Add torchao quant (int4/int8/fp8) to llama models #1341

jerryzh168 · 2024-09-06T00:37:56Z

Summary:
We want to hack before we work on a proper solution

proper solution will be rewrite llama model with tensor parallelism: https://pytorch.org/docs/stable/distributed.tensor.parallel.html
(using DTensor underneath), trying to do it here: pytorch/ao#785

Test Plan:

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8

max_total_num_tokens=432196
Warmup ...
Prefill. latency: 0.03214 s, throughput:   3983.19 token/s
Decode.  latency: 0.01383 s, throughput:     72.31 token/s
Decode.  latency: 0.01354 s, throughput:     73.88 token/s
Decode.  latency: 0.01338 s, throughput:     74.75 token/s
Decode.  latency: 0.01330 s, throughput:     75.17 token/s
Decode.  median latency: 0.01346 s, median throughput:     74.31 token/s
Total. latency:  0.086 s, throughput:   1531.66 token/s
Benchmark ...
Prefill. latency: 0.02514 s, throughput:   5092.40 token/s
Decode.  latency: 0.01337 s, throughput:     74.80 token/s
Decode.  latency: 0.01338 s, throughput:     74.74 token/s
Decode.  latency: 0.01339 s, throughput:     74.68 token/s
Decode.  latency: 0.01321 s, throughput:     75.68 token/s
Decode.  latency: 0.01295 s, throughput:     77.23 token/s
Decode.  median latency: 0.01337 s, median throughput:     74.77 token/s
Total. latency:  0.132 s, throughput:   1032.13 token/s

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --torchao-config int4wo

max_total_num_tokens=505188
Warmup ...
Prefill. latency: 0.10929 s, throughput:   1171.18 token/s
Decode.  latency: 0.00790 s, throughput:    126.57 token/s
Decode.  latency: 0.00738 s, throughput:    135.54 token/s
Decode.  latency: 0.00724 s, throughput:    138.16 token/s
Decode.  latency: 0.00726 s, throughput:    137.71 token/s
Decode.  median latency: 0.00732 s, median throughput:    136.62 token/s
Total. latency:  0.139 s, throughput:    949.17 token/s
Benchmark ...
Prefill. latency: 0.10405 s, throughput:   1230.13 token/s
Decode.  latency: 0.00769 s, throughput:    129.96 token/s
Decode.  latency: 0.00725 s, throughput:    137.85 token/s
Decode.  latency: 0.00724 s, throughput:    138.11 token/s
Decode.  latency: 0.00731 s, throughput:    136.72 token/s
Decode.  latency: 0.00744 s, throughput:    134.47 token/s
Decode.  median latency: 0.00730 s, median throughput:    136.97 token/s
Total. latency:  0.163 s, throughput:    834.99 token/s

with compile
python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --enable-torch-compile

max_total_num_tokens=432196
Warmup ...
Prefill. latency: 0.03095 s, throughput:   4135.63 token/s
Decode.  latency: 0.01258 s, throughput:     79.49 token/s
Decode.  latency: 0.01250 s, throughput:     79.98 token/s
Decode.  latency: 0.01262 s, throughput:     79.22 token/s
Decode.  latency: 0.01246 s, throughput:     80.26 token/s
Decode.  median latency: 0.01254 s, median throughput:     79.73 token/s
Total. latency:  0.081 s, throughput:   1627.28 token/s
Benchmark ...
Prefill. latency: 0.02445 s, throughput:   5234.65 token/s
Decode.  latency: 0.01249 s, throughput:     80.09 token/s
Decode.  latency: 0.01245 s, throughput:     80.30 token/s
Decode.  latency: 0.01195 s, throughput:     83.65 token/s
Decode.  latency: 0.01170 s, throughput:     85.45 token/s
Decode.  latency: 0.01179 s, throughput:     84.85 token/s
Decode.  median latency: 0.01179 s, median throughput:     84.79 token/s
Total. latency:  0.120 s, throughput:   1131.80 token/s

