596 lines
22 KiB
Python
596 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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#
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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 LoopCoder model compatible with HuggingFace weights."""
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from __future__ import annotations
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from collections.abc import Iterable
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from dataclasses import replace
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from typing import Any
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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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, VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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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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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.model_executor.models.llama import LlamaMLP
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from vllm.sequence import IntermediateTensors
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from vllm.v1.attention.backend import AttentionType
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from .utils import (
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AutoWeightsLoader,
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extract_layer_index,
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make_layers,
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maybe_prefix,
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)
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class LoopCoderAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER,
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dual_chunk_attention_config: dict[str, Any] | None = None,
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layer_idx: int = 0,
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) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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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 = self.head_dim**-0.5
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self.dual_chunk_attention_config = dual_chunk_attention_config
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# Get loop_num from config, default to 2 if not specified
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self.loop_num = getattr(config, "loop_num", 2)
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self.loop_window_size = getattr(config, "loop_window_size", 64)
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# Use total number of hidden layers instead of hardcoded 24
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total_layers = config.num_hidden_layers
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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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max_position=max_position,
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rope_parameters=config.rope_parameters,
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dual_chunk_attention_config=dual_chunk_attention_config,
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)
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self.attn = nn.ModuleList()
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base_cache_config = cache_config
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for loop_idx in range(self.loop_num):
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base_layer_idx = extract_layer_index(prefix)
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unique_layer_idx = loop_idx * total_layers + base_layer_idx
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unique_prefix = prefix.replace(
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f"layers.{base_layer_idx}", f"layers.{unique_layer_idx}"
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)
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if loop_idx == 0:
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loop_cache_config = cache_config
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else:
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if base_cache_config is not None:
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loop_cache_config = replace(
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base_cache_config,
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sliding_window=self.loop_window_size,
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)
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else:
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loop_cache_config = CacheConfig(
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sliding_window=self.loop_window_size,
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cache_dtype="auto",
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)
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self.attn.append(
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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=loop_cache_config,
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quant_config=quant_config,
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attn_type=attn_type,
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prefix=f"{unique_prefix}.attn",
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**{
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"layer_idx": unique_layer_idx,
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"dual_chunk_attention_config": dual_chunk_attention_config,
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}
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if dual_chunk_attention_config and loop_idx == 0
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else {},
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)
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)
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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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loop_idx: int,
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gate_proj: LoopGateProjection | None = None,
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) -> torch.Tensor:
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if loop_idx == 0:
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attn = self.attn[0]
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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 = attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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else:
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global_attn = self.attn[0]
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local_attn = self.attn[loop_idx]
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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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num_tokens, _ = q.shape
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num_heads = self.num_heads
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head_dim = self.head_dim
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q_reshaped = q.view(num_tokens, num_heads, head_dim).transpose(0, 1)
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global_attn_output = global_attn(q, None, None)
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local_attn_output = local_attn(q, k, v)
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assert gate_proj is not None, "gate_proj must be provided for loop_idx > 0"
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gate = gate_proj(q_reshaped)
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output = global_attn_output * gate + local_attn_output * (1 - gate)
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output, _ = self.o_proj(output)
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return output
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class LoopCoderDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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layer_idx: int = 0,
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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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dual_chunk_attention_config = getattr(
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config, "dual_chunk_attention_config", None
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)
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self.layer_idx = layer_idx
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if getattr(config, "is_causal", True):
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attn_type = AttentionType.DECODER
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else:
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attn_type = AttentionType.ENCODER_ONLY
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self.self_attn = LoopCoderAttention(
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config=config,
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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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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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attn_type=attn_type,
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dual_chunk_attention_config=dual_chunk_attention_config,
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layer_idx=self.layer_idx,
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)
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self.mlp = LlamaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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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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loop_idx: int,
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gate_proj: LoopGateProjection | None = None,
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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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loop_idx=loop_idx,
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gate_proj=gate_proj,
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)
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hidden_states = hidden_states + residual
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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.mlp(hidden_states)
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hidden_states = hidden_states + residual
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return hidden_states
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class LoopGateProjection(nn.Module):
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"""Gate projection for mixed attention in Loop 2+.
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Computes: g = sigmoid(linear(Q)) for each head independently.
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This gate determines how much to use Loop1's KV (global) vs current
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loop's KV (local).
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Supports tensor parallelism: each GPU handles a subset of heads.
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The weight matrix has shape [num_heads, head_dim] and is split along
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the head dimension.
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"""
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def __init__(
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self,
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total_num_heads: int,
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head_dim: int,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.total_num_heads = total_num_heads
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self.head_dim = head_dim
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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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self.gate_proj = ColumnParallelLinear(
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head_dim,
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self.total_num_heads,
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bias=True,
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gather_output=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_proj",
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)
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def forward(self, query: torch.Tensor) -> torch.Tensor:
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"""Compute gate values from query tensor.
