Pytorch Sweeps tracing - ttnn.mul - Low PCC and other fails

[KalaivaniMCW]

Following parameters failed during Pytorch Sweeps tracing tests: for ttnn.mul
File: tests/sweep_framework/sweeps/eltwise/binary/multiply/mul_tensor_pytorch2.py
Pytorch sweeps: tracker

Parameter		Error
current parameter {'input_shape': {'self': [4, 12, 64, 64], 'other': [12, 1, 1]},		low pcc fail- unequal ranks ?
current parameter {'input_shape': {'self': [4, 16, 64, 64], 'other': [16, 1, 1]},		low pcc fail- unequal ranks ?
current parameter {'input_shape': {'self': [64, 3, 64, 64], 'other': [3, 1, 1]},		low pcc fail- unequal ranks ?
current parameter {'input_shape': {'self': [64, 4, 64, 64], 'other': [4, 1, 1]},		low pcc fail- unequal ranks ?
current parameter {'input_shape': {'self': [16, 1], 'other': [1, 1, 32]}		low pcc fail- unequal ranks and broadcast issue
current parameter {'input_shape': {'self': [16, 6, 64, 64], 'other': [6, 1, 1]},		low pcc fail- unequal ranks ?
current parameter {'input_shape': {'self': [16, 8, 64, 64], 'other': [8, 1, 1]},		low pcc fail- unequal ranks ?
current parameter {'input_shape': {'self': [0], 'other': 0.5},		round_up: multiple must not be 0
current parameter {'input_shape': {'self': [0], 'other': []},		round_up: multiple must not be 0
current parameter {'input_shape': {'self': [0, 1], 'other': [0, 1]},		round_up: multiple must not be 0
current parameter {'input_shape': {'self': [1], 'other': [1]},		message list(expected_pytorch_result.shape)=[1] vs list(actual_pytorch_result.shape)=[1, 1]
current parameter {'input_shape': {'self': [], 'other': [0, 1]},		round_up: multiple must not be 0
current parameter {'input_shape': {'self': [], 'other': [1, 1, 768]}		Layout issue for [ ] shape
current parameter {'input_shape': {'self': [], 'other': [1, 24, 768]},		same as above
current parameter {'input_shape': {'self': [], 'other': [3234, 1]},		same as above
current parameter {'input_shape': {'self': [], 'other': [8732, 1]},		same as above

[KalaivaniMCW]

Out of these 7 shapes for low pcc fails:
[[4, 12, 64, 64], [12, 1, 1]], [[4, 16, 64, 64], [16, 1, 1]], [[64, 3, 64, 64], [3, 1, 1]], [[64, 4, 64, 64], [4, 1, 1]], [[16, 1], [1, 1, 32]], [[16, 6, 64, 64], [6, 1, 1]], [[16, 8, 64, 64], [8, 1, 1]],

Six of them pass when the lesser-ranked is unsqueezed to match rank. i.e. following shapes,

        [[4, 12, 64, 64], [1, 12, 1, 1]], 
        [[4, 16, 64, 64], [1, 16, 1, 1]], 
        [[64, 3, 64, 64], [1, 3, 1, 1]], 
        [[64, 4, 64, 64], [1, 4, 1, 1]], 
        [[16, 6, 64, 64], [1, 6, 1, 1]],
        [[16, 8, 64, 64], [1, 8, 1, 1]],

One shape gives a different error on unit test [[16, 1], [1, 1, 32]],
and it fails, even when unsqueezed i.e [[16, 1], [1, 1, 32]], to [[1, 16, 1], [1, 1, 32]],

On sweeps

On unit tests

E   RuntimeError: TT_ASSERT @ ../ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:150: height_a > height_b and height_b == 1
E       info:
E       ttnn::operations::binary::BinaryDeviceOperation: height mismatch

[KalaivaniMCW]

PR #14803 has fixes for low PCC of 6 shapes mentioned above

[KalaivaniMCW]

Merged #14803
The 6 out 7 pcc fail cases mentioned above are now passing. in this case {'input_shape': {'self': [16, 1], 'other': [1, 1, 32]} , the unequal rank has been fixed but it fails due to broadcast issue which is mentioned in #14710

[KalaivaniMCW]

Torch: {'input_shape': {'self': [], 'other': [0, 1]},

Torch: {'input_shape': {'self': [], 'other': [1, 1, 768]}