ce3f08b25a
* Fix ipex auto importer with Python builtins. * Raise errors if the user imports ipex manually before importing ipex_llm. Do nothing if they import ipex after importing ipex_llm. * Remove import ipex in examples.
511 lines
21 KiB
Python
511 lines
21 KiB
Python
from torch import nn
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import torch
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import torch.distributed as dist
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from typing import List, Optional, Tuple, Union, Iterator
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import time
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from transformers import AutoTokenizer, AutoConfig
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from transformers.utils import logging
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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import numpy as np
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import asyncio, uuid
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import threading
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logger = logging.get_logger(__name__)
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class PPConfig:
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"""Configuration for ModelSlices."""
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def __init__(self, pp_rank: int, pp_world_size: int) -> None:
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self.pp_rank = pp_rank
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self.pp_world_size = pp_world_size
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self.is_head = self.pp_rank == 0
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self.is_tail = self.pp_rank == self.pp_world_size - 1
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# Copied from transformers.models.bart.modeling_bart._make_causal_mask
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def _make_causal_mask(
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input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
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):
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"""
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Make causal mask used for bi-directional self-attention.
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"""
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bsz, tgt_len = input_ids_shape
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mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
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mask_cond = torch.arange(mask.size(-1), device=device)
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(dtype)
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if past_key_values_length > 0:
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mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
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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.to(torch.bool), torch.finfo(dtype).min)
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def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
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# create causal mask
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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combined_attention_mask = None
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if input_shape[-1] > 1:
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combined_attention_mask = _make_causal_mask(
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input_shape,
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inputs_embeds.dtype,
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device=inputs_embeds.device,
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past_key_values_length=past_key_values_length,
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)
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
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inputs_embeds.device
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)
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combined_attention_mask = (
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expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
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)
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return combined_attention_mask
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class DummyLayer(nn.Module):
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pass
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class PipelineBaseModel(nn.Module):
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def __init__(self, config):
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self.pp_config = PPConfig(pp_rank=dist.get_rank(), pp_world_size=dist.get_world_size())
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nr_slices = self.pp_config.pp_world_size
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# self.config.num_hidden_layers = 8
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slice_size = (self.config.num_hidden_layers + nr_slices -
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1) // nr_slices
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self.layer_start = slice_size * self.pp_config.pp_rank
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self.layer_end = self.layer_start + min(slice_size,
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self.config.num_hidden_layers - self.layer_start)
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self.num_layers = self.layer_end - self.layer_start
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# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
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def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
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# create causal mask
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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combined_attention_mask = None
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if input_shape[-1] > 1:
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combined_attention_mask = _make_causal_mask(
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input_shape,
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inputs_embeds.dtype,
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device=inputs_embeds.device,
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past_key_values_length=past_key_values_length,
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)
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
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inputs_embeds.device
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)
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combined_attention_mask = (
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expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
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)
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return combined_attention_mask
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def forward(
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self,
