Support for loading HF models on TPU with `--colab_tpu`
This commit is contained in:
Gnome Ann
parent
86ac562b0c
commit
0a258a6282
31
aiserver.py
31
aiserver.py
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@ -695,7 +695,7 @@ 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.model in ("TPUMeshTransformerGPTJ",)):
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if(args.colab_tpu or vars.model in ("TPUMeshTransformerGPTJ",)):
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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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@ -730,6 +730,7 @@ parser.add_argument("--override_delete", action='store_true', help="Deleting sto
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parser.add_argument("--override_rename", action='store_true', help="Renaming stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow renaming stories if using --remote and prevent renaming stories otherwise.")
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parser.add_argument("--configname", help="Force a fixed configuration name to aid with config management.")
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parser.add_argument("--colab", action='store_true', help="Optimize for Google Colab.")
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parser.add_argument("--colab_tpu", action='store_true', help="If you're running KoboldAI in a Google Colab TPU instance, enable this to load Hugging Face models onto the TPU.")
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parser.add_argument("--nobreakmodel", action='store_true', help="Disables Breakmodel support completely.")
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parser.add_argument("--unblock", action='store_true', default=False, help="Unblocks the KoboldAI port to be accessible from other machines without optimizing for remote play (It is recommended to use --host instead)")
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parser.add_argument("--quiet", action='store_true', default=False, help="If present will suppress any story related text from showing on the console")
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@ -783,7 +784,7 @@ 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(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
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if(vars.model not in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
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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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@ -822,6 +823,8 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
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elif(vars.model_type == "not_found"):
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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 args.colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
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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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@ -1014,7 +1017,7 @@ 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.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
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if(not args.colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
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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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@ -1523,9 +1526,9 @@ 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.model == "TPUMeshTransformerGPTJ"):
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elif(args.colab_tpu or vars.model == "TPUMeshTransformerGPTJ"):
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print("{0}Initializing Mesh Transformer JAX, please wait...{1}".format(colors.PURPLE, colors.END))
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if not vars.custmodpth or not os.path.isdir(vars.custmodpth):
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if vars.model == "TPUMeshTransformerGPTJ" 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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@ -1537,7 +1540,7 @@ 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, **vars.modelconfig)
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tpu_mtj_backend.load_model(vars.custmodpth, hf_checkpoint=args.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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@ -2068,7 +2071,7 @@ 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(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 args.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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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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@ -2084,7 +2087,7 @@ def lua_get_modeltype():
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return "gpt-neo-1.3B"
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if(vars.model in ("EleutherAI/gpt-neo-2.7B",) or (vars.model_type == "gpt_neo" and hidden_size == 2560)):
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return "gpt-neo-2.7B"
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if(vars.model in ("EleutherAI/gpt-j-6B",) or (vars.model == "TPUMeshTransformerGPTJ" and tpu_mtj_backend.params["d_model"] == 4096) or (vars.model_type in ("gpt_neo", "gptj") and hidden_size == 4096)):
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if(vars.model in ("EleutherAI/gpt-j-6B",) or ((args.colab_tpu or vars.model == "TPUMeshTransformerGPTJ") and tpu_mtj_backend.params["d_model"] == 4096) or (vars.model_type in ("gpt_neo", "gptj") and hidden_size == 4096)):
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return "gpt-j-6B"
