mirror of https://github.com/hpcaitech/ColossalAI
1117 lines
49 KiB
Python
1117 lines
49 KiB
Python
# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch OpenMoE model."""
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import math
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from transformers.modeling_utils import PreTrainedModel
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from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRMSNorm
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from transformers.utils import (
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from colossalai.kernel.extensions.flash_attention import HAS_FLASH_ATTN
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from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON
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from colossalai.moe.layers import SparseMLP
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from colossalai.moe.manager import MOE_MANAGER
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from colossalai.moe.utils import get_activation, set_moe_args
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if HAS_TRITON:
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from colossalai.kernel.triton.llama_act_combine_kernel import LlamaActCombine
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logger = logging.get_logger(__name__)
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_CONFIG_FOR_DOC = "LlamaConfig"
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def set_openmoe_args(
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config: LlamaConfig,
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num_experts: int,
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moe_layer_interval: int,
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router_topk: int = 2,
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router_capacity_factor_train: float = 1.25,
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router_capacity_factor_eval: float = 2.0,
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router_min_capacity: int = 4,
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router_noisy_policy: str = None,
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router_drop_tks: bool = True,
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router_aux_loss_factor: float = 0.01,
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router_z_loss_factor: float = 0.0001,
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mlp_gated: bool = True,
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label_smoothing: float = 0.001,
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z_loss_factor: float = 0.01,
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enable_load_balance: bool = False,
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load_balance_tolerance: float = 0.1,
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load_balance_beam_width: int = 8,
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load_balance_group_swap_factor: float = 0.4,
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enable_kernel: bool = False,
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enable_comm_overlap: bool = False,
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enable_hierarchical_alltoall: bool = False,
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) -> None:
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"""
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MoE related arguments.
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It inserts the MoE arguments into the Llama config.
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Args:
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config (LlamaConfig): Transformers Llama config.
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num_experts (int, optional): Number of experts.
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moe_layer_interval (int, optional): The interval moe layer.
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router_topk (int, optional): Moe router top k. Defaults to 2.
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router_capacity_factor_train (float, optional): Moe router max capacity for train. Defaults to 1.25.
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router_capacity_factor_eval (float, optional): Moe router max capacity for eval. Defaults to 2.0.
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router_min_capacity (int, optional): Moe router min capacity. Defaults to 4.
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router_noisy_policy (str, optional): Moe router noisy policy. You can choose [Jitter, Gaussian, None]. Defaults to None.
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router_drop_tks (bool, optional): Whether moe router drop tokens which exceed max capacity. Defaults to True.
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router_aux_loss_factor (float, optional): Moe router aux loss. You can refer to STMoE for details. Defaults to 0.01.
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router_z_loss_factor (float, optional): Moe router z loss. You can refer to STMoE for details. Defaults to 0.01.
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mlp_gated (bool, optional): Use gate in mlp. Defaults to True.
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label_smoothing (float, optional): Label smoothing. Defaults to 0.001.
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z_loss_factor (float, optional): The final outputs' classification z loss factor. Defaults to 0.01.
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enable_load_balance (bool, optional): Expert load balance. Defaults to False.
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load_balance_tolerance (float, optional): Expert load balance search's difference tolerance. Defaults to 0.1.
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load_balance_beam_width (int, optional): Expert load balance search's beam width. Defaults to 8.
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load_balance_group_swap_factor (float, optional): Expert load balance group swap factor. Longer value encourages less swap. Defaults to 0.4.
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enable_kernel (bool, optional): Use kernel optimization. Defaults to False.
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enable_comm_overlap (bool, optional): Use communication overlap for MoE. Recommended to enable for muiti-node training. Defaults to False.
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enable_hierarchical_alltoall (bool, optional): Use hierarchical alltoall for MoE. Defaults to False.
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"""
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moe_args = dict(
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num_experts=num_experts,
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moe_layer_interval=moe_layer_interval,
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router_topk=router_topk,
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router_capacity_factor_train=router_capacity_factor_train,
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router_capacity_factor_eval=router_capacity_factor_eval,
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router_min_capacity=router_min_capacity,
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router_noisy_policy=router_noisy_policy,
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router_drop_tks=router_drop_tks,
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router_aux_loss_factor=router_aux_loss_factor,
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router_z_loss_factor=router_z_loss_factor,
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mlp_gated=mlp_gated,
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label_smoothing=label_smoothing,
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z_loss_factor=z_loss_factor,
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enable_load_balance=enable_load_balance,
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load_balance_tolerance=load_balance_tolerance,
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load_balance_beam_width=load_balance_beam_width,
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load_balance_group_swap_factor=load_balance_group_swap_factor,
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enable_kernel=enable_kernel,
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enable_comm_overlap=enable_comm_overlap,
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enable_hierarchical_alltoall=enable_hierarchical_alltoall,
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)
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set_moe_args(config, moe_args)
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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 generate_fixed_pos_embedding(features, length, min_timescale=1.0, max_timescale=10000.0):
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"""Generate Sin/Cos for Rotary Embeddings.
