TilliFe Comments - Answer Overflow

I am not sure i f this helps you in any way, if not let's just take it as a checkpoint on what Endia can currently do and what not. 🙃 I'd be super happy to hear more of what exactly you would like to see in the long term. Could you create a list of features (possibly with some examples) so that we all can learn a bit more about the powers of function transformations. That would be super awesome! :mojo:

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

    #########################################################
    #  Second round - create second branch
    #########################################################

    # Initialize input to take the else branch in foo...
    x = nd.array("[100.0, 2.0, 3.0]") 

    # Compute result and derivatives (with type hints)
    y = foo_jitted(x)[nd.Array]
    dy_dx = foo_jac(x)[nd.Array]
    d2y_dx2 = foo_hes(x)[nd.Array]

    # Print results
    print(str(y))           # -9913.0
    print(str(dy_dx))       # [-200.0, -4.0, -6.0]
    print(str(d2y_dx2))     # [[-2.0, 0.0, 0.0],
                            #  [0.0, -2.0, 0.0],
                            #  [0.0, 0.0, -2.0]]


    #########################################################
    #  Third round - use second branch again
    #########################################################

    # Initialize input to take the else branch in foo...
    x = nd.array("[200.0, 2.0, 3.0]") 

    # Compute result and derivatives (with type hints)
    y = foo_jitted(x)[nd.Array]
    dy_dx = foo_jac(x)[nd.Array]
    d2y_dx2 = foo_hes(x)[nd.Array]

    # Print results
    print(str(y))           # -39913.0
    print(str(dy_dx))       # [-400.0, -4.0, -6.0]
    print(str(d2y_dx2))     # [[-2.0, 0.0, 0.0],
                            #  [0.0, -2.0, 0.0],
                            #  [0.0, 0.0, -2.0]]

    #########################################################
    #  Second round - create second branch
    #########################################################

    # Initialize input to take the else branch in foo...
    x = nd.array("[100.0, 2.0, 3.0]") 

    # Compute result and derivatives (with type hints)
    y = foo_jitted(x)[nd.Array]
    dy_dx = foo_jac(x)[nd.Array]
    d2y_dx2 = foo_hes(x)[nd.Array]

    # Print results
    print(str(y))           # -9913.0
    print(str(dy_dx))       # [-200.0, -4.0, -6.0]
    print(str(d2y_dx2))     # [[-2.0, 0.0, 0.0],
                            #  [0.0, -2.0, 0.0],
                            #  [0.0, 0.0, -2.0]]


    #########################################################
    #  Third round - use second branch again
    #########################################################

    # Initialize input to take the else branch in foo...
    x = nd.array("[200.0, 2.0, 3.0]") 

    # Compute result and derivatives (with type hints)
    y = foo_jitted(x)[nd.Array]
    dy_dx = foo_jac(x)[nd.Array]
    d2y_dx2 = foo_hes(x)[nd.Array]

    # Print results
    print(str(y))           # -39913.0
    print(str(dy_dx))       # [-400.0, -4.0, -6.0]
    print(str(d2y_dx2))     # [[-2.0, 0.0, 0.0],
                            #  [0.0, -2.0, 0.0],
                            #  [0.0, 0.0, -2.0]]

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

import endia as nd


# Define the function
def foo(x: nd.Array) -> nd.Array:
    if x.load(0) < 10:
        return nd.sum(x**2)
    else: 
        return - nd.sum(x**2) + 100

def main():

    # Create callables for the jacobian and hessian
    foo_jitted = nd.jit(foo)
    foo_jac = nd.grad(foo_jitted)
    foo_hes = nd.grad(foo_jac)
    
    #########################################################
    #  First round - create inital branch
    #########################################################

    # Initialize input to take the if branch in foo...
    x = nd.array("[1.0, 2.0, 3.0]")

    # Compute result and derivatives (with type hints)
    y = foo_jitted(x)[nd.Array]
    dy_dx = foo_jac(x)[nd.Array]
    d2y_dx2 = foo_hes(x)[nd.Array]

    # Print results
    print(str(y))           # 14.0
    print(str(dy_dx))       # [2.0, 4.0, 6.0]
    print(str(d2y_dx2))     # [[2.0, 0.0, 0.0],
                            #  [0.0, 2.0, 0.0],
                            #  [0.0, 0.0, 2.0]]

import endia as nd


# Define the function
def foo(x: nd.Array) -> nd.Array:
    if x.load(0) < 10:
        return nd.sum(x**2)
    else: 
        return - nd.sum(x**2) + 100

def main():

    # Create callables for the jacobian and hessian
    foo_jitted = nd.jit(foo)
    foo_jac = nd.grad(foo_jitted)
    foo_hes = nd.grad(foo_jac)
    
    #########################################################
    #  First round - create inital branch
    #########################################################

    # Initialize input to take the if branch in foo...
    x = nd.array("[1.0, 2.0, 3.0]")

    # Compute result and derivatives (with type hints)
    y = foo_jitted(x)[nd.Array]
    dy_dx = foo_jac(x)[nd.Array]
    d2y_dx2 = foo_hes(x)[nd.Array]

    # Print results
    print(str(y))           # 14.0
    print(str(dy_dx))       # [2.0, 4.0, 6.0]
    print(str(d2y_dx2))     # [[2.0, 0.0, 0.0],
                            #  [0.0, 2.0, 0.0],
                            #  [0.0, 0.0, 2.0]]

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

Hi 🧙, at the moment I'd prefer to answer that it is still a bit too early for these kind of flexible transformations. I am currently overthinking most parts of the endia core and I am planning to rebuild things from scratch (again). So things will hopefully become better and more flexible in the next iteration. Wrt. to custom transformations, I would like to refer to Endia's custom_ops as described in https://endia.vercel.app/docs/custom_ops. Nonetheless, I wrote out a little program which basically gives a more or less comprehensive overview of what Endia can currently do in terms of function transformations and how one might apply control flow: 1. We create a function foo which has some control flow inside of it. 2. We create a jitted (optional, but for the sake of concatenating transforms let's do it here too) version of this function and pass it to the grad and jacobian function transformations. 3. Then, in the three following rounds, we use those transformed functions (which are basically just a bunch of custom structs called Callables) and pass a differently initialized x into them and check if the transformed versions branch correctly.

