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615 lines
25 KiB
Python
615 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only GraniteMoeHybrid model."""
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# Added by the IBM Team, 2025
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from collections.abc import Iterable
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from typing import Optional
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import torch
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from torch import nn
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from transformers import GraniteMoeHybridConfig
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from vllm.attention.layer import Attention
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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 import get_tensor_model_parallel_world_size
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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.linear import (QKVParallelLinear,
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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_mixer2 import MambaMixer2
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from vllm.model_executor.layers.mamba.mamba_utils import (
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MambaStateDtypeCalculator, MambaStateShapeCalculator)
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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.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.sequence import IntermediateTensors
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from .granitemoe import GraniteMoeMoE
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from .granitemoeshared import GraniteMoeSharedMLP
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from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
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SupportsQuant)
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from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class GraniteMoeHybridMambaDecoderLayer(nn.Module):
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def __init__(self,
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config: GraniteMoeHybridConfig,
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layer_idx: int,
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model_config: Optional[ModelConfig] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "") -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.residual_multiplier = config.residual_multiplier
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self.mamba = MambaMixer2(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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use_conv_bias = config.mamba_conv_bias,
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use_bias = config.mamba_proj_bias,
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n_groups=config.mamba_n_groups,
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num_heads=config.mamba_n_heads,
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head_dim=config.mamba_d_head,
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rms_norm_eps=config.rms_norm_eps,
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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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self.block_sparse_moe = None
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if getattr(config, "num_local_experts", 0) > 0:
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self.block_sparse_moe = GraniteMoeMoE(
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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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intermediate_size=config.intermediate_size,
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quant_config=quant_config,
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prefix=f"{prefix}.block_sparse_moe")
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self.shared_mlp = None if \
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getattr(config, 'shared_intermediate_size', 0) == 0 \
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else GraniteMoeSharedMLP(
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config,
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quant_config=quant_config,
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prefix=f"{prefix}.shared_mlp"
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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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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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**kwargs,
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):
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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output = torch.empty_like(hidden_states)
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self.mamba(hidden_states, output)
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hidden_states = residual + output * self.residual_multiplier
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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if self.shared_mlp is None:
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if self.block_sparse_moe is not None:
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hidden_states = self.block_sparse_moe(hidden_states)
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# else: skip
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else:
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# create a copy since block_sparse_moe modifies in-place
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if self.block_sparse_moe is not None:
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moe_hidden_states = hidden_states.clone()
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moe_hidden_states = self.block_sparse_moe(moe_hidden_states)
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hidden_states = moe_hidden_states + self.shared_mlp(
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hidden_states)
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del moe_hidden_states
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else:
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hidden_states = self.shared_mlp(hidden_states)
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hidden_states = residual + hidden_states * self.residual_multiplier
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return hidden_states, residual
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class GraniteMoeHybridAttentionDecoderLayer(nn.Module):
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def __init__(
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self,
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config: GraniteMoeHybridConfig,
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layer_idx: int,
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model_config: Optional[ModelConfig] = None,
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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.hidden_size = config.hidden_size
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self.residual_multiplier = config.residual_multiplier
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self.self_attn = GraniteMoeHybridAttention(
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config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn")
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self.block_sparse_moe = None
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if getattr(config, "num_local_experts", 0) > 0:
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self.block_sparse_moe = GraniteMoeMoE(
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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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intermediate_size=config.intermediate_size,
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quant_config=quant_config,
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prefix=f"{prefix}.block_sparse_moe")
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self.shared_mlp = None if \
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getattr(config, 'shared_intermediate_size', 0) == 0 \
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else GraniteMoeSharedMLP(
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config,
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quant_config=quant_config,
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prefix=f"{prefix}.shared_mlp"
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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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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states = residual + hidden_states * self.residual_multiplier
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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if self.shared_mlp is None:
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if self.block_sparse_moe is not None:
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hidden_states = self.block_sparse_moe(hidden_states)
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# else: skip
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else:
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# create a copy since block_sparse_moe modifies in-place
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if self.block_sparse_moe is not None:
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moe_hidden_states = hidden_states.clone()
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moe_hidden_states = self.block_sparse_moe(moe_hidden_states)
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hidden_states = moe_hidden_states + self.shared_mlp(
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hidden_states)
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del moe_hidden_states
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else:
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hidden_states = self.shared_mlp(hidden_states)
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hidden_states = residual + hidden_states * self.residual_multiplier
