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527 lines
21 KiB
Python
527 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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#
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# Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team.
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# All rights reserved.
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#
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#
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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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"""Inference-only LLaMA model compatible with HuggingFace weights."""
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from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
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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 vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from .llama import LlamaForCausalLM, LlamaMLP, LlamaModel
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from .utils import (AutoWeightsLoader, extract_layer_index,
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is_pp_missing_parameter)
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class Llama4MoE(nn.Module):
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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, router_indices = torch.topk(gating_output, topk, dim=-1)
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router_scores = torch.sigmoid(router_scores.float()).to(
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hidden_states.dtype)
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return (router_scores, router_indices.to(torch.int32))
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def __init__(self,
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config: Llama4TextConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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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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intermediate_size_moe = config.intermediate_size
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self.router = ReplicatedLinear(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=f"{prefix}.router")
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self.experts = FusedMoE(
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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custom_routing_function=Llama4MoE.custom_routing_function,
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intermediate_size=intermediate_size_moe,
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apply_router_weight_on_input=True,
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reduce_results=False,
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renormalize=False,
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quant_config=quant_config,
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prefix=f"{prefix}.experts")
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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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bias=False,
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prefix=f"{prefix}.shared_expert",
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reduce_results=False, # We need to do scatter before reduce
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)
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def forward(self, hidden_states):
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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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routed_out = self.experts(
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hidden_states=hidden_states,
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router_logits=router_logits,
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)
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experts_out = routed_out + shared_out
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if self.tp_size > 1:
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experts_out = tensor_model_parallel_all_reduce(experts_out)
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return experts_out
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class Llama4Attention(nn.Module):
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def __init__(self,
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config: Llama4TextConfig,
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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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cache_config: Optional[CacheConfig] = None,
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prefix: str = "") -> None:
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super().__init__()
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self.layer_idx = extract_layer_index(prefix)
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self.hidden_size = hidden_size
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self.no_rope_layers = config.no_rope_layers
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self.nope = self.no_rope_layers[self.layer_idx] == 0
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self.use_qk_norm = config.use_qk_norm and not self.nope
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= 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 % 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 tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // 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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# TODO: attn_temperature_tuning should be a bool in huggingface
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self.attn_temperature_tuning = self.nope and \
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config.attn_temperature_tuning > 0
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self.floor_scale = getattr(config, "floor_scale", 8192.0)
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self.attn_scale = getattr(config, "attn_scale", 0.1)
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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 = RMSNorm(
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hidden_size=self.head_dim,
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eps=config.rms_norm_eps,
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has_weight=False,
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dtype=torch.float32,
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) if self.use_qk_norm else 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=quant_config,
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prefix=f"{prefix}.qkv_proj",
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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=quant_config,
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prefix=f"{prefix}.o_proj",
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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 == "llama":
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is_neox_style = False
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self.rotary_emb = 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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) if not self.nope else None
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self.attn = Attention(
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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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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=None,
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use_irope=not self.nope,
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prefix=f"{prefix}.attn",
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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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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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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, 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, k = self.rotary_emb(positions, q, k)
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if self.qk_norm is not None:
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q = q.reshape(-1, self.num_heads, self.head_dim)
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q = self.qk_norm(q.float()).reshape(-1, self.q_size).to(q.dtype)
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k = k.reshape(-1, self.num_kv_heads, self.head_dim)
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k = self.qk_norm(k.float()).reshape(-1, self.kv_size).to(k.dtype)
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# We are applying temperature tuning (https://arxiv.org/abs/2501.19399)
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# to NoPE layers, where the inference-time temperature tuning function
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# 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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#
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# We should apply temperature tuning between (after) rotary / QK norm
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# and (before) attention.
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if self.attn_temperature_tuning and self.nope:
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attn_scale = self._get_attn_scale(positions)
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q = (q * attn_scale).to(q.dtype)
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attn_output = self.attn(q, k, v)
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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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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.layer_idx = extract_layer_index(prefix)
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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.self_attn = Llama4Attention(
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config=config,
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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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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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)
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is_moe_layer = (self.layer_idx +
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1) % config.interleave_moe_layer_step == 0
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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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quant_config=quant_config,
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prefix=f"{prefix}.feed_forward",
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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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bias=False,
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prefix=f"{prefix}.feed_forward",
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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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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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(positions=positions,
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hidden_states=hidden_states)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.feed_forward(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class Llama4Model(LlamaModel):
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def __init__(self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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layer_type: type[Llama4DecoderLayer] = Llama4DecoderLayer):
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self.num_experts = vllm_config.model_config.hf_config.num_local_experts
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super().__init__(vllm_config=vllm_config,
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prefix=prefix,
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layer_type=layer_type)
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def load_moe_expert_weights(
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self,
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name: str,
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loaded_weight: torch.Tensor,
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params_dict: Dict[str, nn.Parameter],
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loaded_params: Set[str],
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expert_params_mapping: List[Tuple[str, str, int, str]],
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fused: bool = True,
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) -> bool:
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expert_param_loaded = False
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if "experts.gate_up_proj" in name:
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loaded_weight = loaded_weight.chunk(2, dim=-1)
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for (param_name, weight_name, expert_id,
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shard_id) in expert_params_mapping:
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new_loaded_weight = loaded_weight
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if fused:
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e_str, _, proj_str, _ = weight_name.split('.')
