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ami-iit / jaxsim

A differentiable physics engine and multibody dynamics library for control and robot learning.
https://jaxsim.readthedocs.io/
BSD 3-Clause "New" or "Revised" License
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Error when passing two different pytrees with the same structure to a JIT-compiled function #103

Closed diegoferigo closed 2 months ago

diegoferigo commented 5 months ago

Also with the new functional APIs, the following problem still affects our code:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

A MWE to reproduce the problem is the following:

import jax.numpy as jnp
import jaxsim.api as js
import rod.builder.primitives
import rod.urdf.exporter

# Create on-the-fly a ROD model of a box.
rod_model = (
    rod.builder.primitives.BoxBuilder(x=0.3, y=0.2, z=0.1, mass=1.0, name="box")
    .build_model()
    .add_link()
    .add_inertial()
    .add_visual()
    .add_collision()
    .build()
)

# Export the URDF string.
urdf_string = rod.urdf.exporter.UrdfExporter.sdf_to_urdf_string(
    sdf=rod_model, pretty=True
)

model1 = js.model.JaxSimModel.build_from_model_description(
    model_description=urdf_string,
    gravity=jnp.array([0, 0, -10]),
    is_urdf=True,
)

model2 = js.model.JaxSimModel.build_from_model_description(
    model_description=urdf_string,
    gravity=jnp.array([0, 0, -10]),
    is_urdf=True,
)

# This works
_ = js.contact.estimate_good_soft_contacts_parameters(model=model1)

# This raises
_ = js.contact.estimate_good_soft_contacts_parameters(model=model2)

I've already tried to play a bit with JaxSimModel.__eq__ and JaxSimModel.__hash__ with no luck. Marking this issue as help wanted in case this problem becomes annoying to someone.

Interestingly, running it in a context in which jax.jit is disabled, works fine:

with jax.disable_jit():
    _ = js.contact.estimate_good_soft_contacts_parameters(model=model1)
    _ = js.contact.estimate_good_soft_contacts_parameters(model=model2)

I suspect that there is something strange in the jax cache that we are either overlooking or not handling properly.

diegoferigo commented 5 months ago

This is basically the updated version of the problem occurring in the previous OOP APIs in #84 cc @flferretti

traversaro commented 5 months ago

The example is failing with the following error for me:

traversaro@IITBMP014LW012:~/jaxsimws$ pixi run python mwe103.py
jaxsim[21436] INFO Enabling JAX to use 64bit precision
An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
Traceback (most recent call last):
  File "/home/traversaro/jaxsimws/mwe103.py", line 18, in <module>
    urdf_string = rod.urdf.exporter.UrdfExporter.sdf_to_urdf_string(
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/traversaro/jaxsimws/.pixi/envs/default/lib/python3.12/site-packages/rod/urdf/exporter.py", line 30, in sdf_to_urdf_string
    model = sdf.models()[0]
            ~~~~~~~~~~~~^^^
IndexError: list index out of range

Installed packages:

