HW0.5 Implicit Euler method on Mass-Spring system 以及一些问题

Hello! 做这回作业的时候感觉有一下这些问题:

  1. 请问用Taichi的并行计算有可能实现Gauss-Siedel 方法而不是Jacobi方法嘛?
    (因为感觉上好像是可以的,但是自己试了一下,发现中途调用用于输出的变量的时候,没有什么效果。P.S. 如果理论上可行的话,我再在后面po自己尝试的代码吧:))

  2. 请问同shape的ti.var之间可以进行加减操作嘛?
    (也是自己之前试了下 好像不可以…)

  3. 请问ti.var上有norm操作嘛?

  4. AssertionError: The 0-th index of a Matrix/Vector must be a compile-time constant integer, got <taichi.lang.expr.Expr object at 0x00000239F998D580>
    为什么要有这个规定呢?感觉不太方便呀 (横向或者竖向遍历某张量中的最底层的数值元素)

  5. A s s e r t i o n f a i l e d : g e t O p e r a n d ( 0 ) - > g e t T y p e ( ) = = c a s t < P o i n t e r T y p e > ( g e t O p e r a n d ( 1 ) - > g e t T y p e ( ) ) - > g e t E l e m e n t T y p e ( ) & & " P t r m u s t b e a p o i n t e r t o V a l t y p e ! " , f i l e e : \ r e p o s \ l l v m - 8 . 0 . 1 \ l i b \ i r \ i n s t r u c t i o n s . c p p , l i n e 1 2 1 0
    Init the parameters in this system:
    这个错误感觉挺奇怪的,我把对应代码和具体哪行似乎有问题,放到后面了。但是我没看出来那几行有什么问题,之后我也是绕过这块做的作业。

(Implicit Euler 确实是 unconditionally stable 的呀:))

import taichi as ti
import numpy as np

ti.init(arch=ti.gpu)
pixels = ti.var(ti.u8, shape=(512, 512, 3))
max_num_particles = 256
dt = 8e-3

num_particles = ti.var(ti.i32, shape=())
spring_stiffness = ti.var(ti.f32, shape=())
paused = ti.var(ti.i32, shape=())
damping = ti.var(ti.f32, shape=())

particle_mass = 1
bottom_y = 0.05

x = ti.Vector(2, dt=ti.f32, shape=max_num_particles)
v = ti.Vector(2, dt=ti.f32, shape=max_num_particles)
new_v = ti.var(dt=ti.f32, shape=2*max_num_particles)
diff_vec = ti.var(dt=ti.f32, shape=2*max_num_particles)

A = ti.Matrix(2, 2, dt=ti.f32, shape=(max_num_particles, max_num_particles))
b = ti.Vector(2, dt=ti.f32, shape=max_num_particles)

# rest_length[i, j] = 0 means i and j are not connected
rest_length = ti.var(ti.f32, shape=(max_num_particles, max_num_particles))
A_implicit = ti.Matrix(2, 2, dt=ti.f32, shape=(max_num_particles, max_num_particles))
b_implicit = ti.Vector(2, dt=ti.f32, shape=max_num_particles)

connection_radius = 0.15

gravity = [0, -9.8]