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return "unknown"
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@ -2097,7 +2100,7 @@ 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.model in ("TPUMeshTransformerGPTJ",)):
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if(args.colab_tpu or vars.model in ("TPUMeshTransformerGPTJ",)):
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return "mtj"
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return "transformers"
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@ -3044,22 +3047,22 @@ 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.model in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
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if(not args.colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
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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.model == "TPUMeshTransformerGPTJ"):
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elif(args.colab_tpu or vars.model == "TPUMeshTransformerGPTJ"):
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tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
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else:
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if(not vars.model in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
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if(not args.colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
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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.model == "TPUMeshTransformerGPTJ"):
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elif(args.colab_tpu or vars.model == "TPUMeshTransformerGPTJ"):
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tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
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# For InferKit web API
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@ -5071,7 +5074,7 @@ 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.model in ("TPUMeshTransformerGPTJ",)):
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if(args.colab_tpu or vars.model in ("TPUMeshTransformerGPTJ",)):
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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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@ -1,25 +1,32 @@
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{
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"mtj_compat": "neo",
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"mtj_pe": "fixed",
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"mtj_config_map": {
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"d_model": "hidden_size",
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"n_heads": "num_heads",
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"layers": "num_layers"
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},
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"static_weights": {
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"transformer.wte.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "axis": 1, "transforms": ["transpose", "vocab_pad"]}},
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"transformer.wpe.weight": {"mtj": {"module": "embedding_shard/~/pos_embs", "param": "w", "axis": 2, "transforms": ["transpose"]}},
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"transformer.ln_f.weight": {"mtj": {"module": "projection_shard/~/layer_norm", "param": "scale"}},
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"transformer.ln_f.bias": {"mtj": {"module": "projection_shard/~/layer_norm", "param": "offset"}}
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"transformer.wte.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
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"transformer.wpe.weight": {"mtj": {"module": "embedding_shard", "param": "pos_embs", "transforms": ["no_transpose"]}},
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"transformer.ln_f.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
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"transformer.ln_f.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}}
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},
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"layer_weights": {
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"transformer.h.{layer}.attn.attention.bias": {},
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"transformer.h.{layer}.attn.attention.masked_bias": {},
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"transformer.h.{layer}.attn.attention.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w", "axis": 2}},
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"transformer.h.{layer}.attn.attention.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w", "axis": 2}},
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"transformer.h.{layer}.attn.attention.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w", "axis": 2}},
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"transformer.h.{layer}.attn.attention.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w", "axis": 1}},
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"transformer.h.{layer}.attn.attention.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w"}},
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"transformer.h.{layer}.attn.attention.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w"}},
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"transformer.h.{layer}.attn.attention.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w"}},
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"transformer.h.{layer}.attn.attention.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w"}},