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config int4wo-128
max_total_num_tokens=506932
Warmup ...
Prefill. latency: 0.10922 s, throughput:   1171.92 token/s
Decode.  latency: 0.00739 s, throughput:    135.25 token/s
Decode.  latency: 0.00692 s, throughput:    144.58 token/s
Decode.  latency: 0.00673 s, throughput:    148.68 token/s
Decode.  latency: 0.00673 s, throughput:    148.68 token/s
Decode.  median latency: 0.00682 s, median throughput:    146.60 token/s
Total. latency:  0.137 s, throughput:    963.61 token/s
Benchmark ...
Prefill. latency: 0.10405 s, throughput:   1230.12 token/s
Decode.  latency: 0.00733 s, throughput:    136.34 token/s
Decode.  latency: 0.00689 s, throughput:    145.13 token/s
Decode.  latency: 0.00682 s, throughput:    146.59 token/s
Decode.  latency: 0.00677 s, throughput:    147.67 token/s
Decode.  latency: 0.00676 s, throughput:    147.85 token/s
Decode.  median latency: 0.00677 s, median throughput:    147.76 token/s
Total. latency:  0.159 s, throughput:    856.11 token/s

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config int8wo

max_total_num_tokens=484068
Warmup ...
Prefill. latency: 0.05860 s, throughput:   2184.33 token/s
Decode.  latency: 0.00921 s, throughput:    108.52 token/s
Decode.  latency: 0.00883 s, throughput:    113.29 token/s
Decode.  latency: 0.00880 s, throughput:    113.69 token/s
Decode.  latency: 0.00868 s, throughput:    115.15 token/s
Decode.  median latency: 0.00881 s, median throughput:    113.49 token/s
Total. latency:  0.094 s, throughput:   1402.45 token/s
Benchmark ...
Prefill. latency: 0.05269 s, throughput:   2429.12 token/s
Decode.  latency: 0.00928 s, throughput:    107.80 token/s
Decode.  latency: 0.00892 s, throughput:    112.10 token/s
Decode.  latency: 0.00881 s, throughput:    113.53 token/s
Decode.  latency: 0.00870 s, throughput:    114.90 token/s
Decode.  latency: 0.00864 s, throughput:    115.68 token/s
Decode.  median latency: 0.00876 s, median throughput:    114.15 token/s
Total. latency:  0.123 s, throughput:   1102.80 token/s

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config int8dq
max_total_num_tokens=432196
Warmup ...
Prefill. latency: 0.03086 s, throughput:   4147.16 token/s
Decode.  latency: 0.01249 s, throughput:     80.03 token/s
Decode.  latency: 0.01246 s, throughput:     80.29 token/s
Decode.  latency: 0.01275 s, throughput:     78.42 token/s
Decode.  latency: 0.01244 s, throughput:     80.39 token/s
Decode.  median latency: 0.01247 s, median throughput:     80.16 token/s
Total. latency:  0.081 s, throughput:   1629.54 token/s
Benchmark ...
Prefill. latency: 0.02428 s, throughput:   5270.99 token/s
Decode.  latency: 0.01250 s, throughput:     79.99 token/s
Decode.  latency: 0.01252 s, throughput:     79.87 token/s
Decode.  latency: 0.01260 s, throughput:     79.34 token/s
Decode.  latency: 0.01234 s, throughput:     81.02 token/s
Decode.  latency: 0.01236 s, throughput:     80.92 token/s
Decode.  median latency: 0.01241 s, median throughput:     80.56 token/s
Total. latency:  0.124 s, throughput:   1098.26 token/s

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config fp8wo
# [rank0]: AssertionError: fp8e4nv data type is not supported on CUDA arch < 89

Reviewers:

Subscribers:

Tasks:

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Summary: We want to hack before we work on a proper solution proper solution will be rewrite llama model with tensor parallelism: https://pytorch.org/docs/stable/distributed.tensor.parallel.html (using DTensor underneath), trying to do it here: pytorch/ao#785 Test Plan: change `ENABLE_TORCHAO` to True/False in `python/sglang/srt/models/llama.py` to test the baseline v.s. torchao int4 weight only quant performance python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 ``` max_total_num_tokens=432196 Warmup ... Prefill. latency: 0.03214 s, throughput: 3983.19 token/s Decode. latency: 0.01383 s, throughput: 72.31 token/s Decode. latency: 0.01354 s, throughput: 73.88 token/s Decode. latency: 0.01338 s, throughput: 74.75 token/s Decode. latency: 0.01330 s, throughput: 75.17 token/s Decode. median latency: 0.01346 s, median throughput: 74.31 token/s Total. latency: 0.086 s, throughput: 1531.66 token/s Benchmark ... Prefill. latency: 0.02514 s, throughput: 5092.40 token/s Decode. latency: 0.01337 s, throughput: 74.80 token/s Decode. latency: 0.01338 s, throughput: 74.74 token/s Decode. latency: 0.01339 s, throughput: 74.68 token/s Decode. latency: 0.01321 s, throughput: 75.68 token/s Decode. latency: 0.01295 s, throughput: 77.23 token/s Decode. median latency: 0.01337 s, median throughput: 74.77 token/s Total. latency: 0.132 s, throughput: 1032.13 token/s max_total_num_tokens=505188 Warmup ... Prefill. latency: 0.10929 s, throughput: 1171.18 token/s Decode. latency: 0.00790 s, throughput: 126.57 token/s Decode. latency: 0.00738 s, throughput: 135.54 token/s Decode. latency: 0.00724 s, throughput: 138.16 token/s Decode. latency: 0.00726 s, throughput: 137.71 token/s Decode. median latency: 0.00732 s, median throughput: 136.62 token/s Total. latency: 0.139 s, throughput: 949.17 token/s Benchmark ... Prefill. latency: 0.10405 s, throughput: 1230.13 token/s Decode. latency: 0.00769 s, throughput: 129.96 token/s Decode. latency: 0.00725 s, throughput: 137.85 token/s Decode. latency: 0.00724 s, throughput: 138.11 token/s Decode. latency: 0.00731 s, throughput: 136.72 token/s Decode. latency: 0.00744 s, throughput: 134.47 token/s Decode. median latency: 0.00730 s, median throughput: 136.97 token/s Total. latency: 0.163 s, throughput: 834.99 token/s Warmup ... Prefill. latency: 0.05868 s, throughput: 2181.51 token/s Decode. latency: 0.04475 s, throughput: 22.35 token/s Decode. latency: 0.04463 s, throughput: 22.41 token/s Decode. latency: 0.04467 s, throughput: 22.39 token/s Decode. latency: 0.04478 s, throughput: 22.33 token/s Decode. median latency: 0.04471 s, median throughput: 22.37 token/s Total. latency: 0.238 s, throughput: 555.78 token/s Benchmark ... Prefill. latency: 0.05274 s, throughput: 2427.22 token/s Decode. latency: 0.04463 s, throughput: 22.41 token/s Decode. latency: 0.04456 s, throughput: 22.44 token/s Decode. latency: 0.04453 s, throughput: 22.45 token/s Decode. latency: 0.04469 s, throughput: 22.38 token/s Decode. latency: 0.04457 s, throughput: 22.44 token/s Decode. median latency: 0.04457 s, median throughput: 22.44 token/s Total. latency: 0.409 s, throughput: 332.13 token/s ``` Reviewers: Subscribers: Tasks: Tags:

zhyncs · 2024-09-06T08:46:15Z

Hi @msaroufim @jerryzh168 Nice work! It looks like the CI failures are due to a missing torchao dependency. Could you please add it to https://github.com/sgl-project/sglang/blob/main/python/pyproject.toml? Thanks.

merrymercy

Given the changes are very minimal. I feel that we can even merge this and start iteration. Here are a few thoughts on making it more clean.