@ti.kernel
def createEqn():
    # Construct A_implicit Matrix:
    n = num_particles[None]
    mass_inv = 1.0 / particle_mass
    for tensor_ele_idx in range(n * n):
        # Construct original Jacobian matrix's elements (2x2):
        tensor_ele_idx_row = tensor_ele_idx // n
        tensor_ele_idx_col = tensor_ele_idx - tensor_ele_idx_row * n
        if tensor_ele_idx_row == tensor_ele_idx_col:
            # Populate diagonal elements in the tensor:
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = 0
            for j in range(n):
                if rest_length[tensor_ele_idx_row, j] != 0:
                    xdt = x[tensor_ele_idx_row]
                    xjt = x[j]
                    dist = (xdt - xjt).norm()
                    ti_dist_pow_n3 = ti.pow(dist, -3)
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] += -spring_stiffness[None] * (
                                rest_length[tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[0] - xjt[0]) * (
                                    xdt[0] - xjt[0]) - rest_length[tensor_ele_idx, j] * ti.pow(dist, -1) + 1)
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] += -spring_stiffness[None] * rest_length[
                        tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[0] - xjt[0]) * (xdt[1] - xjt[1])
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] += -spring_stiffness[None] * rest_length[
                        tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[0] - xjt[0]) * (xdt[1] - xjt[1])
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] += -spring_stiffness[None] * (
                                rest_length[tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[1] - xjt[1]) * (
                                    xdt[1] - xjt[1]) - rest_length[tensor_ele_idx, j] * ti.pow(dist, -1) + 1)
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = 1.0 - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 1]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = 1.0 - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 1]
        elif rest_length[tensor_ele_idx_row, tensor_ele_idx_col] != 0:
            # Populate elements that have a connection:
            xrt = x[tensor_ele_idx_row]
            xct = x[tensor_ele_idx_col]
            dist = (xrt - xct).norm()
            dist_pow_n3 = ti.pow(dist, -3)
            lrc = rest_length[tensor_ele_idx_row, tensor_ele_idx_col]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = -spring_stiffness[None] * (
                        -lrc * dist_pow_n3 * (xrt[0] - xct[0]) * (xrt[0] - xct[0]) + lrc * ti.pow(dist, -1) - 1)
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = spring_stiffness[None] * lrc * dist_pow_n3 * (
                        xrt[0] - xct[0]) * (xrt[1] - xct[1])
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = spring_stiffness[None] * lrc * dist_pow_n3 * (
                        xrt[0] - xct[0]) * (xrt[1] - xct[1])
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = -spring_stiffness[None] * (
                        -lrc * dist_pow_n3 * (xrt[1] - xct[1]) * (xrt[1] - xct[1]) + lrc * ti.pow(dist, -1) - 1)
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 1]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 1]
        else:
            # Populate zeros:
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = 0
    # Construct b_implicit Vector:
    for i in range(n):
        # Calculate the overall force on this point:
        total_force = ti.Vector(gravity) * particle_mass
        for j in range(n):
            if rest_length[i, j] != 0:
                x_ij = x[i] - x[j]
                total_force += -spring_stiffness[None] * (x_ij.norm() - rest_length[i, j]) * x_ij.normalized()
        b_implicit[i] = v[n] + dt * mass_inv * total_force


@ti.kernel
def iterate(A_implicit_np: ti.ext_arr(), b_implicit_np: ti.ext_arr(), v_np: ti.ext_arr()) -> ti.f32:
    n = num_particles[None]
    residual = 1
    for i in range(2 * n):
        i_tensor_ele_idx_row = i // 2
        i_internal_ele_idx_row = i % 2
        temp = b_implicit_np[i_tensor_ele_idx_row, i_internal_ele_idx_row]
        for j in range(2 * n):
            if i != j:
                j_tensor_ele_idx_col = j // 2
                j_internal_ele_idx_col = j % 2
                temp -= A_implicit_np[i_tensor_ele_idx_row, j_tensor_ele_idx_col, i_internal_ele_idx_row, j_internal_ele_idx_col] * v_np[j_tensor_ele_idx_col, j_internal_ele_idx_col]
        # Divide everything by the coefficient of that unknown
        new_v[i] = temp / A_implicit_np[i_tensor_ele_idx_row, i_tensor_ele_idx_row, i_internal_ele_idx_row, i_internal_ele_idx_row]
    # Calculate the residual of this iteration by using infinite norm:
    norm1 = -1.0
    norm2 = -1.0
    for i in range(2 * n):
        i_tensor_ele_idx = i // 2
        i_internal_ele_idx = i % 2
        diff_vec[i] = v_np[i_tensor_ele_idx, i_internal_ele_idx] - new_v[i]
        if ti.abs(diff_vec[i]) > norm1:
            norm1 = ti.abs(diff_vec[i])
        if ti.abs(v_np[i_tensor_ele_idx, i_internal_ele_idx]) > norm2:
            norm2 = ti.abs(v_np[i_tensor_ele_idx, i_internal_ele_idx])
    residual = norm1 / norm2
    # Update the unknown vector:
    for i in range(n):
        v[i][0] = new_v[2 * i]
        v[i][1] = new_v[2 * i + 1]

