add support for grouped gemm

[ajassani]

No description provided.

[Copilot]

Pull Request Overview

This PR adds support for grouped GEMM (General Matrix Multiplication) operations to the TraceLens performance modeling framework. Grouped GEMM applies group-specific weight matrices to partitions of input tensors, which is useful for certain neural network architectures.

• Implements a new GroupedGemm performance model class with forward/backward FLOP and byte calculations
• Adds a custom implementation custom_grouped_gemm in the Megatron extension for parsing grouped GEMM events
• Updates the extension mappings to register the new grouped GEMM operation

Reviewed Changes

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

File	Description
TraceLens/PerfModel/perf_model.py	Implements the core GroupedGemm performance model class with comprehensive documentation and computation methods
examples/example_megatron_extension.py	Adds custom_grouped_gemm implementation and registers it in the performance model mappings

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[Copilot]

The error message should be more informative about what data types are supported. Consider mentioning the supported data types or referring to the name2bpe function documentation.

Suggested change

	raise ValueError(f"Expected bpe_in to be 1 or 2, got {bpe_in}")
	raise ValueError(
	f"Unsupported bpe_in value: {bpe_in}. Supported bpe_in values are 1 (float16) and 2 (float32). "
	"Please ensure that the input types are supported. See the name2bpe function documentation for details."
	)

[Copilot]

There's a spelling error: 'ofcourse' should be 'of course'.

Suggested change

	Computation is functionally equivalent to (implementation detail will ofcourse be efficient):
	Computation is functionally equivalent to (implementation detail will of course be efficient):