Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fixing peak memory stats for benchmark #353

Merged
merged 2 commits into from
Jun 13, 2024
Merged

fixing peak memory stats for benchmark #353

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
6ac3880
fixing peak memory stats for benchmark
HDCharles Jun 12, 2024
f7620fe
improve language
HDCharles Jun 12, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 16 additions & 8 deletions torchao/_models/llama/benchmark_results.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,8 +1,16 @@
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
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

should we keep this file you think? Feels subsumed by the table which is significantly clearer to read

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

having full repros of everything can be nice

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
View file
Open in desktop
Original file line number Diff line number Diff line change
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")
Expand Down
30 changes: 16 additions & 14 deletions torchao/quantization/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
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

Expand Down
Loading