746 lines
28 KiB
Python
746 lines
28 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable
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import torch
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import torch.distributed as dist
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from torch import nn
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from transformers import GptOssConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (
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get_dp_group,
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get_ep_group,
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get_pcp_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.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import FusedMoE
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from vllm.model_executor.layers.fused_moe.config import FusedMoEParallelConfig
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
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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.utils import rocm_unquantized_gemm
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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.model_executor.models.utils import sequence_parallel_chunk
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from vllm.utils.math_utils import cdiv
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from vllm.v1.attention.backend import AttentionType
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from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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WeightsMapper,
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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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)
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class OAIAttention(nn.Module):
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def __init__(
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self,
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config: GptOssConfig,
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quant_config: QuantizationConfig | None = None,
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cache_config: CacheConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.layer_idx = extract_layer_index(prefix)
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self.head_dim = config.head_dim
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.hidden_size = config.hidden_size
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self.rotary_emb = get_rope(
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self.head_dim,
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max_position=config.max_position_embeddings,
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dtype=torch.float32,
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rope_parameters={
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"rope_theta": config.rope_parameters["rope_theta"],
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"rope_type": "yarn",
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"factor": config.rope_parameters["factor"],
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"original_max_position_embeddings": config.rope_parameters[
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"original_max_position_embeddings"
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],
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"beta_fast": config.rope_parameters["beta_fast"],
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"beta_slow": config.rope_parameters["beta_slow"],
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"truncate": config.rope_parameters.get("truncate", True),
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},
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is_neox_style=True,
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)
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tp_size = get_tensor_model_parallel_world_size()
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self.sinks = torch.nn.Parameter(
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torch.empty(config.num_attention_heads // tp_size, requires_grad=False)
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)
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self.q_size = self.num_attention_heads * self.head_dim // tp_size
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self.kv_size = self.num_key_value_heads * self.head_dim // tp_size
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size=self.hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.num_attention_heads,
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total_num_kv_heads=self.num_key_value_heads,
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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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input_size=self.num_attention_heads * self.head_dim,
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output_size=self.hidden_size,
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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.num_local_attention_heads = config.num_attention_heads // tp_size
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self.num_local_key_value_heads = config.num_key_value_heads // tp_size
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# Only apply sliding window to every other layer
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sliding_window = config.sliding_window if self.layer_idx % 2 == 0 else None
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self.attn = Attention(
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self.num_local_attention_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_local_key_value_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=sliding_window,
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attn_type=AttentionType.DECODER,
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prefix=f"{prefix}.attn",
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sinks=self.sinks,
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)
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def forward(
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self, hidden_states: torch.Tensor, positions: torch.Tensor
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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v = v.contiguous()
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class MLPBlock(torch.nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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layer_idx: int,
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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_config
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quant_config = vllm_config.quant_config
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parallel_config = vllm_config.parallel_config
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self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
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self.layer_idx = layer_idx
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self.num_experts = config.num_local_experts
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self.hidden_size = config.hidden_size
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self.experts_per_token = config.num_experts_per_tok
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self.world_size = dist.get_world_size() if dist.is_initialized() else 1
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self.router = torch.nn.Linear(config.hidden_size, config.num_local_experts)
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assert config.intermediate_size % self.world_size == 0
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self.experts = FusedMoE(
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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reduce_results=True,
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renormalize=True,
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quant_config=quant_config,
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prefix=f"{prefix}.experts",
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apply_router_weight_on_input=False,
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has_bias=True,
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activation="swigluoai",
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is_sequence_parallel=self.is_sequence_parallel,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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num_tokens = x.shape[0]
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if self.is_sequence_parallel:
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x = sequence_parallel_chunk(x)
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if current_platform.is_rocm():
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g = rocm_unquantized_gemm(
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self, x[:, : self.hidden_size], self.router.weight, self.router.bias
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)
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else:
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g = self.router(x)
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x = self.experts(hidden_states=x, router_logits=g)[:, : self.hidden_size]
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if self.is_sequence_parallel:
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x = tensor_model_parallel_all_gather(x.contiguous(), 0)
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x = x[:num_tokens]
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return x
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class TransformerBlock(torch.nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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quant_config: QuantizationConfig,
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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_config
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cache_config = vllm_config.cache_config
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self.layer_idx = extract_layer_index(prefix)
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self.attn = OAIAttention(
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config,
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prefix=f"{prefix}.attn",
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quant_config=quant_config,
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cache_config=cache_config,
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)
