @pytest.mark.parametrize('has_D', [True]) @pytest.mark.parametrize("varBC_groups", [1, 2]) # @pytest.mark.parametrize("varBC_groups", [1]) # @pytest.mark.parametrize("is_variable_C", [False, True]) @pytest.mark.parametrize("is_variable_C", [True]) # @pytest.mark.parametrize("is_variable_B", [False, True]) @pytest.mark.parametrize("is_variable_B", [True]) def test_selective_scan(is_variable_B, is_variable_C, varBC_groups, has_D, has_z, has_delta_bias, delta_softplus, return_last_state, seqlen, itype, wtype): if varBC_groups > 1 and (not is_variable_B or not is_variable_C): pytest.skip() # This config is not applicable device = 'cuda' rtol, atol = (6e-4, 2e-3) if itype == torch.float32 else (3e-3, 5e-3) if itype == torch.bfloat16: rtol, atol = 3e-2, 5e-2 rtolw, atolw = (1e-3, 1e-3) if has_z: # If we have z, the errors on the weights seem higher rtolw = max(rtolw, rtol) atolw = max(atolw, atol) # set seed torch.random.manual_seed(0) batch_size = 2 dim = 4 dstate = 8 is_complex = wtype == torch.complex64 A = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_() if not is_variable_B: B_shape = (dim, dstate) elif varBC_groups == 1: B_shape = (batch_size, dstate, seqlen if not is_complex else seqlen * 2) else: B_shape = (batch_size, varBC_groups, dstate, seqlen if not is_complex else seqlen * 2) B = torch.randn(*B_shape, device=device, dtype=wtype if not is_variable_B else itype, requires_grad=True) if not is_variable_C: C_shape = (dim, dstate) elif varBC_groups == 1: C_shape = (batch_size, dstate, seqlen if not is_complex else seqlen * 2) else: C_shape = (batch_size, varBC_groups, dstate, seqlen if not is_complex else seqlen * 2) C = torch.randn(*C_shape, device=device, dtype=wtype if not is_variable_C else itype, requires_grad=True) if has_D: D = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) else: D = None if has_z: z = torch.randn(batch_size, dim, seqlen, device=device, dtype=itype, requires_grad=True) else: z = None if has_delta_bias: delta_bias = (0.5 * torch.rand(dim, device=device, dtype=torch.float32)).requires_grad_() else: delta_bias = None u = torch.randn(batch_size, dim, seqlen, device=device, dtype=itype, requires_grad=True) delta = (0.5 * torch.rand(batch_size, dim, seqlen, device=device, dtype=itype)).requires_grad_() A_ref = A.detach().clone().requires_grad_() B_ref = B.detach().clone().requires_grad_() C_ref = C.detach().clone().requires_grad_() D_ref = D.detach().clone().requires_grad_() if D is not None else None z_ref = z.detach().clone().requires_grad_() if z is not None else None u_ref = u.detach().clone().requires_grad_() delta_ref = delta.detach().clone().requires_grad_() delta_bias_ref = delta_bias.detach().clone().requires_grad_() if delta_bias is not None else None out, *rest = selective_scan_fn( u, delta, A, B, C, D, z=z, delta_bias=delta_bias, delta_softplus=delta_softplus, return_last_state=return_last_state ) if return_last_state: state = rest[0] out_ref, *rest = selective_scan_ref( u_ref, delta_ref, A_ref, B_ref, C_ref, D_ref, z=z_ref, delta_bias=delta_bias_ref, delta_softplus=delta_softplus, return_last_state=return_last_state ) if return_last_state: state_ref = rest[0] # dA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A)) # dt_u = delta * u print(f'Output max diff: {(out - out_ref).abs().max().item()}') print(f'Output mean diff: {(out - out_ref).abs().mean().item()}') assert torch.allclose(out, out_ref, rtol=rtol, atol=atol) if return_last_state: print(f'State max diff: {(state - state_ref).abs().max().item()}') assert torch.allclose(state, state_ref, rtol=rtol, atol=atol) g = torch.randn_like(out) out_ref.backward(g) out.backward(g) print(f'du max diff: {(u.grad - u_ref.grad).abs().max().item()}') print(f'ddelta max diff: {(delta.grad - delta_ref.grad).abs().max().item()}') print(f'dA max diff: {(A.grad - A_ref.grad).abs().max().item()}') print(f'dB max diff: {(B.grad - B_ref.grad).abs().max().item()}') print(f'dC