    return residual


@ti.kernel
def substep():
    n = num_particles[None]
    # Collide with ground
    for i in range(n):
        if x[i].y < bottom_y:
            x[i].y = bottom_y
            v[i].y = 0

    # Compute new position
    for i in range(num_particles[None]):
        x[i] += v[i] * dt


@ti.kernel
def new_particle(pos_x: ti.f32, pos_y: ti.f32):  # Taichi doesn't support using Matrices as kernel arguments yet
    new_particle_id = num_particles[None]
    x[new_particle_id] = [pos_x, pos_y]
    v[new_particle_id] = [0, 0]
    num_particles[None] += 1

    # Connect with existing particles
    for i in range(new_particle_id):
        dist = (x[new_particle_id] - x[i]).norm()
        if dist < connection_radius:
            rest_length[i, new_particle_id] = 0.1
            rest_length[new_particle_id, i] = 0.1


gui = ti.GUI('Mass Spring System', res=(512, 512), background_color=0xdddddd)
result_dir = "./results"
video_manger = ti.VideoManager(output_dir=result_dir, framerate=24, automatic_build=False)

spring_stiffness[None] = 10000
damping[None] = 20

new_particle(0.3, 0.3)
new_particle(0.3, 0.4)
new_particle(0.4, 0.4)

while True:
    for e in gui.get_events(ti.GUI.PRESS):
        if e.key in [ti.GUI.ESCAPE, ti.GUI.EXIT]:
            exit()
        elif e.key == gui.SPACE:
            paused[None] = not paused[None]
        elif e.key == ti.GUI.LMB:
            new_particle(e.pos[0], e.pos[1])
        elif e.key == 'c':
            num_particles[None] = 0
            rest_length.fill(0)
        elif e.key == 's':
            if gui.is_pressed('Shift'):
                spring_stiffness[None] /= 1.1
            else:
                spring_stiffness[None] *= 1.1
        elif e.key == 'd':
            if gui.is_pressed('Shift'):
                damping[None] /= 1.1
            else:
                damping[None] *= 1.1

    if not paused[None]:
        # substep:
        for ss in range(8):
            # Construct A, b vectors:
            createEqn()
            b_implicit_np = b_implicit.to_numpy()
            A_implicit_np = A_implicit.to_numpy()
            # Get solution by using Jacobian method. Stop it when its residual is small enough:
            residual = 1000
            while residual > 0.001:
                v_np = v.to_numpy()
                residual = iterate(A_implicit_np, b_implicit_np, v_np)
            # Step forward:
            substep()

    X = x.to_numpy()
    gui.circles(X[:num_particles[None]], color=0xffaa77, radius=5)

    gui.line(begin=(0.0, bottom_y), end=(1.0, bottom_y), color=0x0, radius=1)

    for i in range(num_particles[None]):
        for j in range(i + 1, num_particles[None]):
            if rest_length[i, j] != 0:
                gui.line(begin=X[i], end=X[j], radius=2, color=0x445566)
    gui.text(content=f'C: clear all; Space: pause', pos=(0, 0.95), color=0x0)
    gui.text(content=f'S: Spring stiffness {spring_stiffness[None]:.1f}', pos=(0, 0.9), color=0x0)
    gui.text(content=f'D: damping {damping[None]:.2f}', pos=(0, 0.85), color=0x0)
    gui.show()

Hello!
这块错误应该是能复现的。
问题在这两行代码上:
norm1 = ti.abs(v_np[0, 0] - new_v_np[0, 0])
norm2 = ti.abs(v_np[0, 0])

十分感谢啦!

