# Copyright 2023-2024 SGLang Team # 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. # ============================================================================== # Adapted from: # https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py """Inference-only DeepseekV2 model.""" import logging import os from enum import IntEnum, auto from typing import Any, Dict, Iterable, Optional, Tuple import torch import torch.nn.functional as F from torch import nn from tqdm import tqdm from transformers import PretrainedConfig from sglang.srt.distributed import ( get_tensor_model_parallel_world_size, parallel_state, tensor_model_parallel_all_reduce, ) from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.communicator import ( LayerCommunicator, LayerScatterModes, enable_moe_dense_fully_dp, ) from sglang.srt.layers.dp_attention import ( get_attention_tp_rank, get_attention_tp_size, get_local_attention_dp_size, ) from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( ColumnParallelLinear, MergedColumnParallelLinear, ReplicatedLinear, RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class from sglang.srt.layers.moe.ep_moe.token_dispatcher import DeepEPDispatcher from sglang.srt.layers.moe.topk import select_experts from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.quantization.deep_gemm import _ENABLE_JIT_DEEPGEMM from sglang.srt.layers.quantization.fp8_kernel import ( per_tensor_quant_mla_fp8, per_token_group_quant_mla_deep_gemm_masked_fp8, ) 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, ) from sglang.srt.layers.quantization.int8_utils import ( block_dequant as int8_block_dequant, ) from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.rotary_embedding import get_rope from sglang.srt.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from sglang.srt.managers.expert_distribution import ( get_global_expert_distribution_recorder, ) from sglang.srt.managers.expert_location import ModelConfigForExpertLocation from sglang.srt.managers.expert_location_dispatch import ExpertLocationDispatchInfo from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.model_loader.weight_utils import default_weight_loader from sglang.srt.operations import execute_operations from sglang.srt.operations_strategy import compute_layer_operations from sglang.srt.utils import ( BumpAllocator, DeepEPMode, add_prefix, get_bool_env_var, get_int_env_var, is_cuda, is_hip, log_info_on_rank0, ) _is_hip = is_hip() _is_cuda = is_cuda() if _is_cuda: from sgl_kernel import awq_dequantize, bmm_fp8, merge_state_v2 from sglang.srt.layers.quantization.deep_gemm import ( grouped_gemm_nt_f8f8bf16_masked as deep_gemm_grouped_gemm_nt_f8f8bf16_masked, ) else: from vllm._custom_ops import awq_dequantize if _is_hip: from sglang.srt.layers.attention.triton_ops.rocm_mla_decode_rope import ( decode_attention_fwd_grouped_rope, ) logger = logging.getLogger(__name__) class AttnForwardMethod(IntEnum): # Use multi-head attention MHA = auto() # Use absorbed multi-latent attention MLA = auto() # Use multi-head attention, but with KV cache chunked. # This method can avoid OOM when prefix lengths are long. MHA_CHUNKED_KV = auto() class DeepseekV2MLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = True, prefix: str = "", tp_rank: Optional[int] = None, tp_size: Optional[int] = None, ) -> None: super().__init__() self.tp_size = tp_size self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), tp_rank=tp_rank, tp_size=tp_size, ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=quant_config, reduce_results=reduce_results, prefix=add_prefix("down_proj", prefix), tp_rank=tp_rank, tp_size=tp_size, ) 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, forward_batch=None): if (self.tp_size == 1) and x.shape[0] == 0: return x gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class MoEGate(nn.Module): def __init__( self, config, prefix: str = "", ): super().__init__() self.weight = nn.Parameter( torch.empty((config.n_routed_experts, config.hidden_size)) ) if config.topk_method == "noaux_tc": self.e_score_correction_bias = nn.Parameter( torch.empty((config.n_routed_experts)) ) else: self.e_score_correction_bias = None def forward(self, hidden_states): logits = F.linear(hidden_states, self.weight, None) return logits def is_non_idle_and_non_empty(forward_mode, hidden_states): return ( (forward_mode is not None) and not forward_mode.is_idle() and hidden_states.shape[0] > 0 ) class DeepseekV2MoE(nn.Module): def __init__( self, config: PretrainedConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() self.routed_scaling_factor = config.routed_scaling_factor self.n_shared_experts = config.n_shared_experts self.n_share_experts_fusion = global_server_args_dict["n_share_experts_fusion"] self.layer_id = layer_id 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.gate = MoEGate(config=config, prefix=add_prefix("gate", prefix)) self.experts = get_moe_impl_class()( num_experts=config.n_routed_experts + self.n_share_experts_fusion, top_k=config.num_experts_per_tok + min(self.n_share_experts_fusion, 1), hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, layer_id=self.layer_id, renormalize=config.norm_topk_prob, quant_config=quant_config, use_grouped_topk=True, num_expert_group=config.n_group, topk_group=config.topk_group, correction_bias=self.gate.e_score_correction_bias, routed_scaling_factor=self.routed_scaling_factor, prefix=add_prefix("experts", prefix), **( dict(deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]]) if