549 lines
22 KiB
Python
549 lines
22 KiB
Python
"""Inference-only Jamba model."""
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from typing import 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 JambaConfig
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.attention.layer import Attention
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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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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.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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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.mamba_cache import (MambaCacheManager,
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MambaCacheParams)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from vllm.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
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_get_graph_batch_size)
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from .interfaces import HasInnerState, SupportsLoRA
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from .utils import maybe_prefix
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class JambaMoE(nn.Module):
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def __init__(self,
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config: JambaConfig,
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num_experts: Optional[int] = None,
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top_k: Optional[int] = None,
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params_dtype: Optional[torch.dtype] = None,
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tp_size: Optional[int] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.num_total_experts = num_experts or config.num_experts
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self.top_k = top_k or config.num_experts_per_tok
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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if self.num_total_experts > 1:
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self.router = ReplicatedLinear(self.hidden_size,
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self.num_total_experts,
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bias=False,
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quant_config=None,
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params_dtype=params_dtype)
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self.experts = FusedMoE(self.num_total_experts,
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self.top_k,
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self.hidden_size,
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self.intermediate_size,
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tp_size=tp_size,
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params_dtype=params_dtype,
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reduce_results=True,
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renormalize=False,
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use_grouped_topk=False,
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quant_config=quant_config)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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orig_shape = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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# router_logits: (batch * sequence_length, n_experts)
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if self.num_total_experts > 1:
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router_logits, _ = self.router(hidden_states)
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else:
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router_logits = torch.ones((hidden_states.shape[0], 1),
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device=hidden_states.device,
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dtype=hidden_states.dtype)
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hidden_states = self.experts(hidden_states, router_logits)
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return hidden_states.view(orig_shape)
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class JambaMLP(JambaMoE):
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def __init__(self,
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config: JambaConfig,
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params_dtype: Optional[torch.dtype] = None,
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tp_size: Optional[int] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__(config,
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num_experts=1,
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top_k=1,
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params_dtype=params_dtype,
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tp_size=tp_size,
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quant_config=quant_config)
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class JambaMambaDecoderLayer(nn.Module):
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def __init__(self,
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config: JambaConfig,
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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None) -> None:
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super().__init__()
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self.config = config
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self.mamba = MambaMixer(hidden_size= config.hidden_size,
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ssm_state_size = config.mamba_d_state,
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conv_kernel_size = config.mamba_d_conv,
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intermediate_size = config.mamba_expand *\
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config.hidden_size,
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time_step_rank = config.mamba_dt_rank,
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use_conv_bias = config.mamba_conv_bias,
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use_bias = config.mamba_proj_bias,
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use_rms_norm=True,
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rms_norm_eps=config.rms_norm_eps,
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activation=config.hidden_act)
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num_experts = config.layers_num_experts[layer_idx]
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ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
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self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
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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.pre_ff_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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hidden_states: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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mamba_cache_params: MambaCacheParams,
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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.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.mamba(hidden_states, attn_metadata,
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mamba_cache_params)
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# Fully Connected
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hidden_states, residual = self.pre_ff_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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class JambaAttentionDecoderLayer(nn.Module):
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def __init__(
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self,
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config: JambaConfig,
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layer_idx: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_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 = config.num_key_value_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.hidden_size // self.total_num_heads
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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.qkv_proj = QKVParallelLinear(
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config.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
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config.hidden_size,
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bias=False,
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quant_config=quant_config)
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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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)
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num_experts = config.layers_num_experts[layer_idx]
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ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
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self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
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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.pre_ff_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def self_attention(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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**kwargs,
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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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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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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.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_attention(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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# Fully Connected
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hidden_states, residual = self.pre_ff_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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ALL_DECODER_LAYER_TYPES = {
