M
Modular•2mo ago
taalhaataahir01022001

Reshape MAX API OP giving error

My code: 
fn main() raises:
    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)
fn main() raises:
    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)
Error: candidate not viable: argument #1 cannot be converted from 'List[Dim, 0]' to 'List[Dim, 0]'
2 Replies
capt_falamer
capt_falamer•2mo ago
I'm not getting any errors when running this (same as above but with the import statements): 
from max.graph import Graph, ops
from max.graph.type import TensorType, Dim, Type

fn main() raises:
    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)
from max.graph import Graph, ops
from max.graph.type import TensorType, Dim, Type

fn main() raises:
    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)
what version of mojo/max are you running?
taalhaataahir01022001
taalhaataahir01022001OP•2mo ago
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) 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.
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