# A s s e r t i o n   f a i l e d :   g e t O p e r a n d ( 0 ) - > g e t T y p e ( )   = =   c a s t < P o i n t e r T y p e > ( g e t O p e r a n d ( 1 ) - > g e t T y p e ( ) ) - > g e t E l e m e n t T y p e ( )   & &   " P t r   m u s t   b e   a   p o i n t e r   t o   V a l   t y p e ! " ,   f i l e   e : \ r e p o s \ l l v m - 8 . 0 . 1 \ l i b \ i r \ i n s t r u c t i o n s . c p p ,   l i n e   1 2 1 0
# Init the parameters in this system:


import taichi as ti
import numpy as np

ti.init(debug=True, arch=ti.gpu)

max_num_particles = 256

dt = 1e-3

num_particles = ti.var(ti.i32, shape=())
spring_stiffness = ti.var(ti.f32, shape=())
paused = ti.var(ti.i32, shape=())
damping = ti.var(ti.f32, shape=())

particle_mass = 1
bottom_y = 0.05

x = ti.Vector(2, dt=ti.f32, shape=max_num_particles)
v = ti.Vector(2, dt=ti.f32, shape=max_num_particles)
new_v = ti.Vector(2, dt=ti.f32, shape=max_num_particles)
diff_vec = ti.var(dt=ti.f32, shape=2*max_num_particles)

A = ti.Matrix(2, 2, dt=ti.f32, shape=(max_num_particles, max_num_particles))
b = ti.Vector(2, dt=ti.f32, shape=max_num_particles)

# rest_length[i, j] = 0 means i and j are not connected
rest_length = ti.var(ti.f32, shape=(max_num_particles, max_num_particles))
A_implicit = ti.Matrix(2, 2, dt=ti.f32, shape=(max_num_particles, max_num_particles))
b_implicit = ti.Vector(2, dt=ti.f32, shape=max_num_particles)

connection_radius = 0.15

gravity = [0, -9.8]


@ti.kernel
def createEqn():
    # Construct A_implicit Matrix:
    n = num_particles[None]
    mass_inv = 1.0 / particle_mass
    for tensor_ele_idx in range(n * n):
        # Construct original Jacobian matrix's elements (2x2):
        tensor_ele_idx_row = tensor_ele_idx // n
        tensor_ele_idx_col = tensor_ele_idx - tensor_ele_idx_row * n
        if tensor_ele_idx_row == tensor_ele_idx_col:
            # Populate diagonal elements in the tensor:
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = 0
            for j in range(n):
                if rest_length[tensor_ele_idx_row, j] != 0:
                    xdt = x[tensor_ele_idx_row]
                    xjt = x[j]
                    dist = (xdt - xjt).norm()
                    ti_dist_pow_n3 = ti.pow(dist, -3)
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] += -spring_stiffness[None] * (
                                rest_length[tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[0] - xjt[0]) * (
                                    xdt[0] - xjt[0]) - rest_length[tensor_ele_idx, j] * ti.pow(dist, -1) + 1)
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] += -spring_stiffness[None] * rest_length[
                        tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[0] - xjt[0]) * (xdt[1] - xjt[1])
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] += -spring_stiffness[None] * rest_length[
                        tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[0] - xjt[0]) * (xdt[1] - xjt[1])
                    A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] += -spring_stiffness[None] * (
                                rest_length[tensor_ele_idx_row, j] * ti_dist_pow_n3 * (xdt[1] - xjt[1]) * (
                                    xdt[1] - xjt[1]) - rest_length[tensor_ele_idx, j] * ti.pow(dist, -1) + 1)
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = 1.0 - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 1]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = 1.0 - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 1]
        elif rest_length[tensor_ele_idx_row, tensor_ele_idx_col] != 0:
            # Populate elements that have a connection:
            xrt = x[tensor_ele_idx_row]
            xct = x[tensor_ele_idx_col]
            dist = (xrt - xct).norm()
            dist_pow_n3 = ti.pow(dist, -3)
            lrc = rest_length[tensor_ele_idx_row, tensor_ele_idx_col]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = -spring_stiffness[None] * (
                        -lrc * dist_pow_n3 * (xrt[0] - xct[0]) * (xrt[0] - xct[0]) + lrc * ti.pow(dist, -1) - 1)
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = spring_stiffness[None] * lrc * dist_pow_n3 * (
                        xrt[0] - xct[0]) * (xrt[1] - xct[1])
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = spring_stiffness[None] * lrc * dist_pow_n3 * (
                        xrt[0] - xct[0]) * (xrt[1] - xct[1])
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = -spring_stiffness[None] * (
                        -lrc * dist_pow_n3 * (xrt[1] - xct[1]) * (xrt[1] - xct[1]) + lrc * ti.pow(dist, -1) - 1)
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][0, 1]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 0]
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = - dt * dt * mass_inv * A_implicit[
                tensor_ele_idx_row, tensor_ele_idx_col][1, 1]
        else:
            # Populate zeros:
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][0, 1] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 0] = 0
            A_implicit[tensor_ele_idx_row, tensor_ele_idx_col][1, 1] = 0
    # Construct b_implicit Vector:
    for i in range(n):
        # Calculate the overall force on this point:
        total_force = ti.Vector(gravity) * particle_mass
        for j in range(n):
            if rest_length[i, j] != 0:
                x_ij = x[i] - x[j]
                total_force += -spring_stiffness[None] * (x_ij.norm() - rest_length[i, j]) * x_ij.normalized()
        b_implicit[i] = v[n] + dt * mass_inv * total_force


