Merge branch 'henk717:united' into united
This commit is contained in:
ebolam
GitHub
commit
80ae054cb5
29
aiserver.py
29
aiserver.py
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@ -185,7 +185,7 @@ class vars:
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recentrngm = None # If a new random game was recently generated without Submitting after, this is the memory used (as a string), otherwise this is None
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useprompt = False # Whether to send the full prompt with every submit action
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breakmodel = False # For GPU users, whether to use both system RAM and VRAM to conserve VRAM while offering speedup compared to CPU-only
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bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J only, currently)
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bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J/XGLM only, currently)
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nobreakmodel = False # Something specifically requested Breakmodel to be disabled (For example a models config)
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smandelete = False # Whether stories can be deleted from inside the browser
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smanrename = False # Whether stories can be renamed from inside the browser
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@ -382,18 +382,29 @@ def device_config(model):
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return
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model.half().to('cpu')
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gc.collect()
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if(hasattr(model, "transformer")):
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model.transformer.wte.to(breakmodel.primary_device)
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model.transformer.ln_f.to(breakmodel.primary_device)
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if(hasattr(model, 'lm_head')):
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model.lm_head.to(breakmodel.primary_device)
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if(hasattr(model.transformer, 'wpe')):
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model.transformer.wpe.to(breakmodel.primary_device)
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else:
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model.model.embed_tokens.to(breakmodel.primary_device)
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model.model.layer_norm.to(breakmodel.primary_device)
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model.lm_head.to(breakmodel.primary_device)
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model.model.embed_positions.to(breakmodel.primary_device)
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gc.collect()
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GPTNeoModel.forward = breakmodel.new_forward
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GPTNeoModel.forward = breakmodel.new_forward_neo
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if("GPTJModel" in globals()):
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GPTJModel.forward = breakmodel.new_forward
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GPTJModel.forward = breakmodel.new_forward_neo
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if("XGLMModel" in globals()):
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XGLMModel.forward = breakmodel.new_forward_xglm
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generator = model.generate
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if(hasattr(model, "transformer")):
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breakmodel.move_hidden_layers(model.transformer)
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else:
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breakmodel.move_hidden_layers(model.model, model.model.layers)
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#==================================================================#
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# Allow the models to override some settings
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@ -544,7 +555,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
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loadmodelsettings()
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print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
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vars.hascuda = torch.cuda.is_available()
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vars.bmsupported = vars.model_type in ("gpt_neo", "gptj") and not vars.nobreakmodel
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vars.bmsupported = vars.model_type in ("gpt_neo", "gptj", "xglm") and not vars.nobreakmodel
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if(args.breakmodel is not None and args.breakmodel):
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print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --breakmodel_gpulayers is used (see --help for details).", file=sys.stderr)
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if(args.breakmodel_layers is not None):
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@ -736,8 +747,9 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
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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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for m in ("GPTJModel", "XGLMModel"):
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try:
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from transformers import GPTJModel
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globals()[m] = getattr(__import__("transformers"), m)
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except:
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pass
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import transformers.generation_utils
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@ -753,7 +765,10 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
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if(vars.sp is not None):
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shifted_input_ids = input_ids - self.config.vocab_size
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input_ids.clamp_(max=self.config.vocab_size-1)
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if(hasattr(self, "transformer")):
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inputs_embeds = self.transformer.wte(input_ids)
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else:
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inputs_embeds = self.model.embed_tokens(input_ids) * self.model.embed_scale
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if(vars.sp is not None):
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vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
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inputs_embeds = torch.where(
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@ -766,9 +781,9 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
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cls.forward = new_causallm_forward
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for cls in (GPT2LMHeadModel, GPTNeoForCausalLM):
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patch_causallm(cls)
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for c in ("GPTJForCausalLM", "XGLMForCausalLM"):
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try:
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from transformers import GPTJForCausalLM
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patch_causallm(GPTJForCausalLM)
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patch_causallm(getattr(__import__("transformers"), c))
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except:
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pass
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233
breakmodel.py
233
breakmodel.py
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@ -212,14 +212,17 @@ Copyright 2018 The Hugging Face team
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import torch
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from torch import nn
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import torch.cuda.comm
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import copy
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import gc
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import sys
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import itertools
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import bisect
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import random
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from typing import Optional
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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@ -230,22 +233,40 @@ gpu_blocks = []
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primary_device = 0
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def move_hidden_layers(transformer):
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# Copied from transformers.models.bart.modeling_bart._expand_mask
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def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
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def move_hidden_layers(transformer, h=None):
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if h is None:
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h = transformer.h
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assert len(gpu_blocks) <= torch.cuda.device_count()
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assert sum(gpu_blocks) <= len(transformer.h)
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ram_blocks = len(transformer.h) - sum(gpu_blocks)
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assert sum(gpu_blocks) <= len(h)
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ram_blocks = len(h) - sum(gpu_blocks)