global_server_args_dict["enable_deepep_moe"] else {} ), ) if config.n_shared_experts is not None and self.n_share_experts_fusion == 0: intermediate_size = config.moe_intermediate_size * config.n_shared_experts # disable tp for shared experts when enable deepep moe self.shared_experts = DeepseekV2MLP( hidden_size=config.hidden_size, intermediate_size=intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, reduce_results=False, prefix=add_prefix("shared_experts", prefix), **( dict(tp_rank=0, tp_size=1) if global_server_args_dict["enable_deepep_moe"] else {} ), ) self.top_k = config.num_experts_per_tok if global_server_args_dict["enable_deepep_moe"]: # TODO: we will support tp < ep in the future self.ep_size = get_tensor_model_parallel_world_size() self.num_experts = config.n_routed_experts self.renormalize = config.norm_topk_prob self.topk_group = config.topk_group self.num_expert_group = config.n_group self.correction_bias = ( self.gate.e_score_correction_bias.data if self.gate.e_score_correction_bias is not None else None ) self.deepep_dispatcher = DeepEPDispatcher( group=parallel_state.get_tp_group().device_group, router_topk=self.top_k, permute_fusion=True, num_experts=config.n_routed_experts, num_local_experts=config.n_routed_experts // self.tp_size, hidden_size=config.hidden_size, params_dtype=config.torch_dtype, deepep_mode=DeepEPMode[global_server_args_dict["deepep_mode"]], async_finish=True, # TODO return_recv_hook=True, ) @property def _enable_deepep_moe(self): return global_server_args_dict["enable_deepep_moe"] def op_gate(self, state): if (not self._enable_deepep_moe) or is_non_idle_and_non_empty( state.forward_batch.forward_mode, state.hidden_states_mlp_input ): # router_logits: (num_tokens, n_experts) state.router_logits = self.gate(state.hidden_states_mlp_input) else: state.router_logits = None def op_shared_experts(self, state): if (self.n_share_experts_fusion == 0) and ( (not self._enable_deepep_moe) or is_non_idle_and_non_empty( state.forward_batch.forward_mode, state.hidden_states_mlp_input ) ): state.shared_output = self.shared_experts(state.hidden_states_mlp_input) else: state.shared_output = None def op_select_experts(self, state): router_logits = state.router_logits hidden_states = state.hidden_states_mlp_input if self._enable_deepep_moe: if router_logits is not None: state.topk_weights_local, state.topk_idx_local = select_experts( hidden_states=hidden_states, router_logits=router_logits, top_k=self.top_k, use_grouped_topk=True, renormalize=self.renormalize, topk_group=self.topk_group, num_expert_group=self.num_expert_group, correction_bias=self.correction_bias, routed_scaling_factor=self.routed_scaling_factor, expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new( layer_id=self.layer_id, ), ) else: state.topk_idx_local = torch.full( (0, self.top_k), -1, dtype=torch.int, device=hidden_states.device ) state.topk_weights_local = torch.empty( (0, self.top_k), dtype=torch.float32, device=hidden_states.device ) def op_dispatch_a(self, state): if self._enable_deepep_moe and (self.ep_size > 1): # TODO(ch-wan): allow users to set num_max_dispatch_tokens_per_rank value self.deepep_dispatcher.dispatch_a( hidden_states=state.pop("hidden_states_mlp_input"), topk_idx=state.pop("topk_idx_local"), topk_weights=state.pop("topk_weights_local"), forward_mode=state.forward_batch.forward_mode, ) def op_dispatch_b(self, state): if self._enable_deepep_moe and (self.ep_size > 1): ( state.hidden_states_experts_input, state.topk_idx_dispatched, state.topk_weights_dispatched, state.reorder_topk_ids, state.num_recv_tokens_per_expert, state.seg_indptr, state.masked_m, state.expected_m, ) = self.deepep_dispatcher.dispatch_b() def op_experts(self, state): if self._enable_deepep_moe: state.pop("router_logits") state.hidden_states_experts_output = self.experts( hidden_states=state.pop("hidden_states_experts_input"), topk_idx=state.topk_idx_dispatched, topk_weights=state.topk_weights_dispatched, reorder_topk_ids=state.pop("reorder_topk_ids"), seg_indptr=state.pop("seg_indptr"), masked_m=state.pop("masked_m"), expected_m=state.pop("expected_m"), num_recv_tokens_per_expert=state.pop("num_recv_tokens_per_expert"), forward_mode=state.forward_batch.forward_mode, ) else: state.hidden_states_experts_output = self.experts( hidden_states=state.pop("hidden_states_mlp_input"), router_logits=state.pop("router_logits"), ) def op_combine_a(self, state): if self._enable_deepep_moe and (self.ep_size > 1): self.deepep_dispatcher.combine_a( state.pop("hidden_states_experts_output"), topk_idx=state.pop("topk_idx_dispatched"), topk_weights=state.pop("topk_weights_dispatched"), forward_mode=state.forward_batch.forward_mode, ) def op_combine_b(self, state): if self._enable_deepep_moe and (self.ep_size > 1): state.hidden_states_after_combine = self.deepep_dispatcher.combine_b() def op_output(self, state): final_hidden_states = ( state.pop("hidden_states_after_combine") if self._enable_deepep_moe else state.pop("hidden_states_experts_output") ) final_hidden_states *= self.routed_scaling_factor if (s := state.pop("shared_output")) is not None: final_hidden_states = final_hidden_states + s if (not self._enable_deepep_moe) and (self.tp_size > 1): final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states) state.hidden_states_mlp_output = final_hidden_states def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float: import math if scale <= 1: return 1.0 return 0.1 * mscale * math.log(scale) + 1.0 class DeepseekV2AttentionMLA(nn.Module): def __init__( self, config: PretrainedConfig, hidden_size: int, num_heads: int, qk_nope_head_dim: int, qk_rope_head_dim: int, v_head_dim: int, q_lora_rank: int, kv_lora_rank: int, rope_theta: float = 10000, rope_scaling: Optional[Dict[str, Any]] = None, max_position_embeddings: int = 8192, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = True, layer_id: int = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.layer_id = layer_id self.hidden_size = hidden_size self.qk_nope_head_dim = qk_nope_head_dim self.qk_rope_head_dim = qk_rope_head_dim self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim self.v_head_dim = v_head_dim self.q_lora_rank = q_lora_rank self.kv_lora_rank = kv_lora_rank attn_tp_rank = get_attention_tp_rank() attn_tp_size = get_attention_tp_size() self.num_heads = num_heads assert num_heads % attn_tp_size == 0 self.num_local_heads = num_heads // attn_tp_size self.scaling = self.qk_head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings # For tensor parallel attention if self.q_lora_rank is not None: self.fused_qkv_a_proj_with_mqa = ReplicatedLinear( self.hidden_size, self.q_lora_rank + self.kv_lora_rank + self.qk_rope_head_dim, bias=False, quant_config=quant_config, prefix=add_prefix("fused_qkv_a_proj_with_mqa", prefix), ) self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps) self.q_b_proj = ColumnParallelLinear( q_lora_rank, self.num_heads * self.qk_head_dim, bias=False, quant_config=quant_config, prefix=add_prefix("q_b_proj", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) else: self.q_proj = ColumnParallelLinear( self.hidden_size, self.num_heads * self.qk_head_dim, bias=False, quant_config=quant_config, prefix=add_prefix("q_proj", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) self.kv_a_proj_with_mqa = ReplicatedLinear( self.hidden_size, self.kv_lora_rank + self.qk_rope_head_dim, bias=False, quant_config=quant_config, prefix=add_prefix("kv_a_proj_with_mqa", prefix), ) self.kv_b_proj = ColumnParallelLinear( self.kv_lora_rank, self.num_heads * (self.qk_nope_head_dim + self.v_head_dim), bias=False, quant_config=quant_config, prefix=add_prefix("kv_b_proj", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) # O projection. self.o_proj = RowParallelLinear( self.num_heads * self.v_head_dim, self.hidden_size, bias=False, quant_config=quant_config, reduce_results=reduce_results, prefix=add_prefix("o_proj", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps) if rope_scaling: rope_scaling["rope_type"] = "deepseek_yarn" self.rotary_emb = get_rope( qk_rope_head_dim, rotary_dim=qk_rope_head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, is_neox_style=False, ) if rope_scaling: mscale_all_dim = rope_scaling.get("mscale_all_dim", False) scaling_factor = rope_scaling["factor"] mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim)) self.scaling = self.scaling * mscale * mscale else: self.rotary_emb.forward = self.rotary_emb.forward_native self.attn_mqa = RadixAttention( self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim, self.scaling, num_kv_heads=1, layer_id=layer_id, v_head_dim=self.kv_lora_rank, quant_config=quant_config, prefix=add_prefix("attn_mqa", prefix), ) self.attn_mha = RadixAttention( self.num_local_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, self.scaling, num_kv_heads=self.num_local_heads, layer_id=layer_id, v_head_dim=self.v_head_dim, quant_config=quant_config, prefix=add_prefix("attn_mha", prefix), ) self.alt_stream = alt_stream self.w_kc = None self.w_vc = None self.w_scale = None self.w_scale_k = None self.w_scale_v = None self.use_deep_gemm_bmm = False self.flashinfer_mla_disable_ragged = global_server_args_dict[ "flashinfer_mla_disable_ragged" ] self.disable_chunked_prefix_cache = global_server_args_dict[ "disable_chunked_prefix_cache" ] self.attention_backend = global_server_args_dict["attention_backend"] self.rocm_fused_decode_mla = get_bool_env_var( "SGLANG_ROCM_FUSED_DECODE_MLA", "false" ) # TODO: Design a finer way to determine the threshold self.chunked_prefix_cache_threshold = get_int_env_var( "SGL_CHUNKED_PREFIX_CACHE_THRESHOLD", 8192 ) def dispatch_attn_forward_method( self, forward_batch: ForwardBatch ) -> AttnForwardMethod: if self.attention_backend == "flashinfer": # Flashinfer MLA: Do not absorb when enabling ragged prefill if ( not self.flashinfer_mla_disable_ragged and forward_batch.forward_mode.is_extend() and not forward_batch.forward_mode.is_target_verify() and not forward_batch.forward_mode.is_draft_extend() and sum(forward_batch.extend_prefix_lens_cpu) == 0 ): return AttnForwardMethod.MHA else: return AttnForwardMethod.MLA elif self.attention_backend == "fa3": # Flash Attention: Use MHA with chunked KV cache when prefilling on long sequences. if forward_batch.extend_prefix_lens_cpu is not None: sum_extend_prefix_lens = sum(forward_batch.extend_prefix_lens_cpu) if ( forward_batch.forward_mode.is_extend() and not self.disable_chunked_prefix_cache and not forward_batch.forward_mode.is_target_verify() and not forward_batch.forward_mode.is_draft_extend() and ( sum_extend_prefix_lens >= self.chunked_prefix_cache_threshold or sum_extend_prefix_lens == 0 ) ): return