Repository: kornia/kornia

Issue:
find_essential and run_5point are not working correctly with batch size>1
### Describe the bug

The problem is with run_5point solver, which has several filters:
https://github.com/kornia/kornia/blob/a041d9255f459a0034c75b20fb9ab524d7ae3983/kornia/geometry/epipolar/essential.py#L124
https://github.com/kornia/kornia/blob/a041d9255f459a0034c75b20fb9ab524d7ae3983/kornia/geometry/epipolar/essential.py#L161
https://github.com/kornia/kornia/blob/a041d9255f459a0034c75b20fb9ab524d7ae3983/kornia/geometry/epipolar/essential.py#L164
All of which can skip processing a batch index without appending anything to the output list E_models. The correspondence between index in this list and the index in the batch is lost.
In the result the shape of the returned tensor will be less than 10*B and it is not possible to determine for which batch indices there were no solution.

The solver is used in find_essential.

### Reproduction steps

```bash
Run run_5point with 
points1: (B, 5, 2)
points2: (B, 5, 2)
with a large B, e.g. B=100.

Although, the doc string says N>=8 points. Is that necessary?
```


### Expected behavior

Expecting output of shape 
(B*10, 3,3)
Although the doctoring says "computed essential matrix with shape :math:`(B, 3, 3)`.", the code returns 10 solutions per batch index when not skipping.

### Environment

```shell
kornia version 0.7.2
```


### Additional context

_No response_


Discussion:
@shekhovt thanks for this finding ! What do you suggest, to add an empty tensor when we skip ? Are you open to fix and ideally expand the tests with this corner case ? /cc @weitong8591 as originally implemented this :)
In the 'continue' cases it can be fixed by 
`E_models.append(torch.eye(3, device=cs.device, dtype=cs.dtype).repeat(10, 1).reshape(-1, 9))`
i.e. writing 10 identity matrices for that image pair. This is consistent with how the variable number of solutions is handled in this function later on.
I am not sure how to fix the `singular_filter` and not to break anything.

For the tests, if you have a correctness test of batch_size = 1, then it is easy to test also batch_size > 1, right?
Hi, thanks for pointing out the missing batch indices (shape difference) due to the filters, i will soon have time to fix it. @edgarriba . probably as you said @shekhovt, to return identity matrices when it fails to make sure to return results for all batches.  
Hi @shekhovt ,

I encountered this problem as well and made modifications to the code, which now works stably for my training with a batch size of 256. You can find my implementation in the codebase for [vggsfm](https://vggsfm.github.io/):

https://github.com/facebookresearch/vggsfm/blob/f490bc11b3eb68c1aa832fcd2e230ffacbf575fd/vggsfm/two_view_geo/essential.py#L171

I addressed the `singular_filter` issue using masking operations. Essential matrices are initialized with `eye()` and only those with valid `singular_filter` are replaced by the computed essential matrices. Additionally, I removed the `for` loop inside the run_5point function (shown below) and replaced it with fully matrix operations, speeding up our training by approximately 12 times.

https://github.com/kornia/kornia/blob/bdd07f36cd8c26dd5f67a65009fdb9d3adaa7399/kornia/geometry/epipolar/essential.py#L149

You could have a try on this implementation if in a hurry. Tong @weitong8591  will review this implementation and merge it to kornia soon. 


Hi, thanks a lot! Good job with batching all the components, would be good to merge that to kornia. And an excellent paper too! I was using poselib in the meantime. I will try some of your primitives.

Resolving patch:
diff --git a/kornia/geometry/epipolar/essential.py b/kornia/geometry/epipolar/essential.py
--- a/kornia/geometry/epipolar/essential.py
+++ b/kornia/geometry/epipolar/essential.py
@@ -4,11 +4,10 @@
 
 import torch
 
-import kornia.geometry.epipolar as epi
 from kornia.core import eye, ones_like, stack, where, zeros
 from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_SAME_SHAPE, KORNIA_CHECK_SHAPE
 from kornia.geometry import solvers
-from kornia.utils import eye_like, safe_inverse_with_mask, vec_like
+from kornia.utils import eye_like, vec_like
 from kornia.utils.helpers import _torch_solve_cast, _torch_svd_cast
 
 from .numeric import cross_product_matrix
@@ -30,7 +29,7 @@ def run_5point(points1: torch.Tensor, points2: torch.Tensor, weights: Optional[t
     r"""Compute the essential matrix using the 5-point algorithm from Nister.
 