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"transformer.h.{layer}.attn.attention.out_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "b", "transforms": ["divide_by_shards"]}},
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"transformer.h.{layer}.mlp.c_fc.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w", "axis": 2}},
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"transformer.h.{layer}.mlp.c_fc.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b", "axis": 1}},
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"transformer.h.{layer}.mlp.c_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w", "axis": 1}},
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"transformer.h.{layer}.mlp.c_fc.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w"}},
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"transformer.h.{layer}.mlp.c_fc.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b"}},
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"transformer.h.{layer}.mlp.c_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w"}},
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"transformer.h.{layer}.mlp.c_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "b", "transforms": ["divide_by_shards"]}},
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"transformer.h.{layer}.ln_1.weight": {"mtj": {"module": "layer_{layer}/~/layer_norm", "param": "scale"}},
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"transformer.h.{layer}.ln_1.bias": {"mtj": {"module": "layer_{layer}/~/layer_norm", "param": "offset"}},
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"transformer.h.{layer}.ln_2.weight": {"mtj": {"module": "layer_{layer}/~/layer_norm_1", "param": "scale"}},
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"transformer.h.{layer}.ln_2.bias": {"mtj": {"module": "layer_{layer}/~/layer_norm_1", "param": "offset"}}
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"transformer.h.{layer}.ln_1.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "scale"}},
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"transformer.h.{layer}.ln_1.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "offset"}},
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"transformer.h.{layer}.ln_2.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "scale"}},
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"transformer.h.{layer}.ln_2.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "offset"}}
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}
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}
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@ -1,24 +1,32 @@
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{
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"mtj_compat": "j",
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"mtj_pe": "rotary",
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"mtj_config_map": {
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"pe_rotary_dims": ["rotary_dim", 64],
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"d_model": "n_embd",
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"n_heads": "n_head",
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"layers": "n_layer"
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},
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"static_weights": {
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"transformer.wte.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "axis": 1, "transforms": ["transpose", "vocab_pad"]}},
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"transformer.wte.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
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"transformer.wte.bias": {"mtj": {"module": "embedding_shard/~/linear", "param": "b"}},
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"transformer.ln_f.weight": {"mtj": {"module": "projection_shard/~/layer_norm", "param": "scale"}},
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"transformer.ln_f.bias": {"mtj": {"module": "projection_shard/~/layer_norm", "param": "offset"}},
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"lm_head.weight": {"mtj": {"module": "projection_shard/~/linear", "param": "w", "axis": 2, "transforms": ["vocab_pad"]}},
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"transformer.ln_f.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
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"transformer.ln_f.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}},
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"lm_head.weight": {"mtj": {"module": "projection_shard/~/linear", "param": "w", "transforms": ["vocab_pad"]}},
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"lm_head.bias": {"mtj": {"module": "projection_shard/~/linear", "param": "b"}}
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},
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"layer_weights": {
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"transformer.h.{layer}.attn.bias": {},
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"transformer.h.{layer}.attn.masked_bias": {},
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"transformer.h.{layer}.attn.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w", "axis": 2}},
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"transformer.h.{layer}.attn.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w", "axis": 2}},
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"transformer.h.{layer}.attn.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w", "axis": 2}},
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"transformer.h.{layer}.attn.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w", "axis": 1}},