AttnForwardMethod.MHA_CHUNKED_KV else: return AttnForwardMethod.MLA else: # Triton: Use normal computation for prefill and use weight absorption for extend/decode if ( forward_batch.forward_mode.is_extend() and not forward_batch.forward_mode.is_target_verify() and not forward_batch.forward_mode.is_draft_extend() and sum(forward_batch.extend_prefix_lens_cpu) == 0 ): return AttnForwardMethod.MHA else: return AttnForwardMethod.MLA def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, zero_allocator: BumpAllocator, ) -> torch.Tensor: if hidden_states.shape[0] == 0: assert ( not self.o_proj.reduce_results ), "short-circuiting allreduce will lead to hangs" return hidden_states attn_forward_method = self.dispatch_attn_forward_method(forward_batch) if attn_forward_method == AttnForwardMethod.MHA: return self.forward_normal(positions, hidden_states, forward_batch) elif attn_forward_method == AttnForwardMethod.MHA_CHUNKED_KV: return self.forward_normal_chunked_kv( positions, hidden_states, forward_batch ) else: if _is_hip: if ( self.rocm_fused_decode_mla and forward_batch.forward_mode.is_decode() ): return self.forward_absorb_fused_mla_rope( positions, hidden_states, forward_batch ) else: return self.forward_absorb( positions, hidden_states, forward_batch, zero_allocator ) else: return self.forward_absorb( positions, hidden_states, forward_batch, zero_allocator ) def forward_normal( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: if self.q_lora_rank is not None: q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split( [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1 ) q = self.q_a_layernorm(q) q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim) else: q = self.q_proj(hidden_states)[0].view( -1, self.num_local_heads, self.qk_head_dim ) latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0] _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) latent_cache = latent_cache.unsqueeze(1) kv_a = self.kv_a_layernorm(kv_a.contiguous()) kv = self.kv_b_proj(kv_a)[0] kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim) k_nope = kv[..., : self.qk_nope_head_dim] v = kv[..., self.qk_nope_head_dim :] k_pe = latent_cache[:, :, self.kv_lora_rank :] q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe) q[..., self.qk_nope_head_dim :] = q_pe k = torch.empty_like(q) k[..., : self.qk_nope_head_dim] = k_nope k[..., self.qk_nope_head_dim :] = k_pe latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1) latent_cache[:, :, self.kv_lora_rank :] = k_pe # Save latent cache forward_batch.token_to_kv_pool.set_kv_buffer( self.attn_mha, forward_batch.out_cache_loc, latent_cache, None ) attn_output = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False) attn_output = attn_output.reshape(-1, self.num_local_heads * self.v_head_dim) output, _ = self.o_proj(attn_output) return output def forward_absorb( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, zero_allocator: BumpAllocator, ) -> torch.Tensor: if self.q_lora_rank is not None: q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split( [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1 ) k_nope = latent_cache[..., : self.kv_lora_rank] # overlap qk norm if self.alt_stream is not None and torch.cuda.is_current_stream_capturing(): current_stream = torch.cuda.current_stream() self.alt_stream.wait_stream(current_stream) q = self.q_a_layernorm(q) with torch.cuda.stream(self.alt_stream): k_nope = self.kv_a_layernorm(k_nope) current_stream.wait_stream(self.alt_stream) else: q = self.q_a_layernorm(q) k_nope = self.kv_a_layernorm(k_nope) k_nope = k_nope.unsqueeze(1) q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim) else: q = self.q_proj(hidden_states)[0].view( -1, self.num_local_heads, self.qk_head_dim ) latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0] k_nope = latent_cache[..., : self.kv_lora_rank] k_nope = self.kv_a_layernorm(k_nope).unsqueeze(1) q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) k_pe = latent_cache[..., self.kv_lora_rank :].unsqueeze(1) if self.use_deep_gemm_bmm: q_nope_val, q_nope_scale, masked_m, expected_m, aligned_m = ( per_token_group_quant_mla_deep_gemm_masked_fp8(q_nope.transpose(0, 1)) ) q_nope_out = q_nope.new_empty( (self.num_local_heads, aligned_m, self.kv_lora_rank) ) deep_gemm_grouped_gemm_nt_f8f8bf16_masked( (q_nope_val, q_nope_scale), (self.w_kc, self.w_scale_k), q_nope_out, masked_m, expected_m, ) q_nope_out = q_nope_out[:, :expected_m, :] elif self.w_kc.dtype == torch.float8_e4m3fnuz: # TODO(kernel): add bmm_fp8 for torch.float8_e4m3fnuz q_nope_out = torch.bmm( q_nope.to(torch.bfloat16).transpose(0, 1), self.w_kc.to(torch.bfloat16) * self.w_scale, ) elif self.w_kc.dtype == torch.float8_e4m3fn: q_nope_val, q_nope_scale = per_tensor_quant_mla_fp8( q_nope.transpose(0, 1), zero_allocator.allocate(1), ) q_nope_out = bmm_fp8( q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16 ) else: q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc) q_nope_out = q_nope_out.transpose(0, 1) q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe) if self.attention_backend == "fa3" or self.attention_backend == "flashinfer": attn_output = self.attn_mqa( q_nope_out, k_nope, k_nope, forward_batch, q_rope=q_pe, k_rope=k_pe ) else: q = torch.cat([q_nope_out, q_pe], dim=-1) k = torch.cat([k_nope, k_pe], dim=-1) attn_output = self.attn_mqa(q, k, k_nope, forward_batch) attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank) if