From 1a62600fa3ce36a9949b00908e4ed3a7681a3b68 Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Wed, 20 Jul 2022 12:33:14 -0700 Subject: [PATCH 01/17] update --- _blog.yml | 13 +- .../stas-bekman-300x300.jpg | Bin 0 -> 48110 bytes .../sylvain_gugger.png | Bin 0 -> 451025 bytes bloom-inference-pytorch.md | 143 ++++++++++++++++++ 4 files changed, 155 insertions(+), 1 deletion(-) create mode 100644 assets/bloom-inference-pytorch/stas-bekman-300x300.jpg create mode 100644 assets/bloom-inference-pytorch/sylvain_gugger.png create mode 100644 bloom-inference-pytorch.md diff --git a/_blog.yml b/_blog.yml index d7b00be0d0..a99d88df03 100644 --- a/_blog.yml +++ b/_blog.yml @@ -1006,7 +1006,7 @@ tags: - nlp - llm - + - local: mnist-adversarial title: "How to train your model dynamically using adversarial data" author: chrisjay @@ -1015,6 +1015,7 @@ tags: - mnist - adversarial +<<<<<<< HEAD - guide - local: deep-rl-a2c @@ -1079,3 +1080,13 @@ tags: - announcement - private hub + +- local: bloom-inference-pytorch + title: "The Technology Behind BLOOM Training" + author: stas, sgugger, Narsil + thumbnail: /blog/assets/bloom-inference-pytorch/thumbnail.png + date: July 28, 2022 + tags: + - nlp + - llm + - inference diff --git a/assets/bloom-inference-pytorch/stas-bekman-300x300.jpg b/assets/bloom-inference-pytorch/stas-bekman-300x300.jpg new file mode 100644 index 0000000000000000000000000000000000000000..effbc0e5ca403a8e33db2c393f9d761b8b01e12f GIT binary patch literal 48110 zcma(2RZv__)b|bJ0Rq9@g4^Kk3GVJ1+#$F_(BKY(bK&mpfiMh%26tz0cjx84zvnx6 zs@{Wlo$PAaU0u6&ukPRauYO;A--N-GmzI-;f&DN5EC2@PeFaA1e@p*U^Z#aW|G!GO z|I6V2Z~LD?{?EG~-QnOr`u^V?{=byo2Vk&}|7TM0uvjo~Sg`O|ui2pk_ z%>OY&cm$+R$Z#mIFsL8xB(N}W@E`5}|5lifA`C17BGRY#Wf(O0k5){0%#Sf<12KXQ z@%VdI8rw=u$~Dqvb4G0}1cQ5~PZ}*$>{inP93IW*P_^XLSOJ>O(C?{#S{zXWzRJ!| zMVr}D=ov6trCO#AmV600{sMAhc7fmw&;!JhQ`4TXX^B(K#do(t_`>np<*JVruw$gY z5B+r3E^c9HCmeQg$o~S+ocqNNiehPJqy7r`Ig2(J7s*QgyTZsT73N&JxRk{M 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z9urm+?NU)EFiaiidmQ76D?7sTEWk!_?gK;sJ7DKgxxej@5dqsd1nfY;Z$p^^$iS_K zB}jwEK|nKX+fj+!4zRa-NyHX~^$`jAnp*QZPYDLtL4Z4$$Kjg}OYF`Q60-x>`Rw{@4P~hq%e0j%2MFtk)3RXx)^7CRJLts$d^G^9@zDu6XF_dMRL%rxtSWB3E2T|0q^SO~xiv8V_q_QV6aPe_v+K#q&saw3Q2XOOZx1g|FIB~TC zYE6;10`2C`76go(aJ0@r$ zn^bVe5}d*&F;I{J(D${GGH)GW!3qYthF)61I_cHM6<+)haFZ&x+I6Ct1^DIQ!Uo

Efficient BLOOM Inference on PyTorch

+ + + + + +This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). + +As the model needs 352GB in bf16 weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at as of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. + +Using a single node is ideal since PCIe speed is typically much faster than inter-node network. + +If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course the generation time will be much slower. + + +For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for loading speed time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. + +The solutions are presented in alphabetical order: + + +## Accelerate + +[Accelerate](https://github.com/huggingface/accelerate) + +Accelerate deploys a naive Pipeline Parallelism to load a model that is much larger than the GPU size, by spreading the layers over multiple GPUs. At inference time it then switches control from one GPU to the next until all layers have run. In this situation only one GPU works at any given time. It sounds very inefficient but it produces excellent throughput despite the idling of the GPUs. + + +### Setup + +``` +pip install transformers>=4.20.1 +git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ +cd Megatron-DeepSpeed +``` + +### Run + +``` +python scripts/inference/bloom-accelerate-inference.py --name bigscience/bloom --batch_size 8 --benchmark +``` + + + + + + + +## DeepSpeed-Inference + +[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver an super-fast <1msec per token inference on a large batch size of 128. + + + +### Setup + +``` +pip install deepspeed>=0.6.7 transformers>=4.20.1 +git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ +cd Megatron-DeepSpeed +``` + +### Run + +``` +deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark +``` + + + + + + + +## DeepSpeed-ZeRO + +[DeepSpeed-ZeRO](https://www.deepspeed.ai/tutorials/zero/) was primarily developed for training huge models, and wasn't targeting inference, but it still produces pretty decent results. ZeRO shards the weights across multiple GPUs and then gathers the needed weights magically before each `forward` and `backward` calls, so that the model isn't aware that its weights were ever split up. + + +### Setup + +``` +pip install deepspeed>=0.6.7 transformers>=4.20.1 +git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ +cd Megatron-DeepSpeed +``` + +### Run + +``` +deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark +``` + + + + + +## Rust + +XXX: Nicolas's work here + + +### Setup + +``` +``` + +### Run + +``` +``` From ddea3f29d25bf6c2052559e6f409c6c1d9d964a5 Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Wed, 20 Jul 2022 13:31:19 -0700 Subject: [PATCH 02/17] add perf stats --- bloom-inference-pytorch.md | 31 +++++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 0af41edfda..c79f149a3d 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -69,12 +69,17 @@ cd Megatron-DeepSpeed ### Run ``` -python scripts/inference/bloom-accelerate-inference.py --name bigscience/bloom --batch_size 8 --benchmark -``` - - +python scripts/inference/bloom-accelerate-inference.py --name bigscience/bloom --batch_size 40 --benchmark +[...] +*** Performance stats: +Throughput per token including tokenize: 10.74 msecs +Start to ready to generate: 107.892 secs +Tokenize and generate 20000 (bs=40) tokens: 52.059 secs +Start to finish: 159.952 secs +``` +The highest batch size I was able to run without OOM was 40 in this case. @@ -95,9 +100,17 @@ cd Megatron-DeepSpeed ### Run ``` -deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark +deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 128 --benchmark +[...] +*** Performance stats: +Throughput per token including tokenize: 0.73 msecs +Start to ready to generate: 591.902 secs +Tokenize and generate 64000 (bs=128) tokens: 9.421 secs +Start to finish: 601.323 secs + ``` +The highest batch size I was able to run without OOM was 128 in this case. @@ -121,9 +134,15 @@ cd Megatron-DeepSpeed ``` deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark +[...] +*** Performance stats: +Throughput per token including tokenize: 37.86 msecs +Start to ready to generate: 464.724 secs +Tokenize and generate 32000 (bs=8) tokens: 242.090 secs +Start to finish: 706.813 secs ``` - +The highest batch size I was able to run without OOM was 8 in this case. From bbd217a2a6c4b5f05b808fd383c38773aa8522ed Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Wed, 20 Jul 2022 14:58:59 -0700 Subject: [PATCH 03/17] Performance metrics --- bloom-inference-pytorch.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index c79f149a3d..49b625c877 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -45,9 +45,20 @@ Using a single node is ideal since PCIe speed is typically much faster than inte If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course the generation time will be much slower. - For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for loading speed time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. +## Performance metrics + +For doing occasional inference on someone's desktop, that is a few inputs at a time the two critical metrics are the time to load the framework and the checkpoint shards and then the generation time. + +For doing inference on the server the two important metrics for inference is the overall latency and the throughput of tokens. The loading time is less important since once loaded it becomes persistent. + +The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. + +The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this of course will make the overall throughput lower. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. + +This article primarily discusses simple solutions and new more efficient solutions are being worked on as well. + The solutions are presented in alphabetical order: From 564c400cc641a3060f2da01bf13a3b64130c349e Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Thu, 21 Jul 2022 08:58:55 -0700 Subject: [PATCH 04/17] Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> --- bloom-inference-pytorch.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 49b625c877..84dd7a5571 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -49,15 +49,15 @@ For the sake of consistency, unless stated differently, the benchmarks in this a ## Performance metrics -For doing occasional inference on someone's desktop, that is a few inputs at a time the two critical metrics are the time to load the framework and the checkpoint shards and then the generation time. +For doing occasional inference on someone's desktop, that is a few inputs at a time, the two critical metrics are the time to load the framework and the checkpoint shards and then the generation time. -For doing inference on the server the two important metrics for inference is the overall latency and the throughput of tokens. The loading time is less important since once loaded it becomes persistent. +For doing inference on a server the two important metrics are the overall latency and the throughput of tokens. The loading time is less important since once loaded, the model becomes persistent. The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this of course will make the overall throughput lower. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. -This article primarily discusses simple solutions and new more efficient solutions are being worked on as well. +This article primarily discusses simple solutions in PyTorch. More efficient solutions are being worked on as well. The solutions are presented in alphabetical order: @@ -90,7 +90,7 @@ Tokenize and generate 20000 (bs=40) tokens: 52.059 secs Start to finish: 159.952 secs ``` -The highest batch size I was able to run without OOM was 40 in this case. +The highest batch size we were able to run without OOM was 40 in this case. @@ -121,7 +121,7 @@ Start to finish: 601.323 secs ``` -The highest batch size I was able to run without OOM was 128 in this case. +The highest batch size we were able to run without OOM was 128 in this case. @@ -130,7 +130,7 @@ The highest batch size I was able to run without OOM was 128 in this case. ## DeepSpeed-ZeRO -[DeepSpeed-ZeRO](https://www.deepspeed.ai/tutorials/zero/) was primarily developed for training huge models, and wasn't targeting inference, but it still produces pretty decent results. ZeRO shards the weights across multiple GPUs and then gathers the needed weights magically before each `forward` and `backward` calls, so that the model isn't aware that its weights were ever split up. +[DeepSpeed-ZeRO](https://www.deepspeed.ai/tutorials/zero/) was primarily developed for training huge models, and does not target inference, but it still produces pretty decent results. ZeRO shards the weights across multiple GPUs and then gathers the needed weights magically before each `forward` and `backward` calls, so that the model isn't aware that its weights were ever split up. ### Setup @@ -153,7 +153,7 @@ Tokenize and generate 32000 (bs=8) tokens: 242.090 secs Start to finish: 706.813 secs ``` -The highest batch size I was able to run without OOM was 8 in this case. +The highest batch size we were able to run without OOM was 8 in this case. From e1707ebc52b623da3bc08ea518249005728895d9 Mon Sep 17 00:00:00 2001 From: Sylvain Gugger Date: Fri, 22 Jul 2022 03:42:43 -0400 Subject: [PATCH 05/17] Write a blob about Accelerate --- bloom-inference-pytorch.md | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 84dd7a5571..83c8a525e5 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -39,7 +39,7 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at as of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at as of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. Using a single node is ideal since PCIe speed is typically much faster than inter-node network. @@ -66,13 +66,23 @@ The solutions are presented in alphabetical order: [Accelerate](https://github.com/huggingface/accelerate) -Accelerate deploys a naive Pipeline Parallelism to load a model that is much larger than the GPU size, by spreading the layers over multiple GPUs. At inference time it then switches control from one GPU to the next until all layers have run. In this situation only one GPU works at any given time. It sounds very inefficient but it produces excellent throughput despite the idling of the GPUs. +Accelerate handles big models for inference in the following way: +1. Instantiate the model with empty weights. +2. Analyze the size of each layer and the available space on each device (GPUs, CPU) to decide where each layer should go. +3. Load the model checkpoint bit by bit and put each weight on its device +It then ensures the model runs properly with hooks that transfer the inputs and outputs on the right device and that the model weights offloaded on the CPU (or even the disk) are loaded on a GPU just before the forward pass, before being offloaded again once the forward pass is finished. + +In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce excellent throughput despite the idling of the GPUs. + +It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. It will be slower than running the full model on GPUs of course. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token. + +You can learn more about this solution in [Accelerate documentation](https://huggingface.co/docs/accelerate/big_modeling). ### Setup ``` -pip install transformers>=4.20.1 +pip install accelerate transformers>=4.20.1 git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ cd Megatron-DeepSpeed ``` From 6342bfe9df1b74c8097f662ca2984ca68a105236 Mon Sep 17 00:00:00 2001 From: Thomas Simonini Date: Fri, 22 Jul 2022 10:43:33 +0200 Subject: [PATCH 06/17] Unit 7: Deep Reinforcement Learning Course (A2C) (#433) * Create 89_deep_rl_a2c folder * Add A2C Introduction * Rename deep-rl-a2c to deep-rl-a2c.md * Add files via upload * Add Part 1 A2C * Add PII A A2C Article * Add PII B A2C * Misc updates A2C * Add files via upload * Add Conclusion A2C * Finish A2C before review * Add files via upload * Update deep-rl-a2c.md * Update _blog.yml --- _blog.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/_blog.yml b/_blog.yml index a99d88df03..d18562e2e6 100644 --- a/_blog.yml +++ b/_blog.yml @@ -1015,7 +1015,6 @@ tags: - mnist - adversarial -<<<<<<< HEAD - guide - local: deep-rl-a2c @@ -1085,7 +1084,7 @@ title: "The Technology Behind BLOOM Training" author: stas, sgugger, Narsil thumbnail: /blog/assets/bloom-inference-pytorch/thumbnail.png - date: July 28, 2022 + date: August 4, 2022 tags: - nlp - llm From e2f19a243f69fc37ff4b0a05b9f5e74e760ed757 Mon Sep 17 00:00:00 2001 From: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Date: Tue, 26 Jul 2022 13:12:38 +0200 Subject: [PATCH 07/17] Update bloom-inference-pytorch.md Co-authored-by: Stas Bekman --- bloom-inference-pytorch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 83c8a525e5..1f17b93257 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -75,7 +75,7 @@ It then ensures the model runs properly with hooks that transfer the inputs and In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce excellent throughput despite the idling of the GPUs. -It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. It will be slower than running the full model on GPUs of course. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token. +It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token. You can learn more about this solution in [Accelerate documentation](https://huggingface.co/docs/accelerate/big_modeling). From fe004e748b745b816a56edc64aed493612756c9f Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Tue, 26 Jul 2022 08:40:10 -0700 Subject: [PATCH 08/17] typo --- bloom-inference-pytorch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 1f17b93257..9660bba8d1 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -39,7 +39,7 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at as of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that gas of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. Using a single node is ideal since PCIe speed is typically much faster than inter-node network. From 5f2990a67a88ae53586895569fbb0db50cecde69 Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Tue, 26 Jul 2022 08:41:04 -0700 Subject: [PATCH 09/17] comments --- bloom-inference-pytorch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 9660bba8d1..35b9329ba2 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -75,7 +75,7 @@ It then ensures the model runs properly with hooks that transfer the inputs and In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce excellent throughput despite the idling of the GPUs. -It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token. +It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token as compared to 10 msecs on 8x80 A100s. You can learn more about this solution in [Accelerate documentation](https://huggingface.co/docs/accelerate/big_modeling). From c54aff284068ca94989bdd06177c23522013905d Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Tue, 2 Aug 2022 08:18:52 -0700 Subject: [PATCH 10/17] Apply suggestions from code review Co-authored-by: Nicolas Patry --- bloom-inference-pytorch.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 35b9329ba2..3617c1924c 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -39,7 +39,7 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that gas of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at the time of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. Using a single node is ideal since PCIe speed is typically much faster than inter-node network. @@ -73,7 +73,7 @@ Accelerate handles big models for inference in the following way: It then ensures the model runs properly with hooks that transfer the inputs and outputs on the right device and that the model weights offloaded on the CPU (or even the disk) are loaded on a GPU just before the forward pass, before being offloaded again once the forward pass is finished. -In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce excellent throughput despite the idling of the GPUs. +In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token as compared to 10 msecs on 8x80 A100s. From 60d5fb8b08767934b6639b4f548e01ba0306d9ca Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 4 Aug 2022 11:19:47 +0200 Subject: [PATCH 11/17] Adding my part. 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b/bloom-inference-pytorch.md @@ -55,7 +55,14 @@ For doing inference on a server the two important metrics are the overall latenc The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. -The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this of course will make the overall throughput lower. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. +The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. + +Let's try and show the metrics in a drawing: + + + +In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of room for compute so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. +As you increase your batch_size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. This article primarily discusses simple solutions in PyTorch. More efficient solutions are being worked on as well. @@ -85,6 +92,7 @@ You can learn more about this solution in [Accelerate documentation](https://hug pip install accelerate transformers>=4.20.1 git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ cd Megatron-DeepSpeed +git checkout bloom-inference ``` ### Run @@ -94,6 +102,7 @@ python scripts/inference/bloom-accelerate-inference.py --name bigscience/bloom - [...] *** Performance stats: +Latency (T): 104 msecs Throughput per token including tokenize: 10.74 msecs Start to ready to generate: 107.892 secs Tokenize and generate 20000 (bs=40) tokens: 52.059 secs @@ -124,6 +133,7 @@ cd Megatron-DeepSpeed deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 128 --benchmark [...] *** Performance stats: +Latency (T): 18.8 msecs Throughput per token including tokenize: 0.73 msecs Start to ready to generate: 591.902 secs Tokenize and generate 64000 (bs=128) tokens: 9.421 secs @@ -132,52 +142,95 @@ Start to finish: 601.323 secs ``` The highest batch size we were able to run without OOM was 128 in this case. +You can see two factors at play leading to better performance here. +1/ T was reduced by using Tensor Parallelism (TP) instead of Pipeline Parallelism (PP) of `accelerate`. + Because accelerate is meant to be very generic it is also unfortunately hard to maximize the GPU usage. + Basically all computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means + 7 GPU are idle all the time. + `DeepSpeed` on the other end, uses TP meaning it will send tensors to all GPUs, compute part of the computation + then all GPUs communicate to each other the results, then move on to the next layer. That means all + GPUs are active at once but they need to communicate much more. Overall that decreases **T** by a theoretical + factor of `8x` (the theoretical limit would be `1.3s`). +2/ DeepSpeed also uses custom cuda kernels to avoid allocating too much memory and doing tensor copies + The effect of this is less memory allocation, less kernel starts which does reduce **T** but also + allows for bigger batch sizes (here **128** which also participates in raising the overall throughput. +## Splitting the generation -## DeepSpeed-ZeRO +Another approach we took, was relooking at the overall behavior of the webserver in generation mode. -[DeepSpeed-ZeRO](https://www.deepspeed.ai/tutorials/zero/) was primarily developed for training huge models, and does not target inference, but it still produces pretty decent results. ZeRO shards the weights across multiple GPUs and then gathers the needed weights magically before each `forward` and `backward` calls, so that the model isn't aware that its weights were ever split up. +In practice when users send request to a `text-generation` model, we don't run a single `forward` loop but multiple ones. +They also don't send the same parameters, some want `greedy`, some `sampling` and some `beam_search` for instance, and probably +with never really the same values. +In theory, if we're using `.generate` directly, we are **not** able to do any batching on different parameter sets. So what happens +is that we're less likely to use batching in production, and that as mentionned above is a *big* source of throughput improvement. -### Setup +Let's take a simple example where there's only 1 query running, and another query comes in while the first is being processed. -``` -pip install deepspeed>=0.6.7 transformers>=4.20.1 -git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ -cd Megatron-DeepSpeed -``` + -### Run +As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But for the **request 2** is has to wait +`4.5 x T`. which is bigger. Actually, if there was a **request 3** with yet another parameter set, then you would have to wait `7.5 x T`. +And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. -``` -deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark -[...] -*** Performance stats: -Throughput per token including tokenize: 37.86 msecs -Start to ready to generate: 464.724 secs -Tokenize and generate 32000 (bs=8) tokens: 242.090 secs -Start to finish: 706.813 secs -``` +Another approach is to split entirely the generation loop. We're going to ignore the `use_cache=True` complexity for the sake of readability. + +So now, what we're going to do is have each request, handle the `generate` part on it own, and simply send back the required tensors only for +the `forward` pass. A single thread will be responsible of batching those simple requests. + +Since each request is handling entirely its own way of choosing next ids, there is no problem in handling an infinite amount of different parameters. -The highest batch size we were able to run without OOM was 8 in this case. +You're getting a compute model like this. + +Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), +**request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. -## Rust +As with any sort of optimization, the trick is using all source of optimizations at once, for instance here since every request is doing it's own `.generate` then +the tensors have to be copied a bunch of times (including the past key values). And any additional overhead adds up to increase **T** you need to make good care that doing this generation split is not offsetting all the other benefits. -XXX: Nicolas's work here + +Code here in Rust: https://github.com/Narsil/bloomserver +It's in Rust, mostly because threading/async/multiprocessing have a lot of caveats in Python and was causing a lot of issues. +Rust doesn't provide any performance benefits here (it uses the same PyTorch under the hood) +This code is not meant to be portable and should run on `16xA100(40)` and it also uses PP (like `accelerate`) +and uses a technique to interleave GPU usage to that the throughput should be more comparable to deepspeed, +even if the latency (**T**) is exactly the same as `accelerate`. + + +There are other issues you need to take into account, like padding which might also hit your performance but this is +beyond this blogpost (More info [here](https://huggingface.co/docs/transformers/v4.21.0/en/main_classes/pipelines#pipeline-batching)). ### Setup ``` +git clone https://github.com/Narsil/bloomserver.git +pip install setuptools_rust +pip install -r requirements.txt +python convert_weights.py #libtorch cannot load Python made weights so we have to convert them +TORCH_CUDA_VERSION=cu116 cargo run --release ``` ### Run +**This experiement does not have a 1-1 benchmark analysis since it's running a full fledged webserver and was never ran on the same machines as before** + ``` +*** Performance stats (NUMBERS ARE ESTIMATES FROM QUERIES): +Latency (T): 104 msecs (should be similar to `accelerate`, it's using the roughly same code) +Throughput per token including tokenize: N/A (higher than accelerate, we're able to get 10X throughput compared to `accelerate`, on a similar machine) +Start to ready to generate: 15.902 secs +Tokenize and generate N/A (bs=N/A) tokens: N/A +Start to finish: N/A ``` + +The faster load speed is due to `safetensors` (not a production ready library yet) is using `mmap` + there's 1 thread to load per GPU. +Overall the latency should be exactly the same as `accelerate`, throughput is ~10X better and **most importantly, it can support any amount of different parameters** +without any latency cost. From 3f0f4daf69b97525ce2d7d8a25e6b52ca67ee657 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 4 Aug 2022 12:03:02 +0200 Subject: [PATCH 12/17] Grammarly pass. --- bloom-inference-pytorch.md | 42 +++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 7d90ddc294..a8a87e93b8 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -37,13 +37,13 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png -This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). +This article discusses various PyTorch-based solutions to run efficiently 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at the time of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, at the time of this writing, they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. Using a single node is ideal since PCIe speed is typically much faster than inter-node network. -If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course the generation time will be much slower. +If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course, the generation time will be much slower. For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for loading speed time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. @@ -55,13 +55,13 @@ For doing inference on a server the two important metrics are the overall latenc The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. -The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. +The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then of course there is a small overhead of processing the input, sending data to the server, and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. Let's try and show the metrics in a drawing: -In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of room for compute so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. +In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of computing power so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. As you increase your batch_size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. This article primarily discusses simple solutions in PyTorch. More efficient solutions are being worked on as well. @@ -80,7 +80,7 @@ Accelerate