562 lines
19 KiB
Python
562 lines
19 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Adapted from
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# https://github.com/vllm-project/vllm/blob/v0.8.3/vllm/model_executor/models/llama4.py
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"""Inference-only LLaMA model compatible with HuggingFace weights."""
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import logging
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import Llama4TextConfig
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from sglang.srt.distributed import (
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
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from sglang.srt.layers.dp_attention import (
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get_attention_tp_rank,
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get_attention_tp_size,
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is_dp_attention_enabled,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import (
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.moe.topk import TopK
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from sglang.srt.model_executor.forward_batch_info import (
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ForwardBatch,
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ForwardMode,
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PPProxyTensors,
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)
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from sglang.srt.models.llama import LlamaForCausalLM, LlamaMLP
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from sglang.srt.utils import (
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add_prefix,
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fast_topk,
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get_compiler_backend,
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is_cuda,
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make_layers,
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)
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_is_cuda = is_cuda()
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logger = logging.getLogger(__name__)
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class Llama4MoE(nn.Module):
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@torch.compile(dynamic=True, backend=get_compiler_backend())
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@staticmethod
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def custom_routing_function(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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router_scores_aK, router_indices_aK = fast_topk(gating_output, topk, dim=-1)
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router_scores_aK = torch.sigmoid(router_scores_aK.float()).to(
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hidden_states.dtype
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)
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return (
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router_scores_aK.view(-1).reshape(router_scores_aK.shape),
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router_indices_aK.to(torch.int32),
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)
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def __init__(
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self,
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config: Llama4TextConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.top_k = config.num_experts_per_tok
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self.device_module = torch.get_device_module()
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intermediate_size_moe = config.intermediate_size
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self.router = ReplicatedLinear(
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config.hidden_size,
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config.num_local_experts,
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bias=False,
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quant_config=None,
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prefix=add_prefix("router", prefix),
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)
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self.topk = TopK(
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top_k=self.top_k,
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renormalize=False,
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custom_routing_function=Llama4MoE.custom_routing_function,
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)
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self.experts = FusedMoE(
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num_experts=config.num_local_experts,
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size_moe,
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layer_id=layer_id,
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reduce_results=False,
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quant_config=quant_config,
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apply_router_weight_on_input=True,
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prefix=add_prefix("experts", prefix),
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)
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self.shared_expert = LlamaMLP(
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size_moe,
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hidden_act="silu",
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quant_config=quant_config,
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prefix=add_prefix("shared_expert", prefix),
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reduce_results=False, # We need to do scatter before reduce
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)
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def forward(
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self,
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hidden_states,
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forward_batch: ForwardBatch,
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use_reduce_scatter: bool = False,
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):
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shared_out, routed_out = self._forward_core(
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hidden_states, forward_batch.forward_mode
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)
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out_aD = routed_out + shared_out
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if self.tp_size > 1 and not use_reduce_scatter:
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out_aD = tensor_model_parallel_all_reduce(out_aD)
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return out_aD
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def _forward_core(self, hidden_states, forward_mode: ForwardMode):
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if hidden_states.shape[0] < 4 and _is_cuda:
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return self._forward_core_shared_routed_overlap(hidden_states)
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else:
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return self._forward_core_normal(hidden_states)
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def _forward_core_normal(self, hidden_states):
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# router_scores: [num_tokens, num_experts]
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router_logits, _ = self.router(hidden_states)
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shared_out = self.shared_expert(hidden_states)
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topk_output = self.topk(hidden_states, router_logits)
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routed_out = self.experts(hidden_states, topk_output)
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return shared_out, routed_out
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def _forward_core_shared_routed_overlap(self, hidden_states):
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alt_stream = _get_or_create_alt_stream(self.device_module)
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alt_stream.wait_stream(self.device_module.current_stream())
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shared_out = self.shared_expert(hidden_states)
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with self.device_module.stream(alt_stream):
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# router_scores: [num_tokens, num_experts]
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router_logits, _ = self.router(hidden_states)
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topk_output = self.topk(hidden_states, router_logits)
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routed_out = self.experts(hidden_states, topk_output)
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self.device_module.current_stream().wait_stream(alt_stream)
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return shared_out, routed_out
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_alt_stream = None
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def _get_or_create_alt_stream(device_module):
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global _alt_stream
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if _alt_stream is None:
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_alt_stream = device_module.Stream()
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return _alt_stream
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class Llama4Attention(nn.Module):
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def __init__(
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self,
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config: Llama4TextConfig,
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layer_id: int,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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bias_o_proj: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = hidden_size
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self.use_rope = (layer_id + 1) % 4 != 0
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self.use_qk_norm = config.use_qk_norm and self.use_rope
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % attn_tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
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self.head_dim = config.head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.attn_temperature_tuning = config.attn_temperature_tuning
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self.floor_scale = config.floor_scale
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self.attn_scale = config.attn_scale
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.n_rep = self.num_heads // self.num_kv_heads
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self.qk_norm = (
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RMSNorm(
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hidden_size=self.head_dim,
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eps=config.rms_norm_eps,
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)
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if self.use_qk_norm
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else None
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)
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qkv_quant_config = quant_config
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o_quant_config = quant_config
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if quant_config and hasattr(quant_config, "ignore") and quant_config.ignore:
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if add_prefix("q_proj", prefix) in quant_config.ignore:
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qkv_quant_config = None
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if add_prefix("o_proj", prefix) in quant_config.ignore:
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o_quant_config = None
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=qkv_quant_config,
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prefix=add_prefix("qkv_proj", prefix),
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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)
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias_o_proj,
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quant_config=o_quant_config,
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prefix=add_prefix("o_proj", prefix),
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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reduce_results=False,
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)
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is_neox_style = True
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is_gguf = quant_config and quant_config.get_name() == "gguf"
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if is_gguf and config.model_type in ["llama", "llama4"]:
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is_neox_style = False
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self.rotary_emb = (
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get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=int(rope_theta),
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rope_scaling=rope_scaling if rope_scaling != "default" else None,
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is_neox_style=is_neox_style,
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)
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if self.use_rope
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else None
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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prefix=add_prefix("attn", prefix),
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use_irope=self.use_rope,
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)
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def _get_attn_scale(self, positions: torch.Tensor) -> torch.Tensor:
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floor = torch.floor((positions + 1.0) / self.floor_scale)
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attn_scale = torch.log(floor + 1.0) * self.attn_scale + 1.0
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return attn_scale.unsqueeze(-1)
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@torch.compile(dynamic=True, backend=get_compiler_backend())
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def _mul_attn_scale(self, positions, q):
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attn_scale = self._get_attn_scale(positions)
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return (q * attn_scale).to(q.dtype)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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qk, v = qkv.split([self.q_size + self.kv_size, self.kv_size], dim=-1)
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if self.rotary_emb is not None:
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q_view, k_view = qk.split([self.q_size, self.kv_size], dim=-1)
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q_out_unused, k_out_unused = self.rotary_emb(positions, q_view, k_view)
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del q_view, k_view, q_out_unused, k_out_unused
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if self.qk_norm is not None:
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# TODO there are still 2 redundant direct_copy_kernel_cuda for this `reshape` and (in attn backend) q.contiguous(), maybe we can fuse them later
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qk = qk.reshape(-1, self.head_dim).contiguous().bfloat16()
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qk = self.qk_norm(qk).to(torch.bfloat16)
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qk = qk.reshape(-1, self.q_size + self.kv_size)
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q, k = qk.split([self.q_size, self.kv_size], dim=-1)
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# We are applying temperature tuning (https://arxiv.org/abs/2501.19399) to NoPE layers, where
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# the inference-time temperature tuning function is customized to not affect short context
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# while working at very long context
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# https://arxiv.org/abs/2501.19399
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if self.attn_temperature_tuning and not self.use_rope:
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q = self._mul_attn_scale(positions=positions, q=q)
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attn_output = self.attn(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class Llama4DecoderLayer(nn.Module):
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def __init__(
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self,
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config: Llama4TextConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = config.hidden_size
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rope_theta = config.rope_theta
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rope_scaling = config.rope_scaling
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max_position_embeddings = config.max_position_embeddings
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self.attn_tp_size = get_attention_tp_size()
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self.attn_tp_rank = get_attention_tp_rank()
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self.self_attn = Llama4Attention(
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config=config,
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layer_id=layer_id,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=False,
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bias_o_proj=False,
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prefix=add_prefix("self_attn", prefix),
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)
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self.config = config
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is_moe_layer = self._is_moe_layer(layer_id)
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is_previous_moe_layer = self._is_moe_layer(layer_id - 1)
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if is_moe_layer:
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self.feed_forward = Llama4MoE(
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config=config,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("feed_forward", prefix),
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)
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else:
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self.feed_forward = LlamaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size_mlp,
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hidden_act="silu",
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quant_config=quant_config,
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prefix=add_prefix("feed_forward", prefix),
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.layer_scatter_modes = LayerScatterModes.init_new(
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layer_id=layer_id,
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num_layers=config.num_hidden_layers,
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is_layer_sparse=is_moe_layer,
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is_previous_layer_sparse=is_previous_moe_layer,
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)
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self.layer_communicator = LayerCommunicator(
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layer_scatter_modes=self.layer_scatter_modes,
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input_layernorm=self.input_layernorm,
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post_attention_layernorm=self.post_attention_layernorm,
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allow_reduce_scatter=True,
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)
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def _is_moe_layer(self, layer_id: int) -> bool:
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if self.config.interleave_moe_layer_step == 0:
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return self.config.num_local_experts > 0
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return (layer_id + 1) % self.config.interleave_moe_layer_step == 0
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def get_intermediate_size(self) -> int:
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if isinstance(self.feed_forward, Llama4MoE):
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return self.config.intermediate_size
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else:
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return self.config.intermediate_size_mlp
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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hidden_states, residual = self.layer_communicator.prepare_attn(
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hidden_states, residual, forward_batch
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)
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if hidden_states.shape[0] != 0:
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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hidden_states, residual = self.layer_communicator.prepare_mlp(
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hidden_states, residual, forward_batch
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)
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# For DP with padding, reduce scatter can be used instead of all-reduce.
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use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
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forward_batch
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)
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# Fully Connected
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hidden_states = self.feed_forward(
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hidden_states, forward_batch, use_reduce_scatter
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)
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hidden_states, residual = self.layer_communicator.postprocess_layer(
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hidden_states, residual, forward_batch
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)
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return hidden_states, residual
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class Llama4Model(nn.Module):
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def __init__(
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self,
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config: Llama4TextConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("embed_tokens", prefix),
|
|
enable_tp=not is_dp_attention_enabled(),
|
|
)
|
|
self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda idx, prefix: Llama4DecoderLayer(
|
|
config=config, layer_id=idx, quant_config=quant_config, prefix=prefix
|
|
),
|
|
prefix=add_prefix("layers", prefix),
|
|
)
|
|
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.layers_to_capture = []
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
|
|
if input_embeds is None:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
else:
|
|
hidden_states = input_embeds
|
|
residual = None
|
|
aux_hidden_states = []
|
|
for i in range(len(self.layers)):
|
|
if i in self.layers_to_capture:
|
|
aux_hidden_states.append(hidden_states + residual)
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
forward_batch,
|
|
residual,
|
|
)
|
|
if not forward_batch.forward_mode.is_idle():
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
if len(aux_hidden_states) == 0:
|
|
return hidden_states
|
|
|
|
return hidden_states, aux_hidden_states
|
|
|
|
|
|
class Llama4ForCausalLM(LlamaForCausalLM):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
config: Llama4TextConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__(config, quant_config, prefix)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.embed_tokens
|
|
|
|
def get_layers(self):
|
|
return self.model.layers
|
|
|
|
def _init_model(
|
|
self,
|
|
config: Llama4TextConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
):
|
|
return Llama4Model(config, quant_config=quant_config, prefix=prefix)
|
|
|
|
|
|
EntryClass = [Llama4ForCausalLM]
|