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302 lines
11 KiB
Python
302 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""PyTorch MAMBA2 model."""
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from collections.abc import Iterable
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import torch
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from torch import nn
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from transformers import MambaConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, ModelConfig, VllmConfig
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateDtypeCalculator,
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MambaStateShapeCalculator,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import HasInnerState, IsAttentionFree
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from vllm.sequence import IntermediateTensors
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from .utils import (
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AutoWeightsLoader,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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KVCache = tuple[torch.Tensor, torch.Tensor]
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class Mamba2DecoderLayer(nn.Module):
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def __init__(
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self,
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config: MambaConfig,
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model_config: ModelConfig | None = None,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.mixer = MambaMixer2(
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hidden_size=config.hidden_size,
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ssm_state_size=config.state_size,
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conv_kernel_size=config.conv_kernel,
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intermediate_size=getattr(
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config, "intermediate_size", config.expand * config.hidden_size
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),
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use_conv_bias=config.use_conv_bias,
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use_bias=config.use_bias,
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n_groups=config.n_groups,
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num_heads=config.num_heads,
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head_dim=config.head_dim,
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rms_norm_eps=config.layer_norm_epsilon,
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activation=config.hidden_act,
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model_config=model_config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.mixer",
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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def forward(
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self,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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**kwargs,
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):
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if residual is None:
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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else:
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hidden_states, residual = self.norm(hidden_states, residual)
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output = torch.empty_like(hidden_states)
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self.mixer(hidden_states, output)
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return output, residual
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@support_torch_compile
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class Mamba2Model(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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model_config = vllm_config.model_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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is_lora_enabled = bool(lora_config)
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assert not is_lora_enabled
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self.config = config
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lora_vocab = (
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(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
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if lora_config
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else 0
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)
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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self.embeddings = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: Mamba2DecoderLayer(
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config,
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model_config=model_config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers",
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)
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self.norm_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i, layer in enumerate(self.layers):
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hidden_states, residual = layer(
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positions=positions, hidden_states=hidden_states, residual=residual
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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hidden_states, _ = self.norm_f(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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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 "A_log" in name:
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name = name.replace("A_log", "A")
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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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", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree):
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@classmethod
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def get_mamba_state_dtype_from_config(
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cls,
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vllm_config: "VllmConfig",
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) -> tuple[torch.dtype, torch.dtype]:
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return MambaStateDtypeCalculator.mamba2_state_dtype(
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vllm_config.model_config.dtype,
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vllm_config.cache_config.mamba_cache_dtype,
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vllm_config.cache_config.mamba_ssm_cache_dtype,
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)
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@classmethod
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def get_mamba_state_shape_from_config(
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cls,
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vllm_config: "VllmConfig",
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) -> tuple[tuple[int, int], tuple[int, int, int]]:
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"""Calculate shapes for Mamba's convolutional and state caches.
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Args:
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vllm_config: vLLM config
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Returns:
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Tuple containing:
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- conv_state_shape: Shape for convolutional state cache
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- temporal_state_shape: Shape for state space model cache
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"""
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parallel_config = vllm_config.parallel_config
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hf_config = vllm_config.model_config.hf_config
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intermediate_size = hf_config.expand * hf_config.hidden_size
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return MambaStateShapeCalculator.mamba2_state_shape(
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intermediate_size=intermediate_size,
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tp_world_size=parallel_config.tensor_parallel_size,
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n_groups=hf_config.n_groups,
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num_heads=hf_config.num_heads,
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head_dim=hf_config.head_dim,
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state_size=hf_config.state_size,
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conv_kernel=hf_config.conv_kernel,
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)
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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config = vllm_config.model_config.hf_config
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lora_config = vllm_config.lora_config
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scheduler_config = vllm_config.scheduler_config
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super().__init__()
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self.config = config
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self.vllm_config = vllm_config
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self.scheduler_config = scheduler_config
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self.model_config = vllm_config.model_config
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self.backbone = Mamba2Model(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "backbone")
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)
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config
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else lora_config.lora_vocab_padding_size,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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if config.tie_word_embeddings:
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self.lm_head = self.lm_head.tie_weights(self.backbone.embeddings)
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self.logits_processor = LogitsProcessor(
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self.unpadded_vocab_size, config.vocab_size
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)
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self.make_empty_intermediate_tensors = (
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self.backbone.make_empty_intermediate_tensors
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.backbone.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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**kwargs,
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):
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hidden_states = self.backbone(
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input_ids, positions, intermediate_tensors, inputs_embeds
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)
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return hidden_states
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def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
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return self.mamba_cache.copy_inputs_before_cuda_graphs(input_buffers, **kwargs)
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def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
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return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)
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def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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