handles big models for inference in the following way: It then ensures the model runs properly with hooks that transfer the inputs and outputs on the right device and that the model weights offloaded on the CPU (or even the disk) are loaded on a GPU just before the forward pass, before being offloaded again once the forward pass is finished. -In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. +In a situation where there are multiple GPUs with enough space to accommodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token as compared to 10 msecs on 8x80 A100s. @@ -115,7 +115,7 @@ The highest batch size we were able to run without OOM was 40 in this case. ## DeepSpeed-Inference -[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver an super-fast <1msec per token inference on a large batch size of 128. +[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver a super-fast <1msec per token inference on a large batch size of 128. @@ -144,17 +144,17 @@ Start to finish: 601.323 secs The highest batch size we were able to run without OOM was 128 in this case. You can see two factors at play leading to better performance here. -1/ T was reduced by using Tensor Parallelism (TP) instead of Pipeline Parallelism (PP) of `accelerate`. +1/ T was reduced by using Tensor Parallelism (TP) instead of the Pipeline Parallelism (PP) of `accelerate`. Because accelerate is meant to be very generic it is also unfortunately hard to maximize the GPU usage. - Basically all computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means + All computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means 7 GPU are idle all the time. - `DeepSpeed` on the other end, uses TP meaning it will send tensors to all GPUs, compute part of the computation + `DeepSpeed` on the other end uses TP meaning it will send tensors to all GPUs, compute part of the computation then all GPUs communicate to each other the results, then move on to the next layer. That means all GPUs are active at once but they need to communicate much more. Overall that decreases **T** by a theoretical factor of `8x` (the theoretical limit would be `1.3s`). 2/ DeepSpeed also uses custom cuda kernels to avoid allocating too much memory and doing tensor copies - The effect of this is less memory allocation, less kernel starts which does reduce **T** but also + The effect of this is less memory allocation and fewer kernel starts which does reduce **T** but also allows for bigger batch sizes (here **128** which also participates in raising the overall throughput. @@ -163,25 +163,25 @@ You can see two factors at play leading to better performance here. Another approach we took, was relooking at the overall behavior of the webserver in generation mode. -In practice when users send request to a `text-generation` model, we don't run a single `forward` loop but multiple ones. +In practice when users send requests to a `text-generation` model, we don't run a single `forward` loop but multiple ones. They also don't send the same parameters, some want `greedy`, some `sampling` and some `beam_search` for instance, and probably with never really the same values. In theory, if we're using `.generate` directly, we are **not** able to do any batching on different parameter sets. So what happens -is that we're less likely to use batching in production, and that as mentionned above is a *big* source of throughput improvement. +is that we're less likely to use batching in production and that as mentioned above is a *big* source of throughput improvement. Let's take a simple example where there's only 1 query running, and another query comes in while the first is being processed. -As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But for the **request 2** is has to wait -`4.5 x T`. which is bigger. Actually, if there was a **request 3** with yet another parameter set, then you would have to wait `7.5 x T`. +As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait +for `4.5 x T`. which is bigger. If there was a **request 3** with yet another parameter set, then you would have to wait for `7.5 x T`. And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. Another approach is to split entirely the generation loop. We're going to ignore the `use_cache=True` complexity for the sake of readability. -So now, what we're going to do is have each request, handle the `generate` part on it own, and simply send back the required tensors only for -the `forward` pass. A single thread will be responsible of batching those simple requests. +So now, what we're going to do is have each request, handle the `generate` part on its own, and simply send back the required tensors only for +the `forward` pass. A single thread will be responsible for batching those simple requests. Since each request is handling entirely its own way of choosing next ids, there is no problem in handling an infinite amount of different parameters. @@ -192,7 +192,7 @@ You're getting a compute model like this. Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), **request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. -As with any sort of optimization, the trick is using all source of optimizations at once, for instance here since every request is doing it's own `.generate` then +As with any sort of optimization, the trick is using all sources of optimizations at once, for instance here since every request is doing its own `.generate` then the tensors have to be copied a bunch of times (including the past key values). And any additional overhead adds up to increase **T** you need to make good care that doing this generation split is not offsetting all the other benefits. @@ -201,7 +201,7 @@ It's in Rust, mostly because threading/async/multiprocessing have a lot of cavea Rust doesn't provide any performance benefits here (it uses the same PyTorch under the hood) This code is not meant to be portable and should run on `16xA100(40)` and it also uses PP (like `accelerate`) and uses a technique to interleave GPU usage to that the throughput should be more comparable to deepspeed, -even if the latency (**T**) is exactly the same as `accelerate`. +even if the latency (**T**) is the same as `accelerate`. There are other issues you need to take into account, like padding which might also hit your performance but this is @@ -220,7 +220,7 @@ TORCH_CUDA_VERSION=cu116 cargo run --release ### Run -**This experiement does not have a 1-1 benchmark analysis since it's running a full fledged webserver and was never ran on the same machines as before** +**This experiment does not have a 1-1 benchmark analysis since it's running a full fledged webserver and was never ran on the same machines as before** ``` *** Performance stats (NUMBERS ARE ESTIMATES FROM QUERIES): @@ -232,5 +232,5 @@ Start to finish: N/A ``` The faster load speed is due to `safetensors` (not a production ready library yet) is using `mmap` + there's 1 thread to load per GPU. -Overall the latency should be exactly the same as `accelerate`, throughput is ~10X better and **most importantly, it can support any amount of different parameters** +Overall the latency should be the same as `accelerate`, throughput is ~10X better and **most importantly, it can support any amount of different parameters** without any latency cost. From 28992065779ee02f64f4724cc2ce78a7a780ce5b Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Fri, 5 Aug 2022 08:58:47 -0700 Subject: [PATCH 13/17] restore last version before force pushes --- bloom-inference-pytorch.md | 107 ++++++++++--------------------------- 1 file changed, 27 insertions(+), 80 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index a8a87e93b8..3617c1924c 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -37,13 +37,13 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png -This article discusses various PyTorch-based solutions to run efficiently 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). +This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, at the time of this writing, they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at the time of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. Using a single node is ideal since PCIe speed is typically much faster than inter-node network. -If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course, the generation time will be much slower. +If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course the generation time will be much slower. For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for loading speed time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. @@ -55,14 +55,7 @@ For doing inference on a server the two important metrics are the overall latenc The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. -The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then of course there is a small overhead of processing the input, sending data to the server, and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. - -Let's try and show the metrics in a drawing: - - - -In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of computing power so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. -As you increase your batch_size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. +The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this of course will make the overall throughput lower. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. This article primarily discusses simple solutions in PyTorch. More efficient solutions are being worked on as well. @@ -80,7 +73,7 @@ Accelerate handles big models for inference in the following way: It then ensures the model runs properly with hooks that transfer the inputs and outputs on the right device and that the model weights offloaded on the CPU (or even the disk) are loaded on a GPU just before the forward pass, before being offloaded again once the forward pass is finished. -In a situation where there are multiple GPUs with enough space to accommodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. +In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token as compared to 10 msecs on 8x80 A100s. @@ -92,7 +85,6 @@ You can learn more about this solution in [Accelerate documentation](https://hug pip install accelerate transformers>=4.20.1 git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ cd Megatron-DeepSpeed -git checkout bloom-inference ``` ### Run @@ -102,7 +94,6 @@ python scripts/inference/bloom-accelerate-inference.py --name bigscience/bloom - [...] *** Performance stats: -Latency (T): 104 msecs Throughput per token including tokenize: 10.74 msecs Start to ready to generate: 107.892 secs Tokenize and generate 20000 (bs=40) tokens: 52.059 secs @@ -115,7 +106,7 @@ The highest batch size we were able to run without OOM was 40 in this case. ## DeepSpeed-Inference -[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver a super-fast <1msec per token inference on a large batch size of 128. +[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver an super-fast <1msec per token inference on a large batch size of 128. @@ -133,7 +124,6 @@ cd Megatron-DeepSpeed deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 128 --benchmark [...] *** Performance stats: -Latency (T): 18.8 msecs Throughput per token including tokenize: 0.73 msecs Start to ready to generate: 591.902 secs Tokenize and generate 64000 (bs=128) tokens: 9.421 secs @@ -142,95 +132,52 @@ Start to finish: 601.323 secs ``` The highest batch size we were able to run without OOM was 128 in this case. -You can see two factors at play leading to better performance here. - -1/ T was reduced by using Tensor Parallelism (TP) instead of the Pipeline Parallelism (PP) of `accelerate`. - Because accelerate is meant to be very generic it is also unfortunately hard to maximize the GPU usage. - All computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means - 7 GPU are idle all the time. - `DeepSpeed` on the other end uses TP meaning it will send tensors to all GPUs, compute part of the computation - then all GPUs communicate to each other the results, then move on to the next layer. That means all - GPUs are active at once but they need to communicate much more. Overall that decreases **T** by a theoretical - factor of `8x` (the theoretical limit would be `1.3s`). - -2/ DeepSpeed also uses custom cuda kernels to avoid allocating too much memory and doing tensor copies - The effect of this is less memory allocation and fewer kernel starts which does reduce **T** but also - allows for bigger batch sizes (here **128** which also participates in raising the overall throughput. - -## Splitting the generation -Another approach we took, was relooking at the overall behavior of the webserver in generation mode. -In practice when users send requests to a `text-generation` model, we don't run a single `forward` loop but multiple ones. -They also don't send the same parameters, some want `greedy`, some `sampling` and some `beam_search` for instance, and probably -with never really the same values. -In theory, if we're using `.generate` directly, we are **not** able to do any batching on different parameter sets. So what happens -is that we're less likely to use batching in production and that as mentioned above is a *big* source of throughput improvement. -Let's take a simple example where there's only 1 query running, and another query comes in while the first is being processed. +## DeepSpeed-ZeRO - +[DeepSpeed-ZeRO](https://www.deepspeed.ai/tutorials/zero/) was primarily developed for training huge models, and does not target inference, but it still produces pretty decent results. ZeRO shards the weights across multiple GPUs and then gathers the needed weights magically before each `forward` and `backward` calls, so that the model isn't aware that its weights were ever split up. -As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait -for `4.5 x T`. which is bigger. If there was a **request 3** with yet another parameter set, then you would have to wait for `7.5 x T`. -And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. -Another approach is to split entirely the generation loop. We're going to ignore the `use_cache=True` complexity for the sake of readability. - -So now, what we're going to do is have each request, handle the `generate` part on its own, and simply send back the required tensors only for -the `forward` pass. A single thread will be responsible for batching those simple requests. - -Since each request is handling entirely its own way of choosing next ids, there is no problem in handling an infinite amount of different parameters. +### Setup -You're getting a compute model like this. +``` +pip install deepspeed>=0.6.7 transformers>=4.20.1 +git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ +cd Megatron-DeepSpeed +``` - +### Run -Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), -**request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. +``` +deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark +[...] +*** Performance stats: +Throughput per token including tokenize: 37.86 msecs +Start to ready to generate: 464.724 secs +Tokenize and generate 32000 (bs=8) tokens: 242.090 secs +Start to finish: 706.813 secs +``` -As with any sort of optimization, the trick is using all sources of optimizations at once, for instance here since every request is doing its own `.generate` then -the tensors have to be copied a bunch of times (including the past key values). And any additional overhead adds up to increase **T** you need to make good care that doing this generation split is not offsetting all the other benefits. +The highest batch size we were able to run without OOM was 8 in this case. -Code here in Rust: https://github.com/Narsil/bloomserver -It's in Rust, mostly because threading/async/multiprocessing have a lot of caveats in Python and was causing a lot of issues. -Rust doesn't provide any performance benefits here (it uses the same PyTorch under the hood) -This code is not meant to be portable and should run on `16xA100(40)` and it also uses PP (like `accelerate`) -and uses a technique to interleave GPU usage to that the throughput should be more comparable to deepspeed, -even if the latency (**T**) is the same as `accelerate`. +## Rust -There are other issues you need to take into account, like padding which might also hit your performance but this is -beyond this blogpost (More info [here](https://huggingface.co/docs/transformers/v4.21.0/en/main_classes/pipelines#pipeline-batching)). +XXX: Nicolas's work here ### Setup ``` -git clone https://github.com/Narsil/bloomserver.git -pip install setuptools_rust -pip install -r requirements.txt -python convert_weights.py #libtorch cannot load Python made weights so we have to convert them -TORCH_CUDA_VERSION=cu116 cargo run --release ``` ### Run -**This experiment does not have a 1-1 benchmark analysis since it's running a full fledged webserver and was never ran on the same machines as before** - ``` -*** Performance stats (NUMBERS ARE ESTIMATES FROM QUERIES): -Latency (T): 104 msecs (should be similar to `accelerate`, it's using the roughly same code) -Throughput per token including tokenize: N/A (higher than accelerate, we're able to get 10X throughput compared to `accelerate`, on a similar machine) -Start to ready to generate: 15.902 secs -Tokenize and generate N/A (bs=N/A) tokens: N/A -Start to finish: N/A ``` - -The faster load speed is due to `safetensors` (not a production ready library yet) is using `mmap` + there's 1 thread to load per GPU. -Overall the latency should be the same as `accelerate`, throughput is ~10X better and **most importantly, it can support any amount of different parameters** -without any latency cost. From 31b315d80f3c08b07ef94081905b0c8e6f9ab4a5 Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Fri, 5 Aug 2022 09:22:44 -0700 Subject: [PATCH 14/17] only Nicolas version --- bloom-inference-pytorch.md | 107 +++++++++++++++++++++++++++---------- 1 file changed, 80 insertions(+), 27 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 3617c1924c..83077b10b2 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -37,13 +37,13 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png -This article discusses various PyTorch-based solution to efficient 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). +This article discusses various PyTorch-based solutions to run efficiently 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s And, is that at the time of this writing they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s, at the time of this writing, they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. Using a single node is ideal since PCIe speed is typically much faster than inter-node network. -If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course the generation time will be much slower. +If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course, the generation time will be much slower. For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for loading speed time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. @@ -55,7 +55,14 @@ For doing inference on a server the two important metrics are the overall latenc The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. -The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this of course will make the overall throughput