# coding=utf-8 # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch OpenMoE model.""" import math from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import PreTrainedModel from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRMSNorm from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from colossalai.kernel.cuda_native.mha.flash_attn_2 import HAS_FLASH_ATTN from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON from colossalai.moe.layers import SparseMLP from colossalai.moe.manager import MOE_MANAGER from colossalai.moe.utils import get_activation, set_moe_args if HAS_TRITON: from colossalai.kernel.triton.llama_act_combine_kernel import LlamaActCombine logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "LlamaConfig" def set_openmoe_args( config: LlamaConfig, num_experts: int, moe_layer_interval: int, router_topk: int = 2, router_capacity_factor_train: float = 1.25, router_capacity_factor_eval: float = 2.0, router_min_capacity: int = 4, router_noisy_policy: str = None, router_drop_tks: bool = True, router_aux_loss_factor: float = 0.01, router_z_loss_factor: float = 0.0001, mlp_gated: bool = True, label_smoothing: float = 0.001, z_loss_factor: float = 0.01, enable_load_balance: bool = False, load_balance_tolerance: float = 0.1, load_balance_beam_width: int = 8, load_balance_group_swap_factor: float = 0.4, enable_kernel: bool = False, enable_comm_overlap: bool = False, enable_hierarchical_alltoall: bool = False, ) -> None: """ MoE related arguments. It inserts the MoE arguments into the Llama config. Args: config (LlamaConfig): Transformers Llama config. num_experts (int, optional): Number of experts. moe_layer_interval (int, optional): The interval moe layer. router_topk (int, optional): Moe router top k. Defaults to 2. router_capacity_factor_train (float, optional): Moe router max capacity for train. Defaults to 1.25. router_capacity_factor_eval (float, optional): Moe router max capacity for eval. Defaults to 2.0. router_min_capacity (int, optional): Moe router min capacity. Defaults to 4. router_noisy_policy (str, optional): Moe router noisy policy. You can choose [Jitter, Gaussian, None]. Defaults to None. router_drop_tks (bool, optional): Whether moe router drop tokens which exceed max capacity. Defaults to True. router_aux_loss_factor (float, optional): Moe router aux loss. You can refer to STMoE for details. Defaults to 0.01. router_z_loss_factor (float, optional): Moe router z loss. You can refer to STMoE for details. Defaults to 0.01. mlp_gated (bool, optional): Use gate in mlp. Defaults to True. label_smoothing (float, optional): Label smoothing. Defaults to 0.001. z_loss_factor (float, optional): The final outputs' classification z loss factor. Defaults to 0.01. enable_load_balance (bool, optional): Expert load balance. Defaults to False. load_balance_tolerance (float, optional): Expert load balance search's difference tolerance. Defaults to 0.1. load_balance_beam_width (int, optional): Expert load balance search's beam width. Defaults to 8. load_balance_group_swap_factor (float, optional): Expert load balance group swap factor. Longer value encourages less swap. Defaults to 0.4. enable_kernel (bool, optional): Use kernel optimization. Defaults to False. enable_comm_overlap (bool, optional): Use communication overlap for MoE. Recommended to enable for muiti-node training. Defaults to False. enable_hierarchical_alltoall (bool, optional): Use hierarchical alltoall for MoE. Defaults to False. """ moe_args = dict( num_experts=num_experts, moe_layer_interval=moe_layer_interval, router_topk=router_topk, router_capacity_factor_train=router_capacity_factor_train, router_capacity_factor_eval=router_capacity_factor_eval, router_min_capacity=router_min_capacity, router_noisy_policy=router_noisy_policy, router_drop_tks=router_drop_tks, router_aux_loss_factor=router_aux_loss_factor, router_z_loss_factor=router_z_loss_factor, mlp_gated=mlp_gated, label_smoothing=label_smoothing, z_loss_factor=z_loss_factor, enable_load_balance=enable_load_balance, load_balance_tolerance=load_balance_tolerance, load_balance_beam_width=load_balance_beam_width, load_balance_group_swap_factor=load_balance_group_swap_factor, enable_kernel=enable_kernel, enable_comm_overlap=enable_comm_overlap, enable_hierarchical_alltoall=enable_hierarchical_alltoall, ) set_moe_args(config, moe_args) # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) def generate_fixed_pos_embedding(features, length, min_timescale=1.0, max_timescale=10000.0): """Generate Sin/Cos for Rotary Embeddings. Args: features: an integer length: an integer min_timescale: an optional float max_timescale: an optional float Returns: output_sin: a float32 Tensor with shape [length, features] output_cos: a float32 Tensor with shape [length, features] """ fraction = torch.arange(0, features, 2, dtype=torch.float32) / features timescale = min_timescale * (max_timescale / min_timescale) ** fraction rotational_frequency = 1.0 / timescale sinusoid_inp = torch.einsum("i,j->ij", torch.arange(length, dtype=torch.float32), rotational_frequency) sinusoid_inp = torch.cat([sinusoid_inp, sinusoid_inp], dim=-1) return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp) def apply_rotary_embedding(q, k, cos, sin, decode=False, rotary_index=None): # q: (bs, q_len, num_heads, head_dim) # k: (bs, q_len [+past_kv_len], num_heads, head_dim) # cos: (max_seq_len, head_dim) # sin: (max_seq_len, head_dim) # rotary_index: (bs, 1) # only used during decoding, when one query token is input at a time """Helper function to apply Rotary Embeddings.""" cos = cos.to(q.dtype) sin = sin.to(q.dtype) if len(k.shape) == 3: # for multi query attention k = k.unsqueeze(2) multiquery = True else: multiquery = False batch, qlen, qheads, d = q.shape kbatch, klen, kheads, kd = k.shape assert batch == kbatch, f"{batch} != {kbatch}" assert d == kd, f"{d} != {kd}" if decode and qlen == 1 and rotary_index is not None: qcos = cos[rotary_index, :] # (bs, 1, head_dim) qsin = sin[rotary_index, :] # (bs, 1, head_dim) qcos = qcos.unsqueeze(2) # (bs, q_len=1, 1, head_dim) # broadcast to all heads qsin = qsin.unsqueeze(2) # (bs, q_len=1, 1, head_dim) else: qcos, qsin = cos[:qlen, :], sin[:qlen, :] # (q_len, head_dim) qcos = qcos.unsqueeze(0).unsqueeze(2) # (1, q_len, 1, head_dim) qsin = qsin.unsqueeze(0).unsqueeze(2) kcos, ksin = cos[:klen, :], sin[:klen, :] # (k_len, head_dim) kcos = kcos.unsqueeze(0).unsqueeze(2) # (1, k_len, 1, head_dim) # broadcast to the whole batch, broadcast to all heads ksin = ksin.unsqueeze(0).unsqueeze(2) # (1, k_len, 1, head_dim) out_q = (q * qcos) + (rotate_half(q) * qsin) out_k = (k * kcos) + (rotate_half(k) * ksin) if multiquery: out_k = out_k.squeeze(2) return out_q, out_k def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def SwiGLU(x): """Gated linear unit activation function. Args: x : input array axis: the axis along which the split should be computed (default: -1) """ size = x.shape[-1] assert size % 2 == 0, "axis size must be divisible by 2" x1, x2 = torch.split(x, size // 2, -1) return x1 * (x2 * torch.sigmoid(x2)) class OpenMoeMLP(nn.Module): def __init__(self, config: LlamaConfig): super().__init__() self.pretraining_tp = config.pretraining_tp self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.hidden_act = config.hidden_act self.act_fn = get_activation(self.hidden_act) self.use_kernel = config.enable_kernel def forward(self, x): if self.pretraining_tp > 1: slice = self.intermediate_size // self.pretraining_tp gate_proj_slices = self.gate_proj.weight.split(slice, dim=0) up_proj_slices = self.up_proj.weight.split(slice, dim=0) down_proj_slices = self.down_proj.weight.split(slice, dim=1) gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.pretraining_tp)], dim=-1) intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2) down_proj = [F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.pretraining_tp)] down_proj = sum(down_proj) else: if HAS_TRITON and self.use_kernel and self.hidden_act == "swiglu": down_proj = self.down_proj(LlamaActCombine.apply(self.gate_proj(x), self.up_proj(x))) else: down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) return down_proj def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class OpenMoeAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: LlamaConfig): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = config.head_dim self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.pretraining_tp = config.pretraining_tp self.max_position_embeddings = config.max_position_embeddings self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) sin, cos = generate_fixed_pos_embedding(self.head_dim, self.max_position_embeddings, 1.0, 1e4) self.register_buffer('sin', sin) self.register_buffer('cos', cos) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() 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: bool = False, use_cache: bool = False, use_kernel: bool = True, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() if self.pretraining_tp > 1: key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0) key_slices = self.k_proj.weight.split(key_value_slicing, dim=0) value_slices = self.v_proj.weight.split(key_value_slicing, dim=0) query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)] query_states = torch.cat(query_states, dim=-1) key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)] key_states = torch.cat(key_states, dim=-1) value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)] value_states = torch.cat(value_states, dim=-1) else: query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) if past_key_value is not None: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) past_key_value = (key_states, value_states) if use_cache else None query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) max_length = max(query_states.shape[1], key_states.shape[1]) assert max_length <= self.sin.shape[0] sin, cos = self.sin[:max_length], self.cos[:max_length] # TODO: for inference, we can add emb kv into cache to avoid computation query_states, key_states = apply_rotary_embedding( query_states, key_states, cos, sin, decode=True if q_len == 1 else False, rotary_index=position_ids ) query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) if HAS_FLASH_ATTN and use_kernel: # If we use `from flash_attn import flash_attn_func` directly, # AutoModelForCausalLM.from_pretrained will treat flash_attn as a compulsory dependency and raise error if cannot find. # Here is a workaround to avoid the error. exec("from flash_attn import flash_attn_func") query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_output = flash_attn_func(query_states, key_states, value_states, softmax_scale=1.0, causal=True) attn_output = attn_output.transpose(1, 2).contiguous() else: 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, )