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"transformer.h.{layer}.mlp.fc_in.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w", "axis": 2}},
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"transformer.h.{layer}.mlp.fc_in.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b", "axis": 1}},
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"transformer.h.{layer}.mlp.fc_out.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w", "axis": 1}},
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"transformer.h.{layer}.attn.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w"}},
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"transformer.h.{layer}.attn.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w"}},
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"transformer.h.{layer}.attn.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w"}},
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"transformer.h.{layer}.attn.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w"}},
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"transformer.h.{layer}.mlp.fc_in.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w"}},
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"transformer.h.{layer}.mlp.fc_in.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b"}},
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"transformer.h.{layer}.mlp.fc_out.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w"}},
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"transformer.h.{layer}.mlp.fc_out.bias": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "b", "transforms": ["divide_by_shards"]}},
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"transformer.h.{layer}.ln_1.weight": {"mtj": {"module": "layer_{layer}/~/layer_norm", "param": "scale"}},
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"transformer.h.{layer}.ln_1.bias": {"mtj": {"module": "layer_{layer}/~/layer_norm", "param": "offset"}}
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"transformer.h.{layer}.ln_1.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "scale"}},
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"transformer.h.{layer}.ln_1.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "offset"}}
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}
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}
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@ -1,26 +1,33 @@
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{
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"mtj_compat": "fairseq_lm",
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"mtj_pe": "fairseq_sinusoidal",
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"mtj_config_map": {
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"d_model": "d_model",
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"n_heads": "attention_heads",
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"layers": "num_layers"
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},
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"static_weights": {
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"model.embed_tokens.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "axis": 1, "transforms": ["transpose", "vocab_pad"]}},
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"model.layer_norm.weight": {"mtj": {"module": "projection_shard/~/layer_norm", "param": "scale"}},
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"model.layer_norm.bias": {"mtj": {"module": "projection_shard/~/layer_norm", "param": "offset"}},
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"lm_head.weight": {"mtj": {"module": "projection_shard/~/linear", "param": "w", "axis": 2, "transforms": ["vocab_pad"]}}
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"model.embed_tokens.weight": {"mtj": {"module": "embedding_shard/~/linear", "param": "w", "transforms": ["no_transpose", "vocab_pad"]}},
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"model.replicated_layer_norm.weight": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "scale"}},
|
||||
"model.replicated_layer_norm.bias": {"mtj": {"module": "projection_shard/~/replicated_layer_norm", "param": "offset"}},
|
||||
"lm_head.weight": {"mtj": {"module": "projection_shard/~/linear", "param": "w", "transforms": ["vocab_pad"]}}
|
||||
},
|
||||
"layer_weights": {
|
||||
"model.layers.{layer}.self_attn.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w", "axis": 2}},
|
||||
"model.layers.{layer}.self_attn.q_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear", "param": "b", "axis": 1}},
|
||||
"model.layers.{layer}.self_attn.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w", "axis": 2}},
|
||||
"model.layers.{layer}.self_attn.v_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "b", "axis": 1}},
|
||||
"model.layers.{layer}.self_attn.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w", "axis": 2}},
|
||||
"model.layers.{layer}.self_attn.k_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "b", "axis": 1}},
|
||||
"model.layers.{layer}.self_attn.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w", "axis": 1}},
|
||||
"model.layers.{layer}.self_attn.q_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear", "param": "w"}},
|
||||
"model.layers.{layer}.self_attn.q_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear", "param": "b"}},
|
||||
"model.layers.{layer}.self_attn.v_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "w"}},
|
||||
"model.layers.{layer}.self_attn.v_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_1", "param": "b"}},
|
||||
"model.layers.{layer}.self_attn.k_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "w"}},
|
||||
