542 lines
22 KiB
Python
542 lines
22 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only GraniteMoe model."""
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from collections.abc import Iterable
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from itertools import islice
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from typing import Any, Optional
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import torch
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from torch import nn
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from transformers.models.granitemoe import GraniteMoeConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, 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.quantization.base_config import (
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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 (
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default_weight_loader, maybe_remap_kv_scale_name)
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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 .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, is_pp_missing_parameter, make_layers,
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maybe_prefix)
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class GraniteMoeMoE(nn.Module):
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"""A tensor-parallel MoE implementation for GraniteMoe that shards each
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expert across all ranks.
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Each expert's weights are sharded across all ranks and a fused MoE
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kernel is used for the forward pass, and finally we reduce the outputs
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across ranks.
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"""
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def __init__(self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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tp_size: Optional[int] = None,
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prefix: str = ""):
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super().__init__()
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self.hidden_size = hidden_size
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# Gate always runs at half / full precision for now.
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self.gate = ReplicatedLinear(hidden_size,
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num_experts,
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bias=False,
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params_dtype=params_dtype,
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quant_config=None,
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prefix=f"{prefix}.gate")
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self.experts = FusedMoE(num_experts=num_experts,
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top_k=top_k,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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params_dtype=params_dtype,
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reduce_results=True,
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renormalize=True,
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quant_config=quant_config,
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tp_size=tp_size,
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prefix=f"{prefix}.experts")
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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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: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(hidden_states, router_logits)
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return final_hidden_states.view(orig_shape)
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class GraniteMoeAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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rope_scaling: Optional[dict[str, Any]] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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attention_multiplier: Optional[float] = 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 = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = 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 = (attention_multiplier if attention_multiplier
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is not None else self.head_dim**-1)
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self.rope_theta = rope_theta
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self.qkv_proj = QKVParallelLinear(
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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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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position,
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base=int(self.rope_theta),
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is_neox_style=True,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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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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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class GraniteMoeDecoderLayer(nn.Module):
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def __init__(
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self,
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config: GraniteMoeConfig,
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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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# Requires transformers > 4.32.0
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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self.self_attn = GraniteMoeAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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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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attention_multiplier=config.attention_multiplier)
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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.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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self.residual_multiplier = config.residual_multiplier
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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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# Self Attention
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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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hidden_states = self.block_sparse_moe(hidden_states)
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hidden_states = residual + hidden_states * self.residual_multiplier
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return hidden_states
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@support_torch_compile
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class GraniteMoeModel(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.quant_config = quant_config # Required by MixtralModel
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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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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: GraniteMoeDecoderLayer(
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config, cache_config, quant_config=quant_config, prefix=prefix
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),
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prefix=f"{prefix}.layers")
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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],
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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 *= self.embedding_multiplier
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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 layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states = layer(positions, hidden_states)
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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,
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weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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"""
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This function is copied from `MixtralModel.load_weights`, mainly to
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decouple from mixtral, avoiding impact on support like BNB
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quantization.
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"""
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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 for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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expert_params_mapping = FusedMoE.make_expert_params_mapping(
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ckpt_gate_proj_name="w1",
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ckpt_down_proj_name="w2",
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ckpt_up_proj_name="w3",
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num_experts=self.config.num_local_experts)
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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 (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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if name.endswith("scale"):
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param,
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loaded_weight,
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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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break
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else:
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# Skip loading extra bias for GPTQ models.
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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# Remapping the name of FP8 kv-scale.
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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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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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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new_weights = {}
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for n, p in weights:
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if n.endswith('.block_sparse_moe.input_linear.weight'):
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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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assert w1_name not in new_weights
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assert w3_name not in new_weights
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new_weights[w1_name] = w1_param
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new_weights[w3_name] = w3_param
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elif n.endswith('.block_sparse_moe.output_linear.weight'):
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for e in range(p.size(0)):
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w2_name = n.replace(
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'.block_sparse_moe.output_linear.weight',
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f".block_sparse_moe.experts.{e}.w2.weight")
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w2_param = p[e]
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assert w2_name not in new_weights
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new_weights[w2_name] = w2_param
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elif n.endswith('.block_sparse_moe.router.layer.weight'):
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gate_name = n.replace('.block_sparse_moe.router.layer.weight',
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".block_sparse_moe.gate.weight")
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assert gate_name not in new_weights
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new_weights[gate_name] = p
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else:
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new_weights[n] = p
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return self._load_weights(new_weights.items())
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class GraniteMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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fall_back_to_pt_during_load = False
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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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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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|
}
|
|
embedding_padding_modules = ["lm_head"]
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
lora_config = vllm_config.lora_config
|
|
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
|
|
self.model = GraniteMoeModel(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=quant_config,
|
|
)
|
|
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)
|
|
|
|
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,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def make_empty_intermediate_tensors(
|
|
self, batch_size: int, dtype: torch.dtype,
|
|
device: torch.device) -> IntermediateTensors:
|
|
return IntermediateTensors({
|
|
"hidden_states":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
"residual":
|
|
torch.zeros((batch_size, self.config.hidden_size),
|
|
dtype=dtype,
|
|
device=device),
|
|
})
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights)
|