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Vidyasagar Ananthan 92c67a824f [DOCS] Documentation Addition (Readme updates) (#2495)
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Update README.md to clarify CK Tile API description and remove outdated references to the Tile Engine.

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Remove references to the Tile Engine in README files across multiple examples

* GH-2368 Adding a basic glossary

GH-2368 Minor edits

GH-2368 Adding missing READMEs and standardization.

resolving readme updates

GH-2368 Minor improvements to documentation.

Improving some readmes.

Further improvement for readmes.

Cleaned up the documentation in 'client_example' (#2468)

Update for PR

Update ACRONYMS.md to remove trivial terms

Update ACRONYMS.md to provide detailed explanations for BF16 and BF8 formats

Apply suggestion from @spolifroni-amd

Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>

Apply suggestion from @spolifroni-amd

Co-authored-by: spolifroni-amd <Sandra.Polifroni@amd.com>

Update README.md to clarify CK Tile API description and remove outdated references to the Tile Engine.

revise 37_transpose readme

revise 36_copy readme

Remove references to the Tile Engine in README files for 19_gemm_multi_d and 35_batched_transpose, and update distribution links for clarity.

Remove references to the Tile Engine in multiple README files and update distribution links for consistency and clarity.

Remove references to the Tile Engine in README files across multiple examples

Refine README files by removing outdated references to the Tile Engine

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* Content Modification of CK Tile Example

* Modify the ck_tile gemm config

---------

Co-authored-by: AviralGoelAMD <aviral.goel@amd.com>
Co-authored-by: ThomasNing <thomas.ning@amd.com>
2025-10-16 03:10:57 -07:00

2.1 KiB

GEMM with Microscaling

This example demonstrates a GEMM operation with microscaling, an advanced quantization technique that applies fine-grained scaling to small blocks of data. Microscaling enables more precise quantization than traditional methods by using different scale factors for small groups of elements, leading to better accuracy preservation in quantized neural network inference.

Source Code Organization

Build and Run

example_gemm_mx_fp8

Custom verification parameters:

# arg1: verification (0=no, 1=CPU)
# arg2: initialization (0=constant values, 1=integer values, 2=decimal values)
# arg3: time kernel (0=no, 1=yes)
# arg4: verbosity (0=no info, 1=verbose info)
# arg5 to 10: M(256x), N(256x), K(512x), StrideA, StrideB, StrideC
# arg11: KBatch
# arg12: warmup runs pre-timing
# arg13: repeat run count for timing
./bin/example_gemm_mx_fp8 1 1 0 1

Custom tensor shapes:

./bin/example_gemm_mx_fp8 1 2 1 0 256  256  512 -1 -1 -1 1 10 10

Run the Example

Custom verification parameters:

# arg1: verification (0=no, 1=CPU)
# arg2: initialization (0=constant values, 1=integer values, 2=decimal values)
# arg3: time kernel (0=no, 1=yes)
# arg4: verbosity (0=no info, 1=verbose info)
# arg5 to 10: M(128x), N(128x), K(64x), StrideA, StrideB, StrideC
# arg11: KBatch
./bin/example_gemm_mx_fp8 1 1 0 1

Custom tensor shapes:

./bin/example_gemm_mx_fp8 1 2 1 0 128  128  256 -1 -1 -1 1

Default invocation:

# Implies: ./bin/example_gemm_mx_fp8 1 2 0 0
./bin/example_gemm_mx_fp8