max diff: {(C.grad - C_ref.grad).abs().max().item()}') if has_D: print(f'dD max diff: {(D.grad - D_ref.grad).abs().max().item()}') if has_z: print(f'dz max diff: {(z.grad - z_ref.grad).abs().max().item()}') if has_delta_bias: print(f'ddelta_bias max diff: {(delta_bias.grad - delta_bias_ref.grad).abs().max().item()}') assert torch.allclose(u.grad, u_ref.grad.to(dtype=itype), rtol=rtol * 2, atol=atol * 2) assert torch.allclose(delta.grad, delta_ref.grad.to(dtype=itype), rtol=rtol * 5, atol=atol * 10) assert torch.allclose(A.grad, A_ref.grad, rtol=rtolw, atol=atolw * 5) assert torch.allclose(B.grad, B_ref.grad, rtol=rtolw if not is_variable_B else rtol, atol=atolw if not is_variable_B else atol) assert torch.allclose(C.grad, C_ref.grad, rtol=rtolw if not is_variable_C else rtol, atol=atolw if not is_variable_C else atol) if has_D: assert torch.allclose(D.grad, D_ref.grad, rtol=rtolw, atol=atolw) if has_z: assert torch.allclose(z.grad, z_ref.grad, rtol=rtolw, atol=atolw) if has_delta_bias: assert torch.allclose(delta_bias.grad, delta_bias_ref.grad, rtol=rtolw, atol=atolw) @pytest.mark.parametrize('wtype', [torch.float32, torch.complex64]) # @pytest.mark.parametrize('wtype', [torch.complex64]) # @pytest.mark.parametrize('itype', [torch.float32, torch.float16, torch.bfloat16]) @pytest.mark.parametrize('itype', [torch.float32]) # @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 372, 512, 784, 1024, 1134, 2048, 4096]) @pytest.mark.parametrize('seqlen', [128]) @pytest.mark.parametrize("is_variable_C", [False, True]) # @pytest.mark.parametrize("is_variable_C", [False]) @pytest.mark.parametrize("is_variable_B", [False, True]) # @pytest.mark.parametrize("is_variable_B", [True]) def test_mamba_inner_fn(is_variable_B, is_variable_C, seqlen, itype, wtype): device = 'cuda' rtol, atol = (6e-4, 2e-3) if itype == torch.float32 else (3e-3, 5e-3) if itype == torch.bfloat16: rtol, atol = 3e-2, 5e-2 rtolw, atolw = (1e-3, 1e-3) # If we have z, the errors on the weights seem higher rtolw = max(rtolw, rtol) atolw = max(atolw, atol) # set seed torch.random.manual_seed(0) batch_size = 2 dim = 768 dstate = 8 dt_rank = 48 is_complex = wtype == torch.complex64 xz = torch.randn(batch_size, 2 * dim, seqlen, device=device, dtype=itype, requires_grad=True) conv1d_weight = torch.randn(dim, 1, 3, device=device, dtype=torch.float32, requires_grad=True) conv1d_bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) x_proj_weight = torch.randn(dt_rank + (bool(is_variable_B) + bool(is_variable_C)) * dstate * (1 if not is_complex else 2), dim, device=device, dtype=itype, requires_grad=True) delta_proj_weight = torch.randn(dim, dt_rank, device=device, dtype=itype, requires_grad=True) out_proj_weight = torch.randn(dim // 2, dim, device=device, dtype=itype, requires_grad=True) out_proj_bias = None A = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_() B = (torch.randn(dim, dstate, device=device, dtype=wtype, requires_grad=True) if not is_variable_B else None) C = (torch.randn(dim, dstate, device=device, dtype=wtype, requires_grad=True) if not is_variable_C else None) D = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) delta_bias = (0.5 * torch.rand(dim, device=device, dtype=torch.float32)).requires_grad_() B_proj_bias = None C_proj_bias = None xz_ref = xz.detach().clone().requires_grad_() conv1d_weight_ref = conv1d_weight.detach().clone().requires_grad_() conv1d_bias_ref = conv1d_bias.detach().clone().requires_grad_() x_proj_weight_ref = x_proj_weight.detach().clone().requires_grad_() delta_proj_weight_ref = delta_proj_weight.detach().clone().requires_grad_() out_proj_weight_ref = out_proj_weight.detach().clone().requires_grad_() out_proj_bias_ref = (out_proj_bias.detach().clone().requires_grad_() if out_proj_bias is not None else None) A_ref = A.detach().clone().requires_grad_() B_ref = B.detach().clone().requires_grad_() if B is not None else None C_ref = C.detach().clone().requires_grad_() if C is not None else None D_ref = D.detach().clone().requires_grad_() delta_bias_ref = delta_bias.detach().clone().requires_grad_() if delta_bias is not None else None out = mamba_inner_fn(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight, out_proj_weight, out_proj_bias, A, B, C, D, delta_bias=delta_bias, delta_softplus=True) out_ref = mamba_inner_ref(xz_ref, conv1d_weight_ref, conv1d_bias_ref, x_proj_weight_ref, delta_proj_weight_ref, out_proj_weight_ref, out_proj_bias_ref, A_ref, B_ref, C_ref, D_ref, delta_bias=delta_bias_ref, delta_softplus=True) # dA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A)) # dt_u = delta * u print("mamba_inner_fn") print(f'Output max diff: {(out - out_ref).abs().max().item()}') print(f'Output mean diff: {(out - out_ref).abs().mean().item()}') assert torch.allclose(out, out_ref, rtol=rtol, atol=atol) g = torch.randn_like(out) out_ref.backward(g) out.backward(g) print(f'dxz max diff: {(xz.grad - xz_ref.grad).abs().max().item()}') print(f'dA max diff: {(A.grad - A_ref.grad).abs().max().item()}') if not is_variable_B: print(f'dB max diff: {(B.grad - B_ref.grad).abs().max().item()}') if not is_variable_C: print(f'dC max diff: {(C.grad - C_ref.grad).abs().max().item()}') print(f'dD max diff: {(D.grad - D_ref.grad).abs().max().item()}') print(f'ddelta_bias max diff: {(delta_bias.grad - delta_bias_ref.grad).abs().max().item()}') print(f'dout_proj_weight max diff: {(out_proj_weight.grad - out_proj_weight_ref.grad).abs().max().item()}') print(f'ddelta_proj_weight max diff: {(delta_proj_weight.grad - delta_proj_weight_ref.grad).abs().max().item()}') print(f'dx_proj_weight max diff: {(x_proj_weight.grad - x_proj_weight_ref.grad).abs().max().item()}') print(f'dconv1d_weight max diff: {(conv1d_weight.grad - conv1d_weight_ref.grad).abs().max().item()}') print(f'dconv1d_bias max diff: {(conv1d_bias.grad - conv1d_bias_ref.grad).abs().max().item()}') # assert torch.allclose(xz.grad, xz_ref.grad.to(dtype=itype), rtol=rtol * 2, atol=atol * 2) # assert torch.allclose(delta.grad, delta_ref.grad.to(dtype=itype), rtol=rtol * 5, atol=atol * 10) # assert torch.allclose(A.grad, A_ref.grad, rtol=rtolw, atol=atolw * 5) # assert torch.allclose(B.grad, B_ref.grad, rtol=rtolw if not is_variable_B else rtol, # atol=atolw if not is_variable_B else atol) # assert torch.allclose(C.grad, C_ref.grad, rtol=rtolw if not is_variable_C else rtol, # atol=atolw if not is_variable_C else atol) # assert torch.allclose(D.grad, D_ref.grad, rtol=rtolw, atol=atolw) # assert torch.allclose(delta_bias.grad, delta_bias_ref.grad, rtol=rtolw, atol=atolw) # test_mamba_inner_fn(False, False, 128, torch.float32, torch.float32) @pytest.mark.parametrize('wtype', [torch.float32, torch.complex64]) # @pytest.mark.parametrize('wtype', [torch.complex64]) # @pytest.mark.parametrize('itype', [torch.float32, torch.float16, torch.bfloat16]) @pytest.mark.parametrize('itype', [torch.float32]) # @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 372, 512, 784, 1024, 1134, 2048, 4096]) @pytest.mark.parametrize('seqlen', [128]) @pytest.mark.parametrize("is_variable_C", [False, True]) # @pytest.mark.parametrize("is_variable_C", [False]) @pytest.mark.parametrize("is_variable_B", [False, True]) # @pytest.mark.parametrize("is_variable_B", [True]) def test_bimamba_inner_fn(is_variable_B, is_variable_C, seqlen, itype, wtype): device = 'cuda' rtol, atol = (6e-4, 2e-3) if itype == torch.float32 else (3e-3, 5e-3) if itype == torch.bfloat16: rtol, atol = 3e-2, 5e-2 rtolw, atolw = (1e-3, 1e-3) # If we have z, the errors on the weights seem higher rtolw = max(rtolw, rtol) atolw = max(atolw, atol) # set seed torch.random.manual_seed(0) batch_size = 2 dim = 768 dstate = 8 dt_rank = 48 is_complex = wtype == torch.complex64 xz = torch.randn(batch_size, 2 * dim, seqlen, device=device, dtype=itype, requires_grad=True) conv1d_weight = torch.randn(dim, 1, 