# Apache License, Version 2.0: # 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. # # MIT License: # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import concurrent.futures import logging from typing import Iterable, List, Optional, Tuple import torch from torch import nn from sglang.srt.configs import LongcatFlashConfig from sglang.srt.distributed import ( get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce, ) from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation from sglang.srt.layers import deep_gemm_wrapper from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes from sglang.srt.layers.dp_attention import ( get_attention_tp_rank, get_attention_tp_size, is_dp_attention_enabled, ) from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( MergedColumnParallelLinear, ReplicatedLinear, RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.moe.ep_moe.kernels import zero_experts_compute_triton from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, get_moe_impl_class from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE from sglang.srt.layers.moe.topk import StandardTopKOutput, TopK from sglang.srt.layers.moe.utils import filter_moe_weight_param_global_expert from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz from sglang.srt.layers.quantization.fp8_utils import ( block_quant_dequant, block_quant_to_tensor_quant, channel_quant_to_tensor_quant, normalize_e4m3fn_to_e4m3fnuz, requant_weight_ue8m0_inplace, ) from sglang.srt.layers.quantization.int8_utils import ( block_dequant as int8_block_dequant, ) from sglang.srt.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.model_loader.utils import ( maybe_executor_submit, should_async_load, should_deepgemm_weight_requant_ue8m0, ) from sglang.srt.model_loader.weight_utils import default_weight_loader from sglang.srt.models.deepseek_v2 import DeepseekV2AttentionMLA from sglang.srt.server_args import get_global_server_args from sglang.srt.utils import ( BumpAllocator, add_prefix, bind_or_assign, cpu_has_amx_support, get_bool_env_var, get_device_sm, is_cpu, is_cuda, is_hip, is_npu, ) _is_hip = is_hip() _is_cuda = is_cuda() _is_npu = is_npu() _is_fp8_fnuz = is_fp8_fnuz() _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip _is_cpu_amx_available = cpu_has_amx_support() _is_cpu = is_cpu() _device_sm = get_device_sm() if _is_cuda: from sgl_kernel import awq_dequantize elif _is_cpu and _is_cpu_amx_available: pass elif _is_hip: from sglang.srt.layers.quantization.awq_triton import ( awq_dequantize_triton as awq_dequantize, ) else: pass logger = logging.getLogger(__name__) class LongcatFlashMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = False, prefix: str = "", ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=quant_config, reduce_results=reduce_results, prefix=add_prefix("down_proj", prefix), ) if hidden_act != "silu": raise ValueError( f"Unsupported activation: {hidden_act}. " "Only silu is supported for now." ) self.act_fn = SiluAndMul() def forward( self, x, ): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class LongcatFlashRouter(nn.Module): def __init__( self, config, zero_expert_num=0, rounter_params_dtype=torch.float32, prefix: str = "", ): super().__init__() self.n_routed_experts = config.n_routed_experts self.n_routed_experts = self.n_routed_experts + zero_expert_num self.rounter_params_dtype = rounter_params_dtype self.classifier = ReplicatedLinear( config.hidden_size, self.n_routed_experts, bias=config.router_bias, params_dtype=rounter_params_dtype, quant_config=None, prefix=add_prefix("classifier", prefix), ) self.e_score_correction_bias = nn.Parameter( torch.zeros((self.n_routed_experts), dtype=rounter_params_dtype) ) def forward(self, hidden_states): logits, _ = self.classifier(hidden_states.to(self.rounter_params_dtype)) return logits class LongcatFlashMoE(nn.Module): def __init__( self, config: LongcatFlashConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.config = config self.layer_id = layer_id self.routed_scaling_factor = config.routed_scaling_factor self.num_experts = config.n_routed_experts self.top_k = config.moe_topk self.zero_expert_num = config.zero_expert_num self.zero_expert_type = config.zero_expert_type if config.rounter_params_dtype == "float32": self.rounter_params_dtype = torch.float32 else: self.rounter_params_dtype = torch.bfloat16 self.tp_size = get_tensor_model_parallel_world_size() if self.tp_size > config.n_routed_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " f"the number of experts {config.n_routed_experts}." ) if config.hidden_act != "silu": raise ValueError( f"Unsupported activation: {config.hidden_act}. " "Only silu is supported for now." ) self.router = LongcatFlashRouter( config=self.config, zero_expert_num=self.zero_expert_num, rounter_params_dtype=self.rounter_params_dtype, prefix=add_prefix("router", prefix), ) self.topk = TopK( top_k=self.top_k, renormalize=False, use_grouped_topk=False, correction_bias=self.router.e_score_correction_bias.data, layer_id=layer_id, ) self.topk.forward = self.topk.forward_native self.experts = get_moe_impl_class(quant_config)( num_experts=self.num_experts, top_k=self.top_k, layer_id=self.layer_id, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, quant_config=quant_config, prefix=add_prefix("experts", prefix), ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (num_tokens, n_experts) router_logits = self.router(hidden_states) topk_weights, topk_idx, _ = self.topk( hidden_states, router_logits, ) if self.zero_expert_type is not None: zero_expert_result = zero_experts_compute_triton( expert_indices=topk_idx, expert_scales=topk_weights, num_experts=self.num_experts, zero_expert_type=self.zero_expert_type, hidden_states=hidden_states, ) topk_output = StandardTopKOutput(topk_weights, topk_idx, _) final_hidden_states = self.experts(hidden_states, topk_output) final_hidden_states *= self.routed_scaling_factor if self.zero_expert_type is not None and hidden_states.shape[0] > 0: final_hidden_states += zero_expert_result.to(final_hidden_states.device) if self.tp_size > 1: final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states) return final_hidden_states.view(num_tokens, hidden_dim) def get_moe_weights(self): return [ x.data for name, x in self.experts.named_parameters() if name not in ["correction_bias"] and filter_moe_weight_param_global_expert( name, x, self.experts.num_local_experts ) ] class LongcatFlashDecoderLayer(nn.Module): def __init__( self, config: LongcatFlashConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.layer_id = layer_id self.alt_stream = alt_stream self.self_attn = nn.ModuleList( [ DeepseekV2AttentionMLA( config=config, hidden_size=config.hidden_size, num_heads=config.num_attention_heads, qk_nope_head_dim=config.qk_nope_head_dim, qk_rope_head_dim=config.qk_rope_head_dim, v_head_dim=config.v_head_dim, q_lora_rank=config.q_lora_rank, kv_lora_rank=config.kv_lora_rank, rope_theta=config.rope_theta, rope_scaling=None, max_position_embeddings=config.max_position_embeddings, quant_config=( None if "self_attn" in getattr(config, "disable_quant_module", []) else quant_config ), layer_id=layer_id * 2 + i, reduce_results=False, prefix=add_prefix(f"self_attn.{i}", prefix), alt_stream=self.alt_stream, ) for i in range(2) ] ) self.input_layernorm = nn.ModuleList( [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)] ) self.post_attention_layernorm = nn.ModuleList( [RMSNorm(config.hidden_size, eps=config.rms_norm_eps) for i in range(2)] ) self.mlps = nn.ModuleList( [ LongcatFlashMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=( None if "mlps" in getattr(config, "disable_quant_module", []) else quant_config ), prefix=add_prefix(f"mlps.