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fixing peak memory stats for benchmark (pytorch#353)
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* fixing peak memory stats for benchmark

Summary: we were hitting the peak upon model load, not during model
runtime, this is an issue since users can load model to cpu/meta which
significantly reduces mem usage during model load/quant.

Test Plan: sh benchmarks.sh

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* improve language

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@HDCharles
HDCharles authored Jun 13, 2024
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24 changes: 16 additions & 8 deletions torchao/_models/llama/benchmark_results.txt
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20240610164534, tok/s= 94.91, mem/s=1424.58 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610164738, tok/s=179.41, mem/s= 757.45 GB/s, peak_mem=23.44 GB, model_size= 4.22 GB quant: int4wo-64, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610164952, tok/s=136.75, mem/s=1028.38 GB/s, peak_mem=19.16 GB, model_size= 7.52 GB quant: int8wo, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610165423, tok/s= 8.41, mem/s= 63.23 GB/s, peak_mem=19.16 GB, model_size= 7.52 GB quant: int8dq, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610165618, tok/s=105.02, mem/s=1387.78 GB/s, peak_mem=13.88 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610165808, tok/s=199.81, mem/s= 746.45 GB/s, peak_mem=15.92 GB, model_size= 3.74 GB quant: int4wo-64, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610170005, tok/s=147.03, mem/s= 973.54 GB/s, peak_mem=14.50 GB, model_size= 6.62 GB quant: int8wo, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240610170408, tok/s= 9.40, mem/s= 62.26 GB/s, peak_mem=14.50 GB, model_size= 6.62 GB quant: int8dq, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611210704, tok/s= 29.44, mem/s= 883.80 GB/s, peak_mem=32.34 GB, model_size=30.02 GB quant: None, mod: Meta-Llama-3-8B, compile: False, compile_prefill: False, dtype: torch.float32, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.float32 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611210907, tok/s= 26.22, mem/s= 393.56 GB/s, peak_mem=16.16 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: False, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611211226, tok/s= 94.57, mem/s=1419.48 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611215300, tok/s= 95.57, mem/s=1434.47 GB/s, peak_mem=16.43 GB, model_size=15.01 GB quant: None, mod: Meta-Llama-3-8B, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611215559, tok/s=180.13, mem/s= 760.48 GB/s, peak_mem= 6.88 GB, model_size= 4.22 GB quant: int4wo-64, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611215907, tok/s=139.34, mem/s=1047.90 GB/s, peak_mem=10.42 GB, model_size= 7.52 GB quant: int8wo, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611220524, tok/s= 8.46, mem/s= 63.59 GB/s, peak_mem= 9.24 GB, model_size= 7.52 GB quant: int8dq, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611221358, tok/s= 9.26, mem/s= 138.97 GB/s, peak_mem=10.60 GB, model_size=15.01 GB quant: autoquant, mod: Meta-Llama-3-8B, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization autoquant --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Meta-Llama-3-8B/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611221551, tok/s= 30.18, mem/s= 797.58 GB/s, peak_mem=27.23 GB, model_size=26.43 GB quant: None, mod: Llama-2-7b-chat-hf, compile: False, compile_prefill: False, dtype: torch.float32, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.float32 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611221743, tok/s= 26.09, mem/s= 344.72 GB/s, peak_mem=13.62 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: False, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611222048, tok/s=105.41, mem/s=1393.00 GB/s, peak_mem=13.90 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611230037, tok/s=106.78, mem/s=1411.01 GB/s, peak_mem=13.88 GB, model_size=13.21 GB quant: None, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: True, dtype: torch.bfloat16, device: cuda repro: python generate.py --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --compile_prefill --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611230250, tok/s=199.72, mem/s= 746.13 GB/s, peak_mem= 4.75 GB, model_size= 3.74 GB quant: int4wo-64, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int4wo-64 --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611230440, tok/s=149.32, mem/s= 988.73 GB/s, peak_mem= 8.95 GB, model_size= 6.62 GB quant: int8wo, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8wo --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611231027, tok/s= 9.35, mem/s= 61.94 GB/s, peak_mem= 8.61 GB, model_size= 6.62 GB quant: int8dq, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization int8dq --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
20240611231759, tok/s= 9.56, mem/s= 126.32 GB/s, peak_mem= 8.53 GB, model_size=13.22 GB quant: autoquant, mod: Llama-2-7b-chat-hf, compile: True, compile_prefill: False, dtype: torch.bfloat16, device: cuda repro: python generate.py --quantization autoquant --checkpoint_path ../../../../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth --device cuda --precision torch.bfloat16 --compile --num_samples 5 --max_new_tokens 200 --top_k 200 --temperature 0.8
1 change: 1 addition & 0 deletions torchao/_models/llama/generate.py
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Expand Up @@ -285,6 +285,7 @@ def callback(x):
)
if i == -1:
print(f"Compilation time: {time.perf_counter() - t0:.2f} seconds")
torch.cuda.reset_peak_memory_stats()
continue
if hasattr(prof, "export_chrome_trace"):
prof.export_chrome_trace(f"{profile}.json")
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30 changes: 16 additions & 14 deletions torchao/quantization/README.md
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Expand Up @@ -2,20 +2,22 @@
Typically quantization algorithms will have different schemes for how the activation and weights are quantized so A16W8 for instance means the activations are quantized to 16 bits wheras the weights are quantized to 8 bits. Trying out different quantization schemes in `torchao` is generally a 1 line change. Note: exact APIs are not stable, we may change them in the future.