     The linear system is solved by Nister's 5-point algorithm [@nister2004efficient],
-    and the solver implemented referred to [@barath2020magsac++][@wei2023generalized].
+    and the solver implemented referred to [@barath2020magsac++][@wei2023generalized][@wang2023vggsfm].
 
     Args:
         points1: A set of carlibrated points in the first image with a tensor shape :math:`(B, N, 2), N>=8`.
@@ -51,7 +50,7 @@ def run_5point(points1: torch.Tensor, points2: torch.Tensor, weights: Optional[t
     x2, y2 = torch.chunk(points2, dim=-1, chunks=2)  # Bx1xN
     ones = ones_like(x1)
 
-    # build equations system and find null space.
+    # build the equation system and find the null space.
     # https://www.cc.gatech.edu/~afb/classes/CS4495-Fall2013/slides/CS4495-09-TwoViews-2.pdf
     # [x * x', x * y', x, y * x', y * y', y, x', y', 1]
     # BxNx9
@@ -63,32 +62,59 @@ def run_5point(points1: torch.Tensor, points2: torch.Tensor, weights: Optional[t
     else:
         w_diag = torch.diag_embed(weights)
         X = X.transpose(-2, -1) @ w_diag @ X
+
+    # use Nister's 5PC to solve essential matrix
+    E_Nister = null_to_Nister_solution(X, batch_size)
+
+    return E_Nister
+
+
+def fun_select(null_mat: torch.Tensor, i: int, j: int, ratio: int = 3) -> torch.Tensor:
+    return null_mat[:, ratio * j + i]
+
+
+def null_to_Nister_solution(X: torch.Tensor, batch_size: int) -> torch.Tensor:
+    r"""Use Nister's 5PC to solve essential matrix.
+
+    The linear system is solved by Nister's 5-point algorithm [@nister2004efficient],
+    and the solver implemented referred to [@barath2020magsac++][@wei2023generalized][@wang2023vggsfm].
+
+    Args:
+        X: Coefficients for the null space :math:`(B, N, 2), N>=8`.
+        batch_size: batcs size of the input, the number of image pairs :math:`B`.
+
+    Returns:
+        the computed essential matrix with shape :math:`(B, 3, 3)`.
+        Note that the returned E matrices should be the same batch size with the input.
+    """
+
     # compute eigenvectors and retrieve the one with the smallest eigenvalue, using SVD
     # turn off the grad check due to the unstable gradients from SVD.
     # several close to zero values of eigenvalues.
     _, _, V = _torch_svd_cast(X)  # torch.svd
+
     null_ = V[:, :, -4:]  # the last four rows
     nullSpace = V.transpose(-1, -2)[:, -4:, :]
 
     coeffs = zeros(batch_size, 10, 20, device=null_.device, dtype=null_.dtype)
     d = zeros(batch_size, 60, device=null_.device, dtype=null_.dtype)
 
-    def fun(i: int, j: int) -> torch.Tensor:
-        return null_[:, 3 * j + i]
-
     # Determinant constraint
     coeffs[:, 9] = (
         solvers.multiply_deg_two_one_poly(
-            solvers.multiply_deg_one_poly(fun(0, 1), fun(1, 2)) - solvers.multiply_deg_one_poly(fun(0, 2), fun(1, 1)),
-            fun(2, 0),
+            solvers.multiply_deg_one_poly(fun_select(null_, 0, 1), fun_select(null_, 1, 2))
+            - solvers.multiply_deg_one_poly(fun_select(null_, 0, 2), fun_select(null_, 1, 1)),
+            fun_select(null_, 2, 0),
         )
         + solvers.multiply_deg_two_one_poly(
-            solvers.multiply_deg_one_poly(fun(0, 2), fun(1, 0)) - solvers.multiply_deg_one_poly(fun(0, 0), fun(1, 2)),
-            fun(2, 1),
+            solvers.multiply_deg_one_poly(fun_select(null_, 0, 2), fun_select(null_, 1, 0))
+            - solvers.multiply_deg_one_poly(fun_select(null_, 0, 0), fun_select(null_, 1, 2)),
+            fun_select(null_, 2, 1),
         )
         + solvers.multiply_deg_two_one_poly(
-            solvers.multiply_deg_one_poly(fun(0, 0), fun(1, 1)) - solvers.multiply_deg_one_poly(fun(0, 1), fun(1, 0)),
-            fun(2, 2),
+            solvers.multiply_deg_one_poly(fun_select(null_, 0, 0), fun_select(null_, 1, 1))
+            - solvers.multiply_deg_one_poly(fun_select(null_, 0, 1), fun_select(null_, 1, 0)),
+            fun_select(null_, 2, 2),
         )
     )
 