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return hidden_states, residual
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class GraniteMoeHybridAttention(nn.Module):
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def __init__(
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self,
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config: GraniteMoeHybridConfig,
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model_config: Optional[ModelConfig] = None,
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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.causal = True
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self.hidden_size = config.hidden_size
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self.attention_bias = config.attention_bias
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self.attention_multiplier = config.attention_multiplier
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self.total_num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.total_num_heads
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self.total_num_kv_heads = config.num_key_value_heads
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# TensorParallel logic
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tp_size = get_tensor_model_parallel_world_size()
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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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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_key_value_heads = max(1, self.total_num_kv_heads // tp_size)
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self.qkv_proj = QKVParallelLinear(self.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=self.attention_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj")
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self.o_proj = RowParallelLinear(self.hidden_size,
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self.hidden_size,
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bias=self.attention_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj")
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if config.position_embedding_type == "rope":
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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=config.max_position_embeddings,
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base=int(config.rope_theta),
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rope_scaling=config.rope_scaling \
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if hasattr(config, "rope_scaling") \
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and config.rope_scaling is not None else None,
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is_neox_style=True,
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)
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else:
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self.rotary_emb = None
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.attention_multiplier,
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num_kv_heads=self.num_key_value_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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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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query, key, value = qkv.split([
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self.num_heads * self.head_dim, self.num_key_value_heads *
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self.head_dim, self.num_key_value_heads * self.head_dim
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],
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dim=-1)
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if self.rotary_emb is not None:
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query, key = self.rotary_emb(positions, query, key)
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hidden_states = self.attn(query, key, value)
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del query, key, value
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hidden_states = self.o_proj(hidden_states)[0]
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return hidden_states
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ALL_DECODER_LAYER_TYPES = {
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"attention": GraniteMoeHybridAttentionDecoderLayer,
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"mamba": GraniteMoeHybridMambaDecoderLayer,
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}
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@support_torch_compile
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class GraniteMoeHybridModel(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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self.config = config
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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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self.embedding_multiplier = config.embedding_multiplier
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def get_layer(prefix: str):
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layer_idx = int(prefix.rsplit(".", 1)[1])
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layer_class = ALL_DECODER_LAYER_TYPES[
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config.layer_types[layer_idx]]
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return layer_class(
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config,
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layer_idx,
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model_config,
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cache_config,
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quant_config=quant_config,
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prefix=prefix,
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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, get_layer, prefix=f"{prefix}.layers")
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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self.norm = RMSNorm(config.hidden_size, 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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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = 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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hidden_states = hidden_states * self.embedding_multiplier
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residual = None
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else:
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if intermediate_tensors is None:
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raise RuntimeError('Intermediate tensors may not be None!')
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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num_attn = 0
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for i, layer in enumerate(self.layers):
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if isinstance(layer, GraniteMoeHybridAttentionDecoderLayer):
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num_attn += 1
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hidden_states, residual = layer(positions=positions,
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hidden_states=hidden_states,
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residual=residual)
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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,
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"residual": residual
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})
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hidden_states = self.norm(hidden_states)
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return hidden_states
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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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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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def _load(n, p):
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param = params_dict[n]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, p)
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loaded_params.add(n)
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def _load_shard(n, p, shard_id):
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# Skip layers on other devices.
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if not is_pp_missing_parameter(n, self):
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param = params_dict[n]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, p, shard_id)
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loaded_params.add(n)
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def _load_expert(n, p, name, shard_id, expert_id):
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param = params_dict[n]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param,
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p,
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name,
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shard_id=shard_id,
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expert_id=expert_id)
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loaded_params.add(n)
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for n, p in weights:
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if "A_log" in n:
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n = n.replace("A_log", "A")
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# Logic analogous to: https://github.com/vllm-project/vllm/blob/f49e5aff11c986ed4d45202b1716c5d74786efa9/vllm/model_executor/models/granitemoeshared.py#L215
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# Mapping different experts' layout:
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# from HF (input_linear, output_linear, router)
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# to vLLM (experts_w13({e}.w1, {e}.w2), experts_w3({e}.w3), gate)
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# The renaming and parameter loading logic is the same for weight
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# and weight_scale tensors so we can reuse them without issues.