57 replies

MModular

•Created by Martin Dudek on 9/16/2024 in #questions

to_numpy with Mojo 24.5

I think I have been there too. Does the following example from the MAX repo help as a reference? https://github.com/modularml/max/blob/434daac5b52226d8c7ea024d08df127af5fce9dd/examples/serve/openclip-mojo-onnx/python_utils.mojo#L61

9 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

A small update https://x.com/fe_tilli/status/1835592247779193091

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

Hi Martin, I am indeed planning to integrate those features, at least on a high level. Things like high level modules and some standard nn models. The image shall simply display some possible applications (stuff I am personally really interested in), but it definitely does not cover all possible applications of a comprehensive Array library. (Just look at where NumPy is used nowadays, even in satelites floating through space...) Thank you for mentioning this, I should write down something like a roadmap to make things more transparent.

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

I fixed JIT compilation with MAX. :mojo: What was the problem? If you previously ran the simple MLP benchmarks inside Endia's benchmarks directory, you might have noticed that the version using MAX for JIT compiling Endia Subgraphs, took for ages compared to not using MAX. Why? When transferring data from the Endia Graph to the MAX Graph/Model and back, we did not properly make use of TensorMaps, but converted arguments (a List of Endia Arrays) to a list of NumPy arrays first (a List of PythonObjects, expensive!), which are then again being converted to a set of MAX Tensors for further use by the MAX engine). This sounds indeed terrible 🤦‍♂️ and I only saw the obvious alternative now: We can create MAX Tensors as inputs to an executable MAX Model which do not own their data pointer! From now on, the inputs to a MAX Model merely borrow UnsafePointers from Endia Arrays for the duration of the MAX Model execution. Additionally, outputs from a MAX Model were previously copied (also super expensive). Now, since those outputs will usually be destroyed, we can just steal the outputs' UnsafePointers and let Endia Array own them after execution. All in all, there are no unnecessary data copies anymore, and Endia and MAX can now work on the same data. This dramatically speeds up JIT compilation in Endia. Due to MAX' highly optimized ops, this speedup will be especially significant, when training larger Neural Networks. Ultimately, this also gives me confidence, that Endia can greatly benefit from using MAX once it'll support GPU. Cheers! 🧙

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

partying in nd-space... https://x.com/fe_tilli/status/1829431929688465500

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

Absolutely! :mojo: I had a similar idea and wanted to reach out to you on that as well.

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

Endia's FFT implementation, despite its compactness, delivers performance not far behind established frameworks. Further optimizations and algorithmic refinements could push Endia's performance to fully match or even exceed existing solutions.

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

New Fast Fourier Transform Module in Endia. 🌊 https://x.com/fe_tilli/status/1827434391330558226

57 replies

MModular

•Created by HALFPOTATO on 8/8/2024 in #questions

Autodiff For Mojo

Hi, yeah usability-wise (and also performance-wise) definitely still a ton of difference, since Endia is in very active development. Endia shall soon become very similar to PyTorch, however the goal is to make - what Pytorch currently has in its functional Module - a true first class citzen in Endia. If this will work as I envision, Endia can also be used like a more functional ML framwork, similar to JAX. This would mean people with different philosophies of coding could come together and use just one engine seamlessly, which would be pretty nice. As Benny mentioned, implementation-wise, Endia is completely different to any other ML framework, since it is written in Mojo. A single layer stack basically. Chears 🧙

10 replies

MModular

•Created by HALFPOTATO on 8/8/2024 in #questions

Autodiff For Mojo

I agree.

10 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

Endia nightly now uses MAX/Mojo nightly. This was long due. 👷‍♀️ 👷‍♂️ 🧙

57 replies

MModular

•Created by TilliFe on 7/18/2024 in #community-showcase

Endia

Good question, lets first test if the import of modules work at all on your machine, then we try to generalize this for any kind of external module that shall be used in a (nested) project. Basic Import Test: Create a new directory test and copy the endia.package inside of it. Then next to it create a file where you try to import endia. If that works you can checkout the next step. Example:

.
├── endia.mojopkg
└── use_endia_here.mojo

.
├── endia.mojopkg
└── use_endia_here.mojo

General Usage: - If you build a nested module/a project with a lot of subdirectories, make sure that all subfolders that use an external module (e.g. Endia) have a __init__.mojo file. This will modularize the subfolders. Check out the docs for more information: https://docs.modular.com/mojo/manual/packages. - Once you have modularized your project, you can place the endia.package at the top level of your directory. Then you should be able to import endia at any level. Example:

.
├── endia.mojopkg
├── level1_dir
│   ├── __init__.mojo
│   └── use_endia_here.mojo
└── run_level1stuff_here.mojo

.
├── endia.mojopkg
├── level1_dir
│   ├── __init__.mojo
│   └── use_endia_here.mojo
└── run_level1stuff_here.mojo

57 replies

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