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155 lines
6.3 KiB
Python
155 lines
6.3 KiB
Python
from typing import Optional
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import hivemind
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import torch
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import torch.nn as nn
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from hivemind.utils.logging import get_logger
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from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
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from transformers.models.falcon import (
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FalconForCausalLM,
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FalconForSequenceClassification,
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FalconModel,
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FalconPreTrainedModel,
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)
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from petals.client.from_pretrained import FromPretrainedMixin
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from petals.client.lm_head import LMHead
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from petals.client.ptune import PTuneMixin
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from petals.client.remote_generation import RemoteGenerationMixin, RemotePastKeyValues
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from petals.client.remote_sequential import RemoteSequential
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from petals.models.falcon.config import DistributedFalconConfig
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from petals.utils.auto_config import DefaultRevisionMixin
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logger = get_logger(__name__)
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class DistributedFalconModel(DefaultRevisionMixin, FromPretrainedMixin, PTuneMixin, FalconModel):
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"""FalconModel, but all transformer layers are hosted by the swarm"""
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_keys_to_ignore_on_load_missing = PTuneMixin._keys_to_ignore_on_load_missing
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_keys_to_ignore_on_load_unexpected = [r"^transformer\.h\."]
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config_class = DistributedFalconConfig
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def __init__(self, config: DistributedFalconConfig, *, dht: Optional[hivemind.DHT] = None):
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n_layer, config.num_hidden_layers = config.num_hidden_layers, 0 # Prevent initialization
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super().__init__(config)
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assert len(self.h) == 0
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config.num_hidden_layers = n_layer
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self.h = RemoteSequential(config, dht=dht)
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self.requires_grad_(False) # Forbid accumulate grads for embeddings and layernorm
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self.init_prompts(config)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[RemotePastKeyValues] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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head_mask: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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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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# The causal mask will be added on the server-side
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assert (
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attention_mask is None or (attention_mask == 1).all()
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), f"Custom attention masks are not supported, {attention_mask=}"
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assert (
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position_ids is None or (position_ids[:, 1:] - position_ids[:, :-1] == 1).all()
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), f"Non-consecutive position_ids are not supported, {position_ids=}"
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assert head_mask is None, f"Custom head masks are not supported, {head_mask=}"
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assert use_cache is None or use_cache, f"{use_cache=} is not supported"
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assert not output_attentions, f"{output_attentions=} is not supported"
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assert not output_hidden_states, f"{output_hidden_states=} is not supported"
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assert return_dict is None or return_dict, f"{return_dict=} is not supported"
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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use_prompts = self.config.tuning_mode and "ptune" in self.config.tuning_mode and self.h.position == 0
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if use_prompts:
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batch_size = inputs_embeds.shape[0]
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prompts, intermediate_prompts = self.get_prompt(batch_size)
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inputs_embeds = torch.cat([prompts, inputs_embeds], dim=1)
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else:
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prompts = intermediate_prompts = None
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hidden_states = self.word_embeddings_layernorm(inputs_embeds)
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output_shape = input_shape + (hidden_states.size(-1),)
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hidden_states = self.h(
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hidden_states,
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prompts=intermediate_prompts,
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hypo_ids=past_key_values.hypo_ids if past_key_values is not None else None,
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)
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# Remove prefix
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if use_prompts:
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hidden_states = hidden_states[:, self.pre_seq_len :]
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# Add last hidden state
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hidden_states = self.ln_f(hidden_states)
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hidden_states = hidden_states.view(output_shape)
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return BaseModelOutputWithPastAndCrossAttentions(
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last_hidden_state=hidden_states,
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past_key_values=RemotePastKeyValues(),
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hidden_states=None,
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attentions=None,
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)
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@property
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def word_embeddings_layernorm(self) -> nn.Module: # For compatibility with RemoteGenerationMixin
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return nn.Identity()
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class DistributedFalconForCausalLM(DefaultRevisionMixin, FromPretrainedMixin, RemoteGenerationMixin, FalconForCausalLM):
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_keys_to_ignore_on_load_missing = DistributedFalconModel._keys_to_ignore_on_load_missing
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_keys_to_ignore_on_load_unexpected = DistributedFalconModel._keys_to_ignore_on_load_unexpected
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config_class = DistributedFalconConfig
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def __init__(self, config: DistributedFalconConfig):
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FalconPreTrainedModel.__init__(self, config)
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self.transformer = DistributedFalconModel(config)
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self.lm_head = LMHead(config)
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# Initialize weights and apply final processing
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self.post_init()
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def get_output_embeddings(self):
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return self.lm_head
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class DistributedFalconForSequenceClassification(
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DefaultRevisionMixin, FromPretrainedMixin, FalconForSequenceClassification
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):
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_keys_to_ignore_on_load_missing = DistributedFalconModel._keys_to_ignore_on_load_missing
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_keys_to_ignore_on_load_unexpected = DistributedFalconModel._keys_to_ignore_on_load_unexpected
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config_class = DistributedFalconConfig
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def __init__(self, config: DistributedFalconConfig):
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FalconPreTrainedModel.__init__(self, config)
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self.num_labels = config.num_labels
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self.transformer = DistributedFalconModel(config)
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self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
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# Initialize weights and apply final processing
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self.post_init()
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