# Copyright 2026 Sarvam AI team. All rights reserved. # # This code is based on Llama and Deepseek MoE implementations # in this library. It has been modified from its original forms to # accommodate Sarvam's MLA (multi-latent attention) MoE architecture. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import warnings from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache from transformers.modeling_attn_mask_utils import ( AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask, ) from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel, ALL_ATTENTION_FUNCTIONS from transformers.pytorch_utils import ( ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13, ) from transformers.utils import ( add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from transformers.utils.import_utils import is_torch_fx_available import torch.distributed as dist import numpy as np from .configuration_sarvam_moe import SarvamMLAConfig if is_torch_fx_available(): if not is_torch_greater_or_equal_than_1_13: import torch.fx _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "SarvamMLAConfig" 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.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) def _get_usable_past_kv_length(cache: Cache, new_seq_length: int, layer_idx: int = 0) -> int: previous_length = cache.get_seq_length(layer_idx) # Dynamic layers return -1, static layers return an int max_length = cache.get_max_cache_shape(layer_idx) if max_length is not None and max_length != -1 and previous_length + new_seq_length > max_length: return max_length - new_seq_length return previous_length class SarvamMLARMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ SarvamMLARMSNorm 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) ALL_LAYERNORM_LAYERS.append(SarvamMLARMSNorm) class SarvamMLARotaryEmbedding(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, persistent=False) self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(), ) self.max_seq_len_cached = None 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.outer(t, self.inv_freq.to(t.device)) 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): if self.max_seq_len_cached is None or 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), ) def yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048): return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base)) def yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048): low = math.floor(yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)) high = math.ceil(yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)) return max(low, 0), min(high, dim - 1) def yarn_get_mscale(scale=1, mscale=1): if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 def yarn_linear_ramp_mask(min_val, max_val, dim): if min_val == max_val: max_val += 0.001 linear_func = (torch.arange(dim, dtype=torch.float32) - min_val) / (max_val - min_val) return torch.clamp(linear_func, 0, 1) class SarvamMLAYarnRotaryEmbedding(SarvamMLARotaryEmbedding): def __init__( self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=40.0, original_max_position_embeddings=4096, beta_fast=32, beta_slow=1, mscale=1.0, mscale_all_dim=1.0, ): self.scaling_factor = float(scaling_factor) self.original_max_position_embeddings = int(original_max_position_embeddings) self.beta_fast = float(beta_fast) self.beta_slow = float(beta_slow) self.mscale = float(mscale) self.mscale_all_dim = float(mscale_all_dim) 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 dim = self.dim freq_extra = 1.0 / (self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)) freq_inter = 1.0 / ( self.scaling_factor * self.base ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim) ) low, high = yarn_find_correction_range( self.beta_fast, self.beta_slow, dim, self.base, self.original_max_position_embeddings, ) inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(device=device, dtype=torch.float32) inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(seq_len, device=device, dtype=torch.float32) freqs = torch.outer(t, inv_freq) _mscale = float( yarn_get_mscale(self.scaling_factor, self.mscale) / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim) ) emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False) self.register_buffer("sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False) # 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.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): cos = cos[position_ids].unsqueeze(unsqueeze_dim) sin = sin[position_ids].unsqueeze(unsqueeze_dim) b, h, s, d = q.shape q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) b, h, s, d = k.shape k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class SarvamMLAMLP(nn.Module): def __init__(self, config, hidden_size=None, intermediate_size=None): super().