1446 lines
52 KiB
Python
1446 lines
52 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2026 MBZUAI-IFM.
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# Copyright 2024 The Qwen team.
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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 K2Horizon model compatible with HuggingFace weights."""
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import math
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import typing
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from collections.abc import Callable, Iterable
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from itertools import islice
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from typing import Any
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import torch
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import torch.nn.functional as F
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from torch import nn
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from vllm import _custom_ops as ops
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
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from vllm.distributed import (
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get_ep_group,
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_gather,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import (
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FusedMoEFactory,
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fused_moe_make_expert_params_mapping,
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)
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from vllm.model_executor.layers.fused_moe.config import (
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_get_config_dtype_str,
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)
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from vllm.model_executor.layers.fused_moe.fused_moe import (
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_get_config_quant_dtype,
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_prepare_expert_assignment,
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dispatch_fused_moe_kernel,
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try_get_optimal_moe_config,
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)
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from vllm.model_executor.layers.fused_moe.utils import (
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moe_kernel_quantize_input,
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)
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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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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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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.interfaces import (
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EagleModelMixin,
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MixtureOfExperts,
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SupportsEagle,
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SupportsEagle3,
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SupportsLoRA,
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SupportsPP,
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)
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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sequence_parallel_chunk,
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)
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from vllm.sequence import IntermediateTensors
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from vllm.triton_utils import tl
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logger = init_logger(__name__)
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def split_to_interleaved(x):
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# Split halves: x0 x1 x2 x3 ... y0 y1 y2 y3 ...
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# Interleaved: x0 y0 x1 y1 x2 y2 x3 y3 ...
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return x.reshape(*x.shape[:-1], 2, -1).transpose(-1, -2).reshape(*x.shape[:-1], -1)
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def interleaved_to_split(x):
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# Interleaved: x0 y0 x1 y1 x2 y2 x3 y3 ...
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# Split halves: x0 x1 x2 x3 ... y0 y1 y2 y3 ...
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return x.reshape(*x.shape[:-1], -1, 2).transpose(-1, -2).reshape(*x.shape[:-1], -1)
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def calc_router_weights(
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router_logits: torch.Tensor,
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e_score_correction_bias: torch.Tensor | None,
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score_func: str,
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top_k: int,
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scaling_factor: float | None,
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renormalize: bool | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if score_func == "softmax":
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routing_scores = F.softmax(router_logits, dim=-1, dtype=torch.float32)
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else:
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assert score_func == "sigmoid", (
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f"Unsupported router score function: {score_func}"
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)
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routing_scores = torch.sigmoid(router_logits.to(torch.float32))
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selection_scores = routing_scores
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if e_score_correction_bias is not None:
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selection_scores = selection_scores + e_score_correction_bias.to(
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selection_scores
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)
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selected_indices = torch.topk(selection_scores, top_k, dim=-1).indices
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routing_weights = torch.gather(routing_scores, dim=-1, index=selected_indices)
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if renormalize is None:
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renormalize = top_k > 1
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if renormalize:
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routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True)
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if scaling_factor is not None:
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routing_weights = routing_weights * scaling_factor
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return routing_weights.to(router_logits.dtype), selected_indices
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def fused_mova_impl(
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config,
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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use_fp8_w8a8: bool = False,
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use_int8_w8a8: bool = False,
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use_int8_w8a16: bool = False,
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use_int4_w4a16: bool = False,
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ocp_mx_scheme: str | None = None,
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per_channel_quant: bool = False,
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global_num_experts: int = -1,
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expert_map: torch.Tensor | None = None,
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w1_scale: torch.Tensor | None = None,
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w1_zp: torch.Tensor | None = None,
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a1_scale: torch.Tensor | None = None,
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block_shape: list[int] | None = None,
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w1_bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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if ocp_mx_scheme is not None:
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raise NotImplementedError(
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f"Using ocp_mx_scheme={ocp_mx_scheme} in functional fused_experts call is "
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"deprecated. Please use OCP_MXQuantizationEmulationTritonExperts."
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)
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# Check constraints.
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if use_int4_w4a16:
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assert hidden_states.size(1) // 2 == w1.size(2), "Hidden size mismatch"
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else:
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assert hidden_states.size(1) == w1.size(2), (
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f"Hidden size mismatch {hidden_states.size(1)} != {w1.size(2)}"
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)
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assert topk_weights.size() == topk_ids.size(), "topk shape mismatch"
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assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
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assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
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assert hidden_states.dtype in [torch.float32, torch.float16, torch.bfloat16]
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num_tokens = hidden_states.size(0)
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E, N, K = w1.size()
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if global_num_experts == -1:
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global_num_experts = E
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top_k_num = topk_ids.size(1)
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M = num_tokens
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# Note: for use_int8_w8a16 or use_int4_w4a16, the activations are
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# quantized prior to calling fused_experts.