{i}", prefix), ) for i in range(2) ] ) self.mlp = LongcatFlashMoE( layer_id=self.layer_id, config=config, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) self.attn_tp_size = get_attention_tp_size() self.attn_tp_rank = get_attention_tp_rank() self.mlp_layer_scatter_modes = [ LayerScatterModes.init_new( layer_id=self.layer_id * 2 + i, num_layers=config.num_hidden_layers, is_layer_sparse=False, is_previous_layer_sparse=False, # TODO: Check if the following is correct. is_next_layer_sparse=False, ) for i in range(2) ] self.mlp_layer_communicator = [ LayerCommunicator( layer_scatter_modes=self.mlp_layer_scatter_modes[i], input_layernorm=self.input_layernorm[i], post_attention_layernorm=self.post_attention_layernorm[i], qkv_latent_func=self.self_attn[i].prepare_qkv_latent, ) for i in range(2) ] self.moe_layer_scatter_modes = LayerScatterModes.init_new( layer_id=self.layer_id, num_layers=config.num_hidden_layers, is_layer_sparse=True, is_previous_layer_sparse=True, # TODO: Check if the following is correct. is_next_layer_sparse=True, ) self.moe_layer_communicator = LayerCommunicator( layer_scatter_modes=self.moe_layer_scatter_modes, input_layernorm=self.input_layernorm[0], post_attention_layernorm=self.post_attention_layernorm[0], qkv_latent_func=self.self_attn[0].prepare_qkv_latent, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], zero_allocator: BumpAllocator, ) -> torch.Tensor: # first_attn hidden_states, residual = self.moe_layer_communicator.prepare_attn( hidden_states, residual, forward_batch ) if hidden_states.shape[0] != 0: hidden_states = self.self_attn[0]( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, zero_allocator=zero_allocator, ) # moe hidden_states, residual = self.moe_layer_communicator.prepare_mlp( hidden_states, residual, forward_batch ) moe_hidden_states = hidden_states.clone() moe_residual = residual.clone() moe_hidden_states = self.mlp(moe_hidden_states) moe_hidden_states, moe_residual = self.moe_layer_communicator.postprocess_layer( moe_hidden_states, moe_residual, forward_batch ) hidden_states, residual = self.forward_mlp( hidden_states, positions, residual, forward_batch, zero_allocator ) hidden_states = moe_hidden_states + hidden_states return hidden_states, residual def forward_mlp( self, hidden_states, positions, residual, forward_batch, zero_allocator ): # first_mlp hidden_states = self.mlps[0](hidden_states) # TP all_reduce hidden_states = tensor_model_parallel_all_reduce(hidden_states) # second_attn hidden_states, residual = self.mlp_layer_communicator[1].prepare_attn( hidden_states, residual, forward_batch ) if hidden_states.shape[0] != 0: hidden_states = self.self_attn[1]( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, zero_allocator=zero_allocator, ) # second_mlp hidden_states, residual = self.mlp_layer_communicator[1].prepare_mlp( hidden_states, residual, forward_batch ) hidden_states = self.mlps[1](hidden_states) # TP all_reduce hidden_states = tensor_model_parallel_all_reduce(hidden_states) hidden_states, residual = self.mlp_layer_communicator[1].postprocess_layer( hidden_states, residual, forward_batch ) return hidden_states, residual class LongcatFlashModel(nn.Module): fall_back_to_pt_during_load = False def __init__( self, config: LongcatFlashConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, use_attn_tp_group=is_dp_attention_enabled(), ) self.alt_stream = torch.cuda.Stream() self.layers = nn.ModuleList( [ LongcatFlashDecoderLayer( config, layer_id, quant_config=quant_config, prefix=add_prefix(f"layers.{layer_id}", prefix), alt_stream=self.alt_stream, ) for layer_id in range(config.num_hidden_layers) ] ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.layers_to_capture = [] def get_input_embeddings(self) -> torch.Tensor: return self.embed_tokens def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, ) -> torch.Tensor: total_num_layers = len(self.layers) device = input_embeds.device if input_embeds is not None else input_ids.device zero_allocator = BumpAllocator( buffer_size=total_num_layers * 2 * (2 if forward_batch.can_run_tbo else 1), dtype=torch.float32, device=device, ) if input_embeds is None: hidden_states = self.embed_tokens(input_ids) else: hidden_states = input_embeds residual = None aux_hidden_states = [] for i in range(total_num_layers): if i in self.layers_to_capture: aux_hidden_states.append(hidden_states + residual) with get_global_expert_distribution_recorder().with_current_layer(i): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, forward_batch, residual, zero_allocator ) if hidden_states.shape[0] != 0: if residual is None: hidden_states = self.norm(hidden_states) else: hidden_states, _ = self.norm(hidden_states, residual) if len(aux_hidden_states) == 0: return hidden_states return hidden_states, aux_hidden_states class LongcatFlashForCausalLM(nn.Module): # for quark model load packed_modules_mapping = {} def __init__( self, config: LongcatFlashConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() # for quark model load # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None self.fuse_qkv_a_proj = ( hasattr(config, "q_lora_rank") and config.q_lora_rank is not None ) if self.fuse_qkv_a_proj: self.packed_modules_mapping["fused_qkv_a_proj_with_mqa"] = [ "q_a_proj", "kv_a_proj_with_mqa", ] self.config = config self.tp_size = get_tensor_model_parallel_world_size() self.quant_config = quant_config self.model = LongcatFlashModel( config, quant_config, prefix=add_prefix("model", prefix) ) self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=add_prefix("lm_head", prefix), use_attn_tp_group=get_global_server_args().enable_dp_lm_head, ) self.logits_processor = LogitsProcessor(config) self.capture_aux_hidden_states = False def get_input_embeddings(self) -> nn.Embedding: return self.model.embed_tokens @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, ) -> torch.Tensor: hidden_states = self.model(input_ids, positions, forward_batch, input_embeds) aux_hidden_states = None if self.capture_aux_hidden_states: hidden_states, aux_hidden_states = hidden_states return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states ) def post_load_weights(self, weight_names=None): # Perform post-processing after loading weights if weight_names is None: layer_ids = range(self.config.num_hidden_layers) else: layer_ids = set() for name in weight_names: if "kv_b_proj" in name: layer_id = int(name.split(".")[2]) if layer_id < self.config.num_hidden_layers: layer_ids.add(layer_id) for layer_id in layer_ids: for i in range(2): self_attn = self.model.layers[layer_id].self_attn[i] if hasattr(self_attn.kv_b_proj, "qweight"): # AWQ compatible if _is_cuda or _is_hip: w = awq_dequantize( self_attn.kv_b_proj.qweight, self_attn.kv_b_proj.scales, self_attn.kv_b_proj.qzeros, ).T else: w = awq_dequantize( self_attn.kv_b_proj.qweight, self_attn.kv_b_proj.scales, self_attn.kv_b_proj.qzeros, 0, 0, 0, ).T else: w = self_attn.kv_b_proj.weight use_deep_gemm_bmm = False if w.dtype in ( torch.float8_e4m3fn, torch.float8_e4m3fnuz, ): if ( hasattr(self.quant_config, "weight_block_size") and self.quant_config.weight_block_size is not None ): weight_block_size = self.quant_config.weight_block_size assert hasattr(self_attn.kv_b_proj, "weight_scale_inv") if _is_fp8_fnuz: weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=w, weight_scale=self_attn.kv_b_proj.weight_scale_inv, input_scale=None, ) else: weight = w weight_scale = self_attn.kv_b_proj.weight_scale_inv if ( _is_cuda and weight_block_size[0] == 128 and weight_block_size[1] == 128 ): if ( deep_gemm_wrapper.ENABLE_JIT_DEEPGEMM and not deep_gemm_wrapper.DEEPGEMM_BLACKWELL and get_bool_env_var("SGL_USE_DEEPGEMM_BMM", "false") ): block_scale = weight_scale use_deep_gemm_bmm = True else: w = block_quant_dequant( weight, weight_scale, weight_block_size, torch.bfloat16, ) else: w, scale = block_quant_to_tensor_quant( weight, weight_scale, weight_block_size ) self_attn.w_scale = scale else: if _is_fp8_fnuz: weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz( weight=w, weight_scale=self_attn.kv_b_proj.weight_scale, input_scale=None, ) else: weight = w weight_scale = self_attn.kv_b_proj.weight_scale w, scale = channel_quant_to_tensor_quant(weight, weight_scale) self_attn.w_scale = scale if w.dtype == torch.int8: if hasattr(self.quant_config, "weight_block_size"): # block-wise int8 need it weight_block_size = self.quant_config.weight_block_size if weight_block_size is not None: assert hasattr(self_attn.kv_b_proj, "weight_scale_inv") weight = w weight_scale = self_attn.kv_b_proj.weight_scale_inv w = int8_block_dequant( weight, weight_scale, weight_block_size ).to(torch.bfloat16) else: # channel-wise int8 need it w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to( torch.bfloat16 ) w_kc, w_vc = w.unflatten( 0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim) ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1) if not use_deep_gemm_bmm: self_attn.w_kc = bind_or_assign( self_attn.w_kc, w_kc.transpose(1, 2).contiguous().transpose(1, 2), ) self_attn.w_vc = bind_or_assign( self_attn.w_vc, w_vc.contiguous().transpose(1, 2) ) if ( hasattr(self_attn.kv_b_proj, "weight_scale") and self_attn.w_scale is None ): self_attn.w_scale = bind_or_assign( self_attn.w_scale, self_attn.kv_b_proj.weight_scale ) if _is_hip: self_attn.w_scale *= 2.0 # TODO: remove this after adding FP8 support in bmm cpu kernel if ( _is_cpu and _is_cpu_amx_available and w.dtype == torch.float8_e4m3fn ): self_attn.w_kc = ( self_attn.w_kc.to(torch.bfloat16) * self_attn.w_scale ) self_attn.w_vc = ( self_attn.w_vc.to(torch.bfloat16) * self_attn.w_scale ) else: num_tiles_k = self_attn.qk_nope_head_dim // weight_block_size[1] num_tiles_n = self_attn.v_head_dim // weight_block_size[0] ws_kc, ws_vc = block_scale.unflatten( 0, (-1, (num_tiles_k + num_tiles_n)) ).split([num_tiles_k, num_tiles_n], dim=1) self_attn.w_scale_k = bind_or_assign( self_attn.w_scale_k, ws_kc.transpose(1, 2).contiguous() ) self_attn.w_scale_v = bind_or_assign( self_attn.w_scale_v, ws_vc.contiguous() ) self_attn.w_kc = bind_or_assign( self_attn.w_kc, w_kc.transpose(1, 2).contiguous() ) self_attn.w_vc = bind_or_assign(self_attn.w_vc, w_vc.contiguous()) self_attn.use_deep_gemm_bmm = True if self.config.mla_scale_q_lora: self_attn.q_a_layernorm.weight.data *= ( self.config.hidden_size / self.config.q_lora_rank ) ** 0.5 if self.config.mla_scale_kv_lora: self_attn.kv_a_layernorm.weight.data *= ( self.config.hidden_size / self.config.kv_lora_rank ) ** 0.5 # TODO(linguoyuan) EPMoE not support DEEPGEMM_BLACKWELL, DeepEP needs to be supported in the future deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0 = False if should_deepgemm_weight_requant_ue8m0( weight_block_size=getattr(self.quant_config, "weight_block_size", None) ): self._weight_requant_ue8m0() def _weight_requant_ue8m0(self): weight_block_size = self.quant_config.weight_block_size for layer_id in range(self.config.num_hidden_layers): layer = self.model.layers[layer_id] for i in range(2): self_attn = layer.self_attn[i] module_list = [ self_attn.kv_b_proj, self_attn.o_proj, ] if self.config.q_lora_rank is not None: module_list.append(self_attn.fused_qkv_a_proj_with_mqa) module_list.append(self_attn.q_b_proj) else: module_list.append(self_attn.kv_a_proj_with_mqa) module_list.append(self_attn.q_proj) for module in module_list: if hasattr(module, "weight_scale_inv"): requant_weight_ue8m0_inplace( module.weight, module.weight_scale_inv, weight_block_size ) mlp = layer.mlps[i] assert isinstance(mlp, LongcatFlashMLP) for module in [ mlp.gate_up_proj, mlp.down_proj, ]: if hasattr(module, "weight_scale_inv"): requant_weight_ue8m0_inplace( module.weight, module.weight_scale_inv, weight_block_size ) for layer_id in range(self.config.num_hidden_layers): experts = layer.mlp.experts if isinstance(experts, DeepEPMoE): for w in [ (experts.w13_weight, experts.w13_weight_scale_inv), (experts.w2_weight, experts.w2_weight_scale_inv), ]: requant_weight_ue8m0_inplace(w[0], w[1], weight_block_size) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.n_routed_experts, ) # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and ( self.config.q_lora_rank is not None ) cached_a_proj = {} if fuse_qkv_a_proj else None