__init__() self.config = config self.hidden_size = config.hidden_size if hidden_size is None else hidden_size self.intermediate_size = config.intermediate_size if intermediate_size is None else 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 MoEGate(nn.Module): def __init__(self, config): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.n_routed_experts = config.num_experts self.routed_scaling_factor = config.routed_scaling_factor self.scoring_func = "sigmoid" self.topk_method = "noaux_tc" self.n_group = getattr(config, "n_group", self.n_routed_experts // 8) self.topk_group = getattr(config, "topk_group", 2) self.norm_topk_prob = True self.gating_dim = config.hidden_size self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) if self.topk_method == "noaux_tc": self.e_score_correction_bias = nn.Parameter(torch.empty((self.n_routed_experts))) self.reset_parameters() def reset_parameters(self) -> None: import torch.nn.init as init init.kaiming_uniform_(self.weight, a=math.sqrt(5)) if hasattr(self, "e_score_correction_bias"): init.zeros_(self.e_score_correction_bias) def forward(self, hidden_states): bsz, seq_len, h = hidden_states.shape hidden_states = hidden_states.view(-1, h) logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32), None) if self.scoring_func == "sigmoid": scores = logits.sigmoid() else: raise NotImplementedError(f"insupportable scoring function for MoE gating: {self.scoring_func}") if self.topk_method == "noaux_tc": assert not self.training scores_for_choice = scores.view(bsz * seq_len, -1) + self.e_score_correction_bias.unsqueeze(0) group_scores = ( scores_for_choice.view(bsz * seq_len, self.n_group, -1).topk(2, dim=-1)[0].sum(dim=-1) ) # [n, n_group] group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1] # [n, top_k_group] group_mask = torch.zeros_like(group_scores) # [n, n_group] group_mask.scatter_(1, group_idx, 1) # [n, n_group] score_mask = ( group_mask.unsqueeze(-1) .expand(bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group) .reshape(bsz * seq_len, -1) ) # [n, e] tmp_scores = scores_for_choice.masked_fill(~score_mask.bool(), float("-inf")) # [n, e] _, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False) topk_weight = scores.gather(1, topk_idx) else: raise NotImplementedError(f"insupportable TopK function for MoE gating: {self.topk_method}") ### norm gate to sum 1 if self.top_k > 1 and self.norm_topk_prob: denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 topk_weight = topk_weight / denominator topk_weight = topk_weight * self.routed_scaling_factor # must multiply the scaling factor return topk_idx, topk_weight class SarvamMLAMoE(nn.Module): def __init__(self, config): super().__init__() self.config = config self.num_experts_per_tok = config.num_experts_per_tok if hasattr(config, "ep_size") and config.ep_size > 1: assert config.ep_size == dist.get_world_size() self.ep_size = config.ep_size self.experts_per_rank = config.num_experts // config.ep_size self.ep_rank = dist.get_rank() self.experts = nn.ModuleList( [ ( SarvamMLAMLP(config, intermediate_size=config.moe_intermediate_size) if i >= self.ep_rank * self.experts_per_rank and i < (self.ep_rank + 1) * self.experts_per_rank else None ) for i in range(config.num_experts) ] ) else: self.ep_size = 1 self.experts_per_rank = config.num_experts self.ep_rank = 0 self.experts = nn.ModuleList( [ SarvamMLAMLP(config, intermediate_size=config.moe_intermediate_size) for i in range(config.num_experts) ] ) self.gate = MoEGate(config) if ( hasattr(config, "num_shared_experts") and config.num_shared_experts is not None and config.num_shared_experts > 0 ): intermediate_size = config.moe_intermediate_size * config.num_shared_experts self.shared_experts = SarvamMLAMLP(config=config, intermediate_size=intermediate_size) else: self.shared_experts = None def forward(self, hidden_states): identity = hidden_states orig_shape = hidden_states.shape topk_idx, topk_weight = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) flat_topk_idx = topk_idx.view(-1) if not self.training: y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape) else: # Training mode - simple implementation # In practice, you'd want a more sophisticated training implementation y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape) if self.shared_experts is not None: y = y + self.shared_experts(identity) return y @torch.no_grad() def moe_infer(self, x, topk_ids, topk_weight): cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts))) cnts.scatter_(1, topk_ids, 1) tokens_per_expert = cnts.sum(dim=0) idxs = topk_ids.view(-1).argsort() sorted_tokens = x[idxs // topk_ids.shape[1]] sorted_tokens_shape = sorted_tokens.shape if self.ep_size > 1: tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1) tokens_per_expert_group = tokens_per_expert.new_empty(tokens_per_expert.shape[0]) dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert) output_splits = tokens_per_expert_group.view(self.ep_size, -1).sum(1).cpu().numpy().tolist() gathered_tokens = sorted_tokens.new_empty( tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1] ) input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist() dist.all_to_all( list(gathered_tokens.split(output_splits)), list(sorted_tokens.split(input_split_sizes)), ) tokens_per_expert_post_gather = tokens_per_expert_group.view(self.ep_size, self.experts_per_rank).sum(dim=0) gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32) s = 0 for i, k in enumerate(tokens_per_expert_group.cpu().numpy()): gatherd_idxs[s : s + k] = i % self.experts_per_rank s += k gatherd_idxs = gatherd_idxs.argsort() sorted_tokens = gathered_tokens[gatherd_idxs] tokens_per_expert = tokens_per_expert_post_gather tokens_per_expert = tokens_per_expert.cpu().numpy() outputs = [] start_idx = 0 for i, num_tokens in enumerate(tokens_per_expert): end_idx = start_idx + num_tokens if num_tokens == 0: continue expert = self.experts[i + self.ep_rank * self.experts_per_rank] if expert is None: continue tokens_for_this_expert = sorted_tokens[start_idx:end_idx] expert_out = expert(tokens_for_this_expert) outputs.append(expert_out) start_idx = end_idx outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0) if self.ep_size > 1: new_x = torch.empty_like(outs) new_x[gatherd_idxs] = outs gathered_tokens = new_x.new_empty(*sorted_tokens_shape) dist.all_to_all( list(gathered_tokens.split(input_split_sizes)), list(new_x.split(output_splits)), ) outs = gathered_tokens new_x = torch.empty_like(outs) new_x[idxs] = outs final_out = ( new_x.view(*topk_ids.shape, -1) .type(topk_weight.dtype) .mul_(topk_weight.unsqueeze(dim=-1)) .sum(dim=1) .type(new_x.dtype) ) return final_out # Copied from transformers.models.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: 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 SarvamMLAAttention(nn.Module): is_causal = True def __init__(self, config: SarvamMLAConfig, 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 `layer_idx` is not recommended and will " "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " "when creating this class." ) self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.q_lora_rank = getattr(config, "q_lora_rank", None) self.qk_rope_head_dim = config.qk_rope_head_dim self.kv_lora_rank = config.kv_lora_rank self.v_head_dim = config.v_head_dim self.qk_nope_head_dim = config.qk_nope_head_dim self.q_head_dim = config.q_head_dim if self.q_lora_rank is None: self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.q_head_dim, bias=False) else: self.q_a_proj = nn.Linear( self.hidden_size, config.q_lora_rank, bias=getattr(config, "attention_bias", False) ) self.q_a_layernorm = SarvamMLARMSNorm(config.q_lora_rank) self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False) self.kv_a_proj_with_mqa = nn.Linear( self.hidden_size, config.kv_lora_rank + config.qk_rope_head_dim, bias=getattr(config, "attention_bias", False), ) self.kv_a_layernorm = SarvamMLARMSNorm(config.kv_lora_rank) self.kv_b_proj = nn.Linear( config.kv_lora_rank, self.num_heads * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim), bias=False, ) self.o_proj = nn.Linear( self.num_heads * self.v_head_dim, self.hidden_size, bias=getattr(config, "attention_bias", False), ) self._init_rope() self.softmax_scale = self.q_head_dim ** (-0.5) if self.config.rope_scaling is not None: mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0) scaling_factor = self.config.rope_scaling["factor"] if mscale_all_dim: mscale = yarn_get_mscale(scaling_factor, mscale_all_dim) self.softmax_scale = self.softmax_scale * mscale * mscale def _init_rope(self): rope_scaling = getattr(self.config, "rope_scaling", None) if rope_scaling is None or rope_scaling.get("type", None) in (None, "default"): self.rotary_emb = SarvamMLARotaryEmbedding( self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) return rope_type = rope_scaling.get("type") if rope_type == "deepseek_yarn": self.rotary_emb = SarvamMLAYarnRotaryEmbedding( self.qk_rope_head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, scaling_factor=rope_scaling.get("factor", 40.0), original_max_position_embeddings=rope_scaling.get("original_max_position_embeddings", 4096), beta_fast=rope_scaling.get("beta_fast", 32), beta_slow=rope_scaling.get("beta_slow", 1), mscale=rope_scaling.get("mscale", 1.0), mscale_all_dim=rope_scaling.get("mscale_all_dim", 1.0), ) return raise ValueError(f"Unknown rope_scaling type: {rope_type}") def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.v_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]]]: bsz, q_len, _ = hidden_states.size() if self.q_lora_rank is None: q = self.q_proj(hidden_states) else: q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states))) q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2) q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) compressed_kv = self.kv_a_proj_with_mqa(hidden_states) compressed_kv, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2) kv = ( self.kv_b_proj(self.kv_a_layernorm(compressed_kv)) .