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self.mlp = MLPBlock(vllm_config, self.layer_idx, prefix=f"{prefix}.mlp")
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self.input_layernorm = RMSNorm(config.hidden_size, eps=1e-5)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=1e-5)
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def forward(
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self,
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hidden_states: torch.Tensor,
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positions: torch.Tensor,
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residual: torch.Tensor | None,
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) -> torch.Tensor:
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# Self Attention
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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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hidden_states = self.attn(hidden_states, positions)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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output = self.mlp(hidden_states)
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return output, residual
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@support_torch_compile
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class GptOssModel(nn.Module):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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):
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super().__init__()
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self.config = vllm_config.model_config.hf_config
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self.quant_config = vllm_config.quant_config
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self.parallel_config = vllm_config.parallel_config
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self.config.hidden_size = self.config.hidden_size
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self.embedding = VocabParallelEmbedding(
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self.config.vocab_size,
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self.config.hidden_size,
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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self.config.num_hidden_layers,
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lambda prefix: TransformerBlock(
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vllm_config,
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prefix=prefix,
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quant_config=self.quant_config,
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),
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prefix=f"{prefix}.layers",
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)
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self.norm = RMSNorm(self.config.hidden_size, eps=1e-5)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], self.config.hidden_size
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)
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self.aux_hidden_state_layers = tuple[int, ...]()
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embedding(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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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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x = inputs_embeds
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else:
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x = 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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x = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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aux_hidden_states = []
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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if i in self.aux_hidden_state_layers:
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aux_hidden_states.append(x if residual is None else x + residual)
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x, residual = layer(x, positions, residual)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": x, "residual": residual})
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x, _ = self.norm(x, residual)
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if len(aux_hidden_states) > 0:
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return x, aux_hidden_states
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return x
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def _load_weights_mxfp4(
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self,
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ep_rank_end: int,
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ep_rank_start: int,
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heads_per_rank: int,
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head_start: int,
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weights: Iterable[tuple[str, torch.Tensor]],
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stacked_params_mapping: list[tuple[str, ...]],
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) -> set[str]:
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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mxfp4_block = 32
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use_ep = self.parallel_config.enable_expert_parallel
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num_experts = self.config.num_local_experts
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# In MoE, we need to flatten the tensor parallel size across the data
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# parallel size when EP is disabled.
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tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
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tp_size=get_tensor_model_parallel_world_size(),
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dp_size=get_dp_group().world_size,
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dp_rank=get_dp_group().rank_in_group,
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pcp_size=get_pcp_group().world_size,
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pcp_rank=get_pcp_group().rank_in_group,
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)
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intermediate_size = self.config.intermediate_size
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intermediate_size_block = intermediate_size // mxfp4_block
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per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
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per_rank_intermediate_size = per_rank_intermediate_size_block * mxfp4_block
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# Calculate common slicing bounds for current rank
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tp_rank_start = tp_rank * per_rank_intermediate_size
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tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)
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for name, weight in weights:
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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if ".w13_weight_scale" in name:
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# Handle MLP gate and up projection weights scale
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if use_ep:
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narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
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else:
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narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end, ...]
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(
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param,
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narrow_weight,
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weight_name=name,
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shard_id=None,
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expert_id=None,
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)
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loaded_params.add(name)
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continue
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elif ".w2_weight_scale" in name:
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# Handle MLP down projection weights
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if use_ep:
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narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
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else:
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narrow_weight = weight[
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..., tp_rank_start // mxfp4_block : tp_rank_end // mxfp4_block
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]
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(
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param,
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narrow_weight,
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weight_name=name,
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shard_id=None,
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expert_id=None,
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)
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loaded_params.add(name)
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continue
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elif ".w13_weight" in name:
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# Handle MLP gate and up projection weights
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# flat weight from (E, 2 * N, block_size, entry_per_block)
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# to (E, 2 * N, -1), shouldn't trigger copy for contiguous
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weight = weight.view(
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num_experts, 2 * intermediate_size, -1
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).contiguous()
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# Extract gate and up projection parts
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# since the weight is shuffled, we can slice directly
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if use_ep:
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narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
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else:
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narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end, ...]