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quant_dtype = _get_config_quant_dtype(
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use_fp8_w8a8=use_fp8_w8a8,
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use_int8_w8a8=use_int8_w8a8,
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)
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intermediate_cache1 = torch.empty(
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(M, top_k_num, N), device=hidden_states.device, dtype=hidden_states.dtype
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)
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if hidden_states.dtype == torch.bfloat16:
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compute_type = tl.bfloat16
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elif hidden_states.dtype == torch.float16:
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compute_type = tl.float16
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elif hidden_states.dtype == torch.float32:
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compute_type = tl.float32
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else:
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raise ValueError(f"Unsupported compute_type: {hidden_states.dtype}")
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out_hidden_states = torch.empty(
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(M, N),
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device=hidden_states.device,
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dtype=hidden_states.dtype,
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)
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qhidden_states, a1q_scale = moe_kernel_quantize_input(
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A=hidden_states,
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A_scale=a1_scale,
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quant_dtype=quant_dtype,
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per_act_token_quant=per_channel_quant,
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block_shape=block_shape,
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)
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sorted_token_ids, expert_ids, num_tokens_post_padded = _prepare_expert_assignment(
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topk_ids,
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config,
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num_tokens,
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top_k_num,
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global_num_experts,
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expert_map,
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use_int8_w8a16=use_int8_w8a16,
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use_int4_w4a16=use_int4_w4a16,
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block_shape=block_shape,
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ignore_invalid_experts=True,
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)
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dispatch_fused_moe_kernel(
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qhidden_states,
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w1,
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intermediate_cache1,
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a1q_scale,
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w1_scale,
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w1_zp,
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topk_weights,
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sorted_token_ids,
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expert_ids,
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num_tokens_post_padded,
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False, # apply_router_weight_on_input,
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top_k_num,
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config,
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compute_type=compute_type,
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use_fp8_w8a8=use_fp8_w8a8,
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use_int8_w8a8=use_int8_w8a8,
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use_int8_w8a16=use_int8_w8a16,
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use_int4_w4a16=use_int4_w4a16,
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per_channel_quant=per_channel_quant,
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block_shape=block_shape,
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B_bias=w1_bias,
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)
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intermediate_cache1 = F.silu(intermediate_cache1)
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intermediate_cache1 *= topk_weights[:, :, None].to(intermediate_cache1.dtype)
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ops.moe_sum(intermediate_cache1, out_hidden_states)
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return out_hidden_states
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class K2HorizonRMSNorm(RMSNorm):
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def __init__(self, hidden_size: int, n_groups: int, eps: float = 1e-6):
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assert hidden_size % n_groups == 0
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super().__init__(hidden_size, eps=eps)
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self.hidden_size = hidden_size
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self.n_groups = n_groups
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self.group_size = hidden_size // n_groups
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def forward(self, x, residual=None):
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if self.n_groups == 1:
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return super().forward(x, residual)
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if residual is not None:
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x = x + residual
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residual = x
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orig_shape = x.shape
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x_grouped = x.reshape(-1, self.n_groups, self.group_size).reshape(
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-1, self.group_size
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)
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y_grouped = torch.empty_like(x_grouped)
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# Reuse vLLM's RMSNorm CUDA kernel on each group.
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ops.rms_norm(
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y_grouped,
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x_grouped,
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torch.ones(self.group_size, dtype=x.dtype, device=x.device),
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self.variance_epsilon,
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)
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y = y_grouped.reshape(orig_shape) * self.weight
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return (y, residual) if residual is not None else y
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class K2HorizonMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: QuantizationConfig | None = None,
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reduce_results: bool = True,
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expert_gate: torch.nn.Linear | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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self.expert_gate = expert_gate
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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out = self.act_fn(gate_up)
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out, _ = self.down_proj(out)
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if self.expert_gate is not None:
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out = F.sigmoid(self.expert_gate(x)[0]) * out
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return out
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class K2HorizonSparseMoeBlock(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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):
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super().__init__()
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config = vllm_config.model_config.hf_text_config
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parallel_config = vllm_config.parallel_config
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quant_config = vllm_config.quant_config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.ep_group = get_ep_group().device_group
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self.ep_rank = get_ep_group().rank_in_group
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self.ep_size = self.ep_group.size()
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self.n_routed_experts = config.num_experts
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self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
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if self.tp_size > config.num_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.num_experts}."
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)
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# Load balancing settings.