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim) .transpose(1, 2) ) k_nope, value_states = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1) kv_seq_len = value_states.shape[-2] if past_key_value is not None: if self.layer_idx is None: raise ValueError( f"The cache structure has changed in a previous version. 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 += _get_usable_past_kv_length(past_key_value, kv_seq_len, self.layer_idx) cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids) query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) query_states[:, :, :, : self.qk_nope_head_dim] = q_nope query_states[:, :, :, self.qk_nope_head_dim :] = q_pe key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim) key_states[:, :, :, : self.qk_nope_head_dim] = k_nope key_states[:, :, :, self.qk_nope_head_dim :] = k_pe 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) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale 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()}" ) assert attention_mask is not None 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 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.v_head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class SarvamMLADecoderLayer(nn.Module): def __init__(self, config: SarvamMLAConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = SarvamMLAAttention(config=config, layer_idx=layer_idx) use_moe = ( hasattr(config, "num_experts") and config.num_experts is not None and layer_idx >= getattr(config, "first_k_dense_replace", 0) and layer_idx % getattr(config, "moe_layer_freq", 1) == 0 ) self.mlp = SarvamMLAMoE(config) if use_moe else SarvamMLAMLP(config) self.input_layernorm = SarvamMLARMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = SarvamMLARMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) 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, **kwargs, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class SarvamMLAPreTrainedModel(PreTrainedModel): config_class = SarvamMLAConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["SarvamMLADecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = False # Not implemented yet _supports_cache_class = 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_() class SarvamMLAModel(SarvamMLAPreTrainedModel): def __init__(self, config: SarvamMLAConfig): 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( [SarvamMLADecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self._use_flash_attention_2 = False # Not implemented yet self.norm = SarvamMLARMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") past_key_values_length = 0 if use_cache: use_legacy_cache = not isinstance(past_key_values, Cache) if use_legacy_cache: past_key_values = DynamicCache.from_legacy_cache(past_key_values) past_key_values_length = _get_usable_past_kv_length(past_key_values, seq_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) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) attention_mask = _prepare_4d_causal_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, ) hidden_states = inputs_embeds all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None next_decoder_cache = None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache = layer_outputs[2 if output_attentions else 1] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = None if use_cache: next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) class SarvamMLAForCausalLM(SarvamMLAPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.model = SarvamMLAModel(config) self.vocab_size = config.vocab_size self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) 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: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: 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 outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] 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) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs, ): if past_key_values is not None: if isinstance(past_key_values, Cache): cache_length = past_key_values.get_seq_length() past_length = past_key_values.get_seq_length() if past_key_values is not None else 0 if hasattr(past_key_values, "get_max_length"): max_cache_length = past_key_values.get_max_length() else: max_cache_length = None else: cache_length = past_length = past_key_values[0][0].shape[2] max_cache_length = None if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] if ( max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length ): attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[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