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Args:
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features: an integer
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length: an integer
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min_timescale: an optional float
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max_timescale: an optional float
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Returns:
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output_sin: a float32 Tensor with shape [length, features]
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output_cos: a float32 Tensor with shape [length, features]
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"""
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fraction = torch.arange(0, features, 2, dtype=torch.float32).cuda() / features
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timescale = min_timescale * (max_timescale / min_timescale) ** fraction
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rotational_frequency = 1.0 / timescale
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sinusoid_inp = torch.einsum("i,j->ij", torch.arange(length, dtype=torch.float32).cuda(), rotational_frequency)
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sinusoid_inp = torch.cat([sinusoid_inp, sinusoid_inp], dim=-1)
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return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)
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def apply_rotary_embedding(q, k, cos, sin, decode=False, rotary_index=None):
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"""Helper function to apply Rotary Embeddings."""
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cos = cos.to(q.dtype)
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sin = sin.to(q.dtype)
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if len(k.shape) == 3:
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# for multi query attention
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k = k.unsqueeze(2)
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multiquery = True
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else:
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multiquery = False
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batch, qlen, qheads, d = q.shape
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kbatch, klen, kheads, kd = k.shape
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assert batch == kbatch, f"{batch} != {kbatch}"
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assert d == kd, f"{d} != {kd}"
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if decode and qlen == 1 and rotary_index is not None:
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qcos = cos[rotary_index + 1, :]
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qsin = sin[rotary_index + 1, :]
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qcos = qcos.unsqueeze(2)
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qsin = qsin.unsqueeze(2)
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kcos, ksin = cos[:klen, :], sin[:klen, :]
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kcos = kcos.unsqueeze(0).unsqueeze(2)
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ksin = ksin.unsqueeze(0).unsqueeze(2)
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else:
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qcos, qsin = cos[:qlen, :], sin[:qlen, :]
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qcos = qcos.unsqueeze(0).unsqueeze(2)
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qsin = qsin.unsqueeze(0).unsqueeze(2)
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kcos, ksin = qcos, qsin
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out_q = (q * qcos) + (rotate_half(q) * qsin)
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out_k = (k * kcos) + (rotate_half(k) * ksin)
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if multiquery:
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out_k = out_k.squeeze(2)
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return out_q, out_k
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def SwiGLU(x):
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"""Gated linear unit activation function.
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Args:
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x : input array
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axis: the axis along which the split should be computed (default: -1)
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"""
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size = x.shape[-1]
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assert size % 2 == 0, "axis size must be divisible by 2"
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x1, x2 = torch.split(x, size // 2, -1)
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return x1 * (x2 * torch.sigmoid(x2))
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class OpenMoeMLP(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.pretraining_tp = config.pretraining_tp
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.hidden_act = config.hidden_act
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self.act_fn = get_activation(self.hidden_act)
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self.use_kernel = config.enable_kernel
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def forward(self, x):
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if self.pretraining_tp > 1:
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slice = self.intermediate_size // self.pretraining_tp
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gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
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up_proj_slices = self.up_proj.weight.split(slice, dim=0)
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down_proj_slices = self.down_proj.weight.split(slice, dim=1)
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gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
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up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1)
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intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
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down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)]
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down_proj = sum(down_proj)
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else:
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if HAS_TRITON and self.use_kernel and self.hidden_act == "swiglu":
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down_proj = self.down_proj(LlamaActCombine.apply(self.gate_proj(x), self.up_proj(x)))
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else:
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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class OpenMoeAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = config.head_dim