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vllm_config = get_current_vllm_config()
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eplb_config = vllm_config.parallel_config.eplb_config
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self.enable_eplb = parallel_config.enable_eplb
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self.n_logical_experts = self.n_routed_experts
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self.n_redundant_experts = eplb_config.num_redundant_experts
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self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
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self.n_local_physical_experts = self.n_physical_experts // self.ep_size
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self.physical_expert_start = self.ep_rank * self.n_local_physical_experts
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self.physical_expert_end = (
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self.physical_expert_start + self.n_local_physical_experts
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.num_experts,
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bias=config.moe_gate_bias,
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skip_bias_add=True,
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quant_config=quant_config,
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prefix=f"{prefix}.gate",
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)
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if self.gate.bias is not None:
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self.gate.bias = nn.Parameter(self.gate.bias.float(), requires_grad=False)
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self.num_shared_experts = config.num_shared_experts
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if config.num_shared_experts > 0:
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self.shared_experts = K2HorizonMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size
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* config.num_shared_experts,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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expert_gate=None,
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prefix=f"{prefix}.shared_experts",
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)
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else:
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self.shared_experts = None
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self.experts = FusedMoEFactory(
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shared_experts=self.shared_experts,
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gate=self.gate,
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num_experts=self.n_routed_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.moe_intermediate_size,
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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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enable_eplb=self.enable_eplb,
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num_redundant_experts=self.n_redundant_experts,
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is_sequence_parallel=self.is_sequence_parallel,
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scoring_func=config.router_score_func,
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e_score_correction_bias=self.gate.bias,
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routed_scaling_factor=config.router_scaling_factor,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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assert hidden_states.dim() <= 2, (
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"K2HorizonSparseMoeBlock only supports 1D or 2D inputs"
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)
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is_input_1d = hidden_states.dim() == 1
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.is_sequence_parallel:
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hidden_states = sequence_parallel_chunk(hidden_states)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=hidden_states
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)
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if self.is_sequence_parallel:
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final_hidden_states = tensor_model_parallel_all_gather(
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final_hidden_states, 0
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)
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final_hidden_states = final_hidden_states[:num_tokens]
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# return to 1d if input is 1d
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return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states
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|
|
|
|
class K2HorizonAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
|
|
num_heads: int,
|
|
num_kv_heads: int,
|
|
rope_parameters: dict[str, Any],
|
|
rope_head_dim: int,
|
|
query_key_norm: bool,
|
|
max_position_embeddings: int = 8192,
|
|
head_dim: int | None = None,
|
|
rms_norm_eps: float = 1e-06,
|
|
qkv_bias: bool = False,
|
|