lower. Then of course there is a small overhead of processing the input, sending data to the server and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. +The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then of course there is a small overhead of processing the input, sending data to the server, and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. + +Let's try and show the metrics in a drawing: + + + +In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of computing power so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. +As you increase your batch_size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. This article primarily discusses simple solutions in PyTorch. More efficient solutions are being worked on as well. @@ -73,7 +80,7 @@ Accelerate handles big models for inference in the following way: It then ensures the model runs properly with hooks that transfer the inputs and outputs on the right device and that the model weights offloaded on the CPU (or even the disk) are loaded on a GPU just before the forward pass, before being offloaded again once the forward pass is finished. -In a situation where there are multiple GPUs with enough space to accomodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. +In a situation where there are multiple GPUs with enough space to accommodate the whole model, it switches control from one GPU to the next until all layers have run. Only one GPU works at any given time, which sounds very inefficient but it does produce decent throughput despite the idling of the GPUs. It is also very flexible since the same code can run on any given setup. Accelerate will use all available GPUs first, then offload on the CPU until the RAM is full, and finally on the disk. Offloading to CPU or disk will make things slower. As an example, users have reported running BLOOM with no code changes on just 2 A100s with a throughput of 15s per token as compared to 10 msecs on 8x80 A100s. @@ -85,6 +92,7 @@ You can learn more about this solution in [Accelerate documentation](https://hug pip install accelerate transformers>=4.20.1 git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ cd Megatron-DeepSpeed +git checkout bloom-inference ``` ### Run @@ -94,6 +102,7 @@ python scripts/inference/bloom-accelerate-inference.py --name bigscience/bloom - [...] *** Performance stats: +Latency (T): 104 msecs Throughput per token including tokenize: 10.74 msecs Start to ready to generate: 107.892 secs Tokenize and generate 20000 (bs=40) tokens: 52.059 secs @@ -106,7 +115,7 @@ The highest batch size we were able to run without OOM was 40 in this case. ## DeepSpeed-Inference -[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver an super-fast <1msec per token inference on a large batch size of 128. +[DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/) uses Tensor-Parallelism and efficient fused CUDA kernels to deliver a super-fast <1msec per token inference on a large batch size of 128. @@ -124,6 +133,7 @@ cd Megatron-DeepSpeed deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 128 --benchmark [...] *** Performance stats: +Latency (T): 18.8 msecs Throughput per token including tokenize: 0.73 msecs Start to ready to generate: 591.902 secs Tokenize and generate 64000 (bs=128) tokens: 9.421 secs @@ -132,52 +142,95 @@ Start to finish: 601.323 secs ``` The highest batch size we were able to run without OOM was 128 in this case. +You can see two factors at play leading to better performance here. +1/ T was reduced by using Tensor Parallelism (TP) instead of the Pipeline Parallelism (PP) of `accelerate`. + Because accelerate is meant to be very generic it is also unfortunately hard to maximize the GPU usage. + All computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means + 7 GPU are idle all the time. + `DeepSpeed` on the other end uses TP meaning it will send tensors to all GPUs, compute part of the computation + then all GPUs communicate to each other the results, then move on to the next layer. That means all + GPUs are active at once but they need to communicate much more. Overall that decreases **T** by a theoretical + factor of `8x` (the theoretical limit would be `1.3s`). +2/ DeepSpeed also uses custom cuda kernels to avoid allocating too much memory and doing tensor copies + The effect of this is less memory allocation and fewer kernel starts which does reduce **T** but also + allows for bigger batch sizes (here **128** which also participates in raising the overall throughput. +## Splitting the generation -## DeepSpeed-ZeRO +Another approach we took, was relooking at the overall behavior of the webserver in generation mode. -[DeepSpeed-ZeRO](https://www.deepspeed.ai/tutorials/zero/) was primarily developed for training huge models, and does not target inference, but it still produces pretty decent results. ZeRO shards the weights across multiple GPUs and then gathers the needed weights magically before each `forward` and `backward` calls, so that the model isn't aware that its weights were ever split up. +In practice when users send requests to a `text-generation` model, we don't run a single `forward` loop but multiple ones. +They also don't send the same parameters, some want `greedy`, some `sampling` and some `beam_search` for instance, and probably +with never really the same values. +In theory, if we're using `.generate` directly, we are **not** able to do any batching on different parameter sets. So what happens +is that we're less likely to use batching in production and that as mentioned above is a *big* source of throughput improvement. -### Setup +Let's take a simple example where there's only 1 query running, and another query comes in while the first is being processed. -``` -pip install deepspeed>=0.6.7 transformers>=4.20.1 -git clone https://github.com/bigscience-workshop/Megatron-DeepSpeed/ -cd Megatron-DeepSpeed -``` + -### Run +As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait +for `4.5 x T`. which is bigger. If there was a **request 3** with yet another parameter set, then you would have to wait for `7.5 x T`. +And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. -``` -deepspeed --num_gpus 8 scripts/inference/bloom-ds-inference.py --name bigscience/bloom --batch_size 8 --benchmark -[...] -*** Performance stats: -Throughput per token including tokenize: 37.86 msecs -Start to ready to generate: 464.724 secs -Tokenize and generate 32000 (bs=8) tokens: 242.090 secs -Start to finish: 706.813 secs -``` +Another approach is to split entirely the generation loop. We're going to ignore the `use_cache=True` complexity for the sake of readability. + +So now, what we're going to do is have each request, handle the `generate` part on its own, and simply send back the required tensors only for +the `forward` pass. A single thread will be responsible for batching those simple requests. + +Since each request is handling entirely its own way of choosing next ids, there is no problem in handling an infinite amount of different parameters. -The highest batch size we were able to run without OOM was 8 in this case. +You're getting a compute model like this. + +Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), +**request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. -## Rust +As with any sort of optimization, the trick is using all sources of optimizations at once, for instance here since every request is doing its own `.generate` then +the tensors have to be copied a bunch of times (including the past key values). And any additional overhead adds up to increase **T** you need to make good care that doing this generation split is not offsetting all the other benefits. -XXX: Nicolas's work here + +Code here in Rust: https://github.com/Narsil/bloomserver +It's in Rust, mostly because threading/async/multiprocessing have a lot of caveats in Python and was causing a lot of issues. +Rust doesn't provide any performance benefits here (it uses the same PyTorch under the hood) +This code is not meant to be portable and should run on `16xA100(40)` and it also uses PP (like `accelerate`) +and uses a technique to interleave GPU usage to that the throughput should be more comparable to deepspeed, +even if the latency (**T**) is the same as `accelerate`. + + +There are other issues you need to take into account, like padding which might also hit your performance but this is +beyond this blogpost (More info [here](https://huggingface.co/docs/transformers/v4.21.0/en/main_classes/pipelines#pipeline-batching)). ### Setup ``` +git clone https://github.com/Narsil/bloomserver.git +pip install setuptools_rust +pip install -r requirements.txt +python convert_weights.py #libtorch cannot load Python made weights so we have to convert them +TORCH_CUDA_VERSION=cu116 cargo run --release ``` ### Run +**This experiment does not have a 1-1 benchmark analysis since it's running a full fledged webserver and was never ran on the same machines as before** + ``` +*** Performance stats (NUMBERS ARE ESTIMATES FROM QUERIES): +Latency (T): 104 msecs (should be similar to `accelerate`, it's using the roughly same code) +Throughput per token including tokenize: N/A (higher than accelerate, we're able to get 10X throughput compared to `accelerate`, on a similar machine) +Start to ready to generate: 15.902 secs +Tokenize and generate N/A (bs=N/A) tokens: N/A +Start to finish: N/A ``` + +The faster load speed is due to `safetensors` (not a production ready library yet) is using `mmap` + there's 1 thread to load per GPU. +Overall the latency should be the same as `accelerate`, throughput is ~10X better and **most importantly, it can support any amount of different parameters** +without any latency cost. From 5c116e748ab82d1a2e43530dc5fd5496ee48db7c Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Fri, 5 Aug 2022 09:27:38 -0700 Subject: [PATCH 15/17] fix image urls --- bloom-inference-pytorch.