[GPU] Refactor onednn::layout_to_memory_desc function

[jade-cho]

Description of the issue

• Do refactoring onednn::layout_to_memory_desc() function to improve readability and maintainability.

The code and line that caused this issue

• openvino/src/plugins/intel_gpu/src/graph/impls/onednn/utils.hpp

  Line 48 in 6b0c6a3

  dnnl::memory::desc layout_to_memory_desc(cldnn::layout l,

Checklist

• Is it a proper fix? (not a workaround)
• Did you include test case for this fix, if necessary?
• Did you review existing test that can be extended to cover this scenario? Which test did you review?

Tickets:

• 175118

[Copilot]

Pull request overview

This PR refactors the onednn::layout_to_memory_desc function by decomposing it into specialized variants based on specific usage patterns. The original function used bit flags (mem_flags) to control different behaviors, which has been replaced with dedicated functions for each use case.

Key Changes

• Replaced flag-based layout_to_memory_desc function with specialized variants: layout_to_memory_desc_flatten, layout_to_memory_desc_blocked, layout_to_memory_desc_grouped, and layout_to_memory_desc_strides
• Refactored dimension and stride calculation logic into separate helper functions with switch statements instead of if-else chains
• Updated all call sites across multiple files to use the appropriate specialized function instead of passing flags

Reviewed changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
utils.hpp	Added declarations for new specialized layout_to_memory_desc variants and updated signature of flag-based function
utils.cpp	Implemented new specialized functions with refactored helper functions for dimension and stride calculations; commented out original implementation
program_node.cpp	Updated binary post-op creation to use ternary operator with specialized functions instead of flag-based calls
reorder_onednn.cpp	Replaced flag-based calls with layout_to_memory_desc_blocked
reduce_onednn.cpp	Replaced flag-based calls with layout_to_memory_desc_blocked
primitive_onednn_base.h	Updated fused operation handling to use ternary operator with layout_to_memory_desc_flatten
fully_connected_onednn.cpp	Replaced flag-based calls with layout_to_memory_desc_flatten and conditional logic for weights handling
deconvolution_onednn.cpp	Replaced flag-based calls with layout_to_memory_desc_flatten for bias
convolution_onednn.cpp	Replaced flag-based calls with layout_to_memory_desc_flatten and added explicit target_fmt parameter

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[isanghao]

random spot)
Some thoughts about this PR:

• Separation of layout_to_memory_desc looks good. Especially against use_strides case.
• Does it cleanup the lambda function that was introduced in https://github.com/openvinotoolkit/openvino/pull/32391/files?
• layout_to_memory_desc is separated into multiple functions, but it is implemented based on calculate_memory_dims. Does it bring value?
• I think some call path is too deep. Is it inevitable? For example:
  layout_to_memory_desc_grouped -> calculate_memory_dims -> calculate_default_dims -> calculate_3d_tensor_dims

[jade-cho]

Updated