cache_config: CacheConfig | None = None,
|
|
quant_config: QuantizationConfig | None = None,
|
|
prefix: str = "",
|
|
dual_chunk_attention_config: dict[str, Any] | None = None,
|
|
gate_func: str | None = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = hidden_size
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.total_num_heads = num_heads
|
|
assert self.total_num_heads % tp_size == 0
|
|
self.num_heads = self.total_num_heads // tp_size
|
|
self.total_num_kv_heads = num_kv_heads
|
|
if self.total_num_kv_heads >= tp_size:
|
|
# Number of KV heads is greater than TP size, so we partition
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert self.total_num_kv_heads % tp_size == 0
|
|
else:
|
|
# Number of KV heads is less than TP size, so we replicate
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert tp_size % self.total_num_kv_heads == 0
|
|
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
|
self.head_dim = head_dim or (hidden_size // self.total_num_heads)
|
|
self.q_size = self.num_heads * self.head_dim
|
|
self.kv_size = self.num_kv_heads * self.head_dim
|
|
self.scaling = self.head_dim**-0.5
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.dual_chunk_attention_config = dual_chunk_attention_config
|
|
|
|
self.qkv_proj = QKVParallelLinear(
|
|
hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads,
|
|
self.total_num_kv_heads,
|
|
bias=qkv_bias,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.qkv_proj",
|
|
)
|
|
|
|
self.o_proj = RowParallelLinear(
|
|
self.total_num_heads * self.head_dim,
|
|
hidden_size,
|
|
bias=qkv_bias,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
)
|
|
|
|
self.rope_head_dim = rope_head_dim or self.head_dim
|
|
self.rotary_emb = get_rope(
|
|
self.rope_head_dim,
|
|
max_position=max_position_embeddings,
|
|
rope_parameters=rope_parameters,
|
|
dual_chunk_attention_config=dual_chunk_attention_config,
|
|
)
|
|
self.attn = Attention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
**{
|
|
"layer_idx": extract_layer_index(prefix),
|
|
"dual_chunk_attention_config": dual_chunk_attention_config,
|
|
}
|
|
if dual_chunk_attention_config
|
|
else {},
|
|
)
|
|
|
|
self.query_key_norm = query_key_norm
|
|
if self.query_key_norm:
|
|
self.q_norm = K2HorizonRMSNorm(
|
|
hidden_size=self.q_size, n_groups=self.num_heads, eps=rms_norm_eps
|
|
)
|
|
self.k_norm = K2HorizonRMSNorm(
|
|
hidden_size=self.kv_size, n_groups=self.num_kv_heads, eps=rms_norm_eps
|
|
)
|
|
|
|
self.gate_func = gate_func
|
|
if self.gate_func is not None:
|
|
self.gate_proj = ColumnParallelLinear(
|
|
hidden_size,
|
|
self.total_num_heads * self.head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.gate_proj",
|
|
)
|
|
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
if self.query_key_norm:
|
|
q = self.q_norm(q)
|
|
k = self.k_norm(k)
|
|
|
|
if self.rope_head_dim == self.head_dim:
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
else:
|
|
q = q.reshape(*q.shape[:-1], self.num_heads, self.head_dim)
|
|
k = k.reshape(*k.shape[:-1], self.num_kv_heads, self.head_dim)
|
|
|
|
q_rope, q_nope = torch.split(
|
|
split_to_interleaved(q),
|
|
split_size_or_sections=[
|
|
self.rope_head_dim,
|
|
self.head_dim - self.rope_head_dim,
|
|
],
|
|
dim=-1,
|
|
)
|
|
k_rope, k_nope = torch.split(
|
|
split_to_interleaved(k),
|
|
split_size_or_sections=[
|
|
self.rope_head_dim,
|
|
self.head_dim - self.rope_head_dim,
|
|
],
|
|
dim=-1,
|
|
)
|
|
|
|
q_rope, k_rope = self.rotary_emb(
|
|
positions, interleaved_to_split(q_rope), interleaved_to_split(k_rope)
|
|
)
|
|
|
|
q = interleaved_to_split(
|
|
torch.cat([split_to_interleaved(q_rope), q_nope], dim=-1)
|
|
).reshape(*q.shape[:-2], -1)
|
|
|
|
k = interleaved_to_split(
|
|
torch.cat([split_to_interleaved(k_rope), k_nope], dim=-1)
|
|
).reshape(*k.shape[:-2], -1)
|
|
|
|
attn_output = self.attn(q, k, v)
|
|
|
|
if self.gate_func is not None:
|
|
gate, _ = self.gate_proj(hidden_states)
|
|
if self.gate_func == "silu":
|
|
gate = F.silu(gate)
|
|
else:
|
|
assert self.gate_func == "softplus"
|
|
gate = F.softplus(gate, beta=math.log(2))
|
|
|
|
attn_output = attn_output * gate
|
|
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
|
|
class K2HorizonMoVAAttention(nn.Module):
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
num_kv_heads: int,
|
|
rope_parameters: dict[str, Any],
|
|
rope_head_dim: int,
|
|
query_key_norm: bool,
|
|
num_experts: int,
|
|
num_experts_per_tok: int,
|
|
moe_gate_bias: bool,
|
|
router_score_func: str,
|
|
router_scaling_factor: float,
|
|
max_position_embeddings: int = 8192,
|
|
head_dim: int | None = None,
|
|
rms_norm_eps: float = 1e-06,
|
|
qkv_bias: bool = False,
|
|
cache_config: CacheConfig | None = None,
|
|
quant_config: QuantizationConfig | None = None,
|
|
prefix: str = "",
|
|
dual_chunk_attention_config: dict[str, Any] | None = None,
|
|
gate_func: str | None = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = hidden_size
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
self.total_num_heads = num_heads
|
|
assert self.total_num_heads % tp_size == 0
|
|
self.num_heads = self.total_num_heads // tp_size
|
|
self.total_num_kv_heads = num_kv_heads
|
|
if self.total_num_kv_heads >= tp_size:
|
|
# Number of KV heads is greater than TP size, so we partition
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert self.total_num_kv_heads % tp_size == 0
|
|
else:
|
|
# Number of KV heads is less than TP size, so we replicate
|
|
# the KV heads across multiple tensor parallel GPUs.
|
|
assert tp_size % self.total_num_kv_heads == 0
|
|
|
|
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
|
self.head_dim = head_dim or (hidden_size // self.total_num_heads)
|
|
self.q_size = self.num_heads * self.head_dim
|
|
self.kv_size = self.num_kv_heads * self.head_dim
|
|
self.scaling = self.head_dim**-0.5
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.dual_chunk_attention_config = dual_chunk_attention_config
|
|
|
|
self.num_experts = num_experts
|
|
self.num_experts_per_tok = num_experts_per_tok
|
|
self.router_score_func = router_score_func
|
|
self.router_scaling_factor = router_scaling_factor
|
|
|
|
self.qk_proj = MergedColumnParallelLinear(
|
|
hidden_size,
|
|
[self.total_num_heads * self.head_dim, self.kv_size * tp_size],
|
|
bias=qkv_bias,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.qk_proj",
|
|
)
|
|
|
|
self.v_router = ReplicatedLinear(
|
|
hidden_size,
|
|
num_experts,
|
|
bias=moe_gate_bias,
|
|
skip_bias_add=True,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.v_router",
|
|
)
|
|
if self.v_router.bias is not None:
|
|
self.v_router.bias = nn.Parameter(
|
|
self.v_router.bias.float(), requires_grad=False
|
|
)
|
|
|
|
self.v_experts = nn.ModuleList(
|
|
[
|
|
ColumnParallelLinear(
|
|
hidden_size,
|
|
self.kv_size * tp_size,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.v_experts.{value}",
|
|
)
|
|
for value in range(num_experts)
|
|
]
|
|
)
|
|
|
|
self.o_proj = RowParallelLinear(
|
|
self.total_num_heads * self.head_dim,
|
|
hidden_size,
|
|
bias=qkv_bias,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.o_proj",
|
|
)
|
|
|
|
self.rope_head_dim = rope_head_dim or self.head_dim
|
|
self.rotary_emb = get_rope(
|
|
self.rope_head_dim,
|
|
max_position=max_position_embeddings,
|
|
rope_parameters=rope_parameters,
|
|
dual_chunk_attention_config=dual_chunk_attention_config,
|
|
)
|
|
self.attn = Attention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn",
|
|
**{
|
|
"layer_idx": extract_layer_index(prefix),
|
|
"dual_chunk_attention_config": dual_chunk_attention_config,
|
|
}
|
|
if dual_chunk_attention_config
|
|
else {},
|
|
)
|
|
|
|
self.query_key_norm = query_key_norm
|
|
if self.query_key_norm:
|
|
self.q_norm = K2HorizonRMSNorm(
|
|
hidden_size=self.q_size, n_groups=self.num_heads, eps=rms_norm_eps
|
|
)
|
|
self.k_norm = K2HorizonRMSNorm(
|
|
hidden_size=self.kv_size, n_groups=self.num_kv_heads, eps=rms_norm_eps
|
|
)
|
|
|
|
self.gate_func = gate_func
|
|
if self.gate_func is not None:
|
|
self.gate_proj = ColumnParallelLinear(
|
|
hidden_size,
|
|
self.total_num_heads * self.head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.gate_proj",
|
|
)
|
|
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
|
|
w1_shape = torch.Size(
|
|
[
|
|
self.num_experts,
|
|
self.total_num_kv_heads * self.head_dim,
|
|
hidden_size,
|
|
]
|
|
)
|
|
config_dtype = _get_config_dtype_str(
|
|
use_fp8_w8a8=False,
|
|
use_int8_w8a16=False,
|
|
use_int4_w4a16=False,
|
|
dtype=vllm_config.model_config.dtype,
|
|
)
|
|
self.fused_mova_config = try_get_optimal_moe_config(
|
|
w1_shape=w1_shape,
|
|
w2_shape=torch.Size([w1_shape[0], w1_shape[2], w1_shape[1]]),
|
|
top_k=self.num_experts_per_tok,
|
|
dtype=config_dtype,
|
|
M=vllm_config.scheduler_config.max_num_batched_tokens,
|
|
block_shape=None,
|
|
)
|
|
|
|
def compute_mova_v_sparse(self, hidden_states):
|
|
router_logits, _ = self.v_router(hidden_states)
|
|
|
|
routing_weights, selected_values = calc_router_weights(
|
|
router_logits=router_logits,
|
|
e_score_correction_bias=self.v_router.bias,
|
|
score_func=self.router_score_func,
|
|
top_k=self.num_experts_per_tok,
|
|
scaling_factor=self.router_scaling_factor,
|
|
)
|
|
|
|
w1 = torch.stack(
|
|
[expert.weight for expert in self.v_experts], dim=0
|
|
).contiguous()
|
|
|
|
v = fused_mova_impl(
|
|
config=self.fused_mova_config,
|
|
hidden_states=hidden_states.contiguous(),
|
|
w1=w1,
|
|
topk_weights=routing_weights.contiguous(),
|
|
topk_ids=selected_values.contiguous(),
|
|
global_num_experts=self.num_experts,
|
|
expert_map=None,
|
|
)
|
|
|
|
return v
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
qk, _ = self.qk_proj(hidden_states)
|
|
q, k = qk.split([self.q_size, self.kv_size], dim=-1)
|
|
v = self.compute_mova_v_sparse(hidden_states)
|
|
|
|
if self.query_key_norm:
|
|
q = self.q_norm(q)
|
|
k = self.k_norm(k)
|
|
|
|
if self.rope_head_dim == self.head_dim:
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
else:
|
|