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 83077b10b2..174a903085 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -59,7 +59,7 @@ The latency is more difficult to define. If the server is idle and the model is Let's try and show the metrics in a drawing: - +![initial_drawing](assets/bloom-inference-pytorch/initial_drawing.png) In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of computing power so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. As you increase your batch_size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. @@ -172,7 +172,7 @@ is that we're less likely to use batching in production and that as mentioned ab Let's take a simple example where there's only 1 query running, and another query comes in while the first is being processed. - +![overall_latency](assets/bloom-inference-pytorch/overall_latency.png) As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait for `4.5 x T`. which is bigger. If there was a **request 3** with yet another parameter set, then you would have to wait for `7.5 x T`. @@ -187,7 +187,7 @@ Since each request is handling entirely its own way of choosing next ids, there You're getting a compute model like this. - +![reducing_server_latency](assets/bloom-inference-pytorch/reducing_server_latency.png) Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), **request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. From cf190edaada04ed2b05e866b5fa5987a3e50257a Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Fri, 5 Aug 2022 10:17:14 -0700 Subject: [PATCH 16/17] edit the new text --- bloom-inference-pytorch.md | 73 +++++++++++++------------------------- 1 file changed, 24 insertions(+), 49 deletions(-) diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 174a903085..09341f381a 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -39,30 +39,32 @@ thumbnail: /blog/assets/86_bloom_megatron_deepspeed/thumbnail.png This article discusses various PyTorch-based solutions to run efficiently 176B parameter [BLOOM model](https://huggingface.co/bigscience/bloom). -As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100. The second best is 2x8x40GB A100s. The main reason for using A100s, at the time of this writing, they provide the largest GPU memory. But other GPUs can be used as well. It'd probably take 24x32GB V100 as another possibility. +As the model needs 352GB in bf16 (bfloat16) weights (`176*2`), the most efficient set-up is 8x80GB A100 GPUs. Also 2x8x40GB A100s or 2x8x48GB A6000 can be used. The main reason for using these GPUs is that at the time of this writing they provide the largest GPU memory, but other GPUs can be used as well. For example, 24x32GB V100s can be used. -Using a single node is ideal since PCIe speed is typically much faster than inter-node network. +Using a single node will deliver the fastest througput since PCIe speed is typically much faster than inter-node network. If you don't have that much hardware, it's still possible to run BLOOM inference on smaller GPUs, by using CPU or NVME offload, but of course, the generation time will be much slower. -For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for loading speed time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. +For the sake of consistency, unless stated differently, the benchmarks in this article were all done on the same 8x80GB A100 node on [Jean Zay HPC](http://www.idris.fr/eng/jean-zay/index.html). The HPC users enjoy a very fast IO of about 3GB/s read speed (GPFS). This is important for checkpoint loading time. A slow disc will result in slow loading time. Especially since we are concurrently doing IO in multiple processes. ## Performance metrics -For doing occasional inference on someone's desktop, that is a few inputs at a time, the two critical metrics are the time to load the framework and the checkpoint shards and then the generation time. +For doing occasional inference on someone's desktop, that is a few inputs at a time, the two critical metrics are: + +1. the time to load the framework and the checkpoint files +2. the generation time. For doing inference on a server the two important metrics are the overall latency and the throughput of tokens. The loading time is less important since once loaded, the model becomes persistent. -The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. +The time to generate a token is straightforward - this is just how long it takes for a new token to be generated. As long as `use_cache=True` (the default argument to `generate`) all the previous logits are saved and therefore each token takes the same amount of time to generate. This of course comes at a memory cost - as the model is huge it can easily take many GBs of memory to keep the cache. One can turn the caching off, and save memory, but it'd mean recalculating all the previous logits - which would make a long sequence generation very slow. -The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then of course there is a small overhead of processing the input, sending data to the server, and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. +The latency is more difficult to define. If the server is idle and the model is ready to generate, and one or more prompts are sent from the same user at once, the latency would be just the time to generate a single token multiplied by the number of new tokens to generate. However, if you have multiple users sending prompts at different times, things become much more complicated. The first user gets the fastest response and thus fast latency, but subsequent users if they are unlucky to submit their request while the previous request is being processed may have to first wait till the first request has been generated and only then their request will be attended. It's possible to design a very efficient system that uses a pipeline parallelism, where a new request can join the pipeline at any moment. But this has caveats. Then, of course, there is a small overhead of processing the input, sending data to the server, and sending the results back to the user, but this is usually insignificant compared to the cost of generation on a huge model. Let's try and show the metrics in a drawing: ![initial_drawing](assets/bloom-inference-pytorch/initial_drawing.png) -In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of computing power so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. -As you increase your batch_size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. +In this image, **T** is the simple forward latency. This is determined by how efficiently the **forward** pass is coded. But as you can see, using `8xA100` gives plenty of computing power so we can increase the batch size, and that's (to a point) not going to change **T**. But you will be generating more tokens within **T** so your throughput is effectively going up. As you increase your batch size, your memory consumption will likely go up too, so you might hit OOM errors, **before** actually reaching your computational boundary. This article primarily discusses simple solutions in PyTorch. More efficient solutions are being worked on as well. @@ -144,44 +146,28 @@ Start to finish: 601.323 secs The highest batch size we were able to run without OOM was 128 in this case. You can see two factors at play leading to better performance here. -1/ T was reduced by using Tensor Parallelism (TP) instead of the Pipeline Parallelism (PP) of `accelerate`. - Because accelerate is meant to be very generic it is also unfortunately hard to maximize the GPU usage. - All computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means - 7 GPU are idle all the time. - `DeepSpeed` on the other end uses TP meaning it will send tensors to all GPUs, compute part of the computation - then all GPUs communicate to each other the results, then move on to the next layer. That means all - GPUs are active at once but they need to communicate much more. Overall that decreases **T** by a theoretical - factor of `8x` (the theoretical limit would be `1.3s`). +1. T was reduced by using Tensor Parallelism (TP) instead of the Pipeline Parallelism (PP) of Accelerate. Because Accelerate is meant to be very generic it is also unfortunately hard to maximize the GPU usage. All computations are done first on GPU 0, then on GPU 1, etc... until GPU 8, which means 7 GPU are idle all the time. DeepSpeed-Inference on the other hand uses TP, meaning it will send tensors to all GPUs, compute part of the generation on each GPU and then all GPUs communicate to each other the results, then move on to the next layer. That means all GPUs are active at once but they need to communicate much more. Overall that decreases **T** by a theoretical factor of `8x` (the theoretical limit would be `1.3s`). -2/ DeepSpeed also uses custom cuda kernels to avoid allocating too much memory and doing tensor copies - The effect of this is less memory allocation and fewer kernel starts which does reduce **T** but also - allows for bigger batch sizes (here **128** which also participates in raising the overall throughput. +2. DeepSpeed-Inference also uses custom cuda kernels to avoid allocating too much memory and doing tensor copying to and from GPUs. The effect of this is lesser memory requirements and fewer kernel starts which reduces **T** and allows for bigger batch sizes leading to higher overall throughput. ## Splitting the generation +Let's additionally look at generation