@@ -98,9 +124,9 @@ def fun(i: int, j: int) -> torch.Tensor:
     for i in range(3):
         for j in range(3):
             d[:, indices[i, j] : indices[i, j] + 10] = (
-                solvers.multiply_deg_one_poly(fun(i, 0), fun(j, 0))
-                + solvers.multiply_deg_one_poly(fun(i, 1), fun(j, 1))
-                + solvers.multiply_deg_one_poly(fun(i, 2), fun(j, 2))
+                solvers.multiply_deg_one_poly(fun_select(null_, i, 0), fun_select(null_, j, 0))
+                + solvers.multiply_deg_one_poly(fun_select(null_, i, 1), fun_select(null_, j, 1))
+                + solvers.multiply_deg_one_poly(fun_select(null_, i, 2), fun_select(null_, j, 2))
             )
 
     for i in range(10):
@@ -113,9 +139,9 @@ def fun(i: int, j: int) -> torch.Tensor:
     for i in range(3):
         for j in range(3):
             row = (
-                solvers.multiply_deg_two_one_poly(d[:, indices[i, 0] : indices[i, 0] + 10], fun(0, j))
-                + solvers.multiply_deg_two_one_poly(d[:, indices[i, 1] : indices[i, 1] + 10], fun(1, j))
-                + solvers.multiply_deg_two_one_poly(d[:, indices[i, 2] : indices[i, 2] + 10], fun(2, j))
+                solvers.multiply_deg_two_one_poly(d[:, indices[i, 0] : indices[i, 0] + 10], fun_select(null_, 0, j))
+                + solvers.multiply_deg_two_one_poly(d[:, indices[i, 1] : indices[i, 1] + 10], fun_select(null_, 1, j))
+                + solvers.multiply_deg_two_one_poly(d[:, indices[i, 2] : indices[i, 2] + 10], fun_select(null_, 2, j))
             )
             coeffs[:, cnt] = row
             cnt += 1
@@ -125,10 +151,17 @@ def fun(i: int, j: int) -> torch.Tensor:
         torch.linalg.matrix_rank(coeffs), ones_like(torch.linalg.matrix_rank(coeffs[:, :, :10])) * 10
     )
 
+    # check if there is no solution
+    if singular_filter.sum() == 0:
+        return torch.eye(3, dtype=coeffs.dtype, device=coeffs.device)[None].expand(batch_size, 10, -1, -1).clone()
+
     eliminated_mat = _torch_solve_cast(coeffs[singular_filter, :, :10], b[singular_filter])
 
     coeffs_ = torch.cat((coeffs[singular_filter, :, :10], eliminated_mat), dim=-1)
 
+    # check the batch size after singular filter, for batch operation afterwards
+    batch_size_filtered = coeffs_.shape[0]
+
     A = zeros(coeffs_.shape[0], 3, 13, device=coeffs_.device, dtype=coeffs_.dtype)
 
     for i in range(3):
@@ -142,66 +175,88 @@ def fun(i: int, j: int) -> torch.Tensor:
         A[:, i : i + 1, 9:13] = coeffs_[:, 4 + 2 * i : 5 + 2 * i, 16:20]
         A[:, i : i + 1, 8:12] -= coeffs_[:, 5 + 2 * i : 6 + 2 * i, 16:20]
 