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transformer.extrastorage = {}
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torch.cuda.empty_cache()
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able_to_pin_layers = True
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for i in range(ram_blocks):
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transformer.h[i].to("cpu")
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transformer.extrastorage[i] = copy.deepcopy(transformer.h[i])
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h[i].to("cpu")
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transformer.extrastorage[i] = copy.deepcopy(h[i])
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smalltensor = torch.tensor(0).to(primary_device)
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for param1 in transformer.h[i].parameters():
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for param1 in h[i].parameters():
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param1.data = smalltensor
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transformer.h[i].to(primary_device)
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h[i].to(primary_device)
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for param in transformer.extrastorage[i].parameters():
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param.requires_grad = False
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param.data = param.data.detach()
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@ -259,20 +280,20 @@ def move_hidden_layers(transformer):
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torch.cuda.empty_cache()
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if ram_blocks:
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for param1,param2 in zip(transformer.h[0].parameters(),transformer.extrastorage[0].parameters()):
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for param1,param2 in zip(h[0].parameters(),transformer.extrastorage[0].parameters()):
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param1.data = param2.data.to(primary_device, non_blocking=False).detach()
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for param1,param2 in zip(transformer.h[ram_blocks-1].parameters(),transformer.extrastorage[ram_blocks-1].parameters()):
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for param1,param2 in zip(h[ram_blocks-1].parameters(),transformer.extrastorage[ram_blocks-1].parameters()):
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param1.data = param2.data.to(primary_device, non_blocking=False).detach()
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i = ram_blocks
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for j in range(len(gpu_blocks)):
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for _ in range(gpu_blocks[j]):
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transformer.h[i].to(j)
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h[i].to(j)
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i += 1
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def new_forward(
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def new_forward_neo(
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self,
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input_ids=None,
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past_key_values=None,
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@ -477,3 +498,191 @@ def new_forward(
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hidden_states=all_hidden_states,
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attentions=all_self_attentions,
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)
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def new_forward_xglm(
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self,
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input_ids=None,
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attention_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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head_mask=None,
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cross_attn_head_mask=None,
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past_key_values=None,
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inputs_embeds=None,
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use_cache=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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assert len(gpu_blocks) <= torch.cuda.device_count()
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assert sum(gpu_blocks) <= len(self.layers)
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ram_blocks = len(self.layers) - sum(gpu_blocks)
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cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# retrieve input_ids and inputs_embeds
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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
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elif input_ids is not None:
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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elif inputs_embeds is not None:
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input_shape = inputs_embeds.size()[:-1]
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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# past_key_values_length
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past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
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if inputs_embeds is None:
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if breakmodel:
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input_ids = input_ids.to(primary_device)
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inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
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attention_mask = self._prepare_decoder_attention_mask(
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attention_mask, input_shape, inputs_embeds, past_key_values_length
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)
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# expand encoder attention mask
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if encoder_hidden_states is not None and encoder_attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
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# embed positions
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if breakmodel:
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inputs_embeds = inputs_embeds.to(primary_device)
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positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
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if breakmodel:
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positions = positions.to(primary_device)
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hidden_states = inputs_embeds + positions
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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# decoder layers
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all_hidden_states = () if output_hidden_states else None
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all_self_attns = () if output_attentions else None
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all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
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next_decoder_cache = () if use_cache else None
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if breakmodel and ram_blocks:
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copystream = torch.cuda.Stream(device=primary_device, priority=-1)
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# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
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for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
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if attn_mask is not None:
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assert attn_mask.size()[0] == (
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len(self.layers)
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), f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
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for idx, decoder_layer in enumerate(self.layers):
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i = idx
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if breakmodel:
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if i in range(ram_blocks):
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index1 = (i+1)%ram_blocks
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for param1,param2 in zip(self.layers[index1].parameters(),self.layers[(i-1)%ram_blocks].parameters()):
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param1.data = param2.data
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for param1,param2 in zip(self.layers[index1].parameters(),self.extrastorage[index1].parameters()):
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with torch.cuda.stream(copystream):
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torch.cuda.comm.broadcast(param2.data,out = [param1.data])
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# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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dropout_probability = random.uniform(0, 1)
|
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if self.training and (dropout_probability < self.layerdrop):
|
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continue
|
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|
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past_key_value = past_key_values[idx] if past_key_values is not None else None
|
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|
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if self.gradient_checkpointing and self.training:
|
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|
||||
if use_cache:
|
||||
logger.warning(
|
||||
"`use_cache = True` is incompatible with gradient checkpointing`. Setting `use_cache = False`..."