q = q.reshape(*q.shape[:-1], self.num_heads, self.head_dim)
|
|
k = k.reshape(*k.shape[:-1], self.num_kv_heads, self.head_dim)
|
|
|
|
q_rope, q_nope = torch.split(
|
|
split_to_interleaved(q),
|
|
split_size_or_sections=[
|
|
self.rope_head_dim,
|
|
self.head_dim - self.rope_head_dim,
|
|
],
|
|
dim=-1,
|
|
)
|
|
k_rope, k_nope = torch.split(
|
|
split_to_interleaved(k),
|
|
split_size_or_sections=[
|
|
self.rope_head_dim,
|
|
self.head_dim - self.rope_head_dim,
|
|
],
|
|
dim=-1,
|
|
)
|
|
|
|
q_rope, k_rope = self.rotary_emb(
|
|
positions, interleaved_to_split(q_rope), interleaved_to_split(k_rope)
|
|
)
|
|
|
|
q = interleaved_to_split(
|
|
torch.cat([split_to_interleaved(q_rope), q_nope], dim=-1)
|
|
).reshape(*q.shape[:-2], -1)
|
|
|
|
k = interleaved_to_split(
|
|
torch.cat([split_to_interleaved(k_rope), k_nope], dim=-1)
|
|
).reshape(*k.shape[:-2], -1)
|
|
|
|
attn_output = self.attn(q, k, v)
|
|
|
|
if self.gate_func is not None:
|
|
gate, _ = self.gate_proj(hidden_states)
|
|
if self.gate_func == "silu":
|
|
gate = F.silu(gate)
|
|
else:
|
|
assert self.gate_func == "softplus"
|
|
gate = F.softplus(gate, beta=math.log(2))
|
|
|
|
attn_output = attn_output * gate
|
|
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
|
|
class K2HorizonDecoderLayer(nn.Module):
|
|
def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_text_config
|
|
cache_config = vllm_config.cache_config
|
|
quant_config = vllm_config.quant_config
|
|
|
|
self.hidden_size = config.hidden_size
|
|
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
|
dual_chunk_attention_config = getattr(
|
|
config, "dual_chunk_attention_config", None
|
|
)
|
|
|
|
# `mlp_only_layers` in the config.
|
|
layer_idx = extract_layer_index(prefix)
|
|
mlp_only_layers = (
|
|
[] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers
|
|
)
|
|
is_sparse_layer = (layer_idx not in mlp_only_layers) and (
|
|
config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
|
|
)
|
|
|
|
if is_sparse_layer and config.mova_num_experts > 0:
|
|
self.self_attn = K2HorizonMoVAAttention(
|
|
vllm_config=vllm_config,
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
rope_parameters=config.rope_parameters,
|
|
rope_head_dim=config.rope_head_dim,
|
|
query_key_norm=config.query_key_norm,
|
|
max_position_embeddings=max_position_embeddings,
|
|
rms_norm_eps=config.rms_norm_eps,
|
|
qkv_bias=config.attention_bias,
|
|
head_dim=config.head_dim,
|
|
gate_func=config.attention_gate_func,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.self_attn",
|
|
dual_chunk_attention_config=dual_chunk_attention_config,
|
|
num_experts=config.mova_num_experts,
|
|
num_experts_per_tok=config.mova_num_experts_per_tok,
|
|
moe_gate_bias=config.moe_gate_bias,
|
|
router_score_func=config.router_score_func,
|
|
router_scaling_factor=config.router_scaling_factor,
|
|
)
|
|
else:
|
|
self.self_attn = K2HorizonAttention(
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
rope_parameters=config.rope_parameters,
|
|
rope_head_dim=config.rope_head_dim,
|
|
query_key_norm=config.query_key_norm,
|
|
max_position_embeddings=max_position_embeddings,
|
|
rms_norm_eps=config.rms_norm_eps,
|
|
qkv_bias=config.attention_bias,
|
|
head_dim=config.head_dim,
|
|
gate_func=config.attention_gate_func,
|
|
cache_config=cache_config,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.self_attn",
|
|
dual_chunk_attention_config=dual_chunk_attention_config,
|
|
)
|
|
|
|
if is_sparse_layer:
|
|
self.mlp = K2HorizonSparseMoeBlock(
|
|
vllm_config=vllm_config, prefix=f"{prefix}.mlp"
|
|
)
|
|
else:
|
|
self.mlp = K2HorizonMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.mlp",
|
|
)
|
|
|
|
self.input_layernorm = K2HorizonRMSNorm(
|
|
hidden_size=config.hidden_size,
|
|
n_groups=config.layernorm_num_groups,
|
|
eps=config.rms_norm_eps,
|
|
)
|
|
|
|
self.post_attention_layernorm = K2HorizonRMSNorm(
|
|
hidden_size=config.hidden_size,
|
|
n_groups=config.layernorm_num_groups,
|
|
eps=config.rms_norm_eps,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
residual: torch.Tensor | None,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
# Self Attention
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
|
|
|
hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
|
|
|
|
# Fully Connected
|
|
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
|
hidden_states = self.mlp(hidden_states)
|
|
return hidden_states, residual
|
|
|
|
|
|
@support_torch_compile
|
|
class K2HorizonModel(nn.Module, EagleModelMixin):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
vllm_config: VllmConfig,
|
|
prefix: str = "",
