[example] update Grok-1 inference

examples/language/grok-1/README.md

@@ -5,7 +5,7 @@ An easy-to-use Python + PyTorch + HuggingFace version of 314B Grok-1.
 [[blog]](https://hpc-ai.com/blog/grok-1-of-pytorch-huggingface-version-is-now-available)
 [[HuggingFace Grok-1 PyTorch model weights]](https://huggingface.co/hpcai-tech/grok-1)
 
-## Install
+## Installation
 
 ```bash
 # Make sure you install colossalai from the latest source code
@@ -16,33 +16,36 @@ cd examples/language/grok-1
 pip install -r requirements.txt
 ```
 
-## Tokenizer preparation
-
-You should download the tokenizer from the official grok-1 repository.
-
-```bash
-wget https://github.com/xai-org/grok-1/raw/main/tokenizer.model
-```
-
 ## Inference
 
 You need 8x A100 80GB or equivalent GPUs to run the inference.
 
-We provide two scripts for inference. `run_inference_fast.sh` uses tensor parallelism provided by ColossalAI, and it is faster. `run_inference_slow.sh` uses auto device provided by transformers, and it is slower.
+We provide two scripts for inference. `run_inference_fast.sh` uses tensor parallelism provided by ColossalAI, which is faster for generation, while `run_inference_slow.sh` uses auto device provided by transformers, which is relatively slower.
 
-Command format:
+Command example:
 
 ```bash
-./run_inference_fast.sh <model_name_or_path> <tokenizer_path>
-./run_inference_slow.sh <model_name_or_path> <tokenizer_path>
+./run_inference_fast.sh <MODEL_NAME_OR_PATH>
+./run_inference_slow.sh <MODEL_NAME_OR_PATH>
 ```
 
-`model_name_or_path` can be a local path or a model name from Hugging Face model hub. We provided weights on model hub, named `hpcaitech/grok-1`.
-
-Command example:
-
+`MODEL_NAME_OR_PATH` can be a model name from Hugging Face model hub or a local path to PyTorch-version model checkpoints. We provided weights on model hub, named `hpcaitech/grok-1`. And you could also download the weights in advance using `git`:
 ```bash
-./run_inference_fast.sh hpcaitech/grok-1 tokenizer.model
+git lfs install
+git clone https://huggingface.co/hpcai-tech/grok-1
 ```
 
-It will take 5-10 minutes to load checkpoints. Don't worry, it's not stuck.
+It will take, depending on your Internet speed, several hours to tens of hours to download checkpoints (about 600G!), and 5-10 minutes to load checkpoints when it's ready to launch the inference. Don't worry, it's not stuck.
+
+
+## Performance
+
+For request of batch size set to 1 and maximum length set to 100:
+
+| Method                  | Initialization-Duration(sec) | Average-Generation-Latency(sec) |
+|-------------------------|------------------------------|---------------------------------|
+| ColossalAI              | 431.45                       | 14.92                           |
+| HuggingFace Auto-Device | 426.96                       | 48.38                           |
+| JAX                     | 147.61                       | 56.25                           |
+
+Tested on 8x80G NVIDIA H800.

examples/language/grok-1/inference.py

@@ -1,8 +1,7 @@
 import time
 
 import torch
-from sentencepiece import SentencePieceProcessor
-from transformers import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, LlamaTokenizerFast
 from utils import get_defualt_parser, inference, print_output
 
 if __name__ == "__main__":
@@ -16,17 +15,32 @@
         device_map="auto",
         torch_dtype=torch.bfloat16,
     )
-    sp = SentencePieceProcessor(model_file=args.tokenizer)
+    model.eval()
+    init_time = time.time() - start
+
+    # A transformers-compatible version of the grok-1 tokenizer by Xenova
+    # https://huggingface.co/Xenova/grok-1-tokenizer
+    tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
+
     for text in args.text:
         output = inference(
             model,
-            sp,
+            tokenizer,
             text,
             max_new_tokens=args.max_new_tokens,
             do_sample=args.do_sample,
             temperature=args.temperature,
             top_k=args.top_k,
             top_p=args.top_p,
         )
-        print_output(text, sp.decode(output))
-    print(f"Overall time: {time.time() - start} seconds.")
+        print_output(text, tokenizer.decode(output))
+
+    overall_time = time.time() - start
+    gen_latency = overall_time - init_time
+    avg_gen_latency = gen_latency / len(args.text)
+    print(
+        f"Initializing time: {init_time:.2f} seconds.\n"
+        f"Overall time: {overall_time:.2f} seconds. \n"
+        f"Generation latency: {gen_latency:.2f} seconds. \n"
+        f"Average generation latency: {avg_gen_latency:.2f} seconds. \n"
+    )