self.use_deep_gemm_bmm: attn_output_val, attn_output_scale, masked_m, expected_m, aligned_m = ( per_token_group_quant_mla_deep_gemm_masked_fp8( attn_output.transpose(0, 1) ) ) attn_bmm_output = attn_output.new_empty( (self.num_local_heads, aligned_m, self.v_head_dim) ) deep_gemm_grouped_gemm_nt_f8f8bf16_masked( (attn_output_val, attn_output_scale), (self.w_vc, self.w_scale_v), attn_bmm_output, masked_m, expected_m, ) attn_bmm_output = attn_bmm_output[:, :expected_m, :] elif self.w_vc.dtype == torch.float8_e4m3fnuz: # TODO(kernel): add bmm_fp8 for torch.float8_e4m3fnuz attn_bmm_output = torch.bmm( attn_output.to(torch.bfloat16).transpose(0, 1), self.w_vc.to(torch.bfloat16) * self.w_scale, ) elif self.w_vc.dtype == torch.float8_e4m3fn: attn_output_val, attn_output_scale = per_tensor_quant_mla_fp8( attn_output.transpose(0, 1), zero_allocator.allocate(1), ) attn_bmm_output = bmm_fp8( attn_output_val, self.w_vc, attn_output_scale, self.w_scale, torch.bfloat16, ) else: attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc) attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2) output, _ = self.o_proj(attn_output) return output def forward_absorb_fused_mla_rope( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, zero_allocator: BumpAllocator, ) -> torch.Tensor: enable_rope_fusion = ( os.getenv("SGLANG_FUSED_MLA_ENABLE_ROPE_FUSION", "1") == "1" ) q_len = hidden_states.shape[0] q_input = hidden_states.new_empty( q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim ) if self.q_lora_rank is not None: q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split( [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1 ) q = self.q_a_layernorm(q) q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim) else: q = self.q_proj(hidden_states)[0].view( -1, self.num_local_heads, self.qk_head_dim ) latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0] q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) if self.w_kc.dtype == torch.float8_e4m3fnuz: # TODO(kernel): add bmm_fp8 for torch.float8_e4m3fnuz q_nope_out = torch.bmm( q_nope.to(torch.bfloat16).transpose(0, 1), self.w_kc.to(torch.bfloat16) * self.w_scale, ) elif self.w_kc.dtype == torch.float8_e4m3fn: q_nope_val, q_nope_scale = per_tensor_quant_mla_fp8( q_nope.transpose(0, 1), zero_allocator.allocate(1), dtype=torch.float8_e4m3fn, ) q_nope_out = bmm_fp8( q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16 ) else: q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc) q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1) v_input = latent_cache[..., : self.kv_lora_rank] v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1) k_input = latent_cache.unsqueeze(1) k_input[..., : self.kv_lora_rank] = v_input if not enable_rope_fusion: k_pe = k_input[..., self.kv_lora_rank :] q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe) q_input[..., self.kv_lora_rank :] = q_pe k_input[..., self.kv_lora_rank :] = k_pe k_pe_output = None else: k_pe_output = torch.empty_like(k_input[..., self.kv_lora_rank :]) q_input[..., self.kv_lora_rank :] = q_pe # attn_output = self.attn_mqa(q_input, k_input, v_input, forward_batch) # Use Fused ROPE with use_rope=OFF. attn_output = torch.empty( (q_len, self.num_local_heads, self.kv_lora_rank), dtype=q.dtype, device=q.device, ) attn_logits, _, kv_indptr, kv_indices, _, _, _ = ( forward_batch.attn_backend.forward_metadata ) cos_sin_cache = self.rotary_emb.cos_sin_cache num_kv_split = forward_batch.attn_backend.num_kv_splits sm_scale = self.attn_mqa.scaling if attn_logits is None: attn_logits = torch.empty( ( forward_batch.batch_size, self.num_local_heads, num_kv_split, self.kv_lora_rank + 1, ), dtype=torch.float32, device=q.device, ) # save current latent cache. forward_batch.token_to_kv_pool.set_kv_buffer( self.attn_mqa, forward_batch.out_cache_loc, k_input, None ) key_cache_buf = forward_batch.token_to_kv_pool.get_key_buffer( self.attn_mqa.layer_id ) val_cache_buf = key_cache_buf[..., : self.kv_lora_rank] decode_attention_fwd_grouped_rope( q_input, key_cache_buf, val_cache_buf, attn_output, kv_indptr, kv_indices, k_pe_output, self.kv_lora_rank, self.rotary_emb.rotary_dim, cos_sin_cache, positions, attn_logits, num_kv_split, sm_scale, logit_cap=self.attn_mqa.logit_cap, use_rope=enable_rope_fusion, is_neox_style=self.rotary_emb.is_neox_style, ) if enable_rope_fusion: k_input[..., self.kv_lora_rank :] = k_pe_output forward_batch.token_to_kv_pool.set_kv_buffer( self.attn_mqa, forward_batch.out_cache_loc, k_input, None ) attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank) if self.w_vc.dtype == torch.float8_e4m3fnuz: # TODO(kernel): add bmm_fp8 for torch.float8_e4m3fnuz attn_bmm_output = torch.bmm( attn_output.to(torch.bfloat16).transpose(0, 1), self.w_vc.to(torch.bfloat16) * self.w_scale, ) elif self.w_vc.dtype == torch.float8_e4m3fn: attn_output_val, attn_output_scale = per_tensor_quant_mla_fp8( attn_output.transpose(0, 1), zero_allocator.allocate(1), dtype=torch.float8_e4m3fn, ) attn_bmm_output = bmm_fp8( attn_output_val, self.w_vc, attn_output_scale, self.w_scale, torch.bfloat16, ) else: attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc) attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2) output, _ = self.o_proj(attn_output) return output def _chunked_prefix_attn_mha( self, q: torch.Tensor, accum_output: torch.Tensor, accum_lse: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: assert forward_batch.num_prefix_chunks is not None for i in range(forward_batch.num_prefix_chunks): forward_batch.set_prefix_chunk_idx(i) # Fetch latent cache from memory pool with precomputed chunked kv indices latent_cache_buf = forward_batch.token_to_kv_pool.get_key_buffer( self.attn_mha.layer_id ) latent_cache = latent_cache_buf[ forward_batch.prefix_chunk_kv_indices[i] ].contiguous() kv_a_normed, k_pe = latent_cache.split( [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1 ) kv_a_normed = kv_a_normed.squeeze(1).contiguous() kv = self.kv_b_proj(kv_a_normed)[0] kv = kv.view( -1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim ) v = kv[..., self.qk_nope_head_dim :] k_nope = kv[..., : self.qk_nope_head_dim] k = torch.empty( ( k_nope.shape[0], self.num_local_heads, self.qk_nope_head_dim + self.qk_rope_head_dim, ), dtype=v.dtype, device=v.device, ) k[..., : self.qk_nope_head_dim] = k_nope k[..., self.qk_nope_head_dim :] = k_pe output, lse = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False) lse = torch.transpose(lse, 0, 1).contiguous() tmp_output = torch.empty_like(accum_output) tmp_lse = torch.empty_like(accum_lse) merge_state_v2(output, lse, accum_output, accum_lse, tmp_output, tmp_lse) accum_output, accum_lse = tmp_output, tmp_lse return accum_output def forward_normal_chunked_kv( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: # In normal mha, the k and v tensors will become overly large when the prefix length is long. # To avoid this, we split the kv cache into chunks and process them one after another. # Since mha is compute friendly, the for loop induced here will not introduce significant overhead. # The top comments in https://github.com/vllm-project/vllm/blob/main/vllm/v1/attention/backends/mla/common.py # will be helpful for understanding the purpose of this function. # First do normal mha forward to get output for extended part if self.q_lora_rank is not None: q, latent_cache = self.fused_qkv_a_proj_with_mqa(hidden_states)[0].split( [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], dim=-1 ) q = self.q_a_layernorm(q) q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim) else: q = self.q_proj(hidden_states)[0].view( -1, self.num_local_heads, self.qk_head_dim ) latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0] _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1) latent_cache = latent_cache.unsqueeze(1) kv_a = self.kv_a_layernorm(kv_a.contiguous()) kv = self.kv_b_proj(kv_a)[0] kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim) k_nope = kv[..., : self.qk_nope_head_dim] v = kv[..., self.qk_nope_head_dim :] k_pe = latent_cache[:, :, self.kv_lora_rank :] q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe) q[..., self.qk_nope_head_dim :] = q_pe k = torch.empty_like(q) k[..., : self.qk_nope_head_dim] = k_nope k[..., self.qk_nope_head_dim :] = k_pe latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1) latent_cache[:, :, self.kv_lora_rank :] = k_pe # Save latent cache forward_batch.token_to_kv_pool.set_kv_buffer( self.attn_mha, forward_batch.out_cache_loc, latent_cache, None ) # Do mha for extended part without prefix forward_batch.set_attn_attend_prefix_cache(False) attn_output, lse = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False) lse = torch.transpose(lse, 0, 1).contiguous() # Do mha attention with chunked prefix cache if there are any sequence with prefix if any(forward_batch.extend_prefix_lens_cpu): # Only initialize the info once if forward_batch.num_prefix_chunks is None: forward_batch.prepare_chunked_prefix_cache_info(q.device) forward_batch.set_attn_attend_prefix_cache(True) attn_output = self._chunked_prefix_attn_mha( q=q, accum_output=attn_output, accum_lse=lse, forward_batch=forward_batch, ) attn_output = attn_output.reshape(-1, self.num_local_heads * self.v_head_dim) output, _ = self.o_proj(attn_output) return output class DeepseekV2DecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, layer_id: int, quant_config: Optional[QuantizationConfig] = None, is_nextn: bool = False, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size self.config = config rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.enable_dp_attention = global_server_args_dict["enable_dp_attention"] self.layer_id = layer_id self.self_attn = DeepseekV2AttentionMLA( config=config, hidden_size=self.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 if hasattr(config, "q_lora_rank") else None ), kv_lora_rank=config.kv_lora_rank, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, quant_config=quant_config, layer_id=layer_id, reduce_results=False, prefix=add_prefix("self_attn", prefix), alt_stream=alt_stream, ) self.is_layer_sparse = self._is_layer_sparse(layer_id, is_nextn=is_nextn) is_previous_layer_sparse = self._is_layer_sparse(layer_id - 1, is_nextn=False) self.layer_scatter_modes = LayerScatterModes.init_new( layer_id=layer_id, num_layers=config.num_hidden_layers, is_layer_sparse=self.is_layer_sparse, is_previous_layer_sparse=is_previous_layer_sparse, ) if self.is_layer_sparse: self.mlp = DeepseekV2MoE( config=config, quant_config=quant_config, prefix=add_prefix("mlp", prefix), layer_id=self.layer_id, ) else: if enable_moe_dense_fully_dp(): mlp_tp_rank, mlp_tp_size = 0, 1 else: mlp_tp_rank, mlp_tp_size = None, None self.mlp = DeepseekV2MLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix), tp_rank=mlp_tp_rank, tp_size=mlp_tp_size, ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.layer_communicator = LayerCommunicator( layer_scatter_modes=self.layer_scatter_modes, input_layernorm=self.input_layernorm, post_attention_layernorm=self.post_attention_layernorm, ) def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool: return is_nextn or ( self.config.n_routed_experts is not None and layer_id >= self.config.first_k_dense_replace and layer_id % self.config.moe_layer_freq == 0 ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], zero_allocator: BumpAllocator, ) -> torch.Tensor: return execute_operations( inputs=dict( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, residual=residual, zero_allocator=zero_allocator, ), operations=compute_layer_operations(self), ) def op_comm_prepare_attn( self, state, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], zero_allocator: BumpAllocator, ): state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = ( self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch) ) state.update( dict( forward_batch=forward_batch, positions=positions, zero_allocator=zero_allocator, ) ) def op_attn(self, state): state.hidden_states_after_attn = self.self_attn( positions=state.positions, hidden_states=state.pop("hidden_states_after_comm_pre_attn"), forward_batch=state.forward_batch, zero_allocator=state.zero_allocator, ) def op_comm_prepare_mlp(self, state): state.hidden_states_mlp_input, state.residual_after_comm_pre_mlp = ( self.layer_communicator.prepare_mlp( state.pop("hidden_states_after_attn"), state.pop("residual_after_input_ln"), state.forward_batch, ) ) def op_mlp(self, state): hidden_states = state.pop("hidden_states_mlp_input") if not ( enable_moe_dense_fully_dp() and (not self.is_layer_sparse) and hidden_states.shape[0] == 0 ): state.hidden_states_mlp_output = self.mlp( hidden_states, state.forward_batch.forward_mode ) else: state.hidden_states_mlp_output = hidden_states def op_comm_postprocess_layer(self, state): hidden_states, residual = self.layer_communicator.postprocess_layer( state.pop("hidden_states_mlp_output"), state.pop("residual_after_comm_pre_mlp"), state.forward_batch, ) state.clear(expect_keys={"positions", "forward_batch", "zero_allocator"}) return hidden_states, residual class DeepseekV2Model(nn.Module): fall_back_to_pt_during_load = False def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.padding_id = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, enable_tp=not global_server_args_dict["enable_dp_attention"], ) self.alt_stream = torch.cuda.Stream() if _is_cuda else None self.layers = nn.ModuleList( [ DeepseekV2DecoderLayer( 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.dp_size = get_local_attention_dp_size() 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: zero_allocator = BumpAllocator( # TODO for two-batch-overlap, we need a larger buffer size buffer_size=len(self.layers) * 2, dtype=torch.float32, device=( input_embeds.device if input_embeds is not None else input_ids.device ), ) if input_embeds is None: hidden_states = self.embed_tokens(input_ids) else: hidden_states = input_embeds residual = None for i in range(len(self.layers)): 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 not forward_batch.forward_mode.is_idle(): if residual is None: hidden_states = self.norm(hidden_states) else: hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class DeepseekV2ForCausalLM(nn.Module): def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.config = config self.tp_size = get_tensor_model_parallel_world_size() self.quant_config = quant_config self.determine_n_share_experts_fusion() self.model = DeepseekV2Model( 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=global_server_args_dict["enable_dp_lm_head"], ) self.logits_processor = LogitsProcessor(config) self.dp_size = get_local_attention_dp_size() def determine_n_share_experts_fusion( self, architecture: str = "DeepseekV3ForCausalLM" ): self.n_share_experts_fusion = global_server_args_dict["n_share_experts_fusion"] if self.n_share_experts_fusion > 0: # Only Deepseek V3/R1 can use shared experts fusion optimization now. if ( not _is_cuda or self.config.architectures[0] != architecture or self.config.n_routed_experts != 256 ): self.n_share_experts_fusion = 0 global_server_args_dict["n_share_experts_fusion"] = 0 log_info_on_rank0( logger, "Only Deepseek V3/R1 on NV-platform can use shared experts fusion optimization. Shared experts fusion optimization is disabled.", ) else: assert ( self.n_share_experts_fusion == self.tp_size ), f"Shared experts fusion optimization is enabled in DeepSeek V3/R1, set it to {self.tp_size} can get best optimized performance." elif self.n_share_experts_fusion == 0: if ( _is_cuda and torch.cuda.get_device_capability("cuda") >= (9, 0) and self.config.architectures[0] == architecture and self.config.n_routed_experts == 256 and (not global_server_args_dict["enable_deepep_moe"]) ): self.n_share_experts_fusion = self.tp_size global_server_args_dict["n_share_experts_fusion"] = self.tp_size log_info_on_rank0( logger, "Deepseek V3/R1 with fp8 can use shared experts fusion optimization when SM version >=90. Shared experts fusion optimization is enabled.", ) 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) return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch ) def post_load_weights(self, is_nextn=False): # Perform