+    # Bx11
     cs = solvers.determinant_to_polynomial(A)
-    E_models = []
-
-    # for loop because of different numbers of solutions
-    for bi in range(A.shape[0]):
-        A_i = A[bi]
-        null_i = nullSpace[bi]
-
-        # companion matrix solver for polynomial
-        C = zeros((10, 10), device=cs.device, dtype=cs.dtype)
-        C[0:-1, 1:] = eye(C[0:-1, 0:-1].shape[0], device=cs.device, dtype=cs.dtype)
-        C[-1, :] = -cs[bi][:-1] / cs[bi][-1]
-
-        roots = torch.real(torch.linalg.eigvals(C))
-
-        if roots is None:
-            continue
-        n_sols = roots.size()
-        if n_sols == 0:
-            continue
-        Bs = stack(
-            (
-                A_i[:3, :1] * (roots**3) + A_i[:3, 1:2] * roots.square() + A_i[0:3, 2:3] * roots + A_i[0:3, 3:4],
-                A_i[0:3, 4:5] * (roots**3) + A_i[0:3, 5:6] * roots.square() + A_i[0:3, 6:7] * roots + A_i[0:3, 7:8],
-            ),
-            dim=0,
-        ).transpose(0, -1)
-
-        bs = (
-            A_i[0:3, 8:9] * (roots**4)
-            + A_i[0:3, 9:10] * (roots**3)
-            + A_i[0:3, 10:11] * roots.square()
-            + A_i[0:3, 11:12] * roots
-            + A_i[0:3, 12:13]
-        ).T.unsqueeze(-1)
-
-        # We try to solve using top two rows,
-        xzs = (safe_inverse_with_mask(Bs[:, 0:2, 0:2])[0]) @ (bs[:, 0:2])
-
-        mask = (abs(Bs[:, 2].unsqueeze(1) @ xzs - bs[:, 2].unsqueeze(1)) > 1e-3).flatten()
-        if torch.sum(mask) != 0:
-            q, r = torch.linalg.qr(Bs[mask].clone())  #
-            xzs[mask] = _torch_solve_cast(r, q.transpose(-1, -2) @ bs[mask])  # [mask]
-
-        # models
-        Es = null_i[0] * (-xzs[:, 0]) + null_i[1] * (-xzs[:, 1]) + null_i[2] * roots.unsqueeze(-1) + null_i[3]
-
-        # Since the rows of N are orthogonal unit vectors, we can normalize the coefficients instead
-        inv = 1.0 / torch.sqrt((-xzs[:, 0]) ** 2 + (-xzs[:, 1]) ** 2 + roots.unsqueeze(-1) ** 2 + 1.0)
-        Es *= inv
-        if Es.shape[0] < 10:
-            Es = torch.cat(
-                (Es.clone(), eye(3, device=Es.device, dtype=Es.dtype).repeat(10 - Es.shape[0], 1).reshape(-1, 9))
-            )
-        E_models.append(Es)
 
