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717 lines
26 KiB
Python
717 lines
26 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only Jurassic model."""
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from collections.abc import Iterable
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from itertools import islice
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from typing import Any
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import torch
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from torch import nn
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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 AFDConfig, CacheConfig, ModelConfig, VllmConfig
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from vllm.distributed import (
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get_pp_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from vllm.distributed.afd_transfer.afd_connector.metadata import (
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AFDConnectorMetadata,
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)
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from vllm.forward_context import AFDMetadata, get_forward_context
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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.fused_moe import FusedMoE
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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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 default_weight_loader
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.step3_vl import Step3TextConfig
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from vllm.v1.worker.ubatching import dbo_current_ubatch_id, dbo_enabled, dbo_yield
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from .interfaces import SupportsPP
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from .utils import (
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PPMissingLayer,
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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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)
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logger = init_logger(__name__)
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class FusedMoEBlock(nn.Module):
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def __init__(
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self,
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config: ModelConfig,
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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.tp_size = get_tensor_model_parallel_world_size()
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if self.tp_size > config.moe_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.moe_num_experts}."
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)
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self.experts = FusedMoE(
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num_experts=config.moe_num_experts,
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top_k=config.moe_top_k,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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reduce_results=False,
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renormalize=config.norm_expert_weight,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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)
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self.gate = ReplicatedLinear(
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config.hidden_size,
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config.moe_num_experts,
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bias=False,
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quant_config=None,
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prefix=f"{prefix}.gate",
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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orig_shape = hidden_states.shape
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hidden_dim = hidden_states.shape[-1]
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hidden_states = hidden_states.view(-1, hidden_dim)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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)
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(orig_shape)
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class Step3TextMLP(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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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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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.hidden_size = hidden_size
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(hidden_states)
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intermediate_act = self.act_fn(gate_up)
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output, _ = self.down_proj(intermediate_act)
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return output
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class Step3TextAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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norm_eps: float,
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rope_parameters: dict[str, Any],
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share_q_dim: int | None = None,
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max_position_embedding: int = 8192,
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head_dim: int = 256,
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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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):
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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if num_kv_heads != 1:
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raise ValueError(
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f"Step3TextAttention num_kv_heads must be 1, but got {num_kv_heads}."
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)
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self.num_kv_heads = num_kv_heads
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self.head_dim = head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.q_size = share_q_dim if share_q_dim else self.head_dim
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self.qkv_proj = ReplicatedLinear(
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hidden_size,
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self.q_size + self.kv_size * 2,
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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.inter_norm = RMSNorm(self.q_size, eps=norm_eps)
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self.wq = ColumnParallelLinear(
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self.q_size,
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self.head_dim * self.total_num_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.wq",
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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_embedding,
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rope_parameters=rope_parameters,
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)
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scaling = self.head_dim**-0.5
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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scaling,
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self.num_kv_heads,
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cache_config=cache_config,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self, positions: torch.Tensor, hidden_states: torch.Tensor
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q = self.inter_norm(q)
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q = self.wq(q)[0]
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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residual, _ = self.o_proj(attn_output)
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return residual
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class Step3TextDecoderLayer(nn.Module):
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def __init__(
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self,
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config: Step3TextConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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afd_config: AFDConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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self.afd_role = afd_config.afd_role if afd_config is not None else None
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if self.afd_role is None or self.afd_role == "attention":
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self.self_attn = Step3TextAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=1,
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cache_config=cache_config,
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quant_config=quant_config,
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norm_eps=config.rms_norm_eps,
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max_position_embedding=config.max_position_embedding,
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head_dim=config.head_dim,
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share_q_dim=config.share_q_dim,
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rope_parameters=config.rope_parameters,
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prefix=f"{prefix}.self_attn",
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)
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self.layer_idx = int(prefix.split("layers.")[1].split(".")[0])
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if self.afd_role is None or self.afd_role == "ffn":
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moe_layers_enum = getattr(config, "moe_layers_enum", None)
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if moe_layers_enum is not None:
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moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
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else:
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# Default to 1dense.
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moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
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if self.layer_idx in moe_layers_idx:
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self.moe = FusedMoEBlock(
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config=config, quant_config=quant_config, prefix=f"{prefix}.moe"
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)
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self.share_expert = Step3TextMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.share_expert_dim,
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hidden_act="silu",
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quant_config=quant_config,
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prefix=f"{prefix}.share_expert",
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)
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self.use_moe = True
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else:
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self.mlp = Step3TextMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act="silu",
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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.use_moe = False
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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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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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# query, key and positions must have the same number of tokens
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# /model_executor/layers/rotary_embedding/base.py
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# positions.shape=torch.Size([8192]), hidden_states.shape=torch.Size([4096, 3712]）
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logger.info(f"{positions.shape=}, {hidden_states.shape=}")
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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if self.afd_role == "attention":
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return hidden_states, residual
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if self.use_moe:
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share_output = self.share_expert(hidden_states)
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moe_output = self.moe(hidden_states)
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hidden_states = share_output + moe_output
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else:
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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def compute_attn_output(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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):
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pass
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def compute_ffn_output(self, hidden_states):
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assert self.afd_role == "ffn"
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if self.use_moe:
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share_output = self.share_expert(hidden_states)
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moe_output = self.moe(hidden_states)
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hidden_states = share_output + moe_output
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else:
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hidden_states = self.mlp(hidden_states)
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logger.info(f"{type(hidden_states)=}")
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return hidden_states
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@support_torch_compile
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class Step3TextModel(nn.Module):
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def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None:
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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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logger.info(f"{quant_config=}")
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afd_config = vllm_config.afd_config
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self.vocab_size = config.vocab_size
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self.config = config
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if get_pp_group().is_first_rank or (
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config.tie_word_embeddings and get_pp_group().is_last_rank
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):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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)
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else:
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self.embed_tokens = PPMissingLayer()
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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: Step3TextDecoderLayer(
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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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afd_config=afd_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states"], config.hidden_size
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)
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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_with_afd(
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self,
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hidden_states: torch.Tensor,
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residual: torch.Tensor,
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positions: torch.Tensor,
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afd_metadata: AFDMetadata,
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) -> tuple[torch.Tensor, torch.Tensor]:
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recv_handle = None
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logger.info(f"{__file__}: forward with afd called, may blocked here")
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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afd_connector = afd_metadata.afd_connector
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afd_metadata.afd_stage_idx = dbo_current_ubatch_id()
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if layer.layer_idx > 0:
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hidden_states, recv_metadata = afd_connector.recv_ffn_output()
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if recv_metadata.recv_handle_list is not None:
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recv_handle = recv_metadata.recv_handle_list
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if recv_handle is not None:
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for work in recv_handle:
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work.wait()
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logger.info(f"Step3TextModel {layer.layer_idx=}: {hidden_states.shape=}, {positions.shape=}")
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current_hidden, residual = layer(positions, hidden_states, residual)
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logger.info(f"create attn metadata: {current_hidden.shape=}")
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metadata = AFDConnectorMetadata.create_attention_metadata(
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layer_idx=layer.layer_idx,
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stage_idx=afd_metadata.afd_stage_idx,
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seq_len=current_hidden.shape[0],
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dtype=current_hidden.dtype,
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device=current_hidden.device,
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num_of_stages=afd_metadata.num_of_stages,
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afd_tokens_lens=afd_metadata.afd_tokens_lens,
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)
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afd_connector.send_attn_output(current_hidden, metadata)
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if dbo_enabled():
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dbo_yield()
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hidden_states, recv_metadata = afd_connector.recv_ffn_output()
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if recv_metadata.recv_handle_list is not None:
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recv_handle = recv_metadata.recv_handle_list
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if recv_handle is not None:
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for work in recv_handle:
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work.wait()
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return hidden_states, residual
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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:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_input_ids(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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forward_ctx = get_forward_context()
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afd_metadata = forward_ctx.afd_metadata if forward_ctx is not None else None
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if afd_metadata is not None:
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hidden_states, residual = self.forward_with_afd(
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hidden_states,
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residual,
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positions,
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afd_metadata,
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)
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else:
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for layer in islice(self.layers, self.start_layer, self.end_layer):
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hidden_states, residual = layer(positions, hidden_states, residual)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{
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"hidden_states": hidden_states,
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"residual": residual,
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}
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def compute_ffn_output(
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self,
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hidden_states,
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layer_idx,
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) -> torch.Tensor | IntermediateTensors:
|
||
logger.info(f"{type(self.layers)=}, {type(layer_idx)=}")
|
||
hidden_states = self.layers[layer_idx].compute_ffn_output(hidden_states)
|
||
return hidden_states
|
||
|
||
|
||
class Step3TextForCausalLM(nn.Module, SupportsPP):
|
||
def __init__(
|
||
self,
|
||
*,
|
||
vllm_config: VllmConfig,
|
||
prefix: str = "",
|
||
):
|
||
super().__init__()
|
||
config = vllm_config.model_config.hf_config
|
||
|
||
self.config = config
|
||
self.vllm_config = vllm_config
|
||
|
||
self.afd_config = vllm_config.afd_config
|
||
self.afd_role = (
|
||
self.afd_config.afd_role if self.afd_config is not None else None
|
||
)
|
||
|
||
self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
|
||
|
||
if get_pp_group().is_last_rank:
|
||
self.lm_head = ParallelLMHead(
|
||
config.vocab_size,
|
||
config.hidden_size,
|
||
prefix=maybe_prefix(prefix, "lm_head"),
|
||
)
|
||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||
else:
|
||
self.lm_head = PPMissingLayer()
|
||
|
||
self.make_empty_intermediate_tensors = (
|
||
self.model.make_empty_intermediate_tensors
|
||
)
|
||
|
||
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,
|
||
):
|
||
hidden_states = self.model(
|
||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||
)
|
||
return hidden_states
|
||
|
||
def compute_ffn_output(
|
||
self,
|
||
hidden_states,
|
||
current_layer_idx,
|
||
) -> torch.Tensor | IntermediateTensors:
|
||
hidden_states = self.model.compute_ffn_output(hidden_states, current_layer_idx)
|
||
return hidden_states
|
||
|
||
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||
return logits
|
||
|
||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||
logger.info(f"{__file__}: load_weights!")
|
||
qkv_params_mapping = [
|
||
# (param_name, shard_name, relative_start_idx, relative_end_idx)
|
||
(
|
||
".qkv_proj",
|
||
".q_proj",
|
||
0,
|
||
self.config.share_q_dim
|
||
/ (self.config.share_q_dim + self.config.head_dim * 2),
|
||
),
|
||
(
|
||
".qkv_proj",
|
||
".k_proj",
|
||
self.config.share_q_dim
|
||
/ (self.config.share_q_dim + self.config.head_dim * 2),
|
||
(self.config.share_q_dim + self.config.head_dim)
|
||
/ (self.config.share_q_dim + self.config.head_dim * 2),
|
||
),
|
||
(
|
||
".qkv_proj",
|
||
".v_proj",
|
||
(self.config.share_q_dim + self.config.head_dim)
|
||
/ (self.config.share_q_dim + self.config.head_dim * 2),
|
||
(self.config.share_q_dim + self.config.head_dim * 2)
|
||
/ (self.config.share_q_dim + self.config.head_dim * 2),
|
||
),
|
||
]
|
||
stacked_params_mapping = [
|
||
# (param_name, shard_name, shard_id)
|
||
(".gate_up_proj", ".gate_proj", 0),
|
||
(".gate_up_proj", ".up_proj", 1),
|
||
]
|
||
params_dict = dict(self.named_parameters())
|
||
# logger.info(f"{params_dict.keys()=}")
|
||
loaded_params: set[str] = set()
|
||
|
||
expert_params_mapping = [
|
||
(".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
|
||
(".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
|
||
(".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
|
||
]
|
||
|
||
disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
|
||
|
||
for name, loaded_weight in weights:
|
||
# logger.info(
|
||
# f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
|
||
# f"is_common: {self.is_common_weight(name)}"
|
||
# )
|
||
if self.afd_role == "attention" and self.is_moe_weight(name):
|
||
continue
|
||
|
||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||
if weight_name not in name:
|
||
continue
|
||
|
||
if any(
|
||
disable_moe_stacked_param in name
|
||
for disable_moe_stacked_param in disable_moe_stacked_params
|
||
):
|
||
continue
|
||
name = name.replace(weight_name, param_name)
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = param.weight_loader
|
||
weight_loader(param, loaded_weight, shard_id)
|
||
loaded_params.add(name)
|
||
break
|
||
else:
|
||
for mapping in expert_params_mapping:
|
||
param_name, weight_name, shard_id = mapping
|
||
if weight_name not in name:
|
||
continue
|
||
|
||
if self.afd_role is not None and self.afd_role == "attention":
|
||
continue
|
||
|
||
name = name.replace(weight_name, param_name)
|
||
# Skip layers on other devices.
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
# Skip loading extra bias for GPTQ models.
|
||
if (
|
||
name.endswith(".bias") or name.endswith("_bias")
|
||
) and name not in params_dict:
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = param.weight_loader
|
||
for expert_id in range(loaded_weight.shape[0]):
|
||
loaded_weight_expert = loaded_weight[expert_id]
|
||
weight_loader(
|
||
param,
|
||
loaded_weight_expert,
|
||
name,
|
||
shard_id=shard_id,
|
||
expert_id=expert_id,
|
||
)
|
||
loaded_params.add(name)
|
||
break
|
||
else:
|
||
if (
|
||
self.afd_role == "ffn"
|
||
and not self.is_moe_weight(name)
|
||
and not self.is_common_weight(name)
|
||
):
|
||
continue
|
||
for (
|
||
param_name,
|
||
weight_name,
|
||
start_idx,
|
||
end_idx,
|
||
) in qkv_params_mapping:
|
||
# logger.info(f"{weight_name=}, {name=}")
|
||
if weight_name not in name:
|
||
continue
|
||
name = name.replace(weight_name, param_name)
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
param = params_dict[name]
|
||
dim = param.shape[param.output_dim]
|
||
begin_idx = int(start_idx * dim)
|
||
end_idx = int(end_idx * dim)
|
||
param_slice = param.narrow(
|
||
param.output_dim, begin_idx, end_idx - begin_idx
|
||
)
|
||
param_slice.copy_(loaded_weight)
|
||
loaded_params.add(name)
|
||
break
|
||
else:
|
||
if is_pp_missing_parameter(name, self):
|
||
continue
|
||
param = params_dict[name]
|
||
weight_loader = getattr(
|
||
param, "weight_loader", default_weight_loader
|
||
)
|
||
weight_loader(param, loaded_weight)
|
||
loaded_params.add(name)
|
||
return loaded_params
|
||
|
||
def is_moe_weight(self, name):
|
||
if (
|
||
"shared_expert" in name
|
||
or "experts" in name
|
||
or "gate" in name
|
||
or "up" in name
|
||
or "down" in name
|
||
):
|
||
return True
|
||
return False
|
||
|
||
def is_common_weight(self, name):
|
||
if (
|
||
"lm_head" in name
|
||
or "model.norm.weight" in name
|
||
or "embed" in name
|
||
or "input_layernorm" in name
|
||
or "post_attention_layernorm" in name
|
||
):
|
||
return True
|
||
return False
|