@ti.kernel
def iterate(A_implicit_np: ti.ext_arr(), b_implicit_np: ti.ext_arr(), v_np: ti.ext_arr(), new_v_np: ti.ext_arr()) -> ti.f32:
    n = num_particles[None]
    for i in range(2 * n):
        i_tensor_ele_idx_row = i // 2
        i_internal_ele_idx_row = i % 2
        # i_tensor_ele_idx_row = 5
        # i_internal_ele_idx_row = 6
        # print('i_tensor_ele_idx_row:', i_tensor_ele_idx_row)
        # print('i_internal_ele_idx_row:', i_internal_ele_idx_row)
        # Temporary index system, which doesn't have physical meaning.
        # temp = b_implicit[i_tensor_ele_idx_row][i_internal_ele_idx_row]
        temp = b_implicit_np[i_tensor_ele_idx_row, i_internal_ele_idx_row]
        for j in range(2 * n):
            if i != j:
                j_tensor_ele_idx_col = j // 2
                j_internal_ele_idx_col = j % 2
                # temp -= A_implicit[i_tensor_ele_idx_row, j_tensor_ele_idx_col][i_internal_ele_idx_row, j_internal_ele_idx_col] * v[j_tensor_ele_idx_col][j_internal_ele_idx_col]
                temp -= A_implicit_np[i_tensor_ele_idx_row, j_tensor_ele_idx_col, i_internal_ele_idx_row, j_internal_ele_idx_col] * v_np[j_tensor_ele_idx_col, j_internal_ele_idx_col]
        # Divide everything by the coefficient of that unknown
        # new_v[i_tensor_ele_idx_row][i_internal_ele_idx_row] = temp / A[i_tensor_ele_idx_row, i_tensor_ele_idx_row][i_internal_ele_idx_row, i_internal_ele_idx_row]
        new_v_np[i_tensor_ele_idx_row, i_internal_ele_idx_row] = temp / A_implicit_np[i_tensor_ele_idx_row, i_tensor_ele_idx_row, i_internal_ele_idx_row, i_internal_ele_idx_row]
    # Calculate the residual of this iteration by using infinite norm:
    norm1 = ti.abs(v_np[0, 0] - new_v_np[0, 0])
    norm2 = ti.abs(v_np[0, 0])
    for i in range(2 * n):
        i_tensor_ele_idx = i // 2
        i_internal_ele_idx = i % 2
        diff_vec[i] = v_np[i_tensor_ele_idx, i_internal_ele_idx] - new_v_np[i_tensor_ele_idx, i_internal_ele_idx]
        if ti.abs(diff_vec[i]) > norm1:
            norm1 = ti.abs(diff_vec[i])
        if ti.abs(v_np[i_tensor_ele_idx, i_internal_ele_idx]) > norm2:
            norm2 = ti.abs(v_np[i_tensor_ele_idx, i_internal_ele_idx])
    residual = norm1 / norm2
    # Update the unknown vector:
    for i in range(n):
        v[n][0] = new_v_np[n, 0]
        v[n][1] = new_v_np[n, 1]