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input_ids: torch.LongTensor = 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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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds: Optional[torch.FloatTensor] = 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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) -> Union[Tuple, BaseModelOutputWithPast]:
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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 decoder_input_ids and decoder_inputs_embeds at the same time")
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elif input_ids is not None:
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assert self.pp_config.is_head, "input_ids is only supported on the head stage"
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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assert not self.pp_config.is_head, "inputs_embeds is only supported on the tail stage"
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
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seq_length_with_past = seq_length
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past_key_values_length = 0
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if past_key_values is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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if position_ids is None:
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device = input_ids.device if input_ids is not None else inputs_embeds.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(
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(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
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)
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attention_mask = self._prepare_decoder_attention_mask(
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attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
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)
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hidden_states = inputs_embeds
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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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next_decoder_cache = () if use_cache else None
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for idx in range(self.num_layers):
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decoder_layer = self.layers[self.layer_start + idx]
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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past_key_value = past_key_values[idx] if past_key_values is not None else None
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layer_outputs = decoder_layer(
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hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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)
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hidden_states = layer_outputs[0]
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if use_cache:
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next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
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if output_attentions:
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all_self_attns += (layer_outputs[1],)
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if self.pp_config.is_tail:
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hidden_states = self.norm(hidden_states)
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# add hidden states from the last decoder layer
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if output_hidden_states:
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all_hidden_states += (hidden_states,)
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next_cache = next_decoder_cache if use_cache else None
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if not return_dict:
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return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
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return BaseModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=next_cache,
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hidden_states=all_hidden_states,
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attentions=all_self_attns,
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)
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def load_model(checkpoint):
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from llama_models import LlamaForCausalLM
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if 'llama' in checkpoint.lower():
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model = LlamaForCausalLM.from_pretrained(checkpoint, low_cpu_mem_usage=True, torch_dtype=torch.float16)
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return model
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from pydantic import BaseModel
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class BatchTask(BaseModel):
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batch_id: str
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request_ids: List[str]
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max_tokens: int
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batch_size: int
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input_len: int
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# plain_texts: List[str]
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prompt_lengths: List[int]
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stopped: bool
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# input_ids: torch.Tensor
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# attention_mask: torch.Tensor
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def make_attention_mask(prompt_lengths):
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max_length = max(prompt_lengths)
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attention_mask = torch.zeros((len(prompt_lengths), max_length), dtype=torch.int64)
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for i, length in enumerate(prompt_lengths):
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attention_mask[i, max_length - length:] = 1
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return attention_mask
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class ModelRunner:
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def __init__(self, checkpoint, rank, world_size, low_bit, max_num_seqs):
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import sys
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self.pp_config = PPConfig(rank, world_size)
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start = time.perf_counter()
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model = load_model(checkpoint)
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end = time.perf_counter()
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logger.info(f"Time to load weights: {end - start:.2f}s")
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from ipex_llm import optimize_model
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model = optimize_model(model, low_bit=low_bit)
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model = model.to(torch.float16).to(f'xpu:{rank}')
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self.model = model
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self.rank = rank
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self.world_size = world_size
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self.pre_rank = (self.rank - 1) % self.world_size