Add a new argument --enable-torchao similar to --enable-torch-compile here

sglang/python/sglang/srt/server_args.py

Line 432 in ab4a83b

"--enable-torch-compile",

.
Alternatively, instead of adding --enable-torchao, we can add a new quantization format torchao-int4 here

sglang/python/sglang/srt/server_args.py

Line 224 in ab4a83b

"--quantization",

You can pass the above arguments to this global variable

sglang/python/sglang/srt/model_executor/model_runner.py

Lines 93 to 100 in ab4a83b

    
           global_server_args_dict.update( 
        
               { 
        
                   "disable_flashinfer": server_args.disable_flashinfer, 
        
                   "disable_flashinfer_sampling": server_args.disable_flashinfer_sampling, 
        
                   "triton_attention_reduce_in_fp32": server_args.triton_attention_reduce_in_fp32, 
        
                   "enable_mla": server_args.enable_mla, 
        
               } 
        
           )

and read it in llama.py

python/sglang/srt/models/llama.py

jerryzh168 · 2024-09-07T01:26:30Z

2. Alternatively, instead of adding --enable-torchao, we can add a new quantization format torchao-int4 here

@merrymercy thanks for the suggestions, I can start with https://github.com/pytorch/ao/blob/1ce7da941b311ef6ab416d9c7a22be20e65b7495/torchao/_models/llama/generate.py#L462 I think ideally we'd want to be able to specify different types of quantization like quantization_config = TorchAoConfig("int4_weight_only", group_size=128) (see https://huggingface.co/docs/transformers/main/en/quantization/torchao) so people can try out different quantization methods

I could be using

sglang/python/sglang/srt/models/llama.py

Line 248 in ab4a83b

quant_config: Optional[QuantizationConfig] = None,

actually, but this requires updates to vllm, unless we copy paste the quantization config code here

merrymercy · 2024-09-08T04:26:25Z

Feel free to copy over the code as we will gradually remove the dependency of vLLM (likely in one month or so)

merrymercy · 2024-09-09T12:32:47Z

@jerryzh168 Thanks for the contribution. It is merged.

I pushed some updates:

Move utils to python/sglang/srt/layers/torchao_utils.py
Added unit test test/srt/test_torchao.py

merrymercy · 2024-09-09T12:38:44Z

I verified the performance on H100. Here are the results with a few comments:

int4 decoding speed is pretty good. However, the prefill throughput seems much worse than the fp16 baseline. Do you have any ongoing efforts to improve it?
int8 w/o torch.compile is very bad, but we cannot afford torch.compile for all batch sizes. Since fp8 is a much better alternative at the budget of 8-bit, we will likely only use fp8.

# fp16 baseline
# Decode.  median latency: 0.00728 s, median throughput:    137.41 token/s
python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8

# fp8 baseline
# Decode.  median latency: 0.00590 s, median throughput:    169.51 token/s
python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --quant fp8

# torchao int4
# Decode.  median latency: 0.00448 s, median throughput:    223.43 token/s
python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --torchao-config int4wo-128

# torchao int8 w/o compile
# Decode.  median latency: 0.02629 s, median throughput:     38.04 token/s
python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --torchao-config int8wo

I also got some errors with the current torchao release on Pypi when trying the following settings. Which version should I use?

fp8 is not supported

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --torchao-config fp8wo

[rank0]:   File "/root/sglang/python/sglang/srt/layers/torchao_utils.py", line 31, in torchao_quantize_param_data
[rank0]:     from torchao.quantization import float8_weight_only
[rank0]: ImportError: cannot import name 'float8_weight_only' from 'torchao.quantization' (/usr/local/lib/python3.10/dist-packages/torchao/quantization/__init__.py)

torch.compile + torchao is not supported

python3 -m sglang.bench_latency --model meta-llama/Meta-Llama-3-8B --batch-size 1 --input 128 --output 8 --enable-torch-compile --torchao-config int4wo-128

[rank0]:   File "/usr/local/lib/python3.10/dist-packages/torchao/dtypes/affine_quantized_tensor.py", line 155, in dequantize
[rank0]:     int_data, scale, zero_point = self.layout_tensor.get_plain()
[rank0]: torch._dynamo.exc.InternalTorchDynamoError: 'FakeTensor' object has no attribute 'get_plain'

jerryzh168 · 2024-09-09T17:20:41Z

Thanks @merrymercy for the fixes, tests and merging the PR!

int4 decoding speed is pretty good. However, the prefill throughput seems much worse than the fp16 baseline. Do you have any ongoing efforts to improve it?

not yet I think, but we can take a look to understand why

int8 w/o torch.compile is very bad, but we cannot afford torch.compile for all batch sizes. Since fp8 is a much better alternative at the budget of 8-bit, we will likely only use fp8.

