taalhaataahir01022001 Comments

taalhaataahir01022001

•Created by TomLucidor on 11/15/2024 in #questions

Has anyone tried to get an LLM to code in Mojo, or convert Python into Mojo?

https://github.com/tairov/llama2.mojo There are also few examples on modular github repo via MAX: https://github.com/modularml/max/tree/main/examples

3 replies

MModular

•Created by taalhaataahir01022001 on 10/8/2024 in #questions

Reshape MAX API OP giving error

And it's not the case with reshape only. e.g., this program uses split op:

fn main() raises:
    seed(43)
    var session = engine.InferenceSession()
    var t = Tensor[DType.float32].randn((6, 4))
    print("t:\n", t)

    var splitting = Graph(in_types=List[Type](TensorType(DType.float32, 6,4)))
    var s = ops.split[2](splitting[0], ((2, 4)))
    var o = List[Symbol] ()
    o.append(s[0])
    o.append(s[1])
    splitting.output(o)
    splitting.verify()
    var splt = session.load(splitting)

    results = splt.execute("input0", t)
    var output = results.get[DType.float32]("output0")
    print("output:\n",output)

    var output1 = results.get[DType.float32] ("output1")
    print("output1:\n", output1)

fn main() raises:
    seed(43)
    var session = engine.InferenceSession()
    var t = Tensor[DType.float32].randn((6, 4))
    print("t:\n", t)

    var splitting = Graph(in_types=List[Type](TensorType(DType.float32, 6,4)))
    var s = ops.split[2](splitting[0], ((2, 4)))
    var o = List[Symbol] ()
    o.append(s[0])
    o.append(s[1])
    splitting.output(o)
    splitting.verify()
    var splt = session.load(splitting)

    results = splt.execute("input0", t)
    var output = results.get[DType.float32]("output0")
    print("output:\n",output)

    var output1 = results.get[DType.float32] ("output1")
    print("output1:\n", output1)

And this is the output log:

t:
 Tensor([[1.2788280248641968, -1.2873831987380981, -0.56482106447219849, 1.5331770181655884],
[0.80901926755905151, -0.24991747736930847, -0.8085181713104248, 0.42681333422660828],
[1.8432825803756714, -0.60748469829559326, 0.24848183989524841, -0.3757627010345459],
[1.6167833805084229, 1.0938529968261719, 0.35813808441162109, 0.071096442639827728],
[1.5071860551834106, -0.54008448123931885, -0.18514452874660492, 0.30583590269088745],
[0.34934023022651672, -1.6976139545440674, -1.2638821601867676, -0.97573941946029663]], dtype=float32, shape=6x4)
output:
 Tensor([[2.2701208643344754e+37, 4.2833490159016683e-41, -0.56482106447219849, 1.5331770181655884],
[0.80901926755905151, -0.24991747736930847, -0.8085181713104248, 0.42681333422660828]], dtype=float32, shape=2x4)
output1:
 Tensor([[1.8432825803756714, -0.60748469829559326, 0.24848183989524841, -0.3757627010345459],
[1.6167833805084229, 1.0938529968261719, 0.35813808441162109, 0.071096442639827728],
[1.5071860551834106, -0.54008448123931885, -0.18514452874660492, 0.30583590269088745],
[0.34934023022651672, -1.6976139545440674, -1.2638821601867676, -0.97573941946029663]], dtype=float32, shape=4x4)

t:
 Tensor([[1.2788280248641968, -1.2873831987380981, -0.56482106447219849, 1.5331770181655884],
[0.80901926755905151, -0.24991747736930847, -0.8085181713104248, 0.42681333422660828],
[1.8432825803756714, -0.60748469829559326, 0.24848183989524841, -0.3757627010345459],
[1.6167833805084229, 1.0938529968261719, 0.35813808441162109, 0.071096442639827728],
[1.5071860551834106, -0.54008448123931885, -0.18514452874660492, 0.30583590269088745],
[0.34934023022651672, -1.6976139545440674, -1.2638821601867676, -0.97573941946029663]], dtype=float32, shape=6x4)
output:
 Tensor([[2.2701208643344754e+37, 4.2833490159016683e-41, -0.56482106447219849, 1.5331770181655884],
[0.80901926755905151, -0.24991747736930847, -0.8085181713104248, 0.42681333422660828]], dtype=float32, shape=2x4)
output1:
 Tensor([[1.8432825803756714, -0.60748469829559326, 0.24848183989524841, -0.3757627010345459],
[1.6167833805084229, 1.0938529968261719, 0.35813808441162109, 0.071096442639827728],
[1.5071860551834106, -0.54008448123931885, -0.18514452874660492, 0.30583590269088745],
[0.34934023022651672, -1.6976139545440674, -1.2638821601867676, -0.97573941946029663]], dtype=float32, shape=4x4)

It is also extracting the wrong first 2 outputs in row 1.