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if (n.endswith('.block_sparse_moe.input_linear.weight') or
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n.endswith('.block_sparse_moe.input_linear.weight_scale')):
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for e in range(p.size(0)):
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w1_name = n.replace(
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'.block_sparse_moe.input_linear.weight',
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f".block_sparse_moe.experts.{e}.w1.weight")
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w3_name = n.replace(
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'.block_sparse_moe.input_linear.weight',
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f".block_sparse_moe.experts.{e}.w3.weight")
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w1_param, w3_param = p[e].chunk(2, dim=0)
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_load_expert(n.replace('.input_linear.', '.experts.w13_'),
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w1_param,
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w1_name,
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shard_id='w1',
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expert_id=e)
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_load_expert(n.replace('.input_linear.', '.experts.w13_'),
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w3_param,
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w3_name,
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shard_id='w3',
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expert_id=e)
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elif (n.endswith('.block_sparse_moe.output_linear.weight') or
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n.endswith('.block_sparse_moe.output_linear.weight_scale')):
|
|
for e in range(p.size(0)):
|
|
w2_name = n.replace(
|
|
'.block_sparse_moe.output_linear.weight',
|
|
f".block_sparse_moe.experts.{e}.w2.weight")
|
|
w2_param = p[e]
|
|
_load_expert(n.replace('.output_linear.', '.experts.w2_'),
|
|
w2_param,
|
|
w2_name,
|
|
shard_id='w2',
|
|
expert_id=e)
|
|
elif n.endswith('.block_sparse_moe.router.layer.weight'):
|
|
gate_name = n.replace('.block_sparse_moe.router.layer.weight',
|
|
".block_sparse_moe.gate.weight")
|
|
_load(gate_name, p)
|
|
else:
|
|
loaded = False
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name in n:
|
|
_load_shard(n.replace(weight_name, param_name),
|
|
p,
|
|
shard_id=shard_id)
|
|
loaded = True
|
|
if not loaded:
|
|
_load(n, p)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
|
|
SupportsPP, IsHybrid, SupportsQuant):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
}
|
|
embedding_modules = {
|
|
"embed_tokens": "input_embeddings",
|
|
"lm_head": "output_embeddings",
|
|
}
|
|
embedding_padding_modules = ["lm_head"]
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
) -> tuple[torch.dtype, torch.dtype]:
|
|
|
|
return MambaStateDtypeCalculator.mamba2_state_dtype(
|
|
vllm_config.model_config.dtype,
|
|
vllm_config.cache_config.mamba_cache_dtype,
|
|
vllm_config.cache_config.mamba_ssm_cache_dtype,
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(
|
|
cls,
|
|
vllm_config: "VllmConfig",
|
|
) -> tuple[tuple[int, int], tuple[int, int, int]]:
|
|
"""Calculate shapes for Mamba's convolutional and state caches.
|
|
|
|
Args:
|
|
vllm_config: vLLM config
|
|
|
|
Returns:
|
|
Tuple containing:
|
|
- conv_state_shape: Shape for convolutional state cache
|
|
- temporal_state_shape: Shape for state space model cache
|
|
"""
|
|
parallel_config = vllm_config.parallel_config
|
|
hf_config = vllm_config.model_config.hf_config
|
|
intermediate_size = hf_config.mamba_expand * hf_config.hidden_size
|
|
|
|
return MambaStateShapeCalculator.mamba2_state_shape(
|
|
intermediate_size=intermediate_size,
|
|
tp_world_size=parallel_config.tensor_parallel_size,
|
|
n_groups=hf_config.mamba_n_groups,
|
|
num_heads=hf_config.mamba_n_heads,
|
|
head_dim=hf_config.mamba_d_head,
|
|
state_size=hf_config.mamba_d_state,
|
|
conv_kernel=hf_config.mamba_d_conv,
|
|
)
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_config
|
|
self.vllm_config = vllm_config
|
|
self.model_config = vllm_config.model_config
|
|
cache_config = vllm_config.cache_config
|
|
lora_config = vllm_config.lora_config
|
|
scheduler_config = vllm_config.scheduler_config
|
|
if cache_config.enable_prefix_caching:
|
|
raise RuntimeError(
|
|
"GraniteMoeHybrid currently does not support prefix caching")
|
|
|
|
self.quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.scheduler_config = scheduler_config
|
|
self.model = GraniteMoeHybridModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(
|
|
prefix, "model"))
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
|
# We need bigger padding if using lora for kernel
|
|
# compatibility
|
|
if not lora_config else lora_config.lora_vocab_padding_size,
|
|
quant_config=self.quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"))
|
|
if config.tie_word_embeddings:
|
|
self.lm_head.weight = self.model.embed_tokens.weight
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size,
|
|
scale=1 /
|
|
self.config.logits_scaling)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs):
|
|
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|