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.pretraining_tp = config.pretraining_tp
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self.max_position_embeddings = config.max_position_embeddings
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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self.sin, self.cos = generate_fixed_pos_embedding(self.head_dim, self.max_position_embeddings, 1.0, 1e4)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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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_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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use_kernel: bool = True,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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if self.pretraining_tp > 1:
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key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
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query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0)
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
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query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)]
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query_states = torch.cat(query_states, dim=-1)
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key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)]
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key_states = torch.cat(key_states, dim=-1)
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value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)]
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value_states = torch.cat(value_states, dim=-1)
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else:
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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# cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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# query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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max_length = max(query_states.shape[1], key_states.shape[1])
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assert max_length <= self.sin.shape[0]
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sin, cos = self.sin[:max_length], self.cos[:max_length]
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# TODO: for inference, we can add emb kv into cache to avoid computation
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query_states, key_states = apply_rotary_embedding(
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query_states, key_states, cos, sin, decode=True if q_len == 1 else False, rotary_index=position_ids
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)
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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if HAS_FLASH_ATTN and use_kernel:
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from flash_attn import flash_attn_func
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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attn_output = flash_attn_func(query_states, key_states, value_states, softmax_scale=1.0, causal=True)
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attn_output = attn_output.transpose(1, 2).contiguous()
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else:
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|
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
|
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
|
raise ValueError(
|
|
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
|
raise ValueError(
|
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
|
)
|
|
if self.training:
|
|
attention_mask = attention_mask.clone().detach()
|
|
attention_mask[:, :, :, 0] = 0
|
|
attn_weights = attn_weights + attention_mask
|
|
|
|
# upcast attention to fp32
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
|
raise ValueError(
|
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}"
|
|
)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_dim)
|
|
|
|
if self.pretraining_tp > 1:
|
|
attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
|
|
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1)
|
|
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)])
|
|
else:
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
if not output_attentions:
|
|
attn_weights = None
|
|
|
|
return attn_output, attn_weights, past_key_value
|
|
|
|
|
|
class OpenMoeDecoderLayer(nn.Module):
|
|
def __init__(self, config: LlamaConfig, moe: bool):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.moe = moe
|
|
self.self_attn = OpenMoeAttention(config=config)
|
|
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
if self.moe:
|
|
self.mlp = SparseMLP(
|
|
num_experts=config.num_experts,
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
router_top_k=config.router_topk,
|
|
router_capacity_factor_train=config.router_capacity_factor_train,
|
|
router_capacity_factor_eval=config.router_capacity_factor_eval,
|
|
router_min_capacity=config.router_min_capacity,
|
|
router_noisy_policy=config.router_noisy_policy,
|
|
router_drop_tks=config.router_drop_tks,
|
|
mlp_activation=config.hidden_act,
|
|
mlp_gated=config.mlp_gated,
|
|
enable_load_balance=config.enable_load_balance,
|
|
load_balance_tolerance=config.load_balance_tolerance,
|
|
load_balance_beam_width=config.load_balance_beam_width,
|
|
load_balance_group_swap_factor=config.load_balance_group_swap_factor,
|
|
enable_kernel=config.enable_kernel,
|
|
enable_comm_overlap=config.enable_comm_overlap,
|
|
)
|
|
self.pre_extra_mlp_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.extra_mlp = OpenMoeMLP(config)
|
|
else:
|
|
self.mlp = OpenMoeMLP(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
"""
|
|
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
if self.moe:
|
|
residual = hidden_states
|
|
hidden_states = self.pre_extra_mlp_layernorm(hidden_states)
|
|
hidden_states = self.extra_mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
LLAMA_START_DOCSTRING = r"""
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`LlamaConfig`]):
|
|
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
|
load the weights associated with the model, only the configuration. Check out the
|
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
|
LLAMA_START_DOCSTRING,
|
|
)
|
|
class OpenMoePreTrainedModel(PreTrainedModel):
|
|
config_class = LlamaConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["LlamaDecoderLayer"]
|
|
_skip_keys_device_placement = "past_key_values"
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.initializer_range
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
if isinstance(module, OpenMoeModel):
|
|
module.gradient_checkpointing = value
|
|
|
|
|
|
LLAMA_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
|
information on the default strategy.