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self.next_rank = (self.rank + 1) % self.world_size
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self.hidden_size = self.model.config.hidden_size
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self.max_num_seqs = max_num_seqs
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self.on_going_batches = [None] * self.world_size
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self.input_ids_dict = {}
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# self.attention_mask_dict = {}
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self.past_key_values_dict = {}
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self.tokens = {}
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self.token_times = {}
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self.dtype = torch.float16
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self.waiting_requests = asyncio.Queue()
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self.send_buff = None
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self.dict_lock = threading.Lock()
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# def generate(self, input_ids=None, max_tokens=5, attention_mask=None):
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# times = []
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# with torch.no_grad():
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# _input_ids = None
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# _past_key_values = None
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# bs = input_ids.shape[0]
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# output_ids = input_ids.clone()
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# for i in range(max_tokens):
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# start = time.perf_counter()
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# if _input_ids is None:
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# _input_ids = input_ids
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# if self.rank == 0:
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# outputs = self.model(input_ids=_input_ids, attention_mask=attention_mask, past_key_values=_past_key_values, use_cache=True)
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# else:
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# inputs_embeds = torch.empty(_input_ids.shape + (self.hidden_size,) , device=f'xpu:{self.rank}', dtype=torch.float32)
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# dist.recv(inputs_embeds, src=self.pre_rank)
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# outputs = self.model(inputs_embeds=inputs_embeds, attention_mask=attention_mask, past_key_values=_past_key_values, use_cache=True)
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# if self.rank == self.world_size - 1:
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# logits = outputs.logits
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# next_ids = torch.argmax(logits[:, -1:, :], dim=-1)
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# assert next_ids.shape == (bs, 1)
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# dist.broadcast(next_ids, src=self.rank)
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# else:
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# dist.send(outputs.last_hidden_state, dst=self.next_rank)
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# next_ids = torch.empty((bs, 1), device=f'xpu:{self.rank}', dtype=torch.int64)
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# dist.broadcast(next_ids, src=self.world_size - 1)
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# _input_ids = next_ids
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# output_ids = torch.cat([output_ids, next_ids], dim=-1)
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# _past_key_values = outputs.past_key_values
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# end = time.perf_counter()
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# times.append(end - start)
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# if self.rank == 0:
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# logger.info(f"first token latency: {times[0]}, rest token avg latecy: {np.mean(times[1:])}")
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# return output_ids
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def model_step(self, input, cur_batch):
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if cur_batch is None or cur_batch.stopped or input is None:
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return None
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cur_id = cur_batch.batch_id
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_past_key_values = self.past_key_values_dict.get(cur_id, None)
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# attention_mask = self.attention_mask_dict[cur_id]
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attention_mask = make_attention_mask(cur_batch.prompt_lengths)
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if self.rank == 0:
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input_ids = input
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inputs_embeds = None
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else:
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input_ids = None
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inputs_embeds = input
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output = self.model(
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input_ids=input_ids,
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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past_key_values=_past_key_values,
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use_cache=True
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)
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self.past_key_values_dict[cur_id] = output.past_key_values
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if not self.pp_config.is_tail:
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return output.last_hidden_state
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else:
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# logger.info(f"logits: {output.logits.shape}")
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return output.logits
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def is_initialized(self):
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return True
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async def add_request(self, tokenizer):
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request_ids, prompt_requests = [], []
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for _ in range(self.max_num_seqs):
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if self.waiting_requests.empty():
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break
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tmp_result = await self.waiting_requests.get()
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# logger.info(tmp_result)
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request_id, prompt_request = tmp_result
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request_ids.append(request_id)
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prompt_requests.append(prompt_request)
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plain_texts = [req.prompt for req in prompt_requests]