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weight_name = f"{e_str}.{proj_str}"
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param_name = f"{param_name}weight"
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if weight_name not in name:
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continue
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full_param_name = name.replace(weight_name, param_name)
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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param = params_dict[full_param_name]
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weight_loader = param.weight_loader
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if fused:
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if "w13" in full_param_name:
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shard_idx = 0 if shard_id == "w1" else 1
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new_loaded_weight = new_loaded_weight[shard_idx]
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new_loaded_weight = new_loaded_weight.transpose(-1, -2)
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layer_idx = extract_layer_index(name)
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# EP mapping
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expert_map = self.layers[
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layer_idx].feed_forward.experts.expert_map
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if expert_map is not None:
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local_expert_indices = (expert_map != -1) \
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.nonzero() \
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.flatten() \
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.to(new_loaded_weight.device)
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new_loaded_weight = new_loaded_weight[local_expert_indices]
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expert_id = local_expert_indices[0].item()
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else:
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# TODO: add EP support for non fused weights
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pass
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weight_loader(param,
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new_loaded_weight,
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full_param_name,
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shard_id=shard_id,
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expert_id=expert_id)
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loaded_params.add(full_param_name)
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expert_param_loaded = True
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return expert_param_loaded
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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fused_experts_params = False
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.num_experts)
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expert_params_mapping_fused = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_up_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="gate_up_proj",
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num_experts=1)
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params_dict = dict(self.named_parameters())
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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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if "experts.gate_up_proj" in name or "experts.down_proj" in name:
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fused_experts_params = True
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expert_params_mapping = expert_params_mapping_fused
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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name or "experts" in name:
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continue
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name = name.replace(weight_name, param_name)
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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loaded_params.add(name)
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break
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else:
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moe_loaded = self.load_moe_expert_weights(
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name,
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loaded_weight,
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params_dict,
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loaded_params,
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expert_params_mapping,
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fused=fused_experts_params)
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if not moe_loaded:
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
|
|
weight_loader(param, loaded_weight)
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|
loaded_params.add(name)
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|
return loaded_params
|
|
|
|
|
|
class Llama4ForCausalLM(LlamaForCausalLM):
|
|
|
|
packed_modules_mapping = {
|
|
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
|
"gate_up_proj": ["gate_proj", "up_proj"],
|
|
}
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
# Update temperature tuning config from generation config
|
|
gen_config = vllm_config.model_config.try_get_generation_config()
|
|
gen_config.update(vllm_config.model_config.override_generation_config)
|
|
vllm_config.model_config.hf_config.attn_temperature_tuning \
|
|
= gen_config.get("attn_temperature_tuning", False)
|
|
|
|
super().__init__(vllm_config=vllm_config,
|
|
prefix=prefix,
|
|
layer_type=Llama4DecoderLayer)
|
|
|
|
def _init_model(self,
|
|
vllm_config: VllmConfig,
|
|
prefix: str = "",
|
|
layer_type: type[Llama4DecoderLayer] = Llama4DecoderLayer):
|
|
return Llama4Model(vllm_config=vllm_config,
|
|
prefix=prefix,
|
|
layer_type=layer_type)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
weights = [
|
|
self.permute_qk_weight_for_rotary(name, loaded_weight)
|
|
for name, loaded_weight in weights
|
|
]
|
|
return loader.load_weights(weights)
|
|
|
|
def permute_qk_weight_for_rotary(
|
|
self,
|
|
name: str,
|
|
loaded_weight: torch.Tensor,
|
|
) -> Tuple[str, torch.Tensor]:
|
|
|
|
def permute(w: torch.Tensor, n_heads: int):
|
|
attn_in = self.config.head_dim * n_heads
|
|
attn_out = self.config.hidden_size
|
|
|
|
return w.view(n_heads, attn_in // n_heads // 2, 2,
|
|
attn_out).transpose(1, 2).reshape(attn_in, attn_out)
|
|
|
|
modules = name.split(".")
|
|
|
|
# rotary embeds should be sliced
|
|
if ("wk" in modules or "k_proj" in modules) \
|
|
and modules[-1] == "weight":
|
|
loaded_weight = permute(loaded_weight,
|
|
self.config.num_key_value_heads)
|
|
elif ("wq" in modules or "q_proj" in modules) \
|
|
and modules[-1] == "weight":
|
|
loaded_weight = permute(loaded_weight,
|
|
self.config.num_attention_heads)
|
|
|
|
return name, loaded_weight
|