examples/language/grok-1/inference_tp.py

@@ -2,8 +2,7 @@
 
 import torch
 from grok1_policy import Grok1ForCausalLMPolicy
-from sentencepiece import SentencePieceProcessor
-from transformers import AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, LlamaTokenizerFast
 from utils import get_defualt_parser, inference, print_output
 
 import colossalai
@@ -33,11 +32,17 @@
             args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16
         )
     model, *_ = booster.boost(model)
-    sp = SentencePieceProcessor(model_file=args.tokenizer)
+    model.eval()
+    init_time = time.time() - start
+
+    # A transformers-compatible version of the grok-1 tokenizer by Xenova
+    # https://huggingface.co/Xenova/grok-1-tokenizer
+    tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
+
     for text in args.text:
         output = inference(
             model.unwrap(),
-            sp,
+            tokenizer,
             text,
             max_new_tokens=args.max_new_tokens,
             do_sample=args.do_sample,
@@ -46,5 +51,14 @@
             top_p=args.top_p,
         )
         if coordinator.is_master():
-            print_output(text, sp.decode(output))
-    coordinator.print_on_master(f"Overall time: {time.time() - start} seconds.")
+            print_output(text, tokenizer.decode(output))
+
+    overall_time = time.time() - start
+    gen_latency = overall_time - init_time
+    avg_gen_latency = gen_latency / len(args.text)
+    coordinator.print_on_master(
+        f"Initializing time: {init_time:.2f} seconds.\n"
+        f"Overall time: {overall_time:.2f} seconds. \n"
+        f"Generation latency: {gen_latency:.2f} seconds. \n"
+        f"Average generation latency: {avg_gen_latency:.2f} seconds. \n"
+    )

examples/language/grok-1/requirements.txt

@@ -1,4 +1,3 @@
 torch>=2.1.0,<2.2.0
 colossalai>=0.3.6
-sentencepiece==0.1.99
 transformers==4.35.0

examples/language/grok-1/run_inference_fast.sh

@@ -1,11 +1,9 @@
 #!/usr/bin/env bash
 
 PRETRAINED=${1:-"hpcaitech/grok-1"}
-TOKENIZER=${2:-"tokenizer.model"}
 
 torchrun --standalone --nproc_per_node 8 inference_tp.py --pretrained "$PRETRAINED" \
-    --tokenizer "$TOKENIZER" \
-    --max_new_tokens 64 \
+    --max_new_tokens 100 \
     --text "The company's annual conference, featuring keynote speakers and exclusive product launches, will be held at the Los Angeles Convention Center from October 20th to October 23rd, 2021. Extract the date mentioned in the above sentence." \
             "将以下句子翻译成英语。 我喜欢看电影和读书。" \
             "All books have the same weight, 10 books weigh 5kg, what is the weight of 2 books?"

examples/language/grok-1/run_inference_slow.sh

@@ -1,11 +1,9 @@
 #!/usr/bin/env bash
 
 PRETRAINED=${1:-"hpcaitech/grok-1"}
-TOKENIZER=${2:-"tokenizer.model"}
 
 python3 inference.py --pretrained "$PRETRAINED" \
-    --tokenizer "$TOKENIZER" \
-    --max_new_tokens 64 \
+    --max_new_tokens 100 \
     --text "The company's annual conference, featuring keynote speakers and exclusive product launches, will be held at the Los Angeles Convention Center from October 20th to October 23rd, 2021. Extract the date mentioned in the above sentence." \
             "将以下句子翻译成英语。 我喜欢看电影和读书。" \
             "All books have the same weight, 10 books weigh 5kg, what is the weight of 2 books?"

examples/language/grok-1/utils.py

@@ -20,9 +20,9 @@ def print_output(text, output):
 
 
 @torch.no_grad()
-def inference(model, sp, text, **generate_kwargs):
-    input_ids = sp.encode(text)
-    input_ids = torch.tensor([input_ids]).cuda()
+def inference(model, tokenizer, text, **generate_kwargs):
+    input_ids = tokenizer(text, return_tensors="pt").input_ids
+    input_ids = input_ids.cuda()
     attention_mask = torch.ones_like(input_ids)
     inputs = {
         "input_ids": input_ids,