4 replies

MModular

•Created by taalhaataahir01022001 on 10/8/2024 in #questions

Reshape MAX API OP giving error

Sorry my bad!! Didn't make the correct imports Also I'm facing some issues with the code. ops.reshapes is showing unexpected behaviour. Here's my code:

from max.graph.type import TensorType, Dim, Type
from random import seed
from max import engine
from max.tensor import Tensor
from max.graph import Graph, ops

fn main() raises:
    seed(43)
    var session = engine.InferenceSession()
    var t = Tensor[DType.float32].randn((1, 2, 2))
    print("t:\n", t)
    dim1 = Dim(2)
    dim2 = Dim(2)
    x = List[Dim]()
    x.append(dim1)
    x.append(dim2)

    var graph0 = Graph(in_types=List[Type](TensorType(DType.float32, 1, 2, 2)))
    var r = ops.reshape(graph0[0], x)
    graph0.output(r)
    graph0.verify()
    var rs = session.load(graph0)
    results = rs.execute("input0", t)
    var output = results.get[DType.float32]("output0")
    print("output:\n",output)

from max.graph.type import TensorType, Dim, Type
from random import seed
from max import engine
from max.tensor import Tensor
from max.graph import Graph, ops

fn main() raises:
    seed(43)
    var session = engine.InferenceSession()
    var t = Tensor[DType.float32].randn((1, 2, 2))
    print("t:\n", t)
    dim1 = Dim(2)
    dim2 = Dim(2)
    x = List[Dim]()
    x.append(dim1)
    x.append(dim2)

    var graph0 = Graph(in_types=List[Type](TensorType(DType.float32, 1, 2, 2)))
    var r = ops.reshape(graph0[0], x)
    graph0.output(r)
    graph0.verify()
    var rs = session.load(graph0)
    results = rs.execute("input0", t)
    var output = results.get[DType.float32]("output0")
    print("output:\n",output)

Output: t: Tensor([[[1.2788280248641968, -1.2873831987380981], [-0.56482106447219849, 1.5331770181655884]]], dtype=float32, shape=1x2x2) output: Tensor([[0.0, 0.0], [-0.56482106447219849, 1.5331770181655884]], dtype=float32, shape=2x2) But if I add the following code at the end of my code:

print("t.reshape((2,2)):\n", t.reshape((2,2)))

print("t.reshape((2,2)):\n", t.reshape((2,2)))

The output becomes correct: t: Tensor([[[1.2788280248641968, -1.2873831987380981], [-0.56482106447219849, 1.5331770181655884]]], dtype=float32, shape=1x2x2) output: Tensor([[1.2788280248641968, -1.2873831987380981], [-0.56482106447219849, 1.5331770181655884]], dtype=float32, shape=2x2) t.reshape((2,2)): Tensor([[1.2788280248641968, -1.2873831987380981], [-0.56482106447219849, 1.5331770181655884]], dtype=float32, shape=2x2)

4 replies

MModular

•Created by taalhaataahir01022001 on 8/29/2024 in #questions

Dynamic Shapes to MAX GRAPH API

Now getting same error issue with Layer norm i.e.,

var graph2 = Graph(in_types=List[Type](TensorType(DType.float32, "a", "b", "c"),TensorType(DType.float32, "c"), TensorType(DType.float32, "c")))

var graph2 = Graph(in_types=List[Type](TensorType(DType.float32, "a", "b", "c"),TensorType(DType.float32, "c"), TensorType(DType.float32, "c")))

gives error Unhandled exception caught during execution: Unsupported dim type: symbolic expression Previously it was working fine!!

3 replies

MModular

•Created by JulianJS on 7/22/2024 in #questions

Matrix Multiplication (matmul): `numpy` hard to beat? even by mojo?

https://www.linkedin.com/posts/pavanmv_benchmarking-mojo-the-supercharged-superset-activity-7220047088577888256-siYI/ You can use MAX Engine for speedy matmul. There's a whole discussing here as well: https://github.com/modularml/mojo/issues/2660

4 replies

MModular

•Created by taalhaataahir01022001 on 6/4/2024 in #questions

Time taken in Inference session

I'm new and sorry for asking such basic questions. 1. I am compiling my code using mojo i.e.,

mojo build matmul.mojo

mojo build matmul.mojo

and then running the executable. Does this mean that the compilation of the graph using the MAX Engine compiler occurs at runtime and that optimizations on the graph are also performed during runtime? 2. During execution, does the Mojo compiler invoke the MAX Engine compiler to compile and optimize the graph? 3. I have written my entire model in Mojo. Now, I am planning to break down my model into custom operations in MAX Graph. Will this provide performance benefits since the MAX Engine compiler might further optimize my custom operations written in Mojo?