3, device=device, dtype=torch.float32, requires_grad=True) conv1d_bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) x_proj_weight = torch.randn(dt_rank + (bool(is_variable_B) + bool(is_variable_C)) * dstate * (1 if not is_complex else 2), dim, device=device, dtype=itype, requires_grad=True) delta_proj_weight = torch.randn(dim, dt_rank, device=device, dtype=itype, requires_grad=True) out_proj_weight = torch.randn(dim // 2, dim, device=device, dtype=itype, requires_grad=True) out_proj_bias = None A = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_() A_b = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_() B = (torch.randn(dim, dstate, device=device, dtype=wtype, requires_grad=True) if not is_variable_B else None) C = (torch.randn(dim, dstate, device=device, dtype=wtype, requires_grad=True) if not is_variable_C else None) D = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) delta_bias = (0.5 * torch.rand(dim, device=device, dtype=torch.float32)).requires_grad_() B_proj_bias = None C_proj_bias = None xz_ref = xz.detach().clone().requires_grad_() conv1d_weight_ref = conv1d_weight.detach().clone().requires_grad_() conv1d_bias_ref = conv1d_bias.detach().clone().requires_grad_() x_proj_weight_ref = x_proj_weight.detach().clone().requires_grad_() delta_proj_weight_ref = delta_proj_weight.detach().clone().requires_grad_() out_proj_weight_ref = out_proj_weight.detach().clone().requires_grad_() out_proj_bias_ref = (out_proj_bias.detach().clone().requires_grad_() if out_proj_bias is not None else None) A_ref = A.detach().clone().requires_grad_() A_b_ref = A_b.detach().clone().requires_grad_() B_ref = B.detach().clone().requires_grad_() if B is not None else None C_ref = C.detach().clone().requires_grad_() if C is not None else None D_ref = D.detach().clone().requires_grad_() delta_bias_ref = delta_bias.detach().clone().requires_grad_() if delta_bias is not None else None out = bimamba_inner_fn(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight, out_proj_weight, out_proj_bias, A, A_b, B, C, D, delta_bias=delta_bias, delta_softplus=True) out_ref = bimamba_inner_fn(xz_ref, conv1d_weight_ref, conv1d_bias_ref, x_proj_weight_ref, delta_proj_weight_ref, out_proj_weight_ref, out_proj_bias_ref, A_ref, A_b_ref, B_ref, C_ref, D_ref, delta_bias=delta_bias_ref, delta_softplus=True) # dA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A)) # dt_u = delta * u print("bimamba_inner_fn") print(f'Output max diff: {(out - out_ref).abs().max().item()}') print(f'Output mean diff: {(out - out_ref).abs().mean().item()}') assert torch.allclose(out, out_ref, rtol=rtol, atol=atol) g = torch.randn_like(out) out_ref.backward(g) out.backward(g) print(f'dxz max diff: {(xz.grad - xz_ref.grad).abs().max().item()}') print(f'dA max diff: {(A.grad - A_ref.grad).abs().max().item()}') print(f'dA_b max diff: {(A_b.grad - A_b_ref.grad).abs().max().item()}') if not is_variable_B: print(f'dB max diff: {(B.grad - B_ref.grad).abs().max().item()}') if not is_variable_C: print(f'dC max diff: {(C.grad - C_ref.grad).abs().max().item()}') print(f'dD max diff: {(D.grad - D_ref.grad).abs().max().item()}') print(f'ddelta_bias max diff: {(delta_bias.grad - delta_bias_ref.grad).abs().max().item()}') print(f'dout_proj_weight max diff: {(out_proj_weight.grad - out_proj_weight_ref.grad).abs().max().item()}') print(f'ddelta_proj_weight max diff: {(delta_proj_weight.grad - delta_proj_weight_ref.grad).abs().max().item()}') print(f'dx_proj_weight max diff: {(x_proj_weight.grad - x_proj_weight_ref.grad).abs().max().item()}') print(f'dconv1d_weight max diff: {(conv1d_weight.grad - conv1d_weight_ref.grad).abs().max().item()}') print(f'dconv1d_bias max diff: {(conv1d_bias.grad - conv1d_bias_ref.grad).abs().max().item()}') @pytest.mark.parametrize('wtype', [torch.float32, torch.complex64]) # @pytest.mark.parametrize('wtype', [torch.complex64]) # @pytest.mark.parametrize('itype', [torch.float32, torch.float16, torch.bfloat16]) @pytest.mark.parametrize('itype', [torch.float32]) # @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 