import math import warnings from dataclasses import dataclass from typing import Optional, Tuple import torch import torch.utils.checkpoint import torch.nn as nn import torch.nn.functional as F from torch.distributions.normal import Normal from transformers.modeling_outputs import ( CausalLMOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.activations import ACT2FN from transformers.utils import ModelOutput, logging from .configuration_llama_moe import LlamaMoEConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "LlamaMoEConfig" @dataclass class CalculatorOutput(ModelOutput): hidden_states: Optional[torch.FloatTensor] = None num_dropped_tokens: Optional[int] = None @dataclass class BaseMoEModelOutputWithPast(ModelOutput): """ Args: num_dropped_tokens: layer idx to the number of dropped tokens """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None balance_loss: Optional[float] = None num_dropped_tokens: Optional[Tuple[torch.Tensor]] = None gate_load: Optional[Tuple[list]] = None gate_importance: Optional[Tuple[list]] = None @dataclass class MoECausalLMOutputWithPast(CausalLMOutputWithPast): balance_loss: Optional[float] = None num_dropped_tokens: Optional[Tuple[int]] = None gate_load: Optional[Tuple[list[torch.Tensor]]] = None gate_importance: Optional[Tuple[list[torch.Tensor]]] = None @dataclass class MoEMlpOutput(ModelOutput): hidden_states: Optional[torch.FloatTensor] = None balance_loss: Optional[torch.FloatTensor] = None num_dropped_tokens: Optional[int] = None gate_load: Optional[list] = None gate_importance: Optional[list] = None 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) class LlamaRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ LlamaRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) class LlamaRotaryEmbedding(torch.nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), ) class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ( (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) ) ** (self.dim / (self.dim - 2)) inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) 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 apply_rotary_pos_emb(q, k, cos, sin, position_ids): # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class LlamaMLP(nn.Module): def __init__(self, config): 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, 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.act_fn = ACT2FN[config.hidden_act] 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: 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 LlamaAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: LlamaMoEConfig): super().__init__() self.config = config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads 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 if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads})." ) 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) self._init_rope() def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = LlamaLinearScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor ) elif scaling_type == "dynamic": self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") 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, ) -> 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 # 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) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) 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()}" ) 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.hidden_size) 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 TopKBalancedNoisyGate(nn.Module): def __init__( self, input_size, num_experts, num_selects, gate_network="mlp", use_softmax=True, use_balance=True, balance_loss_weight=1e-2, add_noise=True, noise_epsilon=1e-2, ): super(TopKBalancedNoisyGate, self).__init__() assert num_selects <= num_experts self.input_size = input_size self.num_experts = num_experts self.num_selects = num_selects self.gate_network_type = gate_network self.gate_network = self.get_gate_network(gate_network, input_size, num_experts) self.use_softmax = use_softmax self.softmax = nn.Softmax(1) self.use_balance = use_balance self.balance_loss_weight = balance_loss_weight # add_noise self.add_noise = add_noise self.noise_epsilon = noise_epsilon self.warned = False if self.add_noise: self.weight_noise = nn.Linear(input_size, num_experts, bias=False) self.weight_noise.weight.data = torch.zeros( (num_experts, input_size), requires_grad=True, device=self.weight_noise.weight.data.device, dtype=self.weight_noise.weight.data.dtype, ) self.mean = 0.0 self.std = 1.0 self.normal = Normal(self.mean, self.std) self.softplus = nn.Softplus() self.reset_parameters() def get_gate_network(self, gate_type, input_size, num_experts): gate_type = gate_type.lower() if gate_type == "linear": gate_network = nn.Linear(input_size, num_experts, bias=False) nn.init.zeros_(gate_network.weight) elif gate_type == "mlp": gate_network = torch.nn.Sequential( torch.nn.Linear(input_size, num_experts, bias=False), torch.nn.Tanh(), torch.nn.Linear(num_experts, num_experts, bias=False), ) else: raise ValueError(f'Unexpected gate_type: {gate_type}.') return gate_network def reset_gate_network(self): if "gate_network_type" not in vars(self): raise KeyError(f"{type(self)} does not have a gate network.") else: self.gate_network = self.get_gate_network( self.gate_network_type, self.input_size, self.num_experts ) def reset_parameters(self): if self.add_noise: nn.init.zeros_(self.weight_noise.weight) # nn.init.zeros_(self.weight_noise) def cv_squared(self, x, eps=1e-10): """The squared coefficient of variation of a sample. Useful as a loss to encourage a positive distribution to be more uniform. Epsilons added for numerical stability. Returns 0 for an empty Tensor. Args: x: a `Tensor`. Returns: a `Scalar`.s """ if x.shape[0] == 1: return torch.tensor(0.0, device=x.device) return x.float().var() / (x.float().mean() ** 2 + eps) def forward(self, x): logits_gate = self.gate_network(x) if self.training and self.add_noise: noise_mm = self.weight_noise(x) noise_control = self.softplus(noise_mm) + self.noise_epsilon logits_noise = torch.randn_like(logits_gate) * noise_control logits = logits_gate + logits_noise else: logits = logits_gate top_logits, top_indices = logits.topk(min(self.num_selects + 1, self.num_experts), dim=1) # 选择并排序前k+1个权重 top_k_logits = top_logits[:, :self.num_selects] top_k_indices = top_indices[:, :self.num_selects] top_k_scores = self.softmax(top_k_logits.to(torch.float32)) if self.use_softmax else top_k_logits top_k_scores = top_k_scores.to(logits.dtype) zeros = torch.zeros_like(logits, requires_grad=True, device=logits.device) scores_filtered = zeros.scatter(dim=1, index=top_k_indices, src=top_k_scores) # shape(batch_size, num_experts) importance = scores_filtered.sum(0) # shape(num_experts) if self.training: if self.add_noise and self.num_selects != self.num_experts: batch_size = top_logits.size(0) m = top_logits.size(1) top_values_flat = top_logits.flatten() threshold_positions_if_in = torch.arange(batch_size, device=x.device) * m + self.num_selects threshold_if_in = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_in), 1) is_in = torch.gt(logits_noise, threshold_if_in) threshold_positions_if_out = threshold_positions_if_in - 1 threshold_if_out = torch.unsqueeze(torch.gather(top_values_flat, 0, threshold_positions_if_out), 1) # is each value currently in the top k. prob_if_in = self.normal.cdf((logits_gate - threshold_if_in) / noise_control) prob_if_out = self.normal.cdf((logits_gate - threshold_if_out) / noise_control) prob = torch.where(is_in, prob_if_in, prob_if_out) load = prob.sum(0) else: load = (scores_filtered > 0).sum(0) if not self.add_noise and not self.warned: warnings.warn('Gradient-trackable implementation for load calculation is only available when "add_noise=True". ' 'Training without noise will block the gradient from "load" path and lead to inconsistency in optimization objectives.') self.warned = True else: load = (scores_filtered > 0).sum(0) if self.use_balance: balance_loss = self.cv_squared(importance) + self.cv_squared(load) balance_loss *= self.balance_loss_weight else: balance_loss = torch.tensor(-100.0, device=x.device) return { "topK_indices": top_k_indices, "topK_scores": top_k_scores, "balance_loss": balance_loss, "load": load, "importance": importance, } class LinearGLUExperts(nn.Module): """ Modified from transformers.models.llama.modeling_llama.LlamaMLP """ __constants__ = [ "bias", "in_features", "hidden_features", "out_features", "hidden_act", "num_experts", "size_experts", ] def __init__( self, in_features, hidden_features, out_features, hidden_act, num_experts, size_experts=None, bias=True, device=None, dtype=None, ): factory_kwargs = {"device": device, "dtype": dtype} super(LinearGLUExperts, self).__init__() self.in_features = in_features self.hidden_features = hidden_features self.out_features = out_features self.hidden_act = hidden_act self.num_experts = num_experts if size_experts is None: # all experts share the same number of hidden neurons assert hidden_features % num_experts == 0 size_per_expert = hidden_features // num_experts size_experts = [size_per_expert for _ in range(num_experts)] else: # use specified expert sizes assert ( len(size_experts) == num_experts and sum(size_experts) == hidden_features ) self.size_experts = size_experts self.act_fn = ACT2FN[hidden_act] self.weight_gate = nn.ParameterList() self.weight_up = nn.ParameterList() self.weight_down = nn.ParameterList() for i in range(num_experts): # this matrix will be transposed when performing linear forwarding this_expert_weight_gate = nn.Parameter( torch.empty((size_experts[i], in_features), **factory_kwargs) ) # this matrix will be transposed when performing linear forwarding this_expert_weight_up = nn.Parameter( torch.empty((size_experts[i], in_features), **factory_kwargs) ) # this matrix will be transposed when performing linear forwarding this_expert_weight_down = nn.Parameter( torch.empty((out_features, size_experts[i]), **factory_kwargs) ) self.weight_gate.append(this_expert_weight_gate) self.weight_up.append(this_expert_weight_up) self.weight_down.append(this_expert_weight_down) if bias: self.bias_gate = nn.ParameterList() self.bias_up = nn.ParameterList() self.bias_down = nn.ParameterList() for i in range(num_experts): this_expert_bias_gate = nn.Parameter( torch.empty((size_experts[i],), **factory_kwargs) ) this_expert_bias_up = nn.Parameter( torch.empty((size_experts[i],), **factory_kwargs) ) this_expert_bias_down = nn.Parameter( torch.empty((out_features,), **factory_kwargs) ) self.bias_gate.append(this_expert_bias_gate) self.bias_up.append(this_expert_bias_up) self.bias_down.append(this_expert_bias_down) else: self.register_parameter("bias_gate", None) self.register_parameter("bias_up", None) self.register_parameter("bias_down", None) self.reset_parameters() def reset_parameters(self): for i in range(self.num_experts): nn.init.kaiming_uniform_(self.weight_gate[i], a=math.sqrt(5)) nn.init.kaiming_uniform_(self.weight_up[i], a=math.sqrt(5)) nn.init.kaiming_uniform_(self.weight_down[i], a=math.sqrt(5)) if self.bias_gate is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_gate[i]) bound = 1 / math.sqrt(fan_in) nn.init.uniform_(self.bias_gate[i], -bound, bound) if self.bias_up is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_up[i]) bound = 1 / math.sqrt(fan_in) nn.init.uniform_(self.bias_up[i], -bound, bound) if self.bias_down is not None: fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight_down[i]) bound = 1 / math.sqrt(fan_in) nn.init.uniform_(self.bias_down[i], -bound, bound) def forward(self, input, i): gate = self.act_fn( F.linear( input, self.weight_gate[i], self.bias_gate[i] if self.bias_gate is not None else None, ) ) up = F.linear( input, self.weight_up[i], self.bias_up[i] if self.bias_up is not None else None, ) down = F.linear( gate * up, self.weight_down[i], self.bias_down[i] if self.bias_down is not None else None, ) return down def extra_repr(self): return ( "in_features={}, hidden_features={}, out_features={}, hidden_act={}," " num_experts={}, size_experts={}, bias={}".format( self.in_features, self.hidden_features, self.out_features, self.hidden_act, self.num_experts, self.size_experts, self.bias_gate is not None, ) ) class UniversalCalculator(nn.Module): def __init__( self, experts: LinearGLUExperts, multiply_gate_scores=True, score_scale_factor=1.0, add_weight_norm: bool = False, ): super(UniversalCalculator, self).__init__() self.experts = experts # TODO (zhutong): use vmap to boost the training efficiency # self.experts_vmap = torch.vmap(self.experts) self.multiply_gate_scores = multiply_gate_scores self.score_scale_factor = score_scale_factor self.num_experts = experts.num_experts self.mlp_norm = None if multiply_gate_scores and add_weight_norm: raise NotImplementedError def reset_experts(self): self.experts.reset_parameters() def forward( self, x, topK_indices, topK_scores, expert_batch_size=None, **kwargs ) -> CalculatorOutput: batch_size = topK_indices.size(0) # topK_indices: (bsz*seq_len, num_selects) num_selects = topK_indices.size(1) topK_indices = topK_indices.flatten() # shape(batch_size*num_selects) topK_scores = topK_scores.flatten() # shape(batch_size*num_selects) batch_indices = torch.arange( batch_size, device=topK_scores.device ).repeat_interleave(num_selects) _, index_sorted_topK_indices = topK_indices.sort(0) sorted_topK_scores = topK_scores.index_select(0, index_sorted_topK_indices) sorted_batch_indices = batch_indices.index_select(0, index_sorted_topK_indices) if expert_batch_size is None: expert_batch_size = topK_indices.bincount( minlength=self.num_experts ).tolist() sorted_x = x.index_select(0, sorted_batch_indices) split_x = torch.split(sorted_x, expert_batch_size, dim=0) expert_outputs = [ self.experts(split_x[i], i) for i in range(self.num_experts) if split_x[i].shape[0] > 0 ] # (bsz*seq_len*num_selects, hidden_size) cat_expert_outputs = torch.cat(expert_outputs, 0) output_dim = cat_expert_outputs.size(1) if self.multiply_gate_scores: if self.mlp_norm is None: cat_expert_outputs = torch.mul( cat_expert_outputs, sorted_topK_scores.reshape(-1, 1) * self.score_scale_factor, ) # cat_expert_outputs = torch.mul(cat_expert_outputs, sorted_topK_scores.reshape(-1, 1) * 1.0) else: cat_expert_outputs = torch.mul( cat_expert_outputs, sorted_topK_scores.reshape(-1, 1) ) cat_expert_outputs = self.mlp_norm(cat_expert_outputs) zeros = torch.zeros( (batch_size, output_dim), device=cat_expert_outputs.device, dtype=cat_expert_outputs.dtype, ) y = zeros.index_add(0, sorted_batch_indices, cat_expert_outputs) return CalculatorOutput(hidden_states=y, num_dropped_tokens=torch.tensor(-1.0)) class BaseMoELayer(nn.Module): def __init__(self): super(BaseMoELayer, self).__init__() self.gate: TopKBalancedNoisyGate self.calculator: UniversalCalculator def _create_gate(self, **kwargs): self.gate_type = kwargs.get("gate_type", "TopKBalancedNoisyGate") if self.gate_type == "TopKBalancedNoisyGate": # noisy gate self.gate = TopKBalancedNoisyGate( self.input_size, self.num_experts, self.num_selects, gate_network=kwargs.get("gate_network", "mlp"), use_softmax=kwargs.get("gate_use_softmax", True), use_balance=kwargs.get("gate_use_balance", True), balance_loss_weight=kwargs.get("gate_balance_loss_weight", 1e-2), add_noise=kwargs.get("gate_add_noise", True), noise_epsilon=kwargs.get("gate_noise_epsilon", 1e-2), ) else: raise NotImplementedError def _create_calculator(self, experts, **kwargs): self.calculator_type = kwargs.get("calculator_type", "UniversalCalculator") if self.calculator_type == "UniversalCalculator": # top K calculator self.calculator = UniversalCalculator( experts, multiply_gate_scores=kwargs.get("multiply_gate_scores", True), score_scale_factor=kwargs.get("score_scale_factor", 1.0), add_weight_norm=kwargs.get("add_weight_norm", False), ) else: raise NotImplementedError def forward(self, x) -> MoEMlpOutput: original_shape = x.shape[:-1] x = x.reshape(-1, self.input_size) gate_outputs: dict = self.gate(x) calc_outs: CalculatorOutput = self.calculator(x, **gate_outputs) y = calc_outs.hidden_states y = y.reshape(original_shape + (self.output_size,)) return MoEMlpOutput( hidden_states=y, balance_loss=gate_outputs.get("balance_loss"), num_dropped_tokens=calc_outs.num_dropped_tokens, gate_load=gate_outputs.get("load", torch.tensor(-1)), gate_importance=gate_outputs.get("importance", torch.tensor(-1)), ) def set_num_selects(self, num_selects): if "num_selects" not in vars(self.gate): raise KeyError(f'{self.gate_type} does not have a key named "num_selects".') elif num_selects > self.gate.num_experts: raise ValueError( 'The value of "num_selects" must satisfy "num_selects <= num_experts"!' ) elif self.gate_type in ("SwitchBalancedGate",): raise ValueError( f"{self.gate_type} doesn't support manually setting num_selects." ) else: self.num_selects = num_selects self.gate.num_selects = num_selects def set_gate_use_softmax(self, use_softmax): if "use_softmax" not in vars(self.gate): raise KeyError(f'{self.gate_type} does not have a key named "use_softmax".') else: self.gate.use_softmax = use_softmax def set_gate_use_balance(self, use_balance): if "use_balance" not in vars(self.gate): raise KeyError(f'{self.gate_type} does not have a key named "use_balance".') else: self.gate.use_balance = use_balance def set_gate_balance_loss_weight(self, balance_loss_weight): if "balance_loss_weight" not in vars(self.gate): raise KeyError( f'{self.gate_type} does not have a key named "balance_loss_weight".' ) else: self.gate.balance_loss_weight = balance_loss_weight def set_gate_add_noise(self, add_noise): if "add_noise" not in vars(self.gate): raise KeyError(f'{self.gate_type} does not have a key named "add_noise".') else: self.gate.add_noise = add_noise def set_gate_noise_epsilon(self, noise_epsilon): if "noise_epsilon" not in vars(self.gate): raise KeyError( f'{self.gate_type} does not have a key named "noise_epsilon".' ) else: self.gate.noise_epsilon = noise_epsilon def set_calculator_multiply_gate_scores(self, multiply_gate_scores): if "multiply_gate_scores" not in vars(self.calculator): raise KeyError( f'{self.gate_type} does not have a key named "multiply_gate_scores".' ) else: self.calculator.multiply_gate_scores = multiply_gate_scores def set_calculator_score_scale_factor(self, score_scale_factor): if "score_scale_factor" not in vars(self.calculator): raise KeyError( f'{self.gate_type} does not have a key named "score_scale_factor".' ) else: self.calculator.score_scale_factor = score_scale_factor def set_calculator_drop_tokens(self, drop_tokens): if "drop_tokens" not in vars(self.calculator): raise KeyError(f'{self.gate_type} does not have a key named "drop_tokens".') elif ( drop_tokens and self.calculator.dropped_padding != "zero" and self.input_size != self.output_size ): warnings.warn( 'Setting "drop_tokens=True" without zero dropped padding when "input_size != output_size" will cause error!' ) else: self.calculator.drop_tokens = drop_tokens def set_calculator_dropped_padding(self, dropped_padding): if "dropped_padding" not in vars(self.calculator): raise KeyError( f'{self.gate_type} does not have a key named "dropped_padding".' ) elif dropped_padding not in self.calculator.available_dropped_padding_choices: raise ValueError( f"'dropped_padding' type not available! (available choices: {self.calculator.available_dropped_padding_choices})" ) elif ( self.calculator.drop_tokens and dropped_padding != "zero" and self.input_size != self.output_size ): warnings.warn( f'Setting "dropped_padding={dropped_padding}" with "drop_tokens=True" when "input_size != output_size" will cause error!' ) else: self.calculator.dropped_padding = dropped_padding def set_calculator_capacity_factor(self, capacity_factor): if "capacity_factor" not in vars(self.calculator): raise KeyError( f'{self.gate_type} does not have a key named "capacity_factor".' ) else: self.calculator.capacity_factor = capacity_factor def reset_gate_network(self): self.gate.reset_gate_network() def reset_experts(self): self.calculator.reset_experts() class LinearGLUMoELayer(BaseMoELayer): def __init__( self, input_size, hidden_size, output_size, hidden_act, num_experts, num_selects, size_experts=None, bias=True, **kwargs, ): super(LinearGLUMoELayer, self).__init__() assert num_selects <= num_experts self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.hidden_act = hidden_act self.num_experts = num_experts self.num_selects = num_selects self.size_experts = size_experts self.bias = bias experts = LinearGLUExperts( input_size, hidden_size, output_size, hidden_act, num_experts, size_experts=size_experts, bias=bias, ) self._create_gate(**kwargs) self._create_calculator(experts, **kwargs) class LlamaMoEDecoderLayer(nn.Module): def __init__(self, config: LlamaMoEConfig, layer_index): super().__init__() self.hidden_size = config.hidden_size self.self_attn = LlamaAttention(config=config) self.mlp = LlamaMLP(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) gating_config = { # all gates "gate_type": config.gate_type, "gate_network": config.gate_network, "gate_use_softmax": config.gate_use_softmax, "gate_use_balance": config.gate_use_balance, "gate_balance_loss_weight": config.gate_balance_loss_weight, "gate_add_noise": config.gate_add_noise, # TopKBalancedNoisyGate "gate_noise_epsilon": config.gate_noise_epsilon, } calculator_config = { # all calculators "calculator_type": config.calculator_type, "multiply_gate_scores": config.multiply_gate_scores, "score_scale_factor": ( config.score_scale_factor[layer_index] if isinstance(config.score_scale_factor, list) else config.score_scale_factor ), "add_weight_norm": config.add_weight_norm, # SwitchDropTokenCalculator "drop_tokens": config.drop_tokens, "dropped_padding": config.dropped_padding, "capacity_factor": config.capacity_factor, } self.mlp = LinearGLUMoELayer( input_size=self.hidden_size, hidden_size=config.intermediate_size, output_size=self.hidden_size, hidden_act=config.hidden_act, num_experts=config.num_experts, num_selects=config.num_selects, size_experts=( config.size_experts[layer_index] if config.size_experts is not None else None ), bias=False, **gating_config, **calculator_config, ) def forward( self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, ) -> tuple: 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) mlp_outs: MoEMlpOutput = self.mlp(hidden_states) hidden_states = residual + mlp_outs.hidden_states outputs = ( hidden_states, mlp_outs.balance_loss, mlp_outs.num_dropped_tokens, mlp_outs.gate_load, mlp_outs.gate_importance, ) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs def set_moe_num_selects(self, num_selects): self.mlp.set_num_selects(num_selects) def set_moe_gate_use_softmax(self, use_softmax): self.mlp.set_gate_use_softmax(use_softmax) def set_moe_gate_use_balance(self, use_balance): self.mlp.set_gate_use_balance(use_balance) def set_moe_gate_balance_loss_weight(self, balance_loss_weight): self.mlp.set_gate_balance_loss_weight(balance_loss_weight) def set_moe_gate_add_noise(self, add_noise): self.mlp.set_gate_add_noise(add_noise) def set_moe_gate_noise_epsilon(self, noise_epsilon): self.mlp.set_gate_noise_epsilon(noise_epsilon) def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores): self.mlp.set_calculator_multiply_gate_scores(multiply_gate_scores) def set_moe_calculator_score_scale_factor(self, score_scale_factor): self.mlp.set_calculator_score_scale_factor(score_scale_factor) def set_moe_calculator_drop_tokens(self, drop_tokens): self.mlp.set_calculator_drop_tokens(drop_tokens) def set_moe_calculator_dropped_padding(self, dropped_padding): self.mlp.set_calculator_dropped_padding(dropped_padding) def set_moe_calculator_capacity_factor(self, capacity_factor): self.mlp.set_calculator_capacity_factor(capacity_factor) def reset_gate_network(self): self.mlp.reset_gate_network() def reset_experts(self): self.mlp.reset_experts() class LlamaMoEPreTrainedModel(PreTrainedModel): config_class = LlamaMoEConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["LlamaMoEDecoderLayer"] _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, LlamaMoEModel): module.gradient_checkpointing = value class LlamaMoEModel(LlamaMoEPreTrainedModel): def __init__(self, config: LlamaMoEConfig): 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( [LlamaMoEDecoderLayer(config, i) for i in range(config.num_hidden_layers)] ) self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False 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 def forward( self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): 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 balance_loss = 0.0 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 num_dropped_tokens = () gate_load = () gate_importance = () 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: tuple = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), hidden_states, attention_mask, position_ids, None, ) else: layer_outputs: tuple = 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 layer_outputs[1] is not None: balance_loss += layer_outputs[1] if use_cache: next_decoder_cache += (layer_outputs[6 if output_attentions else 5],) if output_attentions: all_self_attns += (layer_outputs[5],) num_dropped_tokens += (layer_outputs[2],) gate_load += (layer_outputs[3],) gate_importance += (layer_outputs[4],) 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 BaseMoEModelOutputWithPast( last_hidden_state=hidden_states, balance_loss=balance_loss, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, num_dropped_tokens=num_dropped_tokens, gate_load=gate_load, gate_importance=gate_importance, ) def update_config(self): self.config.vocab_size = self.config.vocab_size self.config.max_position_embeddings = self.config.max_position_embeddings # ↓↓↓↓↓↓↓↓↓↓↓↓ changed here ↓↓↓↓↓↓↓↓↓↓↓↓ # self.config.hidden_size = self.layers[0].mlp.input_size self.config.intermediate_size = self.layers[0].mlp.hidden_size self.config.num_hidden_layers = len(self.layers) self.config.num_attention_heads = self.layers[0].self_attn.num_heads self.config.hidden_act = self.layers[0].mlp.hidden_act # ↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑ # self.config.initializer_range = self.config.initializer_range self.config.rms_norm_eps = self.config.rms_norm_eps self.config.pretraining_tp = self.config.pretraining_tp self.config.use_cache = self.config.use_cache self.config.rope_scaling = self.config.rope_scaling self.config._rope_scaling_validation() self.config.num_experts = self.layers[0].mlp.num_experts self.config.num_selects = self.layers[0].mlp.num_selects self.config.size_experts = [ self.layers[i].mlp.calculator.experts.size_experts for i in range(self.config.num_hidden_layers) ] self.config.gate_type = vars(self.layers[0].mlp).get( "gate_type", "TopKBalancedNoisyGate" ) self.config.gate_network = vars(self.layers[0].mlp.gate).get( "gate_network_type", "mlp" ) self.config.gate_use_softmax = vars(self.layers[0].mlp.gate).get( "use_softmax", True ) self.config.gate_use_balance = vars(self.layers[0].mlp.gate).get( "use_balance", True ) self.config.gate_balance_loss_weight = vars(self.layers[0].mlp.gate).get( "balance_loss_weight", 1e-2 ) self.config.gate_add_noise = vars(self.layers[0].mlp.gate).get( "add_noise", True ) self.config.gate_noise_epsilon = vars(self.layers[0].mlp.gate).get( "noise_epsilon", 1e-2 ) self.config.calculator_type = vars(self.layers[0].mlp).get( "calculator_type", "UniversalCalculator" ) self.config.multiply_gate_scores = vars(self.layers[0].mlp.calculator).get( "multiply_gate_scores", True ) self.config.score_scale_factor = [ vars(self.layers[i].mlp.calculator).get("score_scale_factor", 1.0) for i in range(self.config.num_hidden_layers) ] self.config.drop_tokens = vars(self.layers[0].mlp.calculator).get( "drop_tokens", True ) self.config.dropped_padding = vars(self.layers[0].mlp.calculator).get( "dropped_padding", "zero" ) self.config.capacity_factor = vars(self.layers[0].mlp.calculator).get( "capacity_factor", 1.25 ) def set_moe_num_selects(self, num_selects): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_num_selects(num_selects) def set_moe_gate_use_softmax(self, use_softmax): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_gate_use_softmax(use_softmax) def set_moe_gate_use_balance(self, use_balance): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_gate_use_balance(use_balance) def set_moe_gate_balance_loss_weight(self, balance_loss_weight): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_gate_balance_loss_weight(balance_loss_weight) def set_moe_gate_add_noise(self, add_noise): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_gate_add_noise(add_noise) def set_moe_gate_noise_epsilon(self, noise_epsilon): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_gate_noise_epsilon(noise_epsilon) def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_calculator_multiply_gate_scores(multiply_gate_scores) def set_moe_calculator_score_scale_factor( self, score_scale_factor, layer_index=None ): if layer_index is None: for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_calculator_score_scale_factor(score_scale_factor) else: self.layers[layer_index].set_moe_calculator_score_scale_factor( score_scale_factor ) def set_moe_calculator_drop_tokens(self, drop_tokens): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_calculator_drop_tokens(drop_tokens) def set_moe_calculator_dropped_padding(self, dropped_padding): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_calculator_dropped_padding(dropped_padding) def set_moe_calculator_capacity_factor(self, capacity_factor): for idx, decoder_layer in enumerate(self.layers): decoder_layer.set_moe_calculator_capacity_factor(capacity_factor) def reset_gate_network(self): for idx, decoder_layer in enumerate(self.layers): decoder_layer.reset_gate_network() def reset_experts(self): for idx, decoder_layer in enumerate(self.layers): decoder_layer.reset_experts() class LlamaMoEForCausalLM(LlamaMoEPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = LlamaMoEModel(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 def forward( self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs, ): 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: BaseMoEModelOutputWithPast = 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.last_hidden_state logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if outputs.balance_loss is not None and outputs.balance_loss > 0: loss += outputs.balance_loss if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return MoECausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, num_dropped_tokens=outputs.num_dropped_tokens, balance_loss=outputs.balance_loss, gate_load=outputs.gate_load, gate_importance=outputs.gate_importance, ) 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 update_config(self): self.model.update_config() def set_moe_num_selects(self, num_selects): self.model.set_moe_num_selects(num_selects) def set_moe_gate_use_softmax(self, use_softmax): self.model.set_moe_gate_use_softmax(use_softmax) def set_moe_gate_use_balance(self, use_balance): self.model.set_moe_gate_use_balance(use_balance) def set_moe_gate_balance_loss_weight(self, balance_loss_weight): self.model.set_moe_gate_balance_loss_weight(balance_loss_weight) def set_moe_gate_add_noise(self, add_noise): self.model.set_moe_gate_add_noise(add_noise) def set_moe_gate_noise_epsilon(self, noise_epsilon): self.model.set_moe_gate_noise_epsilon(noise_epsilon) def set_moe_calculator_multiply_gate_scores(self, multiply_gate_scores): self.model.set_moe_calculator_multiply_gate_scores(multiply_gate_scores) def set_moe_calculator_score_scale_factor( self, score_scale_factor, layer_index=None ): self.model.set_moe_calculator_score_scale_factor( score_scale_factor, layer_index=layer_index ) def set_moe_calculator_drop_tokens(self, drop_tokens): self.model.set_moe_calculator_drop_tokens(drop_tokens) def set_moe_calculator_dropped_padding(self, dropped_padding): self.model.set_moe_calculator_dropped_padding(dropped_padding) def set_moe_calculator_capacity_factor(self, capacity_factor): self.model.set_moe_calculator_capacity_factor(capacity_factor) def reset_gate_network(self): self.model.reset_gate_network() def reset_experts(self): self.model.reset_experts()