我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用torch.addr()。
def forward(self, add_matrix, vector1, vector2): self.save_for_backward(vector1, vector2) output = self._get_output(add_matrix) return torch.addr(output, self.alpha, add_matrix, self.beta, vector1, vector2)
def forward(ctx, add_matrix, vector1, vector2, alpha=1, beta=1, inplace=False): ctx.alpha = alpha ctx.beta = beta ctx.save_for_backward(vector1, vector2) output = _get_output(ctx, add_matrix, inplace=inplace) return torch.addr(alpha, add_matrix, beta, vector1, vector2, out=output)
def test_functional_blas(self): def compare(fn, *args): unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg for arg in args) self.assertEqual(fn(*args).data, fn(*unpacked_args)) def test_blas_add(fn, x, y, z): # Checks all signatures compare(fn, x, y, z) compare(fn, 0.5, x, y, z) compare(fn, 0.5, x, 0.25, y, z) def test_blas(fn, x, y): compare(fn, x, y) test_blas(torch.mm, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas_add(torch.addmm, Variable(torch.randn(2, 4)), Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas(torch.mv, Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas_add(torch.addmv, Variable(torch.randn(2)), Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas(torch.ger, Variable(torch.randn(5)), Variable(torch.randn(6))) test_blas_add(torch.addr, Variable(torch.randn(5, 6)), Variable(torch.randn(5)), Variable(torch.randn(6)))
def forward(ctx, add_matrix, vector1, vector2, alpha=1, beta=1, inplace=False): ctx.alpha = alpha ctx.beta = beta ctx.add_matrix_size = add_matrix.size() ctx.save_for_backward(vector1, vector2) output = _get_output(ctx, add_matrix, inplace=inplace) return torch.addr(alpha, add_matrix, beta, vector1, vector2, out=output)
def test_addr(self): types = { 'torch.DoubleTensor': 1e-8, 'torch.FloatTensor': 1e-4, } def run_test(m, v1, v2, m_transform=lambda x: x): m = m_transform(m.clone()) ref = m.clone() torch.addr(m, v1, v2, out=m) for i in range(m.size(0)): for j in range(m.size(1)): ref[i, j] += v1[i] * v2[j] self.assertEqual(m, ref) for tname, _prec in types.items(): for h, w in [(100, 110), (1, 20), (200, 2)]: m = torch.randn(h, w).type(tname) v1 = torch.randn(h).type(tname) v2 = torch.randn(w).type(tname) run_test(m, v1, v2) # test transpose run_test(m, v2, v1, lambda x: x.transpose(0, 1)) # test 0 strided v1 = torch.randn(1).type(tname).expand(h) run_test(m, v1, v2) run_test(m, v2, v1, lambda x: x.transpose(0, 1))
def _test_broadcast_fused_matmul(self, cast): fns = ["baddbmm", "addbmm", "addmm", "addmv", "addr"] for fn in fns: batch_dim = random.randint(1, 8) n_dim = random.randint(1, 8) m_dim = random.randint(1, 8) p_dim = random.randint(1, 8) def dims_full_for_fn(): if fn == "baddbmm": return ([batch_dim, n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim]) elif fn == "addbmm": return ([n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim]) elif fn == "addmm": return ([n_dim, p_dim], [n_dim, m_dim], [m_dim, p_dim]) elif fn == "addmv": return ([n_dim], [n_dim, m_dim], [m_dim]) elif fn == "addr": return ([n_dim, m_dim], [n_dim], [m_dim]) else: raise AssertionError("unknown function") (t0_dims_full, t1_dims, t2_dims) = dims_full_for_fn() (t0_dims_small, _, _) = self._select_broadcastable_dims(t0_dims_full) t0_small = cast(torch.randn(*t0_dims_small).float()) t1 = cast(torch.randn(*t1_dims).float()) t2 = cast(torch.randn(*t2_dims).float()) t0_full = cast(t0_small.expand(*t0_dims_full)) fntorch = getattr(torch, fn) r0 = fntorch(t0_small, t1, t2) r1 = fntorch(t0_full, t1, t2) self.assertEqual(r0, r1)
def test_functional_blas(self): def compare(fn, *args): unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg for arg in args) unpacked_result = fn(*unpacked_args) packed_result = fn(*args).data # if non-Variable torch function returns a scalar, compare to scalar if not torch.is_tensor(unpacked_result): assert packed_result.dim() == 1 assert packed_result.nelement() == 1 packed_result = packed_result[0] self.assertEqual(packed_result, unpacked_result) def test_blas_add(fn, x, y, z): # Checks all signatures compare(fn, x, y, z) compare(fn, 0.5, x, y, z) compare(fn, 0.5, x, 0.25, y, z) def test_blas(fn, x, y): compare(fn, x, y) test_blas(torch.mm, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas_add(torch.addmm, Variable(torch.randn(2, 4)), Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas(torch.mv, Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas_add(torch.addmv, Variable(torch.randn(2)), Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas(torch.ger, Variable(torch.randn(5)), Variable(torch.randn(6))) test_blas_add(torch.addr, Variable(torch.randn(5, 6)), Variable(torch.randn(5)), Variable(torch.randn(6))) test_blas(torch.matmul, Variable(torch.randn(6)), Variable(torch.randn(6))) test_blas(torch.matmul, Variable(torch.randn(10, 4)), Variable(torch.randn(4))) test_blas(torch.matmul, Variable(torch.randn(5)), Variable(torch.randn(5, 6))) test_blas(torch.matmul, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas(torch.matmul, Variable(torch.randn(5, 2, 10)), Variable(torch.randn(5, 10, 4))) test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(3, 5, 10, 4))) test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(10))) test_blas(torch.matmul, Variable(torch.randn(10)), Variable(torch.randn(3, 5, 10, 4)))