372, 512, 784, 1024, 1134, 2048, 4096]) @pytest.mark.parametrize('seqlen', [128]) @pytest.mark.parametrize("is_variable_C", [False, True]) # @pytest.mark.parametrize("is_variable_C", [False]) @pytest.mark.parametrize("is_variable_B", [False, True]) # @pytest.mark.parametrize("is_variable_B", [True]) def test_bimamba_inner_fn_grad_check(is_variable_B, is_variable_C, seqlen, itype, wtype): device = 'cuda' rtol, atol = (6e-4, 2e-3) if itype == torch.float32 else (3e-3, 5e-3) if itype == torch.bfloat16: rtol, atol = 3e-2, 5e-2 rtolw, atolw = (1e-3, 1e-3) # If we have z, the errors on the weights seem higher rtolw = max(rtolw, rtol) atolw = max(atolw, atol) # set seed torch.random.manual_seed(0) batch_size = 2 // 2 dim = 768 // 8 dstate = 8 // 8 dt_rank = 48 // 8 is_complex = wtype == torch.complex64 xz = torch.randn(batch_size, 2 * dim, seqlen, device=device, dtype=itype, requires_grad=True) conv1d_weight = torch.randn(dim, 1, 3, device=device, dtype=torch.float32, requires_grad=True) conv1d_bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) x_proj_weight = torch.randn(dt_rank + (bool(is_variable_B) + bool(is_variable_C)) * dstate * (1 if not is_complex else 2), dim, device=device, dtype=itype, requires_grad=True) delta_proj_weight = torch.randn(dim, dt_rank, device=device, dtype=itype, requires_grad=True) out_proj_weight = torch.randn(dim // 2, dim, device=device, dtype=itype, requires_grad=True) out_proj_bias = None A = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_() A_b = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype)).requires_grad_() B = (torch.randn(dim, dstate, device=device, dtype=wtype, requires_grad=True) if not is_variable_B else None) C = (torch.randn(dim, dstate, device=device, dtype=wtype, requires_grad=True) if not is_variable_C else None) D = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True) delta_bias = (0.5 * torch.rand(dim, device=device, dtype=torch.float32)).requires_grad_() B_proj_bias = None C_proj_bias = None xz_ref = xz.detach().clone().requires_grad_() conv1d_weight_ref = conv1d_weight.detach().clone().requires_grad_() conv1d_bias_ref = conv1d_bias.detach().clone().requires_grad_() x_proj_weight_ref = x_proj_weight.detach().clone().requires_grad_() delta_proj_weight_ref = delta_proj_weight.detach().clone().requires_grad_() out_proj_weight_ref = out_proj_weight.detach().clone().requires_grad_() out_proj_bias_ref = (out_proj_bias.detach().clone().requires_grad_() if out_proj_bias is not None else None) A_ref = A.detach().clone().requires_grad_() A_b_ref = A_b.detach().clone().requires_grad_() B_ref = B.detach().clone().requires_grad_() if B is not None else None C_ref = C.detach().clone().requires_grad_() if C is not None else None D_ref = D.detach().clone().requires_grad_() delta_bias_ref = delta_bias.detach().clone().requires_grad_() if delta_bias is not None else None # func = bimamba_inner_fn # func = mamba_inner_fn func = mamba_inner_ref # gradok = gradcheck(func, (xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,out_proj_weight, out_proj_bias, A, A_b, B, C, D, delta_bias, None, None, True)) gradok = gradcheck(func, (xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,out_proj_weight, out_proj_bias, A, B, C, D, delta_bias, None, None, True), eps=1e-6, atol=1e-4, nondet_tol=1.) print(f'* {gradok} check_gradient_numerical bimamba_inner_fn') # test_bimamba_inner_fn(True, True, 128, torch.float32, torch.float32) # test_mamba_inner_fn(True, True, 128, torch.float32, torch.float32) test_bimamba_inner_fn_grad_check(True, True, 128, torch.float32, torch.float32) # input = (torch.randn(20,20,dtype=torch.double,requires_grad=True), torch.randn(30,20,dtype=torch.double,requires_grad=True)) # test = gradcheck(torch.nn.functional.linear, input, eps=1e-6, atol=1e-4) # print(test)