|
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)
|
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use_cache = False
|
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|
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def create_custom_forward(module):
|
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def custom_forward(*inputs):
|
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# None for past_key_value
|
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return module(*inputs, output_attentions, use_cache)
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|
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return custom_forward
|
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|
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layer_outputs = torch.utils.checkpoint.checkpoint(
|
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create_custom_forward(decoder_layer),
|
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hidden_states,
|
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attention_mask,
|
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encoder_hidden_states,
|
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encoder_attention_mask,
|
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head_mask[idx] if head_mask is not None else None,
|
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cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
|
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None,
|
||||
)
|
||||
else:
|
||||
if breakmodel:
|
||||
device = primary_device if i < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, i - ram_blocks)
|
||||
layer_outputs = decoder_layer(
|
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hidden_states.to(device) if breakmodel and hidden_states is not None else hidden_states,
|
||||
attention_mask=attention_mask.to(device) if breakmodel and attention_mask is not None else attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states.to(device) if encoder_hidden_states is not None else None,
|
||||
encoder_attention_mask=encoder_attention_mask.to(device) if encoder_attention_mask is not None else None,
|
||||
layer_head_mask=((head_mask[idx].to(device) if head_mask[idx] is not None else None) if head_mask is not None else None),
|
||||
cross_attn_layer_head_mask=(
|
||||
(cross_attn_head_mask[idx].to(device) if cross_attn_head_mask[idx] is not None else None) if cross_attn_head_mask is not None else None
|
||||
),
|
||||
past_key_value=tuple(v.to(device) for v in past_key_value if v is not None) if breakmodel and past_key_value is not None and i >= ram_blocks and len(past_key_value) and past_key_value[0].device.index != device else past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
if encoder_hidden_states is not None:
|
||||
all_cross_attentions += (layer_outputs[2],)
|
||||
|
||||
if breakmodel:
|
||||
if i in range(ram_blocks):
|
||||
torch.cuda.synchronize()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
if breakmodel:
|
||||
if ram_blocks:
|
||||
del copystream
|
||||
torch.cuda.empty_cache()
|
||||
hidden_states = hidden_states.to(primary_device)
|
||||
hidden_states = self.layer_norm(hidden_states)
|
||||
if breakmodel:
|
||||
hidden_states = hidden_states.to(primary_device)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
if not return_dict:
|
||||
return tuple(
|
||||
v
|
||||
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
|
||||
if v is not None
|
||||
)
|
||||
return BaseModelOutputWithPastAndCrossAttentions(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
cross_attentions=all_cross_attentions,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@
|
|||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"<a href=\"https://colab.research.google.com/github/henk717/KoboldAI/blob/united/colab/TPU.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
"<a href=\"https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -18,7 +18,7 @@
|
|||
"\n",
|
||||
"For more information about KoboldAI check our our Github readme : https://github.com/KoboldAI/KoboldAI-Client/blob/main/readme.md\n",
|
||||
"\n",
|
||||
"More (smaller) models are available in the **[GPU edition](https://colab.research.google.com/github/koboldai/KoboldAI-Client/blob/united/colab/GPU.ipynb)**!"
|
||||
"More (smaller) models are available in the **[GPU edition](https://colab.research.google.com/github/koboldai/KoboldAI-Client/blob/main/colab/GPU.ipynb)**!"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "zrLGxVCEaqZx"
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ function launch
|
|||
else
|
||||
cd /content/KoboldAI-Client
|
||||
echo "Launching KoboldAI with the following options : python3 aiserver.py$model$kmpath$configname$ngrok --remote --override_delete --override_rename"
|
||||
python3 aiserver.py$model$kmpath$configname$ngrok --remote --override_delete --override_rename
|
||||
python3 aiserver.py$model$kmpath$configname$ngrok --colab
|
||||
exit
|
||||
fi
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue