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ales +Ġe commerce +ĠH elper +ãĤ¸ãĥ£ ãĥ¼ +úss ia +éĢļè¿ĩ äºĨ +ĠProf it +éħ ® +Ġph yt +ĠCont ains +Ġwor ding +ĠBin ance +re x +ÙħÙĨ Ø© +åħ³éĶ® åŃĹ +Ġbud s +Ġre building +ir ons +ï¼ħ Ċ +Ġher edit +ahren heit +S pr +Ġapplic ability +# pragma +ock ing +Ġdistingu ishing +\ quad +Ġpisc ina +ann ie +åı¯æĮģç»Ń åıijå±ķ +ins ured +ĠMas jid +ĠB IG +ĠObject ives +Ġshort comings +ĠDiput ación +Ġadult o +Ġpin point +itor io +âĢĻ urgence +n od +ãĤĬãģ¾ãģĻ ãģĮ +B ring +éķ· ãģ¯ +ĠاÙĦÙĨ ÙĪ +Ġthin ner +ĠD reams +ĠAbr il +ĠIndust ri +reib ung +Ġextrem es +d emo +ur red +_by te +cion ario +ä¿ ĺ +-C al +_ role +# wiki +Ġaument ado +Ġf used +æĿ¥ 临 +Pl aintiff +ï¼Į æĽ´å¤ļ +Ġmenurun kan +Ġplay ful +ãģĿãĤĮ ãģ§ãģ¯ +Ġassert ions +.m p +Ñīе е +Ġmetast asis +embl ance +ther os +ĠA unt +Ġdraw backs +Ext ract +ä¼ļåĵ¡ çĻ»éĮ² +æĥħ ä¾£ +ãģĶ ç´¹ä»ĭãģĹãģ¾ãģĻ +ãĥĶ ãĥ¼ãĥ« +ĠMaj esty +Ġnum ériques +ĠØ· اÙĦب +ĠBry ant +$ / +æ²³åĮĹ çľģ +Gr ound +Ġtransfer t +Ġdiscuss ão +Ġmemut uskan +Ġreiv indic +ĉ def +Ġagr icole +ãĥ¼ãĥģ ãĥ£ 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Index +ÑĨÑĸ й +ãĤĢ ãģĹãĤį +âĢĿ è¿Ļ个 +ĠDavid son +ãĥŀ ãĥĥãĥĪ +Ġerre ur +ì ¤Ħ +è¿Ļ æĺ¯ä¸Ģ个 +' expérience +é¡¹çĽ® 建设 +ãĥŀ ãĤ¹ãĤ¿ãĥ¼ +GB R +æīĭ å·¥ +주 ëĭ¹ +(r ange +'es prit +ĠProv idence +Argent ina +Op acity +ç´«å¤ĸ ç·ļ +åĢ¾ æĸľ +ï¼Į å·´ +ëŀ« íı¼ +Ġfamil jen +Ġgr ids +ä¸Ģ 带 +ì¹ Ń +ĠSup rema +ãģ«ãģªãĤĬ ãģŁãģĦ +ĠRab bi +Ġconcentr ating +ĠD MA +ãģ® 使çĶ¨ +Ġperm utation +ãģ¨ ãģķãĤĮãĤĭ +/ about +ãĤĪãģĨ ãģł +ĠØ¥ÙĨ تاج +決 ç®Ĺ +.T ensor +ger ald +Ġsie ge +IV ATE +Ġincl in +Ġdiplom acy +溶 解 +wick lung +_ global +ĠSek olah +为 æ°ij +表 è¿° +Ġmor bidity +Ġred ress +Ġcr us +Ġline up +çĮĽ åľ° +åĨĨ ãģ§ +Ġgr ac +(g ame +çĻ ľ +oso cial +ï¼Į æ°¸è¿ľ +ãģĹ ãĤĦãģĻ +Ġpersu aded +ç«ĭ ãģŁ +Ġspr ite +Ġaccueill ir +AN TS +Ġber per +èĥ¸ åı£ +ĠاÙĦاÙĤتص ادÙĬØ© +è¿ĺæĺ¯ æľī +Ġg ep +Ġcontinu ará +log s +_ close +(s ervice +確 ãģĭ +int ro +éĻ¢ 士 +ĠÐĴ ели +e os +âĨĵ Ċ +级 åĪ«çļĦ +Ġador able +Ġret rait +in co +Ġint enta +Ġ åħ¶ä»ĸ +emph asis +Input s +Pre ference +ĠDes k +å·¥ä½ľ ç»ıéªĮ +ç© Ĺ +Ġfic am +ĠV ox +Ġsoph ist +ät z +k au +Ġultraviol et +| lat +} ");Ċ +_s ingle +Span ish +ĠEm presas +å¥ij æľº +Ass im +Ġfut uras +Ġsub groups +Ġh ut +åIJį åı« +æĮ ļ +Ñĥ л +Ġbert ujuan +_ once +ãģĹãģªãģĦ ãģ¨ +æĽ¸ 士 +Ġ ä¸ĢèĪ¬ +Ġgan hou +èĥ½å¤Ł åľ¨ +ĠCom bine +æĸ¯ åŁº +Set ter +_ HOST +缺 失 +åѦçĶŁ åľ¨ +Ġpet ty +d ic +ÙĤ ص +ĠاÙĦÙĤ رار +Ġinvest isseurs +Ġprest ige +＿ ＿ +/ | +Ġ\ _ +å± ¬ +Ġgre c +Ġbol ts +Ġpatent ed +·· ·· +رÙĬ ØŃات +ï¼ĮåıĪ æĺ¯ +ĠMa o +ĠCirc ular +æ¹ĸåįĹ çľģ +Str ateg +Ġpro claim +ĉ expected +Ġdiscrepan cy +Car actéristiques +Ġ èİ·åıĸ +ãĢĤ äºĭå®ŀä¸Ĭ +ĠWe ak +Ġcelular es +ĠØ´ Ùĩد +æľĪ ç»ı +[ self +Ġind ican +æĮĩ çļĦæĺ¯ +好 åIJĹ +Ġenv oyer +Ġwh im +å« £ +lich keit +Ġsum ar +it ário +ĠìŀĪ ì§Ģë§Į +_T AG +ĠImpro ved +in ine +Ġreact ing +ĠO c +на д +Ġderiv ados +Ġprocur ar +ĠC arson +Ġcontradict ory +Ġlie utenant +åģľ çķĻ +ĠIde ally +æĪIJ éķ¿çļĦ +ãĥķãĤ¡ãĤ¤ãĥ« ãĤĴ +cul us +çijŀ 士 +ãĢĤ çī¹åĪ«æĺ¯ +we is +M ot +ur go +Ġp k +* >( +ãģ¤ ãģĦãģ¦ +cl k +Ġ$ " +ph antom +âĢĻ aur +% å·¦åı³ +èµ· ä¼ı +[ modifier +ĠCom ercial +Ġside walk +ĠK m +ĠاÙĦØ® دÙħات +Ġplag iarism +é«ĺ æ½® +Ġf iable +Ġvari antes +.f ull +, {\ +âĢĶâĢĶ Ċ +Ins pect +Ġz ou +Ġarr ests +åĢĭ 人ãģ® +Ġaliment ation +ĠOld er +ãĤĴ æºĢãģŁ +ç§į æĹı +å¾Ģ æĿ¥ +Ġres umed +èĥ½ 让 +ĠLiter acy +el ijk +ãĢģ ãģĭ +çļĦ åĬ¨ä½ľ +Ġd v +Ġpo che +: layout +ĠBro ck +Ġimp atient +Kep ala +çŁ¥èŃĺ ãĤĴ +å¼· åĬĽ +ĠM OS +ĠD V +ï¼ĮæĪij们 å°Ĩ +p ain +Ġmús ico +iè ne +åį ī +å¤Ħ åĪĨ ++ d +ĠSp read +åĪ» æĦı +Ġbén év +_ interface +Ġhospital ization +-d igit +æ´Ĺ é¡Ķ +ĠI p +ĠEqu ations +á rt +Ġbrain storm +åŃĺåľ¨ çļĦéĹ®é¢ĺ +inal e +emon ic +n ico +å±± åı£ +ĠâĶ ľâĶĢâĶĢ +好 åIJĥ +ĠJ uz +æłĩ çļĦ +ik ers +Ġsol ves +ter os +åıª æĥ³ +Ġmatéri au +Ġher idos +Ġib adah +ĠMu eller +Ġsynchron ous +ĠØ¥ ÙĦ +or é +Ġground breaking +_re al +åľ¨ åĨħ +ĠEst at +Ġb icarbon +Ġdream ed +Ġcl ergy +ĠRo of +ó wn +? � +ĠH IST +Ġlanz ar +ма ÑĤи +ĠX en +社 åĽ¢ +Ġden en +ĉĉĉĉ ĉĉĉĉĉĉ +ĠLi ability +Ġmarch and +æĻļ ä¼ļ +Ġh acker +Fund ing +ute urs +Ġobten ido +Ġp istes +on ado +ï¼Į æĸ¹ä¾¿ +) ]ĊĊ +âĢĻ âĢĻ +Qu iz +çĻ ĸ +Ġintend ente +Ġflash ing +Ch ars +ĠPro yecto +å¿ĥ åºķ +Ġdata frame +ad era +课åłĤ æķĻåѦ +ĠB ACK +Ġcompr im +æĹ¥ ãģ¾ãģ§ +il yn +ĠD anger +æķ° å¹´ +综 èīº +pos itories +ĠB N +it ize +Ġrab bits +orm an +uar te +Ġcontin ual +ä¸į çĪ± +ĠP ek +Ġis omorphism +Ġext in +\ pm +åıį åĩ» +èī ¦ +Ġsep ak +. 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_prepare_4d_causal_attention_mask, +) +from transformers.modeling_outputs import ( + MoeCausalLMOutputWithPast, + MoeModelOutputWithPast, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) +from transformers.utils.import_utils import is_torch_fx_available +from .configuration_minimax_text_01 import MiniMaxText01Config + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_func, flash_attn_varlen_func + from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa + + _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) + +# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. +# It means that the function will not be traced through and simply appear as a node in the graph. +if is_torch_fx_available(): + _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) + +use_triton = eval(os.environ.get("use_triton", default="False")) +debug = eval(os.environ.get("debug", default="False")) +do_eval = eval(os.environ.get("do_eval", default="False")) +eval_and_not_generate = eval(os.environ.get("eval_and_not_generate", default="False")) +BLOCK = 256 + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "MiniMaxText01Config" + + +def get_activation_fn(activation): + if debug: + logger.info(f"activation: {activation}") + if activation == "gelu": + return F.gelu + elif activation == "relu": + return F.relu + elif activation == "elu": + return F.elu + elif activation == "sigmoid": + return F.sigmoid + elif activation == "exp": + + def f(x): + with torch.no_grad(): + x_max = torch.max(x, dim=-1, keepdims=True).values + y = torch.exp(x - x_max) + + return y + + return f + elif activation == "leak": + return F.leaky_relu + elif activation == "1+elu": + + def f(x): + return 1 + F.elu(x) + + return f + elif activation == "2+elu": + + def f(x): + return 2 + F.elu(x) + + return f + elif activation == "silu" or activation == "swish": + return F.silu + elif activation == "sine": + return torch.sin + else: + logger.info( + f"activation: does not support {activation}, use Identity!!!") + return lambda x: x + + +def load_balancing_loss_func( + gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, + attention_mask: Optional[torch.Tensor] = None +) -> float: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss + function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between + experts is too unbalanced. + + Args: + gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]): + Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + attention_mask (`torch.Tensor`, None): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + num_experts (`int`, *optional*): + Number of experts + + Returns: + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) + + _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) + + expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) + + if attention_mask is None: + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.mean(expert_mask.float(), dim=0) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.mean(routing_weights, dim=0) + else: + batch_size, sequence_length = attention_mask.shape + num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) + + # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask + expert_attention_mask = ( + attention_mask[None, :, :, None, None] + .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) + .reshape(-1, top_k, num_experts) + .to(compute_device) + ) + + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( + expert_attention_mask, dim=0 + ) + + # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert + router_per_expert_attention_mask = ( + attention_mask[None, :, :, None] + .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) + .reshape(-1, num_experts) + .to(compute_device) + ) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( + router_per_expert_attention_mask, dim=0 + ) + + overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) + return overall_loss * num_experts + + +# Copied from transformers.models.llama.modeling_llama._get_unpad_data +def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + +class GLU(nn.Module): + + def __init__(self, d1, d2, bias=False): + super().__init__() + + self.l1 = nn.Linear(d1, d2, bias=bias) + self.l2 = nn.Linear(d1, d2, bias=bias) + self.l3 = nn.Linear(d2, d1, bias=bias) + + def forward(self, x): + o1 = self.l1(x) + o2 = self.l2(x) + output = o1 * o2 + output = self.l3(output) + return output + + +class MiniMaxText01LightningAttention(nn.Module): + def __init__(self, config: MiniMaxText01Config, layer_idx: Optional[int] = None): + super().__init__() + bias = False + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, 'head_dim', self.hidden_size // self.num_heads) + + self.out_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=bias) + self.act = get_activation_fn(config.hidden_act) + self.norm = MiniMaxText01RMSNorm(self.head_dim * self.num_heads) + + self.qkv_proj = nn.Linear(self.hidden_size, 3 * self.head_dim * self.num_heads, bias=bias) + self.output_gate = nn.Linear(self.hidden_size, self.head_dim * self.num_heads, bias=bias) + + # for inference only + self.offset = 0 + self.layer_idx = layer_idx + + def forward( + self, + hidden_states, + attn_mask: Optional[torch.Tensor] = None, # (b, h, n, m) + output_attentions: bool = False, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + use_cache: bool = False, + slope_rate: Optional[torch.Tensor] = None, + **kwargs + ): + if (not self.training) and (not do_eval): + return self.inference( + hidden_states, + attn_mask, + output_attentions, + past_key_value, + use_cache, + slope_rate, + ) + + def inference( + self, + x, + attn_mask: Optional[torch.Tensor] = None, # (b, n) + output_attentions: bool = False, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + use_cache: bool = False, + slope_rate: Optional[torch.Tensor] = None, # (h, 1, 1) + ): + # x: b n d + b, n, d = x.shape + # linear map + qkv = self.act(self.qkv_proj(x)) + new_shape = qkv.size()[:-1] + (self.num_heads, -1) + qkv = qkv.view(*new_shape) + q, k, v = torch.split(qkv, [self.head_dim] * 3, dim=3) + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + + if past_key_value is None: + self.offset = q.shape[-2] + else: + self.offset += 1 + + # for align with metaseq + ratio = torch.exp(-slope_rate) + + # only use for the first time + if past_key_value is None: + slope_rate = slope_rate.to(torch.float32) + if attn_mask is not None: + v = v.masked_fill((1 - attn_mask).unsqueeze(1).unsqueeze(-1).to(torch.bool), 0) + NUM_BLOCK = (n + BLOCK - 1) // BLOCK + b, h, n, d = q.shape + e = v.shape[-1] + # other + array = torch.arange(BLOCK).to(q) + 1 + q_decay = torch.exp(-slope_rate * array.reshape(-1, 1)) + k_decay = torch.exp(-slope_rate * (BLOCK - array.reshape(-1, 1))) + index = array[:, None] - array[None, :] + s_index = slope_rate * index[ + None, + None, + ] + s_index = torch.where(index >= 0, -s_index, float("-inf")) + diag_decay = torch.exp(s_index) + + kv = torch.zeros(b, h, d, e).to(torch.float32).to(q.device) + output = torch.empty((b, h, n, e), dtype=q.dtype, device=q.device) + for i in range(NUM_BLOCK): + si = i * BLOCK + ei = min(si + BLOCK, n) + m = ei - si + qi = q[:, :, si:ei].contiguous() + ki = k[:, :, si:ei].contiguous() + vi = v[:, :, si:ei].contiguous() + qkv_none_diag = torch.matmul(qi * q_decay[:, :m], kv).to(torch.float32) + + # diag + qk = torch.matmul(qi, ki.transpose(-1, -2)).to(torch.float32) * diag_decay[:, :, :m, :m] + qkv_diag = torch.matmul(qk, vi.to(torch.float32)) + block_decay = torch.exp(-slope_rate * m) + output[:, :, si:ei] = qkv_none_diag + qkv_diag + kv = block_decay * kv + torch.matmul((ki * k_decay[:, -m:]).transpose(-1, -2).to(vi.dtype), vi) + + else: + kv = past_key_value + output = [] + for i in range(n): + kv = ratio * kv + torch.einsum( + "... n d, ... n e -> ... d e", + k[:, :, i:i + 1], + v[:, :, i:i + 1], + ) + qkv = torch.einsum("... n e, ... e d -> ... n d", q[:, :, i:i + 1], kv.to(q.dtype)) + output.append(qkv) + output = torch.concat(output, dim=-2) + # reshape + output = rearrange(output, "b h n d -> b n (h d)") + # normalize + output = self.norm(output) + # gate + output = F.sigmoid(self.output_gate(x)) * output + # outproj + output = self.out_proj(output) + + attn_weights = None + + return output, attn_weights, kv + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MiniMaxText01 +class MiniMaxText01RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + MiniMaxText01RMSNorm 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) + + +# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->MiniMaxText01 +class MiniMaxText01RotaryEmbedding(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, dtype=torch.int64).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # 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.float32 + ) + + 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=torch.int64).type_as(self.inv_freq) + + freqs = torch.outer(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().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().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=torch.float32) + + return ( + self.cos_cached[:seq_len].to(dtype=torch.float32), + self.sin_cached[:seq_len].to(dtype=torch.float32), + ) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +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) + + +# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + dtype = q.dtype + rot_dim = cos.shape[-1] + q_, q_pass = q[..., :rot_dim], q[..., rot_dim:] + k_, k_pass = k[..., :rot_dim], k[..., rot_dim:] + cos = cos[position_ids].unsqueeze(unsqueeze_dim) + sin = sin[position_ids].unsqueeze(unsqueeze_dim) + q_embed = (q_ * cos) + (rotate_half(q_) * sin) + k_embed = (k_ * cos) + (rotate_half(k_) * sin) + return torch.cat((q_embed, q_pass), dim=-1).to(dtype), torch.cat((k_embed, k_pass), dim=-1).to(dtype) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +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) + + +# Copied from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->MiniMaxText01 +class MiniMaxText01Attention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: MiniMaxText01Config, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, '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.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + self.attention_dropout = config.attention_dropout + + 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.rotary_dim = getattr(config, 'rotary_dim', self.head_dim) + + self.rotary_emb = MiniMaxText01RotaryEmbedding( + self.rotary_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + 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[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + bsz, q_len, _ = hidden_states.size() + + 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: + if self.layer_idx is None: + raise ValueError( + f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " + "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " + "with a layer index." + ) + kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) + 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: + cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # 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_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + 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) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->MiniMaxText01 +class MiniMaxText01FlashAttention2(MiniMaxText01Attention): + """ + MiniMaxText01 flash attention module. This module inherits from `MiniMaxText01Attention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Union[Cache, Tuple[torch.Tensor]]] = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ): + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + + # overwrite attention_mask with padding_mask + attention_mask = kwargs.pop("padding_mask") + bsz, q_len, _ = hidden_states.size() + + 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[-3] + + # Because the input can be padded, the absolute sequence length depends on the max position id. + rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1 + cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len) + + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + + use_sliding_windows = ( + _flash_supports_window_size + and getattr(self.config, "sliding_window", None) is not None + and kv_seq_len > self.config.sliding_window + ) + + if not _flash_supports_window_size: + logger.warning_once( + "The current flash attention version does not support sliding window attention, for a more memory efficient implementation" + " make sure to upgrade flash-attn library." + ) + + dropout_rate = 0.0 if not self.training else self.attention_dropout + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in float16 just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + # Reshape to the expected shape for Flash Attention + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + if past_key_value is not None: + # reuse k, v, for evaluation only + key_states = torch.cat([past_key_value[0], key_states], dim=-3) + value_states = torch.cat([past_key_value[1], value_states], dim=-3) + + past_key_value = (key_states, value_states) if use_cache else None + + attn_output = self._flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + dropout=dropout_rate, + use_sliding_windows=use_sliding_windows, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + def _flash_attention_forward( + self, + query_states, + key_states, + value_states, + attention_mask, + query_length, + dropout=0.0, + softmax_scale=None, + use_sliding_windows=False, + ): + """ + Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token + first unpad the input, then computes the attention scores and pad the final attention scores. + + Args: + query_states (`torch.Tensor`): + Input query states to be passed to Flash Attention API + key_states (`torch.Tensor`): + Input key states to be passed to Flash Attention API + value_states (`torch.Tensor`): + Input value states to be passed to Flash Attention API + attention_mask (`torch.Tensor`): + The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the + position of padding tokens and 1 for the position of non-padding tokens. + dropout (`float`): + Attention dropout + softmax_scale (`float`, *optional*): + The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) + use_sliding_windows (`bool`, *optional*): + Whether to activate sliding window attention. + """ + if not self._flash_attn_uses_top_left_mask: + causal = self.is_causal + else: + # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. + causal = self.is_causal and query_length != 1 + + # Contains at least one padding token in the sequence + if attention_mask is not None: + batch_size = query_states.shape[0] + query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( + query_states, key_states, value_states, attention_mask, query_length + ) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if not use_sliding_windows: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) + else: + if not use_sliding_windows: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + ) + else: + attn_output = flash_attn_func( + query_states, + key_states, + value_states, + dropout, + softmax_scale=softmax_scale, + causal=causal, + window_size=(self.config.sliding_window, self.config.sliding_window), + ) + + return attn_output + + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape + + # On the first iteration we need to properly re-create the padding mask + # by slicing it on the proper place + if kv_seq_len != attention_mask.shape[-1]: + attention_mask_num_tokens = attention_mask.shape[-1] + attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len:] + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k) + + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + +class MiniMaxText01MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + 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): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class MiniMaxText01BlockSparseTop2MLP(nn.Module): + def __init__(self, config: MiniMaxText01Config): + super().__init__() + self.ffn_dim = config.intermediate_size + self.hidden_dim = config.hidden_size + + self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) + self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False) + self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) + + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_states): + current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states) + current_hidden_states = self.w2(current_hidden_states) + return current_hidden_states + + +class MiniMaxText01BLockSparseTop2MLP(MiniMaxText01BlockSparseTop2MLP): + def __init__(self, *args, **kwargs): + logger.warning_once( + "MiniMaxText01BLockSparseTop2MLP is deprecated by MiniMaxText01BlockSparseTop2MLP and will be removed in v4.40." + ) + super().__init__(*args, **kwargs) + + +class MiniMaxText01SparseMoeBlock(nn.Module): + """ + This implementation is + strictly equivalent to standard MoE with full capacity (no + dropped tokens). It's faster since it formulates MoE operations + in terms of block-sparse operations to accomodate imbalanced + assignments of tokens to experts, whereas standard MoE either + (1) drop tokens at the cost of reduced performance or (2) set + capacity factor to number of experts and thus waste computation + and memory on padding. + """ + + def __init__(self, config): + super().__init__() + self.hidden_dim = config.hidden_size + self.ffn_dim = config.intermediate_size + self.num_experts = config.num_local_experts + self.top_k = config.num_experts_per_tok + + # gating + self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False) + + self.experts = nn.ModuleList([MiniMaxText01BlockSparseTop2MLP(config) for _ in range(self.num_experts)]) + + # Jitter parameters + self.jitter_noise = config.router_jitter_noise + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + """ """ + batch_size, sequence_length, hidden_dim = hidden_states.shape + if self.training and self.jitter_noise > 0: + hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise) + hidden_states = hidden_states.view(-1, hidden_dim) + # router_logits: (batch * sequence_length, n_experts) + router_logits = self.gate(hidden_states) + + routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) + routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) + routing_weights /= routing_weights.sum(dim=-1, keepdim=True) + # we cast back to the input dtype + routing_weights = routing_weights.to(hidden_states.dtype) + + final_hidden_states = torch.zeros( + (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device + ) + + # One hot encode the selected experts to create an expert mask + # this will be used to easily index which expert is going to be sollicitated + expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0) + + # Loop over all available experts in the model and perform the computation on each expert + for expert_idx in range(self.num_experts): + expert_layer = self.experts[expert_idx] + idx, top_x = torch.where(expert_mask[expert_idx]) + + # Index the correct hidden states and compute the expert hidden state for + # the current expert. We need to make sure to multiply the output hidden + # states by `routing_weights` on the corresponding tokens (top-1 and top-2) + current_state = hidden_states[None, top_x].reshape(-1, hidden_dim) + current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None] + + # However `index_add_` only support torch tensors for indexing so we'll use + # the `top_x` tensor here. + final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) + final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) + return final_hidden_states, router_logits + + +class MiniMaxText01DecoderLayer(nn.Module): + def __init__(self, config: MiniMaxText01Config, layer_idx: int): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + self.self_attn = self.build_attn(config, layer_idx) + + self.layer_idx = layer_idx + + self.block_sparse_moe = MiniMaxText01SparseMoeBlock(config) + self.input_layernorm = MiniMaxText01RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = MiniMaxText01RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.postnorm = getattr(config, 'postnorm', False) + self.layernorm_attention_alpha = getattr(config, 'layernorm_linear_attention_alpha', 1) \ + if config.attention_type == 0 else getattr(config, 'layernorm_full_attention_alpha', 1) + self.layernorm_attention_beta = getattr(config, 'layernorm_linear_attention_beta', 1) \ + if config.attention_type == 0 else getattr(config, 'layernorm_full_attention_beta', 1) + self.layernorm_mlp_alpha = getattr(config, 'layernorm_mlp_alpha', 1) + self.layernorm_mlp_beta = getattr(config, 'layernorm_mlp_beta', 1) + + shared_intermediate = getattr(config, 'shared_intermediate_size', 0) + self.shared_moe = False + if shared_intermediate > 0: + self.shared_moe = True + self.shared_mlp = MiniMaxText01MLP(config) + self.coefficient = torch.nn.Linear(self.hidden_size, 1, bias=False) + + def build_attn(self, config, layer_idx): + if config.attention_type == 0: + Attention_module = MiniMaxText01LightningAttention + else: + Attention_module = MiniMaxText01FlashAttention2 + + return Attention_module( + config, + layer_idx + ) + + 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, + output_router_logits: Optional[bool] = False, + use_cache: Optional[bool] = False, + slope_rate: Optional[float] = None, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + if "padding_mask" in kwargs: + warnings.warn( + "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" + ) + """ + 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, sequence_length)` where padding elements are indicated by 0. + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + 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_router_logits (`bool`, *optional*): + Whether or not to return the logits of all the routers. They are useful for computing the router loss, and + should not be returned during inference. + 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`). + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + if self.postnorm: + residual = hidden_states + + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + position_ids=position_ids, + attn_mask=attention_mask, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + slope_rate=slope_rate, + ) + + hidden_states = residual * self.layernorm_attention_alpha \ + + hidden_states * self.layernorm_attention_beta + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + if self.postnorm: + residual = hidden_states + + moe_hidden_states, router_logits = self.block_sparse_moe(hidden_states) + if self.shared_moe: + output_mlp = self.shared_mlp(hidden_states) + weight_fp32 = self.coefficient.weight.float() + coef = hidden_states.to(torch.float32) @ weight_fp32.T + coef = torch.nn.functional.sigmoid(coef).to(hidden_states.dtype) + hidden_states = moe_hidden_states * (1 - coef) + output_mlp * coef + else: + hidden_states = moe_hidden_states + + hidden_states = residual * self.layernorm_mlp_alpha \ + + hidden_states * self.layernorm_mlp_beta + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + if output_router_logits: + outputs += (router_logits,) + + return outputs + + +MIXTRAL_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 ([`MiniMaxText01Config`]): + 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 MiniMaxText01 Model outputting raw hidden-states without any specific head on top.", + MIXTRAL_START_DOCSTRING, +) +# Copied from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->MiniMaxText01 +class MiniMaxText01PreTrainedModel(PreTrainedModel): + config_class = MiniMaxText01Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["MiniMaxText01DecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + + 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_() + + +MIXTRAL_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. + output_router_logits (`bool`, *optional*): + Whether or not to return the logits of all the routers. They are useful for computing the router loss, and + should not be returned during inference. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare MiniMaxText01 Model outputting raw hidden-states without any specific head on top.", + MIXTRAL_START_DOCSTRING, +) +# Copied from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->MIXTRAL,Mistral->MiniMaxText01 +class MiniMaxText01Model(MiniMaxText01PreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniMaxText01DecoderLayer`] + + Args: + config: MiniMaxText01Config + """ + + def __init__(self, config: MiniMaxText01Config): + 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.attn_type_list = config.attn_type_list + config_copy = copy.deepcopy(config) + + self.layers = nn.ModuleList([]) + for i in range(config.num_hidden_layers): + _config = copy.deepcopy(config) + if self.attn_type_list[i] == 0: + _config._attn_implementation = 'linear_attention' + _config.attention_type = 0 + else: + _config._attn_implementation = config_copy._attn_implementation + _config.attention_type = 1 + self.layers.append(MiniMaxText01DecoderLayer(_config, i)) + + self._attn_implementation = config_copy._attn_implementation + self.norm = MiniMaxText01RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + self.slopes = self._build_slope_tensor(config.num_attention_heads) + # mask + self._linear_attn_mask = torch.empty(0) + + # 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 + + @staticmethod + def _build_slope_tensor(n_attention_heads: int): + + def get_slopes(n): + + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio ** i for i in range(n)] + + if math.log2(n).is_integer(): + return get_slopes_power_of_2( + n) # In the paper, we only train models that have 2^a heads for some a. This function has + else: # some good properties that only occur when the input is a power of 2. To maintain that even + closest_power_of_2 = 2 ** math.floor( + math.log2(n)) # when the number of heads is not a power of 2, we use this workaround. + return (get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][:n - closest_power_of_2]) + + # h, 1, 1 + slopes = torch.tensor(get_slopes(n_attention_heads), dtype=torch.float32).reshape(n_attention_heads, 1, 1) + + return slopes + + # Ignore copy + @add_start_docstrings_to_model_forward(MIXTRAL_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, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MoeModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + 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 + default_device = input_ids.device + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + default_device = inputs_embeds.device + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + past_key_values_length = 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 + + seq_length_with_past = seq_length + if past_key_values is not None: + for idx in range(len(past_key_values)): + if self.attn_type_list[idx] == 1: + past_key_values_length = past_key_values[idx][0].shape[-3] + seq_length_with_past = seq_length_with_past + past_key_values_length + break + + 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) + + if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache: + is_padding_right = attention_mask[:, -1].sum().item() != batch_size + if is_padding_right: + raise ValueError( + "You are attempting to perform batched generation