GPT-NeoX-20B support in Colab TPU instances
This commit is contained in:
Gnome Ann
parent
4892556059
commit
88f247d535
34
aiserver.py
34
aiserver.py
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@ -317,7 +317,7 @@ def getmodelname():
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if(args.configname):
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modelname = args.configname
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return modelname
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if(vars.model in ("NeoCustom", "GPT2Custom", "TPUMeshTransformerGPTJ")):
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if(vars.model in ("NeoCustom", "GPT2Custom", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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modelname = os.path.basename(os.path.normpath(vars.custmodpth))
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return modelname
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else:
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@ -699,7 +699,7 @@ def spRequest(filename):
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vars.sp_length = tensor.shape[-2]
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vars.spmeta["n_tokens"] = vars.sp_length
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if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ",)):
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if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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rows = tensor.shape[0]
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padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows
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tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
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@ -790,7 +790,7 @@ else:
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getModelSelection(mainmenu)
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# If transformers model was selected & GPU available, ask to use CPU or GPU
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if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
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if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
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vars.allowsp = True
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# Test for GPU support
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import torch
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@ -830,7 +830,7 @@ if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMe
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print("WARNING: No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)")
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vars.model_type = "gpt_neo"
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if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
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if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
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loadmodelsettings()
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loadsettings()
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print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
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@ -1032,7 +1032,7 @@ socketio = SocketIO(app, async_method="eventlet")
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print("{0}OK!{1}".format(colors.GREEN, colors.END))
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# Start transformers and create pipeline
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if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]):
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if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
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if(not vars.noai):
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print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
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from transformers import StoppingCriteria, GPT2TokenizerFast, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM, AutoTokenizer
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@ -1050,7 +1050,7 @@ if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "Go
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if not vars.lazy_load:
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return
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from tqdm import tqdm
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from tqdm.auto import tqdm
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if "breakmodel" in globals():
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gpu_blocks = breakmodel.gpu_blocks
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@ -1553,9 +1553,9 @@ else:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", cache_dir="cache/")
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loadsettings()
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# Load the TPU backend if requested
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elif(vars.use_colab_tpu or vars.model == "TPUMeshTransformerGPTJ"):
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elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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print("{0}Initializing Mesh Transformer JAX, please wait...{1}".format(colors.PURPLE, colors.END))
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if vars.model == "TPUMeshTransformerGPTJ" and (not vars.custmodpth or not os.path.isdir(vars.custmodpth)):
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if vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (not vars.custmodpth or not os.path.isdir(vars.custmodpth)):
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raise FileNotFoundError(f"The specified model path {repr(vars.custmodpth)} is not the path to a valid folder")
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import tpu_mtj_backend
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tpu_mtj_backend.vars = vars
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@ -1567,7 +1567,7 @@ else:
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vars.allowsp = True
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loadmodelsettings()
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loadsettings()
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tpu_mtj_backend.load_model(vars.custmodpth, hf_checkpoint=vars.model != "TPUMeshTransformerGPTJ" and vars.use_colab_tpu, **vars.modelconfig)
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tpu_mtj_backend.load_model(vars.custmodpth, hf_checkpoint=vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and vars.use_colab_tpu, **vars.modelconfig)
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vars.modeldim = int(tpu_mtj_backend.params["d_model"])
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tokenizer = tpu_mtj_backend.tokenizer
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else:
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@ -2098,7 +2098,7 @@ def lua_get_modeltype():
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return "readonly"
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if(vars.model in ("Colab", "OAI", "InferKit")):
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return "api"
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if(not vars.use_colab_tpu and vars.model not in ("TPUMeshTransformerGPTJ",) and (vars.model in ("GPT2Custom", "NeoCustom") or vars.model_type in ("gpt2", "gpt_neo", "gptj"))):
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if(not vars.use_colab_tpu and vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (vars.model in ("GPT2Custom", "NeoCustom") or vars.model_type in ("gpt2", "gpt_neo", "gptj"))):
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hidden_size = get_hidden_size_from_model(model)
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if(vars.model in ("gpt2",) or (vars.model_type == "gpt2" and hidden_size == 768)):
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return "gpt2"
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@ -2127,7 +2127,7 @@ def lua_get_modelbackend():
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return "readonly"
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if(vars.model in ("Colab", "OAI", "InferKit")):
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return "api"
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if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ",)):
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if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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return "mtj"
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return "transformers"
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@ -2136,7 +2136,7 @@ def lua_get_modelbackend():
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#==================================================================#
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@bridged_kwarg()
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def lua_is_custommodel():
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return vars.model in ("GPT2Custom", "NeoCustom", "TPUMeshTransformerGPTJ")
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return vars.model in ("GPT2Custom", "NeoCustom", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")
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#==================================================================#
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#
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@ -3074,22 +3074,22 @@ def calcsubmit(txt):