yeah int8 requires torch.compile to speedup, using fp8 sounds good to us as well.

fp8 is not supported

yeah currently we have torchao 0.4 in pypi, we are doing a new 0.5 release this week so fp8 should be supported this week

torch.compile + torchao is not supported

I've seen this before, this is because of some issue in pytorch dynamo/inductor code I think, and it should be fixed in pytorch nightly, maybe you can try installing pytorch nightly first to verify that the issue can be fix first. also do you have a version requirement for pytorch in sglang?

zhyncs · 2024-09-09T17:23:08Z

also do you have a version requirement for pytorch in sglang?

@jerryzh168 We use torch==2.4.0

jerryzh168 · 2024-09-09T20:32:08Z

I see, for 2.4.0 and below, we'd need to call https://github.com/pytorch/ao/blob/e1039abac7f429a8d7f489d047d9b34d6ac6afe2/torchao/utils.py#L269 for the model to be compilable I think, I'll try to verify as well when I got a chance today

jerryzh168 · 2024-09-10T21:18:33Z

for 2.4.0 there seems to be some issue even with work around, trying to debug now

jerryzh168 · 2024-09-11T16:48:39Z

@merrymercy @zhyncs

for the second issue "torch.compile + torchao is not supported" I can only repro in 2.4.0 and not sure how to fix, also the fix could just be needed in pytorch itself I think and we can't really back port the fix to 2.4.0. I'm wondering if it's OK for you to use torch nightly for now for testing? pytorch 2.5.0 is going to be released in one month.

Summary: Similar to sgl-project#1341 we add torchao quantization to mixtral model Test Plan: Note: compile is not working yet, and I can't install torchnightly locally and make it work either. I'll wait for pytorch 2.5 release which happens in mid Oct, or check that again later python3 -m sglang.bench_latency --model Qwen/Qwen1.5-MoE-A2.7B --batch-size 1 --input 128 --output 8  Warmup ... Prefill. latency: 0.05532 s, throughput: 2313.73 token/s Decode. latency: 0.00896 s, throughput: 111.65 token/s Decode. latency: 0.00833 s, throughput: 120.04 token/s Decode. latency: 0.00869 s, throughput: 115.06 token/s Decode. latency: 0.00842 s, throughput: 118.79 token/s Decode. median latency: 0.00855 s, median throughput: 116.89 token/s Total. latency: 0.090 s, throughput: 1471.26 token/s Benchmark ... Prefill. latency: 0.04294 s, throughput: 2980.61 token/s Decode. latency: 0.00839 s, throughput: 119.12 token/s Decode. latency: 0.00828 s, throughput: 120.78 token/s Decode. latency: 0.00857 s, throughput: 116.64 token/s Decode. latency: 0.00853 s, throughput: 117.19 token/s Decode. latency: 0.00859 s, throughput: 116.39 token/s Decode. median latency: 0.00853 s, median throughput: 117.17 token/s Total. latency: 0.111 s, throughput: 1226.84 token/s python3 -m sglang.bench_latency --model Qwen/Qwen1.5-MoE-A2.7B --batch-size 1 --input 128 --output 8 --torchao-config int4wo-128 Warmup ... Prefill. latency: 0.06413 s, throughput: 1996.05 token/s Decode. latency: 0.00764 s, throughput: 130.84 token/s Decode. latency: 0.00748 s, throughput: 133.73 token/s Decode. latency: 0.00725 s, throughput: 137.84 token/s Decode. latency: 0.00721 s, throughput: 138.74 token/s Decode. median latency: 0.00737 s, median throughput: 135.76 token/s Total. latency: 0.094 s, throughput: 1408.61 token/s Benchmark ... Prefill. latency: 0.05239 s, throughput: 2443.43 token/s Decode. latency: 0.00739 s, throughput: 135.25 token/s Decode. latency: 0.00720 s, throughput: 138.90 token/s Decode. latency: 0.00718 s, throughput: 139.21 token/s Decode. latency: 0.00722 s, throughput: 138.42 token/s Decode. latency: 0.00745 s, throughput: 134.30 token/s Decode. median latency: 0.00731 s, median throughput: 136.82 token/s Total. latency: 0.111 s, throughput: 1223.51 token/s A100, no compile python3 -m sglang.bench_latency --model Qwen/Qwen1.5-MoE-A2.7B --batch-size 1 --input 128 --output 8 --torchao-config fp8wo max_total_num_tokens=199454 Warmup ... Prefill. latency: 0.06958 s, throughput: 1839.60 token/s Decode. latency: 0.02343 s, throughput: 42.68 token/s Decode. latency: 0.02342 s, throughput: 42.70 token/s Decode. latency: 0.02368 s, throughput: 42.23 token/s Decode. latency: 0.02337 s, throughput: 42.80 token/s Decode. median latency: 0.02342 s, median throughput: 42.69 token/s Total. latency: 0.163 s, throughput: 807.48 token/s Benchmark ... Prefill. latency: 0.05767 s, throughput: 2219.36 token/s Decode. latency: 0.02293 s, throughput: 43.61 token/s Decode. latency: 0.02026 s, throughput: 49.36 token/s Decode. latency: 0.02029 s, throughput: 49.29 token/s Decode. latency: 0.02024 s, throughput: 49.41 token/s Decode. latency: 0.02026 s, throughput: 49.36 token/s Decode. median latency: 0.02025 s, median throughput: 49.39 token/s Total. latency: 0.222 s, throughput: 611.87 token/s Reviewers: Subscribers: Tasks: Tags:

zhyncs · 2024-09-19T08:39:58Z

I'm wondering if it's OK for you to use torch nightly for now for testing?

@jerryzh168 I think we can give it a try, I will verify asap.

LIHUA919 · 2024-09-28T02:13:00Z

interesting

zhyncs · 2024-11-18T13:40:25Z

@jerryzh168 The main branch currently supports torch 2.5.1, and the installation method is as follows:

git clone -b main https://github.com/sgl-project/sglang.git
cd sglang

pip install --upgrade pip
pip install -e "python[all]"
# If you want to update torch 2.4 to torch 2.5.1 in an existing development environment, you can add `--force-reinstall`.
# pip install -e "python[all]" --force-reinstall

pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/

If you have any questions, feel free to contact me anytime. Thanks!

merrymercy reviewed Sep 6, 2024

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zhyncs self-assigned this Sep 6, 2024

jerryzh168 added 2 commits September 6, 2024 20:40

add torchao-config

b3696b5

add fp8

281c19b

zhyncs assigned ispobock Sep 7, 2024

merrymercy changed the title ~~Add torchao quant to sgl llama model for testing~~ Add torchao quant (int4/int8) to sgl llama model for testing Sep 9, 2024

merrymercy added 5 commits September 9, 2024 04:33

Merge branch 'main' into enable-torchao

01cc04e

lint

6712230

move utils to torchao_utils.py

8ae193b

update

69d2bb5

update test cases

b76dc72

merrymercy changed the title ~~Add torchao quant (int4/int8) to sgl llama model for testing~~ Add torchao quant (int4/int8/fp8) to llama models Sep 9, 2024

merrymercy merged commit a7c47e0 into sgl-project:main Sep 9, 2024
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merrymercy mentioned this pull request Sep 13, 2024

Development Roadmap (2024 Q3) #634

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jerryzh168 mentioned this pull request Sep 14, 2024

Add torchao quant for mixtral and qwen_moe #1418

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zhyncs added the quant LLM Quantization label Sep 19, 2024

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Add torchao quant (int4/int8/fp8) to llama models #1341

Add torchao quant (int4/int8/fp8) to llama models #1341

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	global_server_args_dict.update(
	{
	"disable_flashinfer": server_args.disable_flashinfer,
	"disable_flashinfer_sampling": server_args.disable_flashinfer_sampling,
	"triton_attention_reduce_in_fp32": server_args.triton_attention_reduce_in_fp32,
	"enable_mla": server_args.enable_mla,
	}
	)

Add torchao quant (int4/int8/fp8) to llama models #1341

Add torchao quant (int4/int8/fp8) to llama models #1341

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