    return residual


@ti.kernel
def substep():
    n = num_particles[None]
    # Collide with ground
    for i in range(n):
        if x[i].y < bottom_y:
            x[i].y = bottom_y
            v[i].y = 0

    # Compute new position
    for i in range(num_particles[None]):
        x[i] += v[i] * dt


@ti.kernel
def new_particle(pos_x: ti.f32, pos_y: ti.f32):  # Taichi doesn't support using Matrices as kernel arguments yet
    new_particle_id = num_particles[None]
    x[new_particle_id] = [pos_x, pos_y]
    v[new_particle_id] = [0, 0]
    num_particles[None] += 1

    # Connect with existing particles
    for i in range(new_particle_id):
        dist = (x[new_particle_id] - x[i]).norm()
        if dist < connection_radius:
            rest_length[i, new_particle_id] = 0.1
            rest_length[new_particle_id, i] = 0.1


gui = ti.GUI('Mass Spring System', res=(512, 512), background_color=0xdddddd)

spring_stiffness[None] = 10000
damping[None] = 20

new_particle(0.3, 0.3)
new_particle(0.3, 0.4)
new_particle(0.4, 0.4)

while True:
    for e in gui.get_events(ti.GUI.PRESS):
        if e.key in [ti.GUI.ESCAPE, ti.GUI.EXIT]:
            exit()
        elif e.key == gui.SPACE:
            paused[None] = not paused[None]
        elif e.key == ti.GUI.LMB:
            new_particle(e.pos[0], e.pos[1])
        elif e.key == 'c':
            num_particles[None] = 0
            rest_length.fill(0)
        elif e.key == 's':
            if gui.is_pressed('Shift'):
                spring_stiffness[None] /= 1.1
            else:
                spring_stiffness[None] *= 1.1
        elif e.key == 'd':
            if gui.is_pressed('Shift'):
                damping[None] /= 1.1
            else:
                damping[None] *= 1.1

    if not paused[None]:
        for step in range(10):
            print('main loop start')
            createEqn()
            print('after create equation')
            b_implicit_np = b_implicit.to_numpy()
            A_implicit_np = A_implicit.to_numpy()
            v_np = v.to_numpy()
            new_v_np = new_v.to_numpy()
            print('after transformation')
            residual = iterate(A_implicit_np, b_implicit_np, v_np, new_v_np)
            print('after iteration')
            substep()
            print('main loop end')

    X = x.to_numpy()
    gui.circles(X[:num_particles[None]], color=0xffaa77, radius=5)

    gui.line(begin=(0.0, bottom_y), end=(1.0, bottom_y), color=0x0, radius=1)

    for i in range(num_particles[None]):
        for j in range(i + 1, num_particles[None]):
            if rest_length[i, j] != 0:
                gui.line(begin=X[i], end=X[j], radius=2, color=0x445566)
    gui.text(content=f'C: clear all; Space: pause', pos=(0, 0.95), color=0x0)
    gui.text(content=f'S: Spring stiffness {spring_stiffness[None]:.1f}', pos=(0, 0.9), color=0x0)
    gui.text(content=f'D: damping {damping[None]:.2f}', pos=(0, 0.85), color=0x0)
    gui.show()

Cause matrix operations are unrolled at compile time for performance reason.
Please use ti.static when iterating matrix elements:

for i in ti.static(range(vec.n)):
  print(vec[i])

You mean element-wise operation? No, for ti.var, you may write one yourself:

@ti.func
def my_add(self: ti.template(), other: ti.template()):
  for i in ti.grouped(ti.ndrange(*self.shape)):
    self[i] = other[i]

ti.Expr.__add__ = my_add

https://www.youtube.com/watch?v=ZipHXFrCk70

Please try upload to somewhere like bilibili if possible :slight_smile:

请问ti.var上有norm操作嘛?

