[Meta] Llama4 EAGLE Support #20591
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| # SPDX-License-Identifier: Apache-2.0 | ||
| # SPDX-FileCopyrightText: Copyright contributors to the vLLM project | ||
|
There was a problem hiding this comment. Can you also add the copyright from Meta side? There was a problem hiding this comment. Absolutely, thanks for the suggestion There was a problem hiding this comment. |
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| # Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team. | ||
| # All rights reserved. | ||
| # | ||
| # | ||
| # 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. | ||
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| from collections.abc import Iterable | ||
| from typing import Optional | ||
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| import torch | ||
| import torch.nn as nn | ||
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| from vllm.compilation.decorators import support_torch_compile | ||
| from vllm.config import VllmConfig | ||
| from vllm.distributed.parallel_state import get_pp_group | ||
| from vllm.logger import init_logger | ||
| from vllm.model_executor.layers.layernorm import RMSNorm | ||
| from vllm.model_executor.layers.logits_processor import LogitsProcessor | ||
| from vllm.model_executor.layers.quantization.base_config import ( | ||
| QuantizationConfig) | ||
| from vllm.model_executor.layers.quantization.torchao import TorchAOConfig | ||
| from vllm.model_executor.layers.vocab_parallel_embedding import ( | ||
| VocabParallelEmbedding) | ||
| from vllm.model_executor.model_loader.weight_utils import default_weight_loader | ||
| from vllm.model_executor.models.llama4 import (Llama4DecoderLayer, | ||
| Llama4ForCausalLM) | ||
| from vllm.model_executor.models.utils import extract_layer_index | ||
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| from .utils import AutoWeightsLoader, maybe_prefix | ||
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| logger = init_logger(__name__) | ||
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| @support_torch_compile | ||
| class LlamaModel(nn.Module): | ||
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| def __init__( | ||
| self, | ||
| *, | ||
| vllm_config: VllmConfig, | ||
| prefix: str = "", | ||
| start_layer_id: int = 0, | ||
| quant_config: Optional[QuantizationConfig] = None, | ||
| ) -> None: | ||
| super().__init__() | ||
| self.config = ( | ||
| vllm_config.speculative_config.draft_model_config.hf_config) | ||
| self.validate_and_update_config(start_layer_id, quant_config) | ||
| self.vocab_size = self.config.vocab_size | ||
| self.embed_tokens = VocabParallelEmbedding( | ||
| self.config.vocab_size, | ||
| self.config.hidden_size, | ||
| prefix=maybe_prefix(prefix, "embed_tokens"), | ||
| ) | ||
|
|
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| self.layers = nn.ModuleList([ | ||
| Llama4DecoderLayer( | ||
| self.config, | ||
| quant_config=quant_config, | ||
| prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"), | ||
| ) for i in range(self.config.num_hidden_layers) | ||
| ]) | ||
| self.fc = torch.nn.Linear(self.config.hidden_size * 2, | ||
| self.config.hidden_size, | ||
| bias=False) | ||
| self.norm = RMSNorm(self.config.hidden_size, | ||
| eps=self.config.rms_norm_eps) | ||
|
|
||
| def forward( | ||
| self, | ||
| input_ids: Optional[torch.Tensor], | ||
| positions: torch.Tensor, | ||
| hidden_states: torch.Tensor, | ||
| ) -> tuple[torch.Tensor, torch.Tensor]: | ||
|
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There was a problem hiding this comment. Will MM input support be added later? There was a problem hiding this comment. Yep that's in #20591 |
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| input_embeds = self.embed_tokens(input_ids) | ||
| hidden_states = self.fc( | ||
| torch.cat((input_embeds, hidden_states), dim=-1)) | ||
| residual = None | ||
| for layer in self.layers: | ||
| hidden_states, residual = layer( | ||
| positions, | ||
| hidden_states, | ||
| residual, | ||
| ) | ||
| hidden_states, _ = self.norm(hidden_states, residual) | ||
| return hidden_states, hidden_states | ||
|
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| def load_weights(self, weights: Iterable[tuple[str, | ||
| torch.Tensor]]) -> set[str]: | ||
| stacked_params_mapping = [ | ||
| # (param_name, shard_name, shard_id) | ||
| (".qkv_proj", ".q_proj", "q"), | ||
| (".qkv_proj", ".k_proj", "k"), | ||
| (".qkv_proj", ".v_proj", "v"), | ||
| (".gate_up_proj", ".gate_proj", 0), | ||
| (".gate_up_proj", ".up_proj", 1), | ||
| ] | ||
| params_dict = dict(self.named_parameters()) | ||
| loaded_params: set[str] = set() | ||
| for name, loaded_weight in weights: | ||
| name = name.removeprefix("model.") | ||
| for param_name, weight_name, shard_id in stacked_params_mapping: | ||
| if weight_name not in name: | ||
| continue | ||
| name = name.replace(weight_name, param_name) | ||
| param = params_dict[name] | ||
| weight_loader = param.weight_loader | ||
| weight_loader(param, loaded_weight, shard_id) | ||
| break | ||
| else: | ||