-    # if not E_models:
-    #     return torch.eye(3, device=cs.device, dtype=cs.dtype).unsqueeze(0)
-    # else:
-    return torch.cat(E_models).view(-1, 3, 3).transpose(-1, -2)
+    # A: Bx3x13
+    # nullSpace: Bx4x9
+    # companion matrices to solve the polynomial, in batch
+    C = zeros((batch_size_filtered, 10, 10), device=cs.device, dtype=cs.dtype)
+    eye_mat = eye(C[0, 0:-1, 0:-1].shape[0], device=cs.device, dtype=cs.dtype)
+    C[:, 0:-1, 1:] = eye_mat
+
+    cs_de = cs[:, -1].unsqueeze(-1)
+    cs_de = torch.where(cs_de == 0, torch.tensor(1e-8, dtype=cs_de.dtype), cs_de)
+    C[:, -1, :] = -cs[:, :-1] / cs_de
+
+    roots = torch.real(torch.linalg.eigvals(C))
+
+    roots_unsqu = roots.unsqueeze(1)
+
+    Bs = stack(
+        (
+            A[:, :3, :1] * (roots_unsqu**3)
+            + A[:, :3, 1:2] * roots_unsqu.square()
+            + A[:, 0:3, 2:3] * roots_unsqu
+            + A[:, 0:3, 3:4],
+            A[:, 0:3, 4:5] * (roots_unsqu**3)
+            + A[:, 0:3, 5:6] * roots_unsqu.square()
+            + A[:, 0:3, 6:7] * roots_unsqu
+            + A[:, 0:3, 7:8],
+        ),
+        dim=1,
+    )
+
+    Bs = Bs.transpose(1, -1)
+
+    bs = (
+        (
+            A[:, 0:3, 8:9] * (roots_unsqu**4)
+            + A[:, 0:3, 9:10] * (roots_unsqu**3)
+            + A[:, 0:3, 10:11] * roots_unsqu.square()
+            + A[:, 0:3, 11:12] * roots_unsqu
+            + A[:, 0:3, 12:13]
+        )
+        .transpose(1, 2)
+        .unsqueeze(-1)
+    )
+
+    xzs = torch.matmul(torch.inverse(Bs[:, :, 0:2, 0:2]), bs[:, :, 0:2])
+
+    mask = (abs(Bs[:, 2].unsqueeze(1) @ xzs - bs[:, 2].unsqueeze(1)) > 1e-3).flatten()
+
+    # mask: bx10x1x1
+    mask = (
+        abs(torch.matmul(Bs[:, :, 2, :].unsqueeze(2), xzs) - bs[:, :, 2, :].unsqueeze(2)) > 1e-3
+    )  # .flatten(start_dim=1)
+
+    # bx10
+    mask = mask.squeeze(3).squeeze(2)
+
+    if torch.any(mask):
+        q_batch, r_batch = torch.linalg.qr(Bs[mask])
+        xyz_to_feed = torch.linalg.solve(r_batch, torch.matmul(q_batch.transpose(-1, -2), bs[mask]))
+        xzs[mask] = xyz_to_feed
+
+    nullSpace_filtered = nullSpace[singular_filter]
+
+    Es = (
+        nullSpace_filtered[:, 0:1] * (-xzs[:, :, 0])
+        + nullSpace_filtered[:, 1:2] * (-xzs[:, :, 1])
+        + nullSpace_filtered[:, 2:3] * roots.unsqueeze(-1)
+        + nullSpace_filtered[:, 3:4]
+    )
+
+    inv = 1.0 / torch.sqrt((-xzs[:, :, 0]) ** 2 + (-xzs[:, :, 1]) ** 2 + roots.unsqueeze(-1) ** 2 + 1.0)
+    Es *= inv
+
+    Es = Es.view(batch_size_filtered, -1, 3, 3).transpose(-1, -2)
+
+    # make sure the returned batch size equals to that of inputs
+    E_return = torch.eye(3, dtype=Es.dtype, device=Es.device)[None].expand(batch_size, 10, -1, -1).clone()
+    E_return[singular_filter] = Es
+
+    return E_return
 
 
 def essential_from_fundamental(F_mat: torch.Tensor, K1: torch.Tensor, K2: torch.Tensor) -> torch.Tensor:
@@ -482,32 +537,17 @@ def find_essential(
     points1: torch.Tensor, points2: torch.Tensor, weights: Optional[torch.Tensor] = None
 ) -> torch.Tensor:
     r"""
-    Args:
-        points1: A set of points in the first image with a tensor shape :math:`(B, N, 2), N>=5`.
-        points2: A set of points in the second image with a tensor shape :math:`(B, N, 2), N>=5`.
-        weights: Tensor containing the weights per point correspondence with a shape of :math:`(5, N)`.
+     Args:
+         points1: A set of points in the first image with a tensor shape :math:`(B, N, 2), N>=5`.
+         points2: A set of points in the second image with a tensor shape :math:`(B, N, 2), N>=5`.
+         weights: Tensor containing the weights per point correspondence with a shape of :math:`(5, N)`.
 
     Returns:
-        the computed essential matrix with shape :math:`(B, 3, 3)`,
-        one model for each batch selected out of ten solutions by Sampson distances.
+         the computed essential matrices with shape :math:`(B, 10, 3, 3)`.
+         Note that all possible solutions are returned, i.e., 10 essential matrices for each image pair.
+         To choose the best one out of 10, try to check the one with the lowest Sampson distance.
 