post-processing after loading weights layer_ids = ( range(self.config.num_hidden_layers) if not is_nextn else [self.config.num_hidden_layers] ) for layer_id in layer_ids: self_attn = ( self.model.layers[layer_id].self_attn if not is_nextn else self.model.decoder.self_attn ) if hasattr(self_attn.kv_b_proj, "qweight"): # AWQ compatible if _is_cuda: 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 # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`. # This may affect the accuracy of fp8 model. # Fix deepseek v3 blockwise bmm by using deep_gemm use_deep_gemm_bmm = False model_dtype = torch.get_default_dtype() if w.dtype in ( torch.float8_e4m3fn, torch.float8_e4m3fnuz, ): if hasattr(self.quant_config, "weight_block_size"): 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") if _is_hip: 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 and model_dtype == torch.bfloat16 ): if _ENABLE_JIT_DEEPGEMM 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, model_dtype, ) else: w, scale = block_quant_to_tensor_quant( weight, weight_scale, weight_block_size ) self_attn.w_scale = scale 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 = w_kc.transpose(1, 2).contiguous().transpose(1, 2) 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 = self_attn.kv_b_proj.weight_scale if _is_hip: self_attn.w_scale *= 2.0 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 = ws_kc.transpose(1, 2).contiguous() self_attn.w_scale_v = ws_vc.contiguous() self_attn.w_kc = w_kc.transpose(1, 2).contiguous() self_attn.w_vc = w_vc.contiguous() self_attn.use_deep_gemm_bmm = True def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False): if is_nextn: if hasattr(self.config, "num_nextn_predict_layers"): num_nextn_layers = self.config.num_nextn_predict_layers assert num_nextn_layers == 1, "Only 1 nextn layer is supported" # compatible with old design nextn_layer_id = ( 0 if self.config.num_hidden_layers == 1 else self.config.num_hidden_layers ) else: raise ValueError("num_nextn_predict_layers is not in the config") stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] if self.n_share_experts_fusion > 0: weights_list = list(weights) weights_dict = dict(weights_list) if self.quant_config is None or self.quant_config.get_name() == "w8a8_int8": suffix_list = [ "down_proj.weight", "down_proj.weight_scale", "gate_proj.weight", "gate_proj.weight_scale", "up_proj.weight", "up_proj.weight_scale", ] else: suffix_list = [ "down_proj.weight", "down_proj.weight_scale_inv", "gate_proj.weight", "gate_proj.weight_scale_inv", "up_proj.weight", "up_proj.weight_scale_inv", ] names_to_remove = [] moe_layers = ( range( self.config.first_k_dense_replace, self.config.num_hidden_layers, self.config.moe_layer_freq, ) if not is_nextn else [nextn_layer_id] ) for moe_layer in tqdm( moe_layers, desc=f"Cloning {self.n_share_experts_fusion} " "replicas of the shared expert into MoE", ): for suffix in suffix_list: shared_expert_weight_name = ( f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}" ) for num_repeat in range(self.n_share_experts_fusion): weights_list.append( ( f"model.layers.{moe_layer}." f"mlp.experts." f"{self.config.n_routed_experts + num_repeat}" f".{suffix}", weights_dict[shared_expert_weight_name], ) ) names_to_remove += [shared_expert_weight_name] weights = [w for w in weights_list if w[0] not in names_to_remove] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = get_moe_impl_class().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 + self.n_share_experts_fusion, ) # 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 if is_nextn: nextn_layer_prefix = f"model.layers.{nextn_layer_id}" nextn_spec_weight_names = [ "shared_head.norm", "eh_proj", "enorm", "hnorm", ] params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: if not is_nextn: if hasattr(self.config, "num_nextn_predict_layers"): num_nextn_layers = self.config.num_nextn_predict_layers if num_nextn_layers > 0 and name.startswith("model.layers"): name_list = name.split(".") if ( len(name_list) >= 3 and int(name_list[2]) >= self.config.num_hidden_layers ): continue else: if not name.startswith(nextn_layer_prefix): continue # Use shared head and embed weights from target model if "shared_head.head" in name or "embed_tokens" in name: continue is_decoder = True # For nextn specific weights for weight_name in nextn_spec_weight_names: if weight_name in name: name = name.replace(nextn_layer_prefix, "model") is_decoder = False break # For decoder layer weights if is_decoder: name = name.replace(nextn_layer_prefix, "model.decoder") 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 weight_loader(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 weight_loader( param, loaded_weight, name, 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] fused_weight = torch.cat( [q_a_proj_weight, kv_a_proj_weight], dim=0 ) param_name = name.replace( "q_a_proj", "fused_qkv_a_proj_with_mqa" ) param = params_dict[param_name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, fused_weight) cached_a_proj.pop(q_a_proj_name) cached_a_proj.pop(kv_a_proj_name) else: param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) self.post_load_weights(is_nextn=is_nextn) 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, num_groups=config.n_group, ) class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM): pass EntryClass = [DeepseekV2ForCausalLM, DeepseekV3ForCausalLM]