Commit Graph
9 Commits
Author SHA1 Message Date
shalinib-ibmandGitHub 3bb2fcc856 llamafile: powerpc: add FP16 MMA path for Q4/Q8 matmul (#19709)
Avoid xvi8ger4pp signed→unsigned bias correction by dequantizing Q4/Q8
inputs to FP16 and using FP16×FP16→FP32 MMA. This removes
post-processing overhead and improves performance.

Performance Impact:
1.5 ~ 2x improvement in PP_Speed for Q4 and Q8 Models,
measured with llama-bench and llama-batched-bench.
Q8 Model: granite-4.0-h-micro-Q8_0.gguf (from huggingface)
Q4 Model: Meta-Llama3-8b Q4 model (generated with llama-quantize from
f32 model)

llama-bench Q8 Model Results:
 model                          	       size 	     params 	 backend    	 threads 	            test 	Base t/s	Patch t/s
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	             pp8 	         64.48 ± 4.72 	         73.99 ± 0.27
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	            pp16 	         80.11 ± 0.32 	        112.53 ± 0.40
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	            pp32 	         89.10 ± 0.27 	        152.95 ± 0.68
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	            pp64 	         93.65 ± 0.25 	        187.83 ± 0.83
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	           pp128 	         99.93 ± 0.02 	        201.32 ± 0.11
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	           pp256 	        102.32 ± 0.40 	        208.32 ± 0.41
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	           pp512 	        103.42 ± 0.40 	        209.98 ± 0.14
 granitehybrid 3B Q8_0          	   3.16 GiB 	     3.19 B 	 CPU        	      10 	           tg128 	         20.35 ± 0.01 	         19.57 ± 0.01

llama-bench Q4 Model Results:
 model                          	       size 	     params 	 backend    	 threads 	            test 	              Base    t/s 	               Patch   t/s
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	             pp8 	         34.77 ± 0.10 	         41.23 ± 0.08
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	            pp16 	         40.81 ± 0.04 	         64.55 ± 0.15
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	            pp32 	         44.65 ± 0.05 	         90.84 ± 0.22
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	            pp64 	         47.49 ± 0.03 	        114.39 ± 0.11
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	           pp128 	         49.29 ± 0.24 	        120.13 ± 0.19
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	           pp256 	         49.77 ± 0.23 	        121.51 ± 0.11
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	           pp512 	         49.89 ± 0.23 	        117.52 ± 0.10
 llama 8B Q4_0                  	   4.33 GiB 	     8.03 B 	 CPU        	      10 	           tg128 	         13.40 ± 0.01 	         13.37 ± 0.00

Llama perplexity Results:

Model	                    Base Final PPL Estimate	Patch Final PPL Estimate
granite-4.0-h-micro-Q8_0    1.3862 +/- 0.04424	        1.3868 +/- 0.04432
Meta-Llama3-8b Q4	    1.3801 +/- 0.04116	        1.3803 +/- 0.04116

Signed-off-by: Shalini.Salomi.Bodapati <Shalini.Salomi.Bodapati@ibm.com>
2026-02-19 14:28:53 +08:00
shalinib-ibmandGitHub 7afdfc9b84 ggml-cpu: Enable FP16 MMA kernels on PPC (#19060) 2026-01-27 11:52:34 +08:00
shalinib-ibmandGitHub 8cc0ba957b ggml-cpu: optimize ggml_vec_dot_bf16 for Power9 (#18837) 2026-01-15 17:31:18 +08:00
shalinib-ibmandGitHub 3a0d10533a Q4/Q8 Tiled Gemm Optimization. (#16999) 2025-12-05 19:41:51 +08:00
shalinib-ibmandGitHub a6a58d6478 llamafile: PowerPC Sgemm Optimization (#15558)
This patch improves GEMM for FP32 Data Type on PowerPC

Implements GEMM on large blocks with configurable block size mc, nc, kc
(default: 256, 256, 256).
Packing Function optimized to access blocks as per memory layout.
GEMM Optimized to work on larger blocks.
Isolated Packing from GEMM Operations for better MMA utilization.

Verified functionality and correctness uing llama-cli and stand alone
test case (performs matmul and compares final mattrix C result with base).

Minor code refactoring changes:
Replace macro with inline function
Code Indent made consistent with 4 spaces

Performance Testing:

Observed 50% ~ 70% improvement in Prompt Processing Speed mesured using
llama-bench with Meta-Llama3-8B FP32 Model.  Similar gains observed with
Mistral-7b-Instruct-v0.3 Model.

model                   Size                Params     Backend       Threads   Test    Patch   Base
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp512   98.58   60.3
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp1024  95.88   57.36
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp2048  85.46   53.26
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp4096  68.66   45.78
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp6144  57.35   40.44

25 ~ 30% improvement in llama-batched-bench with Metla-Llama3-8B in
Prompt Processing Speed for large prompts (256, 512, 1024, 2048, 4096)tokens with various batch
sizes ( 1, 2, 4, 8, 16)

Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
2025-08-26 23:35:25 +08:00
shalinib-ibmandGitHub 55c509daf5 ggml : refactor llamafile_sgemm PPC code (#14673)
Remove un-necessary templates from class definition and packing functions
Reduce deeply nested conditionals, if-else switching in mnapck function
Replace repetitive code with inline functions in Packing functions

2 ~ 7% improvement in Q8 Model
15 ~ 50% improvement in Q4 Model

Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
2025-07-14 16:16:42 +03:00
093e3f1feb cmake : Handle mixed-case 'Power' strings in POWER CPU detection (#13966)
Some systems report the CPU implementation as "Power11" instead of "POWER11".
The existing CMake logic uses a case-sensitive regular expression to extract
the CPU generation, which fails when the casing doesn't exactly match "POWER".

This patch provides a fix by first converting the string to uppercase before applying the regex.

Signed-off-by: root <root@rheldb2v.pperf.tadn.ibm.com>
Co-authored-by: root <root@rheldb2v.pperf.tadn.ibm.com>
2025-06-02 15:18:36 +03:00
shalinib-ibmandGitHub 3f3769ba76 ggml : Enable MMA for BF16 in llamafile_sgemm (#13148)
This patch upstreams llamafile's cpu matrix multiplication kernels for ppc64le using MMA builtins for BF16 data type.

This change results in 9x - 40x gains
in total speed S t/s (ie all tokens/total time), across various batch sizes tested using llama-batched-bench benchmark.

The patch is tested with Meta-Lllama-3-8B,
and Mistral-7B models (BF16 models generated by using llama-quantize from corresponding FP32 models) on an IBM POWER10 machine.

Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
2025-05-02 19:53:12 +03:00
shalinib-ibmandGitHub 416313773b ggml : fix ppc64le build (#13176)
Build fails with compilation error on power pc.
This patch fixes the same.

Tested with unit tests run via
 --build <build_dir> && cd <build_dir> && make test

Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
2025-04-30 13:17:08 +02:00