Click ~~~ traversaro@IITBMP014LW012:~/jaxsimws$ pixi list Package Version Build Size Kind Source _libgcc_mutex 0.1 conda_forge 2.5 KiB conda _libgcc_mutex-0.1-conda_forge.tar.bz2 _openmp_mutex 4.5 2_gnu 23.1 KiB conda _openmp_mutex-4.5-2_gnu.tar.bz2 _sysroot_linux-64_curr_repodata_hack 3 h69a702a_14 20.7 KiB conda _sysroot_linux-64_curr_repodata_hack-3-h69a702a_14.conda absl-py 2.1.0 pyhd8ed1ab_0 104.8 KiB conda absl-py-2.1.0-pyhd8ed1ab_0.conda alsa-lib 1.2.11 hd590300_1 541.7 KiB conda alsa-lib-1.2.11-hd590300_1.conda ampl-mp 3.1.0 h2cc385e_1006 1.1 MiB conda ampl-mp-3.1.0-h2cc385e_1006.tar.bz2 aom 3.8.1 h59595ed_0 2.6 MiB conda aom-3.8.1-h59595ed_0.conda assimp 5.3.1 hfb0e8fe_2 3.4 MiB conda assimp-5.3.1-hfb0e8fe_2.conda asttokens 2.4.1 pyhd8ed1ab_0 28.2 KiB conda asttokens-2.4.1-pyhd8ed1ab_0.conda atk-1.0 2.38.0 hd4edc92_1 539 KiB conda atk-1.0-2.38.0-hd4edc92_1.tar.bz2 attr 2.5.1 h166bdaf_1 69.4 KiB conda attr-2.5.1-h166bdaf_1.tar.bz2 aws-c-auth 0.7.16 h79b3bcb_6 101 KiB conda aws-c-auth-0.7.16-h79b3bcb_6.conda aws-c-cal 0.6.10 hb29e0c7_1 53.8 KiB conda aws-c-cal-0.6.10-hb29e0c7_1.conda aws-c-common 0.9.13 hd590300_0 220.3 KiB conda aws-c-common-0.9.13-hd590300_0.conda aws-c-compression 0.2.18 hecc5fa9_1 18.7 KiB conda aws-c-compression-0.2.18-hecc5fa9_1.conda aws-c-event-stream 0.4.2 hf9b2f7b_4 52.7 KiB conda aws-c-event-stream-0.4.2-hf9b2f7b_4.conda aws-c-http 0.8.1 h5d7533a_5 190.5 KiB conda aws-c-http-0.8.1-h5d7533a_5.conda aws-c-io 0.14.5 h50678d4_1 153.5 KiB conda aws-c-io-0.14.5-h50678d4_1.conda aws-c-mqtt 0.10.2 hf479d2b_4 160.6 KiB conda aws-c-mqtt-0.10.2-hf479d2b_4.conda aws-c-s3 0.5.2 h4ad9680_0 102.8 KiB conda aws-c-s3-0.5.2-h4ad9680_0.conda aws-c-sdkutils 0.1.15 hecc5fa9_1 54.1 KiB conda aws-c-sdkutils-0.1.15-hecc5fa9_1.conda aws-checksums 0.1.18 hecc5fa9_1 49 KiB conda aws-checksums-0.1.18-hecc5fa9_1.conda aws-crt-cpp 0.26.2 h19f5d62_7 326 KiB conda aws-crt-cpp-0.26.2-h19f5d62_7.conda aws-sdk-cpp 1.11.267 h5606698_1 3.4 MiB conda aws-sdk-cpp-1.11.267-h5606698_1.conda azure-core-cpp 1.11.1 h91d86a7_1 334.6 KiB conda azure-core-cpp-1.11.1-h91d86a7_1.conda azure-storage-blobs-cpp 12.10.0 h00ab1b0_1 505 KiB conda azure-storage-blobs-cpp-12.10.0-h00ab1b0_1.conda azure-storage-common-cpp 12.5.0 h94269e2_4 129.3 KiB conda azure-storage-common-cpp-12.5.0-h94269e2_4.conda binutils_impl_linux-64 2.40 hf600244_0 5.2 MiB conda binutils_impl_linux-64-2.40-hf600244_0.conda binutils_linux-64 2.40 hbdbef99_2 27.5 KiB conda binutils_linux-64-2.40-hbdbef99_2.conda blosc 1.21.5 h0f2a231_0 47.6 KiB conda blosc-1.21.5-h0f2a231_0.conda brotli 1.1.0 hd590300_1 18.9 KiB conda brotli-1.1.0-hd590300_1.conda brotli-bin 1.1.0 hd590300_1 18.5 KiB conda brotli-bin-1.1.0-hd590300_1.conda bullet-cpp 3.25 hfb8ada1_2 40.4 MiB conda bullet-cpp-3.25-hfb8ada1_2.conda bzip2 1.0.8 hd590300_5 248.3 KiB conda bzip2-1.0.8-hd590300_5.conda c-ares 1.27.0 hd590300_0 159.7 KiB conda c-ares-1.27.0-hd590300_0.conda ca-certificates 2024.2.2 hbcca054_0 151.8 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MiB conda cuda-nvcc-tools-12.4.99-hd3aeb46_0.conda cuda-nvcc_linux-64 12.4.99 h8a487aa_0 24.5 KiB conda cuda-nvcc_linux-64-12.4.99-h8a487aa_0.conda cuda-nvvm-dev_linux-64 12.4.99 ha770c72_0 23.5 KiB conda cuda-nvvm-dev_linux-64-12.4.99-ha770c72_0.conda cuda-nvvm-impl 12.4.99 h59595ed_0 8.6 MiB conda cuda-nvvm-impl-12.4.99-h59595ed_0.conda cuda-nvvm-tools 12.4.99 h59595ed_0 11.2 MiB conda cuda-nvvm-tools-12.4.99-h59595ed_0.conda cuda-version 12.4 h3060b56_3 20.5 KiB conda cuda-version-12.4-h3060b56_3.conda cycler 0.12.1 pyhd8ed1ab_0 13.1 KiB conda cycler-0.12.1-pyhd8ed1ab_0.conda dartsim 6.13.1 hdbb2bd4_2 13.6 MiB conda dartsim-6.13.1-hdbb2bd4_2.conda dav1d 1.2.1 hd590300_0 742.4 KiB conda dav1d-1.2.1-hd590300_0.conda dbus 1.13.6 h5008d03_3 604.1 KiB conda dbus-1.13.6-h5008d03_3.tar.bz2 decorator 5.1.1 pyhd8ed1ab_0 11.8 KiB conda decorator-5.1.1-pyhd8ed1ab_0.tar.bz2 docstring_parser 0.15 pyhd8ed1ab_0 29.3 KiB conda docstring_parser-0.15-pyhd8ed1ab_0.conda eigen 3.4.0 h00ab1b0_0 1 MiB 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Perhaps a specific rod version is required?

