metal: new q4_0 mat-vec mul kernel #2188
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Please feel free to share results on M1/M2 Pro/Max/Ultra! |
Well, I had not checked out the PR branch. With the PR branch I get on M2 Max with 30-core GPU, 64 GB RAM:
The above is for an empty prompt. If I use |
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Looks promising! From what I've read before there is still a lot of room for Metal code improvements so this could be very welcome. I will try to test it out on my M1 Max machine as well. |
Nice! I update the measurements with new prompt. Looks like this PR brings the M1 series to the same speed as M2 series. I guess M2 series have better memory access prediction so their speed are already good without this PR. Nonetheless for 65B model I guess we can see a larger performance improvement for M2 series. |
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Would be nice to see results on M1 Ultra or M2 Ultra chips. Their two-die design may also suffer from cache miss. |
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ggerganov
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performance
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Here's my benchmark: MBP 14 Apple M2 Max 32GB 12C CPU, 30C GPU
Logs |
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M1 Pro 32GB
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Another data point: M2 Max with 30-core GPU, 65B model,
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ggerganov
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Jul 12, 2023
ggml-metal.m
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| @@ -660,7 +662,11 @@ void ggml_metal_graph_compute( | |||
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| nth0 = 8; | |||
| nth1 = 8; | |||
| [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32]; | |||
| if (ne01 % 8 == 0) { | |||
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Is it somehow possible to remove this restriction without affecting the performance?
If so, we can simply delete the old pipeline_mul_mat_q4_0_f32 kernel
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This is for models like WizardLM. In these models the last mat-vec multiplication in inference will have row number = 32001, while our new kernel consumes 8 rows every time. Let me test later if adding capabilities for dealing with <8 rows in the kernel will make it run slower or faster.
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Removed the old kernel. Might have minor performance gain or loss depending on model size and n_vocab. Tested on llama-7B and wizardlm-30B, generation results are same.
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And here a full list of all Meta LLaMA models on 30-core M2 Max with
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MacBook Pro, M2 Pro, 32GB, Ventura 13.4.1
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minor issue |
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With M2 Mac Studio Ultra, 128GB RAM
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dr3murr
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Jul 12, 2023
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Prefetch data to improve GPU utilization. ~48% faster for 33B model.
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@ggerganov how to properly run benchmarks? I would love to contribute! |
ggerganov
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Jul 13, 2023
| kernel void kernel_mul_mat_q4_0_f32( | ||
| device const void * src0, | ||
| device const float * src1, | ||
| device float * dst, | ||
| constant int64_t & ne00, | ||
| constant int64_t & ne10, | ||
| constant int64_t & ne0, | ||
| threadgroup float * sum [[threadgroup(0)]], | ||
| constant int64_t & ne01[[buffer(4)]], |
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What is the function of [[buffer(4)]] here?
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This gets the value from the buffer at index 4, corresponding the following line in ggml-metal.m:
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
@gauravpathak-infa I also have an M2 ultra, i am getting less than half of your numbers on these models; could you share your command line please? |
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@amj Here are the commands: If you are getting half the numbers, the problem may be somewhere else. It may be useful to check if you have compiled llama.cpp with Metal. If not: |
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yep that did it; LLAMA_METAL had been dropped during some clean/build step. I'm getting the same numbers you were showing. Thanks! |
This was referenced Jul 20, 2023
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Prefetch data to achieve better memory bandwidth utilization. With the new kernel token generation is ~48% faster for 33B model and ~14% faster for 7B model. Tests for 65B model are welcome.
GPUs of M1 Max / M2 Max can sustain 340 GB/s , can we reach that in llama.cpp? 😳
* GPU Read Bandwidth measured using Developer Tools comes with Xcode.
** Measured with the following command on M1 Max 32GB. Generation results are same between this PR and master branch.
./main -m model_file -n 128 -c 512 -s 12 -ngl 1 --no-mmapPrompt 1 “”
Prompt 2 “I believe the meaning of life is”