|
|
decoder_layer_type: type[torch.nn.Module] = K2HorizonDecoderLayer,
|
|
):
|
|
super().__init__()
|
|
|
|
config = vllm_config.model_config.hf_text_config
|
|
quant_config = vllm_config.quant_config
|
|
parallel_config = vllm_config.parallel_config
|
|
eplb_config = parallel_config.eplb_config
|
|
self.num_redundant_experts = eplb_config.num_redundant_experts
|
|
|
|
self.vocab_size = config.vocab_size
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.embed_tokens",
|
|
)
|
|
self.start_layer, self.end_layer, self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
lambda prefix: decoder_layer_type(vllm_config=vllm_config, prefix=prefix),
|
|
prefix=f"{prefix}.layers",
|
|
)
|
|
|
|
self.norm = K2HorizonRMSNorm(
|
|
hidden_size=config.hidden_size,
|
|
n_groups=config.layernorm_num_groups,
|
|
eps=config.rms_norm_eps,
|
|
)
|
|
|
|
self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
|
["hidden_states", "residual"], config.hidden_size
|
|
)
|
|
|
|
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.embed_tokens(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.embed_input_ids(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
aux_hidden_states = self._maybe_add_hidden_state(
|
|
[], self.start_layer, hidden_states, residual
|
|
)
|
|
for layer_idx, layer in enumerate(
|
|
islice(self.layers, self.start_layer, self.end_layer),
|
|
start=self.start_layer,
|
|
):
|
|
hidden_states, residual = layer(positions, hidden_states, residual)
|
|
self._maybe_add_hidden_state(
|
|
aux_hidden_states, layer_idx + 1, hidden_states, residual
|
|
)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors(
|
|
{"hidden_states": hidden_states, "residual": residual}
|
|
)
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
# Return auxiliary hidden states if collected
|
|
if len(aux_hidden_states) > 0:
|
|
return hidden_states, aux_hidden_states
|
|
return hidden_states
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return fused_moe_make_expert_params_mapping(
|
|
self,
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts,
|
|
num_redundant_experts=self.num_redundant_experts,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
# ignore_suffixes = (
|
|
# ".bias",
|
|
# "_bias",
|
|
# ".weight_scale",
|
|
# "_weight_scale",
|
|
# ".input_scale",
|
|
# "_input_scale",
|
|
# )
|
|
ignore_suffixes = ()
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
for name, loaded_weight in weights:
|
|
if "scale" in name or "zero_point" in name:
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
# QK norm weights
|
|
if name.endswith(".self_attn.q_norm.weight") or name.endswith(
|
|
".self_attn.k_norm.weight"
|
|
):
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
if loaded_weight.shape != param.shape:
|
|
if name.endswith(".self_attn.q_norm.weight"):
|
|
loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
|
|
else:
|
|
num_kv_heads = self.config.num_key_value_heads
|
|
if num_kv_heads >= tp_size:
|
|
loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
|
|
else:
|
|
num_kv_head_replicas = tp_size // num_kv_heads
|
|
kv_rank = tp_rank // num_kv_head_replicas
|
|
loaded_weight = loaded_weight.chunk(num_kv_heads, dim=0)[
|
|
kv_rank
|
|
]
|
|
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
|
|
# MoVA v experts
|
|
if ".self_attn.v_experts." in name:
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
num_kv_heads = self.config.num_key_value_heads
|
|
|
|
if loaded_weight.shape != param.shape:
|
|
if num_kv_heads >= tp_size:
|
|
loaded_weight = loaded_weight.chunk(tp_size, dim=0)[tp_rank]
|
|
else:
|
|
num_kv_head_replicas = tp_size // num_kv_heads
|
|
kv_rank = tp_rank // num_kv_head_replicas
|
|
loaded_weight = loaded_weight.chunk(num_kv_heads, dim=0)[
|
|
kv_rank
|
|
]
|
|
|
|
default_weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
continue
|
|
if ".self_attn." in name and param_name == "gate_up_proj":
|
|
assert weight_name == "gate_proj"
|
|
continue
|
|
if (
|
|
".self_attn." in name
|
|
and param_name == "qkv_proj"
|
|
and name.replace(weight_name, param_name) not in params_dict
|
|
):
|
|
qk_name = name.replace(weight_name, "qk_proj")
|
|
if is_pp_missing_parameter(qk_name, self):
|
|
continue
|
|
if qk_name not in params_dict:
|
|
continue
|
|
|
|
assert weight_name in ["q_proj", "k_proj"]
|
|
if weight_name == "k_proj":
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
num_kv_heads = self.config.num_key_value_heads
|
|
if num_kv_heads < tp_size:
|
|
num_kv_head_replicas = tp_size // num_kv_heads
|
|
loaded_weight = (
|
|
loaded_weight.reshape(
|
|
num_kv_heads, -1, *loaded_weight.shape[1:]