in the webserver mode. -Another approach we took, was relooking at the overall behavior of the webserver in generation mode. - -In practice when users send requests to a `text-generation` model, we don't run a single `forward` loop but multiple ones. -They also don't send the same parameters, some want `greedy`, some `sampling` and some `beam_search` for instance, and probably -with never really the same values. +In practice when users send requests to a `text-generation` model, we don't run a single `forward` loop, but multiple ones. Additionally users don't send the same parameters, some want `greedy`, some `sampling` and some `beam_search`, and the prompts are different as well. -In theory, if we're using `.generate` directly, we are **not** able to do any batching on different parameter sets. So what happens -is that we're less likely to use batching in production and that as mentioned above is a *big* source of throughput improvement. +In theory, if we're using `generate` directly, we are **not** able to do any batching on different parameter sets. So what happens is that we're less likely to use large batches in production and that as mentioned above is a *big* source of throughput improvement. Let's take a simple example where there's only 1 query running, and another query comes in while the first is being processed. ![overall_latency](assets/bloom-inference-pytorch/overall_latency.png) -As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait -for `4.5 x T`. which is bigger. If there was a **request 3** with yet another parameter set, then you would have to wait for `7.5 x T`. -And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. +As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait for `4.5 x T`, which is longer. If there was a **request 3** with yet another parameter set, then you would have to wait for, say,`7.5 x T`. And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. -Another approach is to split entirely the generation loop. We're going to ignore the `use_cache=True` complexity for the sake of readability. +Another approach is to split the generation loop entirely. We're going to ignore the `use_cache=True` complexity for the sake of readability. -So now, what we're going to do is have each request, handle the `generate` part on its own, and simply send back the required tensors only for -the `forward` pass. A single thread will be responsible for batching those simple requests. +So now, what we're going to do is to have each request, handle the `generate` part on its own, and simply send back the required tensors only for the `forward` pass. A single thread will be responsible for batching those simple requests. Since each request is handling entirely its own way of choosing next ids, there is no problem in handling an infinite amount of different parameters. @@ -189,20 +175,11 @@ You're getting a compute model like this. ![reducing_server_latency](assets/bloom-inference-pytorch/reducing_server_latency.png) -Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), -**request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. - -As with any sort of optimization, the trick is using all sources of optimizations at once, for instance here since every request is doing its own `.generate` then -the tensors have to be copied a bunch of times (including the past key values). And any additional overhead adds up to increase **T** you need to make good care that doing this generation split is not offsetting all the other benefits. - +Here you can see that at most, **request 2** has to wait for 1 forward pass to join the batch. And so even if **request 1** is super long (as in many tokens need to be generated), **request 2** can start to execute almost immediately, and might even finish before **request 1** is done, and we actually see that happening in practice regularly. -Code here in Rust: https://github.com/Narsil/bloomserver -It's in Rust, mostly because threading/async/multiprocessing have a lot of caveats in Python and was causing a lot of issues. -Rust doesn't provide any performance benefits here (it uses the same PyTorch under the hood) -This code is not meant to be portable and should run on `16xA100(40)` and it also uses PP (like `accelerate`) -and uses a technique to interleave GPU usage to that the throughput should be more comparable to deepspeed, -even if the latency (**T**) is the same as `accelerate`. +As with any sort of optimization, the trick is using all sources of optimizations at once, for instance, here since every request is doing its own `generate` the tensors have to be copied a bunch of times (including the past key values). And any additional overhead adds up to increase **T** you need to take good care that doing this generation split is not offsetting all the other benefits. +Please have a look at the code here: https://github.com/Narsil/bloomserver. It's in Rust, mostly because threading/async/multiprocessing have a lot of caveats in Python and was causing a lot of issues. Rust doesn't provide any performance benefits here (it uses the same PyTorch backend under the hood). This code is not meant to be portable and should run on 16x40 A100s and it also uses PP (like Accelerate) and uses a technique to interleave GPU usage so that the throughput should be more comparable to Deepspeed-Inference, even if the latency (**T**) is the same as that of Accelerate. There are other issues you need to take into account, like padding which might also hit your performance but this is beyond this blogpost (More info [here](https://huggingface.co/docs/transformers/v4.21.0/en/main_classes/pipelines#pipeline-batching)). @@ -224,13 +201,11 @@ TORCH_CUDA_VERSION=cu116 cargo run --release ``` *** Performance stats (NUMBERS ARE ESTIMATES FROM QUERIES): -Latency (T): 104 msecs (should be similar to `accelerate`, it's using the roughly same code) -Throughput per token including tokenize: N/A (higher than accelerate, we're able to get 10X throughput compared to `accelerate`, on a similar machine) +Latency (T): 104 msecs (should be similar to Accelerate, it's using the roughly same code) +Throughput per token including tokenize: N/A (higher than accelerate, we're able to get 10X throughput compared to Accelerate, on a similar machine) Start to ready to generate: 15.902 secs Tokenize and generate N/A (bs=N/A) tokens: N/A Start to finish: N/A ``` -The faster load speed is due to `safetensors` (not a production ready library yet) is using `mmap` + there's 1 thread to load per GPU. -Overall the latency should be the same as `accelerate`, throughput is ~10X better and **most importantly, it can support any amount of different parameters** -without any latency cost. +The faster load speed is due to `safetensors` (not a production ready library yet) is using `mmap` + there's 1 thread to load per GPU. Overall the latency should be the same as that of Accelerate, throughput is ~10X better and **most importantly, it can support any amount of different parameters** without any latency cost. From 134285b116e833e91723c85dca68aadca85854a4 Mon Sep 17 00:00:00 2001 From: Stas Bekman Date: Fri, 5 Aug 2022 11:02:38 -0700 Subject: [PATCH 17/17] edits --- _blog.yml | 24 ++++++++++++------------ bloom-inference-pytorch.md | 2 +- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/_blog.yml b/_blog.yml index bc6105125d..73602057b8 100644 --- a/_blog.yml +++ b/_blog.yml @@ -1015,7 +1015,7 @@ tags: - mnist - adversarial - - guide + - guide - local: deep-rl-a2c title: "Advantage Actor Critic (A2C)" @@ -1033,7 +1033,7 @@ tags: - guide - cv - + - local: tf-xla-generate title: "Faster Text Generation with TensorFlow and XLA" author: joaogante @@ -1053,7 +1053,7 @@ - cv - community - announcement - + - local: us-national-ai-research-resource title: "AI Policy @🤗: Comments on U.S. National AI Research Resource Interim Report" author: irenesolaiman @@ -1081,6 +1081,15 @@ - private hub +- local: deep-rl-ppo + title: "Proximal Policy Optimization (PPO)" + author: ThomasSimonini + thumbnail: /blog/assets/93_deep_rl_ppo/thumbnail.png + date: August 5, 2022 + tags: + - rl + + - local: bloom-inference-pytorch title: "The Technology Behind BLOOM Training" author: stas, sgugger, Narsil @@ -1090,12 +1099,3 @@ - nlp - llm - inference - -- local: deep-rl-ppo - title: "Proximal Policy Optimization (PPO)" - author: ThomasSimonini - thumbnail: /blog/assets/93_deep_rl_ppo/thumbnail.png - date: August 5, 2022 - tags: - - rl - diff --git a/bloom-inference-pytorch.md b/bloom-inference-pytorch.md index 09341f381a..6f011365aa 100644 --- a/bloom-inference-pytorch.md +++ b/bloom-inference-pytorch.md @@ -163,7 +163,7 @@ Let's take a simple example where there's only 1 query running, and another quer ![overall_latency](assets/bloom-inference-pytorch/overall_latency.png) -As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait for `4.5 x T`, which is longer. If there was a **request 3** with yet another parameter set, then you would have to wait for, say,`7.5 x T`. And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. +As you can see here, the first request gets a latency of `3 x T` which is what we would expect. But **request 2** has to wait for `4.5 x T`, which is longer. If there was a **request 3** with yet another parameter set, then you would have to wait for, say, `7.5 x T`. And it piles up. The simple solution is to force a single (or handful) parameter sets so that we can ensure we're capping the overall latency. Another approach is to split the generation loop entirely. We're going to ignore the `use_cache=True` complexity for the sake of readability.

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