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(
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param,
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narrow_weight,
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weight_name=name,
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shard_id=None,
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expert_id=None,
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)
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loaded_params.add(name)
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continue
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elif ".w2_weight" in name:
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# Handle MLP down projection weights
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# same flatten here, but since 2 mx4 value are packed in 1
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# uint8, divide by 2
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weight = weight.view(
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num_experts, -1, intermediate_size // 2
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).contiguous()
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if use_ep:
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narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
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else:
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narrow_weight = weight[..., tp_rank_start // 2 : tp_rank_end // 2]
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(
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param,
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narrow_weight,
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weight_name=name,
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shard_id=None,
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expert_id=None,
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)
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loaded_params.add(name)
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continue
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elif ".w13_bias" in name:
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# Handle MLP gate and up projection biases
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# Extract gate and up projection bias parts
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if use_ep:
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narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
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else:
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narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end]
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(
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param,
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narrow_weight,
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weight_name=name,
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shard_id=None,
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expert_id=None,
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)
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loaded_params.add(name)
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continue
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elif ".w2_bias" in name:
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# Handle MLP down projection bias
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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if use_ep:
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weight = weight[ep_rank_start:ep_rank_end, ...]
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else:
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# (only load on rank 0 to avoid duplication)
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if tp_rank != 0:
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weight.zero_()
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weight_loader(
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param, weight, weight_name=name, shard_id=None, expert_id=None
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)
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loaded_params.add(name)
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continue
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elif "sinks" in name:
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# Handle attention sinks (distributed across ranks)
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param = params_dict[name]
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narrow_weight = weight.narrow(0, head_start, heads_per_rank)
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param.data.copy_(narrow_weight)
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loaded_params.add(name)
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
if weight_loader == default_weight_loader:
|
|
weight_loader(param, weight)
|
|
else:
|
|
weight_loader(param, weight, shard_id)
|
|
break
|
|
else:
|
|
# Handle all other weights with potential renaming
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
def _load_weights_other(
|
|
self,
|
|
ep_rank_end: int,
|
|
ep_rank_start: int,
|
|
heads_per_rank: int,
|
|
head_start: int,
|
|
weights: Iterable[tuple[str, torch.Tensor]],
|
|
stacked_params_mapping: list[tuple[str, ...]],
|
|
) -> set[str]:
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
|
|
use_ep = self.parallel_config.enable_expert_parallel
|
|
|
|
# In MoE, we need to flatten the tensor parallel size across the data
|
|
# parallel size when EP is disabled.
|
|
tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
|
|
tp_size=get_tensor_model_parallel_world_size(),
|
|
dp_size=get_dp_group().world_size,
|
|
dp_rank=get_dp_group().rank_in_group,
|
|
pcp_size=get_pcp_group().world_size,
|
|
pcp_rank=get_pcp_group().rank_in_group,
|
|
)
|
|
|
|
intermediate_size = self.config.intermediate_size
|
|
per_rank_intermediate_size = cdiv(intermediate_size, tp_size)
|
|
# Calculate common slicing bounds for current rank
|
|
tp_rank_start = tp_rank * per_rank_intermediate_size
|
|
tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)
|
|
|
|
for name, weight in weights:
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
|
|
if ".w13_weight" in name:
|
|
# Handle MLP gate and up projection weights
|
|
# Extract gate and up projection parts
|
|
if use_ep:
|
|
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
|
|
else:
|
|
narrow_weight = weight[:, :, 2 * tp_rank_start : 2 * tp_rank_end]
|
|
|
|
narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
|
|
param = params_dict[name]
|
|
|
|
param.copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
elif ".w2_weight" in name:
|
|
# Handle MLP down projection weights
|
|
if use_ep:
|
|
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
|
|
else:
|
|
narrow_weight = weight[:, tp_rank_start:tp_rank_end, :]
|
|
narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
|
|
param = params_dict[name]
|
|
|
|
param.copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
elif ".w13_bias" in name:
|
|
# Handle MLP gate and up projection biases
|
|
# Extract gate and up projection bias parts
|
|
if use_ep:
|
|
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
|
|
else:
|
|
narrow_weight = weight[:, 2 * tp_rank_start : 2 * tp_rank_end]
|
|
|
|
param = params_dict[name]
|
|
param.copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
elif ".w2_bias" in name:
|
|
# Handle MLP down projection bias
|
|
if use_ep:
|
|
weight = weight[ep_rank_start:ep_rank_end, ...]
|
|
else:
|
|
# (only load on rank 0 to avoid duplication)
|
|
if tp_rank != 0:
|
|
weight.zero_()
|
|
param = params_dict[name]
|
|
param.copy_(weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
elif "sinks" in name:
|
|
# Handle attention sinks (distributed across ranks)
|
|
param = params_dict[name]
|
|
narrow_weight = weight.narrow(0, head_start, heads_per_rank)
|
|
param.data.copy_(narrow_weight)
|
|
loaded_params.add(name)
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
if weight_loader == default_weight_loader:
|
|
weight_loader(param, weight)
|
|
else:
|
|
weight_loader(param, weight, shard_id)
|
|
break
|
|
else:
|
|
# Handle all other weights with potential renaming
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
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"),
|
|
]
|
|
|
|
tp_rank = get_tensor_model_parallel_rank()
|
|
tp_size = get_tensor_model_parallel_world_size()
|
|
|
|
# Attention heads per rank
|
|
heads_per_rank = self.config.num_attention_heads // tp_size
|
|
head_start = tp_rank * heads_per_rank
|
|
|
|
ep_size = get_ep_group().world_size
|
|
ep_rank = get_ep_group().rank
|
|
num_experts = self.config.num_local_experts
|
|
experts_per_rank = num_experts // ep_size
|
|
ep_rank_start = ep_rank * experts_per_rank
|
|
ep_rank_end = (ep_rank + 1) * experts_per_rank
|
|
|
|
quant_method = (
|
|
self.config.quantization_config["quant_method"]
|
|
if hasattr(self.config, "quantization_config")
|
|
else None
|
|
)
|
|
if quant_method == "mxfp4":
|
|
return self._load_weights_mxfp4(
|
|
ep_rank_end,
|
|
ep_rank_start,
|
|
heads_per_rank,
|
|
head_start,
|
|
weights,
|
|
stacked_params_mapping,
|
|
)
|
|
else:
|
|
return self._load_weights_other(
|
|
ep_rank_end,
|
|
ep_rank_start,
|
|
heads_per_rank,
|
|
head_start,
|
|
weights,
|
|
stacked_params_mapping,
|
|
)
|
|
|
|
|
|
class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
|
|
is_3d_moe_weight: bool = True
|
|
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
|
|
|
|
hf_to_vllm_mapper = WeightsMapper(
|
|
orig_to_new_substr={
|
|
".self_attn.": ".attn.",
|
|
},
|
|
orig_to_new_suffix={
|
|
".embed_tokens.weight": ".embedding.weight",
|
|
# MoE MXFP4 weights
|
|
".gate_up_proj_blocks": ".w13_weight",
|
|
".down_proj_blocks": ".w2_weight",
|
|
".gate_up_proj_scales": ".w13_weight_scale",
|
|
".down_proj_scales": ".w2_weight_scale",
|
|
# MoE other weights
|
|
".gate_up_proj": ".w13_weight",
|
|
".down_proj": ".w2_weight",
|
|
# MoE Bias
|
|
".gate_up_proj_bias": ".w13_bias",
|
|
".down_proj_bias": ".w2_bias",
|
|
},
|
|
)
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
prefix: str = "",
|
|
):
|
|
super().__init__()
|
|
self.vllm_config = vllm_config
|
|
self.config = vllm_config.model_config.hf_config
|
|
|
|
self.model = GptOssModel(
|
|
vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"),
|
|
)
|
|
self.lm_head = ParallelLMHead(
|
|
self.config.vocab_size,
|
|
self.config.hidden_size,
|
|
prefix=maybe_prefix(prefix, "lm_head"),
|
|
)
|
|
self.logits_processor = LogitsProcessor(self.config.vocab_size)
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors
|
|
)
|
|
|
|
def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
|
|
self.model.aux_hidden_state_layers = layers
|
|
|
|
def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
|
|
num_layers = len(self.model.layers)
|
|
return (2, num_layers // 2, num_layers - 3)
|
|
|
|
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:
|
|
return self.model(input_ids, positions, intermediate_tensors, inputs_embeds)
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states)
|
|
return logits
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
# Params for weights, weight scales, activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
return FusedMoE.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_local_experts,
|
|
num_redundant_experts=0,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|