What do you mean by norm? Magnitude?

A s s e r t i o n f a i l e d : g e t O p e r a n d ( 0 ) - > g e t T y p e ( ) = = c a s t < P o i n t e r T y p e > ( g e t O p e r a n d ( 1 ) - > g e t T y p e ( ) ) - > g e t E l e m e n t T y p e ( ) & & " P t r m u s t b e a p o i n t e r t o V a l t y p e ! " , f i l e e : \ r e p o s \ l l v m - 8 . 0 . 1 \ l i b \ i r \ i n s t r u c t i o n s . c p p , l i n e 1 2 1 0
Init the parameters in this system:
这个错误感觉挺奇怪的,我把对应代码和具体哪行似乎有问题,放到后面了。但是我没看出来那几行有什么问题,之后我也是绕过这块做的作业。

Could you provide a minimal reproduce-able example? This may be a optimizer bug, thank for your report! That will helps us improve.

def iterate(A_implicit_np: ti.ext_arr(), b_implicit_np: ti.ext_arr(), v_np: ti.ext_arr(), new_v_np: ti.ext_arr()) -> ti.f32:
  ...
  ...
            b_implicit_np = b_implicit.to_numpy()
            A_implicit_np = A_implicit.to_numpy()
            v_np = v.to_numpy()
            new_v_np = new_v.to_numpy()
            residual = iterate(A_implicit_np, b_implicit_np, v_np, new_v_np)

Well… why not just simply:

def iterate(A_implicit: ti.template(), b_implicit: ti.template(), v: ti.template(), new_v: ti.template()) -> ti.f32:
  ...
  ...
residual = iterate(A_implicit, b_implicit, v, new_v)

?

It’s very possible that this is a ti.ext_arr() bug, in fact ti.ext_arr() is not tested very well and can be easily buggy… avoid using them.

Well… Actually I tried to do this before, but I didn’t use ti.template() because I didn’t know it. At first, ‘A_implicit’ and ‘b_implicit’ were directly used in the ‘iterate(…)’ function. However, I found out that the compiler didn’t allow me to visit the ‘0th-index’ element of ‘Matrix/Vector’ owing to the fact that I had no idea of the ‘Static’ keyword shown by your example.

Apparently, I need more time to get familiar with ‘Taichi’ :slight_smile:

In terms of reproducing the error mentioned, I will spend some time on it. But, I am not confident that I am able to reproduce it quickly.

Thank you so much!

Well… Actually I tried to do this before, but I didn’t use ti.template() because I didn’t know it. At first, ‘A_implicit’ and ‘b_implicit’ were directly used in the ‘iterate(…)’ function. However, I found out that the compiler didn’t allow me to visit the ‘0th-index’ element of ‘Matrix/Vector’ owing to the fact that I had no idea of the ‘Static’ keyword shown by your example.

Yes, using numpy array is not an suggested walk around…
Make sure you’ve check out the documentation if you need some feature, it’s likely to be already exist there.

In terms of reproducing the error mentioned, I will spend some time on it. But, I am not confident that I am able to reproduce it quickly.

Quick confirm, does ti.init(arch=ti.opengl) fix the issue?

Well, unfortunately, it doesn’t work…

Does the error message changed after using ti.init(arch=ti.opengl)? Sorry, didn’t have OpenGL support on my laptop…

No problem. Yeah, it changes. But, the error is quite long and it looks like it shows some GLSL code.

What’s the last line of error message? e.g.:

error C233: Cannot use keyword 'hello' in GLSL!