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if(vars.model != "InferKit"):
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subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions, submission=txt)
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if(actionlen == 0):
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if(not vars.use_colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
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if(not vars.use_colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
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generate(subtxt, min, max, found_entries=found_entries)
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elif(vars.model == "Colab"):
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sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
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elif(vars.model == "OAI"):
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oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
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elif(vars.use_colab_tpu or vars.model == "TPUMeshTransformerGPTJ"):
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elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
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else:
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if(not vars.use_colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
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if(not vars.use_colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]):
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generate(subtxt, min, max, found_entries=found_entries)
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elif(vars.model == "Colab"):
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sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
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elif(vars.model == "OAI"):
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oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max)
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elif(vars.use_colab_tpu or vars.model == "TPUMeshTransformerGPTJ"):
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elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
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# For InferKit web API
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@ -5105,7 +5105,7 @@ if(path.exists("settings/" + getmodelname().replace('/', '_') + ".settings")):
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file.close()
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# Precompile TPU backend if required
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if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ",)):
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if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")):
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soft_tokens = tpumtjgetsofttokens()
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if(vars.dynamicscan or (not vars.nogenmod and vars.has_genmod)):
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threading.Thread(
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@ -46,6 +46,7 @@ import numpy as np
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import optax
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import haiku as hk
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from transformers import AutoTokenizer, GPT2TokenizerFast, AutoModelForCausalLM, GPTNeoForCausalLM
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from tokenizers import Tokenizer
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from mesh_transformer.checkpoint import read_ckpt_lowmem
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from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard, PlaceholderTensor
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from mesh_transformer.util import to_bf16
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@ -800,6 +801,121 @@ def reshard_reverse(x, total_shards, old_shape):
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return out
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def get_old_shape(t, total_shards, dim=2):
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if len(t.shape) == 2:
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shard_shape = t.shape
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if dim == 1:
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assert shard_shape[0] % total_shards == 0
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return (shard_shape[0] // total_shards, shard_shape[1])
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elif dim == 2:
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assert shard_shape[1] % total_shards == 0
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return (shard_shape[0], shard_shape[1] // total_shards)
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else:
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raise ValueError(f"Unsupported dim {dim}")
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if len(t.shape) == 1:
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assert t.shape[0] % total_shards == 0
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return (t.shape[0] // total_shards,)
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else:
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raise ValueError(f"Unsupported shape {t.shape}")
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def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
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assert config["cores_per_replica"] % checkpoint_shards == 0
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output_shards = config["cores_per_replica"] // checkpoint_shards
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import torch
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from tqdm.auto import tqdm
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move_xmap = jax.experimental.maps.xmap(
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fun=lambda x, _: to_bf16(x),
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in_axes=(["shard", ...], ["batch", ...]),
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out_axes=["shard", ...],
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axis_resources={'shard': 'mp', 'batch': 'dp'}
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)
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path_template = os.path.join(path, "layer_{layer:02d}-model_{shard:02d}-model_states.pt")
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static_mapping = {
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"word_embeddings.weight": {"module": "embedding_shard/~/linear", "param": "w", "axis": 1},
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"final_linear.weight": {"module": "projection_shard/~/linear", "param": "w", "axis": 2},
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"norm.weight": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale", "axis": None},
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"norm.bias": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset", "axis": None},
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}
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layer_mapping = {
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"attention.query_key_value.weight": {"module": "combined_qkv", "param": "w", "axis": 2},
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"attention.query_key_value.bias": {"module": "combined_qkv", "param": "b", "axis": 1},
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"attention.dense.weight": {"module": "linear_3", "param": "w", "axis": 1},
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"attention.dense.bias": {"module": "linear_3", "param": "b", "axis": None},
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"mlp.dense_h_to_4h.weight": {"module": "linear_4", "param": "w", "axis": 2},
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"mlp.dense_h_to_4h.bias": {"module": "linear_4", "param": "b", "axis": 1},
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"mlp.dense_4h_to_h.weight": {"module": "linear_5", "param": "w", "axis": 1},
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"mlp.dense_4h_to_h.bias": {"module": "linear_5", "param": "b", "axis": None},
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"input_layernorm.weight": {"module": "replicated_layer_norm", "param": "scale", "axis": None},
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"input_layernorm.bias": {"module": "replicated_layer_norm", "param": "offset", "axis": None},
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"post_attention_layernorm.weight": {"module": "replicated_layer_norm_1", "param": "scale", "axis": None},
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"post_attention_layernorm.bias": {"module": "replicated_layer_norm_1", "param": "offset", "axis": None},
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}
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tqdm_length = len(static_mapping) + config["layers"]*len(layer_mapping)
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bar = tqdm(total=tqdm_length, desc="Loading from NeoX checkpoint")
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for checkpoint_layer in range(config["layers"] + 5):
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if checkpoint_layer in (1, config["layers"] + 2):
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continue
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layer = checkpoint_layer - 2
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shards = []
