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# this code is in active development, interfaces may change
from typing import Optional, Tuple
import hivemind
import torch
import torch.nn as nn
from hivemind import get_logger, use_hivemind_log_handler
from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
from src.bloom.model import (
BloomConfig,
BloomForCausalLM,
BloomForSequenceClassification,
BloomModel,
BloomPreTrainedModel,
LMHead,
)
from src.client.remote_generation import RemoteGenerationMixin
from src.client.remote_sequential import RemoteSequential
from src.utils.misc import DUMMY
use_hivemind_log_handler("in_root_logger")
logger = get_logger(__file__)
class DistributedBloomConfig(BloomConfig):
"""
A bloom config that contains information about DHT peers.
To create a distributed model, one must provide dht_prefix and either initial_peers or dht.
"""
initial_peers: Tuple[str, ...] = () # a list of initial peers for hivemind DHT
dht_prefix: str # a prefix for all dht keys that correspond to this model (usually equal to model name)
dht: Optional[hivemind.DHT] = None # a running DHT instance, e.g. when using the same DHT for multiple models
chunk_size_for_efficient_fp16_on_cpu: int = 10000 # a chunk size for a LM head for efficient half-precision on CPU
pre_seq_len: int = 0 # a number of tokens for prompt tuning.
tuning_mode: Optional[str] = None # One of the finetune options: [None, 'shallow_ptune', 'deep_ptune', 'adapters']
class DistributedBloomModel(BloomModel):
"""BloomModel, but all transformer layers are hosted by the swarm"""
config_class = DistributedBloomConfig
def __init__(self, config: DistributedBloomConfig):
assert config.dht_prefix, "Could not find dht_prefix in config, please create model with dht_prefix=..."
assert config.initial_peers or config.dht, "Please specify initial_peers=list(...) or dht=hivemind.DHT(...)"
n_layer, config.n_layer = config.n_layer, 0 # temporarily set n_layer to 0 to prevent layer initialization
super().__init__(config)
assert len(self.h) == 0
config.n_layer = n_layer
dht = (
config.dht
if config.dht is not None
else hivemind.DHT(initial_peers=config.initial_peers, client_mode=True, start=True)
)
assert isinstance(dht, hivemind.DHT) and dht.is_alive(), "dht must be a running hivemind.DHT instance"
self.h = RemoteSequential(config, dht, config.dht_prefix)
# Forbid accumulate grads for embeddings and layernorm
self.set_requires_grad(False)
if config.tuning_mode and "ptune" in config.tuning_mode:
assert config.pre_seq_len > 0, "The number of prefix tokens must be > 0"
self.pre_seq_len = config.pre_seq_len
self.prompt_embeddings = nn.Embedding(self.pre_seq_len, config.hidden_size)
self.prefix_tokens = torch.arange(self.pre_seq_len).long()
if config.tuning_mode == "deep_ptune":
self.intermediate_prompt_embeddings = nn.Embedding(
self.pre_seq_len,
config.num_hidden_layers * config.hidden_size
# ^-- TODO: should be num_hidden_layers - 1
)
self.intermediate_prompt_embeddings.weight.data.zero_()
elif config.tuning_mode:
raise NotImplementedError(f"{self.tuning_mode} mode is not supported for now")
def set_requires_grad(self, value):
for p in self.parameters():
p.requires_grad = value
def get_prompt(self, batch_size):
prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1)
prefix_tokens = prefix_tokens.to(self.word_embeddings.weight.device)
prompts = self.prompt_embeddings(prefix_tokens)
if self.config.tuning_mode == "deep_ptune":
intermediate_prompts = self.intermediate_prompt_embeddings(prefix_tokens)
intermediate_prompts = intermediate_prompts.view(
batch_size, self.pre_seq_len, len(self.h), self.config.hidden_size # TODO: should be len(self.h) - 1
)
intermediate_prompts = intermediate_prompts.permute([2, 0, 1, 3])
else:
intermediate_prompts = DUMMY
return prompts, intermediate_prompts
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
):
assert attention_mask is None, "DistributedBloomModel does not support attention masks right now"
for k, v in kwargs.items():
if not (v is None or v is False):
logger.debug(f"Extra keyword arguments are not yet supported (got {k} = {v})")
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if self.config.tuning_mode and "ptune" in self.config.tuning_mode:
batch_size = inputs_embeds.shape[0]
prompts, intermediate_prompts = self.get_prompt(batch_size)
inputs_embeds = torch.cat([prompts, inputs_embeds], dim=1)
hidden_states = self.word_embeddings_layernorm(inputs_embeds.float())
output_shape = input_shape + (hidden_states.size(-1),)
if self.config.tuning_mode and "ptune" in self.config.tuning_mode:
hidden_states = self.h(hidden_states, prompts=intermediate_prompts)
else:
hidden_states = self.h(hidden_states)
# Remove prefix
if self.config.tuning_mode and "ptune" in self.config.tuning_mode:
hidden_states = hidden_states[:, self.pre_seq_len :]
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
hidden_states = hidden_states.view(output_shape)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=None,
hidden_states=None,
attentions=None,
)
class DistributedBloomForCausalLM(RemoteGenerationMixin, BloomForCausalLM):
"""DistributedBloomForCausalLM, but all transformer layers are hosted by the swarm"""
config_class = DistributedBloomConfig
def __init__(self, config: DistributedBloomConfig):
BloomPreTrainedModel.__init__(self, config)
self.transformer = DistributedBloomModel(config)
self.lm_head = LMHead(config, self.transformer.word_embeddings)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.transformer.word_embeddings
def get_output_embeddings(self):
if self.config.tie_word_embeddings:
return None
return self.lm_head
def set_input_embeddings(self, new_embeddings: nn.Embedding):
assert isinstance(new_embeddings, nn.Embedding)
self.transformer.word_embeddings = self.lm_head.word_embeddings = new_embeddings
assert self.lm_head.bias is None or len(self.lm_head.bias) == new_embeddings.num_embeddings
def set_output_embeddings(self, new_lm_head: nn.Linear):
with torch.no_grad():
self.lm_head.word_embeddings.weight[...] = new_lm_head.weight
self.lm_head.bias[...] = new_lm_head.bias
class DistributedBloomForSequenceClassification(BloomForSequenceClassification):
config_class = DistributedBloomConfig
def __init__(self, config: DistributedBloomConfig):
BloomPreTrainedModel.__init__(self, config)
self.num_labels = config.num_labels
self.transformer = DistributedBloomModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
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