with padding_side='right'" + " this may lead to unexpected behaviour for Flash Attention version of MiniMaxText01. Make sure to " + " call `tokenizer.padding_side = 'left'` before tokenizing the input. " + ) + slope_rates = [self.slopes.to(default_device) for _ in range(len(self.layers))] + hidden_states = inputs_embeds + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_router_logits = () if output_router_logits 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) + attn_mask = attention_mask + slope_rate = slope_rates[idx] + slope_rate = slope_rate * (1 - idx / (len(self.layers) - 1) + 1e-5) + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + attention_mask, + position_ids, + past_key_values, + output_attentions, + output_router_logits, + use_cache, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attn_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + output_router_logits=output_router_logits, + use_cache=use_cache, + slope_rate=slope_rate + ) + + 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],) + + if output_router_logits: + all_router_logits += (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, all_router_logits] + if v is not None + ) + return MoeModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + router_logits=all_router_logits, + ) + + +class MiniMaxText01ForCausalLM(MiniMaxText01PreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.model = MiniMaxText01Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.router_aux_loss_coef = config.router_aux_loss_coef + self.num_experts = config.num_local_experts + self.num_experts_per_tok = config.num_experts_per_tok + # 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(MIXTRAL_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + # Ignore copy + 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, + output_router_logits: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MoeCausalLMOutputWithPast]: + 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, MiniMaxText01ForCausalLM + + >>> model = MiniMaxText01ForCausalLM.from_pretrained(PATH_TO_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_WEIGHTS) + + >>> 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." + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + + 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, + output_router_logits=output_router_logits, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + logits = logits.float() + + 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 = 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) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits if return_dict else outputs[-1], + self.num_experts, + self.num_experts_per_tok, + attention_mask, + ) + if labels is not None: + loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device + + if not return_dict: + output = (logits,) + outputs[1:] + if output_router_logits: + output = (aux_loss,) + output + return (loss,) + output if loss is not None else output + + torch.cuda.empty_cache() + return MoeCausalLMOutputWithPast( + loss=loss, + aux_loss=aux_loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) + + 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:] + + # 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({ + "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 + + +@add_start_docstrings( + """ + The MiniMaxText01 Model transformer with a sequence classification head on top (linear layer). + + [`MiniMaxText01ForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + MIXTRAL_START_DOCSTRING, +) +# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->MiniMaxText01, LLAMA->MIXTRAL +class MiniMaxText01ForSequenceClassification(MiniMaxText01PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.model = MiniMaxText01Model(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, 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 + + @add_start_docstrings_to_model_forward(MIXTRAL_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[Union[Cache, 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, + ) -> Union[Tuple, SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.model( + 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 = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) diff --git a/quantization_config.json b/quantization_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6fb5915bf8eb119bb383ec1626000131fd91e885 --- /dev/null +++ b/quantization_config.json @@ -0,0 +1,346 @@ +{ + "bits": 4, + "group_size": 128, + 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