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inputs = tokenizer(plain_texts, return_tensors="pt", padding=True)
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input_ids = inputs.input_ids.to(f'xpu:{self.rank}')
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attention_mask = inputs.attention_mask.to(f'xpu:{self.rank}')
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new_batch = BatchTask(
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batch_id="batch_" + str(uuid.uuid4()),
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request_ids=request_ids,
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max_tokens=max([req.n_predict for req in prompt_requests]),
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batch_size=input_ids.size(0),
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input_len=input_ids.size(1),
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prompt_lengths=[sum(attention_mask[i,:]) for i in range(input_ids.size(0))],
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stopped=False,
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# plain_texts=plain_texts,
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# input_ids=input_ids,
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# attention_mask=attention_mask,
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)
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self.input_ids_dict[new_batch.batch_id] = input_ids
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self.token_times[new_batch.batch_id] = [time.perf_counter()]
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# self.attention_mask_dict[new_batch.batch_id] = attention_mask
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return new_batch
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def clear_batch(self, cur_id):
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self.input_ids_dict.pop(cur_id, None)
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self.tokens.pop(cur_id, None)
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self.token_times.pop(cur_id, None)
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# self.attention_mask_dict.pop(cur_id, None)
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self.past_key_values_dict.pop(cur_id, None)
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# torch.xpu.empty_cache()
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async def process_step(self, tokenizer, result_dict):
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cur_batch = None
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if self.rank == 0:
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if self.send_buff is not None:
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# logger.info(f"rank: {self.rank}, send: {self.send_buff.shape}")
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dist.send(self.send_buff, dst=self.next_rank)
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if self.on_going_batches[0] is not None:
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cur_batch = self.on_going_batches[0]
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cur_input = None
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if cur_batch is None:
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if not self.waiting_requests.empty():
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# await asyncio.sleep(0.01)
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cur_batch = await self.add_request(tokenizer)
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cur_input = self.input_ids_dict[cur_batch.batch_id]
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else:
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cur_batch = None
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cur_input = None
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if (cur_batch is not None) and (not cur_batch.stopped) and (cur_input is None):
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cur_id = cur_batch.batch_id
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next_ids = torch.empty((cur_batch.batch_size, 1,), device=f'xpu:{self.rank}', dtype=torch.int64)
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# logger.info(f"rank: {self.rank}, recv: {next_ids.shape}")
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dist.recv(next_ids, src=self.pre_rank)
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if self.tokens.get(cur_id, None) is None:
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self.tokens[cur_id] = []
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if len(next_ids.shape) == 1:
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next_ids = next_ids.unsqueeze(0)
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self.tokens[cur_id].append(next_ids)
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self.token_times[cur_id].append(time.perf_counter())
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# self.input_ids_dict[cur_id] += next_ids
|
|
cur_input = next_ids
|
|
# cur_batch.input_len += 1
|
|
cur_batch.input_len = 1
|
|
cur_batch.prompt_lengths = [x + 1 for x in cur_batch.prompt_lengths]
|
|
if len(self.tokens[cur_id]) >= cur_batch.max_tokens:
|
|
# Finish a batch
|
|
# logger.info(self.tokens[cur_id])
|
|
outputs = torch.cat(self.tokens[cur_id], dim=1)
|
|
outputs = outputs.cpu()
|
|
output_strs = tokenizer.batch_decode(outputs, skip_special_tokens=False)
|
|
for request_id, output_str in zip(cur_batch.request_ids, output_strs):
|
|
with self.dict_lock:
|
|
result_dict[request_id] = output_str
|
|
|
|
cur_times = self.token_times[cur_id]
|
|
first_token = cur_times[1] - cur_times[0]
|
|
next_token = (cur_times[-1] - cur_times[1]) / (len(self.tokens[cur_id]) - 1)
|
|
logger.info(f"First token latency: {first_token}, next token latency: {next_token}")
|
|
self.clear_batch(cur_id)
|
|
cur_batch.stopped = True
|
|
else:
|
|
if (cur_batch is not None) and cur_batch.stopped:
|
|
cur_batch = None
|
|
|
|
if cur_batch is not None:
|
|
dist.broadcast_object_list([cur_batch], src=0)
|
|
|
|
else:
|
|
if self.send_buff is not None:
|
|
# logger.info(f"rank: {self.rank}, send: {self.send_buff.shape}")
|
|
dist.send(self.send_buff, dst=self.next_rank)
|
|
|
|
batch_list = [None]
|
|
dist.broadcast_object_list(batch_list, src=0)
|
|
|
|
cur_batch = batch_list[0]
|
|
cur_input = None
|
|
|
|
if cur_batch is not None:
|
|
if cur_batch.stopped:
|
|
self.clear_batch(cur_batch.batch_id)
|
|
else:
|
|
cur_len = cur_batch.input_len
|
|
cur_input = torch.empty((cur_batch.batch_size, cur_len, self.hidden_size,), device=f'xpu:{self.rank}', dtype=self.dtype)
|
|
# logger.info(f"rank: {self.rank}, recv: {cur_input.shape}")
|
|
dist.recv(cur_input, src=self.pre_rank)
|
|
|
|
# if self.attention_mask_dict.get(cur_batch.batch_id, None) is None:
|
|
# self.attention_mask_dict[cur_batch.batch_id] = make_attention_mask(cur_batch.prompt_lengths)
|
|
|
|
# if self.rank == 0:
|
|
# logger.info(f"rank: {self.rank}, {batch_list}")
|
|
|
|
output = self.model_step(cur_input, cur_batch)
|
|
if output is not None and self.rank == self.world_size - 1:
|
|
output = torch.argmax(output[:, -1:, :], dim=-1)
|
|
|
|
if output is not None:
|
|
# dist.send(output, dst=self.next_rank)
|
|
self.send_buff = output
|
|
else:
|
|
self.send_buff = None
|
|
if self.rank == 0:
|
|
self.on_going_batches[:-1] = self.on_going_batches[1:]
|
|
self.on_going_batches[self.world_size - 1] = cur_batch
|
|
|