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Args:
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query: [num_heads, num_tokens, head_dim] (vLLM flattened format)
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where num_heads is the number of heads on this TP rank
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and num_tokens = batch * seq_len
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Returns:
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gate: [num_tokens, num_heads * head_dim] (flattened format matching q shape)
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"""
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num_heads, num_tokens, head_dim = query.shape
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assert num_heads == self.num_heads, (
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f"Expected {self.num_heads} heads, got {num_heads}"
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)
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query_flat = query.reshape(-1, head_dim)
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gate_logits_flat, _ = self.gate_proj(query_flat)
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gate_logits = gate_logits_flat.reshape(
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num_heads, num_tokens, self.num_heads
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) # [num_heads, num_tokens, num_heads]
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# Extract diagonal: each head h's query should use output column h
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# gate_logits[h, :, h] gives the output for head h at each token
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gate_logits = torch.diagonal(
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gate_logits, dim1=0, dim2=2
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) # [num_tokens, num_heads]
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gate_logits = gate_logits.transpose(0, 1) # [num_heads, num_tokens]
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gate_logits = gate_logits.unsqueeze(-1) # [num_heads, num_tokens, 1]
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# Apply sigmoid
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gate = torch.sigmoid(gate_logits) # [num_heads, num_tokens, 1]
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# Expand and reshape to match q shape: [num_tokens, num_heads * head_dim]
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gate = gate.transpose(0, 1) # [num_tokens, num_heads, 1]
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gate = gate.expand(-1, -1, head_dim) # [num_tokens, num_heads, head_dim]
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gate = gate.reshape(
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num_tokens, num_heads * head_dim
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) # [num_tokens, num_heads * head_dim]
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return gate
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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}
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)
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class IQuestLoopCoderModel(nn.Module):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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decoder_layer_type: type[nn.Module] = LoopCoderDecoderLayer,
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):
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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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# TODO (@robertgshaw2): see if this can be moved out
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if cache_config.sliding_window is not None and hasattr(
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config, "max_window_layers"
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):
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assert config.max_window_layers == config.num_hidden_layers, (
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"Sliding window for some but all layers is not supported. "
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"This model uses sliding window but `max_window_layers` = {} "
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"is less than `num_hidden_layers` = {}. Please open an issue "
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"to discuss this feature.".format(
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config.max_window_layers,
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config.num_hidden_layers,
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)
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)
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self.config = config
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self.quant_config = quant_config
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.embed_tokens",
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)
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self.loop_num = getattr(self.config, "loop_num", 2)
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self.window_size = getattr(self.config, "loop_window_size", 64)
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# Gate projections for Loop 2+ (one per layer)
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head_dim = config.hidden_size // config.num_attention_heads
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_, _, self.gate_projections = make_layers(
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config.num_hidden_layers,
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lambda prefix: LoopGateProjection(
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total_num_heads=config.num_attention_heads,
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head_dim=head_dim,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.gate_projections",
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: LoopCoderDecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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layer_idx=extract_layer_index(prefix),
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),
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prefix=f"{prefix}.layers",
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)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def embed_input_ids(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: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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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.embed_input_ids(input_ids)
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for loop_idx in range(self.loop_num):
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for layer_idx, layer in enumerate(
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self.layers[self.start_layer : self.end_layer]
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):
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# Get the actual layer index (accounting for pipeline parallelism)
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actual_layer_idx = self.start_layer + layer_idx
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# Get gate_proj for this layer (only for loop_idx > 0)
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gate_proj = (
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self.gate_projections[actual_layer_idx] if loop_idx > 0 else None
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)
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hidden_states = layer(positions, hidden_states, loop_idx, gate_proj)
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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, 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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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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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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):
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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", default_weight_loader)
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loaded_weight = (
|
|
loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(scale_name)
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if "gate_projections" in name:
|
|
continue
|
|
if weight_name not 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
|
|
if name.endswith("scale"):
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
if weight_loader == default_weight_loader:
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
if name.startswith("gate_projections."):
|
|
if name.endswith(".weight"):
|
|
vllm_name = name.replace(".weight", ".gate_proj.weight")
|
|
elif name.endswith(".bias"):
|
|
vllm_name = name.replace(".bias", ".gate_proj.bias")
|
|
else:
|
|
continue
|
|
|
|
if vllm_name in params_dict:
|
|
param = params_dict[vllm_name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(vllm_name)
|
|
continue
|
|
continue
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
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
|
|
|
|
|
|
class IQuestLoopCoderForCausalLM(nn.Module):
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.config = config
|
|
|
|
self.quant_config = quant_config
|
|
self.model = IQuestLoopCoderModel(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
|
|
if config.tie_word_embeddings:
|
|
self.lm_head = self.model.embed_tokens
|
|
else:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.embed_input_ids(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
hidden_states = self.model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds
|
|
)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
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,
|
|
skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights)
|