add codellama 70b

generate/generate.py

@@ -8,7 +8,7 @@
 
 # tpl imports
 import torch
-from transformers import pipeline
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
 # local imports
 from utils import BalancedBracketsCriteria, PromptDataset, clean_output, get_inference_config
@@ -33,6 +33,10 @@
 parser.add_argument('--do_sample', action='store_true', help='Enable sampling (default: False)')
 parser.add_argument('--batch_size', type=int, default=16, help='Batch size for generation (default: 8)')
 parser.add_argument('--prompted', action='store_true', help='Use prompted generation. See StarCoder paper (default: False)')
+device_group = parser.add_mutually_exclusive_group()
+device_group.add_argument('--device_map', help='Path to the device map JSON file or the string "auto"')
+device_group.add_argument('--device', type=int, help='Device to use for inference')
+device_group.add_argument('--axonn', action='store_true', help='Use AxoNN for inference')
 args = parser.parse_args()
 
 """ Load prompts """
@@ -96,12 +100,48 @@
 # and repeat them for however many samples we want to generate per prompt
 prompts_repeated = [p for p in prompts for _ in range(args.num_samples_per_prompt)]
 
+""" Set device kwarg for inference """
+device_kwarg = {}
+USE_AXONN = False
+if args.device_map:
+    if args.device_map == "auto":
+        device_kwarg["device_map"] = "auto"
+    else:
+        with open(args.device_map, 'r') as json_file:
+            device_map = json.load(json_file)
+            device_kwarg["device_map"] = device_map
+elif args.device:
+    device_kwarg["device"] = args.device
+elif args.axonn:
+    from mpi4py import MPI
+    from axonn import axonn as ax
+    from modify_llama import monkey_patch_llama_with_axonn
+    world_size = MPI.COMM_WORLD.Get_size()
+    rank = MPI.COMM_WORLD.Get_rank()
+    if rank == 0:
+        print(f"Using AxoNN with {world_size} GPUs.")
+    ax.init(G_data=1, G_inter=1, G_intra_r=1, G_intra_c=1, G_intra_d=world_size)
+    if "llama" in args.model:
+        monkey_patch_llama_with_axonn()
+    USE_AXONN = True
+    device_kwarg["device"] = "cuda"
+else:
+    device_kwarg["device"] = 0
+
+""" Load model and tokenizer """
+model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=inference_config.get_dtype())
+if USE_AXONN:
+    model = model.to("cuda")
+tokenizer = AutoTokenizer.from_pretrained(args.model)
+
 """ Initialize HuggingFace pipeline for generation """
-generator = pipeline(model=args.model, torch_dtype=inference_config.get_dtype(), device=0)
+generator = pipeline(task='text-generation', model=model, tokenizer=tokenizer, **device_kwarg)
 inference_config.init_padding(generator.tokenizer)
 
 """ Create a prompt data set to pass to generate method """
 prompt_dataset = PromptDataset([inference_config.format_prompt(p["prompt"]) for p in prompts_repeated])
+if USE_AXONN:
+    prompt_dataset = prompt_dataset#.to("cuda")
 generated_outputs = generator(
     prompt_dataset,
     max_new_tokens=args.max_new_tokens,
@@ -114,7 +154,7 @@
 )
 
 """ Iterate over prompts and generate code """
-if not args.restart and args.cache is not None:
+if not args.restart and args.cache is not None and os.path.exists(args.cache):
     with open(args.cache, 'r') as jsonl_file:
         responses = [json.loads(line) for line in jsonl_file]
         responses = [r for r in responses if r["temperature"] == args.temperature and r["prompted"] == args.prompted
@@ -140,8 +180,9 @@
         responses.append(cur_prompt)
 
         if not args.restart and args.cache is not None:
-            with open(args.cache, 'a') as jsonl_file:
-                jsonl_file.write(json.dumps(cur_prompt) + "\n")
+            if not USE_AXONN or rank == 0:
+                with open(args.cache, 'a') as jsonl_file:
+                    jsonl_file.write(json.dumps(cur_prompt) + "\n")
 
     if idx != 0 and idx % args.num_samples_per_prompt == 0:
         print(f"Tokens per second: {total_tokens / (time.time() - start_time):.2f}")
@@ -151,5 +192,6 @@
 print(f"Generated {len(responses)} code samples in {end_time - start_time:.2f} seconds ({tokens_per_second:.2f} tokens per second)")
 
 """ Save responses to JSON file """
-with open(args.output, 'w') as output_file:
-    json.dump(responses, output_file, indent=4)
+if not USE_AXONN or rank == 0:
+    with open(args.output, 'w') as output_file:
+        json.dump(responses, output_file, indent=4)

generate/select_gpu_device

@@ -0,0 +1,6 @@
+#!/bin/bash
+# select_gpu_device wrapper script
+export RANK=${SLURM_PROCID}
+export WORLD_SIZE=${SLURM_NTASKS}
+export LOCAL_RANK=${SLURM_LOCALID}
+exec $*