habana_frameworks.mediapipe.fn.Mult

Class:

• habana_frameworks.mediapipe.fn.Mult(**kwargs)

Define graph call:

• __call__(input1, input2)

Parameter:

• input1 - First input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT16, INT32, FLOAT16, BFLOAT16, FLOAT32.

• input2 - Second input tensor to operator. Supported dimensions: minimum = 1, maximum = 5. Supported data types: INT16, INT32, FLOAT16, BFLOAT16, FLOAT32.

Description:

Resultant tensor formed from the multiplication of the two operands element-wise. This operator performs element-wise multiplication and supports Broadcasting. Computes output as: output = (input1 * input2), element-wise.

Supported backend:

• HPU, CPU

Keyword Arguments

kwargs	Description
dtype	Output data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: FLOAT32 Optional: yes Supported data types: INT32 BFLOAT16 FLOAT32

Note

1. All input/output tensors must be of the same data type and must have the same dimensionality except in broadcast support where dimensionality can be different.

2. This operator is agnostic to the data layout.

Example: Mult Operator

The following code snippet shows usage of Mult operator:

from habana_frameworks.mediapipe import fn
from habana_frameworks.mediapipe.mediapipe import MediaPipe
from habana_frameworks.mediapipe.media_types import dtype as dt
import os
import numpy as np

# Create MediaPipe derived class

  class myMediaPipe(MediaPipe):
    def __init__(self, device, queue_depth, batch_size, num_threads, op_device, dir):
        super(myMediaPipe, self).__init__(
            device,
            queue_depth,
            batch_size,
            num_threads,
            self.__class__.__name__)

        self.inp1 = fn.ReadNumpyDatasetFromDir(num_outputs=1,
                                              shuffle=False,
                                              dir=dir,
                                              pattern="inp_x_*.npy",
                                              dense=True,
                                              dtype=dt.FLOAT32,
                                              device="cpu")

        self.inp2 = fn.ReadNumpyDatasetFromDir(num_outputs=1,
                                              shuffle=False,
                                              dir=dir,
                                              pattern="inp_y_*.npy",
                                              dense=True,
                                              dtype=dt.FLOAT32,
                                              device="cpu")

        self.mul = fn.Mult(dtype=dt.FLOAT32,
                          device=op_device)

    def definegraph(self):
        inp1 = self.inp1()
        inp2 = self.inp2()
        out = self.mul(inp1, inp2)
        return out, inp1, inp2


def run(device, op_device):
    batch_size = 2
    queue_depth = 2
    num_threads = 1
    base_dir = os.environ['DATASET_DIR']
    dir = base_dir+"/npy_data/fp32/"

    # Create MediaPipe object
    pipe = myMediaPipe(device, queue_depth, batch_size,
                      num_threads, op_device, dir)
    # Build MediaPipe
    pipe.build()

    # Initialize MediaPipe iterator
    pipe.iter_init()

    # Run MediaPipe
    out, inp1, inp2 = pipe.run()

    def as_cpu(tensor):
        if (callable(getattr(tensor, "as_cpu", None))):
            tensor = tensor.as_cpu()
        return tensor

    # Copy data to host from device as numpy array
    out = as_cpu(out).as_nparray()
    inp1 = as_cpu(inp1).as_nparray()
    inp2 = as_cpu(inp2).as_nparray()

    print("\ninp1 tensor shape:", inp1.shape)
    print("inp1 tensor dtype:", inp1.dtype)
    print("inp1 tensor data:\n", inp1)

    print("\ninp2 tensor shape:", inp2.shape)
    print("inp2 tensor dtype:", inp2.dtype)
    print("inp2 tensor data:\n", inp2)

    print("\nout tensor shape:", out.shape)
    print("out tensor dtype:", out.dtype)
    print("out tensor data:\n", out)

    del pipe
    return inp1, inp2, out


def compare_ref(inp1, inp2, out):
    ref = inp1 * inp2
    if np.array_equal(ref, out) == False:
        raise ValueError(f"Mismatch w.r.t ref for device")


if __name__ == "__main__":
    dev_opdev = {'cpu': ['cpu'],
                'mixed': ['cpu', 'hpu'],
                'legacy': ['hpu']}
    for dev in dev_opdev.keys():
        for op_dev in dev_opdev[dev]:
            inp1, inp2, out = run(dev, op_dev)
            compare_ref(inp1, inp2, out)

The following is the output of Mult operator:

inp1 tensor shape: (2, 3, 2, 3)
inp1 tensor dtype: float32
inp1 tensor data:
[[[[182. 227. 113.]
  [175. 128. 253.]]

  [[ 58. 140. 136.]
  [ 86.  80. 111.]]

  [[175. 196. 178.]
  [ 20. 163. 108.]]]


[[[186. 254.  96.]
  [180.  64. 132.]]

  [[149.  50. 117.]
  [213.   6. 111.]]

  [[ 77.  11. 160.]
  [129. 102. 154.]]]]

inp2 tensor shape: (2, 3, 2, 3)
inp2 tensor dtype: float32
inp2 tensor data:
[[[[ 56. 168.  82.]
  [157.  42. 155.]]

  [[ 62. 235. 238.]
  [ 94. 125. 192.]]

  [[125. 162.   1.]
  [206.  77. 123.]]]


[[[138. 196. 246.]
  [137. 203.   7.]]

  [[217. 194.  11.]
  [167. 218. 226.]]

  [[ 68. 160. 254.]
  [243.  93.  70.]]]]

out tensor shape: (2, 3, 2, 3)
out tensor dtype: float32
out tensor data:
[[[[10192. 38136.  9266.]
  [27475.  5376. 39215.]]

  [[ 3596. 32900. 32368.]
  [ 8084. 10000. 21312.]]

  [[21875. 31752.   178.]
  [ 4120. 12551. 13284.]]]


[[[25668. 49784. 23616.]
  [24660. 12992.   924.]]

  [[32333.  9700.  1287.]
  [35571.  1308. 25086.]]

  [[ 5236.  1760. 40640.]
  [31347.  9486. 10780.]]]]