|
|
)
|
|
.repeat_interleave(num_kv_head_replicas, dim=0)
|
|
.reshape(-1, *loaded_weight.shape[1:])
|
|
)
|
|
|
|
param = params_dict[qk_name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(
|
|
param, loaded_weight, 0 if weight_name == "q_proj" else 1
|
|
)
|
|
loaded_params.add(qk_name)
|
|
break
|
|
|
|
name = name.replace(weight_name, param_name)
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
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
|
|
if name not in params_dict:
|
|
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:
|
|
is_expert_weight = False
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
# Anyway, this is an expert weight and should not be
|
|
# attempted to load as other weights later
|
|
is_expert_weight = True
|
|
|
|
# Do not modify `name` since the loop may continue here
|
|
# Instead, create a new variable
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
|
|
if is_pp_missing_parameter(name_mapped, self):
|
|
continue
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if (
|
|
name_mapped.endswith(ignore_suffixes)
|
|
and name_mapped not in params_dict
|
|
):
|
|
continue
|
|
|
|
param = params_dict[name_mapped]
|
|
# We should ask the weight loader to return success or not
|
|
# here since otherwise we may skip experts with other
|
|
# available replicas.
|
|
weight_loader = typing.cast(
|
|
Callable[..., bool], param.weight_loader
|
|
)
|
|
success = weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
return_success=True,
|
|
)
|
|
if success:
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# We've checked that this is an expert weight
|
|
# However it's not mapped locally to this rank
|
|
# So we simply skip it
|
|
continue
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class K2HorizonForCausalLM(
|
|
nn.Module, SupportsPP, SupportsLoRA, SupportsEagle, SupportsEagle3, MixtureOfExperts
|
|
):
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
"qk_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
],
|
|
}
|
|
|
|
embedding_modules = {
|
|
"embed_tokens": "input_embeddings",
|
|
"lm_head": "output_embeddings",
|
|
}
|
|
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_text_config
|
|
quant_config = vllm_config.quant_config
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
# Only perform the following mapping when K2HorizonMLP exists
|
|
if getattr(config, "mlp_only_layers", []):
|
|
self.packed_modules_mapping["gate_up_proj"] = ["gate_proj", "up_proj"]
|
|
self.model = K2HorizonModel(
|
|
vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
|
|
)
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
if self.config.tie_word_embeddings:
|
|
self.lm_head.weight = self.model.embed_tokens.weight
|
|
self.logits_processor = LogitsProcessor(config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
# Set MoE hyperparameters
|
|
self.expert_weights = []
|
|
|
|
self.moe_layers = []
|
|
example_layer = None
|
|
for layer in self.model.layers:
|
|
if isinstance(layer, PPMissingLayer):
|
|
continue
|
|
|
|
assert isinstance(layer, K2HorizonDecoderLayer)
|
|
if isinstance(layer.mlp, K2HorizonSparseMoeBlock):
|
|
example_layer = layer.mlp
|
|
self.moe_layers.append(layer.mlp.experts)
|
|
|
|
self.num_moe_layers = len(self.moe_layers)
|
|
if example_layer is not None:
|
|
self.num_logical_experts = example_layer.n_logical_experts
|
|
self.num_physical_experts = example_layer.n_physical_experts
|
|
self.num_local_physical_experts = example_layer.n_local_physical_experts
|
|
self.num_routed_experts = example_layer.n_routed_experts
|
|
self.num_redundant_experts = example_layer.n_redundant_experts
|
|
|
|
def update_physical_experts_metadata(
|
|
self,
|
|
num_physical_experts: int,
|
|
num_local_physical_experts: int,
|
|
) -> None:
|
|
assert self.num_local_physical_experts == num_local_physical_experts
|
|
self.num_physical_experts = num_physical_experts
|
|
self.num_local_physical_experts = num_local_physical_experts
|
|
self.num_redundant_experts = num_physical_experts - self.num_logical_experts
|
|
for layer in self.model.layers:
|
|
if isinstance(layer.mlp, K2HorizonSparseMoeBlock):
|
|
moe = layer.mlp
|
|
moe.n_local_physical_experts = num_local_physical_experts
|
|
moe.n_physical_experts = num_physical_experts
|
|
moe.n_redundant_experts = self.num_redundant_experts
|
|
moe.experts.update_expert_map()
|
|
|
|
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 | None,
|
|
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)
|
|
return loader.load_weights(weights)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|