4 replies

MModular

•Created by taalhaataahir01022001 on 5/21/2024 in #questions

x86 Intrinsics

Thanks working now

5 replies

MModular

•Created by taalhaataahir01022001 on 3/12/2024 in #questions

GRAPH API MOTensor not working

got it! never imported it 😛

from max.graph import MOTensor

from max.graph import MOTensor

2 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

Plus it is not only the case when I update the Dynamic Vector of objects but it also creates new objects using the copy constructor even if I try to access any specific index. e.g.,

print(arg[0].sz)

print(arg[0].sz)

if arg[1].sz == 0

if arg[1].sz == 0

also invokes the copy constructors. And it has nothing to do with weather the vector is passed to a function via reference or not. Even If I access the vector of objects in the same scope where it's created, copy constructor is called. I tried to compare it with the C++ vectors to see if Dynamic Vectors are equivalent to vectors in C++:

class MyClass {
...
...
}
void printData(std::vector<MyClass>& myObjects) {
    for (int i =0; i<3; i++) {
        printf("%d\n",myObjects[i].getData());
    }
}
int main() {
    // Create a vector of objects of MyClass
    std::vector<MyClass> myObjects;
    myObjects.push_back(MyClass(1));
    myObjects.push_back(MyClass(2));
    myObjects.push_back(MyClass(3));
    printf("%d\n", myObjects[0].getData())
    printData(myObjects);
}

class MyClass {
...
...
}
void printData(std::vector<MyClass>& myObjects) {
    for (int i =0; i<3; i++) {
        printf("%d\n",myObjects[i].getData());
    }
}
int main() {
    // Create a vector of objects of MyClass
    std::vector<MyClass> myObjects;
    myObjects.push_back(MyClass(1));
    myObjects.push_back(MyClass(2));
    myObjects.push_back(MyClass(3));
    printf("%d\n", myObjects[0].getData())
    printData(myObjects);
}

Both printf("%d\n", myObjects[0].getData()) and printData function doesnot invoke any constructor which makes sense.

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

And you can download it using apt install perf. Maybe you might need 1-2 more dependencies which can easily be found on web

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

There's a tool named perf It's pretty easy to use. just do

 
perf record ./executable

 
perf record ./executable

And it records the time taken inside each function. To view the output as shown above in the ss:

perf report

perf report

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

sure thanks

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

Although the DynamicVector "c" is updated outside the "newfunc2" but why is it the case that new "HuffmanEntry" object is being created while updating the "sz" argument of the object which is already stored in vector c? And How to avoid this i.e., update the object store within the dynamic vector instead of creating a new object?

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects

@sora I was using Dynamic Vectors previously but I was getting this issue. Here I've created an example code:

struct HuffmanEntry(CollectionElement):
    var sz: Int
    var codeword: Int
    var decoded: Int

    fn __init__(inout self, sz: Int, codeword: Int, decoded: Int):
        self.sz = sz
        self.codeword = codeword
        self.decoded = decoded

    fn __copyinit__(inout self, existing: Self):
        print("CopyInit")
        self.sz = existing.sz
        self.codeword = existing.codeword
        self.decoded = existing.decoded

    fn __moveinit__(inout self, owned existing: Self):
        self.sz = existing.sz
        self.codeword = existing.codeword
        self.decoded = existing.decoded


fn newfunc(inout arg: HuffmanEntry) -> Int:
    arg.sz = 3
    return 0

fn newfunc2(inout arg: DynamicVector[HuffmanEntry]) -> Int:
    for i in range(3):
        arg[i].sz = 4
    return 0

fn main() raises:
    var a = HuffmanEntry(0,0,0)
    let b: Int = newfunc(a)

    var c = DynamicVector[HuffmanEntry] ()

    c.append(a)
    c.append(a)
    c.append(a)

    let d:Int = newfunc2(c)

struct HuffmanEntry(CollectionElement):
    var sz: Int
    var codeword: Int
    var decoded: Int

    fn __init__(inout self, sz: Int, codeword: Int, decoded: Int):
        self.sz = sz
        self.codeword = codeword
        self.decoded = decoded

    fn __copyinit__(inout self, existing: Self):
        print("CopyInit")
        self.sz = existing.sz
        self.codeword = existing.codeword
        self.decoded = existing.decoded

    fn __moveinit__(inout self, owned existing: Self):
        self.sz = existing.sz
        self.codeword = existing.codeword
        self.decoded = existing.decoded


fn newfunc(inout arg: HuffmanEntry) -> Int:
    arg.sz = 3
    return 0

fn newfunc2(inout arg: DynamicVector[HuffmanEntry]) -> Int:
    for i in range(3):
        arg[i].sz = 4
    return 0

fn main() raises:
    var a = HuffmanEntry(0,0,0)
    let b: Int = newfunc(a)

    var c = DynamicVector[HuffmanEntry] ()

    c.append(a)
    c.append(a)
    c.append(a)

    let d:Int = newfunc2(c)

In the above code, the copyinit function is called 6 times. 3 times when I use the append function (which is completely fine). But when I send the DynamicVector "c" ti the nxt function i.e., newfunc2 and update the member variable "sz" , the copy constructor is called again. Why is it the case. According to my understanding when you use "in out" with the argument, the original object is sent to the function as a reference and not the copy of that object.

17 replies

MModular

•Created by taalhaataahir01022001 on 1/14/2024 in #questions

Create 1D array of objects