Update code/inference.py
Browse files- code/inference.py +42 -44
code/inference.py
CHANGED
@@ -1,53 +1,53 @@
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import json
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from typing import List, Dict
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from accelerate import load_checkpoint_and_dispatch
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# Global variables to persist the model and tokenizer between invocations
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model = None
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tokenizer = None
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# Function to format chat messages using Qwen's chat template
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def format_chat(messages: List[Dict[str, str]], tokenizer) -> str:
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return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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# Model loading function for SageMaker with tensor parallelism and
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def model_fn(model_dir, context=None):
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if model is None:
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print("Loading the
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#
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offload_dir = "/tmp/offload_dir"
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os.makedirs(offload_dir, exist_ok=True)
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# Load the tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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# Load the
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model = AutoModelForCausalLM.from_pretrained(
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model_dir,
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torch_dtype=torch.float8, # Specify FP8 dtype
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low_cpu_mem_usage=True,
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device_map="auto",
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offload_folder=offload_dir,
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)
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# Use load_checkpoint_and_dispatch for tensor parallelism
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model = load_checkpoint_and_dispatch(
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model,
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model_dir,
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device_map="auto",
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offload_folder=offload_dir,
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no_split_module_classes=["QWenLMHeadModel"], # Adjust if needed for Qwen architecture
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)
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return model, tokenizer
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# Custom predict function for SageMaker
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def predict_fn(input_data, model_and_tokenizer):
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try:
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model, tokenizer = model_and_tokenizer
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data = json.loads(input_data)
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@@ -57,28 +57,27 @@ def predict_fn(input_data, model_and_tokenizer):
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formatted_prompt = format_chat(messages, tokenizer)
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# Tokenize the input
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inputs = tokenizer([formatted_prompt], return_tensors="pt").to(
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# Generate output
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)
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# Decode the output
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generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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# Build response
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response = {
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"id": "chatcmpl-
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"object": "chat.completion",
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"model": "qwen-72b
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"choices": [{
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"index": 0,
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"message": {
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@@ -89,8 +88,8 @@ def predict_fn(input_data, model_and_tokenizer):
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}],
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"usage": {
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"prompt_tokens": len(inputs['input_ids'][0]),
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"completion_tokens": len(outputs[0])
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"total_tokens": len(outputs[0])
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}
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}
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return response
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@@ -98,16 +97,15 @@ def predict_fn(input_data, model_and_tokenizer):
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except Exception as e:
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return {"error": str(e), "details": repr(e)}
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# Define input format for SageMaker
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def input_fn(serialized_input_data, content_type,
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"""
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Prepare the input data for inference.
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"""
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return serialized_input_data
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# Define output format for SageMaker
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def output_fn(prediction_output, accept
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"""
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Convert the model output to a JSON response.
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"""
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import json
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from typing import List, Dict
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from accelerate import load_checkpoint_and_dispatch
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# Global variables to persist the model and tokenizer between invocations
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model = None
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tokenizer = None
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# Function to format chat messages using Qwen's chat template
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def format_chat(messages: List[Dict[str, str]], tokenizer) -> str:
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"""
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Format chat messages using Qwen's chat template.
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"""
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return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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# Model loading function for SageMaker with tensor parallelism and offloading
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def model_fn(model_dir, context=None):
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"""
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Load the model and tokenizer from the model directory for inference.
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Supports tensor parallelism across multiple GPUs with offloading.
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The model is loaded only once and stored in a global variable.
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"""
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global model, tokenizer # Declare model and tokenizer as global to persist across invocations
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if model is None: # Check if the model is already loaded
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print("Loading the model and tokenizer...")
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# Define an offload directory for any model components that can't fit in GPU memory
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offload_dir = "/tmp/offload_dir" # Ensure SageMaker has write access to this directory
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# Load and dispatch the model across multiple GPUs
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model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.float16)
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model = load_checkpoint_and_dispatch(
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model,
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model_dir,
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device_map="auto", # Automatically map model layers across devices
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offload_folder=offload_dir, # Offload parts of the model to disk if GPU memory is insufficient
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)
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# Load the tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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return model, tokenizer
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# Custom predict function for SageMaker
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def predict_fn(input_data, model_and_tokenizer):
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"""
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Generate predictions for the input data.
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"""
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try:
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model, tokenizer = model_and_tokenizer
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data = json.loads(input_data)
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formatted_prompt = format_chat(messages, tokenizer)
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# Tokenize the input
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inputs = tokenizer([formatted_prompt], return_tensors="pt").to("cuda:0") # Send input to GPU 0 for generation
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# Generate output
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outputs = model.generate(
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inputs['input_ids'],
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max_new_tokens=data.get("max_new_tokens", 512),
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temperature=data.get("temperature", 0.7),
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top_p=data.get("top_p", 0.9),
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repetition_penalty=data.get("repetition_penalty", 1.0),
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length_penalty=data.get("length_penalty", 1.0),
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do_sample=True
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)
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# Decode the output
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generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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# Build response
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response = {
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"id": "chatcmpl-uuid",
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"object": "chat.completion",
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"model": "qwen-72b",
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"choices": [{
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"index": 0,
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"message": {
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}],
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"usage": {
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"prompt_tokens": len(inputs['input_ids'][0]),
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"completion_tokens": len(outputs[0]),
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"total_tokens": len(inputs['input_ids'][0]) + len(outputs[0])
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}
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}
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return response
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except Exception as e:
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return {"error": str(e), "details": repr(e)}
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# Define input format for SageMaker
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def input_fn(serialized_input_data, content_type,context=None):
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"""
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Prepare the input data for inference.
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"""
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return serialized_input_data
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# Define output format for SageMaker
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def output_fn(prediction_output, accept, context=None):
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"""
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Convert the model output to a JSON response.
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"""
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