## Benchmarks
Benchmarks are run on a machine with a single A100 GPU using the script in _models/llama, evaluation was done
Using the lm_eval. The models used were meta-llama/Llama-2-7b-chat-hf and meta-llama/Meta-Llama-3-8B

| Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Model Size (GB) |
| ----------- | ------------------ | ------------------- | ------------- | ----------------------- | --------------- |
| Llama-2-7B | Base (bfloat16) | 12.212 | 105.02 | 1387.78 | 13.21 |
| | int8dq | 12.262 | 9.40 | 62.26 | 6.62 |
| | int8wo | 12.204 | 147.03 | 973.54 | 6.62 |
| | int4wo-64 | 12.843 | 199.81 | 746.45 | 3.74 |
| | int4wo-64-GPTQ | 12.489 | 199.81 | 746.45 | 3.74 |
| Llama-3-8B | Base (bfloat16) | N/A | 94.91 | 1424.58 | 15.01 |
| | int8dq | N/A | 8.41 | 63.23 | 7.52 |
| | int8wo | N/A | 136.75 | 1028.38 | 7.52 |
| | int4wo-64 | N/A | 179.41 | 757.45 | 4.22 |
Benchmarks are run on a machine with a single A100 GPU using the script in _models/llama which generates text in a latency optimized way (batchsize=1), evaluation was done
Using the lm_eval. The models used were meta-llama/Llama-2-7b-chat-hf and meta-llama/Meta-Llama-3-8B.

| Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) |
| ----------- | ------------------ | ------------------- | ------------- | ----------------------- | ---------------- | --------------- |
| Llama-2-7B | Base (bfloat16) | 12.212 | 105.02 | 1387.78 | 13.21 | 13.90 |
| | int8dq | 12.262 | 9.40 | 62.26 | 6.62 | 8.61 |
| | int8wo | 12.204 | 147.03 | 973.54 | 6.62 | 8.95 |
| | int4wo-64 | 12.843 | 199.81 | 746.45 | 3.74 | 4.75 |
| | int4wo-64-GPTQ | 12.489 | 199.81 | 746.45 | 3.74 | 4.75 |
| Llama-3-8B | Base (bfloat16) | N/A | 94.91 | 1424.58 | 15.01 | 16.43 |
| | int8dq | N/A | 8.41 | 63.23 | 7.52 | 9.24 |
| | int8wo | N/A | 136.75 | 1028.38 | 7.52 | 10.42 |
| | int4wo-64 | N/A | 179.41 | 757.45 | 4.22 | 6.88 |

note: Int8 dynamic quantization works best on compute bound models like [SAM](https://github.com/pytorch-labs/segment-anything-fast) whereas Llama with batchsize=1 tends to be memory bound, thus the rather low performance.

## Autoquantization

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