"model.layers.{layer}.self_attn.k_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_2", "param": "b"}},
|
||||
"model.layers.{layer}.self_attn.out_proj.weight": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "w"}},
|
||||
"model.layers.{layer}.self_attn.out_proj.bias": {"mtj": {"module": "layer_{layer}/~/linear_3", "param": "b", "transforms": ["divide_by_shards"]}},
|
||||
"model.layers.{layer}.fc1.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w", "axis": 2}},
|
||||
"model.layers.{layer}.fc1.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b", "axis": 1}},
|
||||
"model.layers.{layer}.fc2.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w", "axis": 1}},
|
||||
"model.layers.{layer}.fc1.weight": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "w"}},
|
||||
"model.layers.{layer}.fc1.bias": {"mtj": {"module": "layer_{layer}/~/linear_4", "param": "b"}},
|
||||
"model.layers.{layer}.fc2.weight": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "w"}},
|
||||
"model.layers.{layer}.fc2.bias": {"mtj": {"module": "layer_{layer}/~/linear_5", "param": "b", "transforms": ["divide_by_shards"]}},
|
||||
"model.layers.{layer}.self_attn_layer_norm.weight": {"mtj": {"module": "layer_{layer}/~/layer_norm", "param": "scale"}},
|
||||
"model.layers.{layer}.self_attn_layer_norm.bias": {"mtj": {"module": "layer_{layer}/~/layer_norm", "param": "offset"}},
|
||||
"model.layers.{layer}.final_layer_norm.weight": {"mtj": {"module": "layer_{layer}/~/layer_norm_1", "param": "scale"}},
|
||||
"model.layers.{layer}.final_layer_norm.bias": {"mtj": {"module": "layer_{layer}/~/layer_norm_1", "param": "offset"}}
|
||||
"model.layers.{layer}.self_attn_layer_norm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "scale"}},
|
||||
"model.layers.{layer}.self_attn_layer_norm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm", "param": "offset"}},
|
||||
"model.layers.{layer}.final_layer_norm.weight": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "scale"}},
|
||||
"model.layers.{layer}.final_layer_norm.bias": {"mtj": {"module": "layer_{layer}/~/replicated_layer_norm_1", "param": "offset"}}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -32,6 +32,9 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar
|
|||
import progressbar
|
||||
import time
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import zipfile
|
||||
import requests
|
||||
import random
|
||||
import jax
|
||||
|
|
@ -41,9 +44,10 @@ import jax.numpy as jnp
|
|||
import numpy as np
|
||||
import optax
|
||||
import haiku as hk
|
||||
import transformers
|
||||
from transformers import AutoTokenizer, GPT2TokenizerFast, AutoModelForCausalLM, GPTNeoForCausalLM
|
||||
from mesh_transformer.checkpoint import read_ckpt_lowmem
|
||||
from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard
|
||||
from mesh_transformer.transformer_shard import CausalTransformer, CausalTransformerShard, PlaceholderTensor
|
||||
from mesh_transformer.util import to_bf16
|
||||
|
||||
|
||||
params: Dict[str, Any] = {}
|
||||
|
|
@ -776,7 +780,26 @@ def infer_static(
|
|||
return samples
|
||||
|
||||
|
||||
def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs) -> None:
|
||||
def reshard_reverse(x, total_shards, old_shape):
|
||||
assert len(x.shape) != 1
|
||||
if len(x.shape) == 2:
|
||||
if old_shape[1] == x.shape[1]:
|
||||
out = x[0:1].tile((total_shards, 1))
|
||||
else:
|
||||
out = x.reshape(old_shape)
|
||||
elif len(x.shape) == 3:
|
||||
if x.shape[0] * x.shape[2] == old_shape[2]:
|
||||
out = x.reshape(old_shape)
|
||||
elif x.shape[0] * x.shape[1] == old_shape[1]:
|
||||
out = x.reshape((old_shape[1], old_shape[0], old_shape[2])).permute((1, 0, 2))
|
||||
else:
|
||||
assert False
|
||||
else:
|
||||
assert False
|
||||
return out
|
||||
|
||||
|
||||
def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpoint=False, **kwargs) -> None:
|
||||
global thread_resources_env, seq, tokenizer, network, params
|
||||
|
||||
default_params = {
|
||||
|
|
@ -795,6 +818,53 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
|
|||
"tokenizer": "gpt2",
|
||||
}
|
||||
params = kwargs
|
||||
|
||||
# Try to convert HF config.json to MTJ config
|
||||
if hf_checkpoint:
|
||||
spec_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "maps", vars.model_type + ".json")
|
||||
if not os.path.isfile(spec_path):
|
||||
raise NotImplementedError(f"Unsupported model type {repr(vars.model_type)}")
|
||||
with open(spec_path) as f:
|
||||
lazy_load_spec = json.load(f)
|
||||
|
||||
if "mtj_compat" in lazy_load_spec:
|
||||
params["compat"] = lazy_load_spec["mtj_compat"]
|
||||
if "mtj_pe" in lazy_load_spec:
|
||||
params["pe"] = lazy_load_spec["mtj_pe"]
|
||||
for k, v in lazy_load_spec.get("mtj_config_map", {}).items():
|
||||
if type(v) is not list:
|
||||
params[k] = params[v]
|
||||
continue
|
||||
for i in range(len(v)):
|
||||
if i == len(v) - 1:
|
||||
params[k] = v[i]
|
||||
elif v[i] in params:
|
||||
params[k] = params[v[i]]
|
||||
break
|
||||
|
||||
params["n_vocab"] = params["vocab_size"]
|
||||
|
||||
if "activation_function" in params:
|
||||
params["activation"] = params["activation_function"]
|
||||
|
||||
# Both the number of attention heads in the model and the embedding
|
||||
# dimension of the model need to be divisible by the number of TPU cores
|
||||
# that we use, and JAX also requires the number of TPU cores used to be
|
||||
# an even number if we're using more than one core, so logically we try
|
||||
# to pick the largest possible even number of TPU cores such that the
|
||||
# number of attention heads and embedding dimension are both divisible
|
||||
# by the number of TPU cores, and fall back to one core if an even
|
||||
# number of TPU cores is not possible.
|
||||
for c in (8, 6, 4, 2, 1):
|
||||
if 0 == params["n_heads"] % c == params["d_model"] % c:
|
||||
params["cores_per_replica"] = c
|
||||
break
|
||||
|
||||
# The vocabulary size of the model also has to be divisible by the
|
||||
# number of TPU cores, so we pad the vocabulary with the minimum
|
||||
# possible number of dummy tokens such that it's divisible.
|
||||
params["n_vocab_padding"] = -(params["n_vocab"] % -params["cores_per_replica"])
|
||||
|
||||
if "compat" in params:
|
||||
default_params["compat"] = params["compat"]
|
||||
if default_params["compat"] == "fairseq_lm":
|
||||
|
|
@ -804,10 +874,11 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
|
|||
params[param] = default_params[param]
|
||||
|
||||
# Load tokenizer
|
||||
if not isinstance(params["tokenizer_class"], str) or not any(params["tokenizer_class"].endswith(s) for s in ("Tokenizer", "TokenizerFast")):
|
||||
raise ValueError("`tokenizer_class` must be a string ending in 'Tokenizer' or 'TokenizerFast'")
|
||||
tokenizer_class = getattr(__import__("transformers"), params["tokenizer_class"])
|
||||
tokenizer = tokenizer_class.from_pretrained(params["tokenizer"])
|
||||
if not hf_checkpoint:
|
||||
if not isinstance(params["tokenizer_class"], str) or not any(params["tokenizer_class"].endswith(s) for s in ("Tokenizer", "TokenizerFast")):
|
||||
raise ValueError("`tokenizer_class` must be a string ending in 'Tokenizer' or 'TokenizerFast'")
|
||||
tokenizer_class = getattr(__import__("transformers"), params["tokenizer_class"])
|
||||
tokenizer = tokenizer_class.from_pretrained(params["tokenizer"])
|
||||
|
||||
# Disable JAX warnings about these two functions having been renamed
|
||||
jax.host_count = jax.process_count
|
||||
|
|
@ -844,5 +915,147 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", **kwargs)
|
|||
path += "/"
|
||||
|
||||
network = PenalizingCausalTransformer(params, dematerialized=True)
|
||||
network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
|
||||
|
||||
if not hf_checkpoint:
|
||||
network.state = read_ckpt_lowmem(network.state, path, devices.shape[1])
|
||||
network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|
||||
return
|
||||
|
||||
# Convert from HF checkpoint
|
||||
|
||||
move_xmap = jax.experimental.maps.xmap(
|
||||
fun=lambda x, _: to_bf16(x),
|
||||
in_axes=(["shard", ...], ["batch", ...]),
|
||||
out_axes=["shard", ...],
|
||||
axis_resources={'shard': 'mp', 'batch': 'dp'}
|
||||
)
|
||||
|
||||
model_spec = {}
|
||||
for key, spec in lazy_load_spec.get("static_weights", {}).items():
|
||||
if spec.get("mtj") is not None:
|
||||
model_spec[key] = spec["mtj"].copy()
|
||||
model_spec[key]["module"] = "causal_transformer_shard/~/" + model_spec[key]["module"]
|
||||
for _key, spec in lazy_load_spec.get("layer_weights", {}).items():
|
||||
for layer in range(params["layers"]):
|
||||
if spec.get("mtj") is not None:
|
||||
key = _key.format(layer=layer)
|
||||
model_spec[key] = spec["mtj"].copy()
|
||||
model_spec[key]["module"] = "causal_transformer_shard/~/" + model_spec[key]["module"].format(layer=layer)
|
||||
|
||||
import torch_lazy_loader
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
def callback(model_dict, f, **_):
|
||||
with zipfile.ZipFile(f, "r") as z:
|
||||
try:
|
||||
last_storage_key = None
|
||||
f = None
|
||||
print("\n\n\nThis model has ", f"{hk.data_structures.tree_size(network.state['params']):,d}".replace(",", " "), " parameters.\n")
|
||||
for key in tqdm(sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors"):