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for checkpoint_shard in range(checkpoint_shards):
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shards.append(torch.load(path_template.format(layer=checkpoint_layer, shard=checkpoint_shard), map_location="cpu"))
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for key in shards[0]:
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if key == "attention.rotary_emb.inv_freq":
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continue
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elif key in static_mapping:
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target_module = "causal_transformer_shard/~/" + static_mapping[key]["module"]
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target_param = static_mapping[key]["param"]
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target_axis = static_mapping[key]["axis"]
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elif key in layer_mapping:
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target_module = f"causal_transformer_shard/~/layer_{layer}/~/" + layer_mapping[key]["module"]
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target_param = layer_mapping[key]["param"]
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target_axis = layer_mapping[key]["axis"]
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else:
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error = f"{repr(key)} not found in mapping"
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print("\n\nERROR: ", error, file=sys.stderr)
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raise RuntimeError(error)
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original_shape = shards[0][key].shape
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for checkpoint_shard in range(checkpoint_shards):
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if key in ("attention.dense.bias", "mlp.dense_4h_to_h.bias"):
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shards[checkpoint_shard][key] /= output_shards
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if key != "word_embeddings.weight":
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shards[checkpoint_shard][key] = shards[checkpoint_shard][key].T
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tensor = shards[checkpoint_shard][key]
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if target_axis is not None:
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target_shape = (output_shards,) + get_old_shape(tensor, total_shards=output_shards, dim=target_axis)
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else:
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target_shape = (output_shards, tensor.shape[0])
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shards[checkpoint_shard][key] = reshard_reverse(tensor.unsqueeze_(0), output_shards, target_shape)
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#print(key, ":", original_shape, "->", shards[0][key].shape)
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tensor = torch.cat([shards[s][key] for s in range(checkpoint_shards)], dim=0)
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target_shape = state["params"][target_module][target_param].shape
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if tensor.shape != target_shape:
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error = f"Weight {repr(key)} has shape {tensor.shape} in checkpoint but shape {target_shape} was requested by MTJ for {target_module} {target_param}"
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print("\n\nERROR: ", error, file=sys.stderr)
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raise RuntimeError(error)
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if tensor.dtype is torch.float16 or tensor.dtype is torch.float32:
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tensor = tensor.bfloat16()
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state["params"][target_module][target_param] = move_xmap(
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jax.dlpack.from_dlpack(torch.utils.dlpack.to_dlpack(tensor)).copy(),
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np.zeros(config["cores_per_replica"]),
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)
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bar.update(1)
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for mk, mv in state["params"].items():
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for pk, pv in mv.items():
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if isinstance(pv, PlaceholderTensor):
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error = f"{mk} {pk} could not be found in the model checkpoint"
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print("\n\nERROR: " + error, file=sys.stderr)
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raise RuntimeError(error)
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def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpoint=False, **kwargs) -> None:
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global thread_resources_env, seq, tokenizer, network, params
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@ -820,6 +936,23 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
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}
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params = kwargs
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if vars.model == "TPUMeshTransformerGPTNeoX":
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default_params = {
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"compat": "neox",
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"layers": 44,
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"d_model": 6144,
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"n_heads": 64,
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"n_vocab": 50432,
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"n_vocab_padding": 0,
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"norm": "doublelayernorm",
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"pe": "rotary",
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"pe_rotary_dims": 24,
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"seq": 2048,
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"cores_per_replica": 8,
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"tokenizer_class": "GPT2TokenizerFast",
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"tokenizer": "gpt2",
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}
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# Try to convert HF config.json to MTJ config
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if hf_checkpoint:
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spec_path = os.path.join("maps", vars.model_type + ".json")
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@ -875,7 +1008,14 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
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params[param] = default_params[param]
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# Load tokenizer
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if not hf_checkpoint:
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if vars.model == "TPUMeshTransformerGPTNeoX":
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tokenizer = Tokenizer.from_file(os.path.join(path, "20B_tokenizer.json"))
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def new_encode(old_encode):
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def encode(s, *args, **kwargs):
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return old_encode(s).ids
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return encode
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tokenizer.encode = new_encode(tokenizer.encode)
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elif not hf_checkpoint:
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if not isinstance(params["tokenizer_class"], str) or not any(params["tokenizer_class"].endswith(s) for s in ("Tokenizer", "TokenizerFast")):
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raise ValueError("`tokenizer_class` must be a string ending in 'Tokenizer' or 'TokenizerFast'")
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tokenizer_class = getattr(__import__("transformers"), params["tokenizer_class"])
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@ -917,9 +1057,13 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
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network = PenalizingCausalTransformer(params, dematerialized=True)
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||||
|
||||
if not hf_checkpoint:
|
||||
if not hf_checkpoint and vars.model != "TPUMeshTransformerGPTNeoX":
|
||||
network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
|
||||
network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|
||||
#network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|
||||
return
|
||||
|
||||
if vars.model == "TPUMeshTransformerGPTNeoX":
|
||||
read_neox_checkpoint(network.state, path, params)
|
||||
return
|
||||
|
||||
# Convert from HF checkpoint
|
||||
|
|
@ -945,7 +1089,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
|
|||
|
||||
import torch_lazy_loader
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
def callback(model_dict, f, **_):
|
||||
with zipfile.ZipFile(f, "r") as z:
|
||||
|
|
@ -1069,4 +1213,4 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
|
|||
except Exception as e:
|
||||
model = GPTNeoForCausalLM.from_pretrained(vars.model, cache_dir="cache")
|
||||
|
||||
network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|
||||
#network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|
||||
|
|
|
|||
Loading…
Reference in New Issue