diegoferigo commented 5 months ago

I guess it needs https://github.com/ami-iit/rod/pull/27/commits/0ce60b22e18432f6a557e83f2a1100cb0bd4cdd4 included in https://github.com/ami-iit/rod/pull/27.

diegoferigo commented 5 months ago

Also #83 actually needed https://github.com/ami-iit/rod/pull/27 for proper color support, we need to bump the dependencies before releasing jaxsim v0.2.0.

traversaro commented 5 months ago

Playing a bit around, I was able to reduce the MWE, to remove jaxsim at all:

import dataclasses

import jax
import jax.numpy as jnp
import jax_dataclasses
from jax_dataclasses import Static
import pathlib
import numpy.typing as npt

import dataclasses
from typing import Dict, Union

import jax.lax
import jax.numpy as jnp
import jax_dataclasses
import numpy as np
from jax_dataclasses import Static

@jax_dataclasses.pytree_dataclass
class MWE103GroundContact:
    body: Static[npt.NDArray] = dataclasses.field(
        default_factory=lambda: np.array([])
    )

    @staticmethod
    def build_from() -> "MWE103GroundContact":
        # Build the object
        gc = MWE103GroundContact(body=np.zeros(8))
        return gc

def local_total_mass(model: MWE103GroundContact) -> float:
    return 1.0

dummy_contact1 = MWE103GroundContact.build_from()
dummy_contact2 = MWE103GroundContact.build_from()

local_total_mass_jit1 = jax.jit(local_total_mass)
local_total_mass_jit2 = jax.jit(local_total_mass)

_ = local_total_mass_jit1(model=dummy_contact1)
_ = local_total_mass_jit2(model=dummy_contact2)

Somehow the problem is related to the body attribute of the GroundContact class: https://github.com/ami-iit/jaxsim/blob/f3d9abb0dd8583c5391db7b47c998ad0a457adea/src/jaxsim/physics/model/ground_contact.py#L26 . Commenting it, the problem disappears.

traversaro commented 5 months ago

Somehow the problem is related to the body attribute of the GroundContact class:

This is aligned with what was found in https://github.com/ami-iit/jaxsim/issues/84#issuecomment-1943719169 .

traversaro commented 5 months ago

I think the answer to this question clarifies everything: https://github.com/google/jax/issues/19547#issuecomment-1913711588 .

traversaro commented 5 months ago

I think I described the root issue behind all the ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() in https://github.com/ami-iit/jaxsim/pull/105 .

flferretti commented 4 months ago

Doing some additional tests, I found out that a similar problem is raised. This time, it seems to be related to the integrators:

Traceback (most recent call last):
  File "/home/flferretti/git/comodo/src/comodo/jaxsimSimulator/test.py", line 55, in <module>
    integrator_state2 = integrator2.init(x0=data2.state, t0=0, dt=1e-3)
  File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 166, in init
    _ = integrator(x0, t0, dt, **kwargs)
  File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 283, in __call__
    z = self._compute_next_state(x0=x0, t0=t0, dt=dt, **kwargs)
  File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 417, in _compute_next_state
    K, _ = jax.lax.scan(
  File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 403, in scan_body
    ki = jax.lax.cond(
  File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 374, in <lambda>
    get_ẋ0 = lambda: self.params.get("dxdt0", f(x0, t0)[0])
  File "/home/flferretti/jaxsim/src/jaxsim/integrators/common.py", line 365, in <lambda>
    f = lambda x, t: self.dynamics(x=x, t=t, **kwargs)
  File "/home/flferretti/jaxsim/src/jaxsim/api/ode.py", line 69, in f
    return system_dynamics(
jax.errors.TracerBoolConversionError: Attempted boolean conversion of traced array with shape bool[1]..
The error occurred while tracing the function <lambda> at /home/flferretti/jaxsim/src/jaxsim/integrators/common.py:374 for cond. This value became a tracer due to JAX operations on these lines:

  operation a:bool[1] = eq b c
    from line /home/flferretti/jaxsim/src/jaxsim/api/ode.py:69 (f)
See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.TracerBoolConversionError
MWE ```python import jax.numpy as jnp import jaxsim.api as js import rod.builder.primitives import rod.urdf.exporter from jaxsim import integrators # Create on-the-fly a ROD model of a box. rod_model = ( rod.builder.primitives.BoxBuilder(x=0.3, y=0.2, z=0.1, mass=1.0, name="box") .build_model() .add_link() .add_inertial() .add_visual() .add_collision() .build() ) # Export the URDF string. urdf_string = rod.urdf.exporter.UrdfExporter.sdf_to_urdf_string( sdf=rod_model, pretty=True ) model1 = js.model.JaxSimModel.build_from_model_description( model_description=urdf_string, is_urdf=True, ) model2 = js.model.JaxSimModel.build_from_model_description( model_description=urdf_string, is_urdf=True, ) # Build the data data1 = js.data.JaxSimModelData.build(model=model1) data2 = js.data.JaxSimModelData.build(model=model2) # Create the integrators integrator1 = integrators.fixed_step.Heun2SO3.build( dynamics=js.ode.wrap_system_dynamics_for_integration( model=model1, data=data1, system_dynamics=js.ode.system_dynamics, ), ) integrator2 = integrators.fixed_step.Heun2SO3.build( dynamics=js.ode.wrap_system_dynamics_for_integration( model=model2, data=data2, system_dynamics=js.ode.system_dynamics, ), ) # ! Try to initialize the integrator integrator_state1 = integrator1.init(x0=data1.state, t0=0, dt=1e-3) integrator_state2 = integrator2.init(x0=data2.state, t0=0, dt=1e-3) ```
flferretti commented 3 months ago

After some testing, I found out that the objects data.state.physics_model are not compatible. Moreover, when data is created for a model without joints, like in the example in https://github.com/ami-iit/jaxsim/issues/103#issuecomment-2058752595, the fact that the attributes joint_positions and joint_velocities are empty raises https://github.com/google/jax/blob/95c05521b457f2572b0704449c76982c85ee72b6/jax/_src/core.py#L646-L647.

Setting hash=False for both attributes partially solves the problem, but still I cannot compare data1 with data2 to get a boolean

flferretti commented 3 months ago

Furthermore, I believe that we should pay attention to the dynamics attribute of Integrator, which being just a Protocol, it cannot be compared with ==

diegoferigo commented 3 months ago

Thanks for the investigation @flferretti. Since JaxSimModelData contains many JAX arrays as attributes, I believe that the __hash__ and __eq__ methods need to be properly implemented. I'll have a look at it, I've already have some WIP work on the implementation of these methods for the jaxsim.parser package, I'll extend it also to JaxSimModelData.