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.n_positions - 1]`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
|
LLAMA_START_DOCSTRING,
|
|
)
|
|
class OpenMoeModel(OpenMoePreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
|
|
|
Args:
|
|
config: LlamaConfig
|
|
"""
|
|
|
|
def __init__(self, config: LlamaConfig):
|
|
super().__init__(config)
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
OpenMoeDecoderLayer(config, moe=True if (i + 1) % config.moe_layer_interval == 0 else False)
|
|
for i in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
|
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
|
# create causal mask
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
combined_attention_mask = None
|
|
if input_shape[-1] > 1:
|
|
combined_attention_mask = _make_causal_mask(
|
|
input_shape,
|
|
inputs_embeds.dtype,
|
|
device=inputs_embeds.device,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
|
inputs_embeds.device
|
|
)
|
|
combined_attention_mask = (
|
|
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
|
)
|
|
|
|
return combined_attention_mask
|
|
|
|
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
batch_size, seq_length = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
batch_size, seq_length, _ = inputs_embeds.shape
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
|
|
if past_key_values is not None:
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
|
|
if position_ids is None:
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
position_ids = torch.arange(
|
|
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
else:
|
|
position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
# embed positions
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(
|
|
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
|
)
|
|
attention_mask = self._prepare_decoder_attention_mask(
|
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
# None for past_key_value
|
|
return module(*inputs, output_attentions, None)
|
|
|
|
return custom_forward
|
|
|
|
layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(decoder_layer),
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
None,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
# 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] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
class OpenMoeForCausalLM(OpenMoePreTrainedModel):
|
|
# _tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = OpenMoeModel(config)
|
|
self.pretraining_tp = config.pretraining_tp
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
chunk_head: Optional[bool] = True,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, LlamaForCausalLM
|
|
|
|
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
|
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
|
```"""
|
|
# reset moe loss
|
|
MOE_MANAGER.reset_loss()
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
if self.pretraining_tp > 1:
|
|
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.pretraining_tp, dim=0)
|
|
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.pretraining_tp)]
|
|
logits = torch.cat(logits, dim=-1)
|
|
|
|
loss = None
|
|
# if no training, just do forward
|
|
if labels is None:
|
|
logits = self.lm_head(hidden_states)
|
|
logits = logits.float()
|
|
# the vocab size for openmoe is 30w+
|
|
# which causes great activation memory in training, up to 20G for one sequence
|
|
# so we use chunk and checkpoint to reduce memory
|
|
else:
|
|
if chunk_head == True:
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs):
|
|
logits = module(inputs[0])
|
|
logits = logits.float()
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous().float()
|
|
shift_labels = inputs[1][..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss = self._calculate_loss(shift_logits, shift_labels)
|
|
return loss
|
|
|
|
return custom_forward
|
|
|
|
aux_loss, z_loss = self._calculate_router_loss()
|
|
loss = aux_loss + z_loss
|
|
for batch_idx in range(hidden_states.shape[0]):
|
|
loss = loss + torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(self.lm_head),
|
|
hidden_states[batch_idx : batch_idx + 1, :],
|
|
labels[batch_idx : batch_idx + 1, :],
|
|
)
|
|
logits = None
|
|
else:
|
|
logits = self.lm_head(hidden_states)
|
|
logits = logits.float()
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
aux_loss, z_loss = self._calculate_router_loss()
|
|
loss = aux_loss + z_loss
|
|
loss = loss + self._calculate_loss(shift_logits, shift_labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
|
):
|
|
if past_key_values:
|
|
input_ids = input_ids[:, -1:]
|
|
|
|
position_ids = kwargs.get("position_ids", None)
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past_key_values:
|
|
position_ids = position_ids[:, -1].unsqueeze(-1)
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if inputs_embeds is not None and past_key_values is None:
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids}
|
|
|
|
model_inputs.update(
|
|
{
|
|
"position_ids": position_ids,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"attention_mask": attention_mask,
|
|
}
|
|
)
|
|
return model_inputs
|
|
|
|
@staticmethod
|
|
def _reorder_cache(past_key_values, beam_idx):
|
|
reordered_past = ()
|
|
for layer_past in past_key_values:
|
|
reordered_past += (
|
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
|
)
|
|
return reordered_past
|
|
|
|
def _calculate_router_loss(self, aux_loss: list = None, z_loss: list = None):
|
|
if aux_loss is None or z_loss is None:
|
|
aux_loss, z_loss = MOE_MANAGER.get_loss()
|
|
assert len(aux_loss) == len(z_loss) == self.config.num_hidden_layers // self.config.moe_layer_interval
|
|
aux_loss = self.config.router_aux_loss_factor * sum(aux_loss) / len(aux_loss)
|
|
z_loss = self.config.router_z_loss_factor * sum(z_loss) / len(z_loss)
|
|
return aux_loss, z_loss
|
|
|
|
def _calculate_loss(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
|
|
"""Compute cross entropy and entropy for log probs and targets.
