Dasor Comments - Answer Overflow

Dasor

•Created by Dasor on 12/6/2024 in #questions

Confused with conv2d op

here is the code:

from random import seed

from max.engine import InferenceSession
from max.graph import Graph, TensorType, ops
from max.tensor import Tensor, TensorShape
from time import perf_counter

alias SIZE = 4096
alias K = 3
alias IN_CH = 3
alias OUT_CH = 3 
alias batch = 1
alias GROUPS = 1

def main():
    graph = Graph(TensorType(DType.float32, SIZE, SIZE,IN_CH//GROUPS,OUT_CH))
    # create a constant tensor value to later create a graph constant symbol
    # NHWC format (batch, height, width, channels)
    filter_shape = TensorShape(batch, K, K, IN_CH)
    constant_value = Tensor[DType.float32](filter_shape).randn(filter_shape) # filter
    # create a constant symbol
    constant_symbol = graph.constant(constant_value)
    # create a convolution node
    mm = ops.conv2d(graph[0],constant_symbol,groups=GROUPS)
    graph.output(mm)
    # verify
    graph.verify()

    # create session, load and compile the graph
    session = InferenceSession()
    model = session.load(graph)

    # RSCF layout (height, width, in_channels/groups, out_channels)
    input_shape = TensorShape(SIZE, SIZE, IN_CH//GROUPS, OUT_CH)   
    input0 = Tensor[DType.float32](input_shape).randn(input_shape)

    # measure time
    start = perf_counter()

    ret = model.execute("input0", input0^)

    print("Inference time:", perf_counter() - start, "seconds")

    print("matmul result:", ret.get[DType.float32]("output0"))

from random import seed

from max.engine import InferenceSession
from max.graph import Graph, TensorType, ops
from max.tensor import Tensor, TensorShape
from time import perf_counter

alias SIZE = 4096
alias K = 3
alias IN_CH = 3
alias OUT_CH = 3 
alias batch = 1
alias GROUPS = 1

def main():
    graph = Graph(TensorType(DType.float32, SIZE, SIZE,IN_CH//GROUPS,OUT_CH))
    # create a constant tensor value to later create a graph constant symbol
    # NHWC format (batch, height, width, channels)
    filter_shape = TensorShape(batch, K, K, IN_CH)
    constant_value = Tensor[DType.float32](filter_shape).randn(filter_shape) # filter
    # create a constant symbol
    constant_symbol = graph.constant(constant_value)
    # create a convolution node
    mm = ops.conv2d(graph[0],constant_symbol,groups=GROUPS)
    graph.output(mm)
    # verify
    graph.verify()

    # create session, load and compile the graph
    session = InferenceSession()
    model = session.load(graph)

    # RSCF layout (height, width, in_channels/groups, out_channels)
    input_shape = TensorShape(SIZE, SIZE, IN_CH//GROUPS, OUT_CH)   
    input0 = Tensor[DType.float32](input_shape).randn(input_shape)

    # measure time
    start = perf_counter()

    ret = model.execute("input0", input0^)

    print("Inference time:", perf_counter() - start, "seconds")

    print("matmul result:", ret.get[DType.float32]("output0"))

3 replies

MModular

•Created by Dasor on 12/1/2024 in #questions

MNIST slower on MAX than on Pytorch

Nevermind I was just missing using the TensorShape as it says on the tensor module example

12 replies

MModular

•Created by Dasor on 12/1/2024 in #questions

MNIST slower on MAX than on Pytorch

I'm checking the driver mojo API but I can't even run the code sample provided, do I need the nightly build?

12 replies

MModular

•Created by Dasor on 12/1/2024 in #questions

MNIST slower on MAX than on Pytorch

Thanks for the replies I will take a look at those docs. I'm measuring time like this just adding a start variable before the loop and checking after I don't know if that's the right approach, here is a code sample:

 
    
    start = perf_counter()

    for i in range(len(test_dataset)):
        item = test_dataset[i]
        image = item[0]
        label = item[1]

        preprocessed_image = preprocess(image)

        output = model.execute("input0", preprocessed_image)
        probs = output.get[DType.float32]("output0")

        predicted = probs.argmax(axis=1)

        label_ = Tensor[DType.index](TensorShape(1), int(label))
        correct += int(predicted == label_)
        total += 1

    print("Inference time:", perf_counter() - start, "seconds")

 
    
    start = perf_counter()

    for i in range(len(test_dataset)):
        item = test_dataset[i]
        image = item[0]
        label = item[1]

        preprocessed_image = preprocess(image)

        output = model.execute("input0", preprocessed_image)
        probs = output.get[DType.float32]("output0")

        predicted = probs.argmax(axis=1)

        label_ = Tensor[DType.index](TensorShape(1), int(label))
        correct += int(predicted == label_)
        total += 1

    print("Inference time:", perf_counter() - start, "seconds")

12 replies

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