|
||||
|
||||
# Some model weights are used by transformers but not by MTJ.
|
||||
# We have to materialize these weights anyways because
|
||||
# transformers will throw a tantrum otherwise. To attain
|
||||
# the least possible memory usage, we create them as meta
|
||||
# tensors, which don't take up any actual CPU or TPU memory.
|
||||
if key not in model_spec:
|
||||
model_dict[key] = torch.empty(model_dict[key].shape, dtype=model_dict[key].storage_type(0).dtype, device="meta")
|
||||
continue
|
||||
|
||||
storage_key = model_dict[key].key
|
||||
if storage_key != last_storage_key:
|
||||
last_storage_key = storage_key
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
f = z.open(f"archive/data/{storage_key}")
|
||||
current_offset = f.tell()
|
||||
if current_offset != model_dict[key].seek_offset:
|
||||
f.seek(model_dict[key].seek_offset - current_offset, 1)
|
||||
spec = model_spec[key]
|
||||
transforms = set(spec.get("transforms", ()))
|
||||
if not isinstance(model_dict[key], torch_lazy_loader.LazyTensor):
|
||||
error = f"Duplicate key {repr(key)}"
|
||||
print("\n\nERROR: " + error, file=sys.stderr)
|
||||
raise RuntimeError(error)
|
||||
tensor = model_dict[key].materialize(f, map_location="cpu")
|
||||
model_dict[key] = tensor.to("meta")
|
||||
|
||||
# MTJ requires certain mathematical operations to be performed
|
||||
# on tensors in order for them to be in the correct format
|
||||
if "divide_by_shards" in transforms:
|
||||
tensor /= params["cores_per_replica"]
|
||||
if "vocab_pad" in transforms:
|
||||
tensor = torch.nn.functional.pad(tensor, (0, 0, 0, params["n_vocab_padding"]))
|
||||
if "no_transpose" not in transforms:
|
||||
tensor = tensor.T
|
||||
tensor.unsqueeze_(0)
|
||||
|
||||
# Shard the tensor so that parts of the tensor can be used
|
||||
# on different TPU cores
|
||||
network.state["params"][spec["module"]][spec["param"]] = move_xmap(
|
||||
jnp.array(
|
||||
reshard_reverse(
|
||||
tensor,
|
||||
params["cores_per_replica"],
|
||||
network.state["params"][spec["module"]][spec["param"]].shape,
|
||||
),
|
||||
dtype=jnp.bfloat16,
|
||||
),
|
||||
np.empty(params["cores_per_replica"]),
|
||||
)
|
||||
|
||||
# Check for tensors that MTJ needs that were not provided in the
|
||||
# HF model
|
||||
for mk, mv in network.state["params"].items():
|
||||
for pk, pv in mv.items():
|
||||
if isinstance(pv, PlaceholderTensor):
|
||||
# The transformers GPT-J models apparently do not
|
||||
# have embedding bias, whereas MTJ GPT-J models do,
|
||||
# so we have to supplement an embedding bias tensor
|
||||
# by creating a tensor with the necessary shape, filled
|
||||
# with zeros.
|
||||
if mk == "causal_transformer_shard/~/embedding_shard/~/linear" and pk == "b":
|
||||
mv[pk] = move_xmap(jnp.zeros(mv[pk].shape, dtype=jnp.bfloat16), np.empty(params["cores_per_replica"]))
|
||||
|
||||
else:
|
||||
error = f"{mk} {pk} could not be found in the model checkpoint"
|
||||
print("\n\nERROR: " + error, file=sys.stderr)
|
||||
raise RuntimeError(error)
|
||||
finally:
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
|
||||
if os.path.isdir(vars.model.replace('/', '_')):
|
||||
import shutil
|
||||
shutil.move(vars.model.replace('/', '_'), "models/{}".format(vars.model.replace('/', '_')))
|
||||
with torch_lazy_loader.use_lazy_torch_load(callback=callback, dematerialized_modules=True):
|
||||
if(os.path.isdir(vars.custmodpth)):
|
||||
try:
|
||||
tokenizer = AutoTokenizer.from_pretrained(vars.custmodpth, cache_dir="cache")
|
||||
except ValueError as e:
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(vars.custmodpth, cache_dir="cache")
|
||||
try:
|
||||
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
|
||||
except ValueError as e:
|
||||
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache")
|
||||
elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
|
||||
try:
|
||||
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
|
||||
except ValueError as e:
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache")
|
||||
try:
|
||||
model = AutoModelForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache", **lowmem)
|
||||
except ValueError as e:
|
||||
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), cache_dir="cache", **lowmem)
|
||||
else:
|
||||
try:
|
||||
tokenizer = AutoTokenizer.from_pretrained(vars.model, cache_dir="cache")
|
||||
except ValueError as e:
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, cache_dir="cache")
|
||||
try:
|
||||
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache")
|
||||
except ValueError as e:
|
||||
model = GPTNeoForCausalLM.from_pretrained(vars.model, cache_dir="cache")
|
||||
|
||||
network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
|
||||
|
|
|
|||
Loading…
Reference in New Issue