|
|
|
|
Args:
|
|
logits: [batch, length, num_classes] float array.
|
|
targets: categorical targets [batch, length] int array.
|
|
|
|
Returns:
|
|
Tuple of scalar loss.
|
|
"""
|
|
if len(logits.shape) != len(targets.shape) + 1:
|
|
raise ValueError(
|
|
"Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
|
|
)
|
|
vocab_size = logits.shape[-1]
|
|
confidence = 1.0 - self.config.label_smoothing
|
|
low_confidence = (1.0 - confidence) / (vocab_size - 1)
|
|
normalizing_constant = -(
|
|
confidence * math.log(confidence) + (vocab_size - 1) * low_confidence * math.log(low_confidence + 1e-20)
|
|
)
|
|
|
|
# one hot
|
|
soft_targets = targets[..., None] == torch.arange(vocab_size, device=targets.device).reshape(
|
|
(1,) * len(targets.shape) + (-1,)
|
|
)
|
|
soft_targets = torch.where(
|
|
soft_targets, torch.full_like(soft_targets, confidence), torch.full_like(soft_targets, low_confidence)
|
|
)
|
|
soft_targets = soft_targets.to(torch.float32)
|
|
|
|
# cross entropy
|
|
total_loss = ZLossCrossEntropy.apply(logits, soft_targets, self.config.z_loss_factor)
|
|
total_loss = total_loss - normalizing_constant
|
|
total_loss = torch.mean(torch.sum(total_loss, dim=-1), dim=0)
|
|
return total_loss
|
|
|
|
|
|
class ZLossCrossEntropy(torch.autograd.Function):
|
|
"""Computes cross entropy loss with stable custom gradient.
|
|
|
|
Computes a stabilized-gradient version of:
|
|
-jnp.sum(targets * nn.log_softmax(logits), axis=-1)
|
|
|
|
If z_loss > 0, then an auxiliary loss equal to z_loss*log(z)^2
|
|
will be added to the cross entropy loss (z = softmax normalization constant).
|
|
The two uses of z_loss are:
|
|
1. To keep the logits from drifting too far from zero, which can cause
|
|
unacceptable roundoff errors in bfloat16.
|
|
2. To encourage the logits to be normalized log-probabilities.
|
|
|
|
Args:
|
|
logits: [batch, length, num_classes] float array.
|
|
targets: categorical one-hot targets [batch, length, num_classes] float
|
|
array.
|
|
z_loss: coefficient for auxilliary z-loss loss term.
|
|
|
|
Returns:
|
|
tuple with the total loss and the z_loss, both
|
|
float arrays with shape [batch, length].
|
|
"""
|
|
|
|
@staticmethod
|
|
def forward(ctx, logits, targets, z_loss):
|
|
max_logit = torch.max(logits, dim=-1, keepdim=True)[0]
|
|
shifted = logits - max_logit
|
|
exp_shifted = torch.exp(shifted)
|
|
sum_exp = torch.sum(exp_shifted, axis=-1, keepdims=True)
|
|
sum_exp_log = torch.log(sum_exp)
|
|
log_softmax = shifted - sum_exp_log
|
|
loss = -torch.sum(targets * log_softmax, axis=-1)
|
|
# Add auxilliary z-loss term.
|
|
log_z = torch.squeeze(sum_exp_log + max_logit, axis=-1)
|
|
total_z_loss = z_loss * torch.square(log_z)
|
|
loss += total_z_loss
|
|
ctx.z_loss = z_loss
|
|
ctx.save_for_backward(logits, targets, exp_shifted, sum_exp, log_softmax, log_z)
|
|
return loss
|
|
|
|
@staticmethod
|
|
def backward(ctx, *grad_outputs):
|
|
assert len(grad_outputs) == 1
|
|
g = grad_outputs[0]
|
|
z_loss = ctx.z_loss
|
|
logits, targets, exp_shifted, sum_exp, log_softmax, log_z = ctx.saved_tensors
|
|
# z-loss term adds the (2 * z_loss * log_z) factor.
|
|
deriv = (1 + 2 * z_loss * log_z).unsqueeze(-1) * exp_shifted / sum_exp - targets
|
|
g_logits = g.unsqueeze(-1) * deriv
|
|
g_targets = -g.unsqueeze(-1) * log_softmax
|
|
|
|
return (
|
|
g_logits.to(logits.dtype),
|
|
g_targets.to(targets.dtype),
|
|
None,
|
|
)
|