most advanced language models, like Meta’s 70B-parameter Llama 2, require multiple GPUs working in concert to deliver responses in real time. Previously, developers looking to achieve the best performance for LLM inference had
接着,对所有 GPU 上的 gradients(fp16) 做一次Reduce-Scatter 操作,使得每块 GPU 都可以获得自己维护的那部分梯度的累加和; 最后,分别更新自己维护的那份 optimizer stats(fp32),再用其中 fp32 格式的 parameters 去更新本地的 fp16 的 parameters。 此时,单卡的显存占用,就变为\frac{(4 + K)\Psi}{N}b...
start=time.time()# divide the prompt list onto the available GPUswithaccelerator.split_between_processes(prompts_all)asprompts: results=dict(outputs=[], num_tokens=0)# have each GPU do inference in batchesprompt_batches=prepare_prompts(prompts, tokenizer, batch_size=16)forprompts_tokenizedinprompt...
return batches_tok # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time() # divide the prompt list onto the available GPUs with accelerator.split_between_processes(prompts_all) as prompts: results=dict(outputs=[], num_tokens=0) # have each GPU do inference in b...
start=time.time()# divide the prompt list onto the available GPUswithaccelerator.split_between_processes(prompts_all)asprompts:# store output of generations in dictresults=dict(outputs=[], num_tokens=0)# have each GPU do inference, prompt by promptforpromptinprompts: ...
# divide the prompt list onto the available GPUs with accelerator.split_between_processes(prompts_all) as prompts: # store output of generations in dict results=dict(outputs=[], num_tokens=0) # have each GPU do inference, prompt by prompt ...
# divide the prompt list onto the available GPUs with accelerator.split_between_processes(prompts_all) as prompts: # store output of generations in dict results=dict(outputs=[], num_tokens=0) # have each GPU do inference, prompt by prompt ...
The llm inference is quite fast and everyhting works as expected. So the problem clearly lies with multiple GPUs. This issue happens with all the models and not particular to just one organisation. Can someone please help me in this regard? What am I doing wrong? Is it something due to...
start=time.time()# divide the prompt list onto the available GPUswithaccelerator.split_between_processes(prompts_all)asprompts: results=dict(outputs=[], num_tokens=0)# have each GPU do inference in batchesprompt_batches=prepare_prompts(prompts, tokenizer, batch_size=16)forprompts_tokenizedinprompt...
# divide the prompt list onto the available GPUs with accelerator.split_between_processes(prompts_all) as prompts: # store output of generations in dict results=dict(outputs=[], num_tokens=0) # have each GPU do inference, prompt by prompt ...