     """
     E = run_5point(points1, points2, weights).to(points1.dtype)
 
-    # select one out of 10 possible solutions from 5PC Nister solver.
-    solution_num = 10
-    batch_size = points1.shape[0]
-
-    error = zeros((batch_size, solution_num))
-
-    for b in range(batch_size):
-        error[b] = epi.sampson_epipolar_distance(points1[b], points2[b], E.view(batch_size, solution_num, 3, 3)[b]).sum(
-            -1
-        )
-
-    KORNIA_CHECK_SHAPE(error, ["f{batch_size}", "10"])
-
-    chosen_indices = torch.argmin(error, dim=-1)
-    result = stack([(E.view(-1, solution_num, 3, 3))[i, chosen_indices[i], :] for i in range(batch_size)])
-
-    return result
+    return E


Test patch:
diff --git a/tests/geometry/epipolar/test_essential.py b/tests/geometry/epipolar/test_essential.py
--- a/tests/geometry/epipolar/test_essential.py
+++ b/tests/geometry/epipolar/test_essential.py
@@ -14,16 +14,16 @@ def test_smoke(self, device, dtype):
         points2 = torch.rand(1, 5, 2, device=device, dtype=dtype)
         weights = torch.ones(1, 5, device=device, dtype=dtype)
         E_mat = epi.essential.find_essential(points1, points2, weights)
-        assert E_mat.shape == (1, 3, 3)
+        assert E_mat.shape == (1, 10, 3, 3)
 
-    @pytest.mark.parametrize("batch_size, num_points", [(1, 5), (2, 6), (3, 7)])
+    @pytest.mark.parametrize("batch_size, num_points", [(1, 5), (2, 6), (3, 7), (1000, 5)])
     def test_shape(self, batch_size, num_points, device, dtype):
         B, N = batch_size, num_points
         points1 = torch.rand(B, N, 2, device=device, dtype=dtype)
         points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
         weights = torch.ones(B, N, device=device, dtype=dtype)
         E_mat = epi.essential.find_essential(points1, points2, weights)
-        assert E_mat.shape == (B, 3, 3)
+        assert E_mat.shape == (B, 10, 3, 3)
 
     @pytest.mark.parametrize("batch_size, num_points", [(1, 5), (2, 6), (3, 7)])
     def test_shape_noweights(self, batch_size, num_points, device, dtype):
@@ -32,7 +32,7 @@ def test_shape_noweights(self, batch_size, num_points, device, dtype):
         points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
         weights = None
         E_mat = epi.essential.find_essential(points1, points2, weights)
-        assert E_mat.shape == (B, 3, 3)
+        assert E_mat.shape == (B, 10, 3, 3)
 
     def test_epipolar_constraint(self, device, dtype):
         calibrated_x1 = torch.tensor(
@@ -49,7 +49,8 @@ def test_epipolar_constraint(self, device, dtype):
         E = epi.essential.find_essential(calibrated_x1, calibrated_x2)
         if torch.all(E != 0):
             distance = epi.symmetrical_epipolar_distance(calibrated_x1, calibrated_x2, E)
-            mean_error = distance.mean()
+            # Note : here we check only the best model, although all solutions are returned
+            mean_error = distance.mean(-1).min()
             self.assert_close(mean_error, torch.tensor(0.0, device=device, dtype=dtype), atol=1e-4, rtol=1e-4)
 
     def test_synthetic_sampson(self, device, dtype):
@@ -68,9 +69,20 @@ def test_synthetic_sampson(self, device, dtype):
         E_est = epi.essential.find_essential(calibrated_x1, calibrated_x2, weights)
         error = epi.sampson_epipolar_distance(calibrated_x1, calibrated_x2, E_est)
         self.assert_close(
-            error, torch.zeros((calibrated_x1.shape[:2]), device=device, dtype=dtype), atol=1e-4, rtol=1e-4
+            error[:, torch.argmin(error.mean(-1).min())],
+            torch.zeros((calibrated_x1.shape[:2]), device=device, dtype=dtype),
+            atol=1e-4,
+            rtol=1e-4,
         )
 
+    @pytest.mark.parametrize("batch_size, num_points", [(5, 5), (10, 5)])
+    def test_degenerate_case(self, batch_size, num_points, device, dtype):
+        B, N = batch_size, num_points
+        points1_deg = torch.rand(B, N, 2, device=device, dtype=dtype)
+        weights = torch.ones_like(points1_deg)[..., 0]
+        E_mat_deg = epi.essential.find_essential(points1_deg, points1_deg, weights)
+        assert E_mat_deg.shape == (B, 10, 3, 3)
+
 
 class TestEssentialFromFundamental(BaseTester):
     def test_smoke(self, device, dtype):