# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py # Copyright 2023 The vLLM team. # Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """Inference-only DeepseekV2/DeepseekV3 model.""" import typing from collections.abc import Callable, Iterable from itertools import islice from typing import Any, Optional, Union import torch from torch.library import custom_op from torch import nn from transformers import DeepseekV2Config, DeepseekV3Config from vllm_kunlun.ops.attention.layer import Attention from vllm.attention.backends.abstract import AttentionBackend from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton from vllm.compilation.decorators import support_torch_compile from vllm.config import (CacheConfig, ParallelConfig, VllmConfig, get_current_vllm_config) from vllm.distributed import (get_ep_group, get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather) from vllm.forward_context import get_forward_context from vllm.logger import init_logger from vllm_kunlun.ops.activation import SiluAndMul from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm from vllm.model_executor.layers.linear import (ColumnParallelLinear, MergedColumnParallelLinear, RowParallelLinear) from vllm_kunlun.ops.linear import ReplicatedLinear from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm_kunlun.ops.attention.mla import MLAModules, MultiHeadLatentAttention from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors from vllm.utils import cdiv, direct_register_custom_op from vllm_kunlun.ops.deep_gemm import int8_mqa_logits, int8_paged_mqa_logits from vllm.v1.attention.backends.mla.indexer import DeepseekV32IndexerBackend from vllm_kunlun.v1.attention.backends.mla.indexer import DeepseekV32IndexerMetadata from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec from vllm.model_executor.models.interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP from vllm.model_executor.models.utils import (PPMissingLayer, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache if current_platform.is_cuda_alike(): from vllm import _custom_ops as ops elif current_platform.is_xpu(): from vllm._ipex_ops import ipex_ops as ops import xspeedgate_ops _is_kunlun = True logger = init_logger(__name__) 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, is_sequence_parallel=False, prefix: str = "", ) -> None: super().__init__() # If is_sequence_parallel, the input and output tensors are sharded # across the ranks within the tp_group. In this case the weights are # replicated and no collective ops are needed. # Otherwise we use standard TP with an allreduce at the end. self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, disable_tp=is_sequence_parallel, prefix=f"{prefix}.gate_up_proj") self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, quant_config=quant_config, reduce_results=reduce_results, disable_tp=is_sequence_parallel, prefix=f"{prefix}.down_proj") 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 DeepseekV2MoE(nn.Module): def __init__( self, config: Union[DeepseekV2Config, DeepseekV3Config], parallel_config: ParallelConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() self.tp_rank = get_tensor_model_parallel_rank() self.routed_scaling_factor = config.routed_scaling_factor self.ep_group = get_ep_group().device_group self.ep_rank = self.ep_group.rank() self.ep_size = self.ep_group.size() self.n_routed_experts: int = config.n_routed_experts self.n_shared_experts: int = config.n_shared_experts self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe if config.hidden_act != "silu": raise ValueError(f"Unsupported activation: {config.hidden_act}. " "Only silu is supported for now.") self.gate = ReplicatedLinear(config.hidden_size, config.n_routed_experts, bias=False, quant_config=None, prefix=f"{prefix}.gate") if config.topk_method == "noaux_tc": self.gate.e_score_correction_bias = nn.Parameter( torch.empty(config.n_routed_experts, dtype=torch.float32)) else: self.gate.e_score_correction_bias = None # Load balancing settings. eplb_config = parallel_config.eplb_config self.enable_eplb = parallel_config.enable_eplb self.n_redundant_experts = eplb_config.num_redundant_experts self.n_logical_experts = self.n_routed_experts self.n_physical_experts = (self.n_logical_experts + self.n_redundant_experts) self.n_local_physical_experts = self.n_physical_experts // self.ep_size self.physical_expert_start = (self.ep_rank * self.n_local_physical_experts) self.physical_expert_end = (self.physical_expert_start + self.n_local_physical_experts) if config.n_shared_experts is None: self.experts = FusedMoE( num_experts=config.n_routed_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, reduce_results=False, renormalize=config.norm_topk_prob, quant_config=quant_config, use_grouped_topk=True, num_expert_group=config.n_group, topk_group=config.topk_group, prefix=f"{prefix}.experts", scoring_func=config.scoring_func, # we do scaling outside, set factor to 1.0 to avoid double mul routed_scaling_factor=1.0, e_score_correction_bias=self.gate.e_score_correction_bias, enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, is_sequence_parallel=self.is_sequence_parallel, ) self.shared_experts = None else: intermediate_size = (config.moe_intermediate_size * config.n_shared_experts) self.shared_experts = DeepseekV2MLP( hidden_size=config.hidden_size, intermediate_size=intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, is_sequence_parallel=self.is_sequence_parallel, reduce_results=False, prefix=f"{prefix}.shared_experts", ) self.experts = SharedFusedMoE( shared_experts=self.shared_experts, num_experts=config.n_routed_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, reduce_results=False, renormalize=config.norm_topk_prob, quant_config=quant_config, use_grouped_topk=True, num_expert_group=config.n_group, topk_group=config.topk_group, prefix=f"{prefix}.experts", scoring_func=config.scoring_func, # we do scaling outside, set factor to 1.0 to avoid double mul routed_scaling_factor=1.0, e_score_correction_bias=self.gate.e_score_correction_bias, enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, is_sequence_parallel=self.is_sequence_parallel, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) # Chunk the hidden states so they aren't replicated across TP ranks. # This avoids duplicate computation in self.experts. # TODO: We can replace the all_reduce at the end of attn with a # reduce_scatter instead of chunking here. if self.is_sequence_parallel: hidden_states = sequence_parallel_chunk(hidden_states) # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) fused_moe_out = self.experts(hidden_states=hidden_states, router_logits=router_logits) if self.shared_experts is not None: shared_output, final_hidden_states = fused_moe_out else: shared_output = None final_hidden_states = fused_moe_out # Fix FP16 overflow # See DeepseekV2DecoderLayer for more details. if hidden_states.dtype != torch.float16: final_hidden_states *= self.routed_scaling_factor elif self.shared_experts is not None: assert shared_output is not None shared_output *= (1. / self.routed_scaling_factor) if self.shared_experts is not None: assert shared_output is not None final_hidden_states += shared_output if self.is_sequence_parallel: final_hidden_states = tensor_model_parallel_all_gather( final_hidden_states, 0) final_hidden_states = final_hidden_states[:num_tokens] elif self.tp_size > 1: final_hidden_states = ( self.experts.maybe_all_reduce_tensor_model_parallel( final_hidden_states)) return final_hidden_states.view(num_tokens, hidden_dim) 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 DeepseekV2Attention(nn.Module): def __init__( self, vllm_config: VllmConfig, config: Union[DeepseekV2Config, DeepseekV3Config], 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, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, topk_indices_buffer: Optional[torch.Tensor] = None, prefix: str = "", ) -> None: super().__init__() 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 self.num_heads = num_heads tp_size = get_tensor_model_parallel_world_size() assert num_heads % tp_size == 0 self.num_local_heads = num_heads // tp_size self.scaling = self.qk_head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings assert topk_indices_buffer is None, "topk_indices_buffer is not \ supported for DeepseekV2Attention" if self.q_lora_rank is not None: self.q_a_proj = ReplicatedLinear(self.hidden_size, self.q_lora_rank, bias=False, quant_config=quant_config, prefix=f"{prefix}.q_a_proj") 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=f"{prefix}.q_b_proj") else: self.q_proj = ColumnParallelLinear(self.hidden_size, self.num_heads * self.qk_head_dim, bias=False, quant_config=quant_config, prefix=f"{prefix}.q_proj") 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=f"{prefix}.kv_a_proj_with_mqa") self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps) 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=f"{prefix}.kv_b_proj") # O projection. self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim, self.hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.o_proj") 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 self.attn = Attention(self.num_local_heads, self.qk_head_dim, self.scaling, num_kv_heads=self.num_local_heads, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.attn") def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: if self.q_lora_rank is not None: q = self.q_a_proj(hidden_states)[0] 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) q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1) latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0] 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) 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, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1) 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 # padding value to qk_head_dim for alignment v = torch.nn.functional.pad( v, [0, self.qk_head_dim - self.v_head_dim], value=0).view(-1, self.num_local_heads * self.qk_head_dim) attn_output = self.attn(q, k, v) attn_output = attn_output.view( -1, self.num_local_heads, self.qk_head_dim)[..., :self.v_head_dim].reshape( -1, self.num_local_heads * self.v_head_dim) output, _ = self.o_proj(attn_output) return output @torch.inference_mode() def cp_gather_indexer_k_quant_cache( kv_cache, # [num_blocks, block_size, head_dim + 1] block_table, # [batch_size, num_blocks] cu_seq_lens, # [batch_size + 1, ] batch_size, head_dim, ): num_blocks, block_size, _ = kv_cache.shape kv_cache = kv_cache.view(num_blocks, -1) expected_value = [] expected_scale = [] for b in range(batch_size): s = cu_seq_lens[b + 1] - cu_seq_lens[b] if s == 0: continue tot = cdiv(s, block_size) blocks = block_table[b, :tot] value = [] scale = [] full_block = torch.arange(tot - 1, device=kv_cache.device, dtype=torch.int32) non_remaining_value = kv_cache[blocks[full_block], :block_size * head_dim].view(-1, head_dim) non_remaining_scale = kv_cache[blocks[full_block], block_size * head_dim:].view(-1, 4) remaining = s - (tot - 1) * block_size value = torch.cat([ non_remaining_value, kv_cache[blocks[-1], :remaining * head_dim].view(-1, head_dim) ], dim=0) scale = torch.cat([ non_remaining_scale, kv_cache[blocks[-1], block_size * head_dim:block_size * head_dim + remaining * 4].view(-1, 4) ], dim=0) expected_value.append(value) expected_scale.append(scale) gather_value = torch.cat(expected_value, dim=0).view(-1, head_dim) gather_scale = torch.cat(expected_scale, dim=0).view(-1, 4) gather_value = gather_value.view(torch.int8) gather_scale = gather_scale.view(torch.float32) return gather_value, gather_scale @torch.inference_mode() def kunlun_indexer_k_quant_cache( k, #[num_tokens, head_dim] kv_cache, # [num_blocks, cache_block_size, head_dim + 1] slot_mapping, # [num_tokens] quant_block_size, ): num_blocks, cache_block_size, cache_stride = kv_cache.shape # num_tokens, head_dim = k.shape head_dim = k.shape[1] num_tokens = slot_mapping.shape[0] assert head_dim % quant_block_size == 0 kv_cache = kv_cache.view(num_blocks, -1) k_fp8 = torch.empty( k.shape, device=k.device, dtype=torch.int8, ) k_scale = torch.empty( [k.shape[0], 1], device=k.device, dtype=torch.float32, ) torch.ops._C.quant2d(k, k_fp8, k_scale, force_sdnn=True) k_scale /= 127 for token_idx in range(num_tokens): slot_idx = slot_mapping[token_idx] if slot_idx < 0: continue block_idx = slot_idx // cache_block_size block_offset = slot_idx % cache_block_size v_offset = block_offset * head_dim kv_cache[block_idx, v_offset:v_offset + head_dim] = k_fp8[token_idx, :].view(torch.uint8).contiguous() s_offset = cache_block_size * head_dim + block_offset * 4 kv_cache[block_idx, s_offset:s_offset + 4] = k_scale[token_idx, :].view(torch.uint8).contiguous() kv_cache = kv_cache.view(num_blocks, cache_block_size, cache_stride) @custom_op("vllm::sparse_attn_indexer_vllm_kunlun", mutates_args=()) def sparse_attn_indexer_vllm_kunlun( hidden_states: torch.Tensor, k_cache_prefix: str, kv_cache: torch.Tensor, q_fp8: torch.Tensor, k: torch.Tensor, weights: torch.Tensor, quant_block_size: int, scale_fmt: Optional[str], topk_tokens: int, head_dim: int, max_model_len: int, total_seq_lens: int, topk_indices_buffer: Optional[torch.Tensor], ) -> None: # careful! this will be None in dummy run attn_metadata = get_forward_context().attn_metadata # assert isinstance(attn_metadata, dict) if not isinstance(attn_metadata, dict): sparse_attn_indexer_vllm_kunlun_fake( hidden_states, k_cache_prefix, kv_cache, q_fp8, k, weights, quant_block_size, scale_fmt, topk_tokens, head_dim, max_model_len, total_seq_lens, topk_indices_buffer, ) return attn_metadata = attn_metadata[k_cache_prefix] assert isinstance(attn_metadata, DeepseekV32IndexerMetadata) slot_mapping = attn_metadata.slot_mapping has_decode = attn_metadata.num_decodes > 0 has_prefill = attn_metadata.num_prefills > 0 num_decode_tokens = attn_metadata.num_decode_tokens # kunlun_indexer_k_quant_cache( # k, # kv_cache, # slot_mapping, # quant_block_size, # ) torch.ops.xspeedgate_ops.indexer_k_quant_and_cache( k, kv_cache, slot_mapping, quant_block_size, scale_fmt, ) topk_indices_buffer[:hidden_states.shape[0]] = -1 if has_prefill: prefill_metadata = attn_metadata.prefill for chunk in prefill_metadata.chunks: k_fp8, k_scale = cp_gather_indexer_k_quant_cache( kv_cache, chunk.block_table, chunk.cu_seq_lens, chunk.num_reqs, head_dim, ) logits = int8_mqa_logits( q_fp8[chunk.token_start:chunk.token_end], (k_fp8, k_scale), weights[chunk.token_start:chunk.token_end], chunk.cu_seqlen_ks, chunk.cu_seqlen_ke, ) topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]), dim=-1)[1] topk_indices -= chunk.cu_seqlen_ks[:, None] mask_lo = topk_indices >= 0 mask_hi = topk_indices - (chunk.cu_seqlen_ke - chunk.cu_seqlen_ks)[:, None] < 0 mask = torch.full_like(topk_indices, False, dtype=torch.bool, device=topk_indices.device) mask = mask_lo & mask_hi topk_indices = topk_indices.masked_fill(~mask, -1) topk_indices_buffer[ chunk.token_start:chunk.token_end, :topk_indices. shape[-1]] = topk_indices.to(dtype=torch.int32) if has_decode: decode_metadata = attn_metadata.decode # kv_cache size requirement [num_block, block_size, n_head, head_dim], # we only have [num_block, block_size, head_dim], kv_cache = kv_cache.unsqueeze(-2) decode_lens = decode_metadata.decode_lens if decode_metadata.requires_padding: # pad in edge case where we have short chunked prefill length < # decode_threshold since we unstrictly split # prefill and decode by decode_threshold # (currently set to 1 + speculative tokens) padded_q_fp8_decode_tokens = pack_seq_triton( q_fp8[:num_decode_tokens], decode_lens) else: padded_q_fp8_decode_tokens = q_fp8[:num_decode_tokens].reshape( decode_lens.shape[0], -1, *q_fp8.shape[1:]) # TODO: move and optimize below logic with triton kernels batch_size = padded_q_fp8_decode_tokens.shape[0] next_n = padded_q_fp8_decode_tokens.shape[1] assert batch_size == decode_metadata.seq_lens.shape[0] num_padded_tokens = batch_size * next_n logits = int8_paged_mqa_logits( padded_q_fp8_decode_tokens, kv_cache, weights[:num_padded_tokens], decode_metadata.seq_lens, decode_metadata.seq_lens_cpu, decode_metadata.block_table, decode_metadata.schedule_metadata, max_model_len=max_model_len, ) # padded query len current_device = padded_q_fp8_decode_tokens.device padded_num_tokens = batch_size * next_n positions = torch.arange(max_model_len, device=current_device).unsqueeze(0).expand( batch_size * next_n, -1) row_indices = torch.arange(padded_num_tokens, device=current_device) // next_n next_n_offset = torch.arange( padded_num_tokens, device=padded_q_fp8_decode_tokens.device) % next_n index_end_pos = (decode_metadata.seq_lens[row_indices] - next_n + next_n_offset).unsqueeze(1) # index_end_pos: [B * N, 1] mask = positions <= index_end_pos # mask: [B * N, L] logits = logits.masked_fill(~mask, float('-inf')) topk_indices = logits.topk(topk_tokens, dim=-1)[1].to(torch.int32) # [B * N, K] # ensure we don't set indices for the top k # that is out of range(masked already) # this will happen if context length is shorter than K topk_indices[topk_indices > index_end_pos] = -1 if decode_metadata.requires_padding: # if padded, we need to unpack # the topk indices removing padded tokens topk_indices = unpack_seq_triton( topk_indices.reshape(batch_size, -1, topk_indices.shape[-1]), decode_lens) topk_indices_buffer[:num_decode_tokens, :topk_indices. shape[-1]] = topk_indices.to(dtype=torch.int32) # return topk_indices_buffer def sparse_attn_indexer_vllm_kunlun_fake( hidden_states: torch.Tensor, k_cache_prefix: str, kv_cache: torch.Tensor, q_fp8: torch.Tensor, k: torch.Tensor, weights: torch.Tensor, quant_block_size: int, scale_fmt: Optional[str], topk_tokens: int, head_dim: int, max_model_len: int, total_seq_lens: int, topk_indices_buffer: Optional[torch.Tensor], ) -> None: return sparse_attn_indexer_vllm_kunlun.register_fake(sparse_attn_indexer_vllm_kunlun_fake) class Indexer(nn.Module): def __init__(self, vllm_config: VllmConfig, config: Union[DeepseekV2Config, DeepseekV3Config], hidden_size: int, q_lora_rank: int, quant_config: Optional[QuantizationConfig], cache_config: Optional[CacheConfig], topk_indices_buffer: Optional[torch.Tensor], prefix: str = ""): super().__init__() self.vllm_config = vllm_config self.config = config # self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"] self.topk_tokens = config.index_topk self.n_head = config.index_n_heads # 64 self.head_dim = config.index_head_dim # 128 self.rope_dim = config.qk_rope_head_dim # 64 self.q_lora_rank = q_lora_rank # 1536 # no tensor parallel, just replicated self.wq_b = ReplicatedLinear(self.q_lora_rank, self.head_dim * self.n_head, bias=False, quant_config=quant_config, prefix=f"{prefix}.wq_b") self.wk = ReplicatedLinear(hidden_size, self.head_dim, bias=False, quant_config=quant_config, prefix=f"{prefix}.wk") self.k_norm = LayerNorm(self.head_dim, eps=1e-6) self.weights_proj = ReplicatedLinear(hidden_size, self.n_head, bias=False, quant_config=None, prefix=f"{prefix}.weights_proj") self.softmax_scale = self.head_dim**-0.5 self.scale_fmt = "ue8m0" self.quant_block_size = 128 # TODO: get from config self.topk_indices_buffer = topk_indices_buffer # NOTE: (zyongye) we use fp8 naive cache, # where we store value in fp8 and scale in fp32 # per self.quant_block_size element self.k_cache = DeepseekV32IndexerCache( head_dim=self.head_dim + self.head_dim // self.quant_block_size * 4, dtype=torch.uint8, prefix=f"{prefix}.k_cache", cache_config=cache_config) self.max_model_len = vllm_config.model_config.max_model_len if self.max_model_len % cache_config.block_size != 0: #由于I8_paged_mqa_logits输入参数的限制，最大长度必须为block_zise的整数倍 self.max_model_len = self.max_model_len + cache_config.block_size - (self.max_model_len % cache_config.block_size) self.prefix = prefix from vllm.v1.attention.backends.mla.indexer import ( get_max_prefill_buffer_size) self.max_total_seq_len = get_max_prefill_buffer_size(vllm_config) def forward(self, hidden_states: torch.Tensor, qr: torch.Tensor, positions, rotary_emb) -> torch.Tensor: q, _ = self.wq_b(qr) q = q.view(-1, self.n_head, self.head_dim) q_pe, q_nope = torch.split( q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1) k, _ = self.wk(hidden_states) k = self.k_norm(k) k_pe, k_nope = torch.split( k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1) q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1)) q = torch.cat([q_pe, q_nope], dim=-1) k = torch.cat([k_pe.squeeze(1), k_nope], dim=-1) # we only quant q here since k quant is fused with cache insertion q = q.view(-1, self.head_dim) q_fp8 = torch.empty( q.shape, device=q.device, dtype=torch.int8, ) q_scale = torch.empty( [q.shape[0], 1], device=q.device, dtype=torch.float32, ) torch.ops._C.quant2d(q, q_fp8, q_scale, force_sdnn=True) q_scale /= 127 q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim) q_scale = q_scale.view(-1, self.n_head) weights, _ = self.weights_proj(hidden_states) weights = weights * self.n_head**-0.5 weights = weights * q_scale * self.softmax_scale torch.ops.vllm.sparse_attn_indexer_vllm_kunlun( hidden_states, self.k_cache.prefix, self.k_cache.kv_cache[0], q_fp8, k, weights, self.quant_block_size, self.scale_fmt, self.topk_tokens, self.head_dim, self.max_model_len, self.max_total_seq_len, self.topk_indices_buffer, ) return self.topk_indices_buffer class DeepseekV2MLAAttention(nn.Module): """ Main reference: DeepseekV2 paper, and FlashInfer Implementation (https://arxiv.org/abs/2405.04434 and https://github.com/flashinfer-ai/flashinfer/pull/551). For more info see MLACommonImpl in: vllm/v1/attention/backends/mla/utils.py """ def __init__( self, vllm_config: VllmConfig, config: Union[DeepseekV2Config, DeepseekV3Config], hidden_size: int, num_heads: int, qk_nope_head_dim: int, qk_rope_head_dim: int, v_head_dim: int, q_lora_rank: Optional[int], kv_lora_rank: int, rope_theta: float = 10000, rope_scaling: Optional[dict[str, Any]] = None, max_position_embeddings: int = 8192, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", topk_indices_buffer: Optional[torch.Tensor] = None, ) -> None: super().__init__() 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 self.num_heads = num_heads tp_size = get_tensor_model_parallel_world_size() assert num_heads % tp_size == 0 self.num_local_heads = num_heads // tp_size self.scaling = self.qk_head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings if self.q_lora_rank is not None: self.fused_qkv_a_proj = MergedColumnParallelLinear( self.hidden_size, [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim], bias=False, quant_config=quant_config, prefix=f"{prefix}.fused_qkv_a_proj", disable_tp=True) else: 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=f"{prefix}.kv_a_proj_with_mqa") if self.q_lora_rank is not None: self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps) self.q_b_proj = ColumnParallelLinear(self.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False, quant_config=quant_config, prefix=f"{prefix}.q_b_proj") else: self.q_proj = ColumnParallelLinear(self.hidden_size, self.num_heads * self.qk_head_dim, bias=False, quant_config=quant_config, prefix=f"{prefix}.q_proj") self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps) 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=f"{prefix}.kv_b_proj") self.o_proj = RowParallelLinear(self.num_heads * self.v_head_dim, self.hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.o_proj") 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 self.is_v32 = hasattr(config, "index_topk") if self.is_v32: self.indexer = Indexer(vllm_config, config, hidden_size, q_lora_rank, quant_config, cache_config, topk_indices_buffer, f"{prefix}.indexer") else: self.indexer = None mla_modules = MLAModules( kv_a_layernorm=self.kv_a_layernorm, kv_b_proj=self.kv_b_proj, rotary_emb=self.rotary_emb, o_proj=self.o_proj, fused_qkv_a_proj=self.fused_qkv_a_proj if self.q_lora_rank is not None else None, kv_a_proj_with_mqa=self.kv_a_proj_with_mqa if self.q_lora_rank is None else None, q_a_layernorm=self.q_a_layernorm if self.q_lora_rank is not None else None, q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None, q_proj=self.q_proj if self.q_lora_rank is None else None, indexer=self.indexer, is_sparse=self.is_v32, topk_indices_buffer=topk_indices_buffer, ) self.mla_attn = MultiHeadLatentAttention( self.hidden_size, self.num_local_heads, self.scaling, self.qk_nope_head_dim, self.qk_rope_head_dim, self.v_head_dim, self.q_lora_rank, self.kv_lora_rank, mla_modules, cache_config, quant_config, prefix, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: return self.mla_attn(positions, hidden_states) class DeepseekV2DecoderLayer(nn.Module): def __init__(self, vllm_config: VllmConfig, prefix: str, topk_indices_buffer: Optional[torch.Tensor] = None) -> None: super().__init__() config = vllm_config.model_config.hf_config model_config = vllm_config.model_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config parallel_config = vllm_config.parallel_config self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) # DecoderLayers are created with `make_layers` which passes the prefix # with the layer's index. layer_idx = int(prefix.split(sep='.')[-1]) self.layer_idx = layer_idx if model_config.use_mla: attn_cls = DeepseekV2MLAAttention else: attn_cls = DeepseekV2Attention self.self_attn = attn_cls( vllm_config=vllm_config, 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, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", topk_indices_buffer=topk_indices_buffer, ) if (config.n_routed_experts is not None and layer_idx >= config.first_k_dense_replace and layer_idx % config.moe_layer_freq == 0): self.mlp = DeepseekV2MoE( config=config, parallel_config=parallel_config, quant_config=quant_config, prefix=f"{prefix}.mlp", ) else: self.mlp = DeepseekV2MLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=f"{prefix}.mlp", ) 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.routed_scaling_factor = config.routed_scaling_factor def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: Optional[torch.Tensor], ) -> torch.Tensor: # Self Attention if residual is None: residual = hidden_states.clone() hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm( hidden_states, residual) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) if hidden_states.dtype == torch.float16: # Fix FP16 overflow # We scale both hidden_states and residual before # rmsnorm, and rmsnorm result would not affect by scale. hidden_states *= 1. / self.routed_scaling_factor if self.layer_idx == 0: # The residual is shared by all layers, we only scale it on # first layer. residual *= 1. / self.routed_scaling_factor # Fully Connected hidden_states, residual = self.post_attention_layernorm( hidden_states, residual) hidden_states = self.mlp(hidden_states) if isinstance(self.mlp, DeepseekV2MLP) and hidden_states.dtype == torch.float16: # Fix FP16 overflow # Scaling the DeepseekV2MLP output, it is the input of # input_layernorm of next decoder layer. # The scaling of DeepseekV2MOE output would be done in the forward # of DeepseekV2MOE hidden_states *= 1. / self.routed_scaling_factor return hidden_states, residual @support_torch_compile class DeepseekV2Model(nn.Module): fall_back_to_pt_during_load = False def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config self.vocab_size = config.vocab_size self.is_v32 = hasattr(config, "index_topk") if self.is_v32: topk_tokens = config.index_topk topk_indices_buffer = torch.empty( vllm_config.scheduler_config.max_num_batched_tokens, topk_tokens, dtype=torch.int32, device="cuda") else: topk_indices_buffer = None if get_pp_group().is_first_rank: self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=f"{prefix}.embed_tokens") else: self.embed_tokens = PPMissingLayer() self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix, topk_indices_buffer), prefix=f"{prefix}.layers") if get_pp_group().is_last_rank: self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer() self.make_empty_intermediate_tensors = ( make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size)) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors], inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.get_input_embeddings(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for layer in islice(self.layers, self.start_layer, self.end_layer): hidden_states, residual = layer(positions, hidden_states, residual) if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": hidden_states, "residual": residual }) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts, SupportsLoRA): packed_modules_mapping = { "gate_up_proj": ["gate_proj", "up_proj"], } def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config # `packed_modules_mapping` needs to be modified before # initializing DeepseekV2Model, as it is passed inplace to # quantization config init and may be used to select the # quant_method for relevant layers during initialization. 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"] = [ "q_a_proj", "kv_a_proj_with_mqa", ] self.model = DeepseekV2Model(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) if get_pp_group().is_last_rank: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) else: self.lm_head = PPMissingLayer() self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) self.expert_weights = [] # Set MoE hyperparameters self.num_moe_layers = (config.num_hidden_layers - config.first_k_dense_replace) self.num_expert_groups = config.n_group self.moe_layers: list[FusedMoE] = [] example_moe = None for layer in self.model.layers: if isinstance(layer, PPMissingLayer): continue assert isinstance(layer, DeepseekV2DecoderLayer) if isinstance(layer.mlp, DeepseekV2MoE): # Pick last one layer since the first ones may be dense layers. example_moe = layer.mlp self.moe_layers.append(layer.mlp.experts) if example_moe is None: raise RuntimeError("No DeepseekV2MoE layer found in model.layers.") self.num_logical_experts = example_moe.n_logical_experts self.num_physical_experts = example_moe.n_physical_experts self.num_local_physical_experts = example_moe.n_local_physical_experts self.num_routed_experts = example_moe.n_routed_experts self.num_shared_experts = example_moe.n_shared_experts self.num_redundant_experts = example_moe.n_redundant_experts def set_eplb_state( self, expert_load_view: torch.Tensor, logical_to_physical_map: torch.Tensor, logical_replica_count: torch.Tensor, ) -> None: for layer_idx, layer in enumerate(self.moe_layers): # Register the expert weights. self.expert_weights.append(layer.get_expert_weights()) layer.set_eplb_state( moe_layer_idx=layer_idx, expert_load_view=expert_load_view, logical_to_physical_map=logical_to_physical_map, logical_replica_count=logical_replica_count, ) def update_physical_experts_metadata( self, num_physical_experts: int, num_local_physical_experts: int, ) -> None: assert self.num_local_physical_experts == num_local_physical_experts self.num_physical_experts = num_physical_experts self.num_local_physical_experts = num_local_physical_experts self.num_redundant_experts = (num_physical_experts - self.num_logical_experts) for layer in self.model.layers: if isinstance(layer.mlp, DeepseekV2MoE): moe = layer.mlp moe.n_local_physical_experts = num_local_physical_experts moe.n_physical_experts = num_physical_experts moe.n_redundant_experts = self.num_redundant_experts moe.experts.update_expert_map() def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: hidden_states = self.model(input_ids, positions, intermediate_tensors, inputs_embeds) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> Optional[torch.Tensor]: logits = self.logits_processor(self.lm_head, hidden_states) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ("fused_qkv_a_proj", "q_a_proj", 0), ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 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, num_redundant_experts=self.num_redundant_experts) params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue spec_layer = get_spec_layer_idx_from_weight_name(self.config, name) if spec_layer is not None: continue # skip spec decode layers for main model 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_mapped = name.replace(weight_name, param_name) # QKV fusion is optional, fall back to normal # weight loading if it's not enabled # if go with fusion option, then update name if ((param_name == "fused_qkv_a_proj") and name_mapped not in params_dict): continue else: name = name_mapped # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, self): continue if name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: is_expert_weight = False for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue # Anyway, this is an expert weight and should not be # attempted to load as other weights later is_expert_weight = True # Do not modify `name` since the loop may continue here # Instead, create a new variable name_mapped = name.replace(weight_name, param_name) if is_pp_missing_parameter(name_mapped, self): continue if name_mapped not in params_dict: continue param = params_dict[name_mapped] # We should ask the weight loader to return success or not # here since otherwise we may skip experts with other # available replicas. weight_loader = typing.cast(Callable[..., bool], param.weight_loader) success = weight_loader(param, loaded_weight, name_mapped, shard_id=shard_id, expert_id=expert_id, return_success=True) if success: name = name_mapped break else: if is_expert_weight: # We've checked that this is an expert weight # However it's not mapped locally to this rank # So we simply skip it continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue if is_pp_missing_parameter(name, self): continue if name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM): pass # Compatibility with # https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/blob/main/configuration_deepseek.py def get_spec_layer_idx_from_weight_name(config: Union[DeepseekV2Config, DeepseekV3Config], weight_name: str) -> Optional[int]: if (hasattr(config, "num_nextn_predict_layers") and config.num_nextn_predict_layers > 0): layer_idx = config.num_hidden_layers for i in range(config.num_nextn_predict_layers): if weight_name.startswith(f"model.layers.{layer_idx+i}."): return layer_idx + i return None