with concurrent.futures.ThreadPoolExecutor() as executor: futures = [] params_dict = dict(self.named_parameters()) weight_names = [] for name, loaded_weight in weights: use_async_loading = should_async_load(loaded_weight) if "mtp" in name: continue weight_names.append(name) if "rotary_emb.inv_freq" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if ("mlp.experts." in name) and name not in params_dict: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader maybe_executor_submit( executor=executor, futures=futures, use_async=use_async_loading, func=weight_loader, func_args=(param, loaded_weight, shard_id), ) break else: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader maybe_executor_submit( executor=executor, futures=futures, use_async=use_async_loading, func=weight_loader, func_args=(param, loaded_weight, name), func_kwargs={ "shard_id": shard_id, "expert_id": expert_id, }, ) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if fuse_qkv_a_proj and ( "q_a_proj" in name or "kv_a_proj_with_mqa" in name ): cached_a_proj[name] = loaded_weight q_a_proj_name = ( name if "q_a_proj" in name else name.replace("kv_a_proj_with_mqa", "q_a_proj") ) kv_a_proj_name = ( name if "kv_a_proj_with_mqa" in name else name.replace("q_a_proj", "kv_a_proj_with_mqa") ) # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter if ( q_a_proj_name in cached_a_proj and kv_a_proj_name in cached_a_proj ): q_a_proj_weight = cached_a_proj[q_a_proj_name] kv_a_proj_weight = cached_a_proj[kv_a_proj_name] cat_dim = 0 if self.quant_config is not None and ( self.quant_config.get_name() == "awq" or self.quant_config.get_name() == "awq_marlin" or self.quant_config.get_name() == "moe_wna16" ): cat_dim = 1 fused_weight = torch.cat( [q_a_proj_weight, kv_a_proj_weight], dim=cat_dim ) param_name = ( name.replace( "q_a_proj", "fused_qkv_a_proj_with_mqa" ) if "q_a_proj" in name else name.replace( "kv_a_proj_with_mqa", "fused_qkv_a_proj_with_mqa", ) ) param = params_dict[param_name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) maybe_executor_submit( executor=executor, futures=futures, use_async=use_async_loading, func=weight_loader, func_args=(param, fused_weight), ) cached_a_proj.pop(q_a_proj_name) cached_a_proj.pop(kv_a_proj_name) else: if ( "k_scale" in name or "v_scale" in name ) and name not in params_dict: # modelopt attn kv scale is named differently for scale in ["k_scale", "v_scale"]: if scale in name: name = name.replace( f"{scale[0]}_proj", "attn_mqa" ) break if name not in params_dict: # modelopt ckpt contains not needed weights for MTP module: # model.decoder.self_attn.attn_mqa.v_scale and # model.decoder.self_attn.attn_mqa.k_scale logger.warning(f"{name} not found in params_dict.") continue param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) maybe_executor_submit( executor=executor, futures=futures, use_async=use_async_loading, func=weight_loader, func_args=(param, loaded_weight), ) # Wait for all tasks to complete and raise any exceptions. for future in concurrent.futures.as_completed(futures): future.result() self.post_load_weights(weight_names=weight_names) def get_embed_and_head(self): return self.model.embed_tokens.weight, self.lm_head.weight def set_embed_and_head(self, embed, head): del self.model.embed_tokens.weight del self.lm_head.weight self.model.embed_tokens.weight = embed self.lm_head.weight = head torch.cuda.empty_cache() torch.cuda.synchronize() @classmethod def get_model_config_for_expert_location(cls, config): return ModelConfigForExpertLocation( num_layers=config.num_hidden_layers, num_logical_experts=config.n_routed_experts, ) def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None): if layer_ids is None: self.capture_aux_hidden_states = True num_layers = self.config.num_hidden_layers self.model.layers_to_capture = [2, num_layers // 2, num_layers - 3] else: self.capture_aux_hidden_states = True self.model.layers_to_capture = [val + 1 for val in layer_ids] EntryClass = [LongcatFlashForCausalLM]