| # if PP disabled then draft will share embed with target | ||
| if get_pp_group().world_size == 1 and \ | ||
| "embed_tokens." in name: | ||
| continue | ||
| param = params_dict[name] | ||
| weight_loader = getattr(param, "weight_loader", | ||
| default_weight_loader) | ||
| weight_loader(param, loaded_weight) | ||
| loaded_params.add(name) | ||
| for name in params_dict: | ||
| # if PP disabled then draft will share embed with target | ||
| if get_pp_group().world_size == 1 and \ | ||
| "embed_tokens." in name: | ||
| continue | ||
| assert name in loaded_params, f"{name} is not loaded!" | ||
| return loaded_params | ||
|
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| def validate_and_update_config( | ||
| self, | ||
| start_layer_id: int, | ||
| quant_config: Optional[QuantizationConfig] = None) -> None: | ||
| # yoco and moe is not supported by draft model yet | ||
| assert self.config.yoco_global_kv_layer is None | ||
| assert self.config.yoco_local_kv_layer is None | ||
| assert len(self.config.moe_layers) == 0 | ||
| # draft model layer index is increased by start_layer_id, | ||
| # so we need to pad relevant configs accordingly | ||
| self.config.no_rope_layers = [ | ||
| 0 | ||
| ] * start_layer_id + self.config.no_rope_layers | ||
| # currently only TorchAO quantization is supported | ||
| if isinstance(quant_config, TorchAOConfig): | ||
|
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| def pad_layer_name(layer: str) -> str: | ||
| layer_index = extract_layer_index(layer) | ||
| return layer.replace(str(layer_index), | ||
| str(layer_index + start_layer_id)) | ||
|
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| quant_config.torchao_config.module_fqn_to_config = { | ||
| pad_layer_name(layer): quantization | ||
| for layer, quantization in | ||
| quant_config.torchao_config.module_fqn_to_config.items() | ||
| } | ||
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| class EagleLlama4ForCausalLM(Llama4ForCausalLM): | ||
|
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| def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): | ||
| nn.Module.__init__(self) | ||
|
There was a problem hiding this comment. The To improve maintainability, consider either using composition over inheritance or ensuring a proper call to |
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| self.config = ( | ||
| vllm_config.speculative_config.draft_model_config.hf_config) | ||
| target_layer_num = vllm_config.model_config.get_num_layers( | ||
| vllm_config.parallel_config) | ||
| # draft model quantization config may differ from target model | ||
| quant_config = VllmConfig.get_quantization_config( | ||
| vllm_config.speculative_config.draft_model_config, | ||
| vllm_config.load_config) | ||
| self.model = LlamaModel(vllm_config=vllm_config, | ||
| prefix="model", | ||
| start_layer_id=target_layer_num, | ||
| quant_config=quant_config) | ||
| logit_scale = getattr(self.config, "logit_scale", 1.0) | ||
| self.logits_processor = LogitsProcessor(self.config.vocab_size, | ||
| scale=logit_scale) | ||
|
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| def forward( | ||
| self, | ||
| input_ids: torch.Tensor, | ||
| positions: torch.Tensor, | ||
| hidden_states: torch.Tensor, | ||
| ) -> tuple[torch.Tensor, torch.Tensor]: | ||
| return self.model(input_ids, positions, hidden_states) | ||
|
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| def load_weights(self, weights: Iterable[tuple[str, | ||
| torch.Tensor]]) -> None: | ||
| loader = AutoWeightsLoader( | ||
| self, | ||
| # lm_head is tied with target model (Llama4ForCausalLM) | ||
| skip_prefixes=(["lm_head."]), | ||
| ) | ||
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| model_weights = {} | ||
| weights = [ | ||
| self.permute_qk_weight_for_rotary(name, loaded_weight) | ||
| for name, loaded_weight in weights | ||
| ] | ||
| for name, loaded_weight in weights: | ||
| if "lm_head" not in name: | ||
| name = "model." + name | ||
| model_weights[name] = loaded_weight | ||
|
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| loader.load_weights(model_weights.items()) | ||
|
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There was a problem hiding this comment. This block of code for processing weights is not memory-efficient. It first creates a list of all permuted weights, and then a dictionary of these weights, both of which can consume a large amount of memory for large models. A more memory-efficient approach is to use a generator to process the weights one by one. This avoids loading all weights into memory at once. def _processed_weights():
for name, loaded_weight in weights:
name, loaded_weight = self.permute_qk_weight_for_rotary(
name, loaded_weight)
if "lm_head" not in name:
name = "model." + name
yield name, loaded_weight
loader.load_weights(_processed_weights()) |
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is this needed due to OOM issue?
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I think this might not be needed, it is just from our internal test, though I think default is smaller than this?Yes this is for OOM as original length with BF16 will be too big