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"attention": JambaAttentionDecoderLayer,
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"mamba": JambaMambaDecoderLayer
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}
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class JambaModel(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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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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self.config = config
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self.padding_idx = config.pad_token_id
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lora_vocab = ((lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0)
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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.embed_tokens = 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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decoder_layers = []
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for i in range(config.num_hidden_layers):
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layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
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decoder_layers.append(
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layer_class(config,
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layer_idx=i,
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cache_config=cache_config,
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quant_config=quant_config))
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self.layers = nn.ModuleList(decoder_layers)
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self.final_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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mamba_cache_params: MambaCacheParams,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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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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for i in range(len(self.layers)):
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layer = self.layers[i]
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kv_cache = None
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layer_mamba_cache_params = None
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if isinstance(layer, JambaAttentionDecoderLayer):
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kv_cache = kv_caches[(i - self.config.attn_layer_offset) //
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self.config.attn_layer_period]
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if isinstance(layer, JambaMambaDecoderLayer):
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current_state_layer = i - (1 +
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(i - self.config.attn_layer_offset)
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// self.config.attn_layer_period)
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layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
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current_state_layer)
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hidden_states, residual = layer(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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residual=residual,
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mamba_cache_params=layer_mamba_cache_params)
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hidden_states, _ = self.final_layernorm(hidden_states, residual)
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return hidden_states
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class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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}
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# LoRA specific attributes
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supported_lora_modules = [
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"qkv_proj",
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"o_proj",
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"embed_tokens",
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"lm_head",
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]
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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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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cache_config = vllm_config.cache_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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assert not cache_config.enable_prefix_caching, \
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"Jamba currently does not support prefix caching"
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super().__init__()
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self.config = config
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self.scheduler_config = scheduler_config
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self.model = JambaModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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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 else lora_config.lora_vocab_padding_size,
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)
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# Used to track and store by the Mamba cache between steps.
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self.mamba_cache: Optional[MambaCacheManager] = None
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size)
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self.sampler = get_sampler()
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[KVCache],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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**kwargs):
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if self.mamba_cache is None:
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max_batch_size = (_get_graph_batch_size(
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self.scheduler_config.max_num_seqs) if self.scheduler_config
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else max(_BATCH_SIZES_TO_CAPTURE) + 2)
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layers_type = self.config.layers_block_type
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num_mamba_layers = sum(
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[layer_type == "mamba" for layer_type in layers_type])
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self.mamba_cache = MambaCacheManager(
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self.lm_head.weight.dtype, num_mamba_layers, max_batch_size,
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*self._get_mamba_cache_shape())
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(
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mamba_cache_tensors,
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state_indices_tensor,
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) = self.mamba_cache.current_run_tensors(input_ids, attn_metadata,
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**kwargs)
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mamba_cache_params = MambaCacheParams(mamba_cache_tensors[0],
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mamba_cache_tensors[1],
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state_indices_tensor)
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, mamba_cache_params,
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inputs_embeds)
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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(
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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 _get_mamba_cache_shape(
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self) -> Tuple[Tuple[int, int], Tuple[int, int]]:
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world_size = get_tensor_model_parallel_world_size()
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hidden_size = self.config.hidden_size
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conv_state_shape = (
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self.config.mamba_expand * hidden_size // world_size,
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self.config.mamba_d_conv - 1,
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)
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temporal_state_shape = (
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self.config.mamba_expand * hidden_size // world_size,
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self.config.mamba_d_state,
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)
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return conv_state_shape, temporal_state_shape
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: Optional[torch.Tensor],
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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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)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: Set[str] = set()
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
|
|
if "A_log" in name:
|
|
name = name.replace("A_log", "A")
|
|
|
|
if ".self_attn." in name:
|
|
name = name.replace(".self_attn", "")
|
|
|
|
if "feed_forward" in name and not _is_moe_layer(name):
|
|
## map MLP layers to expert with ID=0
|
|
name = name.replace("feed_forward", "feed_forward.experts.0")
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
if 'experts' in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
for (
|
|
param_name,
|
|
weight_name,
|
|
expert_id,
|
|
shard_id,
|
|
) in expert_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,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
def _is_moe_layer(name: str):
|
|
return any(
|
|
[experts_name in name for experts_name in [
|
|
"experts",
|
|
"router",
|
|
]])
|